{"id":"node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js","dependencies":[{"name":"/www/ml-classifier/example/node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js.map","includedInParent":true,"mtime":1551550403900},{"name":"/www/ml-classifier/example/package.json","includedInParent":true,"mtime":1551949031632},{"name":"/www/ml-classifier/example/.babelrc","includedInParent":true,"mtime":1551703756168},{"name":"/www/ml-classifier/example/node_modules/@tensorflow/tfjs-core/package.json","includedInParent":true,"mtime":1551550403900},{"name":"crypto","optional":true,"loc":{"line":17,"column":261009},"parent":"/www/ml-classifier/example/node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js","resolved":"/www/ml-classifier/example/node_modules/crypto-browserify/index.js"},{"name":"process","parent":"/www/ml-classifier/example/node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js","resolved":"/www/ml-classifier/example/node_modules/process/browser.js"},{"name":"buffer","parent":"/www/ml-classifier/example/node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js","resolved":"/www/ml-classifier/example/node_modules/buffer/index.js"}],"generated":{"js":"var process = require(\"process\");\nvar global = arguments[3];\nvar Buffer = require(\"buffer\").Buffer;\n\"use strict\";\n\nObject.defineProperty(exports, \"__esModule\", {\n value: true\n});\nexports.nextFrame = nextFrame;\nexports.enableProdMode = enableProdMode;\nexports.disableDeprecationWarnings = disableDeprecationWarnings;\nexports.deprecationWarn = deprecationWarn;\nexports.op = op;\nexports.buffer = buffer;\nexports.toPixels = toPixels$1;\nexports.print = print;\nexports.fill = fill;\nexports.linspace = linspace;\nexports.ones = ones$1;\nexports.range = range;\nexports.scalar = scalar;\nexports.tensor = tensor;\nexports.tensor1d = tensor1d;\nexports.tensor2d = tensor2d;\nexports.tensor3d = tensor3d;\nexports.tensor4d = tensor4d;\nexports.tensor5d = tensor5d;\nexports.tensor6d = tensor6d;\nexports.zeros = zeros;\nexports.customGrad = customGrad;\nexports.grad = grad;\nexports.grads = grads;\nexports.valueAndGrad = valueAndGrad;\nexports.valueAndGrads = valueAndGrads;\nexports.variableGrads = variableGrads;\nexports.sign = exports.sigmoid = exports.rsqrt = exports.round = exports.reciprocal = exports.neg = exports.logSigmoid = exports.log1p = exports.log = exports.floor = exports.expm1 = exports.exp = exports.erf = exports.cosh = exports.cos = exports.clipByValue = exports.ceil = exports.atanh = exports.atan = exports.asinh = exports.asin = exports.acosh = exports.acos = exports.abs = exports.slice4d = exports.slice3d = exports.slice2d = exports.slice1d = exports.slice = exports.pool = exports.avgPool = exports.maxPool = exports.reverse4d = exports.reverse3d = exports.reverse2d = exports.reverse1d = exports.reverse = exports.outerProduct = exports.dot = exports.matMul = exports.conv2dTranspose = exports.separableConv2d = exports.depthwiseConv2d = exports.conv2dDerFilter = exports.conv3d = exports.conv2d = exports.conv1d = exports.split = exports.concat4d = exports.concat3d = exports.concat2d = exports.concat1d = exports.concat = exports.imag = exports.real = exports.complex = exports.batchNorm4d = exports.batchNorm3d = exports.batchNorm2d = exports.batchNorm = exports.batchNormalization = exports.batchNormalization4d = exports.batchNormalization3d = exports.batchNormalization2d = exports.fused = exports.spectral = exports.losses = exports.linalg = exports.image = exports.DataStorage = exports.KernelBackend = exports.Environment = exports.ENV = exports.Reduction = exports.Rank = exports.Variable = exports.variable = exports.TensorBuffer = exports.Tensor = exports.SGDOptimizer = exports.RMSPropOptimizer = exports.Optimizer = exports.MomentumOptimizer = exports.AdamaxOptimizer = exports.AdamOptimizer = exports.AdagradOptimizer = exports.AdadeltaOptimizer = exports.webgl = exports.util = exports.test_util = exports.serialization = exports.math = exports.io = exports.environment = exports.browser = exports.version_core = exports.memory = exports.disposeVariables = exports.getBackend = exports.setBackend = void 0;\nexports.multiRNNCell = exports.basicLSTMCell = exports.unsortedSegmentSum = exports.gather = exports.norm = exports.localResponseNormalization = exports.logSoftmax = exports.softmax = exports.transpose = exports.zerosLike = exports.onesLike = exports.setdiff1dAsync = exports.unstack = exports.truncatedNormal = exports.tile = exports.stack = exports.squeeze = exports.spaceToBatchND = exports.reshape = exports.randomUniform = exports.randomNormal = exports.rand = exports.pad4d = exports.pad3d = exports.pad2d = exports.pad1d = exports.pad = exports.oneHot = exports.multinomial = exports.fromPixels = exports.eye = exports.expandDims = exports.depthToSpace = exports.cumsum = exports.clone = exports.cast = exports.batchToSpaceND = exports.whereAsync = exports.where = exports.logicalXor = exports.logicalOr = exports.logicalNot = exports.logicalAnd = exports.selu = exports.relu = exports.prelu = exports.leakyRelu = exports.elu = exports.subStrict = exports.sub = exports.squaredDifferenceStrict = exports.squaredDifference = exports.powStrict = exports.pow = exports.mulStrict = exports.mul = exports.modStrict = exports.mod = exports.minimumStrict = exports.minimum = exports.maximumStrict = exports.maximum = exports.floorDiv = exports.divStrict = exports.div = exports.atan2 = exports.addStrict = exports.addN = exports.add = exports.notEqualStrict = exports.notEqual = exports.lessStrict = exports.lessEqualStrict = exports.lessEqual = exports.less = exports.greaterStrict = exports.greaterEqualStrict = exports.greaterEqual = exports.greater = exports.equalStrict = exports.equal = exports.prod = exports.sum = exports.moments = exports.min = exports.mean = exports.max = exports.logSumExp = exports.argMin = exports.argMax = exports.any = exports.all = exports.tanh = exports.tan = exports.step = exports.square = exports.sqrt = exports.softplus = exports.sinh = exports.sin = void 0;\nexports.profile = exports.time = exports.dispose = exports.keep = exports.tidy = exports.train = exports.gatherND = exports.sparseToDense = exports.irfft = exports.rfft = exports.ifft = exports.fft = exports.scatterND = exports.topk = exports.stridedSlice = exports.movingAverage = void 0;\n\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nvar extendStatics = function (e, t) {\n return (extendStatics = Object.setPrototypeOf || {\n __proto__: []\n } instanceof Array && function (e, t) {\n e.__proto__ = t;\n } || function (e, t) {\n for (var n in t) t.hasOwnProperty(n) && (e[n] = t[n]);\n })(e, t);\n};\n\nfunction __extends(e, t) {\n function n() {\n this.constructor = e;\n }\n\n extendStatics(e, t), e.prototype = null === t ? 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Found -1 at dim \" + r + \" and dim \" + o);\n r = o;\n } else if (e[o] < 0) throw Error(\"Shapes can not be < 0. Found \" + e[o] + \" at dim \" + o);\n\n if (-1 === r) {\n if (t > 0 && t !== n) throw Error(\"Size(\" + t + \") must match the product of shape \" + e);\n return e;\n }\n\n if (0 === n) throw Error(\"Cannot infer the missing size in [\" + e + \"] when there are 0 elements\");\n if (t % n != 0) throw Error(\"The implicit shape can't be a fractional number. Got \" + t + \" / \" + n);\n var a = e.slice();\n return a[r] = t / n, a;\n}\n\nfunction parseAxisParam(e, t) {\n var n = t.length;\n return assert((e = null == e ? t.map(function (e, t) {\n return t;\n }) : [].concat(e)).every(function (e) {\n return e >= -n && e < n;\n }), \"All values in axis param must be in range [-\" + n + \", \" + n + \") but got axis \" + e), assert(e.every(function (e) {\n return isInt(e);\n }), \"All values in axis param must be integers but got axis \" + e), e.map(function (e) {\n return e < 0 ? n + e : e;\n });\n}\n\nfunction squeezeShape(e, t) {\n for (var n = [], r = [], o = null == t ? null : parseAxisParam(t, e).sort(), a = 0, i = 0; i < e.length; ++i) {\n if (null != o) {\n if (o[a] === i && 1 !== e[i]) throw new Error(\"Can't squeeze axis \" + i + \" since its dim '\" + e[i] + \"' is not 1\");\n (null == o[a] || o[a] > i) && 1 === e[i] && (n.push(e[i]), r.push(i)), o[a] <= i && a++;\n }\n\n 1 !== e[i] && (n.push(e[i]), r.push(i));\n }\n\n return {\n newShape: n,\n keptDims: r\n };\n}\n\nfunction getTypedArrayFromDType(e, t) {\n var n = null;\n if (null == e || \"float32\" === e) n = new Float32Array(t);else if (\"int32\" === e) n = new Int32Array(t);else {\n if (\"bool\" !== e) throw new Error(\"Unknown data type \" + e);\n n = new Uint8Array(t);\n }\n return n;\n}\n\nfunction getArrayFromDType(e, t) {\n var n = null;\n if (null == e || \"float32\" === e) n = new Float32Array(t);else if (\"int32\" === e) n = new Int32Array(t);else if (\"bool\" === e) n = new Uint8Array(t);else {\n if (\"string\" !== e) throw new Error(\"Unknown data type \" + e);\n n = new Array(t);\n }\n return n;\n}\n\nfunction checkComputationForErrors(e, t, n) {\n if (\"float32\" === t) for (var r = 0; r < e.length; r++) {\n var o = e[r];\n if (isNaN(o) || !isFinite(o)) throw Error(\"The result of the '\" + n + \"' is \" + o + \".\");\n }\n}\n\nfunction checkConversionForErrors(e, t) {\n for (var n = 0; n < e.length; n++) {\n var r = e[n];\n if (isNaN(r) || !isFinite(r)) throw Error(\"A tensor of type \" + t + \" being uploaded contains \" + r + \".\");\n }\n}\n\nfunction hasEncodingLoss(e, t) {\n return \"complex64\" !== t && (\"float32\" !== t || \"complex64\" === e) && (\"int32\" !== t || \"float32\" === e || \"complex64\" === e) && (\"bool\" !== t || \"bool\" !== e);\n}\n\nfunction isTypedArray(e) {\n return e instanceof Float32Array || e instanceof Int32Array || e instanceof Uint8Array;\n}\n\nfunction bytesPerElement(e) {\n if (\"float32\" === e || \"int32\" === e) return 4;\n if (\"complex64\" === e) return 8;\n if (\"bool\" === e) return 1;\n throw new Error(\"Unknown dtype \" + e);\n}\n\nfunction bytesFromStringArray(e) {\n if (null == e) return 0;\n var t = 0;\n return e.forEach(function (e) {\n return t += 2 * e.length;\n }), t;\n}\n\nfunction isString(e) {\n return \"string\" == typeof e || e instanceof String;\n}\n\nfunction isBoolean(e) {\n return \"boolean\" == typeof e;\n}\n\nfunction isNumber(e) {\n return \"number\" == typeof e;\n}\n\nfunction inferDtype(e) {\n return Array.isArray(e) ? inferDtype(e[0]) : e instanceof Float32Array ? \"float32\" : e instanceof Int32Array || e instanceof Uint8Array ? \"int32\" : isNumber(e) ? \"float32\" : isString(e) ? \"string\" : isBoolean(e) ? \"bool\" : \"float32\";\n}\n\nfunction isFunction(e) {\n return !!(e && e.constructor && e.call && e.apply);\n}\n\nfunction nearestDivisor(e, t) {\n for (var n = t; n < e; ++n) if (e % n == 0) return n;\n\n return e;\n}\n\nfunction computeStrides(e) {\n var t = e.length;\n if (t < 2) return [];\n var n = new Array(t - 1);\n n[t - 2] = e[t - 1];\n\n for (var r = t - 3; r >= 0; --r) n[r] = n[r + 1] * e[r + 1];\n\n return n;\n}\n\nfunction toTypedArray(e, t, n) {\n if (\"string\" === t) throw new Error(\"Cannot convert a string[] to a TypedArray\");\n if (Array.isArray(e) && (e = flatten(e)), n && checkConversionForErrors(e, t), noConversionNeeded(e, t)) return e;\n if (null == t || \"float32\" === t || \"complex64\" === t) return new Float32Array(e);\n if (\"int32\" === t) return new Int32Array(e);\n\n if (\"bool\" === t) {\n for (var r = new Uint8Array(e.length), o = 0; o < r.length; ++o) 0 !== Math.round(e[o]) && (r[o] = 1);\n\n return r;\n }\n\n throw new Error(\"Unknown data type \" + t);\n}\n\nfunction createNestedArray(e, t, n) {\n var r = new Array();\n if (1 === t.length) for (var o = t[0], a = 0; a < o; a++) r[a] = n[e + a];else {\n o = t[0];\n var i = t.slice(1),\n s = i.reduce(function (e, t) {\n return e * t;\n });\n\n for (a = 0; a < o; a++) r[a] = createNestedArray(e + a * s, i, n);\n }\n return r;\n}\n\nfunction toNestedArray(e, t) {\n if (0 === e.length) return t[0];\n var n = e.reduce(function (e, t) {\n return e * t;\n });\n if (0 === n) return [];\n if (n !== t.length) throw new Error(\"[\" + e + \"] does not match the input size.\");\n return createNestedArray(0, e, t);\n}\n\nfunction noConversionNeeded(e, t) {\n return e instanceof Float32Array && \"float32\" === t || e instanceof Int32Array && \"int32\" === t || e instanceof Uint8Array && \"bool\" === t;\n}\n\nfunction makeOnesTypedArray(e, t) {\n for (var n = makeZerosTypedArray(e, t), r = 0; r < n.length; r++) n[r] = 1;\n\n return n;\n}\n\nfunction makeZerosTypedArray(e, t) {\n if (null == t || \"float32\" === t || \"complex64\" === t) return new Float32Array(e);\n if (\"int32\" === t) return new Int32Array(e);\n if (\"bool\" === t) return new Uint8Array(e);\n throw new Error(\"Unknown data type \" + t);\n}\n\nfunction now() {\n if (\"undefined\" != typeof performance) return performance.now();\n\n if (\"undefined\" != typeof process) {\n var e = process.hrtime();\n return 1e3 * e[0] + e[1] / 1e6;\n }\n\n throw new Error(\"Cannot measure time in this environment. You should run tf.js in the browser or in Node.js\");\n}\n\nfunction monitorPromisesProgress(e, t, n, r) {\n !function (e) {\n assert(null != e && Array.isArray(e) && e.length > 0, \"promises must be a none empty array\");\n }(e), function (e, t) {\n assert(e >= 0 && e <= 1, \"Progress fraction must be in range [0, 1], but got startFraction \" + e), assert(t >= 0 && t <= 1, \"Progress fraction must be in range [0, 1], but got endFraction \" + t), assert(t >= e, \"startFraction must be no more than endFraction, but got startFraction \" + e + \" and endFraction \" + t);\n }(n = null == n ? 0 : n, r = null == r ? 1 : r);\n var o = 0;\n return Promise.all(e.map(function (a) {\n return a.then(function (a) {\n var i = n + ++o / e.length * (r - n);\n return t(i), a;\n }), a;\n }));\n}\n\nvar util = Object.freeze({\n shuffle: shuffle,\n clamp: clamp,\n nearestLargerEven: nearestLargerEven,\n sum: sum,\n randUniform: randUniform,\n distSquared: distSquared,\n assert: assert,\n assertShapesMatch: assertShapesMatch,\n assertNonNull: assertNonNull,\n flatten: flatten,\n sizeFromShape: sizeFromShape,\n isScalarShape: isScalarShape,\n arraysEqual: arraysEqual,\n isInt: isInt,\n tanh: tanh,\n sizeToSquarishShape: sizeToSquarishShape,\n createShuffledIndices: createShuffledIndices,\n rightPad: rightPad,\n repeatedTry: repeatedTry,\n inferFromImplicitShape: inferFromImplicitShape,\n parseAxisParam: parseAxisParam,\n squeezeShape: squeezeShape,\n getTypedArrayFromDType: getTypedArrayFromDType,\n getArrayFromDType: getArrayFromDType,\n checkComputationForErrors: checkComputationForErrors,\n checkConversionForErrors: checkConversionForErrors,\n hasEncodingLoss: hasEncodingLoss,\n isTypedArray: isTypedArray,\n bytesPerElement: bytesPerElement,\n bytesFromStringArray: bytesFromStringArray,\n isString: isString,\n isBoolean: isBoolean,\n isNumber: isNumber,\n inferDtype: inferDtype,\n isFunction: isFunction,\n nearestDivisor: nearestDivisor,\n computeStrides: computeStrides,\n toTypedArray: toTypedArray,\n toNestedArray: toNestedArray,\n makeOnesTypedArray: makeOnesTypedArray,\n makeZerosTypedArray: makeZerosTypedArray,\n now: now,\n monitorPromisesProgress: monitorPromisesProgress\n}),\n Profiler = function () {\n function e(e, t) {\n this.backendTimer = e, this.logger = t, null == t && (this.logger = new Logger());\n }\n\n return e.prototype.profileKernel = function (e, t) {\n var n,\n r = this,\n o = this.backendTimer.time(function () {\n n = t();\n });\n return (Array.isArray(n) ? n : [n]).forEach(function (t) {\n var n = t.dataSync();\n checkComputationForErrors(n, t.dtype, e), o.then(function (o) {\n var a = \"\";\n null != o.getExtraProfileInfo && (a = o.getExtraProfileInfo()), r.logger.logKernelProfile(e, t, n, o.kernelMs, a);\n });\n }), n;\n }, e;\n}(),\n Logger = function () {\n function e() {}\n\n return e.prototype.logKernelProfile = function (e, t, n, r, o) {\n var a = rightPad(r + \"ms\", 9),\n i = rightPad(e, 25),\n s = t.rank,\n u = t.size,\n l = rightPad(t.shape.toString(), 14);\n console.log(\"%c\" + i + \"\\t%c\" + a + \"\\t%c\" + s + \"D \" + l + \"\\t%c\" + u + \"\\t%c\" + o, \"font-weight:bold\", \"color:red\", \"color:blue\", \"color: orange\", \"color: green\");\n }, e;\n}(),\n FORMAT_LIMIT_NUM_VALS = 20,\n FORMAT_NUM_FIRST_LAST_VALS = 3,\n FORMAT_NUM_SIG_DIGITS = 7;\n\nexports.util = util;\n\nfunction tensorToString(e, t, n, r) {\n var o = computeStrides(t),\n a = computeMaxSizePerColumn(e, t, n, o),\n i = t.length,\n s = subTensorToString(e, t, n, o, a),\n u = [\"Tensor\"];\n return r && (u.push(\" dtype: \" + n), u.push(\" rank: \" + i), u.push(\" shape: [\" + t + \"]\"), u.push(\" values:\")), u.push(s.map(function (e) {\n return \" \" + e;\n }).join(\"\\n\")), u.join(\"\\n\");\n}\n\nfunction computeMaxSizePerColumn(e, t, n, r) {\n var o = sizeFromShape(t),\n a = r[r.length - 1],\n i = new Array(a).fill(0),\n s = t.length,\n u = \"complex64\" === n ? createComplexTuples(e) : e;\n if (s > 1) for (var l = 0; l < o / a; l++) for (var c = l * a, p = 0; p < a; p++) i[p] = Math.max(i[p], valToString(u[c + p], 0).length);\n return i;\n}\n\nfunction valToString(e, t) {\n return rightPad(Array.isArray(e) ? parseFloat(e[0].toFixed(FORMAT_NUM_SIG_DIGITS)) + \" + \" + parseFloat(e[1].toFixed(FORMAT_NUM_SIG_DIGITS)) + \"j\" : isString(e) ? \"'\" + e + \"'\" : parseFloat(e.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(), t);\n}\n\nfunction subTensorToString(e, t, n, r, o, a) {\n void 0 === a && (a = !0);\n var i = \"complex64\" === n ? 2 : 1,\n s = t[0],\n u = t.length;\n if (0 === u) return \"complex64\" === n ? [valToString(createComplexTuples(e)[0], 0)] : [e[0].toString()];\n\n if (1 === u) {\n if (s > FORMAT_LIMIT_NUM_VALS) {\n var l = FORMAT_NUM_FIRST_LAST_VALS * i,\n c = Array.from(e.slice(0, l)),\n p = Array.from(e.slice(s - FORMAT_NUM_FIRST_LAST_VALS * i, s));\n return \"complex64\" === n && (c = createComplexTuples(c), p = createComplexTuples(p)), [\"[\" + c.map(function (e, t) {\n return valToString(e, o[t]);\n }).join(\", \") + \", ..., \" + p.map(function (e, t) {\n return valToString(e, o[s - FORMAT_NUM_FIRST_LAST_VALS + t]);\n }).join(\", \") + \"]\"];\n }\n\n return [\"[\" + (\"complex64\" === n ? createComplexTuples(e) : Array.from(e)).map(function (e, t) {\n return valToString(e, o[t]);\n }).join(\", \") + \"]\"];\n }\n\n var d = t.slice(1),\n h = r.slice(1),\n f = r[0] * i,\n m = [];\n\n if (s > FORMAT_LIMIT_NUM_VALS) {\n for (var g = 0; g < FORMAT_NUM_FIRST_LAST_VALS; g++) {\n var v = (y = g * f) + f;\n m.push.apply(m, subTensorToString(e.slice(y, v), d, n, h, o, !1));\n }\n\n m.push(\"...\");\n\n for (g = s - FORMAT_NUM_FIRST_LAST_VALS; g < s; g++) {\n v = (y = g * f) + f;\n m.push.apply(m, subTensorToString(e.slice(y, v), d, n, h, o, g === s - 1));\n }\n } else for (g = 0; g < s; g++) {\n var y;\n v = (y = g * f) + f;\n m.push.apply(m, subTensorToString(e.slice(y, v), d, n, h, o, g === s - 1));\n }\n\n var x = 2 === u ? \",\" : \"\";\n m[0] = \"[\" + m[0] + x;\n\n for (g = 1; g < m.length - 1; g++) m[g] = \" \" + m[g] + x;\n\n var T = \",\\n\";\n\n for (g = 2; g < u; g++) T += \"\\n\";\n\n return m[m.length - 1] = \" \" + m[m.length - 1] + \"]\" + (a ? \"\" : T), m;\n}\n\nfunction createComplexTuples(e) {\n for (var t = [], n = 0; n < e.length; n += 2) t.push([e[n], e[n + 1]]);\n\n return t;\n}\n\nvar TensorBuffer = function () {\n function e(e, t, n) {\n if (this.dtype = t, this.shape = e.slice(), this.size = sizeFromShape(e), null != n) {\n var r = n.length;\n assert(r === this.size, \"Length of values '\" + r + \"' does not match the size inferred by the shape '\" + this.size + \"'.\");\n }\n\n if (\"complex64\" === t) throw new Error(\"complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).\");\n this.values = n || getArrayFromDType(t, sizeFromShape(this.shape)), this.strides = computeStrides(e);\n }\n\n return e.prototype.set = function (e) {\n for (var t = [], n = 1; n < arguments.length; n++) t[n - 1] = arguments[n];\n\n 0 === t.length && (t = [0]), assert(t.length === this.rank, \"The number of provided coordinates (\" + t.length + \") must match the rank (\" + this.rank + \")\");\n var r = this.locToIndex(t);\n this.values[r] = e;\n }, e.prototype.get = function () {\n for (var e = [], t = 0; t < arguments.length; t++) e[t] = arguments[t];\n\n 0 === e.length && (e = [0]);\n\n for (var n = e[e.length - 1], r = 0; r < e.length - 1; ++r) n += this.strides[r] * e[r];\n\n return this.values[n];\n }, e.prototype.locToIndex = function (e) {\n if (0 === this.rank) return 0;\n if (1 === this.rank) return e[0];\n\n for (var t = e[e.length - 1], n = 0; n < e.length - 1; ++n) t += this.strides[n] * e[n];\n\n return t;\n }, e.prototype.indexToLoc = function (e) {\n if (0 === this.rank) return [];\n if (1 === this.rank) return [e];\n\n for (var t = new Array(this.shape.length), n = 0; n < t.length - 1; ++n) t[n] = Math.floor(e / this.strides[n]), e -= t[n] * this.strides[n];\n\n return t[t.length - 1] = e, t;\n }, Object.defineProperty(e.prototype, \"rank\", {\n get: function () {\n return this.shape.length;\n },\n enumerable: !0,\n configurable: !0\n }), e.prototype.toTensor = function () {\n return Tensor.make(this.shape, {\n values: this.values\n }, this.dtype);\n }, e;\n}(),\n trackerFn = null,\n opHandler = null,\n deprecationWarningFn = null;\n\nexports.TensorBuffer = TensorBuffer;\n\nfunction setTensorTracker(e) {\n trackerFn = e;\n}\n\nfunction setOpHandler(e) {\n opHandler = e;\n}\n\nfunction setDeprecationWarningFn(e) {\n deprecationWarningFn = e;\n}\n\nvar Tensor = function () {\n function e(e, t, n, r) {\n this.isDisposedInternal = !1, this.shape = e.slice(), this.dtype = t || \"float32\", this.size = sizeFromShape(e), this.strides = computeStrides(e), this.dataId = null != r ? r : {}, this.id = trackerFn().nextTensorId(), this.rankType = this.rank < 5 ? this.rank.toString() : \"higher\", trackerFn().registerTensor(this), null != n && trackerFn().write(this.dataId, n);\n }\n\n return e.make = function (t, n, r) {\n return new e(t, r, n.values, n.dataId);\n }, e.prototype.flatten = function () {\n return this.throwIfDisposed(), this.as1D();\n }, e.prototype.asScalar = function () {\n return this.throwIfDisposed(), assert(1 === this.size, \"The array must have only 1 element.\"), this.reshape([]);\n }, e.prototype.as1D = function () {\n return this.throwIfDisposed(), this.reshape([this.size]);\n }, e.prototype.as2D = function (e, t) {\n return this.throwIfDisposed(), this.reshape([e, t]);\n }, e.prototype.as3D = function (e, t, n) {\n return this.throwIfDisposed(), this.reshape([e, t, n]);\n }, e.prototype.as4D = function (e, t, n, r) {\n return this.throwIfDisposed(), this.reshape([e, t, n, r]);\n }, e.prototype.as5D = function (e, t, n, r, o) {\n return this.throwIfDisposed(), this.reshape([e, t, n, r, o]);\n }, e.prototype.asType = function (e) {\n return this.throwIfDisposed(), opHandler.cast(this, e);\n }, Object.defineProperty(e.prototype, \"rank\", {\n get: function () {\n return this.shape.length;\n },\n enumerable: !0,\n configurable: !0\n }), e.prototype.get = function () {\n for (var e = [], t = 0; t < arguments.length; t++) e[t] = arguments[t];\n\n deprecationWarningFn(\"Tensor.get() is deprecated. Use Tensor.array() and native array indexing instead.\"), assert(e.length === this.rank, \"Number of coordinates in get() must match the rank of the tensor\"), assert(\"complex64\" !== this.dtype, \"Tensor.get() is not supported for complex64 tensors yet.\"), this.throwIfDisposed(), 0 === e.length && (e = [0]);\n\n for (var n = e[e.length - 1], r = 0; r < e.length - 1; ++r) n += this.strides[r] * e[r];\n\n return this.dataSync()[n];\n }, e.prototype.buffer = function () {\n return deprecationWarningFn(\"Tensor.buffer() is renamed to Tensor.bufferSync() in TensorFlow.js 1.0 and Tensor.buffer() will become an async function.\"), opHandler.buffer(this.shape, this.dtype, this.dataSync());\n }, e.prototype.bufferSync = function () {\n return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n }, e.prototype.array = function () {\n return __awaiter(this, void 0, void 0, function () {\n var e, t;\n return __generator(this, function (n) {\n switch (n.label) {\n case 0:\n return e = toNestedArray, t = [this.shape], [4, this.data()];\n\n case 1:\n return [2, e.apply(void 0, t.concat([n.sent()]))];\n }\n });\n });\n }, e.prototype.arraySync = function () {\n return toNestedArray(this.shape, this.dataSync());\n }, e.prototype.data = function () {\n return __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (e) {\n return this.throwIfDisposed(), [2, trackerFn().read(this.dataId)];\n });\n });\n }, e.prototype.dataSync = function () {\n return this.throwIfDisposed(), trackerFn().readSync(this.dataId);\n }, e.prototype.dispose = function () {\n this.isDisposed || (trackerFn().disposeTensor(this), this.isDisposedInternal = !0);\n }, Object.defineProperty(e.prototype, \"isDisposed\", {\n get: function () {\n return this.isDisposedInternal;\n },\n enumerable: !0,\n configurable: !0\n }), e.prototype.throwIfDisposed = function () {\n if (this.isDisposed) throw new Error(\"Tensor is disposed.\");\n }, e.prototype.toFloat = function () {\n return this.asType(\"float32\");\n }, e.prototype.toInt = function () {\n return this.asType(\"int32\");\n }, e.prototype.toBool = function () {\n return this.asType(\"bool\");\n }, e.prototype.print = function (e) {\n return void 0 === e && (e = !1), opHandler.print(this, e);\n }, e.prototype.reshape = function (e) {\n return this.throwIfDisposed(), opHandler.reshape(this, e);\n }, e.prototype.reshapeAs = function (e) {\n return this.throwIfDisposed(), this.reshape(e.shape);\n }, e.prototype.expandDims = function (e) {\n return void 0 === e && (e = 0), opHandler.expandDims(this, e);\n }, e.prototype.cumsum = function (e, t, n) {\n return void 0 === e && (e = 0), void 0 === t && (t = !1), void 0 === n && (n = !1), opHandler.cumsum(this, e, t, n);\n }, e.prototype.squeeze = function (e) {\n return this.throwIfDisposed(), opHandler.squeeze(this, e);\n }, e.prototype.clone = function () {\n return this.throwIfDisposed(), opHandler.clone(this);\n }, e.prototype.oneHot = function (e, t, n) {\n return this.throwIfDisposed(), opHandler.oneHot(this, e, t, n);\n }, e.prototype.toString = function (e) {\n return void 0 === e && (e = !1), tensorToString(this.dataSync(), this.shape, this.dtype, e);\n }, e.prototype.tile = function (e) {\n return this.throwIfDisposed(), opHandler.tile(this, e);\n }, e.prototype.gather = function (e, t) {\n return void 0 === t && (t = 0), this.throwIfDisposed(), opHandler.gather(this, e, t);\n }, e.prototype.matMul = function (e, t, n) {\n return void 0 === t && (t = !1), void 0 === n && (n = !1), this.throwIfDisposed(), opHandler.matMul(this, e, t, n);\n }, e.prototype.dot = function (e) {\n return this.throwIfDisposed(), opHandler.dot(this, e);\n }, e.prototype.norm = function (e, t, n) {\n return void 0 === e && (e = \"euclidean\"), void 0 === t && (t = null), void 0 === n && (n = !1), this.throwIfDisposed(), opHandler.norm(this, e, t, n);\n }, e.prototype.slice = function (e, t) {\n return this.throwIfDisposed(), opHandler.slice(this, e, t);\n }, e.prototype.reverse = function (e) {\n return this.throwIfDisposed(), opHandler.reverse(this, e);\n }, e.prototype.concat = function (t, n) {\n return void 0 === n && (n = 0), this.throwIfDisposed(), t instanceof e && (t = [t]), opHandler.concat([this].concat(t), n);\n }, e.prototype.split = function (e, t) {\n return void 0 === t && (t = 0), this.throwIfDisposed(), opHandler.split(this, e, t);\n }, e.prototype.stack = function (e, t) {\n return void 0 === t && (t = 0), opHandler.stack([this, e], t);\n }, e.prototype.unstack = function (e) {\n return void 0 === e && (e = 0), opHandler.unstack(this, e);\n }, e.prototype.pad = function (e, t) {\n return void 0 === t && (t = 0), opHandler.pad(this, e, t);\n }, e.prototype.batchNormalization = function (e, t, n, r, o) {\n return void 0 === n && (n = .001), deprecationWarningFn(\"tf.batchNormalization() is going away. Use tf.batchNorm() instead, and note the positional argument change of scale, offset, and varianceEpsilon\"), this.batchNorm(e, t, o, r, n);\n }, e.prototype.batchNorm = function (e, t, n, r, o) {\n return void 0 === o && (o = .001), this.throwIfDisposed(), opHandler.batchNorm(this, e, t, n, r, o);\n }, e.prototype.all = function (e, t) {\n return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), opHandler.all(this, e, t);\n }, e.prototype.any = function (e, t) {\n return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), opHandler.any(this, e, t);\n }, e.prototype.logSumExp = function (e, t) {\n return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), opHandler.logSumExp(this, e, t);\n }, e.prototype.sum = function (e, t) {\n return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), opHandler.sum(this, e, t);\n }, e.prototype.prod = function (e, t) {\n return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), opHandler.prod(this, e, t);\n }, e.prototype.mean = function (e, t) {\n return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), opHandler.mean(this, e, t);\n }, e.prototype.min = function (e, t) {\n return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), opHandler.min(this, e, t);\n }, e.prototype.max = function (e, t) {\n return void 0 === e && (e = null), void 0 === t && (t = !1), this.throwIfDisposed(), opHandler.max(this, e, t);\n }, e.prototype.argMin = function (e) {\n return void 0 === e && (e = null), this.throwIfDisposed(), opHandler.argMin(this, e);\n }, e.prototype.argMax = function (e) {\n return void 0 === e && (e = null), this.throwIfDisposed(), opHandler.argMax(this, e);\n }, e.prototype.cast = function (e) {\n return this.throwIfDisposed(), opHandler.cast(this, e);\n }, e.prototype.add = function (e) {\n return this.throwIfDisposed(), opHandler.add(this, e);\n }, e.prototype.addStrict = function (e) {\n return this.throwIfDisposed(), opHandler.addStrict(this, e);\n }, e.prototype.atan2 = function (e) {\n return this.throwIfDisposed(), opHandler.atan2(this, e);\n }, e.prototype.sub = function (e) {\n return this.throwIfDisposed(), opHandler.sub(this, e);\n }, e.prototype.subStrict = function (e) {\n return this.throwIfDisposed(), opHandler.subStrict(this, e);\n }, e.prototype.pow = function (e) {\n return this.throwIfDisposed(), opHandler.pow(this, e);\n }, e.prototype.powStrict = function (e) {\n return this.throwIfDisposed(), opHandler.powStrict(this, e);\n }, e.prototype.mul = function (e) {\n return this.throwIfDisposed(), opHandler.mul(this, e);\n }, e.prototype.mulStrict = function (e) {\n return this.throwIfDisposed(), opHandler.mulStrict(this, e);\n }, e.prototype.div = function (e) {\n return this.throwIfDisposed(), opHandler.div(this, e);\n }, e.prototype.floorDiv = function (e) {\n return this.throwIfDisposed(), opHandler.floorDiv(this, e);\n }, e.prototype.divStrict = function (e) {\n return this.throwIfDisposed(), opHandler.divStrict(this, e);\n }, e.prototype.minimum = function (e) {\n return this.throwIfDisposed(), opHandler.minimum(this, e);\n }, e.prototype.minimumStrict = function (e) {\n return this.throwIfDisposed(), opHandler.minimumStrict(this, e);\n }, e.prototype.maximum = function (e) {\n return this.throwIfDisposed(), opHandler.maximum(this, e);\n }, e.prototype.maximumStrict = function (e) {\n return this.throwIfDisposed(), opHandler.maximumStrict(this, e);\n }, e.prototype.mod = function (e) {\n return this.throwIfDisposed(), opHandler.mod(this, e);\n }, e.prototype.modStrict = function (e) {\n return this.throwIfDisposed(), opHandler.modStrict(this, e);\n }, e.prototype.squaredDifference = function (e) {\n return this.throwIfDisposed(), opHandler.squaredDifference(this, e);\n }, e.prototype.squaredDifferenceStrict = function (e) {\n return this.throwIfDisposed(), opHandler.squaredDifferenceStrict(this, e);\n }, e.prototype.transpose = function (e) {\n return this.throwIfDisposed(), opHandler.transpose(this, e);\n }, e.prototype.notEqual = function (e) {\n return this.throwIfDisposed(), opHandler.notEqual(this, e);\n }, e.prototype.notEqualStrict = function (e) {\n return this.throwIfDisposed(), opHandler.notEqualStrict(this, e);\n }, e.prototype.less = function (e) {\n return this.throwIfDisposed(), opHandler.less(this, e);\n }, e.prototype.lessStrict = function (e) {\n return this.throwIfDisposed(), opHandler.lessStrict(this, e);\n }, e.prototype.equal = function (e) {\n return this.throwIfDisposed(), opHandler.equal(this, e);\n }, e.prototype.equalStrict = function (e) {\n return this.throwIfDisposed(), opHandler.equalStrict(this, e);\n }, e.prototype.lessEqual = function (e) {\n return this.throwIfDisposed(), opHandler.lessEqual(this, e);\n }, e.prototype.lessEqualStrict = function (e) {\n return this.throwIfDisposed(), opHandler.lessEqualStrict(this, e);\n }, e.prototype.greater = function (e) {\n return this.throwIfDisposed(), opHandler.greater(this, e);\n }, e.prototype.greaterStrict = function (e) {\n return this.throwIfDisposed(), opHandler.greaterStrict(this, e);\n }, e.prototype.greaterEqual = function (e) {\n return this.throwIfDisposed(), opHandler.greaterEqual(this, e);\n }, e.prototype.greaterEqualStrict = function (e) {\n return this.throwIfDisposed(), opHandler.greaterEqualStrict(this, e);\n }, e.prototype.logicalAnd = function (e) {\n return this.throwIfDisposed(), opHandler.logicalAnd(this, e);\n }, e.prototype.logicalOr = function (e) {\n return this.throwIfDisposed(), opHandler.logicalOr(this, e);\n }, e.prototype.logicalNot = function () {\n return this.throwIfDisposed(), opHandler.logicalNot(this);\n }, e.prototype.logicalXor = function (e) {\n return this.throwIfDisposed(), opHandler.logicalXor(this, e);\n }, e.prototype.where = function (e, t) {\n return this.throwIfDisposed(), opHandler.where(e, this, t);\n }, e.prototype.neg = function () {\n return this.throwIfDisposed(), opHandler.neg(this);\n }, e.prototype.ceil = function () {\n return this.throwIfDisposed(), opHandler.ceil(this);\n }, e.prototype.floor = function () {\n return this.throwIfDisposed(), opHandler.floor(this);\n }, e.prototype.sign = function () {\n return this.throwIfDisposed(), opHandler.sign(this);\n }, e.prototype.exp = function () {\n return this.throwIfDisposed(), opHandler.exp(this);\n }, e.prototype.expm1 = function () {\n return this.throwIfDisposed(), opHandler.expm1(this);\n }, e.prototype.log = function () {\n return this.throwIfDisposed(), opHandler.log(this);\n }, e.prototype.log1p = function () {\n return this.throwIfDisposed(), opHandler.log1p(this);\n }, e.prototype.sqrt = function () {\n return this.throwIfDisposed(), opHandler.sqrt(this);\n }, e.prototype.rsqrt = function () {\n return this.throwIfDisposed(), opHandler.rsqrt(this);\n }, e.prototype.square = function () {\n return this.throwIfDisposed(), opHandler.square(this);\n }, e.prototype.reciprocal = function () {\n return this.throwIfDisposed(), opHandler.reciprocal(this);\n }, e.prototype.abs = function () {\n return this.throwIfDisposed(), opHandler.abs(this);\n }, e.prototype.clipByValue = function (e, t) {\n return this.throwIfDisposed(), opHandler.clipByValue(this, e, t);\n }, e.prototype.relu = function () {\n return this.throwIfDisposed(), opHandler.relu(this);\n }, e.prototype.elu = function () {\n return this.throwIfDisposed(), opHandler.elu(this);\n }, e.prototype.selu = function () {\n return this.throwIfDisposed(), opHandler.selu(this);\n }, e.prototype.leakyRelu = function (e) {\n return void 0 === e && (e = .2), this.throwIfDisposed(), opHandler.leakyRelu(this, e);\n }, e.prototype.prelu = function (e) {\n return this.throwIfDisposed(), opHandler.prelu(this, e);\n }, e.prototype.sigmoid = function () {\n return this.throwIfDisposed(), opHandler.sigmoid(this);\n }, e.prototype.logSigmoid = function () {\n return this.throwIfDisposed(), opHandler.logSigmoid(this);\n }, e.prototype.softplus = function () {\n return this.throwIfDisposed(), opHandler.softplus(this);\n }, e.prototype.zerosLike = function () {\n return this.throwIfDisposed(), opHandler.zerosLike(this);\n }, e.prototype.onesLike = function () {\n return this.throwIfDisposed(), opHandler.onesLike(this);\n }, e.prototype.sin = function () {\n return this.throwIfDisposed(), opHandler.sin(this);\n }, e.prototype.cos = function () {\n return this.throwIfDisposed(), opHandler.cos(this);\n }, e.prototype.tan = function () {\n return this.throwIfDisposed(), opHandler.tan(this);\n }, e.prototype.asin = function () {\n return this.throwIfDisposed(), opHandler.asin(this);\n }, e.prototype.acos = function () {\n return this.throwIfDisposed(), opHandler.acos(this);\n }, e.prototype.atan = function () {\n return this.throwIfDisposed(), opHandler.atan(this);\n }, e.prototype.sinh = function () {\n return this.throwIfDisposed(), opHandler.sinh(this);\n }, e.prototype.cosh = function () {\n return this.throwIfDisposed(), opHandler.cosh(this);\n }, e.prototype.tanh = function () {\n return this.throwIfDisposed(), opHandler.tanh(this);\n }, e.prototype.asinh = function () {\n return this.throwIfDisposed(), opHandler.asinh(this);\n }, e.prototype.acosh = function () {\n return this.throwIfDisposed(), opHandler.acosh(this);\n }, e.prototype.atanh = function () {\n return this.throwIfDisposed(), opHandler.atanh(this);\n }, e.prototype.erf = function () {\n return this.throwIfDisposed(), opHandler.erf(this);\n }, e.prototype.round = function () {\n return this.throwIfDisposed(), opHandler.round(this);\n }, e.prototype.step = function (e) {\n return void 0 === e && (e = 0), this.throwIfDisposed(), opHandler.step(this, e);\n }, e.prototype.softmax = function (e) {\n return void 0 === e && (e = -1), this.throwIfDisposed(), opHandler.softmax(this, e);\n }, e.prototype.logSoftmax = function (e) {\n return void 0 === e && (e = -1), this.throwIfDisposed(), opHandler.logSoftmax(this, e);\n }, e.prototype.resizeBilinear = function (e, t) {\n return void 0 === t && (t = !1), this.throwIfDisposed(), opHandler.image.resizeBilinear(this, e, t);\n }, e.prototype.resizeNearestNeighbor = function (e, t) {\n return void 0 === t && (t = !1), this.throwIfDisposed(), opHandler.image.resizeNearestNeighbor(this, e, t);\n }, e.prototype.conv1d = function (e, t, n, r, o, a) {\n return void 0 === r && (r = \"NWC\"), void 0 === o && (o = 1), this.throwIfDisposed(), opHandler.conv1d(this, e, t, n, r, o, a);\n }, e.prototype.conv2d = function (e, t, n, r, o, a) {\n return void 0 === r && (r = \"NHWC\"), void 0 === o && (o = [1, 1]), this.throwIfDisposed(), opHandler.conv2d(this, e, t, n, r, o, a);\n }, e.prototype.conv2dTranspose = function (e, t, n, r, o) {\n return this.throwIfDisposed(), opHandler.conv2dTranspose(this, e, t, n, r, o);\n }, e.prototype.depthwiseConv2D = function (e, t, n, r, o, a) {\n return void 0 === r && (r = \"NHWC\"), void 0 === o && (o = [1, 1]), this.throwIfDisposed(), opHandler.depthwiseConv2d(this, e, t, n, r, o, a);\n }, e.prototype.separableConv2d = function (e, t, n, r, o, a) {\n return void 0 === o && (o = [1, 1]), void 0 === a && (a = \"NHWC\"), this.throwIfDisposed(), opHandler.separableConv2d(this, e, t, n, r, o, a);\n }, e.prototype.avgPool = function (e, t, n, r) {\n return this.throwIfDisposed(), opHandler.avgPool(this, e, t, n, r);\n }, e.prototype.maxPool = function (e, t, n, r) {\n return this.throwIfDisposed(), opHandler.maxPool(this, e, t, n, r);\n }, e.prototype.localResponseNormalization = function (e, t, n, r) {\n return void 0 === e && (e = 5), void 0 === t && (t = 1), void 0 === n && (n = 1), void 0 === r && (r = .5), opHandler.localResponseNormalization(this, e, t, n, r);\n }, e.prototype.pool = function (e, t, n, r, o) {\n return this.throwIfDisposed(), opHandler.pool(this, e, t, n, r, o);\n }, e.prototype.variable = function (e, t, n) {\n return void 0 === e && (e = !0), this.throwIfDisposed(), Variable.variable(this, e, t, n);\n }, e.prototype.unsortedSegmentSum = function (e, t) {\n return this.throwIfDisposed(), opHandler.unsortedSegmentSum(this, e, t);\n }, e.prototype.batchToSpaceND = function (e, t) {\n return this.throwIfDisposed(), opHandler.batchToSpaceND(this, e, t);\n }, e.prototype.spaceToBatchND = function (e, t) {\n return this.throwIfDisposed(), opHandler.spaceToBatchND(this, e, t);\n }, e.prototype.topk = function (e, t) {\n return void 0 === e && (e = 1), void 0 === t && (t = !0), this.throwIfDisposed(), opHandler.topk(this, e, t);\n }, e.prototype.stridedSlice = function (e, t, n, r, o, a, i, s) {\n return void 0 === r && (r = 0), void 0 === o && (o = 0), void 0 === a && (a = 0), void 0 === i && (i = 0), void 0 === s && (s = 0), this.throwIfDisposed(), opHandler.stridedSlice(this, e, t, n, r, o, a, i, s);\n }, e.prototype.depthToSpace = function (e, t) {\n return this.throwIfDisposed(), opHandler.depthToSpace(this, e, t);\n }, e.prototype.fft = function () {\n return this.throwIfDisposed(), opHandler.spectral.fft(this);\n }, e.prototype.ifft = function () {\n return this.throwIfDisposed(), opHandler.spectral.ifft(this);\n }, e.prototype.rfft = function () {\n return this.throwIfDisposed(), opHandler.spectral.rfft(this);\n }, e.prototype.irfft = function () {\n return this.throwIfDisposed(), opHandler.spectral.irfft(this);\n }, e;\n}();\n\nexports.Tensor = Tensor;\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n value: function (e) {\n return !!e && null != e.dataId && null != e.shape && null != e.dtype;\n }\n});\n\nvar Variable = function (e) {\n function t(t, n, r) {\n void 0 === n && (n = !0);\n var o = e.call(this, t.shape, t.dtype, null, t.dataId) || this;\n o.trainable = n, o.name = r, null == o.name && (o.name = trackerFn().nextVariableId().toString());\n\n try {\n trackerFn().registerVariable(o);\n } catch (e) {\n throw trackerFn().disposeTensor(o), e;\n }\n\n return o;\n }\n\n return __extends(t, e), t.variable = function (e, n, r, o) {\n return void 0 === n && (n = !0), null != o && o !== e.dtype && (e = e.asType(o)), new t(e, n, r);\n }, t.prototype.assign = function (e) {\n if (e.dtype !== this.dtype) throw new Error(\"dtype of the new value (\" + e.dtype + \") and previous value (\" + this.dtype + \") must match\");\n if (!arraysEqual(e.shape, this.shape)) throw new Error(\"shape of the new value (\" + e.shape + \") and previous value (\" + this.shape + \") must match\");\n trackerFn().disposeTensor(this), this.dataId = e.dataId, trackerFn().registerTensor(this);\n }, t;\n}(Tensor);\n\nexports.Variable = Variable;\nObject.defineProperty(Variable, Symbol.hasInstance, {\n value: function (e) {\n return e instanceof Tensor && null != e.assign && e.assign instanceof Function;\n }\n});\nvar Rank,\n UpcastInt32AndMap,\n UpcastBoolAndMap,\n UpcastFloat32AndMap,\n UpcastComplex64AndMap,\n variable = Variable.variable;\nexports.variable = variable;\nexports.Rank = Rank;\n\nfunction getFilteredNodesXToY(e, t, n) {\n for (var r = {}, o = {}, a = 0; a < t.length; a++) r[t[a].id] = !0;\n\n for (a = 0; a < e.length; a++) {\n var i = (f = e[a]).inputs;\n\n for (var s in i) {\n for (var u = i[s], l = !1, c = 0; c < t.length; c++) if (r[u.id]) {\n f.outputs.forEach(function (e) {\n return r[e.id] = !0;\n }), l = !0, o[f.id] = !0;\n break;\n }\n\n if (l) break;\n }\n }\n\n var p = {};\n p[n.id] = !0;\n var d = {};\n\n for (a = e.length - 1; a >= 0; a--) for (i = (f = e[a]).inputs, c = 0; c < f.outputs.length; c++) if (p[f.outputs[c].id]) {\n for (var s in i) p[i[s].id] = !0, d[f.id] = !0;\n\n break;\n }\n\n var h = [];\n\n for (a = 0; a < e.length; a++) {\n var f;\n\n if (o[(f = e[a]).id] && d[f.id]) {\n var m = {};\n\n for (var s in f.inputs) {\n var g = f.inputs[s];\n r[g.id] && (m[s] = g);\n }\n\n var v = Object.assign({}, f);\n v.inputs = m, v.outputs = f.outputs, h.push(v);\n }\n }\n\n return h;\n}\n\nfunction backpropagateGradients(e, t) {\n for (var n = function (n) {\n var r = t[n],\n o = [];\n if (r.outputs.forEach(function (t) {\n var n = e[t.id];\n if (null != n) o.push(n);else {\n var r = Tensor.make(t.shape, {\n values: makeZerosTypedArray(t.size, t.dtype)\n }, t.dtype);\n o.push(r);\n }\n }), null == r.gradient) throw new Error(\"Cannot compute gradient: gradient function not found for \" + r.name + \".\");\n var a = r.gradient(1 === r.outputs.length ? o[0] : o);\n\n for (var i in r.inputs) {\n if (!(i in a)) throw new Error(\"Cannot backprop through input \" + i + \". Available gradients found: \" + Object.keys(a) + \".\");\n var s = a[i]();\n if (\"float32\" !== s.dtype) throw new Error(\"Error in gradient for op \" + r.name + \". The gradient of input \" + i + \" must have 'float32' dtype, but has '\" + s.dtype + \"'\");\n var u = r.inputs[i];\n if (!arraysEqual(s.shape, u.shape)) throw new Error(\"Error in gradient for op \" + r.name + \". The gradient of input '\" + i + \"' has shape '\" + s.shape + \"', which does not match the shape of the input '\" + u.shape + \"'\");\n if (null == e[u.id]) e[u.id] = s;else {\n var l = e[u.id];\n e[u.id] = l.add(s), l.dispose();\n }\n }\n }, r = t.length - 1; r >= 0; r--) n(r);\n}\n\n!function (e) {\n e.R0 = \"R0\", e.R1 = \"R1\", e.R2 = \"R2\", e.R3 = \"R3\", e.R4 = \"R4\", e.R5 = \"R5\", e.R6 = \"R6\";\n}(Rank || (exports.Rank = Rank = {})), function (e) {\n e.float32 = \"float32\", e.int32 = \"int32\", e.bool = \"int32\", e.complex64 = \"complex64\";\n}(UpcastInt32AndMap || (UpcastInt32AndMap = {})), function (e) {\n e.float32 = \"float32\", e.int32 = \"int32\", e.bool = \"bool\", e.complex64 = \"complex64\";\n}(UpcastBoolAndMap || (UpcastBoolAndMap = {})), function (e) {\n e.float32 = \"float32\", e.int32 = \"float32\", e.bool = \"float32\", e.complex64 = \"complex64\";\n}(UpcastFloat32AndMap || (UpcastFloat32AndMap = {})), function (e) {\n e.float32 = \"complex64\", e.int32 = \"complex64\", e.bool = \"complex64\", e.complex64 = \"complex64\";\n}(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nvar upcastTypeMap = {\n float32: UpcastFloat32AndMap,\n int32: UpcastInt32AndMap,\n bool: UpcastBoolAndMap,\n complex64: UpcastComplex64AndMap\n};\n\nfunction upcastType(e, t) {\n if (\"string\" === e || \"string\" === t) {\n if (\"string\" === e && \"string\" === t) return \"string\";\n throw new Error(\"Can not upcast \" + e + \" with \" + t);\n }\n\n return upcastTypeMap[e][t];\n}\n\nfunction sumOutType(e) {\n return upcastType(e, \"int32\");\n}\n\nfunction makeTypesMatch(e, t) {\n if (e.dtype === t.dtype) return [e, t];\n var n = upcastType(e.dtype, t.dtype);\n return [e.cast(n), t.cast(n)];\n}\n\nfunction assertTypesMatch(e, t) {\n assert(e.dtype === t.dtype, \"The dtypes of the first(\" + e.dtype + \") and second(\" + t.dtype + \") input must match\");\n}\n\nfunction isTensorInList(e, t) {\n for (var n = 0; n < t.length; n++) if (t[n].id === e.id) return !0;\n\n return !1;\n}\n\nfunction getTensorsInContainer(e) {\n var t = [];\n return walkTensorContainer(e, t, new Set()), t;\n}\n\nfunction walkTensorContainer(e, t, n) {\n if (null != e) if (e instanceof Tensor) t.push(e);else if (isIterable(e)) {\n var r = e;\n\n for (var o in r) {\n var a = r[o];\n n.has(a) || (n.add(a), walkTensorContainer(a, t, n));\n }\n }\n}\n\nfunction isIterable(e) {\n return Array.isArray(e) || \"object\" == typeof e;\n}\n\nvar Type,\n Engine = function () {\n function e(e, t, n) {\n this.backend = e, this.safeMode = t, this.debugMode = n, this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.profiling = !1, this.gradientScopeCount = 0, this.customGradientDepth = 0, this.scopeStack = [], this.keepTensors = new Set(), this.tensorInfo = new WeakMap(), this.profiler = new Profiler(e), this.activeProfile = {\n newBytes: 0,\n newTensors: 0,\n peakBytes: 0,\n kernels: [],\n result: null\n };\n }\n\n return e.prototype.moveData = function (e) {\n this.write(e, this.readSync(e));\n }, e.prototype.tidy = function (e, t, n) {\n var r = this;\n void 0 === n && (n = !1);\n var o,\n a = null;\n\n if (null == t) {\n if (\"function\" != typeof e) throw new Error(\"Please provide a function to tidy()\");\n t = e;\n } else {\n if (\"string\" != typeof e && !(e instanceof String)) throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");\n if (\"function\" != typeof t) throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");\n a = e;\n }\n\n return this.scopedRun(function () {\n return r.startScope(a, n);\n }, function () {\n return r.endScope(o, n);\n }, function () {\n return (o = t()) instanceof Promise && console.error(\"Cannot return a Promise inside of tidy.\"), o;\n });\n }, e.prototype.scopedRun = function (e, t, n) {\n e();\n\n try {\n var r = n();\n return t(), r;\n } catch (e) {\n throw t(), e;\n }\n }, e.prototype.nextTensorId = function () {\n return e.nextTensorId++;\n }, e.prototype.nextVariableId = function () {\n return e.nextVariableId++;\n }, e.prototype.runKernel = function (e, t, n) {\n var r,\n o = this,\n a = [],\n i = function (e) {\n return a.push(e), e;\n },\n s = null != this.activeScope ? this.activeScope.name : \"\",\n u = this.numBytes,\n l = this.numTensors;\n\n if (this.scopedRun(function () {\n return o.customGradientDepth++;\n }, function () {\n return o.customGradientDepth--;\n }, function () {\n r = o.debugMode() ? o.profiler.profileKernel(s, function () {\n return e(o.backend, i);\n }) : e(o.backend, i);\n }), this.shouldRecord()) {\n var c = {\n id: this.nextTapeNodeId++,\n name: s,\n inputs: t,\n outputs: Array.isArray(r) ? r : [r]\n };\n null != n && (c.gradient = function (e) {\n return n(e, a);\n }), this.activeTape.push(c);\n }\n\n return this.profiling && this.activeProfile.kernels.push({\n name: s,\n bytesAdded: this.numBytes - u,\n totalBytesSnapshot: this.numBytes,\n tensorsAdded: this.numTensors - l,\n totalTensorsSnapshot: this.numTensors,\n inputShapes: Object.keys(t).map(function (e) {\n return t[e].shape;\n }),\n outputShape: Array.isArray(r) ? r.map(function (e) {\n return e.shape;\n }) : r.shape\n }), r;\n }, e.prototype.registerTensor = function (e) {\n var t = this.tensorInfo.has(e.dataId) ? this.tensorInfo.get(e.dataId).refCount : 0;\n\n if (this.numTensors++, \"string\" === e.dtype && this.numStringTensors++, 0 === t) {\n this.numDataBuffers++;\n var n = 0;\n \"complex64\" !== e.dtype && \"string\" !== e.dtype && (n = sizeFromShape(e.shape) * bytesPerElement(e.dtype)), this.tensorInfo.set(e.dataId, {\n backend: this.backend,\n dtype: e.dtype,\n shape: e.shape,\n bytes: n,\n refCount: 0\n }), this.numBytes += n, this.backend.register(e.dataId, e.shape, e.dtype);\n }\n\n this.tensorInfo.get(e.dataId).refCount++, e instanceof Variable || this.track(e);\n }, e.prototype.registerVariable = function (e) {\n if (null != this.registeredVariables[e.name]) throw new Error(\"Variable with name \" + e.name + \" was already registered\");\n this.registeredVariables[e.name] = e;\n }, e.prototype.disposeTensor = function (e) {\n if (this.tensorInfo.has(e.dataId)) {\n this.keepTensors.has(e.id) && this.keepTensors.delete(e.id), this.numTensors--, \"string\" === e.dtype && this.numStringTensors--;\n var t = this.tensorInfo.get(e.dataId);\n t.refCount <= 1 ? (\"complex64\" !== e.dtype && (this.numBytes -= t.bytes), this.numDataBuffers--, t.backend.disposeData(e.dataId), this.tensorInfo.delete(e.dataId)) : this.tensorInfo.get(e.dataId).refCount--;\n }\n }, e.prototype.disposeVariables = function () {\n for (var e in this.registeredVariables) {\n var t = this.registeredVariables[e];\n this.disposeTensor(t), delete this.registeredVariables[e];\n }\n }, e.prototype.memory = function () {\n var e = this.backend.memory();\n return e.numTensors = this.numTensors, e.numDataBuffers = this.numDataBuffers, e.numBytes = this.numBytes, this.numStringTensors > 0 && (e.unreliable = !0, null == e.reasons && (e.reasons = []), e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")), e;\n }, e.prototype.profile = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n;\n return __generator(this, function (r) {\n return this.profiling = !0, t = this.numBytes, n = this.numTensors, this.activeProfile.kernels = [], this.activeProfile.result = e(), this.profiling = !1, this.activeProfile.peakBytes = Math.max.apply(Math, this.activeProfile.kernels.map(function (e) {\n return e.totalBytesSnapshot;\n })), this.activeProfile.newBytes = this.numBytes - t, this.activeProfile.newTensors = this.numTensors - n, [2, this.activeProfile];\n });\n });\n }, e.prototype.shouldRecord = function () {\n return null != this.activeTape && 0 === this.customGradientDepth;\n }, e.prototype.addTapeNode = function (e, t, n) {\n var r = {};\n e.forEach(function (e, t) {\n r[t] = e;\n });\n var o = {\n id: this.nextTapeNodeId++,\n name: this.activeScope.name,\n inputs: r,\n outputs: [t],\n gradient: function (e) {\n var t = {};\n return n(e).forEach(function (e, n) {\n t[n] = function () {\n return e;\n };\n }), t;\n }\n };\n this.activeTape.push(o);\n }, e.prototype.keep = function (e) {\n if (1 === this.scopeStack.length && this.safeMode) throw new Error(\"Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {...}) to avoid memory leaks.\");\n return this.keepTensors.add(e.id), e;\n }, e.prototype.startScope = function (e, t) {\n void 0 === t && (t = !1), t && 0 === this.gradientScopeCount && (this.activeTape = []), t && this.gradientScopeCount++;\n var n = {\n track: [],\n name: \"unnamed scope\"\n };\n e && (n.name = e), this.scopeStack.push(n), this.activeScope = n;\n }, e.prototype.endScope = function (e, t) {\n var n = this;\n void 0 === t && (t = !1), t && (this.gradientScopeCount--, 0 === this.gradientScopeCount && (this.activeTape = null));\n var r = new Set(this.keepTensors),\n o = getTensorsInContainer(e);\n o.forEach(function (e) {\n return r.add(e.id);\n });\n\n for (var a = 0; a < this.activeScope.track.length; a++) {\n var i = this.activeScope.track[a];\n r.has(i.id) || (null != this.activeTape ? o.push(i) : i.dispose());\n }\n\n var s = this.scopeStack.pop();\n this.activeScope = 0 === this.scopeStack.length ? null : this.scopeStack[this.scopeStack.length - 1], o.forEach(function (e) {\n !n.keepTensors.has(e.id) && isTensorInList(e, s.track) && n.track(e);\n });\n }, e.prototype.gradients = function (e, t, n, r) {\n var o = this;\n if (void 0 === r && (r = !1), assert(t.length > 0, \"gradients() received an empty list of xs.\"), null != n && \"float32\" !== n.dtype) throw new Error(\"dy must have 'float32' dtype, but has '\" + n.dtype + \"'\");\n return this.tidy(\"gradients\", function () {\n var a = e();\n assert(a instanceof Tensor, \"The result y returned by f() must be a tensor.\");\n var i = getFilteredNodesXToY(o.activeTape, t, a);\n if (!r && 0 === i.length && t.length > 0) throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");\n var s = {};\n return s[a.id] = null == n ? ones(a.shape) : n, backpropagateGradients(s, i), {\n value: a,\n grads: t.map(function (e) {\n return s[e.id];\n })\n };\n }, !0);\n }, e.prototype.customGrad = function (e) {\n var t = this;\n return assert(isFunction(e), \"The f passed in customGrad(f) must be a function.\"), function () {\n for (var n, r, o = [], a = 0; a < arguments.length; a++) o[a] = arguments[a];\n\n if (assert(o.every(function (e) {\n return e instanceof Tensor;\n }), \"The args passed in customGrad(f)(x1, x2,...) must all be tensors\"), t.scopedRun(function () {\n return t.customGradientDepth++;\n }, function () {\n return t.customGradientDepth--;\n }, function () {\n r = t.tidy(e.name, function () {\n var t = e.apply(void 0, o),\n r = t.value,\n a = t.gradFunc;\n return assert(r instanceof Tensor, \"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"), assert(isFunction(a), \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"), n = a, r;\n }, !0);\n }), t.shouldRecord()) {\n t.addTapeNode(o, r, function (e) {\n var t = n(e),\n r = Array.isArray(t) ? t : [t];\n return assert(r.length === o.length, \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"), assert(r.every(function (e) {\n return e instanceof Tensor;\n }), \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\"), r;\n });\n }\n\n return r;\n };\n }, e.prototype.write = function (e, t) {\n var n = this.tensorInfo.get(e);\n\n if (\"string\" === n.dtype) {\n var r = bytesFromStringArray(t);\n this.numBytes += r - n.bytes, n.bytes = r;\n }\n\n this.backend !== n.backend && (n.backend.disposeData(e), n.backend = this.backend, this.backend.register(e, n.shape, n.dtype)), this.backend.write(e, t);\n }, e.prototype.readSync = function (e) {\n return this.tensorInfo.get(e).backend.readSync(e);\n }, e.prototype.read = function (e) {\n return this.tensorInfo.get(e).backend.read(e);\n }, e.prototype.fromPixels = function (e, t) {\n return this.backend.fromPixels(e, t);\n }, e.prototype.time = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n;\n return __generator(this, function (r) {\n switch (r.label) {\n case 0:\n return t = now(), [4, this.backend.time(e)];\n\n case 1:\n return (n = r.sent()).wallMs = now() - t, [2, n];\n }\n });\n });\n }, e.prototype.track = function (e) {\n if (1 === this.scopeStack.length && this.safeMode) throw new Error(\"Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {op();...}); to avoid memory leaks.\");\n return null != this.activeScope && this.activeScope.track.push(e), e;\n }, e.nextTensorId = 0, e.nextVariableId = 0, e;\n}();\n\nfunction ones(e) {\n var t = makeOnesTypedArray(sizeFromShape(e), \"float32\");\n return Tensor.make(e, {\n values: t\n });\n}\n\n!function (e) {\n e[e.NUMBER = 0] = \"NUMBER\", e[e.BOOLEAN = 1] = \"BOOLEAN\", e[e.STRING = 2] = \"STRING\";\n}(Type || (Type = {}));\nvar MAX_TEXTURE_SIZE,\n MAX_TEXTURES_IN_SHADER,\n URL_PROPERTIES = [{\n name: \"DEBUG\",\n type: Type.BOOLEAN\n}, {\n name: \"IS_BROWSER\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_LAZILY_UNPACK\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_CPU_FORWARD\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_PACK\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_PACK_BATCHNORMALIZATION\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_PACK_CLIP\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_PACK_DEPTHWISECONV\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_PACK_BINARY_OPERATIONS\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_PACK_ARRAY_OPERATIONS\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_CONV_IM2COL\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_MAX_TEXTURE_SIZE\",\n type: Type.NUMBER\n}, {\n name: \"WEBGL_NUM_MB_BEFORE_PAGING\",\n type: Type.NUMBER\n}, {\n name: \"WEBGL_MAX_TEXTURES_IN_SHADER\",\n type: Type.NUMBER\n}, {\n name: \"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",\n type: Type.NUMBER\n}, {\n name: \"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_VERSION\",\n type: Type.NUMBER\n}, {\n name: \"WEBGL_RENDER_FLOAT32_ENABLED\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_DOWNLOAD_FLOAT_ENABLED\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_FENCE_API_ENABLED\",\n type: Type.BOOLEAN\n}, {\n name: \"WEBGL_SIZE_UPLOAD_UNIFORM\",\n type: Type.NUMBER\n}, {\n name: \"BACKEND\",\n type: Type.STRING\n}, {\n name: \"EPSILON\",\n type: Type.NUMBER\n}, {\n name: \"PROD\",\n type: Type.BOOLEAN\n}, {\n name: \"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\",\n type: Type.BOOLEAN\n}, {\n name: \"DEPRECATION_WARNINGS_ENABLED\",\n type: Type.BOOLEAN\n}];\n\nfunction isWebGLVersionEnabled(e) {\n try {\n if (null != getWebGLContext(e)) return !0;\n } catch (e) {\n return !1;\n }\n\n return !1;\n}\n\nfunction getWebGLMaxTextureSize(e) {\n if (null == MAX_TEXTURE_SIZE) {\n var t = getWebGLContext(e);\n MAX_TEXTURE_SIZE = t.getParameter(t.MAX_TEXTURE_SIZE);\n }\n\n return MAX_TEXTURE_SIZE;\n}\n\nfunction getMaxTexturesInShader(e) {\n if (null == MAX_TEXTURES_IN_SHADER) {\n var t = getWebGLContext(e);\n MAX_TEXTURES_IN_SHADER = t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS);\n }\n\n return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\n\nfunction getWebGLDisjointQueryTimerVersion(e) {\n if (0 === e) return 0;\n var t = getWebGLContext(e);\n return hasExtension(t, \"EXT_disjoint_timer_query_webgl2\") && 2 === e ? 2 : hasExtension(t, \"EXT_disjoint_timer_query\") ? 1 : 0;\n}\n\nfunction isRenderToFloatTextureEnabled(e) {\n if (0 === e) return !1;\n var t = getWebGLContext(e);\n\n if (1 === e) {\n if (!hasExtension(t, \"OES_texture_float\")) return !1;\n } else if (!hasExtension(t, \"EXT_color_buffer_float\")) return !1;\n\n return createFloatTextureAndBindToFramebuffer(t, e);\n}\n\nfunction isDownloadFloatTextureEnabled(e) {\n if (0 === e) return !1;\n var t = getWebGLContext(e);\n\n if (1 === e) {\n if (!hasExtension(t, \"OES_texture_float\")) return !1;\n if (!hasExtension(t, \"WEBGL_color_buffer_float\")) return !1;\n } else if (!hasExtension(t, \"EXT_color_buffer_float\")) return !1;\n\n return createFloatTextureAndBindToFramebuffer(t, e);\n}\n\nfunction isWebGLFenceEnabled(e) {\n return 2 === e && null != getWebGLContext(e).fenceSync;\n}\n\nfunction isChrome() {\n return \"undefined\" != typeof navigator && null != navigator && null != navigator.userAgent && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor);\n}\n\nvar TENSORFLOWJS_FLAGS_PREFIX = \"tfjsflags\";\n\nfunction getFeaturesFromURL() {\n var e = {};\n if (\"undefined\" == typeof window || void 0 === window.location || void 0 === window.location.search) return e;\n var t = getQueryParams(window.location.search);\n\n if (TENSORFLOWJS_FLAGS_PREFIX in t) {\n var n = {};\n t[TENSORFLOWJS_FLAGS_PREFIX].split(\",\").forEach(function (e) {\n var t = e.split(\":\"),\n r = t[0],\n o = t[1];\n n[r] = o;\n }), URL_PROPERTIES.forEach(function (t) {\n t.name in n && (console.log(\"Setting feature override from URL \" + t.name + \": \" + n[t.name]), t.type === Type.NUMBER ? e[t.name] = +n[t.name] : t.type === Type.BOOLEAN ? e[t.name] = \"true\" === n[t.name] : t.type === Type.STRING ? e[t.name] = n[t.name] : console.warn(\"Unknown URL param: \" + t.name + \".\"));\n });\n }\n\n return e;\n}\n\nfunction hasExtension(e, t) {\n return null != e.getExtension(t);\n}\n\nfunction createFloatTextureAndBindToFramebuffer(e, t) {\n var n = e.createFramebuffer(),\n r = e.createTexture();\n e.bindTexture(e.TEXTURE_2D, r);\n var o = 2 === t ? e.RGBA32F : e.RGBA;\n e.texImage2D(e.TEXTURE_2D, 0, o, 1, 1, 0, e.RGBA, e.FLOAT, null), e.bindFramebuffer(e.FRAMEBUFFER, n), e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, r, 0);\n var a = e.checkFramebufferStatus(e.FRAMEBUFFER) === e.FRAMEBUFFER_COMPLETE;\n return e.bindTexture(e.TEXTURE_2D, null), e.bindFramebuffer(e.FRAMEBUFFER, null), e.deleteTexture(r), e.deleteFramebuffer(n), a;\n}\n\nfunction getQueryParams(e) {\n var t = {};\n return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, function (e) {\n for (var n = [], r = 1; r < arguments.length; r++) n[r - 1] = arguments[r];\n\n return decodeParam(t, n[0], n[1]), n.join(\"=\");\n }), t;\n}\n\nfunction decodeParam(e, t, n) {\n e[decodeURIComponent(t)] = decodeURIComponent(n || \"\");\n}\n\nvar BEFORE_PAGING_CONSTANT = 600;\n\nfunction getNumMBBeforePaging() {\n return window.screen.height * window.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024;\n}\n\nvar EPSILON_FLOAT16 = 1e-4,\n TEST_EPSILON_FLOAT16 = .1,\n EPSILON_FLOAT32 = 1e-7,\n TEST_EPSILON_FLOAT32 = .001,\n Environment = function () {\n function e(e) {\n this.features = {}, this.registry = {}, null != e && (this.features = e), this.get(\"DEBUG\") && console.warn(\"Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.\");\n }\n\n return e.setBackend = function (e, t) {\n if (void 0 === t && (t = !1), !(e in ENV.registry)) throw new Error(\"Backend name '\" + e + \"' not found in registry\");\n ENV.engine.backend = ENV.findBackend(e), ENV.backendName = e;\n }, e.getBackend = function () {\n return ENV.initEngine(), ENV.backendName;\n }, e.disposeVariables = function () {\n ENV.engine.disposeVariables();\n }, e.memory = function () {\n return ENV.engine.memory();\n }, e.profile = function (e) {\n return ENV.engine.profile(e);\n }, e.tidy = function (e, t) {\n return ENV.engine.tidy(e, t);\n }, e.dispose = function (e) {\n getTensorsInContainer(e).forEach(function (e) {\n return e.dispose();\n });\n }, e.keep = function (e) {\n return ENV.engine.keep(e);\n }, e.time = function (e) {\n return ENV.engine.time(e);\n }, e.prototype.get = function (e) {\n return e in this.features ? this.features[e] : (this.features[e] = this.evaluateFeature(e), this.features[e]);\n }, e.prototype.getFeatures = function () {\n return this.features;\n }, e.prototype.set = function (e, t) {\n this.features[e] = t;\n }, e.prototype.getBestBackendName = function () {\n var e = this;\n if (0 === Object.keys(this.registry).length) throw new Error(\"No backend found in registry.\");\n return Object.keys(this.registry).map(function (t) {\n return {\n name: t,\n entry: e.registry[t]\n };\n }).sort(function (e, t) {\n return t.entry.priority - e.entry.priority;\n })[0].name;\n }, e.prototype.evaluateFeature = function (e) {\n if (\"DEBUG\" === e) return !1;\n if (\"IS_BROWSER\" === e) return \"undefined\" != typeof window;\n if (\"IS_NODE\" === e) return \"undefined\" != typeof process && void 0 !== process.versions && void 0 !== process.versions.node;\n if (\"IS_CHROME\" === e) return isChrome();\n if (\"WEBGL_CPU_FORWARD\" === e) return !0;\n if (\"WEBGL_PACK\" === e) return !1;\n if (\"WEBGL_PACK_BATCHNORMALIZATION\" === e) return this.get(\"WEBGL_PACK\");\n if (\"WEBGL_PACK_CLIP\" === e) return this.get(\"WEBGL_PACK\");\n if (\"WEBGL_PACK_DEPTHWISECONV\" === e) return this.get(\"WEBGL_PACK\");\n if (\"WEBGL_PACK_BINARY_OPERATIONS\" === e) return this.get(\"WEBGL_PACK\");\n if (\"WEBGL_PACK_ARRAY_OPERATIONS\" === e) return this.get(\"WEBGL_PACK\");\n if (\"WEBGL_LAZILY_UNPACK\" === e) return this.get(\"WEBGL_PACK\");\n if (\"WEBGL_CONV_IM2COL\" === e) return this.get(\"WEBGL_PACK\");\n if (\"WEBGL_NUM_MB_BEFORE_PAGING\" === e) return this.get(\"PROD\") || !this.get(\"IS_BROWSER\") ? Number.POSITIVE_INFINITY : getNumMBBeforePaging();\n if (\"WEBGL_MAX_TEXTURE_SIZE\" === e) return getWebGLMaxTextureSize(this.get(\"WEBGL_VERSION\"));\n if (\"WEBGL_MAX_TEXTURES_IN_SHADER\" === e) return getMaxTexturesInShader(this.get(\"WEBGL_VERSION\"));\n if (\"IS_TEST\" === e) return !1;\n if (\"BACKEND\" === e) return this.getBestBackendName();\n\n if (\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\" === e) {\n var t = this.get(\"WEBGL_VERSION\");\n return 0 === t ? 0 : getWebGLDisjointQueryTimerVersion(t);\n }\n\n if (\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\" === e) return this.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0 && !isMobile();\n if (\"HAS_WEBGL\" === e) return this.get(\"WEBGL_VERSION\") > 0;\n if (\"WEBGL_VERSION\" === e) return isWebGLVersionEnabled(2) ? 2 : isWebGLVersionEnabled(1) ? 1 : 0;\n if (\"WEBGL_RENDER_FLOAT32_ENABLED\" === e) return isRenderToFloatTextureEnabled(this.get(\"WEBGL_VERSION\"));\n if (\"WEBGL_DOWNLOAD_FLOAT_ENABLED\" === e) return isDownloadFloatTextureEnabled(this.get(\"WEBGL_VERSION\"));\n if (\"WEBGL_FENCE_API_ENABLED\" === e) return isWebGLFenceEnabled(this.get(\"WEBGL_VERSION\"));\n if (\"WEBGL_SIZE_UPLOAD_UNIFORM\" === e) return this.get(\"WEBGL_RENDER_FLOAT32_ENABLED\") ? 4 : 0;\n if (\"TEST_EPSILON\" === e) return 32 === this.backend.floatPrecision() ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;\n if (\"EPSILON\" === e) return 32 === this.backend.floatPrecision() ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n if (\"PROD\" === e) return !1;\n if (\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\" === e) return !this.get(\"PROD\");\n if (\"DEPRECATION_WARNINGS_ENABLED\" === e) return !0;\n throw new Error(\"Unknown feature \" + e + \".\");\n }, e.prototype.setFeatures = function (e) {\n this.features = Object.assign({}, e);\n }, e.prototype.reset = function () {\n this.features = getFeaturesFromURL(), null != this.globalEngine && (this.globalEngine = null);\n }, Object.defineProperty(e.prototype, \"backend\", {\n get: function () {\n return this.engine.backend;\n },\n enumerable: !0,\n configurable: !0\n }), e.prototype.findBackend = function (e) {\n return e in this.registry ? this.registry[e].backend : null;\n }, e.prototype.registerBackend = function (e, t, n, r) {\n var o = this;\n if (void 0 === n && (n = 1), e in this.registry) return console.warn(e + \" backend was already registered. Reusing existing backend\"), null != r && r(function () {\n return o.engine;\n }), !1;\n\n try {\n var a = t();\n return a.setDataMover({\n moveData: function (e) {\n return o.engine.moveData(e);\n }\n }), this.registry[e] = {\n backend: a,\n priority: n\n }, !0;\n } catch (t) {\n return console.warn(\"Registration of backend \" + e + \" failed\"), console.warn(t.stack || t.message), !1;\n }\n }, e.prototype.removeBackend = function (e) {\n if (!(e in this.registry)) throw new Error(e + \" backend not found in registry\");\n this.registry[e].backend.dispose(), delete this.registry[e];\n }, Object.defineProperty(e.prototype, \"engine\", {\n get: function () {\n return this.initEngine(), this.globalEngine;\n },\n enumerable: !0,\n configurable: !0\n }), e.prototype.initEngine = function () {\n var e = this;\n\n if (null == this.globalEngine) {\n this.backendName = this.get(\"BACKEND\");\n var t = this.findBackend(this.backendName);\n this.globalEngine = new Engine(t, !1, function () {\n return e.get(\"DEBUG\");\n });\n }\n }, e;\n}();\n\nexports.Environment = Environment;\n\nfunction getGlobalNamespace() {\n var e;\n if (\"undefined\" != typeof window) e = window;else {\n if (\"undefined\" == typeof process) throw new Error(\"Could not find a global object\");\n e = process;\n }\n return e;\n}\n\nfunction getOrMakeEnvironment() {\n var e = getGlobalNamespace();\n return null == e.ENV && (e.ENV = new Environment(getFeaturesFromURL()), setTensorTracker(function () {\n return e.ENV.engine;\n })), e.ENV;\n}\n\nfunction enableProdMode() {\n ENV.set(\"PROD\", !0);\n}\n\nfunction disableDeprecationWarnings() {\n ENV.set(\"DEPRECATION_WARNINGS_ENABLED\", !1), console.warn(\"TensorFlow.js deprecation warnings have been disabled.\");\n}\n\nfunction deprecationWarn(e) {\n ENV.get(\"DEPRECATION_WARNINGS_ENABLED\") && console.warn(e + \" You can disable deprecation warnings with tf.disableDeprecationWarnings().\");\n}\n\nsetDeprecationWarningFn(deprecationWarn);\nvar ENV = getOrMakeEnvironment(),\n environment = Object.freeze({\n EPSILON_FLOAT16: EPSILON_FLOAT16,\n EPSILON_FLOAT32: EPSILON_FLOAT32,\n Environment: Environment,\n enableProdMode: enableProdMode,\n disableDeprecationWarnings: disableDeprecationWarnings,\n deprecationWarn: deprecationWarn,\n ENV: ENV\n});\nexports.environment = environment;\nexports.ENV = ENV;\n\nfunction grad(e) {\n return assert(isFunction(e), \"The f passed in grad(f) must be a function\"), function (t, n) {\n return assert(t instanceof Tensor, \"The x passed in grad(f)(x) must be a tensor\"), assert(null == n || n instanceof Tensor, \"The dy passed in grad(f)(x, dy) must be a tensor\"), ENV.engine.tidy(function () {\n var r = ENV.engine.gradients(function () {\n return e(t);\n }, [t], n),\n o = r.value,\n a = r.grads;\n return null != n && assertShapesMatch(o.shape, n.shape, \"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)\"), checkGrads(a), a[0];\n });\n };\n}\n\nfunction grads(e) {\n return assert(isFunction(e), \"The f passed in grads(f) must be a function\"), function (t, n) {\n return assert(Array.isArray(t) && t.every(function (e) {\n return e instanceof Tensor;\n }), \"The args passed in grads(f)(args) must be an array of tensors\"), assert(null == n || n instanceof Tensor, \"The dy passed in grads(f)(args, dy) must be a tensor\"), ENV.engine.tidy(function () {\n var r = ENV.engine.gradients(function () {\n return e.apply(void 0, t);\n }, t, n),\n o = r.value,\n a = r.grads;\n return null != n && assertShapesMatch(o.shape, n.shape, \"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])\"), checkGrads(a), a;\n });\n };\n}\n\nfunction valueAndGrad(e) {\n return assert(isFunction(e), \"The f passed in valueAndGrad(f) must be a function\"), function (t, n) {\n assert(t instanceof Tensor, \"The x passed in valueAndGrad(f)(x) must be a tensor\"), assert(null == n || n instanceof Tensor, \"The dy passed in valueAndGrad(f)(x, dy) must be a tensor\");\n var r = ENV.engine.gradients(function () {\n return e(t);\n }, [t], n),\n o = r.grads,\n a = r.value;\n return checkGrads(o), {\n grad: o[0],\n value: a\n };\n };\n}\n\nfunction valueAndGrads(e) {\n return assert(isFunction(e), \"The f passed in valueAndGrads(f) must be a function\"), function (t, n) {\n assert(Array.isArray(t) && t.every(function (e) {\n return e instanceof Tensor;\n }), \"The args passed in valueAndGrads(f)(args) must be array of tensors\"), assert(null == n || n instanceof Tensor, \"The dy passed in valueAndGrads(f)(args, dy) must be a tensor\");\n var r = ENV.engine.gradients(function () {\n return e.apply(void 0, t);\n }, t, n);\n return null != n && assertShapesMatch(r.value.shape, n.shape, \"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])\"), checkGrads(r.grads), r;\n };\n}\n\nfunction variableGrads(e, t) {\n if (assert(isFunction(e), \"The f passed in variableGrads(f) must be a function\"), assert(null == t || Array.isArray(t) && t.every(function (e) {\n return e instanceof Variable;\n }), \"The varList passed in variableGrads(f, varList) must be an array of variables\"), null == t) for (var n in t = [], ENV.engine.registeredVariables) t.push(ENV.engine.registeredVariables[n]);\n var r = t.length;\n assert((t = t.filter(function (e) {\n return e.trainable;\n })).length > 0, \"variableGrads() expects at least one of the input variables to be trainable, but none of the \" + r + \" variables is trainable.\");\n var o = ENV.engine.gradients(e, t, null, !0),\n a = o.value,\n i = o.grads;\n assert(i.some(function (e) {\n return null != e;\n }), \"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().\"), assert(0 === a.rank, \"The f passed in variableGrads(f) must return a scalar, but it returned a rank-\" + a.rank + \" tensor\");\n var s = {};\n return t.forEach(function (e, t) {\n null != i[t] && (s[e.name] = i[t]);\n }), {\n value: a,\n grads: s\n };\n}\n\nfunction customGrad(e) {\n return ENV.engine.customGrad(e);\n}\n\nfunction checkGrads(e) {\n if (e.filter(function (e) {\n return null == e;\n }).length > 0) throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that\\n the f you passed encloses all operations that lead from x to y.\");\n}\n\nvar tidy = Environment.tidy,\n keep = Environment.keep,\n dispose = Environment.dispose,\n time = Environment.time,\n profile = Environment.profile;\nexports.profile = profile;\nexports.time = time;\nexports.dispose = dispose;\nexports.keep = keep;\nexports.tidy = tidy;\n\nfunction warn() {\n for (var e = [], t = 0; t < arguments.length; t++) e[t] = arguments[t];\n\n ENV.get(\"IS_TEST\") || console.warn.apply(console, e);\n}\n\nfunction getReshaped(e, t, n, r) {\n void 0 === r && (r = !0);\n var o = [];\n if (r) (o = o.concat(t.slice(0))).push(e[0] / n), o = o.concat(e.slice(1));else {\n o = o.concat(e[0]);\n\n for (var a = t.length, i = 0; i < a; ++i) o = o.concat([e[i + 1] / t[i], t[i]]);\n\n o = o.concat(e.slice(a + 1));\n }\n return o;\n}\n\nfunction getPermuted(e, t, n) {\n void 0 === n && (n = !0);\n var r = [];\n\n if (n) {\n r.push(t);\n\n for (var o = t + 1; o < e; ++o) o <= 2 * t ? (r.push(o), r.push(o - (t + 1))) : r.push(o);\n } else {\n var a = [],\n i = [];\n\n for (o = 1; o < e; ++o) o >= 2 * t + 1 || o % 2 == 1 ? i.push(o) : a.push(o);\n\n r.push.apply(r, a), r.push(0), r.push.apply(r, i);\n }\n\n return r;\n}\n\nfunction getReshapedPermuted(e, t, n, r) {\n void 0 === r && (r = !0);\n var o = [];\n r ? o.push(e[0] / n) : o.push(e[0] * n);\n\n for (var a = 1; a < e.length; ++a) a <= t.length ? r ? o.push(t[a - 1] * e[a]) : o.push(e[a] / t[a - 1]) : o.push(e[a]);\n\n return o;\n}\n\nfunction getSliceBeginCoords(e, t) {\n for (var n = [0], r = 0; r < t; ++r) n.push(e[r][0]);\n\n return n;\n}\n\nfunction getSliceSize(e, t, n) {\n for (var r = e.slice(0, 1), o = 0; o < n; ++o) r.push(e[o + 1] - t[o][0] - t[o][1]);\n\n return r;\n}\n\nfunction axesAreInnerMostDims(e, t) {\n for (var n = 0; n < e.length; ++n) if (e[e.length - n - 1] !== t - 1 - n) return !1;\n\n return !0;\n}\n\nfunction combineLocations(e, t, n) {\n for (var r = e.length + t.length, o = [], a = 0, i = 0, s = 0; s < r; s++) -1 === n.indexOf(s) ? o.push(e[a++]) : o.push(t[i++]);\n\n return o;\n}\n\nfunction computeOutAndReduceShapes(e, t) {\n for (var n = [], r = e.length, o = 0; o < r; o++) -1 === t.indexOf(o) && n.push(e[o]);\n\n return [n, t.map(function (t) {\n return e[t];\n })];\n}\n\nfunction expandShapeToKeepDim(e, t) {\n return combineLocations(e, t.map(function (e) {\n return 1;\n }), t);\n}\n\nfunction assertAxesAreInnerMostDims(e, t, n) {\n assert(axesAreInnerMostDims(t, n), e + \" supports only inner-most axes for now. Got axes \" + t + \" and rank-\" + n + \" input.\");\n}\n\nfunction getAxesPermutation(e, t) {\n if (axesAreInnerMostDims(e, t)) return null;\n\n for (var n = [], r = 0; r < t; ++r) -1 === e.indexOf(r) && n.push(r);\n\n return e.forEach(function (e) {\n return n.push(e);\n }), n;\n}\n\nfunction getUndoAxesPermutation(e) {\n return e.map(function (e, t) {\n return [t, e];\n }).sort(function (e, t) {\n return e[1] - t[1];\n }).map(function (e) {\n return e[0];\n });\n}\n\nfunction getInnerMostAxes(e, t) {\n for (var n = [], r = t - e; r < t; ++r) n.push(r);\n\n return n;\n}\n\nfunction assertParamsConsistent(e, t) {\n var n = e[0].length;\n e.forEach(function (e, t) {\n assert(e.length === n, \"Error in concat\" + n + \"D: rank of tensors[\" + t + \"] must be the same as the rank of the rest (\" + n + \")\");\n }), assert(t >= 0 && t < n, \"Error in concat\" + n + \"D: axis must be between 0 and \" + (n - 1) + \".\");\n var r = e[0];\n e.forEach(function (e, o) {\n for (var a = 0; a < n; a++) assert(a === t || e[a] === r[a], \"Error in concat\" + n + \"D: Shape of tensors[\" + o + \"] (\" + e + \") does not match the shape of the rest (\" + r + \") along the non-concatenated axis \" + o + \".\");\n });\n}\n\nfunction computeOutShape(e, t) {\n for (var n = e[0].slice(), r = 1; r < e.length; r++) n[t] += e[r][t];\n\n return n;\n}\n\nfunction prepareAndValidate(e, t) {\n if (e.rank < 1) throw new Error(\"tf.gatherND() expects the input to be rank 1 or higher, but the rank was \" + e.rank + \".\");\n if (t.rank < 1) throw new Error(\"tf.gatherND() expects the indices to be rank 1 or higher, but the rank was \" + t.rank + \".\");\n if (\"int32\" !== t.dtype) throw new Error(\"tf.gatherND() expects the indices to be int32 type, but the dtype was \" + t.dtype + \".\");\n if (t.shape[t.rank - 1] > e.rank) throw new Error(\"index innermost dimension length must be <= tensor rank; saw: \" + t.shape[t.rank - 1] + \" vs. \" + e.rank);\n if (0 === e.size) throw new Error(\"Requested more than 0 entries, but input is empty. Input shape: \" + e.shape + \".\");\n\n for (var n = t.shape, r = n[n.length - 1], o = 1, a = 0; a < n.length - 1; ++a) o *= n[a];\n\n var i = e.shape,\n s = n.slice();\n s.pop();\n var u = 1;\n\n for (a = r; a < e.rank; ++a) u *= i[a], s.push(i[a]);\n\n var l = computeStrides(e.shape).map(function (e) {\n return e / u;\n }).concat([1]).slice(0, r);\n return [s, o, u, l];\n}\n\nvar PARALLELIZE_THRESHOLD = 30;\n\nfunction computeOptimalWindowSize(e) {\n return e <= PARALLELIZE_THRESHOLD ? e : nearestDivisor(e, Math.floor(Math.sqrt(e)));\n}\n\nfunction validateUpdateShape(e, t, n) {\n var r = t.rank > 1 ? t.shape[t.rank - 1] : 1,\n o = t.rank > 1 ? t.rank - 1 : 1,\n a = \"Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: \" + n.shape + \", indices.shape: \" + t.shape + \", shape: \" + e + \", sliceDim: \" + r + \", and batchDim: \" + o + \".\";\n if (n.rank < o) throw new Error(a + \" update.rank < \" + o + \". \");\n if (e.length < r + (n.rank - o)) throw new Error(a + \" Output shape length < \" + (r + (n.rank - o)));\n if (n.rank !== o + e.length - r) throw new Error(a + \" update.rank != \" + (o + e.length - r));\n\n for (var i = 0; i < o; ++i) if (n.shape[i] !== t.shape[i]) throw new Error(a + \" updates.shape[\" + i + \"] (\" + n.shape[i] + \") != indices.shape[\" + i + \"] (\" + t.shape[i] + \").\");\n\n for (i = 0; i < n.rank - o; ++i) if (n.shape[i + o] !== e[i + r]) throw new Error(a + \" updates.shape[\" + (i + o) + \"] (\" + n.shape[i + o] + \") != shape[\" + (i + o) + \"] (\" + e[i + o] + \")\");\n}\n\nfunction validateInput(e, t, n) {\n if (t.rank < 1) throw new Error(\"tf.scatterND() expects the indices to be rank 1 or higher, but the rank was \" + t.rank + \".\");\n if (e.rank < 1) throw new Error(\"tf.scatterND() expects the updates to be rank 1 or higher, but the rank was \" + e.rank + \".\");\n if (\"int32\" !== t.dtype) throw new Error(\"The dtype of 'indices' should be int32, but got dtype: \" + t.dtype);\n if (n.length < 1) throw new Error(\"Output rank must be greater or equal to 1, but got shape: \" + n);\n\n if (0 === n.length) {\n if (0 === t.size) throw new Error(\"Indices specified for empty output. indices shape: \" + t.shape);\n if (0 === e.size) throw new Error(\"Updates specified for empty output. updates shape: \" + e.shape);\n }\n\n validateUpdateShape(n, t, e);\n}\n\nfunction calculateShapes(e, t, n) {\n for (var r = t.rank > 1 ? t.shape[t.rank - 1] : 1, o = n.length, a = 1, i = r; i < o; ++i) a *= n[i];\n\n var s = r < 1 ? 1 : r;\n return {\n sliceRank: r,\n numUpdates: t.size / s,\n sliceSize: a,\n strides: computeStrides(n.slice(0, r)).concat([1]),\n outputSize: sizeFromShape(n)\n };\n}\n\nfunction segOpComputeOptimalWindowSize(e, t) {\n var n,\n r = !1;\n\n for (e <= PARALLELIZE_THRESHOLD ? (n = e, r = !0) : n = nearestDivisor(e, Math.floor(Math.sqrt(e))); !r;) {\n if (n > t || n === e) {\n r = !0;\n break;\n }\n\n n = nearestDivisor(e, n + 1);\n }\n\n return n;\n}\n\nfunction computeOutShape$1(e, t, n) {\n for (var r = [], o = e.length, a = 0; a < o; a++) a !== t ? r.push(e[a]) : r.push(n);\n\n return r;\n}\n\nfunction collectGatherOpShapeInfo(e, t, n) {\n for (var r = e.shape[n], o = [], a = 1, i = 1, s = 0; s < n; s++) o.push(e.shape[s]), a *= e.shape[s];\n\n for (s = 0; s < t.rank; s++) o.push(t.shape[s]);\n\n for (s = n + 1; s < e.rank; s++) o.push(e.shape[s]), i *= e.shape[s];\n\n return {\n batchSize: a,\n sliceSize: i,\n dimSize: r,\n outputShape: o\n };\n}\n\nfunction assertParamsValid(e, t, n) {\n assert(e.rank === t.length, \"Error in slice\" + e.rank + \"D: Length of begin \" + t + \" must match the rank of the array (\" + e.rank + \").\"), assert(e.rank === n.length, \"Error in slice\" + e.rank + \"D: Length of size \" + n + \" must match the rank of the array (\" + e.rank + \").\");\n\n for (var r = 0; r < e.rank; ++r) assert(t[r] + n[r] <= e.shape[r], \"Error in slice\" + e.rank + \"D: begin[\" + r + \"] + size[\" + r + \"] (\" + (t[r] + n[r]) + \") would overflow input.shape[\" + r + \"] (\" + e.shape[r] + \")\");\n}\n\nfunction getStridedSlicedInfo(e, t, n, r, o, a, i, s, u) {\n if (void 0 === o && (o = 0), void 0 === a && (a = 0), void 0 === i && (i = 0), void 0 === s && (s = 0), void 0 === u && (u = 0), 0 !== i) throw new Error(\"ellipsis mask is not yet supported\");\n if (0 !== s) throw new Error(\"new axis mask is not yet supported\");\n\n for (var l = [], c = [], p = [], d = 0; d < e.length; d++) l[d] = startForAxis(o, t, r, e, d), c[d] = stopForAxis(a, n, r, e, d), u & 1 << d && (c[d] = l[d] + 1, p.push(d));\n\n var h = new Array(e.length).fill(0);\n return h = h.map(function (e, t) {\n for (var n = 0, o = l[t]; !(r[t] > 0 ? o >= c[t] : o <= c[t]); o += r[t]) n += 1;\n\n return n;\n }), [l, h, p];\n}\n\nfunction startForAxis(e, t, n, r, o) {\n var a = t[o];\n e & 1 << o && (a = n[o] > 0 ? Number.MIN_SAFE_INTEGER : Number.MAX_SAFE_INTEGER);\n var i = r[o];\n return a < 0 && (a += i), a = clamp(0, a, i - 1);\n}\n\nfunction stopForAxis(e, t, n, r, o) {\n var a = t[o];\n e & 1 << o && (a = n[o] > 0 ? Number.MAX_SAFE_INTEGER : Number.MIN_SAFE_INTEGER);\n var i = r[o];\n return a < 0 && (a += i), a = n[o] > 0 ? clamp(0, a, i) : clamp(-1, a, i - 1);\n}\n\nfunction isSliceContinous(e, t, n) {\n for (var r = n.length, o = 0; o < n.length; o++) if (n[o] > 1) {\n r = o;\n break;\n }\n\n for (o = r + 1; o < n.length; o++) if (t[o] > 0 || n[o] !== e[o]) return !1;\n\n return !0;\n}\n\nfunction computeFlatOffset(e, t) {\n for (var n = e.length > 0 ? e[e.length - 1] : 1, r = 0; r < e.length - 1; r++) n += e[r] * t[r];\n\n return n;\n}\n\nfunction inferShape(e) {\n var t = e;\n if (isTypedArray(e)) return [e.length];\n if (!Array.isArray(e)) return [];\n\n for (var n = []; Array.isArray(t) || isTypedArray(t);) n.push(t.length), t = t[0];\n\n return Array.isArray(e) && ENV.get(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\") && deepAssertShapeConsistency(e, n, []), n;\n}\n\nfunction deepAssertShapeConsistency(e, t, n) {\n if (n = n || [], Array.isArray(e) || isTypedArray(e)) {\n assert(t.length > 0, function () {\n return \"Element arr[\" + n.join(\"][\") + \"] should be a primitive, but is an array of \" + e.length + \" elements\";\n }), assert(e.length === t[0], function () {\n return \"Element arr[\" + n.join(\"][\") + \"] should have \" + t[0] + \" elements, but has \" + e.length + \" elements\";\n });\n\n for (var r = t.slice(1), o = 0; o < e.length; ++o) deepAssertShapeConsistency(e[o], r, n.concat(o));\n } else assert(0 === t.length, function () {\n return \"Element arr[\" + n.join(\"][\") + \"] is a primitive, but should be an array/TypedArray of \" + t[0] + \" elements\";\n });\n}\n\nfunction assertDtype(e, t, n, r) {\n if (null != e && (\"numeric\" !== e && e !== t || \"numeric\" === e && \"string\" === t)) throw new Error(\"Argument '\" + n + \"' passed to '\" + r + \"' must be \" + e + \" tensor, but got \" + t + \" tensor\");\n}\n\nfunction convertToTensor(e, t, n, r) {\n if (void 0 === r && (r = \"numeric\"), e instanceof Tensor) return assertDtype(r, e.dtype, t, n), e;\n var o = inferDtype(e);\n if (\"string\" !== o && [\"bool\", \"int32\", \"float32\"].indexOf(r) >= 0 && (o = r), assertDtype(r, o, t, n), !isTypedArray(e) && !Array.isArray(e) && \"number\" != typeof e && \"boolean\" != typeof e && \"string\" != typeof e) throw new Error(\"Argument '\" + t + \"' passed to '\" + n + \"' must be a Tensor or TensorLike, but got '\" + e.constructor.name + \"'\");\n var a = inferShape(e);\n isTypedArray(e) || Array.isArray(e) || (e = [e]);\n var i = \"string\" !== o ? toTypedArray(e, o, ENV.get(\"DEBUG\")) : flatten(e);\n return Tensor.make(a, {\n values: i\n }, o);\n}\n\nfunction convertToTensorArray(e, t, n) {\n if (!Array.isArray(e)) throw new Error(\"Argument \" + t + \" passed to \" + n + \" must be a `Tensor[]` or `TensorLike[]`\");\n return e.map(function (e, r) {\n return convertToTensor(e, t + \"[\" + r + \"]\", n);\n });\n}\n\nfunction op(e) {\n var t = Object.keys(e);\n if (1 !== t.length) throw new Error(\"Please provide an object with a single key (operation name) mapping to a function. Got an object with \" + t.length + \" keys.\");\n var n = t[0],\n r = e[n];\n n.endsWith(\"_\") && (n = n.substring(0, n.length - 1));\n\n var o = function () {\n for (var e = [], t = 0; t < arguments.length; t++) e[t] = arguments[t];\n\n ENV.engine.startScope(n);\n\n try {\n var o = r.apply(void 0, e);\n return o instanceof Promise && console.error(\"Cannot return a Promise inside of tidy.\"), ENV.engine.endScope(o), o;\n } catch (e) {\n throw ENV.engine.endScope(null), e;\n }\n };\n\n return Object.defineProperty(o, \"name\", {\n value: n,\n configurable: !0\n }), o;\n}\n\nfunction softmax_(e, t) {\n void 0 === t && (t = -1);\n var n = convertToTensor(e, \"logits\", \"softmax\");\n if (-1 === t && (t = n.rank - 1), t !== n.rank - 1) throw Error(\"Softmax along a non-last dimension is not yet supported. Logits was rank \" + n.rank + \" and dim was \" + t);\n return customGrad(function (e) {\n var n = e.logSumExp([t], !0),\n r = e.toFloat().sub(n).exp();\n return {\n value: r,\n gradFunc: function (e) {\n var n = e.mul(r);\n return n.sub(n.sum([t], !0).mul(r));\n }\n };\n })(n);\n}\n\nfunction logSoftmax_(e, t) {\n void 0 === t && (t = -1);\n var n = convertToTensor(e, \"logits\", \"logSoftmax\");\n if (-1 === t && (t = n.rank - 1), t !== n.rank - 1) throw Error(\"Log Softmax along a non-last dimension is not yet supported. Logits was rank \" + n.rank + \" and axis was \" + t);\n return customGrad(function (e) {\n var n = e.max(t, !0),\n r = e.sub(n),\n o = r.toFloat().sub(r.exp().sum(t, !0).log());\n return {\n value: o,\n gradFunc: function (e) {\n var n = o.exp();\n return e.sub(e.sum(t, !0).mul(n));\n }\n };\n })(n);\n}\n\nvar softmax = op({\n softmax_: softmax_\n}),\n logSoftmax = op({\n logSoftmax_: logSoftmax_\n});\nexports.logSoftmax = logSoftmax;\nexports.softmax = softmax;\n\nfunction complex_(e, t) {\n var n = convertToTensor(e, \"real\", \"complex\"),\n r = convertToTensor(t, \"imag\", \"complex\");\n return assertShapesMatch(n.shape, r.shape, \"real and imag shapes, \" + n.shape + \" and \" + r.shape + \", must match in call to tf.complex().\"), ENV.engine.runKernel(function (e) {\n return e.complex(n, r);\n }, {\n $real: n,\n $imag: r\n });\n}\n\nfunction real_(e) {\n var t = convertToTensor(e, \"input\", \"real\");\n return ENV.engine.runKernel(function (e) {\n return e.real(t);\n }, {\n $input: t\n });\n}\n\nfunction imag_(e) {\n var t = convertToTensor(e, \"input\", \"imag\");\n return ENV.engine.runKernel(function (e) {\n return e.imag(t);\n }, {\n $input: t\n });\n}\n\nvar complex = op({\n complex_: complex_\n}),\n real = op({\n real_: real_\n}),\n imag = op({\n imag_: imag_\n});\nexports.imag = imag;\nexports.real = real;\nexports.complex = complex;\n\nfunction tensor(e, t, n) {\n if (null == n && (n = inferDtype(e)), \"complex64\" === n) throw new Error(\"Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).\");\n if (!isTypedArray(e) && !Array.isArray(e) && \"number\" != typeof e && \"boolean\" != typeof e && \"string\" != typeof e) throw new Error(\"values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray\");\n var r = inferShape(e);\n\n if (null != t) {\n var o = sizeFromShape(t),\n a = sizeFromShape(r);\n assert(o === a, function () {\n return \"Based on the provided shape, [\" + t + \"], the tensor should have \" + o + \" values but has \" + a;\n });\n\n for (var i = 0; i < r.length; ++i) {\n var s = r[i],\n u = i !== r.length - 1 || s !== sizeFromShape(t.slice(i));\n assert(r[i] === t[i] || !u, function () {\n return \"Error creating a new Tensor. Inferred shape (\" + r + \") does not match the provided shape (\" + t + \"). \";\n });\n }\n }\n\n return isTypedArray(e) || Array.isArray(e) || (e = [e]), t = t || r, e = \"string\" !== n ? toTypedArray(e, n, ENV.get(\"DEBUG\")) : flatten(e), Tensor.make(t, {\n values: e\n }, n);\n}\n\nfunction scalar(e, t) {\n if ((isTypedArray(e) || Array.isArray(e)) && \"complex64\" !== t) throw new Error(\"Error creating a new Scalar: value must be a primitive (number|boolean|string)\");\n return tensor(e, [], t);\n}\n\nfunction tensor1d(e, t) {\n assertNonNull(e);\n var n = inferShape(e);\n if (1 !== n.length) throw new Error(\"tensor1d() requires values to be a flat/TypedArray\");\n return tensor(e, n, t);\n}\n\nfunction tensor2d(e, t, n) {\n if (assertNonNull(e), null != t && 2 !== t.length) throw new Error(\"tensor2d() requires shape to have two numbers\");\n var r = inferShape(e);\n if (2 !== r.length && 1 !== r.length) throw new Error(\"tensor2d() requires values to be number[][] or flat/TypedArray\");\n if (1 === r.length && null == t) throw new Error(\"tensor2d() requires shape to be provided when `values` are a flat/TypedArray\");\n return tensor(e, t = t || r, n);\n}\n\nfunction tensor3d(e, t, n) {\n if (assertNonNull(e), null != t && 3 !== t.length) throw new Error(\"tensor3d() requires shape to have three numbers\");\n var r = inferShape(e);\n if (3 !== r.length && 1 !== r.length) throw new Error(\"tensor3d() requires values to be number[][][] or flat/TypedArray\");\n if (1 === r.length && null == t) throw new Error(\"tensor3d() requires shape to be provided when `values` are a flat array\");\n return tensor(e, t = t || r, n);\n}\n\nfunction tensor4d(e, t, n) {\n if (assertNonNull(e), null != t && 4 !== t.length) throw new Error(\"tensor4d() requires shape to have four numbers\");\n var r = inferShape(e);\n if (4 !== r.length && 1 !== r.length) throw new Error(\"tensor4d() requires values to be number[][][][] or flat/TypedArray\");\n if (1 === r.length && null == t) throw new Error(\"tensor4d() requires shape to be provided when `values` are a flat array\");\n return tensor(e, t = t || r, n);\n}\n\nfunction tensor5d(e, t, n) {\n if (assertNonNull(e), null != t && 5 !== t.length) throw new Error(\"tensor5d() requires shape to have five numbers\");\n var r = inferShape(e);\n if (5 !== r.length && 1 !== r.length) throw new Error(\"tensor5d() requires values to be number[][][][][] or flat/TypedArray\");\n if (1 === r.length && null == t) throw new Error(\"tensor5d() requires shape to be provided when `values` are a flat array\");\n return tensor(e, t = t || r, n);\n}\n\nfunction tensor6d(e, t, n) {\n if (assertNonNull(e), null != t && 6 !== t.length) throw new Error(\"tensor6d() requires shape to have six numbers\");\n var r = inferShape(e);\n if (6 !== r.length && 1 !== r.length) throw new Error(\"tensor6d() requires values to be number[][][][][][] or flat/TypedArray\");\n if (1 === r.length && null == t) throw new Error(\"tensor6d() requires shape to be provided when `values` are a flat array\");\n return tensor(e, t = t || r, n);\n}\n\nfunction ones$1(e, t) {\n if (void 0 === t && (t = \"float32\"), \"complex64\" === t) {\n var n = ones$1(e, \"float32\"),\n r = ones$1(e, \"float32\");\n return complex(n, r);\n }\n\n var o = makeOnesTypedArray(sizeFromShape(e), t);\n return Tensor.make(e, {\n values: o\n }, t);\n}\n\nfunction zeros(e, t) {\n if (void 0 === t && (t = \"float32\"), \"complex64\" === t) {\n var n = zeros(e, \"float32\"),\n r = zeros(e, \"float32\");\n return complex(n, r);\n }\n\n var o = makeZerosTypedArray(sizeFromShape(e), t);\n return Tensor.make(e, {\n values: o\n }, t);\n}\n\nfunction fill(e, t, n) {\n var r = getArrayFromDType(n = n || inferDtype(t), sizeFromShape(e));\n return r.fill(t), Tensor.make(e, {\n values: r\n }, n);\n}\n\nfunction onesLike_(e) {\n var t = convertToTensor(e, \"x\", \"onesLike\");\n return ones$1(t.shape, t.dtype);\n}\n\nfunction zerosLike_(e) {\n var t = convertToTensor(e, \"x\", \"zerosLike\");\n return zeros(t.shape, t.dtype);\n}\n\nfunction linspace(e, t, n) {\n if (0 === n) throw new Error(\"Cannot request zero samples\");\n var r = (t - e) / (n - 1),\n o = makeZerosTypedArray(n, \"float32\");\n o[0] = e;\n\n for (var a = 1; a < o.length; a++) o[a] = o[a - 1] + r;\n\n return tensor1d(o, \"float32\");\n}\n\nfunction range(e, t, n, r) {\n if (void 0 === n && (n = 1), void 0 === r && (r = \"float32\"), 0 === n) throw new Error(\"Cannot have a step of zero\");\n if (e === t || e < t && n < 0 || t < e && n > 1) return zeros([0], r);\n var o = makeZerosTypedArray(Math.abs(Math.ceil((t - e) / n)), r);\n t < e && 1 === n && (n = -1), o[0] = e;\n\n for (var a = 1; a < o.length; a++) o[a] = o[a - 1] + n;\n\n return tensor1d(o, r);\n}\n\nvar onesLike = op({\n onesLike_: onesLike_\n}),\n zerosLike = op({\n zerosLike_: zerosLike_\n}),\n DataStorage = function () {\n function e(e) {\n this.dataMover = e, this.data = new WeakMap();\n }\n\n return e.prototype.get = function (e) {\n return this.data.has(e) || this.dataMover.moveData(e), this.data.get(e);\n }, e.prototype.set = function (e, t) {\n this.data.set(e, t);\n }, e.prototype.has = function (e) {\n return this.data.has(e);\n }, e.prototype.delete = function (e) {\n return this.data.delete(e);\n }, e;\n}(),\n KernelBackend = function () {\n function e() {}\n\n return e.prototype.time = function (e) {\n throw new Error(\"Not yet implemented.\");\n }, e.prototype.read = function (e) {\n throw new Error(\"Not yet implemented.\");\n }, e.prototype.readSync = function (e) {\n throw new Error(\"Not yet implemented.\");\n }, e.prototype.disposeData = function (e) {\n throw new Error(\"Not yet implemented.\");\n }, e.prototype.write = function (e, t) {\n throw new Error(\"Not yet implemented.\");\n }, e.prototype.fromPixels = function (e, t) {\n throw new Error(\"Not yet implemented.\");\n }, e.prototype.register = function (e, t, n) {\n throw new Error(\"Not yet implemented.\");\n }, e.prototype.memory = function () {\n throw new Error(\"Not yet implemented.\");\n }, e.prototype.floatPrecision = function () {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.batchMatMul = function (e, t, n, r) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.fusedBatchMatMul = function (e, t, n, r, o, a) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.slice = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.stridedSlice = function (e, t, n, r, o, a, i, s, u) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.unstack = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.reverse = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.concat = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.neg = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.add = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.addN = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.subtract = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.multiply = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.realDivide = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.floorDiv = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.sum = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.prod = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.unsortedSegmentSum = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.argMin = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.argMax = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.equal = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.notEqual = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.less = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.lessEqual = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.greater = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.greaterEqual = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.logicalNot = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.logicalAnd = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.logicalOr = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.where = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.select = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.topk = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.min = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.minimum = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.mod = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.max = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.maximum = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.all = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.any = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.squaredDifference = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.ceil = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.floor = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.round = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.sign = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.pow = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.exp = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.expm1 = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.log = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.log1p = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.sqrt = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.rsqrt = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.square = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.reciprocal = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.relu = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.prelu = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.elu = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.eluDer = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.selu = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.int = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.clip = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.abs = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.complexAbs = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.sigmoid = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.softplus = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.sin = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.cos = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.tan = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.asin = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.acos = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.atan = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.atan2 = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.sinh = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.cosh = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.tanh = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.asinh = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.acosh = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.atanh = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.erf = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.step = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.conv2d = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.conv2dDerInput = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.conv2dDerFilter = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.depthwiseConv2D = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.depthwiseConv2DDerInput = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.depthwiseConv2DDerFilter = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.conv3d = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.conv3dDerInput = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.conv3dDerFilter = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.maxPool = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.maxPoolBackprop = function (e, t, n, r) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.avgPool = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.avgPoolBackprop = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.reshape = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.cast = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.tile = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.pad = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.transpose = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.gather = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.gatherND = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.scatterND = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.batchToSpaceND = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.spaceToBatchND = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.resizeBilinear = function (e, t, n, r) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.resizeBilinearBackprop = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.resizeNearestNeighbor = function (e, t, n, r) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.resizeNearestNeighborBackprop = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.batchNormalization = function (e, t, n, r, o, a) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.localResponseNormalization4D = function (e, t, n, r, o) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.LRNGrad = function (e, t, n, r, o, a, i) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.multinomial = function (e, t, n, r) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.oneHot = function (e, t, n, r) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.cumsum = function (e, t, n, r) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.nonMaxSuppression = function (e, t, n, r, o) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.fft = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.ifft = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.complex = function (e, t) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.real = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.imag = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.cropAndResize = function (e, t, n, r, o, a) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.depthToSpace = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.split = function (e, t, n) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.sparseToDense = function (e, t, n, r) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.setDataMover = function (e) {\n throw new Error(\"Not yet implemented\");\n }, e.prototype.dispose = function () {\n throw new Error(\"Not yet implemented\");\n }, e;\n}();\n\nexports.KernelBackend = KernelBackend;\nexports.DataStorage = DataStorage;\nexports.zerosLike = zerosLike;\nexports.onesLike = onesLike;\n\nfunction castTensor(e, t, n) {\n if (\"complex64\" === t) {\n if (\"complex64\" === e.dtype) return e.clone();\n var r = zeros(e.shape),\n o = e.toFloat(),\n a = n.complex(o, r);\n return r.dispose(), o.dispose(), a;\n }\n\n if (!hasEncodingLoss(e.dtype, t)) return Tensor.make(e.shape, {\n dataId: e.dataId\n }, t);\n\n if (\"complex64\" === e.dtype) {\n var i = n.real(e);\n a = i.cast(t);\n return i.dispose(), a;\n }\n\n if (\"int32\" === t) return n.int(e);\n\n if (\"bool\" === t) {\n var s = scalar(0, e.dtype);\n a = n.notEqual(e, s);\n return s.dispose(), a;\n }\n\n throw new Error(\"Error in Cast: unknown dtype argument (\" + t + \")\");\n}\n\nfunction reshapeTensor(e, t) {\n return Tensor.make(t, {\n dataId: e.dataId\n }, e.dtype);\n}\n\nfunction mergeRealAndImagArrays(e, t) {\n if (e.length !== t.length) throw new Error(\"Cannot merge real and imag arrays of different lengths. real:\" + e.length + \", imag: \" + t.length + \".\");\n\n for (var n = new Float32Array(2 * e.length), r = 0; r < n.length; r += 2) n[r] = e[r / 2], n[r + 1] = t[r / 2];\n\n return n;\n}\n\nfunction splitRealAndImagArrays(e) {\n for (var t = new Float32Array(e.length / 2), n = new Float32Array(e.length / 2), r = 0; r < e.length; r += 2) t[r / 2] = e[r], n[r / 2] = e[r + 1];\n\n return {\n real: t,\n imag: n\n };\n}\n\nfunction complexWithEvenIndex(e) {\n for (var t = Math.ceil(e.length / 4), n = new Float32Array(t), r = new Float32Array(t), o = 0; o < e.length; o += 4) n[Math.floor(o / 4)] = e[o], r[Math.floor(o / 4)] = e[o + 1];\n\n return {\n real: n,\n imag: r\n };\n}\n\nfunction complexWithOddIndex(e) {\n for (var t = Math.floor(e.length / 4), n = new Float32Array(t), r = new Float32Array(t), o = 2; o < e.length; o += 4) n[Math.floor(o / 4)] = e[o], r[Math.floor(o / 4)] = e[o + 1];\n\n return {\n real: n,\n imag: r\n };\n}\n\nfunction getComplexWithIndex(e, t) {\n return {\n real: e[2 * t],\n imag: e[2 * t + 1]\n };\n}\n\nfunction assignToTypedArray(e, t, n, r) {\n e[2 * r] = t, e[2 * r + 1] = n;\n}\n\nfunction exponents(e, t) {\n for (var n = new Float32Array(e / 2), r = new Float32Array(e / 2), o = 0; o < Math.ceil(e / 2); o++) {\n var a = (t ? 2 : -2) * Math.PI * (o / e);\n n[o] = Math.cos(a), r[o] = Math.sin(a);\n }\n\n return {\n real: n,\n imag: r\n };\n}\n\nfunction exponent(e, t, n) {\n var r = (n ? 2 : -2) * Math.PI * (e / t);\n return {\n real: Math.cos(r),\n imag: Math.sin(r)\n };\n}\n\nfunction nonMaxSuppressionImpl(e, t, n, r, o) {\n for (var a = Array.from(t).map(function (e, t) {\n return {\n score: e,\n boxIndex: t\n };\n }).filter(function (e) {\n return e.score > o;\n }).sort(function (e, t) {\n return t.score - e.score;\n }), i = [], s = 0; s < a.length; s++) {\n var u = a[s],\n l = u.score,\n c = u.boxIndex;\n if (l < o) break;\n\n for (var p = !1, d = i.length - 1; d >= 0; --d) {\n if (intersectionOverUnion(e, c, i[d]) >= r) {\n p = !0;\n break;\n }\n }\n\n if (!p && (i.push(c), i.length >= n)) break;\n }\n\n return tensor1d(i, \"int32\");\n}\n\nfunction intersectionOverUnion(e, t, n) {\n var r = e.subarray(4 * t, 4 * t + 4),\n o = e.subarray(4 * n, 4 * n + 4),\n a = Math.min(r[0], r[2]),\n i = Math.min(r[1], r[3]),\n s = Math.max(r[0], r[2]),\n u = Math.max(r[1], r[3]),\n l = Math.min(o[0], o[2]),\n c = Math.min(o[1], o[3]),\n p = Math.max(o[0], o[2]),\n d = Math.max(o[1], o[3]),\n h = (s - a) * (u - i),\n f = (p - l) * (d - c);\n if (h <= 0 || f <= 0) return 0;\n var m = Math.max(a, l),\n g = Math.max(i, c),\n v = Math.min(s, p),\n y = Math.min(u, d),\n x = Math.max(v - m, 0) * Math.max(y - g, 0);\n return x / (h + f - x);\n}\n\nfunction split(e, t, n) {\n var r = new Array(e.rank).fill(0),\n o = e.shape.slice();\n return t.map(function (t) {\n o[n] = t;\n var a = e.slice(r, o);\n return r[n] += t, a;\n });\n}\n\nfunction topkImpl(e, t, n, r, o) {\n for (var a = t[t.length - 1], i = [e.length / a, a], s = i[0], u = i[1], l = getTypedArrayFromDType(n, s * r), c = getTypedArrayFromDType(\"int32\", s * r), p = 0; p < s; p++) {\n for (var d = p * u, h = e.subarray(d, d + u), f = [], m = 0; m < h.length; m++) f.push({\n value: h[m],\n index: m\n });\n\n f.sort(function (e, t) {\n return t.value - e.value;\n });\n var g = p * r,\n v = l.subarray(g, g + r),\n y = c.subarray(g, g + r);\n\n for (m = 0; m < r; m++) v[m] = f[m].value, y[m] = f[m].index;\n }\n\n var x = t.slice();\n return x[x.length - 1] = r, [tensor(l, x, n), tensor(c, x, \"int32\")];\n}\n\nvar ArgMinMaxProgram = function () {\n return function (e, t, n) {\n this.variableNames = [\"A\"];\n var r = e.windowSize,\n o = e.batchSize,\n a = e.inSize,\n i = Math.ceil(a / r);\n n || this.variableNames.push(\"bestIndicesA\"), this.outputShape = [o, i];\n var s = \"max\" === t ? \">\" : \"<\",\n u = n ? \"inOffset + i;\" : \"round(getBestIndicesA(batch, inOffset + i));\";\n this.userCode = \"\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int batch = coords[0];\\n int outIdx = coords[1];\\n int inOffset = outIdx * \" + r + \";\\n\\n int bestIndex = inOffset;\\n float bestValue = getA(batch, bestIndex);\\n\\n for (int i = 0; i < \" + r + \"; i++) {\\n int inIdx = \" + u + \";\\n float candidate = getA(batch, inIdx);\\n if (candidate \" + s + \" bestValue) {\\n bestValue = candidate;\\n bestIndex = inIdx;\\n }\\n }\\n setOutput(float(bestIndex));\\n }\\n \";\n };\n}(),\n AvgPool2DBackpropProgram = function () {\n return function (e) {\n this.variableNames = [\"dy\"], this.outputShape = e.inShape;\n var t = e.filterHeight,\n n = e.filterWidth,\n r = e.strideHeight,\n o = e.strideWidth,\n a = e.dilationHeight,\n i = e.dilationWidth,\n s = e.effectiveFilterHeight,\n u = e.effectiveFilterWidth,\n l = s - 1 - e.padInfo.top,\n c = u - 1 - e.padInfo.left,\n p = 1 / (t * n);\n this.userCode = \"\\n const ivec2 pads = ivec2(\" + l + \", \" + c + \");\\n const float avgMultiplier = float(\" + p + \");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n\\n ivec2 dyRCCorner = coords.yz - pads;\\n int dyRCorner = dyRCCorner.x;\\n int dyCCorner = dyRCCorner.y;\\n\\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n for (int wR = 0; wR < \" + s + \";\\n wR += \" + a + \") {\\n float dyR = float(dyRCorner + wR) / \" + r + \".0;\\n\\n if (dyR < 0.0 || dyR >= \" + e.outHeight + \".0 || fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n for (int wC = 0; wC < \" + u + \";\\n wC+= \" + i + \") {\\n float dyC = float(dyCCorner + wC) / \" + o + \".0;\\n\\n if (dyC < 0.0 || dyC >= \" + e.outWidth + \".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n float dyValue = getDy(b, idyR, idyC, d);\\n\\n dotProd += dyValue * avgMultiplier;\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}();\n\nfunction getBroadcastDims(e, t) {\n for (var n = e.length, r = [], o = 0; o < n; o++) {\n var a = n - 1 - o,\n i = e[a] || 1;\n (t[t.length - 1 - o] || 1) > 1 && 1 === i && r.unshift(a);\n }\n\n return r;\n}\n\nfunction getReductionAxes(e, t) {\n for (var n = [], r = 0; r < t.length; r++) {\n var o = e[e.length - r - 1],\n a = t.length - r - 1,\n i = t[a];\n (null == o || 1 === o && i > 1) && n.unshift(a);\n }\n\n return n;\n}\n\nfunction assertAndGetBroadcastShape(e, t) {\n for (var n = [], r = Math.max(e.length, t.length), o = 0; o < r; o++) {\n var a = e[e.length - o - 1];\n null == a && (a = 1);\n var i = t[t.length - o - 1];\n if (null == i && (i = 1), 1 === a) n.unshift(i);else if (1 === i) n.unshift(a);else {\n if (a !== i) throw Error(\"Operands could not be broadcast together with shapes \" + e + \" and \" + t + \".\");\n n.unshift(a);\n }\n }\n\n return n;\n}\n\nvar BatchNormProgram = function () {\n return function (e, t, n, r, o, a) {\n this.outputShape = [], this.variableNames = [\"x\", \"mean\", \"variance\"], assertAndGetBroadcastShape(e, t), assertAndGetBroadcastShape(e, n);\n var i = \"0.0\";\n null != r && (assertAndGetBroadcastShape(e, r), this.variableNames.push(\"offset\"), i = \"getOffsetAtOutCoords()\");\n var s = \"1.0\";\n null != o && (assertAndGetBroadcastShape(e, o), this.variableNames.push(\"scale\"), s = \"getScaleAtOutCoords()\"), this.outputShape = e, this.userCode = \"\\n void main() {\\n float x = getXAtOutCoords();\\n float mean = getMeanAtOutCoords();\\n float variance = getVarianceAtOutCoords();\\n float offset = \" + i + \";\\n float scale = \" + s + \";\\n float inv = scale * inversesqrt(variance + float(\" + a + \"));\\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\\n }\\n \";\n };\n}(),\n BatchNormPackedProgram = function () {\n return function (e, t, n, r, o, a) {\n this.usesPackedTextures = !0, this.variableNames = [\"x\", \"mean\", \"variance\"], assertAndGetBroadcastShape(e, t), assertAndGetBroadcastShape(e, n);\n var i = \"vec4(0.0)\";\n null != r && (assertAndGetBroadcastShape(e, r), this.variableNames.push(\"offset\"), i = \"getOffsetAtOutCoords()\");\n var s = \"vec4(1.0)\";\n null != o && (assertAndGetBroadcastShape(e, o), this.variableNames.push(\"scale\"), s = \"getScaleAtOutCoords()\"), this.outputShape = e, this.userCode = \"\\n void main() {\\n vec4 offset = \" + i + \";\\n vec4 scale = \" + s + \";\\n\\n vec4 x = getXAtOutCoords();\\n vec4 mean = getMeanAtOutCoords();\\n vec4 variance = getVarianceAtOutCoords();\\n\\n vec4 inv = scale * inversesqrt(variance + vec4(\" + a + \"));\\n\\n setOutput((x - mean) * inv + offset);\\n }\\n \";\n };\n}(),\n COMPLEX_MULTIPLY = {\n REAL: \"return areal * breal - aimag * bimag;\",\n IMAG: \"return areal * bimag + aimag * breal;\"\n},\n BinaryOpComplexProgram = function () {\n return function (e, t, n) {\n this.variableNames = [\"AReal\", \"AImag\", \"BReal\", \"BImag\"], this.outputShape = assertAndGetBroadcastShape(t, n), this.userCode = \"\\n float binaryOpComplex(\\n float areal, float aimag, float breal, float bimag) {\\n \" + e + \"\\n }\\n\\n void main() {\\n float areal = getARealAtOutCoords();\\n float aimag = getAImagAtOutCoords();\\n float breal = getBRealAtOutCoords();\\n float bimag = getBImagAtOutCoords();\\n setOutput(binaryOpComplex(areal, aimag, breal, bimag));\\n }\\n \";\n };\n}(),\n CHECK_NAN_SNIPPET = \"\\n if (isNaN(a)) return a;\\n if (isNaN(b)) return b;\\n\",\n ADD = \"return a + b;\",\n SUB = \"return a - b;\",\n MUL = \"return a * b;\",\n DIV = \"if (a == b) return 1.0;\\n return a / b;\",\n INT_DIV = \"\\n float resultSign = sign(a) * sign(b);\\n int ia = round(a);\\n int ib = round(b);\\n int result = ia / ib;\\n int amodb = ia - ib * result;\\n\\n if (resultSign < 0.0 && amodb != 0) {\\n result -= 1;\\n }\\n return float(result);\\n\",\n POW = \"\\nif(a < 0.0 && floor(b) < b){\\n return NAN;\\n}\\nreturn (round(mod(b, 2.0)) != 1) ?\\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\\n\",\n SQUARED_DIFFERENCE = \"return (a - b) * (a - b);\",\n EQUAL = \"return float(a == b);\",\n NOT_EQUAL = \"return float(a != b);\",\n LESS = \"return float(a < b);\",\n LESS_EQUAL = \"return float(a <= b);\",\n GREATER = \"return float(a > b);\",\n GREATER_EQUAL = \"return float(a >= b);\",\n LOGICAL_AND = \"return float(a >= 1.0 && b >= 1.0);\",\n LOGICAL_OR = \"return float(a >= 1.0 || b >= 1.0);\",\n MAX = CHECK_NAN_SNIPPET + \"\\n return max(a, b);\\n\",\n MIN = CHECK_NAN_SNIPPET + \"\\n return min(a, b);\\n\",\n MOD = \"if (b == 0.0) return NAN;\\n return mod(a, b);\",\n ATAN2 = CHECK_NAN_SNIPPET + \"\\n return atan(a, b);\\n\",\n ELU_DER = \"return (b >= 1.0) ? a : a * (b + 1.0);\",\n PRELU = \"return (a < 0.) ? b * a : a;\",\n BinaryOpProgram = function () {\n function e(e, t, n) {\n this.variableNames = [\"A\", \"B\"], this.outputShape = assertAndGetBroadcastShape(t, n), this.userCode = \"\\n uniform float NAN;\\n float binaryOperation(float a, float b) {\\n \" + e + \"\\n }\\n\\n void main() {\\n float a = getAAtOutCoords();\\n float b = getBAtOutCoords();\\n setOutput(binaryOperation(a, b));\\n }\\n \";\n }\n\n return e.prototype.getCustomSetupFunc = function () {\n var e = this;\n return function (t, n) {\n null == e.startLoc && (e.startLoc = t.getUniformLocationNoThrow(n, \"NAN\"), null == e.startLoc) || t.gl.uniform1f(e.startLoc, NaN);\n };\n }, e;\n}(),\n PACKED_DIV = \"\\n // vec4 one = vec4(equal(a, b));\\n // return one + (vec4(1.0) - one) * a / b;\\n vec4 result = a / b;\\n result.x = a.x == b.x ? 1. : result.x;\\n result.y = a.y == b.y ? 1. : result.y;\\n result.z = a.z == b.z ? 1. : result.z;\\n result.w = a.w == b.w ? 1. : result.w;\\n return result;\\n\",\n PACKED_INT_DIV = \"\\n vec4 resultSign = sign(a) * sign(b);\\n ivec4 ia = round(a);\\n ivec4 ib = round(b);\\n ivec4 result = ia / ib;\\n ivec4 amodb = ia - ib * result;\\n\\n // Vectorize INT_DIV\\n // if (resultSign < 0.0 && amodb != 0) result -= 1;\\n // return float(result);\\n return vec4(result -\\n ivec4(lessThan(resultSign, vec4(0.0))) * ivec4(notEqual(amodb, ivec4(0))));\\n\",\n PACKED_POW = \"\\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\\n vec4 result = multiplier * pow(abs(a), b);\\n\\n vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\\n result.r = isNaN.r == 1.0 ? NAN : result.r;\\n result.g = isNaN.g == 1.0 ? NAN : result.g;\\n result.b = isNaN.b == 1.0 ? NAN : result.b;\\n result.a = isNaN.a == 1.0 ? NAN : result.a;\\n\\n return result;\\n\",\n BinaryOpPackedProgram = function () {\n function e(e, t, n) {\n this.variableNames = [\"A\", \"B\"], this.supportsBroadcasting = !0, this.usesPackedTextures = !0, this.outputShape = assertAndGetBroadcastShape(t, n), this.userCode = \"\\n uniform float NAN;\\n vec4 binaryOperation(vec4 a, vec4 b) {\\n \" + e + \"\\n }\\n\\n void main() {\\n vec4 a = getAAtOutCoords();\\n vec4 b = getBAtOutCoords();\\n setOutput(binaryOperation(a, b));\\n }\\n \";\n }\n\n return e.prototype.getCustomSetupFunc = function () {\n var e = this;\n return function (t, n) {\n null == e.startLoc && (e.startLoc = t.getUniformLocationNoThrow(n, \"NAN\"), null == e.startLoc) || t.gl.uniform1f(e.startLoc, NaN);\n };\n }, e;\n}(),\n ClipProgram = function () {\n function e(e) {\n this.variableNames = [\"A\"], this.outputShape = e, this.userCode = \"\\n uniform float min;\\n uniform float max;\\n\\n void main() {\\n float value = getAAtOutCoords();\\n if (isNaN(value)) {\\n setOutput(value);\\n return;\\n }\\n\\n setOutput(clamp(value, min, max));\\n }\\n \";\n }\n\n return e.prototype.getCustomSetupFunc = function (e, t) {\n var n = this;\n return function (r, o) {\n null == n.minLoc && (n.minLoc = r.getUniformLocationNoThrow(o, \"min\"), n.maxLoc = r.getUniformLocationNoThrow(o, \"max\")), r.gl.uniform1f(n.minLoc, e), r.gl.uniform1f(n.maxLoc, t);\n };\n }, e;\n}(),\n ClipPackedProgram = function () {\n function e(e) {\n this.variableNames = [\"A\"], this.usesPackedTextures = !0, this.outputShape = e, this.userCode = \"\\n uniform float min;\\n uniform float max;\\n\\n void main() {\\n vec4 value = getAAtOutCoords();\\n\\n if (hasNaN(value)) {\\n setOutput(value);\\n return;\\n }\\n\\n setOutput(clamp(value, vec4(min), vec4(max)));\\n }\\n \";\n }\n\n return e.prototype.getCustomSetupFunc = function (e, t) {\n var n = this;\n return function (r, o) {\n null == n.minLoc && (n.minLoc = r.getUniformLocationNoThrow(o, \"min\"), n.maxLoc = r.getUniformLocationNoThrow(o, \"max\")), r.gl.uniform1f(n.minLoc, e), r.gl.uniform1f(n.maxLoc, t);\n };\n }, e;\n}(),\n ComplexAbsProgram = function () {\n return function (e) {\n this.variableNames = [\"real\", \"imag\"], this.outputShape = e, this.userCode = \"\\n void main() {\\n float re = abs(getRealAtOutCoords());\\n float im = abs(getImagAtOutCoords());\\n float mx = max(re, im);\\n\\n // sadly the length function in glsl is not underflow-safe\\n // (at least not on Intel GPUs). So the safe solution is\\n // to ensure underflow-safety in all cases.\\n setOutput(\\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\\n );\\n }\\n \";\n };\n}(),\n ConcatProgram = function () {\n return function (e) {\n this.outputShape = [], this.outputShape = computeOutShape(e, 1), this.variableNames = e.map(function (e, t) {\n return \"T\" + t;\n });\n var t = new Array(e.length - 1);\n t[0] = e[0][1];\n\n for (var n = 1; n < t.length; n++) t[n] = t[n - 1] + e[n][1];\n\n var r = [\"if (yC < \" + t[0] + \") setOutput(getT0(yR, yC));\"];\n\n for (n = 1; n < t.length; n++) {\n var o = t[n - 1];\n r.push(\"else if (yC < \" + t[n] + \") setOutput(getT\" + n + \"(yR, yC-\" + o + \"));\");\n }\n\n var a = t.length,\n i = t[t.length - 1];\n r.push(\"else setOutput(getT\" + a + \"(yR, yC-\" + i + \"));\"), this.userCode = \"\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int yR = coords.x;\\n int yC = coords.y;\\n\\n \" + r.join(\"\\n \") + \"\\n }\\n \";\n };\n}(),\n Conv2DDerFilterProgram = function () {\n return function (e) {\n this.variableNames = [\"x\", \"dy\"], this.outputShape = e.filterShape;\n var t = e.strideHeight,\n n = e.strideWidth,\n r = e.padInfo.top,\n o = e.padInfo.left;\n this.userCode = \"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int wR = coords.x;\\n int wC = coords.y;\\n int d1 = coords.z;\\n int d2 = coords.w;\\n\\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n\\n for (int b = 0; b < \" + e.batchSize + \"; b++) {\\n for (int yR = 0; yR < \" + e.outHeight + \"; yR++) {\\n int xR = wR + yR * \" + t + \" - \" + r + \";\\n\\n if (xR < 0 || xR >= \" + e.inHeight + \") {\\n continue;\\n }\\n\\n for (int yC = 0; yC < \" + e.outWidth + \"; yC++) {\\n int xC = wC + yC * \" + n + \" - \" + o + \";\\n\\n if (xC < 0 || xC >= \" + e.inWidth + \") {\\n continue;\\n }\\n\\n float dyValue = getDy(b, yR, yC, d2);\\n float xValue = getX(b, xR, xC, d1);\\n dotProd += (xValue * dyValue);\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}(),\n Conv2DDerInputProgram = function () {\n return function (e) {\n this.variableNames = [\"dy\", \"W\"], this.outputShape = e.inShape;\n var t = e.filterHeight,\n n = e.filterWidth,\n r = e.strideHeight,\n o = e.strideWidth,\n a = t - 1 - e.padInfo.top,\n i = n - 1 - e.padInfo.left;\n this.userCode = \"\\n const ivec2 pads = ivec2(\" + a + \", \" + i + \");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords[0];\\n int d1 = coords[3];\\n\\n ivec2 dyCorner = coords.yz - pads;\\n int dyRCorner = dyCorner.x;\\n int dyCCorner = dyCorner.y;\\n\\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n for (int wR = 0; wR < \" + t + \"; wR++) {\\n float dyR = float(dyRCorner + wR) / \" + r + \".0;\\n\\n if (dyR < 0.0 || dyR >= \" + e.outHeight + \".0 || fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n int wRPerm = \" + t + \" - 1 - wR;\\n\\n for (int wC = 0; wC < \" + n + \"; wC++) {\\n float dyC = float(dyCCorner + wC) / \" + o + \".0;\\n\\n if (dyC < 0.0 || dyC >= \" + e.outWidth + \".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n int wCPerm = \" + n + \" - 1 - wC;\\n\\n for (int d2 = 0; d2 < \" + e.outChannels + \"; d2++) {\\n float xValue = getDy(batch, idyR, idyC, d2);\\n float wValue = getW(wRPerm, wCPerm, d1, d2);\\n dotProd += xValue * wValue;\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}(),\n Conv3DDerFilterProgram = function () {\n return function (e) {\n this.variableNames = [\"x\", \"dy\"], this.outputShape = e.filterShape;\n var t = e.strideDepth,\n n = e.strideHeight,\n r = e.strideWidth,\n o = e.padInfo.front,\n a = e.padInfo.top,\n i = e.padInfo.left;\n this.userCode = \"\\n void main() {\\n ivec5 coords = getOutputCoords();\\n int wF = coords.x;\\n int wR = coords.y;\\n int wC = coords.z;\\n int d1 = coords.w;\\n int d2 = coords.u;\\n\\n float dotProd = 0.0;\\n\\n for (int b = 0; b < \" + e.batchSize + \"; b++) {\\n for (int yF = 0; yF < \" + e.outDepth + \"; yF++) {\\n int xF = wF + yF * \" + t + \" - \" + o + \";\\n\\n if (xF < 0 || xF >= \" + e.inDepth + \") {\\n continue;\\n }\\n\\n for (int yR = 0; yR < \" + e.outHeight + \"; yR++) {\\n int xR = wR + yR * \" + n + \" - \" + a + \";\\n\\n if (xR < 0 || xR >= \" + e.inHeight + \") {\\n continue;\\n }\\n\\n for (int yC = 0; yC < \" + e.outWidth + \"; yC++) {\\n int xC = wC + yC * \" + r + \" - \" + i + \";\\n\\n if (xC < 0 || xC >= \" + e.inWidth + \") {\\n continue;\\n }\\n\\n float dyValue = getDy(b, yF, yR, yC, d2);\\n float xValue = getX(b, xF, xR, xC, d1);\\n dotProd += (xValue * dyValue);\\n }\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}(),\n Conv3DDerInputProgram = function () {\n return function (e) {\n this.variableNames = [\"dy\", \"W\"], this.outputShape = e.inShape;\n var t = e.filterDepth,\n n = e.filterHeight,\n r = e.filterWidth,\n o = e.strideDepth,\n a = e.strideHeight,\n i = e.strideWidth,\n s = t - 1 - e.padInfo.front,\n u = n - 1 - e.padInfo.top,\n l = r - 1 - e.padInfo.left;\n this.userCode = \"\\n const ivec3 pads = ivec3(\" + s + \", \" + u + \", \" + l + \");\\n\\n void main() {\\n ivec5 coords = getOutputCoords();\\n int batch = coords.x;\\n int d1 = coords.u;\\n\\n\\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\\n int dyFCorner = dyCorner.x;\\n int dyRCorner = dyCorner.y;\\n int dyCCorner = dyCorner.z;\\n\\n float dotProd = 0.0;\\n for (int wF = 0; wF < \" + t + \"; wF++) {\\n float dyF = float(dyFCorner + wF) / \" + o + \".0;\\n\\n if (dyF < 0.0 || dyF >= \" + e.outDepth + \".0 || fract(dyF) > 0.0) {\\n continue;\\n }\\n int idyF = int(dyF);\\n\\n int wFPerm = \" + t + \" - 1 - wF;\\n\\n for (int wR = 0; wR < \" + n + \"; wR++) {\\n float dyR = float(dyRCorner + wR) / \" + a + \".0;\\n\\n if (dyR < 0.0 || dyR >= \" + e.outHeight + \".0 ||\\n fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n int wRPerm = \" + n + \" - 1 - wR;\\n\\n for (int wC = 0; wC < \" + r + \"; wC++) {\\n float dyC = float(dyCCorner + wC) / \" + i + \".0;\\n\\n if (dyC < 0.0 || dyC >= \" + e.outWidth + \".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n int wCPerm = \" + r + \" - 1 - wC;\\n\\n for (int d2 = 0; d2 < \" + e.outChannels + \"; d2++) {\\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\\n dotProd += xValue * wValue;\\n }\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}(),\n DepthwiseConv2DDerFilterProgram = function () {\n return function (e) {\n this.variableNames = [\"x\", \"dy\"], this.outputShape = e.filterShape;\n var t = e.strideHeight,\n n = e.strideWidth,\n r = e.padInfo.top,\n o = e.padInfo.left,\n a = e.outChannels / e.inChannels;\n this.userCode = \"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int wR = coords.x;\\n int wC = coords.y;\\n int d1 = coords.z;\\n int dm = coords.w;\\n int d2 = d1 * \" + a + \" + dm;\\n\\n float dotProd = 0.0;\\n\\n // TODO: Vec4 over the batch size\\n for (int b = 0; b < \" + e.batchSize + \"; b++) {\\n for (int yR = 0; yR < \" + e.outHeight + \"; yR++) {\\n int xR = wR + yR * \" + t + \" - \" + r + \";\\n\\n if (xR < 0 || xR >= \" + e.inHeight + \") {\\n continue;\\n }\\n\\n for (int yC = 0; yC < \" + e.outWidth + \"; yC++) {\\n int xC = wC + yC * \" + n + \" - \" + o + \";\\n\\n if (xC < 0 || xC >= \" + e.inWidth + \") {\\n continue;\\n }\\n\\n float dyValue = getDy(b, yR, yC, d2);\\n float xValue = getX(b, xR, xC, d1);\\n dotProd += (xValue * dyValue);\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}(),\n DepthwiseConv2DDerInputProgram = function () {\n return function (e) {\n this.variableNames = [\"dy\", \"W\"], this.outputShape = e.inShape;\n var t = e.filterHeight,\n n = e.filterWidth,\n r = e.strideHeight,\n o = e.strideWidth,\n a = t - 1 - e.padInfo.top,\n i = n - 1 - e.padInfo.left,\n s = e.outChannels / e.inChannels;\n this.userCode = \"\\n const ivec2 pads = ivec2(\" + a + \", \" + i + \");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords[0];\\n int d1 = coords[3];\\n ivec2 dyCorner = coords.yz - pads;\\n int dyRCorner = dyCorner.x;\\n int dyCCorner = dyCorner.y;\\n\\n float dotProd = 0.0;\\n\\n for (int wR = 0; wR < \" + t + \"; wR++) {\\n float dyR = float(dyRCorner + wR) / \" + r + \".0;\\n\\n if (dyR < 0.0 || dyR >= \" + e.outHeight + \".0 || fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n int wRPerm = \" + t + \" - 1 - wR;\\n\\n for (int wC = 0; wC < \" + n + \"; wC++) {\\n float dyC = float(dyCCorner + wC) / \" + o + \".0;\\n\\n if (dyC < 0.0 || dyC >= \" + e.outWidth + \".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n int wCPerm = \" + n + \" - 1 - wC;\\n\\n // TODO: Vec4 over the channelMul\\n for (int dm = 0; dm < \" + s + \"; dm++) {\\n int d2 = d1 * \" + s + \" + dm;\\n float xValue = getDy(batch, idyR, idyC, d2);\\n float wValue = getW(wRPerm, wCPerm, d1, dm);\\n dotProd += xValue * wValue;\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}(),\n Conv2DProgram = function () {\n return function (e) {\n this.variableNames = [\"x\", \"W\"], this.outputShape = e.outShape;\n var t = e.padInfo.top,\n n = e.padInfo.left,\n r = e.strideHeight,\n o = e.strideWidth,\n a = e.dilationHeight,\n i = e.dilationWidth,\n s = e.filterHeight,\n u = e.filterWidth,\n l = 4 * Math.floor(e.inChannels / 4),\n c = e.inChannels % 4;\n this.userCode = \"\\n const ivec2 strides = ivec2(\" + r + \", \" + o + \");\\n const ivec2 pads = ivec2(\" + t + \", \" + n + \");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords[0];\\n int d2 = coords[3];\\n\\n ivec2 xRCCorner = coords.yz * strides - pads;\\n int xRCorner = xRCCorner.x;\\n int xCCorner = xRCCorner.y;\\n\\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n for (int wR = 0; wR < \" + s + \"; wR++) {\\n int xR = xRCorner + wR * \" + a + \";\\n\\n if (xR < 0 || xR >= \" + e.inHeight + \") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \" + u + \"; wC++) {\\n int xC = xCCorner + wC * \" + i + \";\\n\\n if (xC < 0 || xC >= \" + e.inWidth + \") {\\n continue;\\n }\\n\\n for (int d1 = 0; d1 < \" + l + \"; d1 += 4) {\\n vec4 xValues = vec4(\\n getX(batch, xR, xC, d1),\\n getX(batch, xR, xC, d1 + 1),\\n getX(batch, xR, xC, d1 + 2),\\n getX(batch, xR, xC, d1 + 3)\\n );\\n vec4 wValues = vec4(\\n getW(wR, wC, d1, d2),\\n getW(wR, wC, d1 + 1, d2),\\n getW(wR, wC, d1 + 2, d2),\\n getW(wR, wC, d1 + 3, d2)\\n );\\n\\n dotProd += dot(xValues, wValues);\\n }\\n\\n if (\" + (1 === c) + \") {\\n dotProd +=\\n getX(batch, xR, xC, \" + l + \") *\\n getW(wR, wC, \" + l + \", d2);\\n } else if (\" + (2 === c) + \") {\\n vec2 xValues = vec2(\\n getX(batch, xR, xC, \" + l + \"),\\n getX(batch, xR, xC, \" + l + \" + 1)\\n );\\n vec2 wValues = vec2(\\n getW(wR, wC, \" + l + \", d2),\\n getW(wR, wC, \" + l + \" + 1, d2)\\n );\\n dotProd += dot(xValues, wValues);\\n } else if (\" + (3 === c) + \") {\\n vec3 xValues = vec3(\\n getX(batch, xR, xC, \" + l + \"),\\n getX(batch, xR, xC, \" + l + \" + 1),\\n getX(batch, xR, xC, \" + l + \" + 2)\\n );\\n vec3 wValues = vec3(\\n getW(wR, wC, \" + l + \", d2),\\n getW(wR, wC, \" + l + \" + 1, d2),\\n getW(wR, wC, \" + l + \" + 2, d2)\\n );\\n dotProd += dot(xValues, wValues);\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}(),\n Conv3DProgram = function () {\n return function (e) {\n this.variableNames = [\"x\", \"W\"], this.outputShape = e.outShape;\n var t = e.padInfo.front,\n n = e.padInfo.top,\n r = e.padInfo.left,\n o = e.strideDepth,\n a = e.strideHeight,\n i = e.strideWidth,\n s = e.dilationDepth,\n u = e.dilationHeight,\n l = e.dilationWidth,\n c = e.filterDepth,\n p = e.filterHeight,\n d = e.filterWidth,\n h = 4 * Math.floor(e.inChannels / 4),\n f = e.inChannels % 4;\n this.userCode = \"\\n const ivec3 strides = ivec3(\" + o + \", \" + a + \", \" + i + \");\\n const ivec3 pads = ivec3(\" + t + \", \" + n + \", \" + r + \");\\n\\n void main() {\\n ivec5 coords = getOutputCoords();\\n int batch = coords.x;\\n int d2 = coords.u;\\n\\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\\n int xFCorner = xFRCCorner.x;\\n int xRCorner = xFRCCorner.y;\\n int xCCorner = xFRCCorner.z;\\n\\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\\n // values in that axis.\\n float dotProd = 0.0;\\n for (int wF = 0; wF < \" + c + \"; wF++) {\\n int xF = xFCorner + wF * \" + s + \";\\n\\n if (xF < 0 || xF >= \" + e.inDepth + \") {\\n continue;\\n }\\n\\n for (int wR = 0; wR < \" + p + \"; wR++) {\\n int xR = xRCorner + wR * \" + u + \";\\n\\n if (xR < 0 || xR >= \" + e.inHeight + \") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \" + d + \"; wC++) {\\n int xC = xCCorner + wC * \" + l + \";\\n\\n if (xC < 0 || xC >= \" + e.inWidth + \") {\\n continue;\\n }\\n\\n for (int d1 = 0; d1 < \" + h + \"; d1 += 4) {\\n vec4 xValues = vec4(\\n getX(batch, xF, xR, xC, d1),\\n getX(batch, xF, xR, xC, d1 + 1),\\n getX(batch, xF, xR, xC, d1 + 2),\\n getX(batch, xF, xR, xC, d1 + 3)\\n );\\n vec4 wValues = vec4(\\n getW(wF, wR, wC, d1, d2),\\n getW(wF, wR, wC, d1 + 1, d2),\\n getW(wF, wR, wC, d1 + 2, d2),\\n getW(wF, wR, wC, d1 + 3, d2)\\n );\\n\\n dotProd += dot(xValues, wValues);\\n }\\n\\n if (\" + (1 === f) + \") {\\n dotProd +=\\n getX(batch, xF, xR, xC, \" + h + \") *\\n getW(wF, wR, wC, \" + h + \", d2);\\n } else if (\" + (2 === f) + \") {\\n vec2 xValues = vec2(\\n getX(batch, xF, xR, xC, \" + h + \"),\\n getX(batch, xF, xR, xC, \" + h + \" + 1)\\n );\\n vec2 wValues = vec2(\\n getW(wF, wR, wC, \" + h + \", d2),\\n getW(wF, wR, wC, \" + h + \" + 1, d2)\\n );\\n dotProd += dot(xValues, wValues);\\n } else if (\" + (3 === f) + \") {\\n vec3 xValues = vec3(\\n getX(batch, xF, xR, xC, \" + h + \"),\\n getX(batch, xF, xR, xC, \" + h + \" + 1),\\n getX(batch, xF, xR, xC, \" + h + \" + 2)\\n );\\n vec3 wValues = vec3(\\n getW(wF, wR, wC, \" + h + \", d2),\\n getW(wF, wR, wC, \" + h + \" + 1, d2),\\n getW(wF, wR, wC, \" + h + \" + 2, d2)\\n );\\n dotProd += dot(xValues, wValues);\\n }\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}(),\n DepthwiseConv2DProgram = function () {\n return function (e) {\n this.variableNames = [\"x\", \"W\"], this.outputShape = e.outShape;\n var t = e.inHeight,\n n = e.inWidth,\n r = e.padInfo.top,\n o = e.padInfo.left,\n a = e.strideHeight,\n i = e.strideWidth,\n s = e.dilationHeight,\n u = e.dilationWidth,\n l = e.filterHeight,\n c = e.filterWidth,\n p = e.outChannels / e.inChannels;\n this.userCode = \"\\n const ivec2 strides = ivec2(\" + a + \", \" + i + \");\\n const ivec2 pads = ivec2(\" + r + \", \" + o + \");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords.x;\\n ivec2 xRCCorner = coords.yz * strides - pads;\\n int d2 = coords.w;\\n int d1 = d2 / \" + p + \";\\n int q = d2 - d1 * \" + p + \";\\n\\n int xRCorner = xRCCorner.x;\\n int xCCorner = xRCCorner.y;\\n\\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\\n for (int wR = 0; wR < \" + l + \"; wR++) {\\n int xR = xRCorner + wR * \" + s + \";\\n\\n if (xR < 0 || xR >= \" + t + \") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \" + c + \"; wC++) {\\n int xC = xCCorner + wC * \" + u + \";\\n\\n if (xC < 0 || xC >= \" + n + \") {\\n continue;\\n }\\n\\n float xVal = getX(batch, xR, xC, d1);\\n float wVal = getW(wR, wC, d1, q);\\n dotProd += xVal * wVal;\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}(),\n DepthwiseConvPacked2DProgram = function () {\n return function (e) {\n this.variableNames = [\"x\", \"W\"], this.usesPackedTextures = !0, this.outputShape = e.outShape;\n\n for (var t = e.inHeight, n = e.inWidth, r = e.padInfo.top, o = e.padInfo.left, a = e.strideHeight, i = e.strideWidth, s = e.dilationHeight, u = e.dilationWidth, l = e.filterHeight, c = e.filterWidth, p = c, d = \"int xR; int xC; int xCOffset;\", h = 0; h < l; h++) for (var f = 0; f < c; f++) d += \"\\n vec4 xTexelR\" + h + \"C\" + 2 * f + \" = vec4(0.);\\n vec4 wR\" + h + \"C\" + f + \" = vec4(0.);\\n vec4 xR\" + h + \"C\" + f + \" = vec4(0.);\";\n\n for (h = 0; h < l; h++) for (var m = 0; m < p; m++) {\n if (d += \"\\n xR = xRCorner + \" + h * s + \";\\n xC = xCCorner + \" + (f = 2 * m) * u + \";\\n \", 1 === i) {\n if (f < c && (d += o % 2 == 1 ? \"\\n xCOffset = xC + 1;\\n if(xR >= 0 && xR < \" + t + \" && xCOffset >= 0 && xCOffset < \" + n + \") {\\n xTexelR\" + h + \"C\" + f + \" = getX(batch, xR, xCOffset, d1);\\n } else {\\n xTexelR\" + h + \"C\" + f + \" = vec4(0.);\\n }\\n\\n xCOffset = xC + 1 - 2;\\n if(xR >= 0 && xR < \" + t + \" && xCOffset >= 0 && xCOffset < \" + n + \") {\\n vec4 previous = getX(batch, xR, xCOffset, d1);\\n xR\" + h + \"C\" + f + \" = vec4(previous.zw, xTexelR\" + h + \"C\" + f + \".xy);\\n } else {\\n xR\" + h + \"C\" + f + \" = vec4(0, 0, xTexelR\" + h + \"C\" + f + \".xy);\\n }\\n \" : \"\\n if(xR >= 0 && xR < \" + t + \" && xC >= 0 && xC < \" + n + \") {\\n xTexelR\" + h + \"C\" + f + \" = getX(batch, xR, xC, d1);\\n } else {\\n xTexelR\" + h + \"C\" + f + \" = vec4(0.);\\n }\\n\\n xR\" + h + \"C\" + f + \" = xTexelR\" + h + \"C\" + f + \";\\n \", f + 1 < c)) {\n var g = o % 2 == 0 ? nearestLargerEven(u) : u;\n u % 2 == 0 && o % 2 == 1 || u % 2 != 0 && o % 2 != 1 ? (d += \"\\n xCOffset = xC + \" + o % 2 + \" + \" + g + \";\\n\\n if(xR >= 0 && xR < \" + t + \" &&\\n xCOffset >= 0 && xCOffset < \" + n + \") {\\n xTexelR\" + h + \"C\" + (f + 2) + \" = getX(batch, xR, xCOffset, d1);\\n }\\n \", u > 1 && (d += \"\\n xCOffset -= 2;\\n if(xR >= 0 && xR < \" + t + \" &&\\n xCOffset >= 0 && xCOffset < \" + n + \") {\\n xTexelR\" + h + \"C\" + f + \" = getX(batch, xR, xCOffset, d1);\\n } else {\\n xTexelR\" + h + \"C\" + f + \" = vec4(0.);\\n }\\n \"), d += \"\\n xR\" + h + \"C\" + (f + 1) + \" = vec4(\\n xTexelR\" + h + \"C\" + f + \".zw, xTexelR\" + h + \"C\" + (f + 2) + \".xy);\\n \") : d += \"\\n xCOffset = xC + \" + g + \";\\n\\n if(xR >= 0 && xR < \" + t + \" &&\\n xCOffset >= 0 && xCOffset < \" + n + \") {\\n xTexelR\" + h + \"C\" + (f + 2) + \" = getX(batch, xR, xCOffset, d1);\\n }\\n\\n xR\" + h + \"C\" + (f + 1) + \" = xTexelR\" + h + \"C\" + (f + 2) + \";\\n \";\n }\n } else f < c && (d += \"\\n if(xR >= 0 && xR < \" + t + \") {\\n \", o % 2 == 1 ? (d += \"\\n xCOffset = xC + 1 - \" + i + \";\\n if(xCOffset >= 0 && xCOffset < \" + n + \") {\\n xTexelR\" + h + \"C\" + f + \" = getX(batch, xR, xCOffset, d1);\\n } else {\\n xTexelR\" + h + \"C\" + f + \" = vec4(0.);\\n }\\n\\n if(xC + 1 >= 0 && xC + 1 < \" + n + \") {\\n xTexelR\" + h + \"C\" + (f + 2) + \" = getX(batch, xR, xC + 1, d1);\\n } else {\\n xTexelR\" + h + \"C\" + (f + 2) + \" = vec4(0.);\\n }\\n\\n xR\" + h + \"C\" + f + \" = vec4(\\n xTexelR\" + h + \"C\" + f + \".zw, xTexelR\" + h + \"C\" + (f + 2) + \".zw);\\n \", f + 1 < c && (d += \"\\n vec4 final = vec4(0.);\\n xCOffset = xC + 1 + \" + i + \";\\n if(xCOffset >= 0 && xCOffset < \" + n + \") {\\n final = getX(batch, xR, xCOffset, d1);\\n }\\n xR\" + h + \"C\" + (f + 1) + \" = vec4(xTexelR\" + h + \"C\" + (f + 2) + \".xy, final.xy);\\n \")) : (d += \"\\n if(xC >= 0 && xC < \" + n + \") {\\n xTexelR\" + h + \"C\" + f + \" = getX(batch, xR, xC, d1);\\n } else {\\n xTexelR\" + h + \"C\" + f + \" = vec4(0.);\\n }\\n\\n xCOffset = xC + \" + i + \";\\n if(xCOffset >= 0 && xCOffset < \" + n + \") {\\n xTexelR\" + h + \"C\" + (f + 2) + \" = getX(batch, xR, xCOffset, d1);\\n } else {\\n xTexelR\" + h + \"C\" + (f + 2) + \" = vec4(0.);\\n }\\n\\n xR\" + h + \"C\" + f + \" = vec4(\\n xTexelR\" + h + \"C\" + f + \".xy, xTexelR\" + h + \"C\" + (f + 2) + \".xy);\\n \", f + 1 < c && (d += \"\\n xR\" + h + \"C\" + (f + 1) + \" = vec4(\\n xTexelR\" + h + \"C\" + f + \".zw, xTexelR\" + h + \"C\" + (f + 2) + \".zw);\\n \")), d += \"}\");\n\n f < c && (d += \"\\n vec4 wTexelR\" + h + \"C\" + f + \" = getW(\" + h + \", \" + f + \", d1, q);\\n wR\" + h + \"C\" + f + \" = vec4(wTexelR\" + h + \"C\" + f + \".xz, wTexelR\" + h + \"C\" + f + \".xz);\\n \", f + 1 < c && (d += \"\\n vec4 wTexelR\" + h + \"C\" + (f + 1) + \" = getW(\" + h + \", \" + (f + 1) + \", d1, q);\\n wR\" + h + \"C\" + (f + 1) + \" =\\n vec4(wTexelR\" + h + \"C\" + (f + 1) + \".xz, wTexelR\" + h + \"C\" + (f + 1) + \".xz);\"));\n }\n\n for (h = 0; h < l; h++) for (f = 0; f < c; f++) d += \"result += xR\" + h + \"C\" + f + \" * wR\" + h + \"C\" + f + \";\";\n\n this.userCode = \"\\n const ivec2 strides = ivec2(\" + a + \", \" + i + \");\\n const ivec2 pads = ivec2(\" + r + \", \" + o + \");\\n\\n void main() {\\n\\n ivec4 coords = getOutputCoords();\\n int batch = coords.x;\\n ivec2 xRCCorner = coords.yz * strides - pads;\\n int d2 = coords.w;\\n int d1 = d2;\\n int q = 0;\\n int xRCorner = xRCCorner.x;\\n int xCCorner = xRCCorner.y;\\n\\n vec4 result = vec4(0.);\\n\\n \" + d + \"\\n\\n setOutput(result);\\n }\\n \";\n };\n}(),\n CropAndResizeProgram = function () {\n return function (e, t, n, r, o) {\n this.variableNames = [\"Image\", \"Boxes\", \"BoxInd\"], this.outputShape = [];\n var a = e[0],\n i = e[1],\n s = e[2],\n u = e[3],\n l = t[0],\n c = n[0],\n p = n[1];\n this.outputShape = [l, c, p, u];\n var d = \"bilinear\" === r ? 1 : 0,\n h = [i - 1 + \".0\", s - 1 + \".0\"],\n f = h[0],\n m = h[1],\n g = c > 1 ? [\"\" + (i - 1) / (c - 1), \"(y2-y1) * height_ratio\", \"y1*\" + f + \" + float(y)*(height_scale)\"] : [\"0.0\", \"0.0\", \"0.5 * (y1+y2) * \" + f],\n v = g[0],\n y = g[1],\n x = g[2],\n T = p > 1 ? [\"\" + (s - 1) / (p - 1), \"(x2-x1) * width_ratio\", \"x1*\" + m + \" + float(x)*(width_scale)\"] : [\"0.0\", \"0.0\", \"0.5 * (x1+x2) * \" + m],\n w = T[0],\n E = T[1],\n b = T[2];\n this.userCode = \"\\n const float height_ratio = float(\" + v + \");\\n const float width_ratio = float(\" + w + \");\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int y = coords[1];\\n int x = coords[2];\\n int d = coords[3];\\n\\n // get box vals\\n float y1 = getBoxes(b,0);\\n float x1 = getBoxes(b,1);\\n float y2 = getBoxes(b,2);\\n float x2 = getBoxes(b,3);\\n\\n // get image in batch index\\n int bInd = round(getBoxInd(b));\\n if(bInd < 0 || bInd >= \" + a + \") {\\n return;\\n }\\n\\n float height_scale = \" + y + \";\\n float width_scale = \" + E + \";\\n\\n float in_y = \" + x + \";\\n if( in_y < 0.0 || in_y > \" + f + \" ) {\\n setOutput(float(\" + o + \"));\\n return;\\n }\\n float in_x = \" + b + \";\\n if( in_x < 0.0 || in_x > \" + m + \" ) {\\n setOutput(float(\" + o + \"));\\n return;\\n }\\n\\n vec2 sourceFracIndexRC = vec2(in_y,in_x);\\n if(\" + d + \" == 1) {\\n // Compute the four integer indices.\\n ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\\n ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\\n\\n float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\\n float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\\n float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\\n float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\\n\\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\\n\\n float top = topLeft + (topRight - topLeft) * fracRC.y;\\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\\n float newValue = top + (bottom - top) * fracRC.x;\\n setOutput(newValue);\\n } else {\\n // Compute the coordinators of nearest neighbor point.\\n ivec2 sourceNearestRC = ivec2(floor(\\n sourceFracIndexRC + vec2(0.5,0.5)));\\n float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\\n setOutput(newValue);\\n }\\n }\\n \";\n };\n}();\n\nfunction getGlslDifferences() {\n var e, t, n, r, o, a, i, s;\n return 2 === ENV.get(\"WEBGL_VERSION\") ? (e = \"#version 300 es\", t = \"in\", n = \"out\", r = \"in\", o = \"texture\", a = \"outputColor\", i = \"out vec4 outputColor;\", s = \"\\n #define round(value) newRound(value)\\n int newRound(float value) {\\n return int(floor(value + 0.5));\\n }\\n\\n ivec4 newRound(vec4 value) {\\n return ivec4(floor(value + vec4(0.5)));\\n }\\n \") : (e = \"\", t = \"attribute\", n = \"varying\", r = \"varying\", o = \"texture2D\", a = \"gl_FragColor\", i = \"\", s = \"\\n int round(float value) {\\n return int(floor(value + 0.5));\\n }\\n\\n ivec4 round(vec4 value) {\\n return ivec4(floor(value + vec4(0.5)));\\n }\\n \"), {\n version: e,\n attribute: t,\n varyingVs: n,\n varyingFs: r,\n texture2D: o,\n output: a,\n defineOutput: i,\n defineRound: s\n };\n}\n\nfunction getLogicalCoordinatesFromFlatIndex(e, t, n) {\n void 0 === n && (n = \"index\");\n var r = computeStrides(t);\n return r.map(function (t, o) {\n return \"int \" + e[o] + \" = \" + n + \" / \" + t + \"; \" + (o === r.length - 1 ? \"int \" + e[o + 1] + \" = \" + n + \" - \" + e[o] + \" * \" + t : \"index -= \" + e[o] + \" * \" + t) + \";\";\n }).join(\"\");\n}\n\nfunction buildVec(e) {\n return 1 === e.length ? \"\" + e[0] : \"vec\" + e.length + \"(\" + e.join(\",\") + \")\";\n}\n\nfunction dotify(e, t) {\n if (e.length !== t.length) throw new Error(\"Vectors to be dotted must be of the same length -got \" + e.length + \" and \" + t.length);\n\n for (var n = [], r = Math.floor(e.length / 4), o = e.length % 4, a = 0; a < r; a++) {\n var i = e.slice(4 * a, 4 * a + 4),\n s = t.slice(4 * a, 4 * a + 4);\n n.push(buildVec(i) + \", \" + buildVec(s));\n }\n\n if (0 !== o) {\n i = e.slice(4 * r), s = t.slice(4 * r);\n 1 === i.length && (i = i.map(function (e) {\n return \"float(\" + e + \")\";\n }), s = s.map(function (e) {\n return \"float(\" + e + \")\";\n })), n.push(buildVec(i) + \", \" + buildVec(s));\n }\n\n return n.map(function (e, t) {\n return \"dot(\" + e + \")\";\n }).join(\"+\");\n}\n\nfunction makeShader(e, t, n, r) {\n var o = [];\n e.forEach(function (e) {\n var t = sizeFromShape(e.shapeInfo.logicalShape);\n e.shapeInfo.isUniform ? o.push(\"uniform float \" + e.name + (t > 1 ? \"[\" + t + \"]\" : \"\") + \";\") : (o.push(\"uniform sampler2D \" + e.name + \";\"), o.push(\"uniform int offset\" + e.name + \";\"));\n });\n var a,\n i,\n s = o.join(\"\\n\"),\n u = e.map(function (e) {\n return getInputSamplingSnippet(e, t, r);\n }).join(\"\\n\"),\n l = t.texShape,\n c = getGlslDifferences(),\n p = getFloatTextureSampleSnippet(c),\n d = getShaderPrefix(c);\n return t.isPacked ? (a = getPackedOutputSamplingSnippet(t.logicalShape, l), i = getFloatTextureSetRGBASnippet(c)) : (a = getOutputSamplingSnippet(t.logicalShape, l), i = getFloatTextureSetRSnippet(c)), r && (d += SHADER_PACKED_PREFIX), [d, p, i, s, a, u, n].join(\"\\n\");\n}\n\nfunction getSamplerFromInInfo(e) {\n var t = e.shapeInfo.logicalShape;\n\n switch (t.length) {\n case 0:\n return getSamplerScalar(e);\n\n case 1:\n return getSampler1D(e);\n\n case 2:\n return getSampler2D(e);\n\n case 3:\n return getSampler3D(e);\n\n case 4:\n return getSampler4D(e);\n\n case 5:\n return getSampler5D(e);\n\n case 6:\n return getSampler6D(e);\n\n default:\n throw new Error(t.length + \"-D input sampling is not yet supported\");\n }\n}\n\nfunction getPackedSamplerFromInInfo(e) {\n switch (e.shapeInfo.logicalShape.length) {\n case 0:\n return getPackedSamplerScalar(e);\n\n case 1:\n return getPackedSampler1D(e);\n\n case 2:\n return getPackedSampler2D(e);\n\n case 3:\n return getPackedSampler3D(e);\n\n default:\n return getPackedSamplerND(e);\n }\n}\n\nfunction getInputSamplingSnippet(e, t, n) {\n void 0 === n && (n = !1);\n var r = \"\";\n r += n ? getPackedSamplerFromInInfo(e) : getSamplerFromInInfo(e);\n var o = e.shapeInfo.logicalShape,\n a = t.logicalShape;\n return o.length <= a.length && (r += n ? getPackedSamplerAtOutputCoords(e, t) : getSamplerAtOutputCoords(e, t)), r;\n}\n\nfunction getPackedOutputSamplingSnippet(e, t) {\n switch (e.length) {\n case 0:\n return getOutputScalarCoords();\n\n case 1:\n return getOutputPacked1DCoords(e, t);\n\n case 2:\n return getOutputPacked2DCoords(e, t);\n\n case 3:\n return getOutputPacked3DCoords(e, t);\n\n default:\n return getOutputPackedNDCoords(e, t);\n }\n}\n\nfunction getOutputSamplingSnippet(e, t) {\n switch (e.length) {\n case 0:\n return getOutputScalarCoords();\n\n case 1:\n return getOutput1DCoords(e, t);\n\n case 2:\n return getOutput2DCoords(e, t);\n\n case 3:\n return getOutput3DCoords(e, t);\n\n case 4:\n return getOutput4DCoords(e, t);\n\n case 5:\n return getOutput5DCoords(e, t);\n\n case 6:\n return getOutput6DCoords(e, t);\n\n default:\n throw new Error(e.length + \"-D output sampling is not yet supported\");\n }\n}\n\nfunction getFloatTextureSampleSnippet(e) {\n return \"\\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\\n return \" + e.texture2D + \"(textureSampler, uv).r;\\n }\\n \";\n}\n\nfunction getFloatTextureSetRSnippet(e) {\n return \"\\n void setOutput(float val) {\\n \" + e.output + \" = vec4(val, 0, 0, 0);\\n }\\n \";\n}\n\nfunction getFloatTextureSetRGBASnippet(e) {\n return \"\\n void setOutput(vec4 val) {\\n \" + e.output + \" = val;\\n }\\n \";\n}\n\nfunction getShaderPrefix(e) {\n var t = \"\";\n return t = ENV.get(\"PROD\") ? \"\\n bool isNaN(float val) {\\n return false;\\n }\\n\\n bool hasNaN(vec4 values) {\\n return false;\\n }\\n \" : \"\\n bool isNaN(float val) {\\n return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\\n }\\n\\n bool hasNaN(vec4 values) {\\n return any(bvec4(\\n isNaN(values.x),\\n isNaN(values.y),\\n isNaN(values.z),\\n isNaN(values.w)\\n ));\\n }\\n \", e.version + \"\\n precision highp float;\\n precision highp int;\\n precision highp sampler2D;\\n \" + e.varyingFs + \" vec2 resultUV;\\n \" + e.defineOutput + \"\\n const vec2 halfCR = vec2(0.5, 0.5);\\n\\n struct ivec5\\n {\\n int x;\\n int y;\\n int z;\\n int w;\\n int u;\\n };\\n\\n struct ivec6\\n {\\n int x;\\n int y;\\n int z;\\n int w;\\n int u;\\n int v;\\n };\\n\\n \" + t + \"\\n\\n float getNaN(vec4 values) {\\n return dot(vec4(1), values);\\n }\\n\\n \" + e.defineRound + \"\\n\\n int imod(int x, int y) {\\n return x - y * (x / y);\\n }\\n\\n //Based on the work of Dave Hoskins\\n //https://www.shadertoy.com/view/4djSRW\\n #define HASHSCALE1 443.8975\\n float random(float seed){\\n vec2 p = resultUV * seed;\\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\\n p3 += dot(p3, p3.yzx + 19.19);\\n return fract((p3.x + p3.y) * p3.z);\\n }\\n\\n \" + SAMPLE_1D_SNIPPET + \"\\n \" + SAMPLE_2D_SNIPPET + \"\\n \" + SAMPLE_3D_SNIPPET + \"\\n \" + SAMPLE_5D_SNIPPET + \"\\n \" + SAMPLE_6D_SNIPPET + \"\\n \";\n}\n\nvar SAMPLE_1D_SNIPPET = \"\\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\\n int texR = index / texNumC;\\n int texC = index - texR * texNumC;\\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\\n int texelIndex = index / 2;\\n int texR = texelIndex / texNumC;\\n int texC = texelIndex - texR * texNumC;\\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",\n SAMPLE_2D_SNIPPET = \"\\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\\n int texNumC, int row, int col) {\\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\\n int texR = texelIndex / texNumC;\\n int texC = texelIndex - texR * texNumC;\\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",\n SAMPLE_3D_SNIPPET = \"\\nvec2 packedUVfrom3D(int texNumR, int texNumC,\\n int texelsInBatch, int texelsInLogicalRow, int b,\\n int row, int col) {\\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\\n int texR = index / texNumC;\\n int texC = index - texR * texNumC;\\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",\n SAMPLE_5D_SNIPPET = \"\\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\\n int stride1, int stride2, int stride3, int row, int col, int depth,\\n int depth2, int depth3) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * stride0 + col * stride1 +\\n depth * stride2 + depth2 * stride3 + depth3;\\n int texR = index / texNumC;\\n int texC = index - texR * texNumC;\\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",\n SAMPLE_6D_SNIPPET = \"\\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\\n int stride1, int stride2, int stride3, int stride4,\\n int row, int col, int depth, int depth2, int depth3, int depth4) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\\n stride3 + depth3 * stride4 + depth4;\\n int texR = index / texNumC;\\n int texC = index - texR * texNumC;\\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",\n SHADER_PACKED_PREFIX = \"\\n float getChannel(vec4 frag, vec2 innerDims) {\\n vec2 modCoord = mod(innerDims, 2.);\\n return modCoord.x == 0. ?\\n (modCoord.y == 0. ? frag.r : frag.g) :\\n (modCoord.y == 0. ? frag.b : frag.a);\\n }\\n float getChannel(vec4 frag, int dim) {\\n float modCoord = mod(float(dim), 2.);\\n return modCoord == 0. ? frag.r : frag.g;\\n }\\n\";\n\nfunction getOutputScalarCoords() {\n return \"\\n int getOutputCoords() {\\n return 0;\\n }\\n \";\n}\n\nfunction getOutputPacked1DCoords(e, t) {\n var n = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)];\n return 1 === n[0] ? \"\\n int getOutputCoords() {\\n return 2 * int(resultUV.x * \" + n[1] + \".0);\\n }\\n \" : 1 === n[1] ? \"\\n int getOutputCoords() {\\n return 2 * int(resultUV.y * \" + n[0] + \".0);\\n }\\n \" : \"\\n int getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + n[0] + \", \" + n[1] + \"));\\n return resTexRC.x * \" + n[1] + \" + resTexRC.y;\\n }\\n \";\n}\n\nfunction getOutput1DCoords(e, t) {\n return 1 === t[0] ? \"\\n int getOutputCoords() {\\n return int(resultUV.x * \" + t[1] + \".0);\\n }\\n \" : 1 === t[1] ? \"\\n int getOutputCoords() {\\n return int(resultUV.y * \" + t[0] + \".0);\\n }\\n \" : \"\\n int getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + t[0] + \", \" + t[1] + \"));\\n return resTexRC.x * \" + t[1] + \" + resTexRC.y;\\n }\\n \";\n}\n\nfunction getOutputPacked3DCoords(e, t) {\n var n = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)],\n r = Math.ceil(e[2] / 2),\n o = r * Math.ceil(e[1] / 2);\n return \"\\n ivec3 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + n[0] + \", \" + n[1] + \"));\\n int index = resTexRC.x * \" + n[1] + \" + resTexRC.y;\\n\\n int b = index / \" + o + \";\\n index -= b * \" + o + \";\\n\\n int r = 2 * (index / \" + r + \");\\n int c = imod(index, \" + r + \") * 2;\\n\\n return ivec3(b, r, c);\\n }\\n \";\n}\n\nfunction getOutput3DCoords(e, t) {\n var n = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], e);\n return \"\\n ivec3 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + t[0] + \", \" + t[1] + \"));\\n int index = resTexRC.x * \" + t[1] + \" + resTexRC.y;\\n \" + n + \"\\n return ivec3(r, c, d);\\n }\\n \";\n}\n\nfunction getOutputPackedNDCoords(e, t) {\n for (var n = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)], r = Math.ceil(e[e.length - 1] / 2), o = r * Math.ceil(e[e.length - 2] / 2), a = o, i = \"\", s = \"b, r, c\", u = 2; u < e.length - 1; u++) i = \"\\n int b\" + u + \" = index / \" + (a *= e[e.length - u - 1]) + \";\\n index -= b\" + u + \" * \" + a + \";\\n \" + i, s = \"b\" + u + \", \" + s;\n\n return \"\\n ivec\" + e.length + \" getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + n[0] + \", \" + n[1] + \"));\\n int index = resTexRC.x * \" + n[1] + \" + resTexRC.y;\\n\\n \" + i + \"\\n\\n int b = index / \" + o + \";\\n index -= b * \" + o + \";\\n\\n int r = 2 * (index / \" + r + \");\\n int c = imod(index, \" + r + \") * 2;\\n\\n return ivec\" + e.length + \"(\" + s + \");\\n }\\n \";\n}\n\nfunction getOutput4DCoords(e, t) {\n var n = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\"], e);\n return \"\\n ivec4 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + t[0] + \", \" + t[1] + \"));\\n int index = resTexRC.x * \" + t[1] + \" + resTexRC.y;\\n \" + n + \"\\n return ivec4(r, c, d, d2);\\n }\\n \";\n}\n\nfunction getOutput5DCoords(e, t) {\n var n = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\", \"d3\"], e);\n return \"\\n ivec5 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(\" + t[0] + \",\\n \" + t[1] + \"));\\n\\n int index = resTexRC.x * \" + t[1] + \" + resTexRC.y;\\n\\n \" + n + \"\\n\\n ivec5 outShape = ivec5(r, c, d, d2, d3);\\n return outShape;\\n }\\n \";\n}\n\nfunction getOutput6DCoords(e, t) {\n var n = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\", \"d3\", \"d4\"], e);\n return \"\\n ivec6 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + t[0] + \", \" + t[1] + \"));\\n int index = resTexRC.x * \" + t[1] + \" + resTexRC.y;\\n\\n \" + n + \"\\n\\n ivec6 result = ivec6(r, c, d, d2, d3, d4);\\n return result;\\n }\\n \";\n}\n\nfunction getOutputPacked2DCoords(e, t) {\n var n = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)];\n if (arraysEqual(e, t)) return \"\\n ivec2 getOutputCoords() {\\n return 2 * ivec2(resultUV.yx * vec2(\" + n[0] + \", \" + n[1] + \"));\\n }\\n \";\n var r = Math.ceil(e[1] / 2);\n return \"\\n ivec2 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + n[0] + \", \" + n[1] + \"));\\n\\n int index = resTexRC.x * \" + n[1] + \" + resTexRC.y;\\n int r = 2 * (index / \" + r + \");\\n int c = imod(index, \" + r + \") * 2;\\n\\n return ivec2(r, c);\\n }\\n \";\n}\n\nfunction getOutput2DCoords(e, t) {\n return arraysEqual(e, t) ? \"\\n ivec2 getOutputCoords() {\\n return ivec2(resultUV.yx * vec2(\" + t[0] + \", \" + t[1] + \"));\\n }\\n \" : 1 === e[1] ? \"\\n ivec2 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + t[0] + \", \" + t[1] + \"));\\n int index = resTexRC.x * \" + t[1] + \" + resTexRC.y;\\n return ivec2(index, 0);\\n }\\n \" : 1 === e[0] ? \"\\n ivec2 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + t[0] + \", \" + t[1] + \"));\\n int index = resTexRC.x * \" + t[1] + \" + resTexRC.y;\\n return ivec2(0, index);\\n }\\n \" : \"\\n ivec2 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\" + t[0] + \", \" + t[1] + \"));\\n int index = resTexRC.x * \" + t[1] + \" + resTexRC.y;\\n int r = index / \" + e[1] + \";\\n int c = index - r * \" + e[1] + \";\\n return ivec2(r, c);\\n }\\n \";\n}\n\nfunction getFlatOffsetUniformName(e) {\n return \"offset\" + e;\n}\n\nfunction getPackedSamplerScalar(e) {\n var t = e.name;\n return \"\\n vec4 \" + (\"get\" + t.charAt(0).toUpperCase() + t.slice(1)) + \"() {\\n return \" + getGlslDifferences().texture2D + \"(\" + t + \", halfCR);\\n }\\n \";\n}\n\nfunction getSamplerScalar(e) {\n var t = e.name,\n n = \"get\" + t.charAt(0).toUpperCase() + t.slice(1);\n if (e.shapeInfo.isUniform) return \"float \" + n + \"() {return \" + t + \";}\";\n var r = e.shapeInfo.texShape,\n o = r[0],\n a = r[1];\n if (1 === o && 1 === a) return \"\\n float \" + n + \"() {\\n return sampleTexture(\" + t + \", halfCR);\\n }\\n \";\n var i = e.shapeInfo.texShape;\n return \"\\n float \" + n + \"() {\\n vec2 uv = uvFromFlat(\" + i[0] + \", \" + i[1] + \", \" + getFlatOffsetUniformName(t) + \");\\n return sampleTexture(\" + t + \", uv);\\n }\\n \";\n}\n\nfunction getPackedSampler1D(e) {\n var t = e.name,\n n = \"get\" + t.charAt(0).toUpperCase() + t.slice(1),\n r = e.shapeInfo.texShape,\n o = [Math.ceil(r[0] / 2), Math.ceil(r[1] / 2)],\n a = getGlslDifferences();\n return \"\\n vec4 \" + n + \"(int index) {\\n vec2 uv = packedUVfrom1D(\\n \" + o[0] + \", \" + o[1] + \", index);\\n return \" + a.texture2D + \"(\" + t + \", uv);\\n }\\n \";\n}\n\nfunction getSampler1D(e) {\n var t = e.name,\n n = \"get\" + t.charAt(0).toUpperCase() + t.slice(1);\n if (e.shapeInfo.isUniform) return \"\\n float \" + n + \"(int index) {\\n \" + getUniformSampler(e) + \"\\n }\\n \";\n var r = e.shapeInfo.texShape,\n o = r[0],\n a = r[1];\n if (1 === a && 1 === o) return \"\\n float \" + n + \"(int index) {\\n return sampleTexture(\" + t + \", halfCR);\\n }\\n \";\n var i = getFlatOffsetUniformName(t);\n return 1 === a ? \"\\n float \" + n + \"(int index) {\\n vec2 uv = vec2(0.5, (float(index + \" + i + \") + 0.5) / \" + o + \".0);\\n return sampleTexture(\" + t + \", uv);\\n }\\n \" : 1 === o ? \"\\n float \" + n + \"(int index) {\\n vec2 uv = vec2((float(index + \" + i + \") + 0.5) / \" + a + \".0, 0.5);\\n return sampleTexture(\" + t + \", uv);\\n }\\n \" : \"\\n float \" + n + \"(int index) {\\n vec2 uv = uvFromFlat(\" + o + \", \" + a + \", index + \" + i + \");\\n return sampleTexture(\" + t + \", uv);\\n }\\n \";\n}\n\nfunction getPackedSampler2D(e) {\n var t = e.shapeInfo.logicalShape,\n n = e.name,\n r = \"get\" + n.charAt(0).toUpperCase() + n.slice(1),\n o = e.shapeInfo.texShape,\n a = o[0],\n i = o[1],\n s = getGlslDifferences();\n if (null != o && arraysEqual(t, o)) return \"\\n vec4 \" + r + \"(int row, int col) {\\n vec2 uv = (vec2(col, row) + halfCR) / vec2(\" + i + \".0, \" + a + \".0);\\n\\n return \" + s.texture2D + \"(\" + n + \", uv);\\n }\\n \";\n var u = [Math.ceil(o[0] / 2), Math.ceil(o[1] / 2)];\n return \"\\n vec4 \" + r + \"(int row, int col) {\\n vec2 uv = packedUVfrom2D(\" + Math.ceil(t[1] / 2) + \", \" + u[0] + \", \" + u[1] + \", row, col);\\n return \" + s.texture2D + \"(\" + n + \", uv);\\n }\\n \";\n}\n\nfunction getSampler2D(e) {\n var t = e.shapeInfo.logicalShape,\n n = e.name,\n r = \"get\" + n.charAt(0).toUpperCase() + n.slice(1),\n o = e.shapeInfo.texShape;\n\n if (null != o && arraysEqual(t, o)) {\n var a = o[0];\n return \"\\n float \" + r + \"(int row, int col) {\\n vec2 uv = (vec2(col, row) + halfCR) / vec2(\" + o[1] + \".0, \" + a + \".0);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \";\n }\n\n var i = squeezeShape(t),\n s = i.newShape,\n u = i.keptDims,\n l = s;\n\n if (l.length < t.length) {\n return \"\\n \" + getSamplerFromInInfo(squeezeInputInfo(e, l)) + \"\\n float \" + r + \"(int row, int col) {\\n return \" + r + \"(\" + getSqueezedParams([\"row\", \"col\"], u) + \");\\n }\\n \";\n }\n\n if (e.shapeInfo.isUniform) return \"\\n float \" + r + \"(int row, int col) {\\n int index = round(dot(vec2(row, col), vec2(\" + t[1] + \", 1)));\\n \" + getUniformSampler(e) + \"\\n }\\n \";\n var c = o[0],\n p = o[1],\n d = getFlatOffsetUniformName(n);\n return 1 === p ? \"\\n float \" + r + \"(int row, int col) {\\n float index = dot(vec3(row, col, \" + d + \"), vec3(\" + t[1] + \", 1, 1));\\n vec2 uv = vec2(0.5, (index + 0.5) / \" + c + \".0);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \" : 1 === c ? \"\\n float \" + r + \"(int row, int col) {\\n float index = dot(vec3(row, col, \" + d + \"), vec3(\" + t[1] + \", 1, 1));\\n vec2 uv = vec2((index + 0.5) / \" + p + \".0, 0.5);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \" : \"\\n float \" + r + \"(int row, int col) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * \" + t[1] + \" + col + \" + d + \";\\n vec2 uv = uvFromFlat(\" + c + \", \" + p + \", index);\\n return sampleTexture(\" + n + \", uv);\\n }\\n\";\n}\n\nfunction getPackedSampler3D(e) {\n var t = e.shapeInfo.logicalShape,\n n = e.name,\n r = \"get\" + n.charAt(0).toUpperCase() + n.slice(1),\n o = e.shapeInfo.texShape,\n a = [Math.ceil(o[0] / 2), Math.ceil(o[1] / 2)];\n\n if (1 === t[0]) {\n return \"\\n \" + getPackedSamplerFromInInfo(squeezeInputInfo(e, t.slice(1))) + \"\\n vec4 \" + r + \"(int b, int row, int col) {\\n return \" + r + \"(\" + getSqueezedParams([\"b\", \"row\", \"col\"], [1, 2]) + \");\\n }\\n \";\n }\n\n var i = a[0],\n s = a[1],\n u = Math.ceil(t[2] / 2);\n return \"\\n vec4 \" + r + \"(int b, int row, int col) {\\n vec2 uv = packedUVfrom3D(\\n \" + i + \", \" + s + \", \" + u * Math.ceil(t[1] / 2) + \", \" + u + \", b, row, col);\\n return \" + getGlslDifferences().texture2D + \"(\" + n + \", uv);\\n }\\n \";\n}\n\nfunction getSampler3D(e) {\n var t = e.shapeInfo.logicalShape,\n n = e.name,\n r = \"get\" + n.charAt(0).toUpperCase() + n.slice(1),\n o = t[1] * t[2],\n a = t[2],\n i = squeezeShape(t),\n s = i.newShape,\n u = i.keptDims,\n l = s;\n\n if (l.length < t.length) {\n return \"\\n \" + getSamplerFromInInfo(squeezeInputInfo(e, l)) + \"\\n float \" + r + \"(int row, int col, int depth) {\\n return \" + r + \"(\" + getSqueezedParams([\"row\", \"col\", \"depth\"], u) + \");\\n }\\n \";\n }\n\n if (e.shapeInfo.isUniform) return \"\\n float \" + r + \"(int row, int col, int depth) {\\n int index = round(dot(vec3(row, col, depth),\\n vec3(\" + o + \", \" + a + \", 1)));\\n \" + getUniformSampler(e) + \"\\n }\\n \";\n var c = e.shapeInfo.texShape,\n p = c[0],\n d = c[1],\n h = e.shapeInfo.flatOffset;\n return d === o && null == h ? \"\\n float \" + r + \"(int row, int col, int depth) {\\n float texR = float(row);\\n float texC = dot(vec2(col, depth), vec2(\" + a + \", 1));\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\" + d + \".0, \" + p + \".0);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \" : d === a && null == h ? \"\\n float \" + r + \"(int row, int col, int depth) {\\n float texR = dot(vec2(row, col), vec2(\" + t[1] + \", 1));\\n float texC = float(depth);\\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\" + d + \".0, \" + p + \".0);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \" : \"\\n float \" + r + \"(int row, int col, int depth) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * \" + o + \" + col * \" + a + \" + depth + \" + getFlatOffsetUniformName(n) + \";\\n vec2 uv = uvFromFlat(\" + p + \", \" + d + \", index);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \";\n}\n\nfunction getPackedSamplerND(e) {\n for (var t = e.shapeInfo.logicalShape, n = t.length, r = e.name, o = \"get\" + r.charAt(0).toUpperCase() + r.slice(1), a = e.shapeInfo.texShape, i = [Math.ceil(a[0] / 2), Math.ceil(a[1] / 2)], s = i[0], u = i[1], l = Math.ceil(t[n - 1] / 2), c = l * Math.ceil(t[n - 2] / 2), p = \"int b, int row, int col\", d = \"b * \" + c + \" + (row / 2) * \" + l + \" + (col / 2)\", h = 2; h < n - 1; h++) p = \"int b\" + h + \", \" + p, d = \"b\" + h + \" * \" + (c *= t[n - h - 1]) + \" + \" + d;\n\n return \"\\n vec4 \" + o + \"(\" + p + \") {\\n int index = \" + d + \";\\n int texR = index / \" + u + \";\\n int texC = index - texR * \" + u + \";\\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\" + u + \", \" + s + \");\\n return \" + getGlslDifferences().texture2D + \"(\" + r + \", uv);\\n }\\n \";\n}\n\nfunction getSampler4D(e) {\n var t = e.shapeInfo.logicalShape,\n n = e.name,\n r = \"get\" + n.charAt(0).toUpperCase() + n.slice(1),\n o = t[3],\n a = t[2] * o,\n i = t[1] * a,\n s = squeezeShape(t),\n u = s.newShape,\n l = s.keptDims;\n\n if (u.length < t.length) {\n return \"\\n \" + getSamplerFromInInfo(squeezeInputInfo(e, u)) + \"\\n float \" + r + \"(int row, int col, int depth, int depth2) {\\n return \" + r + \"(\" + getSqueezedParams([\"row\", \"col\", \"depth\", \"depth2\"], l) + \");\\n }\\n \";\n }\n\n if (e.shapeInfo.isUniform) return \"\\n float \" + r + \"(int row, int col, int depth, int depth2) {\\n int index = round(dot(vec4(row, col, depth, depth2),\\n vec4(\" + i + \", \" + a + \", \" + o + \", 1)));\\n \" + getUniformSampler(e) + \"\\n }\\n \";\n var c = e.shapeInfo.flatOffset,\n p = e.shapeInfo.texShape,\n d = p[0],\n h = p[1];\n return h === i && null == c ? \"\\n float \" + r + \"(int row, int col, int depth, int depth2) {\\n float texR = float(row);\\n float texC =\\n dot(vec3(col, depth, depth2),\\n vec3(\" + a + \", \" + o + \", 1));\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\" + h + \".0, \" + d + \".0);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \" : h === o && null == c ? \"\\n float \" + r + \"(int row, int col, int depth, int depth2) {\\n float texR = dot(vec3(row, col, depth),\\n vec3(\" + t[1] * t[2] + \", \" + t[2] + \", 1));\\n float texC = float(depth2);\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\" + h + \".0, \" + d + \".0);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \" : \"\\n float \" + r + \"(int row, int col, int depth, int depth2) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * \" + i + \" + col * \" + a + \" +\\n depth * \" + o + \" + depth2;\\n vec2 uv = uvFromFlat(\" + d + \", \" + h + \", index + \" + getFlatOffsetUniformName(n) + \");\\n return sampleTexture(\" + n + \", uv);\\n }\\n \";\n}\n\nfunction getSampler5D(e) {\n var t = e.shapeInfo.logicalShape,\n n = e.name,\n r = \"get\" + n.charAt(0).toUpperCase() + n.slice(1),\n o = t[4],\n a = t[3] * o,\n i = t[2] * a,\n s = t[1] * i,\n u = squeezeShape(t),\n l = u.newShape,\n c = u.keptDims;\n\n if (l.length < t.length) {\n return \"\\n \" + getSamplerFromInInfo(squeezeInputInfo(e, l)) + \"\\n float \" + r + \"(int row, int col, int depth, int depth2, int depth3) {\\n return \" + r + \"(\" + getSqueezedParams([\"row\", \"col\", \"depth\", \"depth2\", \"depth3\"], c) + \");\\n }\\n \";\n }\n\n if (e.shapeInfo.isUniform) return \"\\n float \" + r + \"(int row, int col, int depth, int depth2, int depth3) {\\n float index = dot(\\n vec4(row, col, depth, depth2),\\n vec4(\" + s + \", \" + i + \", \" + a + \", \" + o + \")) +\\n depth3;\\n \" + getUniformSampler(e) + \"\\n }\\n \";\n var p = e.shapeInfo.flatOffset,\n d = e.shapeInfo.texShape,\n h = d[0],\n f = d[1];\n return f === s && null == p ? \"\\n float \" + r + \"(int row, int col, int depth, int depth2, int depth3) {\\n int texR = row;\\n float texC = dot(vec4(col, depth, depth2, depth3),\\n vec4(\" + i + \", \" + a + \", \" + o + \", 1));\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\" + f + \".0, \" + h + \".0);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \" : f === o && null == p ? \"\\n float \" + r + \"(int row, int col, int depth, int depth2, int depth3) {\\n float texR = dot(\\n vec4(row, col, depth, depth2),\\n vec4(\" + t[1] * t[2] * t[3] + \",\\n \" + t[2] * t[3] + \", \" + t[3] + \", 1));\\n int texC = depth3;\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\" + f + \".0, \" + h + \".0);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \" : \"\\n float \" + r + \"(int row, int col, int depth, int depth2, int depth3) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * \" + s + \" + col * \" + i + \" + depth * \" + a + \" +\\n depth2 * \" + o + \" + depth3 + \" + getFlatOffsetUniformName(n) + \";\\n vec2 uv = uvFromFlat(\" + h + \", \" + f + \", index);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \";\n}\n\nfunction getSampler6D(e) {\n var t = e.shapeInfo.logicalShape,\n n = e.name,\n r = \"get\" + n.charAt(0).toUpperCase() + n.slice(1),\n o = squeezeShape(t),\n a = o.newShape,\n i = o.keptDims;\n\n if (a.length < t.length) {\n return \"\\n \" + getSamplerFromInInfo(squeezeInputInfo(e, a)) + \"\\n float \" + r + \"(int row, int col, int depth,\\n int depth2, int depth3, int depth4) {\\n return \" + r + \"(\" + getSqueezedParams([\"row\", \"col\", \"depth\", \"depth2\", \"depth3\", \"depth4\"], i) + \");\\n }\\n \";\n }\n\n var s = t[5],\n u = t[4] * s,\n l = t[3] * u,\n c = t[2] * l,\n p = t[1] * c;\n if (e.shapeInfo.isUniform) return \"\\n float \" + r + \"(int row, int col, int depth,\\n int depth2, int depth3, int depth4) {\\n int index = round(dot(\\n vec4(row, col, depth, depth2),\\n vec4(\" + p + \", \" + c + \", \" + l + \", \" + u + \")) +\\n dot(\\n vec2(depth3, depth4),\\n vec2(\" + s + \", 1)));\\n \" + getUniformSampler(e) + \"\\n }\\n \";\n var d = e.shapeInfo.flatOffset,\n h = e.shapeInfo.texShape,\n f = h[0],\n m = h[1];\n return m === p && null == d ? \"\\n float \" + r + \"(int row, int col, int depth,\\n int depth2, int depth3, int depth4) {\\n int texR = row;\\n float texC = dot(vec4(col, depth, depth2, depth3),\\n vec4(\" + c + \", \" + l + \", \" + u + \", \" + s + \")) +\\n float(depth4);\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\" + m + \".0, \" + f + \".0);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \" : m === s && null == d ? \"\\n float \" + r + \"(int row, int col, int depth,\\n int depth2, int depth3, int depth4) {\\n float texR = dot(vec4(row, col, depth, depth2),\\n vec4(\" + t[1] * t[2] * t[3] * t[4] + \",\\n \" + t[2] * t[3] * t[4] + \",\\n \" + t[3] * t[4] + \",\\n \" + t[4] + \")) + float(depth3);\\n int texC = depth4;\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\" + m + \".0, \" + f + \".0);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \" : \"\\n float \" + r + \"(int row, int col, int depth,\\n int depth2, int depth3, int depth4) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * \" + p + \" + col * \" + c + \" + depth * \" + l + \" +\\n depth2 * \" + u + \" + depth3 * \" + s + \" + depth4 + \" + getFlatOffsetUniformName(n) + \";\\n vec2 uv = uvFromFlat(\" + f + \", \" + m + \", index);\\n return sampleTexture(\" + n + \", uv);\\n }\\n \";\n}\n\nfunction getUniformSampler(e) {\n var t = e.name,\n n = sizeFromShape(e.shapeInfo.logicalShape);\n return 1 === n ? \"return \" + t + \";\" : \"\\n for (int i = 0; i < \" + n + \"; i++) {\\n if (i == index) {\\n return \" + t + \"[i];\\n }\\n }\\n \";\n}\n\nfunction getPackedSamplerAtOutputCoords(e, t) {\n var n,\n r = e.name,\n o = r.charAt(0).toUpperCase() + r.slice(1),\n a = \"get\" + o + \"AtOutCoords\",\n i = e.shapeInfo.logicalShape.length,\n s = t.logicalShape.length,\n u = getBroadcastDims(e.shapeInfo.logicalShape, t.logicalShape),\n l = getCoordsDataType(s),\n c = s - i,\n p = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n n = 0 === i ? \"\" : s < 2 && u.length >= 1 ? \"coords = 0;\" : u.map(function (e) {\n return \"coords.\" + p[e + c] + \" = 0;\";\n }).join(\"\\n\");\n var d = \"\";\n d = s < 2 && i > 0 ? \"coords\" : e.shapeInfo.logicalShape.map(function (e, t) {\n return \"coords.\" + p[t + c];\n }).join(\", \");\n var h = \"return outputValue;\";\n if (1 === i && s > 1) h = \"\\n return vec4(outputValue.xy, outputValue.xy);\\n \";else if (0 === i && s > 0) h = 1 === s ? \"\\n return vec4(outputValue.x, outputValue.x, 0., 0.);\\n \" : \"\\n return vec4(outputValue.x);\\n \";else if (u.length) {\n var f = i - 2,\n m = i - 1;\n u.indexOf(f) > -1 && u.indexOf(m) > -1 ? h = \"return vec4(outputValue.x);\" : u.indexOf(f) > -1 ? h = \"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\" : u.indexOf(m) > -1 && (h = \"return vec4(outputValue.xx, outputValue.zz);\");\n }\n return \"\\n vec4 \" + a + \"() {\\n \" + l + \" coords = getOutputCoords();\\n \" + n + \"\\n vec4 outputValue = get\" + o + \"(\" + d + \");\\n \" + h + \"\\n }\\n \";\n}\n\nfunction getSamplerAtOutputCoords(e, t) {\n var n = e.name,\n r = n.charAt(0).toUpperCase() + n.slice(1),\n o = \"get\" + r + \"AtOutCoords\",\n a = t.texShape,\n i = e.shapeInfo.texShape,\n s = e.shapeInfo.logicalShape.length,\n u = t.logicalShape.length;\n if (!e.shapeInfo.isUniform && s === u && null == e.shapeInfo.flatOffset && arraysEqual(i, a)) return \"\\n float \" + o + \"() {\\n return sampleTexture(\" + n + \", resultUV);\\n }\\n \";\n var l = getCoordsDataType(u),\n c = getBroadcastDims(e.shapeInfo.logicalShape, t.logicalShape),\n p = u - s,\n d = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n return \"\\n float \" + o + \"() {\\n \" + l + \" coords = getOutputCoords();\\n \" + (0 === s ? \"\" : u < 2 && c.length >= 1 ? \"coords = 0;\" : c.map(function (e) {\n return \"coords.\" + d[e + p] + \" = 0;\";\n }).join(\"\\n\")) + \"\\n return get\" + r + \"(\" + (u < 2 && s > 0 ? \"coords\" : e.shapeInfo.logicalShape.map(function (e, t) {\n return \"coords.\" + d[t + p];\n }).join(\", \")) + \");\\n }\\n \";\n}\n\nfunction getCoordsDataType(e) {\n if (e <= 1) return \"int\";\n if (2 === e) return \"ivec2\";\n if (3 === e) return \"ivec3\";\n if (4 === e) return \"ivec4\";\n if (5 === e) return \"ivec5\";\n if (6 === e) return \"ivec6\";\n throw Error(\"GPU for rank \" + e + \" is not yet supported\");\n}\n\nfunction squeezeInputInfo(e, t) {\n var n = JSON.parse(JSON.stringify(e));\n return n.shapeInfo.logicalShape = t, n;\n}\n\nfunction getSqueezedParams(e, t) {\n return t.map(function (t) {\n return e[t];\n }).join(\", \");\n}\n\nvar CumSumProgram = function () {\n return function (e, t, n) {\n this.variableNames = [\"x\"], this.outputShape = e;\n var r = e.length,\n o = e[e.length - 1],\n a = n ? \"<\" : \">\";\n this.userCode = \"\\n int getIndex(int i) {\\n \" + (n ? \"return \" + o + \" -i - 1;\" : \"return i;\") + \"\\n }\\n\\n void main() {\\n \" + getCoordsDataType(r) + \" coords = getOutputCoords();\\n int end = \" + getFinalCoord(r, \"coords\") + \";\\n float val = 0.0;\\n for (int i = \" + o + \" - 1; i >= 0; i -= 1) {\\n int idx = getIndex(i);\\n if (idx \" + a + \" end) {\\n continue;\\n }\\n if (idx == end && \" + t + \") {\\n continue;\\n }\\n \" + getFinalCoord(r, \"coords\") + \" = idx;\\n val += getX(\" + getCoords(r, \"coords\") + \");\\n }\\n setOutput(val);\\n }\\n \";\n };\n}();\n\nfunction getCoords(e, t) {\n if (1 === e) return \"\" + t;\n if (2 === e) return t + \".x, \" + t + \".y\";\n if (3 === e) return t + \".x, \" + t + \".y, \" + t + \".z\";\n if (4 === e) return t + \".x, \" + t + \".y, \" + t + \".z, \" + t + \".w\";\n throw Error(\"Cumulative sum for rank \" + e + \" is not yet supported\");\n}\n\nfunction getFinalCoord(e, t) {\n if (1 === e) return \"\" + t;\n if (2 === e) return t + \".y\";\n if (3 === e) return t + \".z\";\n if (4 === e) return t + \".w\";\n throw Error(\"Cumulative sum for rank \" + e + \" is not yet supported\");\n}\n\nvar DepthToSpaceProgram = function () {\n function e(e, t, n) {\n this.variableNames = [\"x\"], this.outputShape = [], this.outputShape = e, this.blockSize = t, this.dataFormat = n, this.userCode = \"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int h = \" + this.getHeightCoordString() + \";\\n int w = \" + this.getWidthCoordString() + \";\\n int d = \" + this.getDepthCoordString() + \";\\n\\n int in_h = h / \" + t + \";\\n int offset_h = imod(h, \" + t + \");\\n int in_w = w / \" + t + \";\\n int offset_w = imod(w, \" + t + \");\\n int offset_d = (offset_h * \" + t + \" + offset_w) *\\n \" + this.getOutputDepthSize() + \";\\n int in_d = d + offset_d;\\n\\n float result = \" + this.getInputSamplingString() + \";\\n setOutput(result);\\n }\\n \";\n }\n\n return e.prototype.getHeightCoordString = function () {\n return \"NHWC\" === this.dataFormat ? \"coords[1]\" : \"coords[2]\";\n }, e.prototype.getWidthCoordString = function () {\n return \"NHWC\" === this.dataFormat ? \"coords[2]\" : \"coords[3]\";\n }, e.prototype.getDepthCoordString = function () {\n return \"NHWC\" === this.dataFormat ? \"coords[3]\" : \"coords[1]\";\n }, e.prototype.getOutputDepthSize = function () {\n return \"NHWC\" === this.dataFormat ? this.outputShape[3] : this.outputShape[1];\n }, e.prototype.getInputSamplingString = function () {\n return \"NHWC\" === this.dataFormat ? \"getX(b, in_h, in_w, in_d)\" : \"getX(b, in_d, in_h, in_w)\";\n }, e;\n}(),\n EncodeFloatProgram = function () {\n return function (e) {\n this.variableNames = [\"A\"];\n var t = getGlslDifferences();\n this.outputShape = e, this.userCode = \"\\n const float FLOAT_MAX = 1.70141184e38;\\n const float FLOAT_MIN = 1.17549435e-38;\\n\\n lowp vec4 encode_float(highp float v) {\\n if (isNaN(v)) {\\n return vec4(255, 255, 255, 255);\\n }\\n\\n highp float av = abs(v);\\n\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\\n }\\n\\n highp vec4 c = vec4(0,0,0,0);\\n\\n highp float e = floor(log2(av));\\n highp float m = exp2(fract(log2(av))) - 1.0;\\n\\n c[2] = floor(128.0 * m);\\n m -= c[2] / 128.0;\\n c[1] = floor(32768.0 * m);\\n m -= c[1] / 32768.0;\\n c[0] = floor(8388608.0 * m);\\n\\n highp float ebias = e + 127.0;\\n c[3] = floor(ebias / 2.0);\\n ebias -= c[3] * 2.0;\\n c[2] += floor(ebias) * 128.0;\\n\\n c[3] += 128.0 * step(0.0, -v);\\n\\n return c / 255.0;\\n }\\n\\n void main() {\\n float x = getAAtOutCoords();\\n \" + t.output + \" = encode_float(x);\\n }\\n \";\n };\n}(),\n COMPLEX_FFT = {\n REAL: \"return real * expR - imag * expI;\",\n IMAG: \"return real * expI + imag * expR;\"\n},\n FFTProgram = function () {\n return function (e, t, n) {\n this.variableNames = [\"real\", \"imag\"];\n var r = t[1];\n this.outputShape = t;\n var o = n ? \"2.0 * \" + Math.PI : \"-2.0 * \" + Math.PI,\n a = n ? r + \".0\" : \"1.0\";\n this.userCode = \"\\n const float exponentMultiplier = \" + o + \";\\n\\n float unaryOpComplex(float real, float expR, float imag, float expI) {\\n \" + e + \"\\n }\\n\\n float mulMatDFT(int batch, int index) {\\n float indexRatio = float(index) / float(\" + r + \");\\n float exponentMultiplierTimesIndexRatio =\\n exponentMultiplier * indexRatio;\\n\\n float result = 0.0;\\n\\n for (int i = 0; i < \" + r + \"; i++) {\\n // x = (-2|2 * PI / N) * index * i;\\n float x = exponentMultiplierTimesIndexRatio * float(i);\\n float expR = cos(x);\\n float expI = sin(x);\\n float real = getReal(batch, i);\\n float imag = getImag(batch, i);\\n\\n result +=\\n unaryOpComplex(real, expR, imag, expI) / \" + a + \";\\n }\\n\\n return result;\\n }\\n\\n void main() {\\n ivec2 coords = getOutputCoords();\\n setOutput(mulMatDFT(coords[0], coords[1]));\\n }\\n \";\n };\n}(),\n FromPixelsProgram = function () {\n return function (e) {\n this.variableNames = [\"A\"];\n var t = getGlslDifferences(),\n n = e[0],\n r = e[1];\n this.outputShape = e, this.userCode = \"\\n void main() {\\n ivec3 coords = getOutputCoords();\\n int texR = coords[0];\\n int texC = coords[1];\\n int depth = coords[2];\\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\" + r + \".0, \" + n + \".0);\\n\\n vec4 values = \" + t.texture2D + \"(A, uv);\\n float value;\\n if (depth == 0) {\\n value = values.r;\\n } else if (depth == 1) {\\n value = values.g;\\n } else if (depth == 2) {\\n value = values.b;\\n } else if (depth == 3) {\\n value = values.a;\\n }\\n\\n setOutput(floor(value * 255.0 + 0.5));\\n }\\n \";\n };\n}(),\n GatherProgram = function () {\n return function (e, t, n) {\n this.variableNames = [\"A\", \"indices\"];\n var r = e.slice();\n r[n] = t, this.outputShape = r, this.rank = r.length;\n var o = getCoordsDataType(this.rank),\n a = getSourceCoords(e, n);\n this.userCode = \"\\n void main() {\\n \" + o + \" resRC = getOutputCoords();\\n setOutput(getA(\" + a + \"));\\n }\\n \";\n };\n}();\n\nfunction getSourceCoords(e, t) {\n var n = e.length;\n if (n > 4) throw Error(\"Gather for rank \" + n + \" is not yet supported\");\n if (1 === n) return \"int(getIndices(resRC))\";\n\n for (var r = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"], o = [], a = 0; a < e.length; a++) a === t ? o.push(\"int(getIndices(\" + r[a] + \"))\") : o.push(\"\" + r[a]);\n\n return o.join();\n}\n\nvar TextureUsage,\n PhysicalTextureType,\n GatherNDProgram = function () {\n return function (e, t, n) {\n this.sliceDim = e, this.strides = t, this.variableNames = [\"x\", \"indices\"], this.outputShape = n;\n var r = getCoordsDataType(t.length),\n o = getCoordsDataType(n.length),\n a = this.sliceDim > 1 ? \"strides[j]\" : \"strides\";\n this.userCode = \"\\n \" + r + \" strides = \" + r + \"(\" + this.strides + \");\\n void main() {\\n \" + o + \" coords = getOutputCoords();\\n int flattenIndex = 0;\\n for (int j = 0; j < \" + this.sliceDim + \"; j++) {\\n int index = round(getIndices(coords[0], j));\\n flattenIndex += index * \" + a + \";\\n }\\n setOutput(getX(flattenIndex, coords[1]));\\n }\\n \";\n };\n}();\n\nfunction getUnpackedMatrixTextureShapeWidthHeight(e, t) {\n return [t, e];\n}\n\nfunction getUnpackedArraySizeFromMatrixSize(e, t) {\n return e * t;\n}\n\nfunction getMatrixSizeFromUnpackedArraySize(e, t) {\n if (e % t != 0) throw new Error(\"unpackedSize (\" + e + \") must be a multiple of \" + t);\n return e / t;\n}\n\nfunction encodeMatrixToUnpackedArray(e, t, n) {\n var r = getUnpackedArraySizeFromMatrixSize(e.length, n);\n if (t.length < r) throw new Error(\"unpackedArray length (\" + t.length + \") must be >= \" + r);\n\n for (var o = 0, a = 0; a < e.length; ++a) t[o] = e[a], o += n;\n}\n\nfunction decodeMatrixFromUnpackedArray(e, t, n) {\n var r = getMatrixSizeFromUnpackedArraySize(e.length, n);\n if (t.length < r) throw new Error(\"matrix length (\" + t.length + \") must be >= \" + r);\n\n for (var o = 0, a = 0; a < e.length; a += n) t[o++] = e[a];\n}\n\nfunction getPackedMatrixTextureShapeWidthHeight(e, t) {\n return [Math.max(1, Math.ceil(t / 2)), Math.max(1, Math.ceil(e / 2))];\n}\n\nfunction getPackedRGBAArraySizeFromMatrixShape(e, t) {\n var n = getPackedMatrixTextureShapeWidthHeight(e, t);\n return n[0] * n[1] * 4;\n}\n\nfunction encodeMatrixToPackedRGBA(e, t, n, r, o) {\n for (var a = r % 2 == 1, i = n % 2 == 1, s = Math.floor(r / 2), u = Math.floor(n / 2), l = Math.ceil(r / 2), c = l * Math.ceil(n / 2), p = nearestLargerEven(n) * nearestLargerEven(r), d = 0; d < t; d++) {\n for (var h = d * n * r, f = d * p, m = a ? 4 : 0, g = r, v = f, y = 0; y < u; ++y) {\n for (var x = 2 * y * r, T = 0; T < s; ++T) {\n var w = h + x + 2 * T;\n o[v] = e[w], o[v + 1] = e[w + 1], o[v + 2] = e[w + g], o[v + 3] = e[w + g + 1], v += 4;\n }\n\n v += m;\n }\n\n if (a) {\n w = h + r - 1, v = f + 4 * (l - 1);\n var E = 2 * r;\n\n for (m = 4 * l, y = 0; y < u; ++y) o[v] = e[w], o[v + 2] = e[w + r], w += E, v += m;\n }\n\n if (i) {\n for (w = h + (n - 1) * r, v = f + 4 * (c - l), T = 0; T < s; ++T) o[v++] = e[w++], o[v++] = e[w++], v += 2;\n\n a && i && (o[f + p - 4] = e[w]);\n }\n }\n\n return o;\n}\n\nfunction decodeMatrixFromPackedRGBA(e, t, n, r, o) {\n var a = n * r;\n if (o.length < a) throw new Error(\"matrix length (\" + o.length + \") must be >= \" + a);\n\n for (var i = r % 2 == 1, s = n % 2 == 1, u = Math.floor(r / 2), l = Math.floor(n / 2), c = Math.ceil(r / 2), p = c * Math.ceil(n / 2), d = nearestLargerEven(n) * nearestLargerEven(r), h = 0; h < t; h++) {\n for (var f = h * n * r, m = h * d, g = i ? 4 : 0, v = r + (i ? 1 : 0), y = m, x = f, T = f + r, w = 0; w < l; ++w) {\n for (var E = 0; E < u; ++E) o[x++] = e[y++], o[x++] = e[y++], o[T++] = e[y++], o[T++] = e[y++];\n\n y += g, x += v, T += v;\n }\n\n if (i) {\n y = m + 4 * (c - 1);\n var b = f + r - 1;\n\n for (g = 4 * c, v = 2 * r, w = 0; w < l; ++w) o[b] = e[y], o[b + r] = e[y + 2], y += g, b += v;\n }\n\n if (s) {\n for (y = m + 4 * (p - c), b = f + (n - 1) * r, E = 0; E < u; ++E) o[b++] = e[y++], o[b++] = e[y++], y += 2;\n\n i && (o[f + n * r - 1] = e[y]);\n }\n }\n\n return o;\n}\n\nfunction callAndCheck(e, t) {\n var n = t();\n return checkWebGLError(e), n;\n}\n\n!function (e) {\n e[e.RENDER = 0] = \"RENDER\", e[e.UPLOAD = 1] = \"UPLOAD\", e[e.PIXELS = 2] = \"PIXELS\", e[e.DOWNLOAD = 3] = \"DOWNLOAD\";\n}(TextureUsage || (TextureUsage = {})), function (e) {\n e[e.UNPACKED_FLOAT16 = 0] = \"UNPACKED_FLOAT16\", e[e.UNPACKED_FLOAT32 = 1] = \"UNPACKED_FLOAT32\", e[e.PACKED_4X1_UNSIGNED_BYTE = 2] = \"PACKED_4X1_UNSIGNED_BYTE\", e[e.PACKED_2X2_FLOAT32 = 3] = \"PACKED_2X2_FLOAT32\", e[e.PACKED_2X2_FLOAT16 = 4] = \"PACKED_2X2_FLOAT16\";\n}(PhysicalTextureType || (PhysicalTextureType = {}));\nvar webGLDebugErrorCheckingEnabled = !1;\n\nfunction enableDebugWebGLErrorChecking(e) {\n webGLDebugErrorCheckingEnabled = e;\n}\n\nfunction checkWebGLError(e) {\n if (webGLDebugErrorCheckingEnabled) {\n var t = e.getError();\n if (t !== e.NO_ERROR) throw new Error(\"WebGL Error: \" + getWebGLErrorMessage(e, t));\n }\n}\n\nvar MIN_FLOAT16 = 5.96e-8,\n MAX_FLOAT16 = 65504;\n\nfunction canBeRepresented(e) {\n return !!(ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\") || 0 === e || MIN_FLOAT16 < Math.abs(e) && Math.abs(e) < MAX_FLOAT16);\n}\n\nfunction getWebGLErrorMessage(e, t) {\n switch (t) {\n case e.NO_ERROR:\n return \"NO_ERROR\";\n\n case e.INVALID_ENUM:\n return \"INVALID_ENUM\";\n\n case e.INVALID_VALUE:\n return \"INVALID_VALUE\";\n\n case e.INVALID_OPERATION:\n return \"INVALID_OPERATION\";\n\n case e.INVALID_FRAMEBUFFER_OPERATION:\n return \"INVALID_FRAMEBUFFER_OPERATION\";\n\n case e.OUT_OF_MEMORY:\n return \"OUT_OF_MEMORY\";\n\n case e.CONTEXT_LOST_WEBGL:\n return \"CONTEXT_LOST_WEBGL\";\n\n default:\n return \"Unknown error code \" + t;\n }\n}\n\nfunction getExtensionOrThrow(e, t) {\n return throwIfNull(e, function () {\n return e.getExtension(t);\n }, 'Extension \"' + t + '\" not supported on this browser.');\n}\n\nfunction createVertexShader(e, t) {\n var n = throwIfNull(e, function () {\n return e.createShader(e.VERTEX_SHADER);\n }, \"Unable to create vertex WebGLShader.\");\n if (callAndCheck(e, function () {\n return e.shaderSource(n, t);\n }), callAndCheck(e, function () {\n return e.compileShader(n);\n }), !1 === e.getShaderParameter(n, e.COMPILE_STATUS)) throw console.log(e.getShaderInfoLog(n)), new Error(\"Failed to compile vertex shader.\");\n return n;\n}\n\nfunction createFragmentShader(e, t) {\n var n = throwIfNull(e, function () {\n return e.createShader(e.FRAGMENT_SHADER);\n }, \"Unable to create fragment WebGLShader.\");\n if (callAndCheck(e, function () {\n return e.shaderSource(n, t);\n }), callAndCheck(e, function () {\n return e.compileShader(n);\n }), !1 === e.getShaderParameter(n, e.COMPILE_STATUS)) throw logShaderSourceAndInfoLog(t, e.getShaderInfoLog(n)), new Error(\"Failed to compile fragment shader.\");\n return n;\n}\n\nvar lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\n\nfunction logShaderSourceAndInfoLog(e, t) {\n var n = lineNumberRegex.exec(t);\n if (null == n) return console.log(\"Couldn't parse line number in error: \" + t), void console.log(e);\n\n for (var r = +n[1], o = e.split(\"\\n\"), a = o.length.toString().length + 2, i = o.map(function (e, t) {\n return rightPad((t + 1).toString(), a) + e;\n }), s = 0, u = 0; u < i.length; u++) s = Math.max(i[u].length, s);\n\n var l = i.slice(0, r - 1),\n c = i.slice(r - 1, r),\n p = i.slice(r);\n console.log(l.join(\"\\n\")), console.log(t.split(\"\\n\")[0]), console.log(\"%c \" + rightPad(c[0], s), \"border:1px solid red; background-color:#e3d2d2; color:#a61717\"), console.log(p.join(\"\\n\"));\n}\n\nfunction createProgram(e) {\n return throwIfNull(e, function () {\n return e.createProgram();\n }, \"Unable to create WebGLProgram.\");\n}\n\nfunction linkProgram(e, t) {\n if (callAndCheck(e, function () {\n return e.linkProgram(t);\n }), !1 === e.getProgramParameter(t, e.LINK_STATUS)) throw console.log(e.getProgramInfoLog(t)), new Error(\"Failed to link vertex and fragment shaders.\");\n}\n\nfunction validateProgram(e, t) {\n if (callAndCheck(e, function () {\n return e.validateProgram(t);\n }), !1 === e.getProgramParameter(t, e.VALIDATE_STATUS)) throw console.log(e.getProgramInfoLog(t)), new Error(\"Shader program validation failed.\");\n}\n\nfunction createStaticVertexBuffer(e, t) {\n var n = throwIfNull(e, function () {\n return e.createBuffer();\n }, \"Unable to create WebGLBuffer\");\n return callAndCheck(e, function () {\n return e.bindBuffer(e.ARRAY_BUFFER, n);\n }), callAndCheck(e, function () {\n return e.bufferData(e.ARRAY_BUFFER, t, e.STATIC_DRAW);\n }), n;\n}\n\nfunction createStaticIndexBuffer(e, t) {\n var n = throwIfNull(e, function () {\n return e.createBuffer();\n }, \"Unable to create WebGLBuffer\");\n return callAndCheck(e, function () {\n return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, n);\n }), callAndCheck(e, function () {\n return e.bufferData(e.ELEMENT_ARRAY_BUFFER, t, e.STATIC_DRAW);\n }), n;\n}\n\nfunction getNumChannels() {\n return 2 === ENV.get(\"WEBGL_VERSION\") ? 1 : 4;\n}\n\nfunction createTexture(e) {\n return throwIfNull(e, function () {\n return e.createTexture();\n }, \"Unable to create WebGLTexture.\");\n}\n\nfunction validateTextureSize(e, t) {\n var n = ENV.get(\"WEBGL_MAX_TEXTURE_SIZE\");\n\n if (e <= 0 || t <= 0) {\n var r = \"[\" + e + \"x\" + t + \"]\";\n throw new Error(\"Requested texture size \" + r + \" is invalid.\");\n }\n\n if (e > n || t > n) {\n r = \"[\" + e + \"x\" + t + \"]\";\n throw new Error(\"Requested texture size \" + r + \" greater than WebGL maximum on this browser / GPU \" + (\"[\" + n + \"x\" + n + \"]\") + \".\");\n }\n}\n\nfunction createFramebuffer(e) {\n return throwIfNull(e, function () {\n return e.createFramebuffer();\n }, \"Unable to create WebGLFramebuffer.\");\n}\n\nfunction bindVertexBufferToProgramAttribute(e, t, n, r, o, a, i) {\n var s = e.getAttribLocation(t, n);\n return -1 !== s && (callAndCheck(e, function () {\n return e.bindBuffer(e.ARRAY_BUFFER, r);\n }), callAndCheck(e, function () {\n return e.vertexAttribPointer(s, o, e.FLOAT, !1, a, i);\n }), callAndCheck(e, function () {\n return e.enableVertexAttribArray(s);\n }), !0);\n}\n\nfunction bindTextureUnit(e, t, n) {\n validateTextureUnit(e, n), callAndCheck(e, function () {\n return e.activeTexture(e.TEXTURE0 + n);\n }), callAndCheck(e, function () {\n return e.bindTexture(e.TEXTURE_2D, t);\n });\n}\n\nfunction unbindTextureUnit(e, t) {\n validateTextureUnit(e, t), callAndCheck(e, function () {\n return e.activeTexture(e.TEXTURE0 + t);\n }), callAndCheck(e, function () {\n return e.bindTexture(e.TEXTURE_2D, null);\n });\n}\n\nfunction getProgramUniformLocationOrThrow(e, t, n) {\n return throwIfNull(e, function () {\n return e.getUniformLocation(t, n);\n }, 'uniform \"' + n + '\" not present in program.');\n}\n\nfunction getProgramUniformLocation(e, t, n) {\n return e.getUniformLocation(t, n);\n}\n\nfunction bindTextureToProgramUniformSampler(e, t, n, r, o) {\n callAndCheck(e, function () {\n return bindTextureUnit(e, n, o);\n }), callAndCheck(e, function () {\n return e.uniform1i(r, o);\n });\n}\n\nfunction bindCanvasToFramebuffer(e) {\n callAndCheck(e, function () {\n return e.bindFramebuffer(e.FRAMEBUFFER, null);\n }), callAndCheck(e, function () {\n return e.viewport(0, 0, e.canvas.width, e.canvas.height);\n }), callAndCheck(e, function () {\n return e.scissor(0, 0, e.canvas.width, e.canvas.height);\n });\n}\n\nfunction bindColorTextureToFramebuffer(e, t, n) {\n callAndCheck(e, function () {\n return e.bindFramebuffer(e.FRAMEBUFFER, n);\n }), callAndCheck(e, function () {\n return e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, t, 0);\n });\n}\n\nfunction unbindColorTextureFromFramebuffer(e, t) {\n callAndCheck(e, function () {\n return e.bindFramebuffer(e.FRAMEBUFFER, t);\n }), callAndCheck(e, function () {\n return e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, null, 0);\n });\n}\n\nfunction validateFramebuffer(e) {\n var t = e.checkFramebufferStatus(e.FRAMEBUFFER);\n if (t !== e.FRAMEBUFFER_COMPLETE) throw new Error(\"Error binding framebuffer: \" + getFramebufferErrorMessage(e, t));\n}\n\nfunction getFramebufferErrorMessage(e, t) {\n switch (t) {\n case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n return \"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\";\n\n case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n return \"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\";\n\n case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n return \"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\";\n\n case e.FRAMEBUFFER_UNSUPPORTED:\n return \"FRAMEBUFFER_UNSUPPORTED\";\n\n default:\n return \"unknown error \" + t;\n }\n}\n\nfunction throwIfNull(e, t, n) {\n var r = callAndCheck(e, function () {\n return t();\n });\n if (null == r) throw new Error(n);\n return r;\n}\n\nfunction validateTextureUnit(e, t) {\n var n = e.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1,\n r = t + e.TEXTURE0;\n if (r < e.TEXTURE0 || r > n) throw new Error(\"textureUnit must be in \" + (\"[gl.TEXTURE0, gl.TEXTURE\" + n + \"]\") + \".\");\n}\n\nfunction getBatchDim(e, t) {\n return void 0 === t && (t = 2), sizeFromShape(e.slice(0, e.length - t));\n}\n\nfunction getRowsCols(e) {\n if (0 === e.length) throw Error(\"Cannot get rows and columns of an empty shape array.\");\n return [e.length > 1 ? e[e.length - 2] : 1, e[e.length - 1]];\n}\n\nfunction getTextureShapeFromLogicalShape(e, t) {\n var n;\n void 0 === t && (t = !1);\n var r = ENV.get(\"WEBGL_MAX_TEXTURE_SIZE\");\n\n if (t && (r *= 2, 1 === (e = e.map(function (t, n) {\n return n >= e.length - 2 ? nearestLargerEven(e[n]) : e[n];\n })).length && (e = [2, e[0]])), 2 !== e.length) {\n var o = squeezeShape(e);\n e = o.newShape;\n }\n\n var a = sizeFromShape(e);\n if (e.length <= 1 && a <= r) return [1, a];\n if (2 === e.length && e[0] <= r && e[1] <= r) return e;\n if (3 === e.length && e[0] * e[1] <= r && e[2] <= r) return [e[0] * e[1], e[2]];\n if (3 === e.length && e[0] <= r && e[1] * e[2] <= r) return [e[0], e[1] * e[2]];\n if (4 === e.length && e[0] * e[1] * e[2] <= r && e[3] <= r) return [e[0] * e[1] * e[2], e[3]];\n if (4 === e.length && e[0] <= r && e[1] * e[2] * e[3] <= r) return [e[0], e[1] * e[2] * e[3]];\n\n if (t) {\n var i = getBatchDim(e),\n s = 2,\n u = 2;\n return e.length && (s = (n = getRowsCols(e))[0], u = n[1]), sizeToSquarishShape(a = i * (s / 2) * (u / 2)).map(function (e) {\n return 2 * e;\n });\n }\n\n return sizeToSquarishShape(a);\n}\n\nfunction isEven(e) {\n return e % 2 == 0;\n}\n\nfunction isReshapeFree(e, t) {\n if (arraysEqual(e = e.slice(-2), t = t.slice(-2))) return !0;\n if (!e.length || !t.length) return !0;\n if (0 === e[0] || 0 === e[1] || 0 === t[0] || 0 === t[1]) return !0;\n\n if (e.length !== t.length) {\n var n = e.slice(-1)[0],\n r = t.slice(-1)[0];\n if (n === r) return !0;\n if (isEven(n) && isEven(r) && (1 === e[0] || 1 === t[0])) return !0;\n }\n\n return e[1] === t[1] && isEven(e[0]) && isEven(t[0]);\n}\n\nvar webgl_util = Object.freeze({\n callAndCheck: callAndCheck,\n enableDebugWebGLErrorChecking: enableDebugWebGLErrorChecking,\n checkWebGLError: checkWebGLError,\n canBeRepresented: canBeRepresented,\n getWebGLErrorMessage: getWebGLErrorMessage,\n getExtensionOrThrow: getExtensionOrThrow,\n createVertexShader: createVertexShader,\n createFragmentShader: createFragmentShader,\n createProgram: createProgram,\n linkProgram: linkProgram,\n validateProgram: validateProgram,\n createStaticVertexBuffer: createStaticVertexBuffer,\n createStaticIndexBuffer: createStaticIndexBuffer,\n getNumChannels: getNumChannels,\n createTexture: createTexture,\n validateTextureSize: validateTextureSize,\n createFramebuffer: createFramebuffer,\n bindVertexBufferToProgramAttribute: bindVertexBufferToProgramAttribute,\n bindTextureUnit: bindTextureUnit,\n unbindTextureUnit: unbindTextureUnit,\n getProgramUniformLocationOrThrow: getProgramUniformLocationOrThrow,\n getProgramUniformLocation: getProgramUniformLocation,\n bindTextureToProgramUniformSampler: bindTextureToProgramUniformSampler,\n bindCanvasToFramebuffer: bindCanvasToFramebuffer,\n bindColorTextureToFramebuffer: bindColorTextureToFramebuffer,\n unbindColorTextureFromFramebuffer: unbindColorTextureFromFramebuffer,\n validateFramebuffer: validateFramebuffer,\n getFramebufferErrorMessage: getFramebufferErrorMessage,\n getBatchDim: getBatchDim,\n getRowsCols: getRowsCols,\n getTextureShapeFromLogicalShape: getTextureShapeFromLogicalShape,\n isReshapeFree: isReshapeFree\n});\n\nfunction createVertexShader$1(e) {\n var t = getGlslDifferences();\n return createVertexShader(e, t.version + \"\\n precision highp float;\\n \" + t.attribute + \" vec3 clipSpacePos;\\n \" + t.attribute + \" vec2 uv;\\n \" + t.varyingVs + \" vec2 resultUV;\\n\\n void main() {\\n gl_Position = vec4(clipSpacePos, 1);\\n resultUV = uv;\\n }\");\n}\n\nfunction createVertexBuffer(e) {\n return createStaticVertexBuffer(e, new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]));\n}\n\nfunction createIndexBuffer(e) {\n return createStaticIndexBuffer(e, new Uint16Array([0, 1, 2, 2, 1, 3]));\n}\n\nfunction getTextureConfig(e, t) {\n var n,\n r,\n o,\n a,\n i,\n s,\n u,\n l = e;\n return 2 === ENV.get(\"WEBGL_VERSION\") ? (n = l.R32F, r = l.R16F, o = l.RGBA32F, a = l.RED, i = 4, s = 1, u = l.HALF_FLOAT) : (n = e.RGBA, r = e.RGBA, o = l.RGBA, a = e.RGBA, i = 4, s = 4, u = null != t ? t.HALF_FLOAT_OES : null), {\n internalFormatFloat: n,\n internalFormatHalfFloat: r,\n internalFormatPackedFloat: o,\n textureFormatFloat: a,\n downloadTextureFormat: e.RGBA,\n downloadUnpackNumChannels: i,\n defaultNumChannels: s,\n textureTypeHalfFloat: u\n };\n}\n\nfunction createAndConfigureTexture(e, t, n, r, o, a) {\n validateTextureSize(t, n);\n var i = createTexture(e),\n s = e.TEXTURE_2D;\n return callAndCheck(e, function () {\n return e.bindTexture(s, i);\n }), callAndCheck(e, function () {\n return e.texParameteri(s, e.TEXTURE_WRAP_S, e.CLAMP_TO_EDGE);\n }), callAndCheck(e, function () {\n return e.texParameteri(s, e.TEXTURE_WRAP_T, e.CLAMP_TO_EDGE);\n }), callAndCheck(e, function () {\n return e.texParameteri(s, e.TEXTURE_MIN_FILTER, e.NEAREST);\n }), callAndCheck(e, function () {\n return e.texParameteri(s, e.TEXTURE_MAG_FILTER, e.NEAREST);\n }), callAndCheck(e, function () {\n return e.texImage2D(s, 0, r, t, n, 0, o, a, null);\n }), callAndCheck(e, function () {\n return e.bindTexture(e.TEXTURE_2D, null);\n }), i;\n}\n\nfunction createFloat32MatrixTexture(e, t, n, r) {\n var o = getUnpackedMatrixTextureShapeWidthHeight(t, n);\n return createAndConfigureTexture(e, o[0], o[1], r.internalFormatFloat, r.textureFormatFloat, e.FLOAT);\n}\n\nfunction createFloat16MatrixTexture(e, t, n, r) {\n var o = getUnpackedMatrixTextureShapeWidthHeight(t, n);\n return createAndConfigureTexture(e, o[0], o[1], r.internalFormatFloat, r.textureFormatFloat, r.textureTypeHalfFloat);\n}\n\nfunction createUnsignedBytesMatrixTexture(e, t, n, r) {\n var o = getUnpackedMatrixTextureShapeWidthHeight(t, n);\n return createAndConfigureTexture(e, o[0], o[1], e.RGBA, e.RGBA, e.UNSIGNED_BYTE);\n}\n\nfunction createPackedMatrixTexture(e, t, n, r) {\n var o = getPackedMatrixTextureShapeWidthHeight(t, n);\n return createAndConfigureTexture(e, o[0], o[1], r.internalFormatPackedFloat, e.RGBA, e.FLOAT);\n}\n\nfunction createFloat16PackedMatrixTexture(e, t, n, r) {\n var o = getPackedMatrixTextureShapeWidthHeight(t, n);\n return createAndConfigureTexture(e, o[0], o[1], r.internalFormatHalfFloat, e.RGBA, r.textureTypeHalfFloat);\n}\n\nfunction bindVertexProgramAttributeStreams(e, t, n) {\n return callAndCheck(e, function () {\n return e.bindBuffer(e.ARRAY_BUFFER, n);\n }), bindVertexBufferToProgramAttribute(e, t, \"clipSpacePos\", n, 3, 20, 0) && bindVertexBufferToProgramAttribute(e, t, \"uv\", n, 2, 20, 12);\n}\n\nfunction uploadPixelDataToTexture(e, t, n) {\n callAndCheck(e, function () {\n return e.bindTexture(e.TEXTURE_2D, t);\n }), callAndCheck(e, function () {\n return e.texImage2D(e.TEXTURE_2D, 0, e.RGBA, e.RGBA, e.UNSIGNED_BYTE, n);\n }), callAndCheck(e, function () {\n return e.bindTexture(e.TEXTURE_2D, null);\n });\n}\n\nfunction uploadDataToTexture(e, t, n, r, o, a) {\n validateTextureSize(n, r), callAndCheck(e, function () {\n return e.bindTexture(e.TEXTURE_2D, t);\n }), callAndCheck(e, function () {\n return e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, n, r, a, e.FLOAT, o);\n }), callAndCheck(e, function () {\n return e.bindTexture(e.TEXTURE_2D, null);\n });\n}\n\nfunction uploadMatrixToTexture(e, t, n, r, o, a, i) {\n var s,\n u = getUnpackedMatrixTextureShapeWidthHeight(n, r),\n l = u[0],\n c = u[1],\n p = n * r;\n 1 === i.defaultNumChannels && p === o.length ? s = o : encodeMatrixToUnpackedArray(o, s = new Float32Array(p * a), a), uploadDataToTexture(e, t, l, c, s, i.textureFormatFloat);\n}\n\nfunction uploadMatrixToPackedTexture(e, t, n, r, o, a, i, s, u) {\n var l = getPackedMatrixTextureShapeWidthHeight(a, i),\n c = l[0],\n p = l[1],\n d = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(a, i));\n encodeMatrixToPackedRGBA(s, n, r, o, d), uploadDataToTexture(e, t, c, p, d, e.RGBA);\n}\n\nfunction maybeCreateBufferFromOutputTexture(e, t, n, r, o) {\n var a = t;\n\n if (2 === ENV.get(\"WEBGL_VERSION\")) {\n var i = e,\n s = i.createBuffer();\n callAndCheck(e, function () {\n return e.bindBuffer(i.PIXEL_PACK_BUFFER, s);\n });\n var u = 4 * getUnpackedArraySizeFromMatrixSize(n * r, o.downloadUnpackNumChannels);\n callAndCheck(e, function () {\n return e.bufferData(i.PIXEL_PACK_BUFFER, u, i.STREAM_READ);\n }), callAndCheck(e, function () {\n return i.readPixels(0, 0, r, n, e.RGBA, e.FLOAT, 0);\n }), callAndCheck(e, function () {\n return e.bindBuffer(i.PIXEL_PACK_BUFFER, null);\n }), a = s;\n }\n\n return a;\n}\n\nfunction downloadFloat32MatrixFromBuffer(e, t, n, r, o) {\n var a = e,\n i = new Float32Array(getUnpackedArraySizeFromMatrixSize(n * r, o.downloadUnpackNumChannels));\n a.bindBuffer(a.PIXEL_PACK_BUFFER, t), a.getBufferSubData(a.PIXEL_PACK_BUFFER, 0, i), a.bindBuffer(a.PIXEL_PACK_BUFFER, null);\n var s = new Float32Array(n * r);\n return decodeMatrixFromUnpackedArray(i, s, o.downloadUnpackNumChannels), s;\n}\n\nfunction downloadFloat32MatrixFromOutputTexture(e, t, n, r) {\n var o = getUnpackedMatrixTextureShapeWidthHeight(t, n),\n a = o[0],\n i = o[1],\n s = new Float32Array(getUnpackedArraySizeFromMatrixSize(t * n, r.downloadUnpackNumChannels));\n callAndCheck(e, function () {\n return e.readPixels(0, 0, a, i, r.downloadTextureFormat, e.FLOAT, s);\n });\n var u = new Float32Array(t * n);\n return decodeMatrixFromUnpackedArray(s, u, r.downloadUnpackNumChannels), u;\n}\n\nfunction downloadByteEncodedFloatMatrixFromOutputTexture(e, t, n, r) {\n var o = getUnpackedMatrixTextureShapeWidthHeight(t, n),\n a = o[0],\n i = o[1],\n s = new Uint8Array(getUnpackedArraySizeFromMatrixSize(t * n, 4));\n return callAndCheck(e, function () {\n return e.readPixels(0, 0, a, i, r.downloadTextureFormat, e.UNSIGNED_BYTE, s);\n }), new Float32Array(s.buffer);\n}\n\nfunction downloadPackedMatrixFromBuffer(e, t, n, r, o, a, i, s) {\n var u = e,\n l = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(a, i));\n u.bindBuffer(u.PIXEL_PACK_BUFFER, t), u.getBufferSubData(u.PIXEL_PACK_BUFFER, 0, l), u.bindBuffer(u.PIXEL_PACK_BUFFER, null);\n var c = new Float32Array(sizeFromShape([n, r, o]));\n return decodeMatrixFromPackedRGBA(l, n, r, o, c), c;\n}\n\nfunction downloadMatrixFromPackedOutputTexture(e, t, n, r, o, a, i) {\n var s = getPackedMatrixTextureShapeWidthHeight(o, a),\n u = s[0],\n l = s[1],\n c = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(o, a));\n callAndCheck(e, function () {\n return e.readPixels(0, 0, u, l, e.RGBA, e.FLOAT, c);\n });\n var p = new Float32Array(sizeFromShape([t, n, r]));\n return decodeMatrixFromPackedRGBA(c, t, n, r, p);\n}\n\nvar gpgpu_util = Object.freeze({\n createVertexShader: createVertexShader$1,\n createVertexBuffer: createVertexBuffer,\n createIndexBuffer: createIndexBuffer,\n getTextureConfig: getTextureConfig,\n createFloat32MatrixTexture: createFloat32MatrixTexture,\n createFloat16MatrixTexture: createFloat16MatrixTexture,\n createUnsignedBytesMatrixTexture: createUnsignedBytesMatrixTexture,\n createPackedMatrixTexture: createPackedMatrixTexture,\n createFloat16PackedMatrixTexture: createFloat16PackedMatrixTexture,\n bindVertexProgramAttributeStreams: bindVertexProgramAttributeStreams,\n uploadPixelDataToTexture: uploadPixelDataToTexture,\n uploadMatrixToTexture: uploadMatrixToTexture,\n uploadMatrixToPackedTexture: uploadMatrixToPackedTexture,\n maybeCreateBufferFromOutputTexture: maybeCreateBufferFromOutputTexture,\n downloadFloat32MatrixFromBuffer: downloadFloat32MatrixFromBuffer,\n downloadFloat32MatrixFromOutputTexture: downloadFloat32MatrixFromOutputTexture,\n downloadByteEncodedFloatMatrixFromOutputTexture: downloadByteEncodedFloatMatrixFromOutputTexture,\n downloadPackedMatrixFromBuffer: downloadPackedMatrixFromBuffer,\n downloadMatrixFromPackedOutputTexture: downloadMatrixFromPackedOutputTexture\n}),\n GPGPUContext = function () {\n function e(e) {\n this.outputTexture = null, this.program = null, this.disposed = !1, this.autoDebugValidate = !1, this.vertexAttrsAreBound = !1, this.itemsToPoll = [], this.gl = null != e ? e : getWebGLContext(ENV.get(\"WEBGL_VERSION\")), 1 === ENV.get(\"WEBGL_VERSION\") ? (this.textureFloatExtension = getExtensionOrThrow(this.gl, \"OES_texture_float\"), this.colorBufferFloatExtension = this.gl.getExtension(\"WEBGL_color_buffer_float\"), ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\") || (this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, \"OES_texture_half_float\"), this.colorBufferHalfFloatExtension = this.gl.getExtension(\"EXT_color_buffer_half_float\"))) : this.colorBufferFloatExtension = getExtensionOrThrow(this.gl, \"EXT_color_buffer_float\"), this.vertexBuffer = createVertexBuffer(this.gl), this.indexBuffer = createIndexBuffer(this.gl), this.framebuffer = createFramebuffer(this.gl), this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension);\n }\n\n return e.prototype.dispose = function () {\n var e = this;\n\n if (!this.disposed) {\n null != this.program && console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"), null != this.outputTexture && console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");\n var t = this.gl;\n callAndCheck(t, function () {\n return t.finish();\n }), callAndCheck(t, function () {\n return t.bindFramebuffer(t.FRAMEBUFFER, null);\n }), callAndCheck(t, function () {\n return t.deleteFramebuffer(e.framebuffer);\n }), callAndCheck(t, function () {\n return t.bindBuffer(t.ARRAY_BUFFER, null);\n }), callAndCheck(t, function () {\n return t.bindBuffer(t.ELEMENT_ARRAY_BUFFER, null);\n }), callAndCheck(t, function () {\n return t.deleteBuffer(e.indexBuffer);\n }), this.disposed = !0;\n }\n }, e.prototype.enableAutomaticDebugValidation = function (e) {\n this.autoDebugValidate = e, enableDebugWebGLErrorChecking(e);\n }, e.prototype.createFloat32MatrixTexture = function (e, t) {\n return this.throwIfDisposed(), createFloat32MatrixTexture(this.gl, e, t, this.textureConfig);\n }, e.prototype.createFloat16MatrixTexture = function (e, t) {\n return this.throwIfDisposed(), createFloat16MatrixTexture(this.gl, e, t, this.textureConfig);\n }, e.prototype.createUnsignedBytesMatrixTexture = function (e, t) {\n return this.throwIfDisposed(), createUnsignedBytesMatrixTexture(this.gl, e, t, this.textureConfig);\n }, e.prototype.uploadPixelDataToTexture = function (e, t) {\n this.throwIfDisposed(), uploadPixelDataToTexture(this.gl, e, t);\n }, e.prototype.createFloat16PackedMatrixTexture = function (e, t) {\n return this.throwIfDisposed(), createFloat16PackedMatrixTexture(this.gl, e, t, this.textureConfig);\n }, e.prototype.createPackedMatrixTexture = function (e, t) {\n return this.throwIfDisposed(), createPackedMatrixTexture(this.gl, e, t, this.textureConfig);\n }, e.prototype.deleteMatrixTexture = function (e) {\n var t = this;\n this.throwIfDisposed(), this.outputTexture === e && (unbindColorTextureFromFramebuffer(this.gl, this.framebuffer), this.outputTexture = null), callAndCheck(this.gl, function () {\n return t.gl.deleteTexture(e);\n });\n }, e.prototype.uploadMatrixToTexture = function (e, t, n, r) {\n this.throwIfDisposed();\n var o = getNumChannels();\n return uploadMatrixToTexture(this.gl, e, t, n, r, o, this.textureConfig);\n }, e.prototype.uploadMatrixToPackedTexture = function (e, t, n, r, o, a, i) {\n return this.throwIfDisposed(), uploadMatrixToPackedTexture(this.gl, e, t, n, r, o, a, i, this.textureConfig);\n }, e.prototype.downloadFloat32MatrixFromOutputTexture = function (e, t, n) {\n var r = this;\n return this.downloadMatrixDriver(e, function () {\n return downloadFloat32MatrixFromOutputTexture(r.gl, t, n, r.textureConfig);\n });\n }, e.prototype.downloadByteEncodedFloatMatrixFromOutputTexture = function (e, t, n) {\n var r = this;\n return this.downloadMatrixDriver(e, function () {\n return downloadByteEncodedFloatMatrixFromOutputTexture(r.gl, t, n, r.textureConfig);\n });\n }, e.prototype.downloadPackedMatrixFromBuffer = function (e, t, n, r, o, a) {\n return downloadPackedMatrixFromBuffer(this.gl, e, t, n, r, o, a, this.textureConfig);\n }, e.prototype.downloadFloat32MatrixFromBuffer = function (e, t, n) {\n return downloadFloat32MatrixFromBuffer(this.gl, e, t, n, this.textureConfig);\n }, e.prototype.maybeCreateBufferFromTexture = function (e, t, n) {\n this.bindTextureToFrameBuffer(e);\n var r = maybeCreateBufferFromOutputTexture(this.gl, e, t, n, this.textureConfig);\n return this.unbindTextureToFrameBuffer(), r;\n }, e.prototype.createAndWaitForFence = function () {\n var e = this.createFence(this.gl);\n return this.pollFence(e);\n }, e.prototype.createFence = function (e) {\n var t,\n n,\n r = this;\n\n if (ENV.get(\"WEBGL_FENCE_API_ENABLED\")) {\n var o = e,\n a = o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE, 0);\n e.flush(), n = function () {\n var e = o.clientWaitSync(a, 0, 0);\n return e === o.ALREADY_SIGNALED || e === o.CONDITION_SATISFIED;\n }, t = a;\n } else ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0 ? (t = this.beginQuery(), this.endQuery(), n = function () {\n return r.isQueryAvailable(t, ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"));\n }) : n = function () {\n return !0;\n };\n\n return {\n query: t,\n isFencePassed: n\n };\n }, e.prototype.downloadMatrixFromPackedTexture = function (e, t, n, r, o, a) {\n var i = this;\n return this.downloadMatrixDriver(e, function () {\n return downloadMatrixFromPackedOutputTexture(i.gl, t, n, r, o, a, i.textureConfig);\n });\n }, e.prototype.createProgram = function (e) {\n this.throwIfDisposed();\n var t = this.gl,\n n = createFragmentShader(t, e),\n r = createVertexShader$1(t),\n o = createProgram(t);\n return callAndCheck(t, function () {\n return t.attachShader(o, r);\n }), callAndCheck(t, function () {\n return t.attachShader(o, n);\n }), linkProgram(t, o), this.autoDebugValidate && validateProgram(t, o), this.vertexAttrsAreBound || (this.setProgram(o), this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(t, this.program, this.vertexBuffer)), o;\n }, e.prototype.deleteProgram = function (e) {\n var t = this;\n this.throwIfDisposed(), e === this.program && (this.program = null), null != e && callAndCheck(this.gl, function () {\n return t.gl.deleteProgram(e);\n });\n }, e.prototype.setProgram = function (e) {\n var t = this;\n this.throwIfDisposed(), this.program = e, null != this.program && this.autoDebugValidate && validateProgram(this.gl, this.program), callAndCheck(this.gl, function () {\n return t.gl.useProgram(e);\n });\n }, e.prototype.getUniformLocation = function (e, t, n) {\n return void 0 === n && (n = !0), this.throwIfDisposed(), n ? getProgramUniformLocationOrThrow(this.gl, e, t) : getProgramUniformLocation(this.gl, e, t);\n }, e.prototype.getAttributeLocation = function (e, t) {\n var n = this;\n return this.throwIfDisposed(), callAndCheck(this.gl, function () {\n return n.gl.getAttribLocation(e, t);\n });\n }, e.prototype.getUniformLocationNoThrow = function (e, t) {\n return this.throwIfDisposed(), this.gl.getUniformLocation(e, t);\n }, e.prototype.setInputMatrixTexture = function (e, t, n) {\n this.throwIfDisposed(), this.throwIfNoProgram(), bindTextureToProgramUniformSampler(this.gl, this.program, e, t, n);\n }, e.prototype.setOutputMatrixTexture = function (e, t, n) {\n this.setOutputMatrixTextureDriver(e, n, t);\n }, e.prototype.setOutputPackedMatrixTexture = function (e, t, n) {\n this.throwIfDisposed();\n var r = getPackedMatrixTextureShapeWidthHeight(t, n),\n o = r[0],\n a = r[1];\n this.setOutputMatrixTextureDriver(e, o, a);\n }, e.prototype.setOutputMatrixWriteRegion = function (e, t, n, r) {\n this.setOutputMatrixWriteRegionDriver(n, e, r, t);\n }, e.prototype.setOutputPackedMatrixWriteRegion = function (e, t, n, r) {\n throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\");\n }, e.prototype.debugValidate = function () {\n null != this.program && validateProgram(this.gl, this.program), validateFramebuffer(this.gl);\n }, e.prototype.executeProgram = function () {\n this.throwIfDisposed(), this.throwIfNoProgram();\n var e = this.gl;\n this.autoDebugValidate && this.debugValidate(), callAndCheck(e, function () {\n return e.drawElements(e.TRIANGLES, 6, e.UNSIGNED_SHORT, 0);\n });\n }, e.prototype.blockUntilAllProgramsCompleted = function () {\n var e = this;\n this.throwIfDisposed(), callAndCheck(this.gl, function () {\n return e.gl.finish();\n });\n }, e.prototype.getQueryTimerExtension = function () {\n return null == this.disjointQueryTimerExtension && (this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, 2 === ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") ? \"EXT_disjoint_timer_query_webgl2\" : \"EXT_disjoint_timer_query\")), this.disjointQueryTimerExtension;\n }, e.prototype.getQueryTimerExtensionWebGL2 = function () {\n return this.getQueryTimerExtension();\n }, e.prototype.getQueryTimerExtensionWebGL1 = function () {\n return this.getQueryTimerExtension();\n }, e.prototype.beginQuery = function () {\n if (2 === ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")) {\n var e = this.gl,\n t = this.getQueryTimerExtensionWebGL2(),\n n = e.createQuery();\n return e.beginQuery(t.TIME_ELAPSED_EXT, n), n;\n }\n\n var r = this.getQueryTimerExtensionWebGL1(),\n o = r.createQueryEXT();\n return r.beginQueryEXT(r.TIME_ELAPSED_EXT, o), o;\n }, e.prototype.endQuery = function () {\n if (2 !== ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")) {\n var e = this.getQueryTimerExtensionWebGL1();\n e.endQueryEXT(e.TIME_ELAPSED_EXT);\n } else {\n var t = this.gl,\n n = this.getQueryTimerExtensionWebGL2();\n t.endQuery(n.TIME_ELAPSED_EXT);\n }\n }, e.prototype.waitForQueryAndGetTime = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t = this;\n return __generator(this, function (n) {\n switch (n.label) {\n case 0:\n return [4, repeatedTry(function () {\n return t.disposed || t.isQueryAvailable(e, ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"));\n })];\n\n case 1:\n return n.sent(), [2, this.getQueryTime(e, ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))];\n }\n });\n });\n }, e.prototype.getQueryTime = function (e, t) {\n if (0 === t) return null;\n\n if (2 === t) {\n var n = this.gl;\n return n.getQueryParameter(e, n.QUERY_RESULT) / 1e6;\n }\n\n var r = this.getQueryTimerExtensionWebGL1();\n return r.getQueryObjectEXT(e, r.QUERY_RESULT_EXT) / 1e6;\n }, e.prototype.isQueryAvailable = function (e, t) {\n if (0 === t) return !0;\n\n if (2 === t) {\n var n = this.gl,\n r = this.getQueryTimerExtensionWebGL2(),\n o = n.getQueryParameter(e, n.QUERY_RESULT_AVAILABLE);\n return null == this.disjoint && (this.disjoint = this.gl.getParameter(r.GPU_DISJOINT_EXT)), o && !this.disjoint;\n }\n\n o = (r = this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(e, r.QUERY_RESULT_AVAILABLE_EXT);\n return null == this.disjoint && (this.disjoint = this.gl.getParameter(r.GPU_DISJOINT_EXT)), o && !this.disjoint;\n }, e.prototype.pollFence = function (e) {\n var t = this;\n return new Promise(function (n) {\n t.addItemToPoll(function () {\n return e.isFencePassed();\n }, function () {\n return n();\n });\n });\n }, e.prototype.pollItems = function () {\n for (var e = linearSearchLastTrue(this.itemsToPoll.map(function (e) {\n return e.isDoneFn;\n })), t = 0; t <= e; ++t) {\n (0, this.itemsToPoll[t].resolveFn)();\n }\n\n this.itemsToPoll = this.itemsToPoll.slice(e + 1);\n }, e.prototype.addItemToPoll = function (e, t) {\n var n = this;\n this.itemsToPoll.push({\n isDoneFn: e,\n resolveFn: t\n }), this.itemsToPoll.length > 1 || repeatedTry(function () {\n return n.pollItems(), 0 === n.itemsToPoll.length;\n });\n }, e.prototype.bindTextureToFrameBuffer = function (e) {\n this.throwIfDisposed(), bindColorTextureToFramebuffer(this.gl, e, this.framebuffer), this.autoDebugValidate && validateFramebuffer(this.gl);\n }, e.prototype.unbindTextureToFrameBuffer = function () {\n null != this.outputTexture ? (bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer), this.autoDebugValidate && validateFramebuffer(this.gl)) : unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n }, e.prototype.downloadMatrixDriver = function (e, t) {\n this.bindTextureToFrameBuffer(e);\n var n = t();\n return this.unbindTextureToFrameBuffer(), n;\n }, e.prototype.setOutputMatrixTextureDriver = function (e, t, n) {\n this.throwIfDisposed();\n var r = this.gl;\n bindColorTextureToFramebuffer(r, e, this.framebuffer), this.autoDebugValidate && validateFramebuffer(r), this.outputTexture = e, callAndCheck(r, function () {\n return r.viewport(0, 0, t, n);\n }), callAndCheck(r, function () {\n return r.scissor(0, 0, t, n);\n });\n }, e.prototype.setOutputMatrixWriteRegionDriver = function (e, t, n, r) {\n var o = this;\n this.throwIfDisposed(), callAndCheck(this.gl, function () {\n return o.gl.scissor(e, t, n, r);\n });\n }, e.prototype.throwIfDisposed = function () {\n if (this.disposed) throw new Error(\"Attempted to use disposed GPGPUContext.\");\n }, e.prototype.throwIfNoProgram = function () {\n if (null == this.program) throw new Error(\"No GPU program is currently set.\");\n }, e;\n}();\n\nfunction linearSearchLastTrue(e) {\n for (var t = 0; t < e.length; ++t) {\n if (!e[t]()) break;\n }\n\n return t - 1;\n}\n\nfunction compileProgram(e, t, n, r) {\n for (var o = t.userCode, a = n.map(function (e, n) {\n var r = {\n logicalShape: e.shape,\n texShape: e.isUniform ? null : e.texData.texShape,\n isUniform: e.isUniform,\n isPacked: !e.isUniform && e.texData.isPacked,\n flatOffset: null\n };\n return null != e.texData && null != e.texData.slice && e.texData.slice.flatOffset > 0 && (r.flatOffset = e.texData.slice.flatOffset), {\n name: t.variableNames[n],\n shapeInfo: r\n };\n }), i = a.map(function (e) {\n return e.shapeInfo;\n }), s = {\n logicalShape: r.shape,\n texShape: r.texData.texShape,\n isUniform: !1,\n isPacked: r.texData.isPacked,\n flatOffset: null\n }, u = makeShader(a, s, o, t.usesPackedTextures), l = e.createProgram(u), c = {}, p = 0; p < t.variableNames.length; p++) {\n var d = t.variableNames[p];\n c[d] = e.getUniformLocation(l, d, !1), c[\"offset\" + d] = e.getUniformLocation(l, \"offset\" + d, !1);\n }\n\n return {\n program: t,\n source: u,\n webGLProgram: l,\n uniformLocations: c,\n gpgpu: e,\n inShapeInfos: i,\n outShapeInfo: s\n };\n}\n\nfunction validateBinaryAndProgram(e, t) {\n if (e.length !== t.length) throw Error(\"Binary was compiled with \" + e.length + \" inputs, but was executed with \" + t.length + \" inputs\");\n e.forEach(function (e, n) {\n var r = e.logicalShape,\n o = t[n],\n a = o.shape;\n if (!arraysEqual(r, a)) throw Error(\"Binary was compiled with different shapes than the current args. Shapes \" + r + \" and \" + a + \" must match\");\n\n if (!e.isUniform || !o.isUniform) {\n var i = e.texShape,\n s = o.isUniform ? null : o.texData.texShape;\n if (!arraysEqual(i, s)) throw Error(\"Binary was compiled with different texture shapes than the current args. Shape \" + i + \" and \" + s + \" must match\");\n }\n });\n}\n\nfunction runProgram(e, t, n, r) {\n validateBinaryAndProgram(e.inShapeInfos, t), validateBinaryAndProgram([e.outShapeInfo], [n]);\n var o = n.texData.texture,\n a = n.texData.texShape,\n i = e.gpgpu;\n n.texData.isPacked ? i.setOutputPackedMatrixTexture(o, a[0], a[1]) : i.setOutputMatrixTexture(o, a[0], a[1]), i.setProgram(e.webGLProgram), t.forEach(function (t, n) {\n var r = e.program.variableNames[n],\n o = e.uniformLocations[r],\n a = e.uniformLocations[\"offset\" + r];\n if (null != o) if (t.isUniform) {\n if (1 === sizeFromShape(t.shape)) i.gl.uniform1f(o, t.uniformValues[0]);else {\n var s = t.uniformValues;\n s instanceof Float32Array || (s = new Float32Array(s)), i.gl.uniform1fv(o, s);\n }\n } else null != t.texData.slice && null != a && i.gl.uniform1i(a, t.texData.slice.flatOffset), i.setInputMatrixTexture(t.texData.texture, o, n);\n }), null != r && r(i, e.webGLProgram), i.executeProgram();\n}\n\nfunction makeShaderKey(e, t, n) {\n var r = \"\";\n t.concat(n).forEach(function (e) {\n var t = null != e.texData && null != e.texData.slice && e.texData.slice.flatOffset > 0,\n n = e.isUniform ? \"uniform\" : e.texData.texShape;\n r += e.shape + \"_\" + n + \"_\" + t;\n });\n var o = e.userCode,\n a = e.constructor.name;\n return a += \"_\" + r + \"_\" + o;\n}\n\nvar Im2ColProgram = function () {\n return function (e, t, n) {\n this.variableNames = [\"A\"], this.outputShape = e;\n var r = n.filterWidth,\n o = n.inChannels,\n a = n.strideWidth,\n i = n.strideHeight,\n s = n.padInfo,\n u = n.outWidth,\n l = n.dilationWidth,\n c = n.dilationHeight,\n p = s.left,\n d = s.top,\n h = o * r,\n f = getGlslDifferences();\n this.userCode = \"\\n void main() {\\n ivec2 rc = getOutputCoords();\\n\\n vec4 result = vec4(0);\\n\\n for(int row=0; row<=1; row++) {\\n for(int col=0; col<=1; col++) {\\n int blockIndex = rc.y + col;\\n int pos = rc.x + row;\\n\\n if(blockIndex >= \" + e[1] + \" || pos >= \" + e[0] + \") continue;\\n\\n int offsetY = int(blockIndex / (\" + u + \")) * \" + i + \" - \" + d + \";\\n int d0 = offsetY + \" + c + \" * (pos / \" + h + \");\\n\\n if(d0 >= \" + t[0] + \" || d0 < 0) continue;\\n\\n int offsetX = int(mod(float(blockIndex), \" + u + \".) * \" + a + \". - \" + p + \".);\\n int d1 = offsetX + \" + l + \" * (int(mod(float(pos), \" + h + \".) / \" + o + \".));\\n\\n if(d1 >= \" + t[1] + \" || d1 < 0) continue;\\n\\n result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), \" + o + \".)));\\n }\\n }\\n\\n \" + f.output + \" = result;\\n }\\n \";\n };\n}(),\n LRNProgram = function () {\n return function (e, t, n, r, o) {\n this.variableNames = [\"x\"], this.outputShape = [];\n var a,\n i = t,\n s = e[3] - 1;\n this.outputShape = e;\n var u = \"float(\" + n + \") + float(\" + r + \") * sum\";\n a = .5 === o ? \"inversesqrt(\" + u + \")\" : 1 === o ? \"1.0/(\" + u + \")\" : \"exp(log(\" + u + \") * float(-\" + o + \"));\", this.userCode = \"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int r = coords[1];\\n int c = coords[2];\\n int d = coords[3];\\n float x = getX(b, r, c, d);\\n float sum = 0.0;\\n for (int j = -\" + i + \"; j <= \" + i + \"; j++) {\\n int idx = d + j;\\n if (idx >= 0 && idx <= \" + s + \") {\\n float z = getX(b, r, c, idx);\\n sum += z * z;\\n }\\n }\\n float val = x * \" + a + \";\\n setOutput(val);\\n }\\n \";\n };\n}(),\n LRNGradProgram = function () {\n return function (e, t, n, r, o) {\n this.variableNames = [\"inputImage\", \"outputImage\", \"dy\"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t, this.bias = n, this.alpha = r, this.beta = o, this.userCode = \"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int r = coords[1];\\n int c = coords[2];\\n\\n float result = 0.0;\\n for (int d = 0; d < \" + this.depth + \"; ++d) {\\n int depthBegin = int(max(0.0, float(d - \" + t + \")));\\n int depthEnd = int(min(float(\" + this.depth + \"),\\n float(d + \" + t + \" + 1)));\\n\\n const int MIN_DEPTH_BEGIN = 0;\\n const int MAX_DEPTH_END = \" + this.depth + \";\\n\\n float norm = 0.0;\\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\\n if (k < depthBegin){\\n continue;\\n }\\n else if (k >= depthBegin && k < depthEnd) {\\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\\n }\\n else {\\n break;\\n }\\n }\\n\\n norm = float(\" + r + \") * norm + float(\" + n + \");\\n\\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\\n if (k < depthBegin){\\n continue;\\n }\\n else if (k >= depthBegin && k < depthEnd){\\n float dyi = -2.0 * float(\" + r + \")\\n * float(\" + o + \")\\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\\n / norm;\\n if (k == d) {\\n dyi += pow(norm, -1.0 * \" + o + \");\\n }\\n if (k == coords[3]) {\\n dyi *= getDy(b, r, c, d);\\n result += dyi;\\n }\\n }\\n else {\\n break;\\n }\\n }\\n }\\n setOutput(result);\\n }\\n \";\n };\n}(),\n MaxPool2DBackpropProgram = function () {\n return function (e) {\n this.variableNames = [\"dy\", \"maxPos\"], this.outputShape = e.inShape;\n var t = e.strideHeight,\n n = e.strideWidth,\n r = e.dilationHeight,\n o = e.effectiveFilterHeight,\n a = e.effectiveFilterWidth,\n i = o - 1 - e.padInfo.top,\n s = a - 1 - e.padInfo.left,\n u = o * a - 1;\n this.userCode = \"\\n const ivec2 pads = ivec2(\" + i + \", \" + s + \");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n\\n ivec2 dyRCCorner = coords.yz - pads;\\n int dyRCorner = dyRCCorner.x;\\n int dyCCorner = dyRCCorner.y;\\n\\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n for (int wR = 0; wR < \" + o + \";\\n wR += \" + r + \") {\\n float dyR = float(dyRCorner + wR) / \" + t + \".0;\\n\\n if (dyR < 0.0 || dyR >= \" + e.outHeight + \".0 || fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n for (int wC = 0; wC < \" + a + \"; wC++) {\\n float dyC = float(dyCCorner + wC) / \" + n + \".0;\\n\\n if (dyC < 0.0 || dyC >= \" + e.outWidth + \".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n float dyValue = getDy(b, idyR, idyC, d);\\n int maxPosValue = \" + u + \" - int(getMaxPos(b, idyR, idyC, d));\\n\\n // Get the current value, check it against the value from the\\n // position matrix.\\n int curPosValue = wR * \" + a + \" + wC;\\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\\n\\n dotProd += dyValue * mask;\\n }\\n }\\n setOutput(dotProd);\\n }\\n \";\n };\n}(),\n MatMulProgram = function () {\n return function (e, t, n, r, o, a) {\n void 0 === n && (n = !1), void 0 === r && (r = !1), void 0 === o && (o = !1), void 0 === a && (a = null), this.variableNames = [\"matrixA\", \"matrixB\"];\n var i = e[0],\n s = n ? e[2] : e[1],\n u = r ? t[1] : t[2],\n l = n ? e[1] : e[2];\n this.outputShape = [i, s, u];\n\n var c = function (e, t) {\n return n ? \"batch, \" + t + \" + \" + e + \", aRow\" : \"batch, aRow, \" + t + \" + \" + e;\n },\n p = function (e, t) {\n return r ? \"batch, bCol, \" + t + \" + \" + e : \"batch, \" + t + \" + \" + e + \", bCol\";\n },\n d = 4 * Math.floor(l / 4),\n h = l % 4,\n f = \"\",\n m = \"\";\n\n a && (f = \"float activation(float x) {\\n \" + a + \"\\n }\", m = \"result = activation(result);\");\n var g = o ? \"result += getBiasAtOutCoords();\" : \"\";\n o && this.variableNames.push(\"bias\"), this.userCode = \"\\n \" + f + \"\\n\\n float dotARowBCol(int batch, int aRow, int bCol) {\\n float result = 0.0;\\n for (int i = 0; i < \" + d + \"; i += 4) {\\n vec4 a = vec4(\\n getMatrixA(\" + c(0, \"i\") + \"),\\n getMatrixA(\" + c(1, \"i\") + \"),\\n getMatrixA(\" + c(2, \"i\") + \"),\\n getMatrixA(\" + c(3, \"i\") + \")\\n );\\n vec4 b = vec4(\\n getMatrixB(\" + p(0, \"i\") + \"),\\n getMatrixB(\" + p(1, \"i\") + \"),\\n getMatrixB(\" + p(2, \"i\") + \"),\\n getMatrixB(\" + p(3, \"i\") + \")\\n );\\n\\n result += dot(a, b);\\n }\\n\\n if (\" + (1 === h) + \") {\\n result += getMatrixA(\" + c(0, d) + \") *\\n getMatrixB(\" + p(0, d) + \");\\n } else if (\" + (2 === h) + \") {\\n vec2 a = vec2(\\n getMatrixA(\" + c(0, d) + \"),\\n getMatrixA(\" + c(1, d) + \")\\n );\\n vec2 b = vec2(\\n getMatrixB(\" + p(0, d) + \"),\\n getMatrixB(\" + p(1, d) + \")\\n );\\n result += dot(a, b);\\n } else if (\" + (3 === h) + \") {\\n vec3 a = vec3(\\n getMatrixA(\" + c(0, d) + \"),\\n getMatrixA(\" + c(1, d) + \"),\\n getMatrixA(\" + c(2, d) + \")\\n );\\n vec3 b = vec3(\\n getMatrixB(\" + p(0, d) + \"),\\n getMatrixB(\" + p(1, d) + \"),\\n getMatrixB(\" + p(2, d) + \")\\n );\\n result += dot(a, b);\\n }\\n\\n return result;\\n }\\n\\n void main() {\\n ivec3 resBRC = getOutputCoords();\\n float result = dotARowBCol(resBRC.x, resBRC.y, resBRC.z);\\n\\n \" + g + \"\\n\\n \" + m + \"\\n\\n setOutput(result);\\n }\\n \";\n };\n}(),\n MatMulPackedProgram = function () {\n return function (e, t, n, r, o, a, i) {\n void 0 === r && (r = !1), void 0 === o && (o = !1), void 0 === a && (a = !1), void 0 === i && (i = null), this.variableNames = [\"matrixA\", \"matrixB\"], this.usesPackedTextures = !0, this.outputShape = n;\n var s = r ? e[0] : e[1],\n u = Math.ceil(s / 2),\n l = r ? \"i * 2, rc.x\" : \"rc.x, i * 2\",\n c = o ? \"rc.y, i * 2\" : \"i * 2, rc.y\",\n p = r ? [\"a.xxyy\", \"a.zzww\"] : [\"a.xxzz\", \"a.yyww\"],\n d = o ? [\"b.xzxz\", \"b.ywyw\"] : [\"b.xyxy\", \"b.zwzw\"],\n h = \"\",\n f = \"\";\n i && (h = \"vec4 activation(vec4 x) {\\n \" + i + \"\\n }\", f = \"result = activation(result);\");\n var m = a ? \"result += getBiasAtOutCoords();\" : \"\";\n a && this.variableNames.push(\"bias\"), this.userCode = \"\\n \" + h + \"\\n\\n const float sharedDimension = \" + u + \".0;\\n\\n vec4 dot2x2ARowBCol(ivec2 rc) {\\n vec4 result = vec4(0);\\n for (int i = 0; i < \" + u + \"; i++) {\\n vec4 a = getMatrixA(\" + l + \");\\n vec4 b = getMatrixB(\" + c + \");\\n\\n result += (\" + p[0] + \" * \" + d[0] + \") + (\" + p[1] + \" * \" + d[1] + \");\\n }\\n return result;\\n }\\n\\n void main() {\\n ivec2 rc = getOutputCoords();\\n vec4 result = dot2x2ARowBCol(rc);\\n\\n \" + m + \"\\n\\n \" + f + \"\\n\\n setOutput(result);\\n }\\n \";\n };\n}(),\n MultinomialProgram = function () {\n function e(e, t, n) {\n this.variableNames = [\"probs\"], this.outputShape = [e, n], this.userCode = \"\\n uniform float seed;\\n\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int batch = coords[0];\\n\\n float r = random(seed);\\n float cdf = 0.0;\\n\\n for (int i = 0; i < \" + (t - 1) + \"; i++) {\\n cdf += getProbs(batch, i);\\n\\n if (r < cdf) {\\n setOutput(float(i));\\n return;\\n }\\n }\\n\\n // If no other event happened, last event happened.\\n setOutput(float(\" + (t - 1) + \"));\\n }\\n \";\n }\n\n return e.prototype.getCustomSetupFunc = function (e) {\n var t = this;\n return function (n, r) {\n null == t.seedLoc && (t.seedLoc = n.getUniformLocation(r, \"seed\")), n.gl.uniform1f(t.seedLoc, e);\n };\n }, e;\n}(),\n OneHotProgram = function () {\n return function (e, t, n, r) {\n this.variableNames = [\"indices\"], this.outputShape = [e, t], this.userCode = \"\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int index = round(getIndices(coords.x));\\n setOutput(mix(float(\" + r + \"), float(\" + n + \"),\\n float(index == coords.y)));\\n }\\n \";\n };\n}();\n\nfunction getVecChannels(e, t) {\n return [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, t).map(function (t) {\n return e + \".\" + t;\n });\n}\n\nfunction getChannels(e, t) {\n return 1 === t ? [e] : getVecChannels(e, t);\n}\n\nfunction getSourceCoords$1(e, t) {\n if (1 === e) return \"rc\";\n\n for (var n = \"\", r = 0; r < e; r++) n += t[r], r < e - 1 && (n += \",\");\n\n return n;\n}\n\nvar PackProgram = function () {\n return function (e) {\n this.variableNames = [\"A\"], this.isPackShader = !0, this.outputShape = e;\n var t = e.length;\n if (0 === t) this.userCode = \"\\n void main() {\\n setOutput(vec4(getA(), 0., 0., 0.));\\n }\\n \";else {\n var n = getChannels(\"rc\", t),\n r = getCoordsDataType(t),\n o = getOutOfBoundsCondition(t, e, n),\n a = getSetup(t, e[e.length - 1], e[e.length - 2], n),\n i = getOutput(e, n);\n this.userCode = \"\\n void main() {\\n \" + r + \" rc = getOutputCoords();\\n\\n if(\" + o + \") {\\n setOutput(vec4(0));\\n } else {\\n \" + a + \"\\n\\n setOutput(vec4(\" + i + \"));\\n }\\n }\\n \";\n }\n };\n}();\n\nfunction getSourceCoordsArr(e, t) {\n for (var n = [], r = 0; r <= 1; r++) for (var o = 0; o <= 1; o++) {\n for (var a = (0 === r ? \"r\" : \"rp1\") + \", \" + (0 === o ? \"c\" : \"cp1\"), i = 2; i < e; i++) a = t[t.length - 1 - i] + \",\" + a;\n\n n.push(a);\n }\n\n return n;\n}\n\nfunction getOutOfBoundsCondition(e, t, n) {\n if (1 === e) return \"rc > \" + t[0];\n\n for (var r = \"\", o = e - 2; o < e; o++) r += n[o] + \" >= \" + t[o], o < e - 1 && (r += \"||\");\n\n return r;\n}\n\nfunction getSetup(e, t, n, r) {\n if (1 === e) return \"\";\n var o = r.slice(-2);\n return \"\\n int r = \" + o[0] + \";\\n int c = \" + o[1] + \";\\n int rp1 = r + 1;\\n int cp1 = c + 1;\\n\\n bool cEdge = cp1 >= \" + t + \";\\n bool rEdge = rp1 >= \" + n + \";\\n \";\n}\n\nfunction getOutput(e, t) {\n var n = e.length,\n r = getSourceCoordsArr(n, t);\n return 1 === n ? \"getA(rc),\\n rc + 1 >= \" + e[0] + \" ? 0. : getA(rc + 1),\\n 0, 0\" : \"getA(\" + r[0] + \"),\\n cEdge ? 0. : getA(\" + r[1] + \"),\\n rEdge ? 0. : getA(\" + r[2] + \"),\\n rEdge || cEdge ? 0. : getA(\" + r[3] + \")\";\n}\n\nvar PadProgram = function () {\n return function (e, t, n) {\n this.variableNames = [\"x\"], this.outputShape = t.map(function (t, n) {\n return t[0] + e[n] + t[1];\n });\n var r = e.length,\n o = getCoordsDataType(r),\n a = t.map(function (e) {\n return e[0];\n }).join(\",\"),\n i = t.map(function (t, n) {\n return t[0] + e[n];\n }).join(\",\"),\n s = [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, r);\n this.userCode = 1 !== r ? \"\\n \" + o + \" start = \" + o + \"(\" + a + \");\\n \" + o + \" end = \" + o + \"(\" + i + \");\\n\\n void main() {\\n \" + o + \" outC = getOutputCoords();\\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\\n setOutput(float(\" + n + \"));\\n } else {\\n \" + o + \" coords = outC - start;\\n setOutput(getX(\" + s + \"));\\n }\\n }\\n \" : \"\\n int start = \" + a + \";\\n int end = \" + i + \";\\n\\n void main() {\\n int outC = getOutputCoords();\\n if (outC < start || outC >= end) {\\n setOutput(float(\" + n + \"));\\n } else {\\n setOutput(getX(outC - start));\\n }\\n }\\n \";\n };\n}(),\n PadPackedProgram = function () {\n return function (e, t, n) {\n this.variableNames = [\"x\"], this.usesPackedTextures = !0, this.outputShape = t.map(function (t, n) {\n return t[0] + e[n] + t[1];\n });\n\n for (var r = e.length, o = getCoordsDataType(r), a = t.map(function (e) {\n return e[0];\n }).join(\",\"), i = t.map(function (t, n) {\n return t[0] + e[n];\n }).join(\",\"), s = getChannels(\"rc\", r), u = getChannels(\"source\", r), l = s[r - 1] + \" < \" + this.outputShape[r - 1], c = 1 === r ? \"source\" : \"vec2(\" + u.slice(-2).join() + \")\", p = [o + \" rc = outputLoc;\", s[r - 1] + \" += 1;\\n if(\" + l + \") {\\n \", 1 === r ? \"\" : \"}\\n rc = outputLoc;\\n \" + s[r - 2] + \" += 1;\\n if(\" + s[r - 2] + \" < \" + this.outputShape[r - 2] + \") {\", 1 === r ? \"\" : \" \" + s[r - 1] + \" += 1;\\n if(\" + l + \") {\"], d = 1 === r ? \"rc < start || rc >= end\" : \"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\", h = \"\", f = 0, m = 1 === r ? 2 : 4; f < m; f++) h += \"\\n \" + p[f] + \"\\n if (\" + d + \") {\\n result[\" + f + \"] = float(\" + n + \");\\n } else {\\n \" + o + \" source = rc - start;\\n result[\" + f + \"] = getChannel(getX(\" + u.join() + \"), \" + c + \");\\n }\\n \";\n\n h += 1 === r ? \"} \" : \"}}\", this.userCode = \"\\n const \" + o + \" start = \" + o + \"(\" + a + \");\\n const \" + o + \" end = \" + o + \"(\" + i + \");\\n\\n void main() {\\n \" + o + \" outputLoc = getOutputCoords();\\n vec4 result = vec4(0.);\\n \" + h + \"\\n setOutput(result);\\n }\\n \";\n };\n}(),\n Pool2DProgram = function () {\n return function (e, t, n) {\n if (this.variableNames = [\"x\"], \"avg\" === t && n) throw new Error(\"Cannot compute positions for average pool.\");\n var r = e.filterWidth,\n o = e.strideHeight,\n a = e.strideWidth,\n i = e.dilationHeight,\n s = e.dilationWidth,\n u = e.effectiveFilterHeight,\n l = e.effectiveFilterWidth,\n c = e.padInfo.top,\n p = e.padInfo.left;\n this.outputShape = e.outShape;\n var d = \"avg\" === t,\n h = \"0.0\";\n if (d || (h = \"-1.0 / 1e-20\"), n) this.userCode = \"\\n const ivec2 strides = ivec2(\" + o + \", \" + a + \");\\n const ivec2 pads = ivec2(\" + c + \", \" + p + \");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords[0];\\n int d = coords[3];\\n\\n ivec2 xRCCorner = coords.yz * strides - pads;\\n int xRCorner = xRCCorner.x;\\n int xCCorner = xRCCorner.y;\\n\\n // max/min x(?, ?, d) to get y(yR, yC, d).\\n // ? = to be determined\\n float minMaxValue = 0.0;\\n float minMaxValueFound = 0.0;\\n int minMaxPosition = 0;\\n float avgValue = 0.0;\\n\\n for (int wR = 0; wR < \" + u + \";\\n wR += \" + i + \") {\\n int xR = xRCorner + wR;\\n\\n if (xR < 0 || xR >= \" + e.inHeight + \") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \" + l + \";\\n wC += \" + s + \") {\\n int xC = xCCorner + wC;\\n\\n if (xC < 0 || xC >= \" + e.inWidth + \") {\\n continue;\\n }\\n\\n float value = getX(batch, xR, xC, d);\\n\\n // If a min / max value has already been found, use it. If not,\\n // use the current value.\\n float currMinMaxValue = mix(\\n value, minMaxValue, minMaxValueFound);\\n if (value >= currMinMaxValue) {\\n minMaxValue = value;\\n minMaxValueFound = 1.0;\\n minMaxPosition = wR * \" + l + \" + wC;\\n }\\n }\\n }\\n setOutput(float(minMaxPosition));\\n }\\n \";else {\n var f = t + \"(\" + t + \"(\" + t + \"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\n \"avg\" === t && (f = \"avgValue / count\");\n var m = 4 * Math.floor(r / 4),\n g = r % 4,\n v = \"\\n if (\" + d + \") {\\n avgValue += dot(values, ones);\\n } else {\\n minMaxValue = max(values, minMaxValue);\\n }\\n \";\n this.userCode = \"\\n const ivec2 strides = ivec2(\" + o + \", \" + a + \");\\n const ivec2 pads = ivec2(\" + c + \", \" + p + \");\\n const float initializationValue = \" + h + \";\\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\\n\\n float count = 0.0;\\n\\n float getValue(int batch, int xR, int xC, int d) {\\n if (xC < 0 || xC >= \" + e.inWidth + \") {\\n return initializationValue;\\n }\\n count += 1.0;\\n return getX(batch, xR, xC, d);\\n }\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords[0];\\n int d = coords[3];\\n\\n ivec2 xRCCorner = coords.yz * strides - pads;\\n int xRCorner = xRCCorner.x;\\n int xCCorner = xRCCorner.y;\\n\\n // max/min x(?, ?, d) to get y(yR, yC, d).\\n // ? = to be determined\\n vec4 minMaxValue = vec4(\" + h + \");\\n float avgValue = 0.0;\\n count = 0.0;\\n\\n for (int wR = 0; wR < \" + u + \";\\n wR += \" + i + \") {\\n int xR = xRCorner + wR;\\n\\n if (xR < 0 || xR >= \" + e.inHeight + \") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \" + m + \"; wC += 4) {\\n int xC = xCCorner + wC * \" + s + \";\\n\\n vec4 values = vec4(\\n getValue(batch, xR, xC, d),\\n getValue(batch, xR, xC + \" + s + \", d),\\n getValue(batch, xR, xC + 2 * \" + s + \", d),\\n getValue(batch, xR, xC + 3 * \" + s + \", d)\\n );\\n\\n \" + v + \"\\n }\\n\\n int xC = xCCorner + \" + m + \";\\n if (\" + (1 === g) + \") {\\n vec4 values = vec4(\\n getValue(batch, xR, xC, d),\\n initializationValue,\\n initializationValue,\\n initializationValue\\n );\\n\\n \" + v + \"\\n } else if (\" + (2 === g) + \") {\\n vec4 values = vec4(\\n getValue(batch, xR, xC, d),\\n getValue(batch, xR, xC + \" + s + \", d),\\n initializationValue,\\n initializationValue\\n );\\n\\n \" + v + \"\\n } else if (\" + (3 === g) + \") {\\n vec4 values = vec4(\\n getValue(batch, xR, xC, d),\\n getValue(batch, xR, xC + \" + s + \", d),\\n getValue(batch, xR, xC + 2 * \" + s + \", d),\\n initializationValue\\n );\\n\\n \" + v + \"\\n }\\n }\\n setOutput(\" + f + \");\\n }\\n \";\n }\n };\n}(),\n ReduceProgram = function () {\n return function (e, t) {\n this.variableNames = [\"x\"];\n var n = e.windowSize,\n r = e.batchSize,\n o = e.inSize,\n a = Math.ceil(o / n);\n this.outputShape = [r, a];\n var i = \"0.0\",\n s = \"\";\n \"prod\" === t ? i = \"1.0\" : \"min\" === t ? (i = \"1.0 / 1e-20\", s = \"min\") : \"max\" === t && (i = \"-1.0 / 1e-20\", s = \"max\");\n var u = t + \"(\" + t + \"(\" + t + \"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\n \"sum\" === t ? u = \"sumValue\" : \"prod\" === t ? u = \"prodValue\" : \"all\" === t ? u = \"allValue\" : \"any\" === t && (u = \"anyValue\");\n var l = 4 * Math.floor(n / 4),\n c = n % 4,\n p = \"\\n if (\" + (\"sum\" === t) + \") {\\n sumValue += dot(values, ones);\\n } else if (\" + (\"prod\" === t) + \") {\\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\\n prodValue *= tmp[0] * tmp[1];\\n } else {\\n minMaxValue = \" + s + \"(values, minMaxValue);\\n }\\n \",\n d = \"vec4\";\n \"all\" === t ? (i = \"1.0\", p = \"\\n bool reducedAllValue = all(values);\\n float floatedReducedAllValue = float(reducedAllValue);\\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\\n \", d = \"bvec4\") : \"any\" === t && (i = \"0.0\", p = \"\\n bool reducedAnyValue = any(values);\\n float floatedReducedAnyValue = float(reducedAnyValue);\\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\\n \", d = \"bvec4\");\n var h = \"\";\n o % n > 0 && (h = \"\\n if (inIdx < 0 || inIdx >= \" + o + \") {\\n return initializationValue;\\n }\\n \"), this.userCode = \"\\n const float initializationValue = \" + i + \";\\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\\n\\n float getValue(int batch, int inIdx) {\\n \" + h + \"\\n return getX(batch, inIdx);\\n }\\n\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int batch = coords[0];\\n int outIdx = coords[1];\\n int inOffset = outIdx * \" + n + \";\\n\\n vec4 minMaxValue = vec4(\" + i + \");\\n float prodValue = 1.0;\\n float sumValue = 0.0;\\n float allValue = 1.0;\\n float anyValue = 0.0;\\n\\n for (int i = 0; i < \" + l + \"; i += 4) {\\n int inIdx = inOffset + i;\\n \" + d + \" values = \" + d + \"(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n getValue(batch, inIdx + 2),\\n getValue(batch, inIdx + 3)\\n );\\n\\n \" + p + \"\\n }\\n\\n int inIdx = inOffset + \" + l + \";\\n if (\" + (1 === c) + \") {\\n \" + d + \" values = \" + d + \"(\\n getValue(batch, inIdx),\\n initializationValue,\\n initializationValue,\\n initializationValue\\n );\\n\\n \" + p + \"\\n } else if (\" + (2 === c) + \") {\\n \" + d + \" values = \" + d + \"(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n initializationValue,\\n initializationValue\\n );\\n\\n \" + p + \"\\n } else if (\" + (3 === c) + \") {\\n \" + d + \" values = \" + d + \"(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n getValue(batch, inIdx + 2),\\n initializationValue\\n );\\n\\n \" + p + \"\\n }\\n setOutput(\" + u + \");\\n }\\n \";\n };\n}(),\n ReshapePackedProgram = function () {\n return function (e, t) {\n this.variableNames = [\"A\"], this.usesPackedTextures = !0, this.outputShape = e;\n\n for (var n = \"\", r = 0; r < 4; r++) {\n var o = \"thisRC = rc;\";\n r % 2 == 1 && (o += \"thisRC.z += 1;\"), r > 1 && (o += \"thisRC.y += 1;\"), n += \"\\n \" + o + \"\\n \" + (r > 0 ? \"if(thisRC.y < rows && thisRC.z < cols){\" : \"\") + \"\\n int flatIndex = getFlatIndex(thisRC);\\n\\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\\n\\n result[\" + r + \"] =\\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\\n \" + (r > 0 ? \"}\" : \"\") + \"\\n \";\n }\n\n this.userCode = \"\\n \" + getReshapedInputCoords(t) + \"\\n \" + getFlatIndex(e) + \"\\n\\n void main() {\\n ivec3 rc = getOutputCoords();\\n\\n vec4 result = vec4(0.);\\n\\n ivec3 thisRC;\\n int rows = \" + e[1] + \";\\n int cols = \" + e[2] + \";\\n\\n \" + n + \"\\n\\n setOutput(result);\\n }\\n \";\n };\n}();\n\nfunction getFlatIndex(e) {\n return \"\\n int getFlatIndex(ivec3 coords) {\\n return round(\" + dotify([\"coords.x\", \"coords.y\", \"coords.z\"], computeStrides(e).map(function (e) {\n return e.toString();\n }).concat([\"1.\"])) + \");\\n }\\n \";\n}\n\nfunction getReshapedInputCoords(e) {\n return \"\\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\\n \" + getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], e) + \"\\n return ivec3(r, c, d);\\n }\\n \";\n}\n\nvar ResizeBilinearBackpropProgram = function () {\n return function (e, t, n) {\n this.variableNames = [\"dy\"], this.outputShape = [], this.outputShape = t.shape;\n var r = t.shape,\n o = r[1],\n a = r[2],\n i = e.shape,\n s = i[1],\n u = i[2],\n l = [n && s > 1 ? o - 1 : o, n && u > 1 ? a - 1 : a],\n c = [n && s > 1 ? s - 1 : s, n && u > 1 ? u - 1 : u],\n p = l[0] / c[0],\n d = l[1] / c[1],\n h = 1 / p,\n f = 1 / d,\n m = 2 * Math.ceil(h) + 2,\n g = 2 * Math.ceil(f) + 2;\n this.userCode = \"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n int r = coords[1];\\n int c = coords[2];\\n\\n float accumulator = 0.0;\\n\\n const float heightScale = float(\" + p + \");\\n const float widthScale = float(\" + d + \");\\n\\n const float invHeightScale = float(\" + h + \");\\n const float invWidthScale = float(\" + f + \");\\n\\n const int winHeight = int(\" + m + \");\\n const int winWidth = int(\" + g + \");\\n\\n // Compute bounds for where in dy we will look\\n float startRLerp = floor(float(r) * invHeightScale);\\n int startDyR = int(startRLerp - float(winHeight / 2));\\n\\n float startCLerp = floor(float(c) * invWidthScale);\\n int startDyC = int(startCLerp - float(winWidth / 2));\\n\\n // Loop over dy\\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\\n int dyR = dyROffset + startDyR;\\n\\n // Guard against the window exceeding the bounds of dy\\n if (dyR < 0 || dyR >= \" + s + \") {\\n continue;\\n }\\n\\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\\n int dyC = dyCOffset + startDyC;\\n\\n // Guard against the window exceeding the bounds of dy\\n if (dyC < 0 || dyC >= \" + u + \") {\\n continue;\\n }\\n\\n float dxR = float(dyR) * heightScale;\\n int topDxRIndex = int(floor(dxR));\\n int bottomDxRIndex = int(min(ceil(dxR), \" + (o - 1) + \".0));\\n float dxRLerp = dxR - float(topDxRIndex);\\n float inverseDxRLerp = 1.0 - dxRLerp;\\n\\n float dxC = float(dyC) * widthScale;\\n int leftDxCIndex = int(floor(dxC));\\n int rightDxCIndex = int(min(ceil(dxC), \" + (a - 1) + \".0));\\n float dxCLerp = dxC - float(leftDxCIndex);\\n float inverseDxCLerp = 1.0 - dxCLerp;\\n\\n if (r == topDxRIndex && c == leftDxCIndex) {\\n // topLeft\\n accumulator +=\\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\\n }\\n\\n if (r == topDxRIndex && c == rightDxCIndex) {\\n // topRight\\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\\n }\\n\\n if (r == bottomDxRIndex && c == leftDxCIndex) {\\n // bottomLeft\\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\\n }\\n\\n if (r == bottomDxRIndex && c == rightDxCIndex) {\\n // bottomRight\\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\\n }\\n }\\n }\\n // End loop over dy\\n\\n setOutput(accumulator);\\n }\\n \";\n };\n}(),\n ResizeBilinearProgram = function () {\n return function (e, t, n, r) {\n this.variableNames = [\"A\"], this.outputShape = [];\n var o = e[0],\n a = e[1],\n i = e[2],\n s = e[3];\n this.outputShape = [o, t, n, s];\n var u = [r && t > 1 ? a - 1 : a, r && n > 1 ? i - 1 : i],\n l = [r && t > 1 ? t - 1 : t, r && n > 1 ? n - 1 : n];\n this.userCode = \"\\n const vec2 effectiveInputOverOutputRatioRC = vec2(\\n \" + u[0] / l[0] + \",\\n \" + u[1] / l[1] + \");\\n const vec2 inputShapeRC = vec2(\" + a + \".0, \" + i + \".0);\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n ivec2 yRC = coords.yz;\\n\\n // Fractional source index.\\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\\n\\n // Compute the four integer indices.\\n ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\\n ivec2 sourceCeilRC = ivec2(\\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\\n\\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\\n\\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\\n\\n float top = topLeft + (topRight - topLeft) * fracRC.y;\\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\\n float newValue = top + (bottom - top) * fracRC.x;\\n\\n setOutput(newValue);\\n }\\n \";\n };\n}(),\n ResizeNearestNeigborBackpropProgram = function () {\n return function (e, t, n) {\n this.variableNames = [\"dy\"], this.outputShape = [], this.outputShape = t.shape;\n var r = t.shape,\n o = r[1],\n a = r[2],\n i = e.shape,\n s = i[1],\n u = i[2],\n l = [n && s > 1 ? o - 1 : o, n && u > 1 ? a - 1 : a],\n c = [n && s > 1 ? s - 1 : s, n && u > 1 ? u - 1 : u],\n p = l[0] / c[0],\n d = l[1] / c[1],\n h = 1 / p,\n f = 1 / d,\n m = 2 * Math.ceil(h) + 2,\n g = 2 * Math.ceil(f) + 2;\n this.userCode = \"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n int r = coords[1];\\n int c = coords[2];\\n\\n float accumulator = 0.0;\\n\\n const float heightScale = float(\" + p + \");\\n const float widthScale = float(\" + d + \");\\n\\n const float invHeightScale = float(\" + h + \");\\n const float invWidthScale = float(\" + f + \");\\n\\n const int winHeight = int(\" + m + \");\\n const int winWidth = int(\" + g + \");\\n\\n // Compute bounds for where in dy we will look\\n float startRLerp = floor(float(r) * invHeightScale);\\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\\n\\n float startCLerp = floor(float(c) * invWidthScale);\\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\\n\\n // Loop over dy\\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\\n int dyR = dyROffset + startDyR;\\n\\n // Guard against the window exceeding the bounds of dy\\n if (dyR < 0 || dyR >= \" + s + \") {\\n continue;\\n }\\n\\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\\n int dyC = dyCOffset + startDyC;\\n\\n // Guard against the window exceeding the bounds of dy\\n if (dyC < 0 || dyC >= \" + u + \") {\\n continue;\\n }\\n\\n float sourceFracRow =\\n float(\" + l[0] + \") *\\n (float(dyR) / float(\" + c[0] + \"));\\n\\n float sourceFracCol =\\n float(\" + l[1] + \") *\\n (float(dyC) / float(\" + c[1] + \"));\\n\\n int sourceNearestRow = int(min(\\n float(int(\" + o + \") - 1),\\n \" + n + \" ? float(round(sourceFracRow)) :\\n float(floor(sourceFracRow))));\\n\\n int sourceNearestCol = int(min(\\n float(int(\" + a + \") - 1),\\n \" + n + \" ? float(round(sourceFracCol)) :\\n float(floor(sourceFracCol))));\\n\\n if (r == sourceNearestRow && c == sourceNearestCol) {\\n accumulator += getDy(b, dyR, dyC, d);\\n }\\n }\\n }\\n // End loop over dy\\n\\n setOutput(accumulator);\\n }\\n \";\n };\n}(),\n ResizeNearestNeighborProgram = function () {\n return function (e, t, n, r) {\n this.variableNames = [\"A\"], this.outputShape = [];\n var o = e[0],\n a = e[1],\n i = e[2],\n s = e[3];\n this.outputShape = [o, t, n, s];\n var u = [r && t > 1 ? a - 1 : a, r && n > 1 ? i - 1 : i],\n l = [r && t > 1 ? t - 1 : t, r && n > 1 ? n - 1 : n],\n c = r ? \"0.5\" : \"0.0\";\n this.userCode = \"\\n const vec2 effectiveInputOverOutputRatioRC = vec2(\\n \" + u[0] / l[0] + \",\\n \" + u[1] / l[1] + \");\\n const vec2 inputShapeRC = vec2(\" + a + \".0, \" + i + \".0);\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n ivec2 yRC = coords.yz;\\n\\n // Fractional source index.\\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\\n\\n // Compute the coordinators of nearest neighbor point.\\n ivec2 sourceNearestRC = ivec2(\\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + \" + c + \")));\\n\\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\\n\\n setOutput(newValue);\\n }\\n \";\n };\n}(),\n ReverseProgram = function () {\n return function (e, t) {\n this.variableNames = [\"x\"];\n var n = e.length;\n if (n > 4) throw new Error(\"WebGL backend: Reverse of rank-\" + n + \" tensor is not yet supported\");\n\n if (this.outputShape = e, 1 !== n) {\n var r = e.map(function (n, r) {\n return function (n) {\n return -1 !== t.indexOf(n) && 1 !== e[n] ? e[n] + \" - coords[\" + n + \"] - 1\" : \"coords[\" + n + \"]\";\n }(r);\n }).join(\",\"),\n o = getCoordsDataType(n);\n this.userCode = \"\\n void main() {\\n \" + o + \" coords = getOutputCoords();\\n setOutput(getX(\" + r + \"));\\n }\\n \";\n } else this.userCode = \"\\n void main() {\\n int coord = getOutputCoords();\\n setOutput(getX(\" + e[0] + \" - coord - 1));\\n }\\n \";\n };\n}(),\n ScatterProgram = function () {\n return function (e, t, n, r, o, a, i) {\n void 0 === i && (i = !0), this.variableNames = [\"updates\", \"indices\", \"defaultValue\"], this.outputShape = a;\n var s = getCoordsDataType(o.length),\n u = getCoordsDataType(a.length),\n l = \"\";\n 1 === n ? l = \"i\" : 2 === n && (l = \"i, j\");\n var c = \"getIndices(\" + l + \")\",\n p = \"\";\n 1 === r ? p = \"i\" : 2 === r && (p = \"i, coords[1]\");\n var d = \"getUpdates(\" + p + \")\",\n h = t > 1 ? \"strides[j]\" : \"strides\";\n this.userCode = \"\\n \" + s + \" strides = \" + s + \"(\" + o + \");\\n\\n void main() {\\n \" + u + \" coords = getOutputCoords();\\n float sum = 0.0;\\n bool found = false;\\n for (int i = 0; i < \" + e + \"; i++) {\\n int flattenedIndex = 0;\\n for (int j = 0; j < \" + t + \"; j++) {\\n int index = round(\" + c + \");\\n flattenedIndex += index * \" + h + \";\\n }\\n if (flattenedIndex == coords[0]) {\\n sum += \" + d + \";\\n found = true;\\n }\\n }\\n setOutput(mix(getDefaultValue(), sum, float(found)));\\n }\\n \";\n };\n}(),\n SegmentOpProgram = function () {\n return function (e, t) {\n this.variableNames = [\"x\", \"segmentIds\"];\n var n = e.windowSize,\n r = e.batchSize,\n o = e.inSize,\n a = e.numSegments,\n i = a * Math.ceil(o / n);\n this.outputShape = [r, i];\n var s = 4 * Math.floor(n / 4),\n u = n % 4,\n l = \"\\n sumValue += dot(values, segFilter);\\n \",\n c = \"\";\n o % n > 0 && (c = \"\\n if (inIdx < 0 || inIdx >= \" + o + \") {\\n return initializationValue;\\n }\\n \");\n var p = \"\";\n o % n > 0 && (p = \"\\n if (inIdx < 0 || inIdx >= \" + o + \") {\\n return -1.0;\\n }\\n \"), this.userCode = \"\\n const float initializationValue = 0.0;\\n\\n float getValue(int batch, int inIdx) {\\n \" + c + \"\\n return getX(batch, inIdx);\\n }\\n\\n float getSegmentIdAtIndex(int inIdx) {\\n \" + p + \"\\n return getSegmentIds(inIdx);\\n }\\n\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int batch = coords[0];\\n int outIdx = coords[1];\\n int inOffset = int(floor(float(outIdx) / float(\\n \" + a + \")) * float(\" + n + \"));\\n int currentSeg = int(mod(float(outIdx), float(\" + a + \")));\\n\\n float sumValue = 0.0;\\n\\n for (int i = 0; i < \" + s + \"; i += 4) {\\n int inIdx = inOffset + i;\\n vec4 values = vec4(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n getValue(batch, inIdx + 2),\\n getValue(batch, inIdx + 3)\\n );\\n\\n vec4 segFilter = vec4(\\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\\n );\\n\\n \" + l + \"\\n }\\n\\n int inIdx = inOffset + \" + s + \";\\n if (\" + (1 === u) + \") {\\n vec4 values = vec4(\\n getValue(batch, inIdx),\\n initializationValue,\\n initializationValue,\\n initializationValue\\n );\\n\\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\\n\\n vec4 segFilter = vec4(\\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n 0,\\n 0,\\n 0\\n );\\n\\n \" + l + \"\\n } else if (\" + (2 === u) + \") {\\n vec4 values = vec4(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n initializationValue,\\n initializationValue\\n );\\n\\n vec4 segFilter = vec4(\\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n 0,\\n 0\\n );\\n\\n \" + l + \"\\n } else if (\" + (3 === u) + \") {\\n vec4 values = vec4(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n getValue(batch, inIdx + 2),\\n initializationValue\\n );\\n\\n vec4 segFilter = vec4(\\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\\n 0\\n );\\n\\n \" + l + \"\\n }\\n setOutput(sumValue);\\n }\\n \";\n };\n}(),\n SelectProgram = function () {\n return function (e, t, n) {\n var r, o;\n if (this.variableNames = [\"c\", \"a\", \"b\"], this.outputShape = t, n > 4) throw Error(\"Where for rank \" + n + \" is not yet supported\");\n if (1 === n) o = \"resRC\", r = \"resRC\";else {\n for (var a = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"], i = [], s = [], u = 0; u < t.length; u++) s.push(\"\" + a[u]), u < e && i.push(\"\" + a[u]);\n\n r = i.join(), o = s.join();\n }\n var l = getCoordsDataType(n);\n this.userCode = \"\\n void main() {\\n \" + l + \" resRC = getOutputCoords();\\n float cVal = getC(\" + r + \");\\n if (cVal >= 1.0) {\\n setOutput(getA(\" + o + \"));\\n } else {\\n setOutput(getB(\" + o + \"));\\n }\\n }\\n \";\n };\n}(),\n SliceProgram = function () {\n function e(e) {\n this.variableNames = [\"source\"], this.outputShape = e, this.rank = e.length;\n var t,\n n = getCoordsDataType(this.rank),\n r = \"uniform int start[\" + this.rank + \"];\",\n o = getCoords$1(this.rank);\n t = \"\\n \" + n + \" sourceLoc;\\n \" + n + \" coords = getOutputCoords();\\n \" + e.map(function (e, t) {\n return \"sourceLoc.\" + coords[t] + \" = start[\" + t + \"] + coords.\" + coords[t] + \";\";\n }).join(\"\\n\") + \"\\n \", this.userCode = \"\\n \" + r + \"\\n void main() {\\n \" + t + \"\\n setOutput(getSource(\" + o + \"));\\n }\\n \";\n }\n\n return e.prototype.getCustomSetupFunc = function (e) {\n var t = this;\n if (e.length !== this.rank) throw Error(\"The rank (\" + this.rank + \") of the program must match the length of start (\" + e.length + \")\");\n return function (n, r) {\n null == t.startLoc && (t.startLoc = n.getUniformLocationNoThrow(r, \"start\"), null == t.startLoc) || n.gl.uniform1iv(t.startLoc, e);\n };\n }, e;\n}(),\n coords = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n\nfunction getCoords$1(e) {\n if (1 === e) return \"sourceLoc\";\n if (e <= 6) return coords.slice(0, e).map(function (e) {\n return \"sourceLoc.\" + e;\n }).join(\",\");\n throw Error(\"Slicing for rank \" + e + \" is not yet supported\");\n}\n\nvar StridedSliceProgram = function () {\n return function (e, t, n, r) {\n this.variableNames = [\"x\"];\n var o = n.filter(function (e, t) {\n return -1 === r.indexOf(t);\n });\n this.outputShape = o;\n var a = n.length,\n i = getCoordsDataType(n.length),\n s = getCoordsDataType(o.length),\n u = \"\";\n if (1 === a) u = \"coords * strides + begin\";else {\n var l = 0;\n u = n.map(function (e, t) {\n return -1 === r.indexOf(t) ? (l++, 1 === o.length ? \"coords * strides[\" + t + \"] + begin[\" + t + \"]\" : \"coords[\" + (l - 1) + \"] * strides[\" + t + \"] + begin[\" + t + \"]\") : \"begin[\" + t + \"]\";\n }).join(\",\");\n }\n this.userCode = \"\\n \" + i + \" begin = \" + i + \"(\" + e + \");\\n \" + i + \" strides = \" + i + \"(\" + t + \");\\n\\n void main() {\\n \" + s + \" coords = getOutputCoords();\\n setOutput(getX(\" + u + \"));\\n }\\n \";\n };\n}(),\n TextureManager = function () {\n function e(e) {\n this.gpgpu = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = {}, this.logEnabled = !1, this.usedTextures = {};\n }\n\n return e.prototype.acquireTexture = function (e, t, n) {\n var r,\n o = getPhysicalFromLogicalTextureType(t, n),\n a = getKeyFromTextureShape(e, o, n);\n\n if (a in this.freeTextures || (this.freeTextures[a] = []), a in this.usedTextures || (this.usedTextures[a] = []), this.freeTextures[a].length > 0) {\n this.numFreeTextures--, this.numUsedTextures++, this.log();\n var i = this.freeTextures[a].shift();\n return this.usedTextures[a].push(i), i;\n }\n\n return this.numUsedTextures++, this.log(), o === PhysicalTextureType.PACKED_2X2_FLOAT32 ? r = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : o === PhysicalTextureType.PACKED_2X2_FLOAT16 ? r = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : o === PhysicalTextureType.UNPACKED_FLOAT32 ? r = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : o === PhysicalTextureType.UNPACKED_FLOAT16 ? r = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : o === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE && (r = this.gpgpu.createUnsignedBytesMatrixTexture(e[0], e[1])), this.usedTextures[a].push(r), r;\n }, e.prototype.releaseTexture = function (e, t, n, r) {\n if (null != this.freeTextures) {\n var o = getKeyFromTextureShape(t, getPhysicalFromLogicalTextureType(n, r), r);\n o in this.freeTextures || (this.freeTextures[o] = []), this.freeTextures[o].push(e), this.numFreeTextures++, this.numUsedTextures--;\n var a = this.usedTextures[o],\n i = a.indexOf(e);\n if (i < 0) throw new Error(\"Cannot release a texture that was never provided by this texture manager\");\n a.splice(i, 1), this.log();\n }\n }, e.prototype.log = function () {\n if (this.logEnabled) {\n var e = this.numFreeTextures + this.numUsedTextures;\n console.log(\"Free/Used\", this.numFreeTextures + \" / \" + this.numUsedTextures, \"(\" + e + \")\");\n }\n }, e.prototype.getNumUsedTextures = function () {\n return this.numUsedTextures;\n }, e.prototype.getNumFreeTextures = function () {\n return this.numFreeTextures;\n }, e.prototype.dispose = function () {\n var e = this;\n\n if (null != this.freeTextures) {\n for (var t in this.freeTextures) this.freeTextures[t].forEach(function (t) {\n e.gpgpu.deleteMatrixTexture(t);\n });\n\n for (var t in this.usedTextures) this.usedTextures[t].forEach(function (t) {\n e.gpgpu.deleteMatrixTexture(t);\n });\n\n this.freeTextures = null, this.usedTextures = null, this.numUsedTextures = 0, this.numFreeTextures = 0;\n }\n }, e;\n}();\n\nfunction getPhysicalFromLogicalTextureType(e, t) {\n if (e === TextureUsage.UPLOAD) return t ? PhysicalTextureType.PACKED_2X2_FLOAT32 : PhysicalTextureType.UNPACKED_FLOAT32;\n if (e === TextureUsage.RENDER || null == e) return t ? ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\") ? PhysicalTextureType.PACKED_2X2_FLOAT32 : PhysicalTextureType.PACKED_2X2_FLOAT16 : ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\") ? PhysicalTextureType.UNPACKED_FLOAT32 : PhysicalTextureType.UNPACKED_FLOAT16;\n if (e === TextureUsage.DOWNLOAD || e === TextureUsage.PIXELS) return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n throw new Error(\"Unknown logical texture type \" + e);\n}\n\nfunction getKeyFromTextureShape(e, t, n) {\n return e[0] + \"_\" + e[1] + \"_\" + t + \"_\" + n;\n}\n\nvar TileProgram = function () {\n return function (e, t) {\n this.variableNames = [\"A\"];\n\n for (var n = new Array(e.length), r = 0; r < n.length; r++) n[r] = e[r] * t[r];\n\n this.outputShape = n, this.rank = n.length;\n var o = getCoordsDataType(this.rank),\n a = getSourceCoords$2(e);\n this.userCode = \"\\n void main() {\\n \" + o + \" resRC = getOutputCoords();\\n setOutput(getA(\" + a + \"));\\n }\\n \";\n };\n}();\n\nfunction getSourceCoords$2(e) {\n var t = e.length;\n if (t > 5) throw Error(\"Tile for rank \" + t + \" is not yet supported\");\n if (1 === t) return \"imod(resRC, \" + e[0] + \")\";\n\n for (var n = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\", \"resRC.u\"], r = [], o = 0; o < e.length; o++) r.push(\"imod(\" + n[o] + \", \" + e[o] + \")\");\n\n return r.join();\n}\n\nvar TransposeProgram = function () {\n return function (e, t) {\n this.variableNames = [\"A\"];\n\n for (var n = new Array(e.length), r = 0; r < n.length; r++) n[r] = e[t[r]];\n\n this.outputShape = n, this.rank = n.length;\n var o = getCoordsDataType(this.rank),\n a = getSwitchedCoords(t);\n this.userCode = \"\\n void main() {\\n \" + o + \" resRC = getOutputCoords();\\n setOutput(getA(\" + a + \"));\\n }\\n \";\n };\n}();\n\nfunction getSwitchedCoords(e) {\n var t = e.length;\n if (t > 6) throw Error(\"Transpose for rank \" + t + \" is not yet supported\");\n\n for (var n = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\", \"resRC.u\", \"resRC.v\"], r = new Array(t), o = 0; o < e.length; o++) r[e[o]] = n[o];\n\n return r.join();\n}\n\nvar ERF_P = .3275911,\n ERF_A1 = .254829592,\n ERF_A2 = -.284496736,\n ERF_A3 = 1.421413741,\n ERF_A4 = -1.453152027,\n ERF_A5 = 1.061405429,\n SELU_SCALEALPHA = 1.7580993408473768,\n SELU_SCALE = 1.0507009873554805,\n UnaryOpProgram = function () {\n function e(e, t) {\n this.variableNames = [\"A\"], this.outputShape = e, this.userCode = \"\\n uniform float NAN;\\n float unaryOperation(float x) {\\n \" + t + \"\\n }\\n\\n void main() {\\n float x = getAAtOutCoords();\\n float y = unaryOperation(x);\\n\\n setOutput(y);\\n }\\n \";\n }\n\n return e.prototype.getCustomSetupFunc = function () {\n var e = this;\n return function (t, n) {\n null == e.startLoc && (e.startLoc = t.getUniformLocationNoThrow(n, \"NAN\"), null == e.startLoc) || t.gl.uniform1f(e.startLoc, NaN);\n };\n }, e;\n}(),\n CHECK_NAN_SNIPPET$1 = \"if (isNaN(x)) return x;\",\n LINEAR = \"return x;\",\n ABS = \"return abs(x);\",\n RELU = CHECK_NAN_SNIPPET$1 + \"\\n return (x < 0.0) ? 0.0 : x;\\n\",\n ELU = \"return (x >= 0.0) ? x : (exp(x) - 1.0);\",\n SELU = \"\\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\\n // see: https://arxiv.org/abs/1706.02515\\n float scaleAlpha = \" + SELU_SCALEALPHA + \";\\n float scale = \" + SELU_SCALE + \";\\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\\n\";\n\nfunction STEP(e) {\n return void 0 === e && (e = 0), CHECK_NAN_SNIPPET$1 + \"\\n return x > 0.0 ? 1.0 : float(\" + e + \");\\n \";\n}\n\nvar NEG = \"return -x;\",\n CEIL = \"return ceil(x);\",\n FLOOR = \"return floor(x);\",\n SIGN = \"\\n if (isNaN(x)) { return 0.0; }\\n return sign(x);\\n\",\n ROUND = \"\\n // OpenGL ES does not support round function.\\n // The algorithm is based on banker's rounding.\\n float base = floor(x);\\n if ((x - base) < 0.5) {\\n return floor(x);\\n } else if ((x - base) > 0.5) {\\n return ceil(x);\\n } else {\\n if (mod(base, 2.0) == 0.0) {\\n return base;\\n } else {\\n return base + 1.0;\\n }\\n }\\n\",\n EXP = \"return exp(x);\",\n EXPM1 = \"return exp(x) - 1.0;\",\n LOG = \"if (x < 0.0) return NAN;\\n return log(x);\",\n LOG1P = \"return log(1.0 + x);\",\n SQRT = \"return sqrt(x);\",\n RSQRT = \"return inversesqrt(x);\",\n SIGMOID = \"return 1.0 / (1.0 + exp(-1.0 * x));\",\n SOFTPLUS = \"\\n float epsilon = 1.1920928955078125e-7;\\n float threshold = log(epsilon) + 2.0;\\n\\n bool too_large = x > -threshold;\\n bool too_small = x < threshold;\\n\\n float result;\\n float exp_x = exp(x);\\n\\n if (too_large){\\n result = x;\\n }\\n else if (too_small){\\n result = exp_x;\\n }\\n else{\\n result = log(exp_x + 1.0);\\n }\\n return result;\\n\",\n SIN = CHECK_NAN_SNIPPET$1 + \"\\n return sin(x);\\n\",\n COS = CHECK_NAN_SNIPPET$1 + \"\\n return cos(x);\\n\",\n TAN = \"return tan(x);\",\n ASIN = \"return asin(x);\",\n ACOS = \"return acos(x);\",\n ATAN = CHECK_NAN_SNIPPET$1 + \"\\n return atan(x);\\n\",\n SINH = \"\\n float e2x = exp(x);\\n return (e2x - 1.0 / e2x) / 2.0;\\n\",\n COSH = \"\\n float e2x = exp(-x);\\n return (e2x + 1.0 / e2x) / 2.0;\\n\",\n TANH = \"\\n float e2x = exp(-2.0 * abs(x));\\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\\n\",\n ASINH = \"return log(x + sqrt(x * x + 1.0));\",\n ACOSH = CHECK_NAN_SNIPPET$1 + \"\\n if (x < 1.0) return NAN;\\n return log(x + sqrt(x * x - 1.0));\",\n ATANH = CHECK_NAN_SNIPPET$1 + \"\\n if ((x < -1.0) || (x > 1.0)) return NAN;\\n return (log(1.0 + x) - log(1.0 - x)) / 2.0;\",\n ERF = '\\n // Error function is calculated approximately with elementary function.\\n // See \"Handbook of Mathematical Functions with Formulas,\\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\\n float p = ' + ERF_P + \";\\n float a1 = \" + ERF_A1 + \";\\n float a2 = \" + ERF_A2 + \";\\n float a3 = \" + ERF_A3 + \";\\n float a4 = \" + ERF_A4 + \";\\n float a5 = \" + ERF_A5 + \";\\n\\n float t = 1.0 / (1.0 + p * x);\\n return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\\n\",\n SQUARE = \"return x * x;\",\n RECIPROCAL = \"return 1.0 / x;\",\n LOGICAL_NOT = \"return float(!(x >= 1.0));\",\n TO_INT = \"return float(int(x));\",\n CLONE = \"return x;\",\n LINEAR$1 = \"return x;\",\n LOG$1 = \"\\n vec4 result = log(x);\\n vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\\n result.r = isNaN.r == 1.0 ? NAN : result.r;\\n result.g = isNaN.g == 1.0 ? NAN : result.g;\\n result.b = isNaN.b == 1.0 ? NAN : result.b;\\n result.a = isNaN.a == 1.0 ? NAN : result.a;\\n\\n return result;\\n\",\n RELU$1 = \"\\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\\n\\n result.r = isNaN(x.r) ? x.r : result.r;\\n result.g = isNaN(x.g) ? x.g : result.g;\\n result.b = isNaN(x.b) ? x.b : result.b;\\n result.a = isNaN(x.a) ? x.a : result.a;\\n\\n return result;\\n\",\n UnaryOpPackedProgram = function () {\n function e(e, t) {\n this.variableNames = [\"A\"], this.usesPackedTextures = !0, this.outputShape = e, this.userCode = \"\\n uniform float NAN;\\n vec4 unaryOperation(vec4 x) {\\n \" + t + \"\\n }\\n\\n void main() {\\n vec4 x = getAAtOutCoords();\\n vec4 y = unaryOperation(x);\\n\\n setOutput(y);\\n }\\n \";\n }\n\n return e.prototype.getCustomSetupFunc = function () {\n var e = this;\n return function (t, n) {\n null == e.startLoc && (e.startLoc = t.getUniformLocationNoThrow(n, \"NAN\"), null == e.startLoc) || t.gl.uniform1f(e.startLoc, NaN);\n };\n }, e;\n}(),\n UnpackProgram = function () {\n return function (e) {\n this.variableNames = [\"A\"], this.usesPackedTextures = !0, this.outputShape = e;\n var t = e.length,\n n = getChannels(\"rc\", t),\n r = getCoordsDataType(t),\n o = getSourceCoords$1(t, n),\n a = n.slice(-2),\n i = t <= 1 ? \"rc\" : \"vec2(\" + a.join(\",\") + \")\";\n this.userCode = \"\\n void main() {\\n \" + r + \" rc = getOutputCoords();\\n vec4 packedInput = getA(\" + o + \");\\n\\n setOutput(getChannel(packedInput, \" + i + \"));\\n }\\n \";\n };\n}();\n\nfunction fromPixels_(e, t) {\n if (void 0 === t && (t = 3), t > 4) throw new Error(\"Cannot construct Tensor with more than 4 channels from pixels.\");\n return ENV.engine.fromPixels(e, t);\n}\n\nfunction toPixels(e, t) {\n return __awaiter(this, void 0, void 0, function () {\n var n, r, o, a, i, s, u, l, c, p, d, h, f, m, g, v, y, x, T, w;\n return __generator(this, function (E) {\n switch (E.label) {\n case 0:\n if (n = convertToTensor(e, \"img\", \"toPixels\"), e instanceof Tensor || (n = n.toInt()), 2 !== n.rank && 3 !== n.rank) throw new Error(\"toPixels only supports rank 2 or 3 tensors, got rank \" + n.rank + \".\");\n if (r = n.shape.slice(0, 2), o = r[0], a = r[1], (i = 2 === n.rank ? 1 : n.shape[2]) > 4 || 2 === i) throw new Error(\"toPixels only supports depth of size 1, 3 or 4 but got \" + i);\n return s = n.min(), u = n.max(), [4, s.data()];\n\n case 1:\n return l = E.sent()[0], [4, u.data()];\n\n case 2:\n if (c = E.sent()[0], s.dispose(), u.dispose(), \"float32\" === n.dtype) {\n if (l < 0 || c > 1) throw new Error(\"Tensor values for a float32 Tensor must be in the range [0 - 1] but got range [\" + l + \" - \" + c + \"].\");\n } else {\n if (\"int32\" !== n.dtype) throw new Error(\"Unsupported type for toPixels: \" + n.dtype + \". Please use float32 or int32 tensors.\");\n if (l < 0 || c > 255) throw new Error(\"Tensor values for a int32 Tensor must be in the range [0 - 255] but got range [\" + l + \" - \" + c + \"].\");\n }\n\n return [4, n.data()];\n\n case 3:\n for (p = E.sent(), d = \"float32\" === n.dtype ? 255 : 1, h = new Uint8ClampedArray(a * o * 4), f = 0; f < o * a; ++f) m = void 0, g = void 0, v = void 0, y = void 0, 1 === i ? (m = p[f] * d, g = p[f] * d, v = p[f] * d, y = 255) : 3 === i ? (m = p[3 * f] * d, g = p[3 * f + 1] * d, v = p[3 * f + 2] * d, y = 255) : 4 === i && (m = p[4 * f] * d, g = p[4 * f + 1] * d, v = p[4 * f + 2] * d, y = p[4 * f + 3] * d), h[(x = 4 * f) + 0] = Math.round(m), h[x + 1] = Math.round(g), h[x + 2] = Math.round(v), h[x + 3] = Math.round(y);\n\n return null != t && (t.width = a, t.height = o, T = t.getContext(\"2d\"), w = new ImageData(h, a, o), T.putImageData(w, 0, 0)), n !== e && n.dispose(), [2, h];\n }\n });\n });\n}\n\nvar fromPixels = op({\n fromPixels_: fromPixels_\n}),\n browser = Object.freeze({\n toPixels: toPixels,\n fromPixels: fromPixels\n});\nexports.browser = browser;\n\nfunction concat1d_(e) {\n return concat(e, 0);\n}\n\nfunction concat2d_(e, t) {\n return concat(e, t);\n}\n\nfunction concat3d_(e, t) {\n return concat(e, t);\n}\n\nfunction concat4d_(e, t) {\n return concat(e, t);\n}\n\nfunction concat_(e, t) {\n void 0 === t && (t = 0), assert(e.length >= 1, \"Pass at least one tensor to concat\");\n var n = convertToTensorArray(e, \"tensors\", \"concat\");\n t = parseAxisParam(t, n[0].shape)[0];\n var r = computeOutShape(n.map(function (e) {\n return e.shape;\n }), t);\n if (0 === sizeFromShape(r)) return tensor([], r);\n if (1 === (n = n.filter(function (e) {\n return e.size > 0;\n })).length) return n[0];\n var o = n.map(function (e) {\n return e.shape;\n });\n assertParamsConsistent(o, t);\n var a = n;\n return ENV.engine.runKernel(function (e) {\n return e.concat(n, t);\n }, a, function (e) {\n var n = o.map(function (e) {\n return e[t];\n });\n return split$1(e, n, t).map(function (e) {\n return function () {\n return e;\n };\n });\n });\n}\n\nfunction split_(e, t, n) {\n void 0 === n && (n = 0);\n var r,\n o = convertToTensor(e, \"x\", \"split\");\n n = parseAxisParam(n, o.shape)[0], \"number\" == typeof t ? (assert(o.shape[n] % t == 0, \"Number of splits must evenly divide the axis.\"), r = new Array(t).fill(o.shape[n] / t)) : (assert(o.shape[n] === t.reduce(function (e, t) {\n return e + t;\n }), \"The sum of sizes must match the size of the axis dimension.\"), r = t);\n return ENV.engine.runKernel(function (e) {\n return e.split(o, r, n);\n }, {\n $x: o\n }, function (e) {\n return {\n $x: function () {\n return concat(e, n);\n }\n };\n });\n}\n\nvar concat = op({\n concat_: concat_\n}),\n concat1d = op({\n concat1d_: concat1d_\n}),\n concat2d = op({\n concat2d_: concat2d_\n}),\n concat3d = op({\n concat3d_: concat3d_\n}),\n concat4d = op({\n concat4d_: concat4d_\n}),\n split$1 = op({\n split_: split_\n}),\n commonjsGlobal = \"undefined\" != typeof window ? window : \"undefined\" != typeof global ? global : \"undefined\" != typeof self ? self : {};\nexports.split = split$1;\nexports.concat4d = concat4d;\nexports.concat3d = concat3d;\nexports.concat2d = concat2d;\nexports.concat1d = concat1d;\nexports.concat = concat;\n\nfunction createCommonjsModule(e, t) {\n return e(t = {\n exports: {}\n }, t.exports), t.exports;\n}\n\nvar alea = createCommonjsModule(function (e) {\n !function (e, t, n) {\n function r(e, t) {\n return t.c = e.c, t.s0 = e.s0, t.s1 = e.s1, t.s2 = e.s2, t;\n }\n\n function o(e, t) {\n var n = new function (e) {\n var t,\n n = this,\n r = (t = 4022871197, function (e) {\n e = e.toString();\n\n for (var n = 0; n < e.length; n++) {\n var r = .02519603282416938 * (t += e.charCodeAt(n));\n r -= t = r >>> 0, t = (r *= t) >>> 0, t += 4294967296 * (r -= t);\n }\n\n return 2.3283064365386963e-10 * (t >>> 0);\n });\n n.next = function () {\n var e = 2091639 * n.s0 + 2.3283064365386963e-10 * n.c;\n return n.s0 = n.s1, n.s1 = n.s2, n.s2 = e - (n.c = 0 | e);\n }, n.c = 1, n.s0 = r(\" \"), n.s1 = r(\" \"), n.s2 = r(\" \"), n.s0 -= r(e), n.s0 < 0 && (n.s0 += 1), n.s1 -= r(e), n.s1 < 0 && (n.s1 += 1), n.s2 -= r(e), n.s2 < 0 && (n.s2 += 1), r = null;\n }(e),\n o = t && t.state,\n a = n.next;\n return a.int32 = function () {\n return 4294967296 * n.next() | 0;\n }, a.double = function () {\n return a() + 1.1102230246251565e-16 * (2097152 * a() | 0);\n }, a.quick = a, o && (\"object\" == typeof o && r(o, n), a.state = function () {\n return r(n, {});\n }), a;\n }\n\n t && t.exports ? t.exports = o : n && n.amd ? n(function () {\n return o;\n }) : this.alea = o;\n }(0, e, !1);\n}),\n xor128 = createCommonjsModule(function (e) {\n !function (e, t, n) {\n function r(e, t) {\n return t.x = e.x, t.y = e.y, t.z = e.z, t.w = e.w, t;\n }\n\n function o(e, t) {\n var n = new function (e) {\n var t = this,\n n = \"\";\n t.x = 0, t.y = 0, t.z = 0, t.w = 0, t.next = function () {\n var e = t.x ^ t.x << 11;\n return t.x = t.y, t.y = t.z, t.z = t.w, t.w ^= t.w >>> 19 ^ e ^ e >>> 8;\n }, e === (0 | e) ? t.x = e : n += e;\n\n for (var r = 0; r < n.length + 64; r++) t.x ^= 0 | n.charCodeAt(r), t.next();\n }(e),\n o = t && t.state,\n a = function () {\n return (n.next() >>> 0) / 4294967296;\n };\n\n return a.double = function () {\n do {\n var e = ((n.next() >>> 11) + (n.next() >>> 0) / 4294967296) / (1 << 21);\n } while (0 === e);\n\n return e;\n }, a.int32 = n.next, a.quick = a, o && (\"object\" == typeof o && r(o, n), a.state = function () {\n return r(n, {});\n }), a;\n }\n\n t && t.exports ? t.exports = o : n && n.amd ? n(function () {\n return o;\n }) : this.xor128 = o;\n }(0, e, !1);\n}),\n xorwow = createCommonjsModule(function (e) {\n !function (e, t, n) {\n function r(e, t) {\n return t.x = e.x, t.y = e.y, t.z = e.z, t.w = e.w, t.v = e.v, t.d = e.d, t;\n }\n\n function o(e, t) {\n var n = new function (e) {\n var t = this,\n n = \"\";\n t.next = function () {\n var e = t.x ^ t.x >>> 2;\n return t.x = t.y, t.y = t.z, t.z = t.w, t.w = t.v, (t.d = t.d + 362437 | 0) + (t.v = t.v ^ t.v << 4 ^ e ^ e << 1) | 0;\n }, t.x = 0, t.y = 0, t.z = 0, t.w = 0, t.v = 0, e === (0 | e) ? t.x = e : n += e;\n\n for (var r = 0; r < n.length + 64; r++) t.x ^= 0 | n.charCodeAt(r), r == n.length && (t.d = t.x << 10 ^ t.x >>> 4), t.next();\n }(e),\n o = t && t.state,\n a = function () {\n return (n.next() >>> 0) / 4294967296;\n };\n\n return a.double = function () {\n do {\n var e = ((n.next() >>> 11) + (n.next() >>> 0) / 4294967296) / (1 << 21);\n } while (0 === e);\n\n return e;\n }, a.int32 = n.next, a.quick = a, o && (\"object\" == typeof o && r(o, n), a.state = function () {\n return r(n, {});\n }), a;\n }\n\n t && t.exports ? t.exports = o : n && n.amd ? n(function () {\n return o;\n }) : this.xorwow = o;\n }(0, e, !1);\n}),\n xorshift7 = createCommonjsModule(function (e) {\n !function (e, t, n) {\n function r(e, t) {\n return t.x = e.x.slice(), t.i = e.i, t;\n }\n\n function o(e, t) {\n null == e && (e = +new Date());\n\n var n = new function (e) {\n var t = this;\n t.next = function () {\n var e,\n n,\n r = t.x,\n o = t.i;\n return e = r[o], n = (e ^= e >>> 7) ^ e << 24, n ^= (e = r[o + 1 & 7]) ^ e >>> 10, n ^= (e = r[o + 3 & 7]) ^ e >>> 3, n ^= (e = r[o + 4 & 7]) ^ e << 7, e = r[o + 7 & 7], n ^= (e ^= e << 13) ^ e << 9, r[o] = n, t.i = o + 1 & 7, n;\n }, function (e, t) {\n var n,\n r = [];\n if (t === (0 | t)) r[0] = t;else for (t = \"\" + t, n = 0; n < t.length; ++n) r[7 & n] = r[7 & n] << 15 ^ t.charCodeAt(n) + r[n + 1 & 7] << 13;\n\n for (; r.length < 8;) r.push(0);\n\n for (n = 0; n < 8 && 0 === r[n]; ++n);\n\n for (8 == n ? r[7] = -1 : r[n], e.x = r, e.i = 0, n = 256; n > 0; --n) e.next();\n }(t, e);\n }(e),\n o = t && t.state,\n a = function () {\n return (n.next() >>> 0) / 4294967296;\n };\n\n return a.double = function () {\n do {\n var e = ((n.next() >>> 11) + (n.next() >>> 0) / 4294967296) / (1 << 21);\n } while (0 === e);\n\n return e;\n }, a.int32 = n.next, a.quick = a, o && (o.x && r(o, n), a.state = function () {\n return r(n, {});\n }), a;\n }\n\n t && t.exports ? t.exports = o : n && n.amd ? n(function () {\n return o;\n }) : this.xorshift7 = o;\n }(0, e, !1);\n}),\n xor4096 = createCommonjsModule(function (e) {\n !function (e, t, n) {\n function r(e, t) {\n return t.i = e.i, t.w = e.w, t.X = e.X.slice(), t;\n }\n\n function o(e, t) {\n null == e && (e = +new Date());\n\n var n = new function (e) {\n var t = this;\n t.next = function () {\n var e,\n n,\n r = t.w,\n o = t.X,\n a = t.i;\n return t.w = r = r + 1640531527 | 0, n = o[a + 34 & 127], e = o[a = a + 1 & 127], n ^= n << 13, e ^= e << 17, n ^= n >>> 15, e ^= e >>> 12, n = o[a] = n ^ e, t.i = a, n + (r ^ r >>> 16) | 0;\n }, function (e, t) {\n var n,\n r,\n o,\n a,\n i,\n s = [],\n u = 128;\n\n for (t === (0 | t) ? (r = t, t = null) : (t += \"\\0\", r = 0, u = Math.max(u, t.length)), o = 0, a = -32; a < u; ++a) t && (r ^= t.charCodeAt((a + 32) % t.length)), 0 === a && (i = r), r ^= r << 10, r ^= r >>> 15, r ^= r << 4, r ^= r >>> 13, a >= 0 && (i = i + 1640531527 | 0, o = 0 == (n = s[127 & a] ^= r + i) ? o + 1 : 0);\n\n for (o >= 128 && (s[127 & (t && t.length || 0)] = -1), o = 127, a = 512; a > 0; --a) r = s[o + 34 & 127], n = s[o = o + 1 & 127], r ^= r << 13, n ^= n << 17, r ^= r >>> 15, n ^= n >>> 12, s[o] = r ^ n;\n\n e.w = i, e.X = s, e.i = o;\n }(t, e);\n }(e),\n o = t && t.state,\n a = function () {\n return (n.next() >>> 0) / 4294967296;\n };\n\n return a.double = function () {\n do {\n var e = ((n.next() >>> 11) + (n.next() >>> 0) / 4294967296) / (1 << 21);\n } while (0 === e);\n\n return e;\n }, a.int32 = n.next, a.quick = a, o && (o.X && r(o, n), a.state = function () {\n return r(n, {});\n }), a;\n }\n\n t && t.exports ? t.exports = o : n && n.amd ? n(function () {\n return o;\n }) : this.xor4096 = o;\n }(0, e, !1);\n}),\n tychei = createCommonjsModule(function (e) {\n !function (e, t, n) {\n function r(e, t) {\n return t.a = e.a, t.b = e.b, t.c = e.c, t.d = e.d, t;\n }\n\n function o(e, t) {\n var n = new function (e) {\n var t = this,\n n = \"\";\n t.next = function () {\n var e = t.b,\n n = t.c,\n r = t.d,\n o = t.a;\n return e = e << 25 ^ e >>> 7 ^ n, n = n - r | 0, r = r << 24 ^ r >>> 8 ^ o, o = o - e | 0, t.b = e = e << 20 ^ e >>> 12 ^ n, t.c = n = n - r | 0, t.d = r << 16 ^ n >>> 16 ^ o, t.a = o - e | 0;\n }, t.a = 0, t.b = 0, t.c = -1640531527, t.d = 1367130551, e === Math.floor(e) ? (t.a = e / 4294967296 | 0, t.b = 0 | e) : n += e;\n\n for (var r = 0; r < n.length + 20; r++) t.b ^= 0 | n.charCodeAt(r), t.next();\n }(e),\n o = t && t.state,\n a = function () {\n return (n.next() >>> 0) / 4294967296;\n };\n\n return a.double = function () {\n do {\n var e = ((n.next() >>> 11) + (n.next() >>> 0) / 4294967296) / (1 << 21);\n } while (0 === e);\n\n return e;\n }, a.int32 = n.next, a.quick = a, o && (\"object\" == typeof o && r(o, n), a.state = function () {\n return r(n, {});\n }), a;\n }\n\n t && t.exports ? t.exports = o : n && n.amd ? n(function () {\n return o;\n }) : this.tychei = o;\n }(0, e, !1);\n}),\n seedrandom = createCommonjsModule(function (e) {\n !function (t, n) {\n var r,\n o = this,\n a = 256,\n i = 6,\n s = \"random\",\n u = n.pow(a, i),\n l = n.pow(2, 52),\n c = 2 * l,\n p = a - 1;\n\n function d(e, d, g) {\n var v = [],\n y = f(function e(t, n) {\n var r,\n o = [],\n a = typeof t;\n if (n && \"object\" == a) for (r in t) try {\n o.push(e(t[r], n - 1));\n } catch (e) {}\n return o.length ? o : \"string\" == a ? t : t + \"\\0\";\n }((d = 1 == d ? {\n entropy: !0\n } : d || {}).entropy ? [e, m(t)] : null == e ? function () {\n try {\n var e;\n return r && (e = r.randomBytes) ? e = e(a) : (e = new Uint8Array(a), (o.crypto || o.msCrypto).getRandomValues(e)), m(e);\n } catch (e) {\n var n = o.navigator,\n i = n && n.plugins;\n return [+new Date(), o, i, o.screen, m(t)];\n }\n }() : e, 3), v),\n x = new function (e) {\n var t,\n n = e.length,\n r = this,\n o = 0,\n i = r.i = r.j = 0,\n s = r.S = [];\n n || (e = [n++]);\n\n for (; o < a;) s[o] = o++;\n\n for (o = 0; o < a; o++) s[o] = s[i = p & i + e[o % n] + (t = s[o])], s[i] = t;\n\n (r.g = function (e) {\n for (var t, n = 0, o = r.i, i = r.j, s = r.S; e--;) t = s[o = p & o + 1], n = n * a + s[p & (s[o] = s[i = p & i + t]) + (s[i] = t)];\n\n return r.i = o, r.j = i, n;\n })(a);\n }(v),\n T = function () {\n for (var e = x.g(i), t = u, n = 0; e < l;) e = (e + n) * a, t *= a, n = x.g(1);\n\n for (; e >= c;) e /= 2, t /= 2, n >>>= 1;\n\n return (e + n) / t;\n };\n\n return T.int32 = function () {\n return 0 | x.g(4);\n }, T.quick = function () {\n return x.g(4) / 4294967296;\n }, T.double = T, f(m(x.S), t), (d.pass || g || function (e, t, r, o) {\n return o && (o.S && h(o, x), e.state = function () {\n return h(x, {});\n }), r ? (n[s] = e, t) : e;\n })(T, y, \"global\" in d ? d.global : this == n, d.state);\n }\n\n function h(e, t) {\n return t.i = e.i, t.j = e.j, t.S = e.S.slice(), t;\n }\n\n function f(e, t) {\n for (var n, r = e + \"\", o = 0; o < r.length;) t[p & o] = p & (n ^= 19 * t[p & o]) + r.charCodeAt(o++);\n\n return m(t);\n }\n\n function m(e) {\n return String.fromCharCode.apply(0, e);\n }\n\n if (n[\"seed\" + s] = d, f(n.random(), t), e.exports) {\n e.exports = d;\n\n try {\n r = require(\"crypto\");\n } catch (e) {}\n } else 0;\n }([], Math);\n});\nseedrandom.alea = alea, seedrandom.xor128 = xor128, seedrandom.xorwow = xorwow, seedrandom.xorshift7 = xorshift7, seedrandom.xor4096 = xor4096, seedrandom.tychei = tychei;\n\nvar seedrandom$1 = seedrandom,\n seedrandom_1 = seedrandom$1.alea,\n MPRandGauss = function () {\n function e(e, t, n, r, o) {\n this.mean = e, this.stdDev = t, this.dtype = n, this.nextVal = NaN, this.truncated = r, this.truncated && (this.upper = this.mean + 2 * this.stdDev, this.lower = this.mean - 2 * this.stdDev);\n var a = o || Math.random();\n this.random = seedrandom_1(a.toString());\n }\n\n return e.prototype.nextValue = function () {\n if (!isNaN(this.nextVal)) {\n var e = this.nextVal;\n return this.nextVal = NaN, e;\n }\n\n for (var t, n, r = !1; !r;) {\n var o = void 0,\n a = void 0,\n i = void 0;\n\n do {\n i = (o = 2 * this.random() - 1) * o + (a = 2 * this.random() - 1) * a;\n } while (i >= 1 || 0 === i);\n\n var s = Math.sqrt(-2 * Math.log(i) / i);\n t = this.mean + this.stdDev * o * s, n = this.mean + this.stdDev * a * s, this.truncated && !this.isValidTruncated(t) || (r = !0);\n }\n\n return this.truncated && !this.isValidTruncated(n) || (this.nextVal = this.convertValue(n)), this.convertValue(t);\n }, e.prototype.convertValue = function (e) {\n return null == this.dtype || \"float32\" === this.dtype ? e : Math.round(e);\n }, e.prototype.isValidTruncated = function (e) {\n return e <= this.upper && e >= this.lower;\n }, e;\n}();\n\nfunction clone_(e) {\n var t = convertToTensor(e, \"x\", \"clone\", null);\n return ENV.engine.runKernel(function (e) {\n return Tensor.make(t.shape, {\n dataId: t.dataId\n }, t.dtype);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.toFloat();\n }\n };\n });\n}\n\nfunction eye_(e, t, n, r) {\n void 0 === r && (r = \"float32\"), null == t && (t = e);\n\n for (var o = buffer([e, t], r), a = e <= t ? e : t, i = 0; i < a; ++i) o.set(1, i, i);\n\n var s = o.toTensor().as2D(e, t);\n if (null == n) return s;\n if (1 === n.length) return tile(expandDims(s, 0), [n[0], 1, 1]);\n if (2 === n.length) return tile(expandDims(expandDims(s, 0), 0), [n[0], n[1], 1, 1]);\n if (3 === n.length) return tile(expandDims(expandDims(expandDims(s, 0), 0), 0), [n[0], n[1], n[2], 1, 1]);\n throw new Error(\"eye() currently supports only 1D and 2D batchShapes, but received \" + n.length + \"D.\");\n}\n\nfunction randomNormal_(e, t, n, r, o) {\n if (void 0 === t && (t = 0), void 0 === n && (n = 1), null != r && \"bool\" === r) throw new Error(\"Unsupported data type \" + r);\n\n for (var a = new MPRandGauss(t, n, r, !1, o), i = buffer(e, r), s = 0; s < i.values.length; s++) i.values[s] = a.nextValue();\n\n return i.toTensor();\n}\n\nfunction truncatedNormal_(e, t, n, r, o) {\n if (void 0 === t && (t = 0), void 0 === n && (n = 1), null != r && \"bool\" === r) throw new Error(\"Unsupported data type \" + r);\n\n for (var a = new MPRandGauss(t, n, r, !0, o), i = buffer(e, r), s = 0; s < i.values.length; s++) i.values[s] = a.nextValue();\n\n return i.toTensor();\n}\n\nfunction randomUniform_(e, t, n, r) {\n void 0 === t && (t = 0), void 0 === n && (n = 1), void 0 === r && (r = \"float32\");\n\n for (var o = buffer(e, r), a = 0; a < o.values.length; a++) o.values[a] = randUniform(t, n);\n\n return o.toTensor();\n}\n\nfunction rand_(e, t, n) {\n var r = sizeFromShape(e),\n o = null;\n if (null == n || \"float32\" === n) o = new Float32Array(r);else if (\"int32\" === n) o = new Int32Array(r);else {\n if (\"bool\" !== n) throw new Error(\"Unknown data type \" + n);\n o = new Uint8Array(r);\n }\n\n for (var a = 0; a < r; a++) o[a] = t();\n\n return Tensor.make(e, {\n values: o\n }, n);\n}\n\nfunction multinomial_(e, t, n, r) {\n void 0 === r && (r = !1);\n var o = convertToTensor(e, \"logits\", \"multinomial\"),\n a = o.size,\n i = o.rank;\n if (a < 2) throw new Error(\"Error in multinomial: you need at least 2 outcomes, but got \" + a + \".\");\n if (i > 2) throw new Error(\"Rank of probabilities must be 1 or 2, but is \" + i);\n n = n || Math.random();\n var s = 1 === i ? o.as2D(1, -1) : o,\n u = ENV.engine.runKernel(function (e) {\n return e.multinomial(s, r, t, n);\n }, {\n logits2D: s\n });\n return 1 === i ? u.as1D() : u;\n}\n\nfunction oneHot_(e, t, n, r) {\n if (void 0 === n && (n = 1), void 0 === r && (r = 0), t < 2) throw new Error(\"Error in oneHot: depth must be >=2, but it is \" + t);\n var o = convertToTensor(e, \"indices\", \"oneHot\", \"int32\"),\n a = o.shape.concat([t]);\n o = o.flatten();\n return ENV.engine.runKernel(function (e) {\n return e.oneHot(o, t, n, r);\n }, {\n $indices: o\n }, function (e) {\n return {\n $indices: function () {\n return zeros(o.shape, \"float32\");\n }\n };\n }).reshape(a);\n}\n\nfunction fromPixels_$1(e, t) {\n return void 0 === t && (t = 3), deprecationWarn(\"tf.fromPixels() is renamed to tf.browser.fromPixels(), please switch to the new method as the old method will be removed in TensorFlow.js 1.0.\"), fromPixels(e, t);\n}\n\nfunction toPixels$1(e, t) {\n return __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (n) {\n return deprecationWarn(\"tf.toPixels() is renamed to tf.browser.toPixels(), please switch to the new method as the old method will be removed in TensorFlow.js 1.0.\"), [2, toPixels(e, t)];\n });\n });\n}\n\nfunction reshape_(e, t) {\n var n = convertToTensor(e, \"x\", \"reshape\", null);\n t = inferFromImplicitShape(t, n.size), assert(n.size === sizeFromShape(t), \"new shape and old shape must have the same number of elements.\");\n return ENV.engine.runKernel(function (e) {\n return e.reshape(n, t);\n }, {\n $x: n\n }, function (e) {\n return {\n $x: function () {\n return e.reshape(n.shape);\n }\n };\n });\n}\n\nfunction squeeze_(e, t) {\n var n = convertToTensor(e, \"x\", \"squeeze\");\n return reshape(n, squeezeShape(n.shape, t).newShape);\n}\n\nfunction cast_(e, t) {\n var n = convertToTensor(e, \"x\", \"cast\");\n return ENV.engine.runKernel(function (e) {\n return e.cast(n, t);\n }, {\n $x: n\n }, function (e) {\n return {\n $x: function () {\n return e.clone();\n }\n };\n });\n}\n\nfunction tile_(e, t) {\n var n = convertToTensor(e, \"x\", \"tile\");\n assert(n.rank === t.length, \"Error in transpose: rank of input \" + n.rank + \" must match length of reps \" + t + \".\");\n return ENV.engine.runKernel(function (e) {\n return e.tile(n, t);\n }, {\n $x: n\n }, function (e) {\n return {\n $x: function () {\n var r = zerosLike(n);\n if (1 === n.rank) for (var o = 0; o < t[0]; ++o) r = r.add(e.slice([o * n.shape[0]], [n.shape[0]]));else if (2 === n.rank) for (o = 0; o < t[0]; ++o) for (var a = 0; a < t[1]; ++a) r = r.add(e.slice([o * n.shape[0], a * n.shape[1]], [n.shape[0], n.shape[1]]));else if (3 === n.rank) for (o = 0; o < t[0]; ++o) for (a = 0; a < t[1]; ++a) for (var i = 0; i < t[2]; ++i) r = r.add(e.slice([o * n.shape[0], a * n.shape[1], i * n.shape[2]], [n.shape[0], n.shape[1], n.shape[2]]));else {\n if (4 !== n.rank) throw new Error(\"Gradient for tile operation is not implemented for rank-\" + n.rank + \" tensors yet.\");\n\n for (o = 0; o < t[0]; ++o) for (a = 0; a < t[1]; ++a) for (i = 0; i < t[2]; ++i) for (var s = 0; s < t[3]; ++s) r = r.add(e.slice([o * n.shape[0], a * n.shape[1], i * n.shape[2], s * n.shape[3]], [n.shape[0], n.shape[1], n.shape[2], n.shape[3]]));\n }\n return r;\n }\n };\n });\n}\n\nfunction pad1d_(e, t, n) {\n return void 0 === n && (n = 0), assert(2 === t.length, \"Invalid number of paddings. Must be length of 2.\"), pad(e, [t], n);\n}\n\nfunction pad2d_(e, t, n) {\n return void 0 === n && (n = 0), assert(2 === t.length && 2 === t[0].length && 2 === t[1].length, \"Invalid number of paddings. Must be length of 2 each.\"), pad(e, t, n);\n}\n\nfunction pad3d_(e, t, n) {\n return void 0 === n && (n = 0), assert(3 === t.length && 2 === t[0].length && 2 === t[1].length && 2 === t[2].length, \"Invalid number of paddings. Must be length of 2 each.\"), pad(e, t, n);\n}\n\nfunction pad4d_(e, t, n) {\n return void 0 === n && (n = 0), assert(4 === t.length && 2 === t[0].length && 2 === t[1].length && 2 === t[2].length && 2 === t[3].length, \"Invalid number of paddings. Must be length of 2 each.\"), pad(e, t, n);\n}\n\nfunction pad_(e, t, n) {\n void 0 === n && (n = 0);\n var r = convertToTensor(e, \"x\", \"pad\");\n if (0 === r.rank) throw new Error(\"pad(scalar) is not defined. Pass non-scalar to pad\");\n var o = t.map(function (e) {\n return e[0];\n });\n return ENV.engine.runKernel(function (e) {\n return e.pad(r, t, n);\n }, {\n $x: r\n }, function (e) {\n return {\n $x: function () {\n return e.slice(o, r.shape);\n }\n };\n });\n}\n\nfunction stack_(e, t) {\n void 0 === t && (t = 0);\n var n = convertToTensorArray(e, \"tensors\", \"stack\");\n if (assert(n.length >= 1, \"Pass at least one tensor to tf.stack\"), 1 === n.length) return n[0].expandDims(t);\n var r = n[0].rank,\n o = n[0].shape,\n a = n[0].dtype;\n assert(t <= r, \"Axis must be <= rank of the tensor\"), n.forEach(function (e) {\n assertShapesMatch(o, e.shape, \"All tensors passed to stack must have matching shapes\");\n }), n.forEach(function (e) {\n assert(a === e.dtype, \"All tensors passed to stack must have matching dtypes\");\n });\n var i = n.map(function (e) {\n return e.expandDims(t);\n });\n return concat(i, t);\n}\n\nfunction batchToSpaceND_(e, t, n) {\n var r = convertToTensor(e, \"x\", \"batchToSpaceND\"),\n o = t.reduce(function (e, t) {\n return e * t;\n });\n assert(r.rank >= 1 + t.length, \"input rank is \" + r.rank + \" but should be > than blockShape.length \" + t.length), assert(n.length === t.length, \"crops.length is \" + n.length + \" but should be equal to blockShape.length \" + t.length), assert(r.shape[0] % o == 0, \"input tensor batch is \" + r.shape[0] + \" but is not divisible by the product of the elements of blockShape \" + t.join(\" * \") + \" === \" + o);\n return ENV.engine.runKernel(function (e) {\n return e.batchToSpaceND(r, t, n);\n }, {\n $x: r\n }, function (e) {\n return {\n $x: function () {\n return e.spaceToBatchND(t, n);\n }\n };\n });\n}\n\nfunction spaceToBatchND_(e, t, n) {\n var r = convertToTensor(e, \"x\", \"spaceToBatchND\");\n assert(r.rank >= 1 + t.length, \"input rank \" + r.rank + \" should be > than [blockShape] \" + t.length), assert(n.length === t.length, \"paddings.shape[0] \" + n.length + \" must be equal to [blockShape] \" + t.length), assert(r.shape.reduce(function (e, r, o) {\n return o > 0 && o <= t.length ? e && (r + n[o - 1][0] + n[o - 1][1]) % t[o - 1] == 0 : e;\n }, !0), \"input spatial dimensions \" + r.shape.slice(1) + \" with paddings \" + n.toString() + \" must be divisible by blockShapes \" + t.toString());\n return ENV.engine.runKernel(function (e) {\n return e.spaceToBatchND(r, t, n);\n }, {\n $x: r\n }, function (e) {\n return {\n $x: function () {\n return e.batchToSpaceND(t, n);\n }\n };\n });\n}\n\nfunction unstack_(e, t) {\n void 0 === t && (t = 0), t = t || 0;\n var n = convertToTensor(e, \"x\", \"unstack\");\n assert(t < n.shape.length, \"Axis \" + t + \" is >= to tensor shape length \" + n.shape.length);\n return ENV.engine.runKernel(function (e) {\n return e.unstack(n, t);\n }, {\n $x: n\n }, function (e) {\n return {\n $x: function () {\n return stack(e, t);\n }\n };\n });\n}\n\nfunction cumsum_(e, t, n, r) {\n void 0 === t && (t = 0), void 0 === n && (n = !1), void 0 === r && (r = !1);\n var o = convertToTensor(e, \"x\", \"cumsum\"),\n a = getAxesPermutation([t |= 0], o.rank),\n i = o;\n null != a && (i = o.transpose(a));\n var s = getInnerMostAxes(1, o.rank)[0],\n u = ENV.engine.runKernel(function (e) {\n return e.cumsum(i, s, n, r);\n }, {\n permutedX: i\n }, function (e) {\n return {\n permutedX: function () {\n return e.cumsum(t, n, !r);\n }\n };\n });\n return null != a && (u = u.transpose(a)), u;\n}\n\nfunction expandDims_(e, t) {\n void 0 === t && (t = 0);\n var n = convertToTensor(e, \"x\", \"expandDims\");\n assert(t <= n.rank, \"Axis must be <= rank of the tensor\");\n var r = n.shape.slice();\n return t < 0 && (assert(-(n.rank + 1) <= t, \"Axis must be in the interval [\" + -(n.rank + 1) + \", \" + n.rank + \"]\"), t = n.rank + t + 1), r.splice(t, 0, 1), reshape(n, r);\n}\n\nfunction depthToSpace_(e, t, n) {\n void 0 === n && (n = \"NHWC\");\n var r = convertToTensor(e, \"x\", \"depthToSpace\"),\n o = \"NHWC\" === n ? r.shape[1] : r.shape[2],\n a = \"NHWC\" === n ? r.shape[2] : r.shape[3],\n i = \"NHWC\" === n ? r.shape[3] : r.shape[1];\n return assert(o * t >= 0, \"Negative dimension size caused by overflow when multiplying\\n \" + o + \" and \" + t + \" for depthToSpace with input shape\\n \" + r.shape), assert(a * t >= 0, \"Negative dimension size caused by overflow when multiplying\\n \" + a + \" and \" + t + \" for depthToSpace with input shape\\n \" + r.shape), assert(i % (t * t) == 0, \"Dimension size must be evenly divisible by \" + t * t + \" but is \" + i + \" for depthToSpace with input shape \" + r.shape), ENV.engine.runKernel(function (e) {\n return e.depthToSpace(r, t, n);\n }, {\n $x: r\n });\n}\n\nfunction setdiff1dAsync_(e, t) {\n return __awaiter(this, void 0, void 0, function () {\n var n, r, o, a, i, s, u, l, c, p;\n return __generator(this, function (d) {\n switch (d.label) {\n case 0:\n return n = convertToTensor(e, \"x\", \"setdiff1d\"), r = convertToTensor(t, \"y\", \"setdiff1d\"), assert(n.dtype === r.dtype, \"x and y should have the same dtype, but got x (\" + n.dtype + \") and y (\" + r.dtype + \").\"), assert(1 === n.rank, \"x should be 1D tensor, but got x (\" + n.shape + \").\"), assert(1 === r.rank, \"y should be 1D tensor, but got y (\" + r.shape + \").\"), [4, n.data()];\n\n case 1:\n return o = d.sent(), [4, r.data()];\n\n case 2:\n for (a = d.sent(), i = new Set(a), s = 0, c = 0; c < o.length; c++) i.has(o[c]) || s++;\n\n for (u = new TensorBuffer([s], n.dtype), l = new TensorBuffer([s], \"int32\"), c = 0, p = 0; c < o.length; c++) i.has(o[c]) || (u.values[p] = o[c], l.values[p] = c, p++);\n\n return [2, [u.toTensor(), l.toTensor()]];\n }\n });\n });\n}\n\nfunction buffer(e, t, n) {\n return void 0 === t && (t = \"float32\"), new TensorBuffer(e, t = t || \"float32\", n);\n}\n\nfunction print(e, t) {\n void 0 === t && (t = !1), console.log(e.toString(t));\n}\n\nvar batchToSpaceND = op({\n batchToSpaceND_: batchToSpaceND_\n}),\n cast = op({\n cast_: cast_\n}),\n clone = op({\n clone_: clone_\n}),\n cumsum = op({\n cumsum_: cumsum_\n}),\n depthToSpace = op({\n depthToSpace_: depthToSpace_\n}),\n expandDims = op({\n expandDims_: expandDims_\n}),\n eye = op({\n eye_: eye_\n}),\n fromPixels$1 = fromPixels_$1,\n multinomial = op({\n multinomial_: multinomial_\n}),\n oneHot = op({\n oneHot_: oneHot_\n}),\n pad = op({\n pad_: pad_\n}),\n pad1d = op({\n pad1d_: pad1d_\n}),\n pad2d = op({\n pad2d_: pad2d_\n}),\n pad3d = op({\n pad3d_: pad3d_\n}),\n pad4d = op({\n pad4d_: pad4d_\n}),\n rand = op({\n rand_: rand_\n}),\n randomNormal = op({\n randomNormal_: randomNormal_\n}),\n randomUniform = op({\n randomUniform_: randomUniform_\n}),\n reshape = op({\n reshape_: reshape_\n}),\n spaceToBatchND = op({\n spaceToBatchND_: spaceToBatchND_\n}),\n squeeze = op({\n squeeze_: squeeze_\n}),\n stack = op({\n stack_: stack_\n}),\n tile = op({\n tile_: tile_\n}),\n truncatedNormal = op({\n truncatedNormal_: truncatedNormal_\n}),\n unstack = op({\n unstack_: unstack_\n}),\n setdiff1dAsync = setdiff1dAsync_;\nexports.setdiff1dAsync = setdiff1dAsync;\nexports.unstack = unstack;\nexports.truncatedNormal = truncatedNormal;\nexports.tile = tile;\nexports.stack = stack;\nexports.squeeze = squeeze;\nexports.spaceToBatchND = spaceToBatchND;\nexports.reshape = reshape;\nexports.randomUniform = randomUniform;\nexports.randomNormal = randomNormal;\nexports.rand = rand;\nexports.pad4d = pad4d;\nexports.pad3d = pad3d;\nexports.pad2d = pad2d;\nexports.pad1d = pad1d;\nexports.pad = pad;\nexports.oneHot = oneHot;\nexports.multinomial = multinomial;\nexports.fromPixels = fromPixels$1;\nexports.eye = eye;\nexports.expandDims = expandDims;\nexports.depthToSpace = depthToSpace;\nexports.cumsum = cumsum;\nexports.clone = clone;\nexports.cast = cast;\nexports.batchToSpaceND = batchToSpaceND;\n\nfunction whereImpl(e, t) {\n for (var n = [], r = 0; r < t.length; r++) t[r] && n.push(r);\n\n var o = buffer(e, \"int32\"),\n a = buffer([n.length, e.length], \"int32\");\n\n for (r = 0; r < n.length; r++) {\n var i = o.indexToLoc(n[r]),\n s = r * e.length;\n a.values.set(i, s);\n }\n\n return a.toTensor();\n}\n\nfunction mapActivationToShaderProgram(e, t) {\n if (void 0 === t && (t = !1), \"linear\" === e) return t ? LINEAR$1 : LINEAR;\n if (\"relu\" === e) return t ? RELU$1 : RELU;\n throw new Error(\"Activation \" + e + \" has not been implemented for the WebGL backend.\");\n}\n\nvar CPU_HANDOFF_SIZE_THRESHOLD = 10,\n MATMUL_SHARED_DIM_THRESHOLD = 1e3,\n MathBackendWebGL = function () {\n function e(e, t) {\n if (void 0 === t && (t = !0), this.gpgpu = e, this.delayedStorage = t, this.pendingRead = new WeakMap(), this.pendingDisposal = new WeakSet(), this.dataRefCount = new WeakMap(), this.lruDataGPU = [], this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.binaryCache = {}, this.disposed = !1, ENV.get(\"WEBGL_VERSION\") < 1) throw new Error(\"WebGL is not supported on this device\");\n\n if (null == e) {\n var n = getWebGLContext(ENV.get(\"WEBGL_VERSION\"));\n this.gpgpu = new GPGPUContext(n), this.canvas = n.canvas, this.gpgpuCreatedLocally = !0;\n } else this.gpgpuCreatedLocally = !1, this.canvas = e.gl.canvas;\n\n this.textureManager = new TextureManager(this.gpgpu);\n }\n\n return e.prototype.register = function (e, t, n) {\n if (this.texData.has(e)) throw new Error(\"Data buffer is already registered\");\n this.texData.set(e, {\n shape: t,\n dtype: n\n });\n }, e.prototype.setDataMover = function (e) {\n this.texData = new DataStorage(e);\n }, e.prototype.fromPixels = function (e, t) {\n if (null == e) throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");\n var n = [e.height, e.width],\n r = [e.height, e.width, t];\n if (!(e instanceof HTMLVideoElement || e instanceof HTMLImageElement || e instanceof HTMLCanvasElement || e instanceof ImageData)) throw new Error(\"pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was \" + e.constructor.name);\n\n if (e instanceof HTMLVideoElement) {\n if (null == this.fromPixels2DContext) {\n if (!ENV.get(\"IS_BROWSER\")) throw new Error(\"Can't read pixels from HTMLImageElement outside the browser.\");\n if (\"complete\" !== document.readyState) throw new Error(\"The DOM is not ready yet. Please call tf.fromPixels() once the DOM is ready. One way to do that is to add an event listener for `DOMContentLoaded` on the document object\");\n this.fromPixels2DContext = document.createElement(\"canvas\").getContext(\"2d\");\n }\n\n this.fromPixels2DContext.canvas.width = e.width, this.fromPixels2DContext.canvas.height = e.height, this.fromPixels2DContext.drawImage(e, 0, 0, e.width, e.height), e = this.fromPixels2DContext.canvas;\n }\n\n var o = this.makeTensorHandle(n, \"int32\");\n this.texData.get(o.dataId).usage = TextureUsage.PIXELS, this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId), e);\n var a = new FromPixelsProgram(r),\n i = this.compileAndRun(a, [o]);\n return this.disposeData(o.dataId), i;\n }, e.prototype.makeTensorHandle = function (e, t) {\n var n = {};\n return this.register(n, e, t), {\n dataId: n,\n shape: e,\n dtype: t\n };\n }, e.prototype.write = function (e, t) {\n if (null == t) throw new Error(\"MathBackendWebGL.write(): values can not be null\");\n if (ENV.get(\"DEBUG\")) for (var n = 0; n < t.length; n++) {\n var r = t[n];\n if (!canBeRepresented(r)) throw Error(\"The value \" + r + \" cannot be represented on this device.\");\n }\n var o = this.texData.get(e),\n a = o.texture,\n i = o.texShape,\n s = o.usage,\n u = o.dtype,\n l = o.isPacked;\n if (\"complex64\" === u) throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");\n null != a && (this.releaseTexture(e, a, i, s, l), o.texture = null, o.texShape = null), o.usage = TextureUsage.UPLOAD, o.values = t, this.delayedStorage || this.uploadToGPU(e);\n }, e.prototype.readSync = function (e) {\n var t = this.texData.get(e),\n n = t.values,\n r = t.dtype,\n o = t.complexTensors,\n a = t.slice,\n i = t.shape;\n\n if (null != a) {\n var s = new UnaryOpProgram(i, CLONE),\n u = this.compileAndRun(s, [{\n dataId: e,\n shape: i,\n dtype: r\n }]),\n l = this.readSync(u.dataId);\n return u.dispose(), l;\n }\n\n if (null != n) return this.convertAndCacheOnCPU(e);\n if (\"string\" === r) return n;\n var c,\n p,\n d = null != this.activeTimers;\n (d && (c = performance.now()), \"complex64\" === r) ? p = mergeRealAndImagArrays(o.real.dataSync(), o.imag.dataSync()) : p = this.getValuesFromTexture(e);\n return d && (this.downloadWaitMs += performance.now() - c), this.convertAndCacheOnCPU(e, p);\n }, e.prototype.read = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n, r, o, a, i, s, u, l, c, p, d, h, f, m, g, v, y, x, T, w, E, b, S;\n return __generator(this, function (C) {\n switch (C.label) {\n case 0:\n if (this.pendingRead.has(e)) return r = this.pendingRead.get(e), [2, new Promise(function (e) {\n return r.push(e);\n })];\n if (o = this.texData.get(e), a = o.texture, i = o.values, s = o.texShape, u = o.isPacked, l = o.shape, c = o.slice, p = o.dtype, null != c) return d = new UnaryOpProgram(l, CLONE), h = this.compileAndRun(d, [{\n dataId: e,\n shape: l,\n dtype: p\n }]), f = this.read(h.dataId), h.dispose(), [2, f];\n if (null != i) return [2, this.convertAndCacheOnCPU(e)];\n if (this.pendingRead.set(e, []), !ENV.get(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\") && 2 === ENV.get(\"WEBGL_VERSION\")) throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");\n return m = s[1], g = s[0], u && (t = getPackedMatrixTextureShapeWidthHeight(s[0], s[1]), m = t[0], g = t[1]), v = this.gpgpu.maybeCreateBufferFromTexture(a, g, m), [4, this.gpgpu.createAndWaitForFence()];\n\n case 1:\n return C.sent(), v instanceof WebGLTexture ? y = this.getValuesFromTexture(e) : (x = sizeFromShape(l), u ? (T = getBatchDim(l), w = 1, E = 1, l.length && (n = getRowsCols(l), w = n[0], E = n[1]), y = this.gpgpu.downloadPackedMatrixFromBuffer(v, T, w, E, s[0], s[1]).subarray(0, x)) : y = this.gpgpu.downloadFloat32MatrixFromBuffer(v, s[0], s[1]).subarray(0, x)), b = this.convertAndCacheOnCPU(e, y), S = this.pendingRead.get(e), this.pendingRead.delete(e), S.forEach(function (e) {\n return e(b);\n }), this.pendingDisposal.has(e) && (this.pendingDisposal.delete(e), this.disposeData(e)), [2, b];\n }\n });\n });\n }, e.prototype.getValuesFromTexture = function (e) {\n var t,\n n = this.texData.get(e),\n r = n.shape,\n o = n.dtype,\n a = n.texture,\n i = n.texShape,\n s = sizeFromShape(r);\n\n if (ENV.get(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")) {\n if (this.texData.get(e).isPacked) {\n var u = getBatchDim(r),\n l = 1,\n c = 1;\n return r.length && (l = (t = getRowsCols(r))[0], c = t[1]), this.gpgpu.downloadMatrixFromPackedTexture(a, u, l, c, i[0], i[1]).subarray(0, s);\n }\n\n return this.gpgpu.downloadFloat32MatrixFromOutputTexture(a, i[0], i[1]).subarray(0, s);\n }\n\n var p = this.makeTensorHandle(r, \"float32\");\n p.size = sizeFromShape(r), this.texData.get(p.dataId).usage = TextureUsage.DOWNLOAD;\n var d = new EncodeFloatProgram(r);\n this.compileAndRun(d, [{\n shape: r,\n dtype: o,\n dataId: e\n }], p, null, !1);\n var h = this.texData.get(p.dataId),\n f = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(h.texture, h.texShape[0], h.texShape[1]).subarray(0, s);\n return this.disposeData(p.dataId), f;\n }, e.prototype.time = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n, r, o, a, i, s;\n return __generator(this, function (u) {\n switch (u.label) {\n case 0:\n return t = this.activeTimers, n = [], r = !1, null == this.programTimersStack ? (this.programTimersStack = n, r = !0) : this.activeTimers.push(n), this.activeTimers = n, e(), o = flatten(this.activeTimers.map(function (e) {\n return e.query;\n })).filter(function (e) {\n return null != e;\n }), a = flatten(this.activeTimers.map(function (e) {\n return e.name;\n })).filter(function (e) {\n return null != e;\n }), this.activeTimers = t, r && (this.programTimersStack = null), [4, Promise.all(o)];\n\n case 1:\n return i = u.sent(), s = {\n uploadWaitMs: this.uploadWaitMs,\n downloadWaitMs: this.downloadWaitMs,\n kernelMs: sum(i),\n getExtraProfileInfo: function () {\n return i.map(function (e, t) {\n return {\n name: a[t],\n ms: e\n };\n }).map(function (e) {\n return e.name + \": \" + e.ms;\n }).join(\", \");\n },\n wallMs: null\n }, this.uploadWaitMs = 0, this.downloadWaitMs = 0, [2, s];\n }\n });\n });\n }, e.prototype.memory = function () {\n return {\n unreliable: !1,\n numBytesInGPU: this.numBytesInGPU\n };\n }, e.prototype.startTimer = function () {\n return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0 ? this.gpgpu.beginQuery() : {\n startMs: performance.now(),\n endMs: null\n };\n }, e.prototype.endTimer = function (e) {\n return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0 ? (this.gpgpu.endQuery(), e) : (e.endMs = performance.now(), e);\n }, e.prototype.getQueryTime = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t;\n return __generator(this, function (n) {\n return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0 ? [2, this.gpgpu.waitForQueryAndGetTime(e)] : [2, (t = e).endMs - t.startMs];\n });\n });\n }, e.prototype.disposeData = function (e) {\n if (!this.pendingDisposal.has(e)) if (this.pendingRead.has(e)) this.pendingDisposal.add(e);else if (this.texData.has(e)) {\n var t = this.texData.get(e),\n n = t.texture,\n r = t.texShape,\n o = t.usage,\n a = t.complexTensors,\n i = t.isPacked,\n s = t.slice;\n\n if (null != n) {\n var u = s && s.origDataId || e,\n l = this.dataRefCount.get(u);\n l > 1 ? this.dataRefCount.set(u, l - 1) : (this.dataRefCount.delete(u), this.releaseTexture(e, n, r, o, i), this.texData.delete(e));\n }\n\n null != a && (a.real.dispose(), a.imag.dispose());\n }\n }, e.prototype.getTexture = function (e) {\n return this.uploadToGPU(e), this.texData.get(e).texture;\n }, e.prototype.getCPUBackend = function () {\n return ENV.get(\"WEBGL_CPU_FORWARD\") ? (null == this.cpuBackend && (this.cpuBackend = ENV.findBackend(\"cpu\")), this.cpuBackend) : null;\n }, e.prototype.shouldExecuteOnCPU = function (e, t) {\n var n = this;\n return void 0 === t && (t = CPU_HANDOFF_SIZE_THRESHOLD), null != this.getCPUBackend() && e.every(function (e) {\n return null == n.texData.get(e.dataId).texture && e.size < t;\n });\n }, e.prototype.getGPGPUContext = function () {\n return this.gpgpu;\n }, e.prototype.getCanvas = function () {\n return this.canvas;\n }, e.prototype.complex = function (e, t) {\n var n = this.makeOutputArray(e.shape, \"complex64\");\n return this.texData.get(n.dataId).complexTensors = {\n real: ENV.engine.keep(e.clone()),\n imag: ENV.engine.keep(t.clone())\n }, n;\n }, e.prototype.real = function (e) {\n return this.texData.get(e.dataId).complexTensors.real.clone();\n }, e.prototype.imag = function (e) {\n return this.texData.get(e.dataId).complexTensors.imag.clone();\n }, e.prototype.slice = function (e, t, n) {\n if (this.shouldExecuteOnCPU([e])) return this.cpuBackend.slice(e, t, n);\n var r = this.texData.get(e.dataId).isPacked,\n o = isSliceContinous(e.shape, t, n);\n\n if (r || !o) {\n var a = new SliceProgram(n),\n i = a.getCustomSetupFunc(t);\n return this.compileAndRun(a, [e], null, i);\n }\n\n return this.uploadToGPU(e.dataId), this.shallowSlice(e, t, n);\n }, e.prototype.shallowSlice = function (e, t, n) {\n var r = this.texData.get(e.dataId),\n o = Tensor.make(n, {}, e.dtype),\n a = this.texData.get(o.dataId);\n Object.assign(a, r), a.shape = n, a.dtype = e.dtype;\n var i = computeFlatOffset(t, e.strides);\n r.slice && (i += r.slice.flatOffset), a.slice = {\n flatOffset: i,\n origDataId: r.slice && r.slice.origDataId || e.dataId\n };\n var s = this.dataRefCount.get(a.slice.origDataId) || 1;\n return this.dataRefCount.set(a.slice.origDataId, s + 1), o;\n }, e.prototype.stridedSlice = function (e, t, n, r, o, a, i, s, u) {\n if (this.shouldExecuteOnCPU([e])) return this.cpuBackend.stridedSlice(e, t, n, r, o, a, i, s, u);\n var l = getStridedSlicedInfo(e.shape, t, n, r, o, a, i, s, u),\n c = l[0],\n p = l[1],\n d = l[2],\n h = p.filter(function (e, t) {\n return -1 === d.indexOf(t);\n });\n if (h.some(function (e) {\n return 0 === e;\n })) return tensor([], h);\n var f = new StridedSliceProgram(c, r, p, d);\n return this.compileAndRun(f, [e]);\n }, e.prototype.reverse = function (e, t) {\n var n = new ReverseProgram(e.shape, t);\n return this.compileAndRun(n, [e]);\n }, e.prototype.concat = function (e, t) {\n if (this.shouldExecuteOnCPU(e)) return this.cpuBackend.concat(e, t);\n if (1 === e.length) return e[0];\n\n if (e.length > ENV.get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")) {\n var n = Math.floor(e.length / 2),\n r = this.concat(e.slice(0, n), t),\n o = this.concat(e.slice(n), t);\n return this.concat([r, o], t);\n }\n\n var a = computeOutShape(e.map(function (e) {\n return e.shape;\n }), t),\n i = e.map(function (e) {\n return e.as2D(-1, sizeFromShape(e.shape.slice(t)));\n }),\n s = new ConcatProgram(i.map(function (e) {\n return e.shape;\n }));\n return this.compileAndRun(s, i).reshape(a);\n }, e.prototype.neg = function (e) {\n var t = new UnaryOpProgram(e.shape, NEG);\n return this.compileAndRun(t, [e]);\n }, e.prototype.batchMatMul = function (e, t, n, r) {\n var o = n ? e.shape[2] : e.shape[1],\n a = r ? t.shape[1] : t.shape[2],\n i = n ? e.shape[1] : e.shape[2],\n s = e.shape[0];\n\n if ((1 === o || 1 === a) && i > MATMUL_SHARED_DIM_THRESHOLD) {\n n && (e = e.transpose([0, 2, 1])), r && (t = t.transpose([0, 2, 1]));\n var u = 1 === a ? e : e.as3D(s, i, 1),\n l = 1 === a ? 2 : 1,\n c = 1 === a ? t.as3D(s, 1, i) : t;\n return this.multiply(u, c).sum(l, !0);\n }\n\n var p = upcastType(e.dtype, t.dtype);\n\n if (1 === s) {\n var d = e.as2D(e.shape[1], e.shape[2]),\n h = t.as2D(t.shape[1], t.shape[2]),\n f = new MatMulPackedProgram(d.shape, h.shape, [o, a], n, r),\n m = this.makePackedTensor(f.outputShape, p),\n g = this.compileAndRun(f, [d, h], m);\n return g.reshape([1, g.shape[0], g.shape[1]]);\n }\n\n f = new MatMulProgram(e.shape, t.shape, n, r), m = this.makeOutputArray(f.outputShape, p);\n return this.compileAndRun(f, [e, t], m);\n }, e.prototype.fusedBatchMatMul = function (e, t, n, r, o, a) {\n var i = n ? e.shape[2] : e.shape[1],\n s = r ? t.shape[1] : t.shape[2],\n u = e.shape[0],\n l = upcastType(e.dtype, t.dtype);\n\n if (1 === u) {\n var c = e.as2D(e.shape[1], e.shape[2]),\n p = t.as2D(t.shape[1], t.shape[2]),\n d = new MatMulPackedProgram(c.shape, p.shape, [i, s], n, r, !!o, a ? mapActivationToShaderProgram(a, !0) : null),\n h = this.makePackedTensor(d.outputShape, l),\n f = [c, p];\n o && f.push(o);\n var m = this.compileAndRun(d, f, h);\n return m.reshape([1, m.shape[0], m.shape[1]]);\n }\n\n d = new MatMulProgram(e.shape, t.shape, n, r, !!o, a ? mapActivationToShaderProgram(a) : null), f = [e, t];\n o && f.push(o);\n h = this.makeOutputArray(d.outputShape, l);\n return this.compileAndRun(d, f, h);\n }, e.prototype.multiply = function (e, t) {\n if (\"complex64\" === e.dtype) {\n var n = this.texData.get(e.dataId),\n r = this.texData.get(t.dataId),\n o = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, e.shape, t.shape),\n a = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, e.shape, t.shape),\n i = [this.makeComplexComponentTensorHandle(e, n.complexTensors.real), this.makeComplexComponentTensorHandle(e, n.complexTensors.imag), this.makeComplexComponentTensorHandle(t, r.complexTensors.real), this.makeComplexComponentTensorHandle(t, r.complexTensors.imag)],\n s = this.compileAndRun(o, i),\n u = this.compileAndRun(a, i),\n l = this.complex(s, u);\n return s.dispose(), u.dispose(), l;\n }\n\n if (this.shouldExecuteOnCPU([e, t])) return this.cpuBackend.multiply(e, t);\n if (ENV.get(\"WEBGL_PACK_BINARY_OPERATIONS\")) return this.packedBinaryOp(e, t, MUL, e.dtype);\n var c = new BinaryOpProgram(MUL, e.shape, t.shape),\n p = this.makeOutputArray(c.outputShape, e.dtype);\n return this.compileAndRun(c, [e, t], p);\n }, e.prototype.batchNormalization = function (e, t, n, r, o, a) {\n var i = [e, t, n],\n s = null;\n null != a && (s = a.shape, i.push(a));\n var u = null;\n\n if (null != o && (u = o.shape, i.push(o)), ENV.get(\"WEBGL_PACK_BATCHNORMALIZATION\")) {\n var l = new BatchNormPackedProgram(e.shape, t.shape, n.shape, s, u, r);\n return this.compileAndRun(l, i);\n }\n\n var c = new BatchNormProgram(e.shape, t.shape, n.shape, s, u, r);\n return this.compileAndRun(c, i);\n }, e.prototype.localResponseNormalization4D = function (e, t, n, r, o) {\n var a = new LRNProgram(e.shape, t, n, r, o);\n return this.compileAndRun(a, [e]);\n }, e.prototype.LRNGrad = function (e, t, n, r, o, a, i) {\n var s = new LRNGradProgram(t.shape, r, o, a, i);\n return this.compileAndRun(s, [t, n, e]);\n }, e.prototype.tile = function (e, t) {\n var n = new TileProgram(e.shape, t);\n return this.compileAndRun(n, [e]);\n }, e.prototype.pad = function (e, t, n) {\n var r = ENV.get(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new PadPackedProgram(e.shape, t, n) : new PadProgram(e.shape, t, n);\n return this.compileAndRun(r, [e]);\n }, e.prototype.transpose = function (e, t) {\n var n = new TransposeProgram(e.shape, t);\n return this.compileAndRun(n, [e]);\n }, e.prototype.gather = function (e, t, n) {\n var r = new GatherProgram(e.shape, t.size, n);\n return this.compileAndRun(r, [e, t]);\n }, e.prototype.batchToSpaceND = function (e, t, n) {\n assert(e.rank <= 4, \"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");\n var r = t.reduce(function (e, t) {\n return e * t;\n }),\n o = getReshaped(e.shape, t, r),\n a = getPermuted(o.length, t.length),\n i = getReshapedPermuted(e.shape, t, r),\n s = getSliceBeginCoords(n, t.length),\n u = getSliceSize(i, n, t.length);\n return e.reshape(o).transpose(a).reshape(i).slice(s, u);\n }, e.prototype.spaceToBatchND = function (e, t, n) {\n assert(e.rank <= 4, \"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");\n var r = t.reduce(function (e, t) {\n return e * t;\n }),\n o = [[0, 0]];\n o.push.apply(o, n);\n\n for (var a = 1 + t.length; a < e.shape.length; ++a) o.push([0, 0]);\n\n var i = e.pad(o),\n s = getReshaped(i.shape, t, r, !1),\n u = getPermuted(s.length, t.length, !1),\n l = getReshapedPermuted(i.shape, t, r, !1);\n return i.reshape(s).transpose(u).reshape(l);\n }, e.prototype.reduce = function (e, t, n) {\n var r = e.shape[0],\n o = e.shape[1],\n a = computeOptimalWindowSize(o),\n i = new ReduceProgram({\n windowSize: a,\n inSize: o,\n batchSize: r\n }, t),\n s = i.outputShape,\n u = s[0],\n l = s[1],\n c = this.makeOutputArray([u, l], n);\n return this.compileAndRun(i, [e], c), 1 === c.shape[1] ? c : this.reduce(c, t, n);\n }, e.prototype.argReduce = function (e, t, n) {\n void 0 === n && (n = null);\n var r = e.shape[0],\n o = e.shape[1];\n null != n && (r = n.shape[0], o = n.shape[1]);\n var a = computeOptimalWindowSize(o),\n i = new ArgMinMaxProgram({\n windowSize: a,\n inSize: o,\n batchSize: r\n }, t, null == n),\n s = i.outputShape,\n u = s[0],\n l = s[1],\n c = this.makeOutputArray([u, l], \"int32\"),\n p = [e];\n return null != n && p.push(n), this.compileAndRun(i, p, c), 1 === c.shape[1] ? c : this.argReduce(e, t, c);\n }, e.prototype.sum = function (e, t) {\n assertAxesAreInnerMostDims(\"sum\", t, e.rank);\n var n = computeOutAndReduceShapes(e.shape, t),\n r = n[0],\n o = sizeFromShape(n[1]),\n a = e.as2D(-1, o),\n i = sumOutType(e.dtype);\n return this.reduce(a, \"sum\", i).reshape(r);\n }, e.prototype.prod = function (e, t) {\n var n = computeOutAndReduceShapes(e.shape, t),\n r = n[0],\n o = sizeFromShape(n[1]),\n a = e.as2D(-1, o),\n i = sumOutType(e.dtype);\n return this.reduce(a, \"prod\", i).reshape(r);\n }, e.prototype.unsortedSegmentSum = function (e, t, n) {\n var r = 0,\n o = getAxesPermutation([r], e.rank),\n a = e;\n null != o && (a = e.transpose(o), r = getInnerMostAxes(1, e.rank)[0]);\n var i = computeOutShape$1(a.shape, r, n),\n s = sizeFromShape([a.shape[r]]),\n u = a.as2D(-1, s),\n l = sumOutType(e.dtype),\n c = this.segOpCompute(u, \"unsortedSegmentSum\", t, l, n).reshape(i);\n return null != o && (c = c.transpose(getUndoAxesPermutation(o))), c;\n }, e.prototype.segOpCompute = function (e, t, n, r, o) {\n var a = e.shape[0],\n i = e.shape[1],\n s = segOpComputeOptimalWindowSize(i, o),\n u = new SegmentOpProgram({\n windowSize: s,\n inSize: i,\n batchSize: a,\n numSegments: o\n }, t),\n l = u.outputShape,\n c = l[0],\n p = l[1],\n d = this.makeOutputArray([c, p], r);\n return this.compileAndRun(u, [e, n], d), d.shape[1] === o ? d : (n = range(0, o).tile([i / s]), this.segOpCompute(d, t, n, r, o));\n }, e.prototype.argMin = function (e, t) {\n var n = [t];\n assertAxesAreInnerMostDims(\"argMin\", n, e.rank);\n var r = computeOutAndReduceShapes(e.shape, n),\n o = r[0],\n a = sizeFromShape(r[1]),\n i = e.as2D(-1, a);\n return this.argReduce(i, \"min\").reshape(o);\n }, e.prototype.argMax = function (e, t) {\n var n = [t];\n assertAxesAreInnerMostDims(\"argMax\", n, e.rank);\n var r = computeOutAndReduceShapes(e.shape, n),\n o = r[0],\n a = sizeFromShape(r[1]),\n i = e.as2D(-1, a);\n return this.argReduce(i, \"max\").reshape(o);\n }, e.prototype.cumsum = function (e, t, n, r) {\n if (t !== e.rank - 1) throw new Error(\"WebGL cumsum shader expects an inner-most axis=\" + (e.rank - 1) + \" but got axis=\" + t);\n var o = new CumSumProgram(e.shape, n, r);\n return this.compileAndRun(o, [e]);\n }, e.prototype.equal = function (e, t) {\n var n = new BinaryOpProgram(EQUAL, e.shape, t.shape),\n r = this.makeOutputArray(n.outputShape, \"bool\");\n return this.compileAndRun(n, [e, t], r);\n }, e.prototype.notEqual = function (e, t) {\n var n = new BinaryOpProgram(NOT_EQUAL, e.shape, t.shape),\n r = this.makeOutputArray(n.outputShape, \"bool\");\n return this.compileAndRun(n, [e, t], r);\n }, e.prototype.less = function (e, t) {\n if (this.shouldExecuteOnCPU([e, t])) return this.cpuBackend.less(e, t);\n var n = new BinaryOpProgram(LESS, e.shape, t.shape),\n r = this.makeOutputArray(n.outputShape, \"bool\");\n return this.compileAndRun(n, [e, t], r);\n }, e.prototype.lessEqual = function (e, t) {\n var n = new BinaryOpProgram(LESS_EQUAL, e.shape, t.shape),\n r = this.makeOutputArray(n.outputShape, \"bool\");\n return this.compileAndRun(n, [e, t], r);\n }, e.prototype.greater = function (e, t) {\n if (this.shouldExecuteOnCPU([e, t])) return this.cpuBackend.greater(e, t);\n var n = new BinaryOpProgram(GREATER, e.shape, t.shape),\n r = this.makeOutputArray(n.outputShape, \"bool\");\n return this.compileAndRun(n, [e, t], r);\n }, e.prototype.greaterEqual = function (e, t) {\n var n = new BinaryOpProgram(GREATER_EQUAL, e.shape, t.shape),\n r = this.makeOutputArray(n.outputShape, \"bool\");\n return this.compileAndRun(n, [e, t], r);\n }, e.prototype.logicalNot = function (e) {\n var t = new UnaryOpProgram(e.shape, LOGICAL_NOT);\n return this.compileAndRun(t, [e]);\n }, e.prototype.logicalAnd = function (e, t) {\n var n = new BinaryOpProgram(LOGICAL_AND, e.shape, t.shape),\n r = this.makeOutputArray(n.outputShape, \"bool\");\n return this.compileAndRun(n, [e, t], r);\n }, e.prototype.logicalOr = function (e, t) {\n var n = new BinaryOpProgram(LOGICAL_OR, e.shape, t.shape),\n r = this.makeOutputArray(n.outputShape, \"bool\");\n return this.compileAndRun(n, [e, t], r);\n }, e.prototype.select = function (e, t, n) {\n var r = new SelectProgram(e.rank, t.shape, t.rank),\n o = this.makeOutputArray(r.outputShape, upcastType(t.dtype, n.dtype));\n return this.compileAndRun(r, [e, t, n], o);\n }, e.prototype.where = function (e) {\n warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");\n var t = e.dataSync();\n return whereImpl(e.shape, t);\n }, e.prototype.topk = function (e, t, n) {\n return topkImpl(e.dataSync(), e.shape, e.dtype, t, n);\n }, e.prototype.min = function (e, t) {\n assertAxesAreInnerMostDims(\"min\", t, e.rank);\n var n = computeOutAndReduceShapes(e.shape, t),\n r = n[0],\n o = sizeFromShape(n[1]),\n a = e.as2D(-1, o);\n return this.reduce(a, \"min\", a.dtype).reshape(r);\n }, e.prototype.minimum = function (e, t) {\n if (this.shouldExecuteOnCPU([e, t])) return this.cpuBackend.minimum(e, t);\n var n = new BinaryOpProgram(MIN, e.shape, t.shape);\n return this.compileAndRun(n, [e, t]);\n }, e.prototype.mod = function (e, t) {\n var n = new BinaryOpProgram(MOD, e.shape, t.shape),\n r = n.getCustomSetupFunc();\n return this.compileAndRun(n, [e, t], null, r);\n }, e.prototype.max = function (e, t) {\n assertAxesAreInnerMostDims(\"max\", t, e.rank);\n var n = computeOutAndReduceShapes(e.shape, t),\n r = n[0],\n o = sizeFromShape(n[1]),\n a = e.as2D(-1, o);\n return this.reduce(a, \"max\", a.dtype).reshape(r);\n }, e.prototype.maximum = function (e, t) {\n if (this.shouldExecuteOnCPU([e, t])) return this.cpuBackend.maximum(e, t);\n var n = new BinaryOpProgram(MAX, e.shape, t.shape);\n return this.compileAndRun(n, [e, t]);\n }, e.prototype.all = function (e, t) {\n assertAxesAreInnerMostDims(\"all\", t, e.rank);\n var n = computeOutAndReduceShapes(e.shape, t),\n r = n[0],\n o = sizeFromShape(n[1]),\n a = e.as2D(-1, o);\n return this.reduce(a, \"all\", a.dtype).reshape(r);\n }, e.prototype.any = function (e, t) {\n assertAxesAreInnerMostDims(\"any\", t, e.rank);\n var n = computeOutAndReduceShapes(e.shape, t),\n r = n[0],\n o = sizeFromShape(n[1]),\n a = e.as2D(-1, o);\n return this.reduce(a, \"any\", a.dtype).reshape(r);\n }, e.prototype.squaredDifference = function (e, t) {\n var n = new BinaryOpProgram(SQUARED_DIFFERENCE, e.shape, t.shape);\n return this.compileAndRun(n, [e, t]);\n }, e.prototype.realDivide = function (e, t) {\n var n = DIV;\n if (ENV.get(\"WEBGL_PACK_BINARY_OPERATIONS\")) return this.packedBinaryOp(e, t, PACKED_DIV, \"float32\");\n var r = new BinaryOpProgram(n, e.shape, t.shape),\n o = this.makeOutputArray(r.outputShape, \"float32\");\n return this.compileAndRun(r, [e, t], o);\n }, e.prototype.floorDiv = function (e, t) {\n var n = INT_DIV;\n if (ENV.get(\"WEBGL_PACK_BINARY_OPERATIONS\")) return this.packedBinaryOp(e, t, PACKED_INT_DIV, \"int32\");\n var r = new BinaryOpProgram(n, e.shape, t.shape),\n o = this.makeOutputArray(r.outputShape, \"int32\");\n return this.compileAndRun(r, [e, t], o);\n }, e.prototype.add = function (e, t) {\n if (\"complex64\" === e.dtype && \"complex64\" === t.dtype) return this.complexSeparableBinaryOp(e, t, ADD);\n var n = upcastType(e.dtype, t.dtype);\n if (ENV.get(\"WEBGL_PACK_BINARY_OPERATIONS\")) return this.packedBinaryOp(e, t, ADD, n);\n var r = new BinaryOpProgram(ADD, e.shape, t.shape),\n o = this.makeOutputArray(r.outputShape, n);\n return this.compileAndRun(r, [e, t], o);\n }, e.prototype.packedBinaryOp = function (e, t, n, r) {\n var o = new BinaryOpPackedProgram(n, e.shape, t.shape),\n a = this.makePackedTensor(o.outputShape, r);\n return this.compileAndRun(o, [e, t], a);\n }, e.prototype.complexSeparableBinaryOp = function (e, t, n) {\n var r = this,\n o = this.texData.get(e.dataId),\n a = this.texData.get(t.dataId),\n i = [[o.complexTensors.real, a.complexTensors.real], [o.complexTensors.imag, a.complexTensors.imag]].map(function (o) {\n var a = o[0],\n i = o[1],\n s = new BinaryOpProgram(n, e.shape, t.shape),\n u = r.makeOutputArray(s.outputShape, upcastType(a.dtype, i.dtype)),\n l = r.makeComplexComponentTensorHandle(e, a),\n c = r.makeComplexComponentTensorHandle(t, i);\n return r.compileAndRun(s, [l, c], u);\n }),\n s = i[0],\n u = i[1],\n l = this.complex(s, u);\n return s.dispose(), u.dispose(), l;\n }, e.prototype.makeComplexComponentTensorHandle = function (e, t) {\n return {\n dataId: t.dataId,\n dtype: t.dtype,\n shape: e.shape\n };\n }, e.prototype.addN = function (e) {\n for (var t = e[0], n = 1; n < e.length; n++) t = this.add(t, e[n]);\n\n return t;\n }, e.prototype.subtract = function (e, t) {\n if (\"complex64\" === e.dtype && \"complex64\" === t.dtype) return this.complexSeparableBinaryOp(e, t, SUB);\n if (this.shouldExecuteOnCPU([e, t])) return this.cpuBackend.subtract(e, t);\n var n = upcastType(e.dtype, t.dtype);\n if (ENV.get(\"WEBGL_PACK_BINARY_OPERATIONS\")) return this.packedBinaryOp(e, t, SUB, e.dtype);\n var r = new BinaryOpProgram(SUB, e.shape, t.shape),\n o = this.makeOutputArray(r.outputShape, n);\n return this.compileAndRun(r, [e, t], o);\n }, e.prototype.pow = function (e, t) {\n var n = ENV.get(\"WEBGL_PACK_BINARY_OPERATIONS\"),\n r = n ? new BinaryOpPackedProgram(PACKED_POW, e.shape, t.shape) : new BinaryOpProgram(POW, e.shape, t.shape),\n o = upcastType(e.dtype, t.dtype),\n a = n ? this.makePackedTensor(r.outputShape, o) : this.makeOutputArray(r.outputShape, o),\n i = r.getCustomSetupFunc();\n return this.compileAndRun(r, [e, t], a, i);\n }, e.prototype.ceil = function (e) {\n var t = new UnaryOpProgram(e.shape, CEIL);\n return this.compileAndRun(t, [e]);\n }, e.prototype.floor = function (e) {\n var t = new UnaryOpProgram(e.shape, FLOOR);\n return this.compileAndRun(t, [e]);\n }, e.prototype.sign = function (e) {\n var t = new UnaryOpProgram(e.shape, SIGN);\n return this.compileAndRun(t, [e]);\n }, e.prototype.round = function (e) {\n var t = new UnaryOpProgram(e.shape, ROUND);\n return this.compileAndRun(t, [e]);\n }, e.prototype.exp = function (e) {\n var t;\n return t = ENV.get(\"WEBGL_PACK\") ? new UnaryOpPackedProgram(e.shape, EXP) : new UnaryOpProgram(e.shape, EXP), this.compileAndRun(t, [e]);\n }, e.prototype.expm1 = function (e) {\n var t = new UnaryOpProgram(e.shape, EXPM1);\n return this.compileAndRun(t, [e]);\n }, e.prototype.log = function (e) {\n var t,\n n = (t = ENV.get(\"WEBGL_PACK\") ? new UnaryOpPackedProgram(e.shape, LOG$1) : new UnaryOpProgram(e.shape, LOG)).getCustomSetupFunc();\n return this.compileAndRun(t, [e], null, n);\n }, e.prototype.log1p = function (e) {\n var t = new UnaryOpProgram(e.shape, LOG1P);\n return this.compileAndRun(t, [e]);\n }, e.prototype.sqrt = function (e) {\n var t = new UnaryOpProgram(e.shape, SQRT);\n return this.compileAndRun(t, [e]);\n }, e.prototype.rsqrt = function (e) {\n var t = new UnaryOpProgram(e.shape, RSQRT);\n return this.compileAndRun(t, [e]);\n }, e.prototype.square = function (e) {\n var t = new UnaryOpProgram(e.shape, SQUARE);\n return this.compileAndRun(t, [e]);\n }, e.prototype.reciprocal = function (e) {\n var t = new UnaryOpProgram(e.shape, RECIPROCAL);\n return this.compileAndRun(t, [e]);\n }, e.prototype.relu = function (e) {\n var t;\n return t = ENV.get(\"WEBGL_PACK\") ? new UnaryOpPackedProgram(e.shape, RELU$1) : new UnaryOpProgram(e.shape, RELU), this.compileAndRun(t, [e]);\n }, e.prototype.prelu = function (e, t) {\n var n = new BinaryOpProgram(PRELU, e.shape, t.shape);\n return this.compileAndRun(n, [e, t]);\n }, e.prototype.elu = function (e) {\n var t = new UnaryOpProgram(e.shape, ELU);\n return this.compileAndRun(t, [e]);\n }, e.prototype.eluDer = function (e, t) {\n var n = new BinaryOpProgram(ELU_DER, e.shape, t.shape);\n return this.compileAndRun(n, [e, t]);\n }, e.prototype.selu = function (e) {\n var t = new UnaryOpProgram(e.shape, SELU);\n return this.compileAndRun(t, [e]);\n }, e.prototype.int = function (e) {\n var t = new UnaryOpProgram(e.shape, TO_INT),\n n = this.makeOutputArray(t.outputShape, \"int32\");\n return this.compileAndRun(t, [e], n);\n }, e.prototype.clip = function (e, t, n) {\n var r,\n o = (r = ENV.get(\"WEBGL_PACK_CLIP\") ? new ClipPackedProgram(e.shape) : new ClipProgram(e.shape)).getCustomSetupFunc(t, n);\n return this.compileAndRun(r, [e], null, o);\n }, e.prototype.abs = function (e) {\n var t = new UnaryOpProgram(e.shape, ABS);\n return this.compileAndRun(t, [e]);\n }, e.prototype.complexAbs = function (e) {\n var t = this.texData.get(e.dataId),\n n = new ComplexAbsProgram(e.shape),\n r = [this.makeComplexComponentTensorHandle(e, t.complexTensors.real), this.makeComplexComponentTensorHandle(e, t.complexTensors.imag)];\n return this.compileAndRun(n, r);\n }, e.prototype.sigmoid = function (e) {\n var t = new UnaryOpProgram(e.shape, SIGMOID);\n return this.compileAndRun(t, [e]);\n }, e.prototype.softplus = function (e) {\n var t = new UnaryOpProgram(e.shape, SOFTPLUS);\n return this.compileAndRun(t, [e]);\n }, e.prototype.sin = function (e) {\n var t = new UnaryOpProgram(e.shape, SIN);\n return this.compileAndRun(t, [e]);\n }, e.prototype.cos = function (e) {\n var t = new UnaryOpProgram(e.shape, COS);\n return this.compileAndRun(t, [e]);\n }, e.prototype.tan = function (e) {\n var t = new UnaryOpProgram(e.shape, TAN);\n return this.compileAndRun(t, [e]);\n }, e.prototype.asin = function (e) {\n var t = new UnaryOpProgram(e.shape, ASIN);\n return this.compileAndRun(t, [e]);\n }, e.prototype.acos = function (e) {\n var t = new UnaryOpProgram(e.shape, ACOS);\n return this.compileAndRun(t, [e]);\n }, e.prototype.atan = function (e) {\n var t = new UnaryOpProgram(e.shape, ATAN);\n return this.compileAndRun(t, [e]);\n }, e.prototype.atan2 = function (e, t) {\n var n = new BinaryOpProgram(ATAN2, e.shape, t.shape);\n return this.compileAndRun(n, [e, t]);\n }, e.prototype.sinh = function (e) {\n var t = new UnaryOpProgram(e.shape, SINH);\n return this.compileAndRun(t, [e]);\n }, e.prototype.cosh = function (e) {\n var t = new UnaryOpProgram(e.shape, COSH);\n return this.compileAndRun(t, [e]);\n }, e.prototype.tanh = function (e) {\n var t = new UnaryOpProgram(e.shape, TANH);\n return this.compileAndRun(t, [e]);\n }, e.prototype.asinh = function (e) {\n var t = new UnaryOpProgram(e.shape, ASINH);\n return this.compileAndRun(t, [e]);\n }, e.prototype.acosh = function (e) {\n var t = new UnaryOpProgram(e.shape, ACOSH),\n n = t.getCustomSetupFunc();\n return this.compileAndRun(t, [e], null, n);\n }, e.prototype.atanh = function (e) {\n var t = new UnaryOpProgram(e.shape, ATANH),\n n = t.getCustomSetupFunc();\n return this.compileAndRun(t, [e], null, n);\n }, e.prototype.erf = function (e) {\n var t = new UnaryOpProgram(e.shape, ERF);\n return this.compileAndRun(t, [e]);\n }, e.prototype.step = function (e, t) {\n var n = new UnaryOpProgram(e.shape, STEP(t));\n return this.compileAndRun(n, [e]);\n }, e.prototype.conv2dWithIm2Row = function (e, t, n) {\n var r = n.filterWidth,\n o = n.filterHeight,\n a = n.inChannels,\n i = n.outWidth,\n s = n.outHeight,\n u = r * o * a,\n l = s * i,\n c = [u, l],\n p = e.squeeze([0]),\n d = t.reshape([u, -1]),\n h = new Im2ColProgram(c, p.shape, n),\n f = this.compileAndRun(h, [p]),\n m = new MatMulPackedProgram(f.shape, d.shape, [l, n.outChannels], !0, !1);\n return this.compileAndRun(m, [f, d]).reshape([1, s, i, n.outChannels]);\n }, e.prototype.conv2d = function (e, t, n) {\n if (ENV.get(\"WEBGL_CONV_IM2COL\") && 1 === e.shape[0]) return this.conv2dWithIm2Row(e, t, n);\n var r = new Conv2DProgram(n);\n return this.compileAndRun(r, [e, t]);\n }, e.prototype.conv2dDerInput = function (e, t, n) {\n var r = new Conv2DDerInputProgram(n);\n return this.compileAndRun(r, [e, t]);\n }, e.prototype.conv2dDerFilter = function (e, t, n) {\n var r = new Conv2DDerFilterProgram(n);\n return this.compileAndRun(r, [e, t]);\n }, e.prototype.depthwiseConv2D = function (e, t, n) {\n var r;\n return ENV.get(\"WEBGL_PACK_DEPTHWISECONV\") && n.strideWidth <= 2 && n.outChannels / n.inChannels == 1 ? (r = new DepthwiseConvPacked2DProgram(n), this.compileAndRun(r, [e, t], this.makePackedTensor(n.outShape, e.dtype))) : (r = new DepthwiseConv2DProgram(n), this.compileAndRun(r, [e, t]));\n }, e.prototype.depthwiseConv2DDerInput = function (e, t, n) {\n var r = new DepthwiseConv2DDerInputProgram(n);\n return this.compileAndRun(r, [e, t]);\n }, e.prototype.depthwiseConv2DDerFilter = function (e, t, n) {\n var r = new DepthwiseConv2DDerFilterProgram(n);\n return this.compileAndRun(r, [e, t]);\n }, e.prototype.conv3d = function (e, t, n) {\n var r = new Conv3DProgram(n);\n return this.compileAndRun(r, [e, t]);\n }, e.prototype.conv3dDerInput = function (e, t, n) {\n var r = new Conv3DDerInputProgram(n);\n return this.compileAndRun(r, [e, t]);\n }, e.prototype.conv3dDerFilter = function (e, t, n) {\n var r = new Conv3DDerFilterProgram(n);\n return this.compileAndRun(r, [e, t]);\n }, e.prototype.maxPool = function (e, t) {\n var n = new Pool2DProgram(t, \"max\", !1),\n r = this.makeOutputArray(n.outputShape, e.dtype);\n return this.compileAndRun(n, [e], r);\n }, e.prototype.avgPool = function (e, t) {\n var n = new Pool2DProgram(t, \"avg\", !1),\n r = this.makeOutputArray(n.outputShape, \"float32\");\n return this.compileAndRun(n, [e], r);\n }, e.prototype.maxPoolBackprop = function (e, t, n, r) {\n var o = new Pool2DProgram(r, \"max\", !0),\n a = this.compileAndRun(o, [t]),\n i = new MaxPool2DBackpropProgram(r),\n s = this.makeOutputArray(i.outputShape, t.dtype),\n u = this.compileAndRun(i, [e, a], s);\n return a.dispose(), u;\n }, e.prototype.avgPoolBackprop = function (e, t, n) {\n var r = new AvgPool2DBackpropProgram(n),\n o = this.makeOutputArray(r.outputShape, t.dtype);\n return this.compileAndRun(r, [e], o);\n }, e.prototype.cast = function (e, t) {\n return castTensor(e, t, this);\n }, e.prototype.unstack = function (e, t) {\n for (var n = e.shape[t], r = new Array(e.rank - 1), o = 0, a = 0; a < e.rank; a++) a !== t && (r[o++] = e.shape[a]);\n\n var i = new Array(e.rank).fill(0),\n s = e.shape.slice();\n s[t] = 1;\n var u = new Array(n);\n\n for (a = 0; a < u.length; a++) i[t] = a, u[a] = this.slice(e, i, s).reshape(r);\n\n return u;\n }, e.prototype.reshape = function (e, t) {\n return this.texData.get(e.dataId).isPacked && !isReshapeFree(e.shape, t) ? this.packedReshape(e, t) : reshapeTensor(e, t);\n }, e.prototype.resizeBilinear = function (e, t, n, r) {\n var o = new ResizeBilinearProgram(e.shape, t, n, r);\n return this.compileAndRun(o, [e]);\n }, e.prototype.resizeBilinearBackprop = function (e, t, n) {\n var r = new ResizeBilinearBackpropProgram(e, t, n);\n return this.compileAndRun(r, [e]);\n }, e.prototype.resizeNearestNeighbor = function (e, t, n, r) {\n var o = new ResizeNearestNeighborProgram(e.shape, t, n, r);\n return this.compileAndRun(o, [e]);\n }, e.prototype.resizeNearestNeighborBackprop = function (e, t, n) {\n var r = new ResizeNearestNeigborBackpropProgram(e, t, n);\n return this.compileAndRun(r, [e]);\n }, e.prototype.multinomial = function (e, t, n, r) {\n var o = t ? e : softmax(e),\n a = o.shape[0],\n i = o.shape[1],\n s = new MultinomialProgram(a, i, n),\n u = this.makeOutputArray(s.outputShape, \"int32\"),\n l = s.getCustomSetupFunc(r);\n return this.compileAndRun(s, [o], u, l);\n }, e.prototype.oneHot = function (e, t, n, r) {\n var o = new OneHotProgram(e.size, t, n, r);\n return this.compileAndRun(o, [e]);\n }, e.prototype.nonMaxSuppression = function (e, t, n, r, o) {\n return warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\"), nonMaxSuppressionImpl(e.dataSync(), t.dataSync(), n, r, o);\n }, e.prototype.cropAndResize = function (e, t, n, r, o, a) {\n var i = new CropAndResizeProgram(e.shape, t.shape, r, o, a);\n return this.compileAndRun(i, [e, t, n]);\n }, e.prototype.depthToSpace = function (e, t, n) {\n assert(t > 1, \"blockSize should be > 1 for depthToSpace, but was: \" + t);\n var r = e.shape[0],\n o = (\"NHWC\" === n ? e.shape[1] : e.shape[2]) * t,\n a = (\"NHWC\" === n ? e.shape[2] : e.shape[3]) * t,\n i = (\"NHWC\" === n ? e.shape[3] : e.shape[1]) / (t * t),\n s = new DepthToSpaceProgram(\"NHWC\" === n ? [r, o, a, i] : [r, i, o, a], t, n);\n return this.compileAndRun(s, [e]);\n }, e.prototype.split = function (e, t, n) {\n return split(e, t, n);\n }, e.prototype.scatterND = function (e, t, n) {\n var r = calculateShapes(t, e, n),\n o = r.sliceRank,\n a = r.numUpdates,\n i = r.sliceSize,\n s = r.strides,\n u = r.outputSize,\n l = [u / i, i],\n c = e.reshape([a, o]),\n p = t.reshape([a, i]);\n if (0 === u) return reshapeTensor(tensor([]), n);\n var d = scalar(0),\n h = new ScatterProgram(a, o, c.rank, p.rank, s, l);\n return this.compileAndRun(h, [p, c, d]).reshape(n);\n }, e.prototype.sparseToDense = function (e, t, n, r) {\n var o = calculateShapes(t, e, n),\n a = o.sliceRank,\n i = o.numUpdates,\n s = o.strides,\n u = o.outputSize,\n l = new ScatterProgram(i, a, e.rank, t.rank, s, [u, 1], !1);\n return this.compileAndRun(l, [t, e, r]).reshape(n);\n }, e.prototype.fft = function (e) {\n return this.fftImpl(e, !1);\n }, e.prototype.ifft = function (e) {\n return this.fftImpl(e, !0);\n }, e.prototype.fftImpl = function (e, t) {\n var n = this.texData.get(e.dataId),\n r = new FFTProgram(COMPLEX_FFT.REAL, e.shape, t),\n o = new FFTProgram(COMPLEX_FFT.IMAG, e.shape, t),\n a = [this.makeComplexComponentTensorHandle(e, n.complexTensors.real), this.makeComplexComponentTensorHandle(e, n.complexTensors.imag)],\n i = this.compileAndRun(r, a),\n s = this.compileAndRun(o, a),\n u = this.complex(i, s).as2D(e.shape[0], e.shape[1]);\n return i.dispose(), s.dispose(), u;\n }, e.prototype.gatherND = function (e, t) {\n var n = t.shape,\n r = n[n.length - 1],\n o = prepareAndValidate(e, t),\n a = o[0],\n i = o[1],\n s = o[2],\n u = o[3],\n l = t.reshape([i, r]),\n c = e.reshape([e.size / s, s]),\n p = new GatherNDProgram(r, u, [i, s]);\n return this.compileAndRun(p, [c, l]).reshape(a);\n }, e.prototype.makeOutputArray = function (e, t) {\n return Tensor.make(e, {}, t);\n }, e.prototype.makePackedTensor = function (e, t) {\n var n = Tensor.make(e, {}, t);\n return this.texData.get(n.dataId).isPacked = !0, n;\n }, e.prototype.unpackTensor = function (e) {\n var t = new UnpackProgram(e.shape);\n return this.compileAndRun(t, [e], Tensor.make(t.outputShape, {}, e.dtype));\n }, e.prototype.packedReshape = function (e, t) {\n var n = e.reshape([getBatchDim(e.shape)].concat(getRowsCols(e.shape))),\n r = [getBatchDim(t)].concat(getRowsCols(t)),\n o = new ReshapePackedProgram(r, n.shape);\n return this.compileAndRun(o, [n]).reshape(t);\n }, e.prototype.compileAndRun = function (e, t, n, r, o) {\n var a = this;\n if (void 0 === o && (o = !0), null == n && (n = e.usesPackedTextures ? this.makePackedTensor(e.outputShape, t[0].dtype) : this.makeOutputArray(e.outputShape, t[0].dtype)), 0 === n.size) return this.texData.get(n.dataId).values = getTypedArrayFromDType(n.dtype, 0), n;\n var i = t.map(function (t) {\n if (\"complex64\" === t.dtype) throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");\n var n = a.texData.get(t.dataId);\n\n if (null == n.texture) {\n if (!e.usesPackedTextures && sizeFromShape(t.shape) <= ENV.get(\"WEBGL_SIZE_UPLOAD_UNIFORM\")) return {\n shape: t.shape,\n texData: null,\n isUniform: !0,\n uniformValues: a.readSync(t.dataId)\n };\n e.usesPackedTextures && (n.isPacked = !0, n.shape = t.shape);\n } else if (!!n.isPacked != !!e.usesPackedTextures) {\n var r = void 0,\n o = void 0;\n n.isPacked ? (r = new UnpackProgram(t.shape), o = a.compileAndRun(r, [t], Tensor.make(r.outputShape, {}, t.dtype))) : (r = new PackProgram(t.shape), o = a.compileAndRun(r, [t], a.makePackedTensor(t.shape, t.dtype))), n = a.texData.get(o.dataId), t = o;\n } else if (n.isPacked && !isReshapeFree(n.shape, t.shape)) {\n a.delayedStorage = !1;\n var i = t.dataSync();\n a.delayedStorage = !0, t = Tensor.make(t.shape, {\n values: i\n }, t.dtype), (n = a.texData.get(t.dataId)).isPacked = !0;\n }\n\n return a.uploadToGPU(t.dataId), {\n shape: t.shape,\n texData: n,\n isUniform: !1\n };\n });\n this.uploadToGPU(n.dataId);\n var s,\n u = {\n shape: n.shape,\n texData: this.texData.get(n.dataId),\n isUniform: !1\n },\n l = makeShaderKey(e, i, u),\n c = this.getAndSaveBinary(l, function () {\n return compileProgram(a.gpgpu, e, i, u);\n }),\n p = null != this.activeTimers;\n p && (s = this.startTimer()), runProgram(c, i, u, r);\n var d = 1024 * ENV.get(\"WEBGL_NUM_MB_BEFORE_PAGING\");\n if (o && this.numBytesInGPU > d) for (var h = this.numBytesInGPU - d; h > 0 && this.lruDataGPU.length > 0;) {\n var f = this.lruDataGPU.shift(),\n m = this.texData.get(f),\n g = m.shape,\n v = m.dtype;\n h -= this.computeBytes(g, v), this.read(f);\n }\n return p && (s = this.endTimer(s), this.activeTimers.push({\n name: e.constructor.name,\n query: this.getQueryTime(s)\n })), ENV.get(\"WEBGL_LAZILY_UNPACK\") || !this.texData.get(n.dataId).isPacked || e.isPackShader ? n : this.unpackTensor(n);\n }, e.prototype.getAndSaveBinary = function (e, t) {\n return e in this.binaryCache || (this.binaryCache[e] = t()), this.binaryCache[e];\n }, e.prototype.getTextureManager = function () {\n return this.textureManager;\n }, e.prototype.dispose = function () {\n if (!this.disposed) {\n for (var e in this.binaryCache) this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram);\n\n this.textureManager.dispose(), this.canvas.remove(), null != this.fromPixels2DContext && this.fromPixels2DContext.canvas.remove(), this.gpgpuCreatedLocally && this.gpgpu.dispose(), this.disposed = !0;\n }\n }, e.prototype.floatPrecision = function () {\n var e = this;\n return tidy(function () {\n var t = ENV.get(\"DEBUG\");\n ENV.set(\"DEBUG\", !1);\n var n = e.abs(scalar(1e-8)).dataSync()[0];\n return ENV.set(\"DEBUG\", t), n > 0 ? 32 : 16;\n });\n }, e.prototype.uploadToGPU = function (e) {\n var t,\n n = this.texData.get(e),\n r = n.shape,\n o = n.values,\n a = n.texture,\n i = n.usage,\n s = n.isPacked;\n\n if (null == a) {\n var u,\n l = null != this.activeTimers;\n l && (u = performance.now());\n var c = getTextureShapeFromLogicalShape(r, s);\n n.texShape = c;\n var p = this.acquireTexture(e, c, i, s);\n\n if (n.texture = p, null != o) {\n if (s) {\n var d = getBatchDim(r),\n h = 1,\n f = 1;\n r.length && (h = (t = getRowsCols(r))[0], f = t[1]), this.gpgpu.uploadMatrixToPackedTexture(p, d, h, f, c[0], c[1], typedArrayToFloat32(o));\n } else this.gpgpu.uploadMatrixToTexture(p, c[0], c[1], typedArrayToFloat32(o));\n\n n.values = null, l && (this.uploadWaitMs += performance.now() - u);\n }\n } else {\n ENV.get(\"WEBGL_NUM_MB_BEFORE_PAGING\") < Number.POSITIVE_INFINITY && this.lruDataGPU.indexOf(e) >= 0 && (this.lruDataGPU.splice(this.lruDataGPU.indexOf(e), 1), this.lruDataGPU.push(e));\n }\n }, e.prototype.convertAndCacheOnCPU = function (e, t) {\n var n = this.delayedStorage,\n r = this.texData.get(e),\n o = r.texture,\n a = r.texShape,\n i = r.dtype,\n s = r.usage,\n u = r.isPacked;\n return n && null != o && (this.releaseTexture(e, o, a, s, u), r.texture = null, r.texShape = null, r.isPacked = !1), r.usage = TextureUsage.UPLOAD, null != t && (r.values = float32ToTypedArray(t, i)), r.values;\n }, e.prototype.releaseTexture = function (e, t, n, r, o) {\n var a = this.texData.get(e),\n i = a.shape,\n s = a.dtype;\n\n if (ENV.get(\"WEBGL_NUM_MB_BEFORE_PAGING\") < Number.POSITIVE_INFINITY) {\n var u = this.lruDataGPU.indexOf(e);\n u >= 0 && this.lruDataGPU.splice(u, 1);\n }\n\n this.numBytesInGPU -= this.computeBytes(i, s), this.textureManager.releaseTexture(t, n, r, o);\n }, e.prototype.acquireTexture = function (e, t, n, r) {\n var o = this.texData.get(e),\n a = o.shape,\n i = o.dtype;\n return ENV.get(\"WEBGL_NUM_MB_BEFORE_PAGING\") < Number.POSITIVE_INFINITY && this.lruDataGPU.push(e), this.numBytesInGPU += this.computeBytes(a, i), this.textureManager.acquireTexture(t, n, r);\n }, e.prototype.computeBytes = function (e, t) {\n return sizeFromShape(e) * bytesPerElement(t);\n }, e;\n}();\n\nfunction float32ToTypedArray(e, t) {\n if (\"float32\" === t || \"complex64\" === t) return e;\n\n if (\"int32\" === t || \"bool\" === t) {\n for (var n = \"int32\" === t ? new Int32Array(e.length) : new Uint8Array(e.length), r = 0; r < n.length; ++r) n[r] = Math.round(e[r]);\n\n return n;\n }\n\n throw new Error(\"Unknown dtype \" + t);\n}\n\nfunction typedArrayToFloat32(e) {\n return e instanceof Float32Array ? e : new Float32Array(e);\n}\n\nfunction neg_(e) {\n var t = convertToTensor(e, \"x\", \"neg\");\n return ENV.engine.runKernel(function (e) {\n return e.neg(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.neg();\n }\n };\n });\n}\n\nfunction ceil_(e) {\n var t = convertToTensor(e, \"x\", \"ceil\");\n return ENV.engine.runKernel(function (e) {\n return e.ceil(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return zerosLike(e);\n }\n };\n });\n}\n\nfunction floor_(e) {\n var t = convertToTensor(e, \"x\", \"floor\");\n return ENV.engine.runKernel(function (e) {\n return e.floor(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return zerosLike(e);\n }\n };\n });\n}\n\nfunction sign_(e) {\n var t = convertToTensor(e, \"x\", \"sign\");\n return ENV.engine.runKernel(function (e) {\n return e.sign(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return zerosLike(e);\n }\n };\n });\n}\n\nfunction round_(e) {\n var t = convertToTensor(e, \"x\", \"round\");\n return ENV.engine.runKernel(function (e) {\n return e.round(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return zerosLike(e);\n }\n };\n });\n}\n\nfunction exp_(e) {\n var t = convertToTensor(e, \"x\", \"exp\");\n return ENV.engine.runKernel(function (e, n) {\n return n(e.exp(t));\n }, {\n $x: t\n }, function (e, t) {\n var n = t[0];\n return {\n $x: function () {\n return e.mulStrict(n);\n }\n };\n });\n}\n\nfunction expm1_(e) {\n var t = convertToTensor(e, \"x\", \"expm1\");\n return ENV.engine.runKernel(function (e) {\n return e.expm1(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.mulStrict(t.exp());\n }\n };\n });\n}\n\nfunction log_(e) {\n var t = convertToTensor(e, \"x\", \"log\");\n return ENV.engine.runKernel(function (e) {\n return e.log(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.divStrict(t.toFloat());\n }\n };\n });\n}\n\nfunction log1p_(e) {\n var t = convertToTensor(e, \"x\", \"log1p\");\n return ENV.engine.runKernel(function (e) {\n return e.log1p(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.div(t.add(1));\n }\n };\n });\n}\n\nfunction sqrt_(e) {\n var t = convertToTensor(e, \"x\", \"sqrt\");\n return ENV.engine.runKernel(function (e) {\n return e.sqrt(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.div(t.toFloat().sqrt().mul(2));\n }\n };\n });\n}\n\nfunction rsqrt_(e) {\n var t = convertToTensor(e, \"x\", \"rsqrt\");\n return ENV.engine.runKernel(function (e) {\n return e.rsqrt(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.div(t.pow(1.5).mul(2)).neg();\n }\n };\n });\n}\n\nfunction square_(e) {\n var t = convertToTensor(e, \"x\", \"square\");\n return ENV.engine.runKernel(function (e) {\n return e.square(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.mul(t.toFloat().mul(2));\n }\n };\n });\n}\n\nfunction reciprocal_(e) {\n var t = convertToTensor(e, \"x\", \"reciprocal\");\n return ENV.engine.runKernel(function (e) {\n return e.reciprocal(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.divStrict(t.square().neg());\n }\n };\n });\n}\n\nfunction abs_(e) {\n var t = convertToTensor(e, \"x\", \"abs\");\n if (\"complex64\" === t.dtype) return ENV.engine.runKernel(function (e) {\n return e.complexAbs(t);\n }, {\n $x: t\n });\n return ENV.engine.runKernel(function (e) {\n return e.abs(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.mulStrict(t.toFloat().step(-1));\n }\n };\n });\n}\n\nfunction clipByValue_(e, t, n) {\n var r = convertToTensor(e, \"x\", \"clipByValue\");\n assert(t <= n, \"Error in clip: min (\" + t + \") must be less than or equal to max (\" + n + \").\");\n return ENV.engine.runKernel(function (e) {\n return e.clip(r, t, n);\n }, {\n $x: r\n }, function (e) {\n return {\n $x: function () {\n return e.where(r.greaterEqual(t).logicalAnd(r.lessEqual(n)), zerosLike(e));\n }\n };\n });\n}\n\nfunction sigmoid_(e) {\n var t = convertToTensor(e, \"x\", \"sigmoid\");\n return ENV.engine.runKernel(function (e, n) {\n return n(e.sigmoid(t));\n }, {\n $x: t\n }, function (e, t) {\n var n = t[0];\n return {\n $x: function () {\n return e.mul(n.mul(scalar(1).sub(n)));\n }\n };\n });\n}\n\nfunction logSigmoid_(e) {\n var t = convertToTensor(e, \"x\", \"logSigmoid\");\n return ENV.engine.runKernel(function (e) {\n return e.softplus(t.neg()).neg();\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.mulStrict(t.neg().sigmoid());\n }\n };\n });\n}\n\nfunction softplus_(e) {\n var t = convertToTensor(e, \"x\", \"softplus\");\n return ENV.engine.runKernel(function (e) {\n return e.softplus(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.mulStrict(t.sigmoid());\n }\n };\n });\n}\n\nfunction sin_(e) {\n var t = convertToTensor(e, \"x\", \"sin\");\n return ENV.engine.runKernel(function (e) {\n return e.sin(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return t.toFloat().cos().mulStrict(e);\n }\n };\n });\n}\n\nfunction cos_(e) {\n var t = convertToTensor(e, \"x\", \"cos\");\n return ENV.engine.runKernel(function (e) {\n return e.cos(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return t.toFloat().sin().neg().mulStrict(e);\n }\n };\n });\n}\n\nfunction tan_(e) {\n var t = convertToTensor(e, \"x\", \"tan\");\n return ENV.engine.runKernel(function (e) {\n return e.tan(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.divStrict(t.cos().square());\n }\n };\n });\n}\n\nfunction asin_(e) {\n var t = convertToTensor(e, \"x\", \"asin\");\n return ENV.engine.runKernel(function (e) {\n return e.asin(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt());\n }\n };\n });\n}\n\nfunction acos_(e) {\n var t = convertToTensor(e, \"x\", \"acos\");\n return ENV.engine.runKernel(function (e) {\n return e.acos(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt()).neg();\n }\n };\n });\n}\n\nfunction atan_(e) {\n var t = convertToTensor(e, \"x\", \"atan\");\n return ENV.engine.runKernel(function (e) {\n return e.atan(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.div(t.toFloat().square().add(1));\n }\n };\n });\n}\n\nfunction sinh_(e) {\n var t = convertToTensor(e, \"x\", \"sinh\");\n return ENV.engine.runKernel(function (e) {\n return e.sinh(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return t.toFloat().cosh().mulStrict(e);\n }\n };\n });\n}\n\nfunction cosh_(e) {\n var t = convertToTensor(e, \"x\", \"cosh\");\n return ENV.engine.runKernel(function (e) {\n return e.cosh(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return t.toFloat().sinh().mulStrict(e);\n }\n };\n });\n}\n\nfunction tanh_(e) {\n var t = convertToTensor(e, \"x\", \"tanh\");\n return ENV.engine.runKernel(function (e, n) {\n return n(e.tanh(t));\n }, {\n $x: t\n }, function (e, t) {\n var n = t[0];\n return {\n $x: function () {\n return scalar(1).sub(n.square()).mulStrict(e);\n }\n };\n });\n}\n\nfunction asinh_(e) {\n var t = convertToTensor(e, \"x\", \"asinh\");\n return ENV.engine.runKernel(function (e) {\n return e.asinh(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.divStrict(scalar(1).add(t.toFloat().square()).sqrt());\n }\n };\n });\n}\n\nfunction acosh_(e) {\n var t = convertToTensor(e, \"x\", \"acosh\");\n return ENV.engine.runKernel(function (e) {\n return e.acosh(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.divStrict(t.toFloat().square().sub(1).sqrt());\n }\n };\n });\n}\n\nfunction atanh_(e) {\n var t = convertToTensor(e, \"x\", \"atanh\");\n return ENV.engine.runKernel(function (e) {\n return e.atanh(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.div(scalar(1).sub(t.toFloat().square()));\n }\n };\n });\n}\n\nfunction erf_(e) {\n var t = convertToTensor(e, \"x\", \"erf\");\n assert(\"int32\" === t.dtype || \"float32\" === t.dtype, \"Input dtype must be `int32` or `float32`.\"), \"int32\" === t.dtype && (t = t.toFloat());\n return ENV.engine.runKernel(function (e) {\n return e.erf(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n return e.mul(t.square().neg().exp().mul(2 / Math.sqrt(Math.PI)));\n }\n };\n });\n}\n\nfunction step_(e, t) {\n void 0 === t && (t = 0);\n var n = convertToTensor(e, \"x\", \"step\");\n return ENV.engine.runKernel(function (e) {\n return e.step(n, t);\n }, {\n $x: n\n }, function (e) {\n return {\n $x: function () {\n return zerosLike(e);\n }\n };\n });\n}\n\nENV.get(\"IS_BROWSER\") && ENV.registerBackend(\"webgl\", function () {\n return new MathBackendWebGL();\n}, 2, setTensorTracker);\nvar abs = op({\n abs_: abs_\n}),\n acos = op({\n acos_: acos_\n}),\n acosh = op({\n acosh_: acosh_\n}),\n asin = op({\n asin_: asin_\n}),\n asinh = op({\n asinh_: asinh_\n}),\n atan = op({\n atan_: atan_\n}),\n atanh = op({\n atanh_: atanh_\n}),\n ceil = op({\n ceil_: ceil_\n}),\n clipByValue = op({\n clipByValue_: clipByValue_\n}),\n cos = op({\n cos_: cos_\n}),\n cosh = op({\n cosh_: cosh_\n}),\n erf = op({\n erf_: erf_\n}),\n exp = op({\n exp_: exp_\n}),\n expm1 = op({\n expm1_: expm1_\n}),\n floor = op({\n floor_: floor_\n}),\n log$1 = op({\n log_: log_\n}),\n log1p = op({\n log1p_: log1p_\n}),\n logSigmoid = op({\n logSigmoid_: logSigmoid_\n}),\n neg = op({\n neg_: neg_\n}),\n reciprocal = op({\n reciprocal_: reciprocal_\n}),\n round = op({\n round_: round_\n}),\n rsqrt = op({\n rsqrt_: rsqrt_\n}),\n sigmoid = op({\n sigmoid_: sigmoid_\n}),\n sign = op({\n sign_: sign_\n}),\n sin = op({\n sin_: sin_\n}),\n sinh = op({\n sinh_: sinh_\n}),\n softplus = op({\n softplus_: softplus_\n}),\n sqrt = op({\n sqrt_: sqrt_\n}),\n square = op({\n square_: square_\n}),\n step = op({\n step_: step_\n}),\n tan = op({\n tan_: tan_\n}),\n tanh$1 = op({\n tanh_: tanh_\n});\nexports.tanh = tanh$1;\nexports.tan = tan;\nexports.step = step;\nexports.square = square;\nexports.sqrt = sqrt;\nexports.softplus = softplus;\nexports.sinh = sinh;\nexports.sin = sin;\nexports.sign = sign;\nexports.sigmoid = sigmoid;\nexports.rsqrt = rsqrt;\nexports.round = round;\nexports.reciprocal = reciprocal;\nexports.neg = neg;\nexports.logSigmoid = logSigmoid;\nexports.log1p = log1p;\nexports.log = log$1;\nexports.floor = floor;\nexports.expm1 = expm1;\nexports.exp = exp;\nexports.erf = erf;\nexports.cosh = cosh;\nexports.cos = cos;\nexports.clipByValue = clipByValue;\nexports.ceil = ceil;\nexports.atanh = atanh;\nexports.atan = atan;\nexports.asinh = asinh;\nexports.asin = asin;\nexports.acosh = acosh;\nexports.acos = acos;\nexports.abs = abs;\n\nfunction batchNorm2d_(e, t, n, r, o, a) {\n void 0 === a && (a = .001);\n var i,\n s,\n u = convertToTensor(e, \"x\", \"batchNorm\"),\n l = convertToTensor(t, \"mean\", \"batchNorm\"),\n c = convertToTensor(n, \"variance\", \"batchNorm\");\n return null != o && (i = convertToTensor(o, \"scale\", \"batchNorm\")), null != r && (s = convertToTensor(r, \"offset\", \"batchNorm\")), assert(2 === u.rank, \"Error in batchNorm3D: x must be rank 3 but got rank \" + u.rank + \".\"), assert(2 === l.rank || 1 === l.rank, \"Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank \" + l.rank + \".\"), assert(2 === c.rank || 1 === c.rank, \"Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank \" + c.rank + \".\"), null != i && assert(2 === i.rank || 1 === i.rank, \"Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank \" + i.rank + \".\"), null != s && assert(2 === s.rank || 1 === s.rank, \"Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank \" + s.rank + \".\"), batchNorm_(u, l, c, s, i, a);\n}\n\nfunction batchNorm3d_(e, t, n, r, o, a) {\n void 0 === a && (a = .001);\n var i,\n s,\n u = convertToTensor(e, \"x\", \"batchNorm\"),\n l = convertToTensor(t, \"mean\", \"batchNorm\"),\n c = convertToTensor(n, \"variance\", \"batchNorm\");\n return null != o && (i = convertToTensor(o, \"scale\", \"batchNorm\")), null != r && (s = convertToTensor(r, \"offset\", \"batchNorm\")), assert(3 === u.rank, \"Error in batchNorm3D: x must be rank 3 but got rank \" + u.rank + \".\"), assert(3 === l.rank || 1 === l.rank, \"Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank \" + l.rank + \".\"), assert(3 === c.rank || 1 === c.rank, \"Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank \" + c.rank + \".\"), null != i && assert(3 === i.rank || 1 === i.rank, \"Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank \" + i.rank + \".\"), null != s && assert(3 === s.rank || 1 === s.rank, \"Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank \" + s.rank + \".\"), batchNorm_(u, l, c, s, i, a);\n}\n\nfunction batchNorm4d_(e, t, n, r, o, a) {\n void 0 === a && (a = .001);\n var i,\n s,\n u = convertToTensor(e, \"x\", \"batchNorm\"),\n l = convertToTensor(t, \"mean\", \"batchNorm\"),\n c = convertToTensor(n, \"variance\", \"batchNorm\");\n return null != o && (i = convertToTensor(o, \"scale\", \"batchNorm\")), null != r && (s = convertToTensor(r, \"offset\", \"batchNorm\")), assert(4 === u.rank, \"Error in batchNorm4D: x must be rank 4 but got rank \" + u.rank + \".\"), assert(4 === l.rank || 1 === l.rank, \"Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank \" + l.rank + \".\"), assert(4 === c.rank || 1 === c.rank, \"Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank \" + c.rank + \".\"), null != i && assert(4 === i.rank || 1 === i.rank, \"Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank \" + i.rank + \".\"), null != s && assert(4 === s.rank || 1 === s.rank, \"Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank \" + s.rank + \".\"), batchNorm_(u, l, c, s, i, a);\n}\n\nfunction batchNormalization_(e, t, n, r, o, a) {\n return void 0 === r && (r = .001), warnDeprecation(), batchNorm_(e, t, n, a, o, r);\n}\n\nfunction batchNorm_(e, t, n, r, o, a) {\n void 0 === a && (a = .001);\n var i,\n s,\n u,\n l = convertToTensor(e, \"x\", \"batchNorm\"),\n c = convertToTensor(t, \"mean\", \"batchNorm\"),\n p = convertToTensor(n, \"variance\", \"batchNorm\");\n null != o && (i = convertToTensor(o, \"scale\", \"batchNorm\")), null != r && (s = convertToTensor(r, \"offset\", \"batchNorm\")), assert(c.rank === p.rank, \"Batch normalization gradient requires mean and variance to have equal ranks.\"), assert(null == s || c.rank === s.rank, \"Batch normalization gradient requires mean and offset to have equal ranks.\"), assert(null == i || c.rank === i.rank, \"Batch normalization gradient requires mean and scale to have equal ranks.\"), u = 0 === l.rank || 1 === l.rank ? l.as4D(1, 1, 1, l.size) : 2 === l.rank ? l.as4D(1, 1, l.shape[0], l.shape[1]) : 3 === l.rank ? l.as4D(1, l.shape[0], l.shape[1], l.shape[2]) : l;\n return ENV.engine.runKernel(function (e) {\n return e.batchNormalization(u, batchnormReshape4D(c), batchnormReshape4D(p), a, batchnormReshape4D(i), batchnormReshape4D(s));\n }, {\n $x: l,\n $mean: c,\n $variance: p,\n $scale: i,\n $offset: s\n }, function (e) {\n var t = null == i ? scalar(1) : i,\n n = getReductionAxes(c.shape, u.shape),\n r = [];\n\n if (1 === c.rank) {\n for (var o = 0; o < u.shape.length - 1; ++o) r.push(u.shape[o]);\n\n r.push(1);\n }\n\n var s = l.sub(c),\n d = e.mul(t),\n h = rsqrt(p.add(scalar(a))),\n f = h.mul(h).mul(h).mul(scalar(-.5));\n return {\n $x: function () {\n return 1 === c.rank ? e.mul(tile(h.as4D(1, 1, 1, c.shape[0]), r)).mul(t).reshape(l.shape) : e.mul(h).mul(t).reshape(l.shape);\n },\n $mean: function () {\n var e = h.mul(scalar(-1)).mul(d);\n return 1 === c.rank && (e = e.sum(n)), e.reshape(c.shape);\n },\n $variance: function () {\n var e = f.mul(s).mul(d);\n return 1 === c.rank && (e = e.sum(n)), e.reshape(c.shape);\n },\n $scale: function () {\n var t = s.mul(h),\n r = e.mul(t);\n return 1 === c.rank && (r = r.sum(n)), r.reshape(c.shape);\n },\n $offset: function () {\n var t = e;\n return 1 === c.rank && (t = t.sum(n)), t.reshape(c.shape);\n }\n };\n }).reshape(l.shape);\n}\n\nfunction batchnormReshape4D(e) {\n return null == e ? null : 0 === e.rank ? e.as1D() : 1 === e.rank ? e : 2 === e.rank ? e.as4D(1, 1, e.shape[0], e.shape[1]) : 3 === e.rank ? e.as4D(1, e.shape[0], e.shape[1], e.shape[2]) : e;\n}\n\nfunction batchNormalization2d_(e, t, n, r, o, a) {\n return void 0 === r && (r = .001), warnDeprecation(), batchNorm2d_(e, t, n, a, o, r);\n}\n\nfunction batchNormalization3d_(e, t, n, r, o, a) {\n return void 0 === r && (r = .001), warnDeprecation(), batchNorm3d_(e, t, n, a, o, r);\n}\n\nfunction batchNormalization4d_(e, t, n, r, o, a) {\n return void 0 === r && (r = .001), warnDeprecation(), batchNorm4d_(e, t, n, a, o, r);\n}\n\nfunction warnDeprecation() {\n deprecationWarn(\"tf.batchNormalization() is going away. Use tf.batchNorm() instead, and note the positional argument change of scale, offset, and varianceEpsilon\");\n}\n\nvar batchNormalization2d = op({\n batchNormalization2d_: batchNormalization2d_\n}),\n batchNormalization3d = op({\n batchNormalization3d_: batchNormalization3d_\n}),\n batchNormalization4d = op({\n batchNormalization4d_: batchNormalization4d_\n}),\n batchNormalization = op({\n batchNormalization_: batchNormalization_\n}),\n batchNorm = op({\n batchNorm_: batchNorm_\n}),\n batchNorm2d = op({\n batchNorm2d_: batchNorm2d_\n}),\n batchNorm3d = op({\n batchNorm3d_: batchNorm3d_\n}),\n batchNorm4d = op({\n batchNorm4d_: batchNorm4d_\n});\nexports.batchNorm4d = batchNorm4d;\nexports.batchNorm3d = batchNorm3d;\nexports.batchNorm2d = batchNorm2d;\nexports.batchNorm = batchNorm;\nexports.batchNormalization = batchNormalization;\nexports.batchNormalization4d = batchNormalization4d;\nexports.batchNormalization3d = batchNormalization3d;\nexports.batchNormalization2d = batchNormalization2d;\n\nfunction computePool2DInfo(e, t, n, r, o, a, i) {\n void 0 === i && (i = \"channelsLast\");\n var s,\n u = parseTupleParam(t),\n l = u[0],\n c = u[1];\n if (\"channelsLast\" === i) s = [l, c, e[3], e[3]];else {\n if (\"channelsFirst\" !== i) throw new Error(\"Unknown dataFormat \" + i);\n s = [l, c, e[1], e[1]];\n }\n return computeConv2DInfo(e, s, n, r, o, a, !1, i);\n}\n\nfunction computeConv2DInfo(e, t, n, r, o, a, i, s) {\n void 0 === i && (i = !1), void 0 === s && (s = \"channelsLast\");\n var u = [-1, -1, -1, -1],\n l = u[0],\n c = u[1],\n p = u[2],\n d = u[3];\n if (\"channelsLast\" === s) l = e[0], c = e[1], p = e[2], d = e[3];else {\n if (\"channelsFirst\" !== s) throw new Error(\"Unknown dataFormat \" + s);\n l = e[0], d = e[1], c = e[2], p = e[3];\n }\n var h,\n f = t[0],\n m = t[1],\n g = t[3],\n v = parseTupleParam(n),\n y = v[0],\n x = v[1],\n T = parseTupleParam(r),\n w = T[0],\n E = T[1],\n b = getEffectiveFilterSize(f, w),\n S = getEffectiveFilterSize(m, E),\n C = getPadAndOutInfo(o, c, p, y, x, b, S, a),\n A = C.padInfo,\n N = C.outHeight,\n _ = C.outWidth,\n R = i ? g * d : g;\n return \"channelsFirst\" === s ? h = [l, R, N, _] : \"channelsLast\" === s && (h = [l, N, _, R]), {\n batchSize: l,\n dataFormat: s,\n inHeight: c,\n inWidth: p,\n inChannels: d,\n outHeight: N,\n outWidth: _,\n outChannels: R,\n padInfo: A,\n strideHeight: y,\n strideWidth: x,\n filterHeight: f,\n filterWidth: m,\n effectiveFilterHeight: b,\n effectiveFilterWidth: S,\n dilationHeight: w,\n dilationWidth: E,\n inShape: e,\n outShape: h,\n filterShape: t\n };\n}\n\nfunction computeConv3DInfo(e, t, n, r, o, a, i) {\n void 0 === a && (a = !1), void 0 === i && (i = \"channelsLast\");\n var s = [-1, -1, -1, -1, -1],\n u = s[0],\n l = s[1],\n c = s[2],\n p = s[3],\n d = s[4];\n if (\"channelsLast\" === i) u = e[0], l = e[1], c = e[2], p = e[3], d = e[4];else {\n if (\"channelsFirst\" !== i) throw new Error(\"Unknown dataFormat \" + i);\n u = e[0], d = e[1], l = e[2], c = e[3], p = e[4];\n }\n var h,\n f = t[0],\n m = t[1],\n g = t[2],\n v = t[4],\n y = parse3TupleParam(n),\n x = y[0],\n T = y[1],\n w = y[2],\n E = parse3TupleParam(r),\n b = E[0],\n S = E[1],\n C = E[2],\n A = get3DPadAndOutInfo(o, l, c, p, x, T, w, getEffectiveFilterSize(f, b), getEffectiveFilterSize(m, S), getEffectiveFilterSize(g, C)),\n N = A.padInfo,\n _ = A.outDepth,\n R = A.outHeight,\n I = A.outWidth,\n k = a ? v * d : v;\n return \"channelsFirst\" === i ? h = [u, k, _, R, I] : \"channelsLast\" === i && (h = [u, _, R, I, k]), {\n batchSize: u,\n dataFormat: i,\n inDepth: l,\n inHeight: c,\n inWidth: p,\n inChannels: d,\n outDepth: _,\n outHeight: R,\n outWidth: I,\n outChannels: k,\n padInfo: N,\n strideDepth: x,\n strideHeight: T,\n strideWidth: w,\n filterDepth: f,\n filterHeight: m,\n filterWidth: g,\n dilationDepth: b,\n dilationHeight: S,\n dilationWidth: C,\n inShape: e,\n outShape: h,\n filterShape: t\n };\n}\n\nfunction computeOutputShape3D(e, t, n, r, o, a) {\n null == o && (o = computeDefaultPad(e, t, r));\n var i = e[0],\n s = e[1],\n u = conditionalRound((i - t + 2 * o) / r + 1, a);\n assert(isInt(u), \"The output # of rows (\" + u + \") must be an integer. Change the stride and/or zero pad parameters\");\n var l = conditionalRound((s - t + 2 * o) / r + 1, a);\n return assert(isInt(l), \"The output # of columns (\" + l + \") must be an integer. Change the stride and/or zero pad parameters\"), [u, l, n];\n}\n\nfunction computeDefaultPad(e, t, n, r) {\n void 0 === r && (r = 1);\n var o = getEffectiveFilterSize(t, r);\n return Math.floor((e[0] * (n - 1) - n + o) / 2);\n}\n\nfunction parseTupleParam(e) {\n return \"number\" == typeof e ? [e, e] : e;\n}\n\nfunction parse3TupleParam(e) {\n return \"number\" == typeof e ? [e, e, e] : e;\n}\n\nfunction getEffectiveFilterSize(e, t) {\n return t <= 1 ? e : e + (e - 1) * (t - 1);\n}\n\nfunction getPadAndOutInfo(e, t, n, r, o, a, i, s) {\n var u, l, c;\n\n if (\"number\" == typeof e) {\n u = {\n top: e,\n bottom: e,\n left: e,\n right: e,\n type: 0 === e ? \"VALID\" : \"NUMBER\"\n };\n var p = computeOutputShape3D([t, n, 1], a, 1, r, e, s);\n l = p[0], c = p[1];\n } else if (\"same\" === e) {\n l = Math.ceil(t / r), c = Math.ceil(n / o);\n var d = Math.max(0, (l - 1) * r + a - t),\n h = Math.max(0, (c - 1) * o + i - n),\n f = Math.floor(d / 2),\n m = d - f,\n g = Math.floor(h / 2);\n u = {\n top: f,\n bottom: m,\n left: g,\n right: h - g,\n type: \"SAME\"\n };\n } else {\n if (\"valid\" !== e) throw Error(\"Unknown padding parameter: \" + e);\n u = {\n top: 0,\n bottom: 0,\n left: 0,\n right: 0,\n type: \"VALID\"\n }, l = Math.ceil((t - a + 1) / r), c = Math.ceil((n - i + 1) / o);\n }\n\n return {\n padInfo: u,\n outHeight: l,\n outWidth: c\n };\n}\n\nfunction get3DPadAndOutInfo(e, t, n, r, o, a, i, s, u, l) {\n var c, p, d, h;\n\n if (\"same\" === e) {\n var f = ((p = Math.ceil(t / o)) - 1) * o + s - t,\n m = ((d = Math.ceil(n / a)) - 1) * a + u - n,\n g = ((h = Math.ceil(r / i)) - 1) * i + l - r,\n v = Math.floor(f / 2),\n y = f - v,\n x = Math.floor(m / 2),\n T = m - x,\n w = Math.floor(g / 2);\n c = {\n top: x,\n bottom: T,\n left: w,\n right: g - w,\n front: v,\n back: y,\n type: \"SAME\"\n };\n } else {\n if (\"valid\" !== e) throw Error(\"Unknown padding parameter: \" + e);\n c = {\n top: 0,\n bottom: 0,\n left: 0,\n right: 0,\n front: 0,\n back: 0,\n type: \"VALID\"\n }, p = Math.ceil((t - s + 1) / o), d = Math.ceil((n - u + 1) / a), h = Math.ceil((r - l + 1) / i);\n }\n\n return {\n padInfo: c,\n outDepth: p,\n outHeight: d,\n outWidth: h\n };\n}\n\nfunction conditionalRound(e, t) {\n if (!t) return e;\n\n switch (t) {\n case \"round\":\n return Math.round(e);\n\n case \"ceil\":\n return Math.ceil(e);\n\n case \"floor\":\n return Math.floor(e);\n\n default:\n throw new Error(\"Unknown roundingMode \" + t);\n }\n}\n\nfunction tupleValuesAreOne(e) {\n var t = parseTupleParam(e),\n n = t[0],\n r = t[1];\n return 1 === n && 1 === r;\n}\n\nfunction eitherStridesOrDilationsAreOne(e, t) {\n return tupleValuesAreOne(e) || tupleValuesAreOne(t);\n}\n\nfunction matMul_(e, t, n, r) {\n var o;\n void 0 === n && (n = !1), void 0 === r && (r = !1);\n var a = convertToTensor(e, \"a\", \"matMul\"),\n i = convertToTensor(t, \"b\", \"matMul\");\n a = (o = makeTypesMatch(a, i))[0], i = o[1];\n var s = n ? a.shape[a.rank - 2] : a.shape[a.rank - 1],\n u = r ? i.shape[i.rank - 1] : i.shape[i.rank - 2],\n l = n ? a.shape[a.rank - 1] : a.shape[a.rank - 2],\n c = r ? i.shape[i.rank - 2] : i.shape[i.rank - 1],\n p = a.shape.slice(0, -2),\n d = i.shape.slice(0, -2),\n h = sizeFromShape(p),\n f = sizeFromShape(d);\n assert(a.rank >= 2 && i.rank >= 2 && a.rank === i.rank, \"Error in matMul: inputs must have the same rank of at least 2, got ranks \" + a.rank + \" and \" + i.rank + \".\"), assert(arraysEqual(p, d), \"Error in matMul: outer dimensions (\" + p + \") and (\" + d + \") of Tensors with shapes \" + a.shape + \" and \" + i.shape + \" must match.\"), assert(s === u, \"Error in matMul: inner shapes (\" + s + \") and (\" + u + \") of Tensors with shapes \" + a.shape + \" and \" + i.shape + \" and transposeA=\" + n + \" and transposeB=\" + r + \" must match.\");\n var m = a.shape.slice(0, -2).concat([l, c]),\n g = n ? a.as3D(h, s, l) : a.as3D(h, l, s),\n v = r ? i.as3D(f, c, u) : i.as3D(f, u, c);\n return ENV.engine.runKernel(function (e) {\n return e.batchMatMul(g, v, n, r);\n }, {\n $a: g,\n $b: v\n }, function (e) {\n return n || r ? !n && r ? {\n $a: function () {\n return e.matMul(v, !1, !1);\n },\n $b: function () {\n return e.matMul(g, !0, !1);\n }\n } : n && !r ? {\n $a: function () {\n return v.matMul(e, !1, !0);\n },\n $b: function () {\n return g.matMul(e, !1, !1);\n }\n } : {\n $a: function () {\n return v.matMul(e, !0, !0);\n },\n $b: function () {\n return e.matMul(g, !0, !0);\n }\n } : {\n $a: function () {\n return e.matMul(v, !1, !0);\n },\n $b: function () {\n return g.matMul(e, !0, !1);\n }\n };\n }).reshape(m);\n}\n\nfunction outerProduct_(e, t) {\n var n = convertToTensor(e, \"v1\", \"outerProduct\"),\n r = convertToTensor(t, \"v2\", \"outerProduct\");\n return assert(1 === n.rank && 1 === r.rank, \"Error in outerProduct: inputs must be rank 1, but got ranks \" + n.rank + \" and \" + r.rank + \".\"), n.as2D(-1, 1).matMul(r.as2D(1, -1));\n}\n\nfunction dot_(e, t) {\n var n = convertToTensor(e, \"t1\", \"dot\"),\n r = convertToTensor(t, \"t2\", \"dot\");\n assert(!(1 !== n.rank && 2 !== n.rank || 1 !== r.rank && 2 !== r.rank), \"Error in dot: inputs must all be rank 1 or 2, but got ranks \" + n.rank + \" and \" + r.rank + \".\");\n var o = 1 === n.rank ? n.size : n.shape[1],\n a = 1 === r.rank ? r.size : r.shape[0];\n return assert(o === a, \"Error in dot: inner dimensions of inputs must match, but got \" + o + \" and \" + a + \".\"), 1 === n.rank && 1 === r.rank ? n.as2D(1, -1).matMul(r.as2D(-1, 1)).asScalar() : 1 === n.rank && 2 === r.rank ? n.as2D(1, -1).matMul(r.as2D(r.shape[0], r.shape[1])).as1D() : 2 === n.rank && 1 === r.rank ? n.matMul(r.as2D(-1, 1)).as1D() : n.matMul(r.as2D(r.shape[0], r.shape[1]));\n}\n\nvar matMul = op({\n matMul_: matMul_\n}),\n dot = op({\n dot_: dot_\n}),\n outerProduct = op({\n outerProduct_: outerProduct_\n});\nexports.outerProduct = outerProduct;\nexports.dot = dot;\nexports.matMul = matMul;\n\nfunction conv1d_(e, t, n, r, o, a, i) {\n void 0 === o && (o = \"NWC\"), void 0 === a && (a = 1);\n var s = convertToTensor(e, \"x\", \"conv1d\"),\n u = convertToTensor(t, \"filter\", \"conv1d\"),\n l = s,\n c = !1;\n 2 === s.rank && (c = !0, l = s.as3D(1, s.shape[0], s.shape[1])), assert(3 === l.rank, \"Error in conv1d: input must be rank 3, but got rank \" + l.rank + \".\"), assert(3 === u.rank, \"Error in conv1d: filter must be rank 3, but got rank \" + u.rank + \".\"), null != i && assert(isInt(r), \"Error in conv1d: pad must be an integer when using, dimRoundingMode \" + i + \" but got pad \" + r + \".\"), assert(l.shape[2] === u.shape[1], \"Error in conv1d: depth of input (\" + l.shape[2] + \") must match input depth for filter \" + u.shape[1] + \".\"), assert(eitherStridesOrDilationsAreOne(n, a), \"Error in conv1D: Either stride or dilation must be 1. Got stride \" + n + \" and dilation '\" + a + \"'\"), assert(\"NWC\" === o, \"Error in conv1d: got dataFormat of \" + o + \" but only NWC is currently supported.\");\n var p = u.as4D(1, u.shape[0], u.shape[1], u.shape[2]),\n d = l.as4D(l.shape[0], 1, l.shape[1], l.shape[2]),\n h = conv2d(d, p, [1, n], r, \"NHWC\", [1, a], i);\n return c ? h.as2D(h.shape[2], h.shape[3]) : h.as3D(h.shape[0], h.shape[2], h.shape[3]);\n}\n\nfunction conv2d_(e, t, n, r, o, a, i) {\n void 0 === o && (o = \"NHWC\"), void 0 === a && (a = [1, 1]);\n var s = convertToTensor(e, \"x\", \"conv2d\"),\n u = convertToTensor(t, \"filter\", \"conv2d\"),\n l = s,\n c = !1;\n 3 === s.rank && (c = !0, l = s.as4D(1, s.shape[0], s.shape[1], s.shape[2])), assert(4 === l.rank, \"Error in conv2d: input must be rank 4, but got rank \" + l.rank + \".\"), assert(4 === u.rank, \"Error in conv2d: filter must be rank 4, but got rank \" + u.rank + \".\"), null != i && assert(isInt(r), \"Error in conv2d: pad must be an integer when using, dimRoundingMode \" + i + \" but got pad \" + r + \".\"), assert(l.shape[3] === u.shape[2], \"Error in conv2d: depth of input (\" + l.shape[3] + \") must match input depth for filter \" + u.shape[2] + \".\"), assert(eitherStridesOrDilationsAreOne(n, a), \"Error in conv2D: Either strides or dilations must be 1. Got strides \" + n + \" and dilations '\" + a + \"'\"), assert(\"NHWC\" === o, \"Error in conv2d: got dataFormat of \" + o + \" but only NHWC is currently supported.\");\n var p,\n d = computeConv2DInfo(l.shape, u.shape, n, a, r, i);\n\n if (1 !== d.filterHeight || 1 !== d.filterWidth || 1 !== d.dilationHeight || 1 !== d.dilationWidth || 1 !== d.strideHeight || 1 !== d.strideWidth || \"SAME\" !== d.padInfo.type && \"VALID\" !== d.padInfo.type) {\n p = ENV.engine.runKernel(function (e) {\n return e.conv2d(l, u, d);\n }, {\n x: l,\n $filter: u\n }, function (e) {\n return assert(tupleValuesAreOne(a), \"Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '\" + a + \"'\"), {\n x: function () {\n return conv2dDerInput_(l.shape, e, u, n, r);\n },\n $filter: function () {\n return conv2dDerFilter_(l, e, u.shape, n, r);\n }\n };\n });\n } else {\n var h = l.reshape([-1, d.inChannels]),\n f = u.reshape([d.inChannels, d.outChannels]);\n p = matMul(h, f).reshape(d.outShape);\n }\n\n return c ? p.as3D(p.shape[1], p.shape[2], p.shape[3]) : p;\n}\n\nfunction conv2dDerInput_(e, t, n, r, o, a) {\n assert(e.length === t.rank, \"Length of inShape (\" + e.length + \") and rank of dy (\" + t.rank + \") must match\");\n var i = e,\n s = t,\n u = !1;\n 3 === t.rank && (u = !0, s = t.as4D(1, t.shape[0], t.shape[1], t.shape[2]), i = [1, e[0], e[1], e[2]]);\n var l = i[3],\n c = s.shape[3];\n assert(4 === i.length, \"Error in conv2dDerInput: inShape must be length 4, but got length \" + i.length + \".\"), assert(4 === s.rank, \"Error in conv2dDerInput: dy must be rank 4, but got rank \" + s.rank), assert(4 === n.rank, \"Error in conv2dDerInput: filter must be rank 4, but got rank \" + n.rank), assert(l === n.shape[2], \"Error in conv2dDerInput: depth of input (\" + l + \") must match input depth for filter \" + n.shape[2] + \".\"), assert(c === n.shape[3], \"Error in conv2dDerInput: depth of output (\" + c + \") must match output depth for filter \" + n.shape[3] + \".\"), null != a && assert(isInt(o), \"Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode \" + a + \" but got pad \" + o + \".\");\n var p = computeConv2DInfo(i, n.shape, r, 1, o, a),\n d = ENV.engine.runKernel(function (e) {\n return e.conv2dDerInput(s, n, p);\n }, {\n dy4D: s,\n filter: n\n }, function (e) {\n return {\n dy4D: function () {\n return conv2d(e, n, r, o, \"NHWC\", 1, a);\n },\n filter: function () {\n return conv2dDerFilter(e, s, n.shape, r, o, a);\n }\n };\n });\n return u ? d.as3D(d.shape[1], d.shape[2], d.shape[3]) : d;\n}\n\nfunction conv2dDerFilter_(e, t, n, r, o, a) {\n var i = e;\n 3 === e.rank && (i = e.as4D(1, e.shape[0], e.shape[1], e.shape[2]));\n var s = t;\n 3 === s.rank && (s = t.as4D(1, t.shape[0], t.shape[1], t.shape[2])), assert(4 === i.rank, \"Error in conv2dDerFilter: input must be rank 4, but got shape \" + i.shape + \".\"), assert(4 === s.rank, \"Error in conv2dDerFilter: dy must be rank 4, but got shape \" + s.shape + \".\"), assert(4 === n.length, \"Error in conv2dDerFilter: filterShape must be length 4, but got \" + n + \".\"), assert(i.shape[3] === n[2], \"Error in conv2dDerFilter: depth of input \" + i.shape[3] + \") must match input depth in filter (\" + n[2] + \".\"), assert(s.shape[3] === n[3], \"Error in conv2dDerFilter: depth of dy (\" + s.shape[3] + \") must match output depth for filter (\" + n[3] + \").\"), null != a && assert(isInt(o), \"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode \" + a + \" but got pad \" + o + \".\");\n var u = computeConv2DInfo(i.shape, n, r, 1, o, a);\n return ENV.engine.runKernel(function (e) {\n return e.conv2dDerFilter(i, s, u);\n }, {\n x4D: i,\n dy4D: s\n });\n}\n\nfunction conv2dTranspose_(e, t, n, r, o, a) {\n return conv2dDerInput_(n, convertToTensor(e, \"x\", \"conv2dTranspose\"), convertToTensor(t, \"filter\", \"conv2dTranspose\"), r, o, a);\n}\n\nfunction depthwiseConv2d_(e, t, n, r, o, a, i) {\n void 0 === o && (o = \"NHWC\"), void 0 === a && (a = [1, 1]);\n var s = convertToTensor(e, \"x\", \"depthwiseConv2d\"),\n u = convertToTensor(t, \"filter\", \"depthwiseConv2d\"),\n l = s,\n c = !1;\n 3 === s.rank && (c = !0, l = s.as4D(1, s.shape[0], s.shape[1], s.shape[2])), assert(4 === l.rank, \"Error in depthwiseConv2d: input must be rank 4, but got rank \" + l.rank + \".\"), assert(4 === u.rank, \"Error in depthwiseConv2d: filter must be rank 4, but got rank \" + u.rank + \".\"), assert(l.shape[3] === u.shape[2], \"Error in depthwiseConv2d: number of input channels (\" + l.shape[3] + \") must match the inChannels dimension in filter \" + u.shape[2] + \".\"), null == a && (a = [1, 1]), assert(eitherStridesOrDilationsAreOne(n, a), \"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides \" + n + \" and dilations '\" + a + \"'\"), null != i && assert(isInt(r), \"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode \" + i + \" but got pad \" + r + \".\");\n var p = computeConv2DInfo(l.shape, u.shape, n, a, r, i, !0),\n d = ENV.engine.runKernel(function (e) {\n return e.depthwiseConv2D(l, u, p);\n }, {\n x: l,\n $filter: u\n }, function (e) {\n return assert(tupleValuesAreOne(a), \"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '\" + a + \"'\"), {\n x: function () {\n return depthwiseConv2dDerInput(l.shape, e, u, p);\n },\n $filter: function () {\n return depthwiseConv2dDerFilter(l, e, u.shape, p);\n }\n };\n });\n return c ? d.as3D(d.shape[1], d.shape[2], d.shape[3]) : d;\n}\n\nfunction separableConv2d_(e, t, n, r, o, a, i) {\n void 0 === a && (a = [1, 1]), void 0 === i && (i = \"NHWC\");\n var s = convertToTensor(e, \"x\", \"separableConv2d\"),\n u = convertToTensor(t, \"depthwiseFilter\", \"separableConv2d\"),\n l = convertToTensor(n, \"pointwiseFilter\", \"separableConv2d\"),\n c = s,\n p = !1;\n if (3 === s.rank && (p = !0, c = s.as4D(1, s.shape[0], s.shape[1], s.shape[2])), \"NCHW\" === i) throw new Error(\"separableConv2d currently does not support dataFormat NCHW; only NHWC is supported\");\n assert(4 === c.rank, \"Error in separableConv2d: input must be rank 4, but got rank \" + c.rank + \".\"), assert(4 === u.rank, \"Error in separableConv2d: depthwise filter must be rank 4, but got rank \" + u.rank + \".\"), assert(4 === l.rank, \"Error in separableConv2d: pointwise filter must be rank 4, but got rank \" + u.rank + \".\"), assert(1 === l.shape[0], \"Error in separableConv2d: the first dimension of pointwise filter must be 1, but got \" + l.shape[0] + \".\"), assert(1 === l.shape[1], \"Error in separableConv2d: the second dimension of pointwise filter must be 1, but got \" + l.shape[1] + \".\");\n var d = u.shape[2],\n h = u.shape[3];\n assert(l.shape[2] === d * h, \"Error in separableConv2d: the third dimension of pointwise filter must be \" + d * h + \", but got \" + l.shape[2] + \".\");\n var f = depthwiseConv2d(c, u, r, o, i, a),\n m = conv2d(f, l, 1, \"valid\", i);\n return p ? m.as3D(m.shape[1], m.shape[2], m.shape[3]) : m;\n}\n\nfunction parseTupleParam$1(e) {\n return \"number\" == typeof e ? [e, e, e] : 2 === e.length ? [e[0], e[1], 1] : e;\n}\n\nfunction tupleValuesAreOne$1(e) {\n var t = parseTupleParam$1(e),\n n = t[0],\n r = t[1],\n o = t[2];\n return 1 === n && 1 === r && 1 === o;\n}\n\nfunction eitherStridesOrDilationsAreOne$1(e, t) {\n return tupleValuesAreOne$1(e) || tupleValuesAreOne$1(t);\n}\n\nfunction depthwiseConv2dDerInput(e, t, n, r) {\n var o = t,\n a = !1;\n 3 === t.rank && (a = !0, o = t.as4D(1, t.shape[0], t.shape[1], t.shape[2]));\n var i = ENV.engine.runKernel(function (e) {\n return e.depthwiseConv2DDerInput(o, n, r);\n }, {\n dy4D: o\n });\n return a ? i.as3D(i.shape[1], i.shape[2], i.shape[3]) : i;\n}\n\nfunction depthwiseConv2dDerFilter(e, t, n, r) {\n var o = e;\n 3 === e.rank && (o = e.as4D(1, e.shape[0], e.shape[1], e.shape[2]));\n var a = t;\n return 3 === a.rank && (a = t.as4D(1, t.shape[0], t.shape[1], t.shape[2])), ENV.engine.runKernel(function (e) {\n return e.depthwiseConv2DDerFilter(o, a, r);\n }, {\n x4D: o,\n dy4D: a\n });\n}\n\nfunction conv3d_(e, t, n, r, o, a) {\n void 0 === o && (o = \"NHWC\"), void 0 === a && (a = [1, 1, 1]);\n var i = convertToTensor(e, \"x\", \"conv3d\"),\n s = convertToTensor(t, \"filter\", \"conv3d\"),\n u = i,\n l = !1;\n 4 === i.rank && (l = !0, u = i.as5D(1, i.shape[0], i.shape[1], i.shape[2], i.shape[3])), assert(5 === u.rank, \"Error in conv3d: input must be rank 5, but got rank \" + u.rank + \".\"), assert(5 === s.rank, \"Error in conv3d: filter must be rank 5, but got rank \" + s.rank + \".\"), assert(u.shape[4] === s.shape[3], \"Error in conv3d: depth of input (\" + u.shape[4] + \") must match input depth for filter \" + s.shape[3] + \".\"), assert(eitherStridesOrDilationsAreOne$1(n, a), \"Error in conv3D: Either strides or dilations must be 1. Got strides \" + n + \" and dilations '\" + a + \"'\"), assert(\"NHWC\" === o, \"Error in conv3d: got dataFormat of \" + o + \" but only NHWC is currently supported.\");\n var c = computeConv3DInfo(u.shape, s.shape, n, a, r),\n p = ENV.engine.runKernel(function (e) {\n return e.conv3d(u, s, c);\n }, {\n x: u,\n $filter: s\n }, function (e) {\n return assert(tupleValuesAreOne$1(a), \"Error in gradient of conv3D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '\" + a + \"'\"), {\n x: function () {\n return conv3dDerInput_(u.shape, e, s, n, r);\n },\n $filter: function () {\n return conv3dDerFilter_(u, e, s.shape, n, r);\n }\n };\n });\n return l ? p.as4D(p.shape[1], p.shape[2], p.shape[3], p.shape[4]) : p;\n}\n\nfunction conv3dDerInput_(e, t, n, r, o) {\n assert(e.length === t.rank, \"Length of inShape (\" + e.length + \") and rank of dy (\" + t.rank + \") must match\");\n var a = e,\n i = t,\n s = !1;\n 4 === t.rank && (s = !0, i = t.as5D(1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]), a = [1, e[0], e[1], e[2], e[3]]);\n var u = a[4],\n l = i.shape[4];\n assert(5 === a.length, \"Error in conv3dDerInput: inShape must be length 5, but got length \" + a.length + \".\"), assert(5 === i.rank, \"Error in conv3dDerInput: dy must be rank 5, but got rank \" + i.rank), assert(5 === n.rank, \"Error in conv3dDerInput: filter must be rank 5, but got rank \" + n.rank), assert(u === n.shape[3], \"Error in conv3dDerInput: depth of input (\" + u + \") must match input depth for filter \" + n.shape[3] + \".\"), assert(l === n.shape[4], \"Error in conv3dDerInput: depth of output (\" + l + \") must match output depth for filter \" + n.shape[4] + \".\");\n var c = computeConv3DInfo(a, n.shape, r, 1, o),\n p = ENV.engine.runKernel(function (e) {\n return e.conv3dDerInput(i, n, c);\n }, {\n dy5D: i\n });\n return s ? p.as4D(p.shape[1], p.shape[2], p.shape[3], p.shape[4]) : p;\n}\n\nfunction conv3dDerFilter_(e, t, n, r, o) {\n var a = e;\n 4 === e.rank && (a = e.as5D(1, e.shape[0], e.shape[1], e.shape[2], e.shape[3]));\n var i = t;\n 4 === i.rank && (i = t.as5D(1, t.shape[0], t.shape[1], t.shape[2], t.shape[3])), assert(5 === a.rank, \"Error in conv3dDerFilter: input must be rank 5, but got shape \" + a.shape + \".\"), assert(5 === i.rank, \"Error in conv3dDerFilter: dy must be rank 5, but got shape \" + i.shape + \".\"), assert(5 === n.length, \"Error in conv3dDerFilter: filterShape must be length 5, but got \" + n + \".\"), assert(a.shape[4] === n[3], \"Error in conv3dDerFilter: depth of input \" + a.shape[4] + \") must match input depth in filter (\" + n[3] + \".\"), assert(i.shape[4] === n[4], \"Error in conv3dDerFilter: depth of dy (\" + i.shape[4] + \") must match output depth for filter (\" + n[4] + \").\");\n var s = computeConv3DInfo(a.shape, n, r, 1, o);\n return ENV.engine.runKernel(function (e) {\n return e.conv3dDerFilter(a, i, s);\n }, {\n x5D: a,\n dy5D: i\n });\n}\n\nvar conv1d = op({\n conv1d_: conv1d_\n}),\n conv2d = op({\n conv2d_: conv2d_\n}),\n conv3d = op({\n conv3d_: conv3d_\n}),\n conv2dDerFilter = op({\n conv2dDerFilter_: conv2dDerFilter_\n}),\n depthwiseConv2d = op({\n depthwiseConv2d_: depthwiseConv2d_\n}),\n separableConv2d = op({\n separableConv2d_: separableConv2d_\n}),\n conv2dTranspose = op({\n conv2dTranspose_: conv2dTranspose_\n});\nexports.conv2dTranspose = conv2dTranspose;\nexports.separableConv2d = separableConv2d;\nexports.depthwiseConv2d = depthwiseConv2d;\nexports.conv2dDerFilter = conv2dDerFilter;\nexports.conv3d = conv3d;\nexports.conv2d = conv2d;\nexports.conv1d = conv1d;\n\nfunction reverse1d_(e) {\n var t = convertToTensor(e, \"x\", \"reverse\");\n return assert(1 === t.rank, \"Error in reverse1D: x must be rank 1 but got\\n rank \" + t.rank + \".\"), reverse(t, 0);\n}\n\nfunction reverse2d_(e, t) {\n var n = convertToTensor(e, \"x\", \"reverse\");\n return assert(2 === n.rank, \"Error in reverse2D: x must be rank 2 but got\\n rank \" + n.rank + \".\"), reverse(n, t);\n}\n\nfunction reverse3d_(e, t) {\n var n = convertToTensor(e, \"x\", \"reverse\");\n return assert(3 === n.rank, \"Error in reverse3D: x must be rank 3 but got\\n rank \" + n.rank + \".\"), reverse(n, t);\n}\n\nfunction reverse4d_(e, t) {\n var n = convertToTensor(e, \"x\", \"reverse\");\n return assert(4 === n.rank, \"Error in reverse4D: x must be rank 4 but got\\n rank \" + n.rank + \".\"), reverse(n, t);\n}\n\nfunction reverse_(e, t) {\n var n = convertToTensor(e, \"x\", \"reverse\");\n if (0 === n.rank) return n.clone();\n var r = parseAxisParam(t, n.shape);\n return ENV.engine.runKernel(function (e) {\n return e.reverse(n, r);\n }, {\n $x: n\n }, function (e) {\n return {\n $x: function () {\n return e.reverse(r);\n }\n };\n }).reshapeAs(n);\n}\n\nvar reverse = op({\n reverse_: reverse_\n}),\n reverse1d = op({\n reverse1d_: reverse1d_\n}),\n reverse2d = op({\n reverse2d_: reverse2d_\n}),\n reverse3d = op({\n reverse3d_: reverse3d_\n}),\n reverse4d = op({\n reverse4d_: reverse4d_\n});\nexports.reverse4d = reverse4d;\nexports.reverse3d = reverse3d;\nexports.reverse2d = reverse2d;\nexports.reverse1d = reverse1d;\nexports.reverse = reverse;\n\nfunction maxPoolImpl_(e, t, n, r, o, a) {\n var i = convertToTensor(e, \"x\", \"maxPool\"),\n s = i,\n u = !1;\n 3 === i.rank && (u = !0, s = i.as4D(1, i.shape[0], i.shape[1], i.shape[2])), null == r && (r = [1, 1]), assert(4 === s.rank, \"Error in maxPool: input must be rank 4 but got rank \" + s.rank + \".\"), assert(eitherStridesOrDilationsAreOne(n, r), \"Error in maxPool: Either strides or dilations must be 1. Got strides \" + n + \" and dilations '\" + r + \"'\"), null != a && assert(isInt(o), \"Error in maxPool: pad must be an integer when using, dimRoundingMode \" + a + \" but got pad \" + o + \".\");\n var l = computePool2DInfo(s.shape, t, n, r, o, a),\n c = ENV.engine.runKernel(function (e, t) {\n return t(e.maxPool(s, l));\n }, {\n x: s\n }, function (e, a) {\n var i = a[0];\n return {\n x: function () {\n return maxPoolBackprop(e, s, i, t, n, r, o);\n }\n };\n });\n return u ? c.as3D(c.shape[1], c.shape[2], c.shape[3]) : c;\n}\n\nfunction maxPool_(e, t, n, r, o) {\n return maxPoolImpl_(e, t, n, 1, r, o);\n}\n\nfunction avgPoolImpl_(e, t, n, r, o, a) {\n var i = convertToTensor(e, \"x\", \"avgPool\", \"float32\");\n null == r && (r = [1, 1]), assert(eitherStridesOrDilationsAreOne(n, r), \"Error in avgPool: Either strides or dilations must be 1. Got strides \" + n + \" and dilations '\" + r + \"'\");\n var s = i,\n u = !1;\n 3 === i.rank && (u = !0, s = i.as4D(1, i.shape[0], i.shape[1], i.shape[2])), assert(4 === s.rank, \"Error in avgPool: x must be rank 4 but got rank \" + s.rank + \".\"), null != a && assert(isInt(o), \"Error in avgPool: pad must be an integer when using, dimRoundingMode \" + a + \" but got pad \" + o + \".\");\n var l = computePool2DInfo(s.shape, t, n, r, o, a),\n c = ENV.engine.runKernel(function (e) {\n return e.avgPool(s, l);\n }, {\n x: s\n }, function (e) {\n return {\n x: function () {\n return avgPoolBackprop(e, s, t, n, r, o);\n }\n };\n });\n return c = c.cast(i.dtype), u ? c.as3D(c.shape[1], c.shape[2], c.shape[3]) : c;\n}\n\nfunction avgPool_(e, t, n, r, o) {\n return avgPoolImpl_(e, t, n, 1, r, o);\n}\n\nfunction pool_(e, t, n, r, o, a) {\n null == o && (o = [1, 1]), null == a && (a = 1), 0 === r && (r = \"valid\");\n var i = convertToTensor(e, \"x\", \"maxPool\"),\n s = i,\n u = !1;\n 3 === i.rank && (u = !0, s = i.as4D(1, i.shape[0], i.shape[1], i.shape[2])), assert(eitherStridesOrDilationsAreOne(a, o), \"Error in pool: Either strides or dilations must be 1. Got strides \" + a + \" and dilations '\" + o + \"'\");\n var l,\n c = computePool2DInfo(s.shape, t, a, o, r),\n p = [c.dilationHeight, c.dilationWidth];\n l = \"same\" === r ? withSpaceToBatchBasePaddings([c.filterHeight, c.filterWidth], p) : [[0, 0], [0, 0]];\n var d = 1 === p[0] && 1 === p[1],\n h = requiredSpaceToBatchPaddings([c.inHeight, c.inWidth], p, l),\n f = h[0],\n m = h[1],\n g = d ? r : \"valid\",\n v = d ? s : spaceToBatchND(s, p, f),\n y = (\"avg\" === n ? function () {\n return avgPoolImpl_(v, t, a, 1, g);\n } : function () {\n return maxPoolImpl_(v, t, a, 1, g);\n })(),\n x = d ? y : batchToSpaceND(y, p, m);\n return u ? x.as3D(x.shape[1], x.shape[2], x.shape[3]) : x;\n}\n\nfunction maxPoolBackprop(e, t, n, r, o, a, i, s) {\n var u = convertToTensor(e, \"dy\", \"maxPoolBackprop\"),\n l = convertToTensor(t, \"input\", \"maxPoolBackprop\"),\n c = convertToTensor(n, \"output\", \"maxPoolBackprop\");\n assert(l.rank === u.rank, \"Rank of input (\" + l.rank + \") does not match rank of dy (\" + u.rank + \")\"), null == a && (a = [1, 1]), assert(eitherStridesOrDilationsAreOne(o, a), \"Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides \" + o + \" and dilations '\" + a + \"'\"), assert(4 === u.rank, \"Error in maxPoolBackprop: dy must be rank 4 but got rank \" + u.rank + \".\"), assert(4 === l.rank, \"Error in maxPoolBackprop: input must be rank 4 but got rank \" + l.rank + \".\"), null != s && assert(isInt(i), \"Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode \" + s + \" but got pad \" + i + \".\");\n var p = computePool2DInfo(l.shape, r, o, a, i, s);\n return ENV.engine.runKernel(function (e) {\n return e.maxPoolBackprop(u, l, c, p);\n }, {\n $dy: u,\n $input: l\n });\n}\n\nfunction avgPoolBackprop(e, t, n, r, o, a) {\n var i = convertToTensor(e, \"dy\", \"avgPoolBackprop\"),\n s = convertToTensor(t, \"input\", \"avgPoolBackprop\");\n assert(s.rank === i.rank, \"Rank of input (\" + s.rank + \") does not match rank of dy (\" + i.rank + \")\"), null == o && (o = [1, 1]), assert(eitherStridesOrDilationsAreOne(r, o), \"Error in avgPoolBackprop: Either strides or dilations must be 1. Got strides \" + r + \" and dilations '\" + o + \"'\");\n var u = s,\n l = i,\n c = !1;\n 3 === s.rank && (c = !0, u = s.as4D(1, s.shape[0], s.shape[1], s.shape[2]), l = i.as4D(1, i.shape[0], i.shape[1], i.shape[2])), assert(4 === l.rank, \"Error in avgPoolBackprop: dy must be rank 4 but got rank \" + l.rank + \".\"), assert(4 === u.rank, \"Error in avgPoolBackprop: input must be rank 4 but got rank \" + u.rank + \".\");\n var p = computePool2DInfo(u.shape, n, r, o, a),\n d = ENV.engine.runKernel(function (e) {\n return e.avgPoolBackprop(l, u, p);\n }, {\n dy4D: l,\n input4D: u\n });\n return c ? d.as3D(d.shape[1], d.shape[2], d.shape[3]) : d;\n}\n\nfunction requiredSpaceToBatchPaddings(e, t, n) {\n var r = n.map(function (e) {\n return e[0];\n }),\n o = n.map(function (e) {\n return e[1];\n }),\n a = e.concat(r, o),\n i = t.map(function (e, t) {\n return (e - a[t] % e) % e;\n }),\n s = o.map(function (e, t) {\n return e + i[t];\n });\n return [t.map(function (e, t) {\n return [r[t], s[t]];\n }), t.map(function (e, t) {\n return [0, i[t]];\n })];\n}\n\nfunction withSpaceToBatchBasePaddings(e, t) {\n var n = e.map(function (e, n) {\n return e + (e - 1) * (t[n] - 1);\n }).map(function (e) {\n return e - 1;\n }),\n r = n.map(function (e) {\n return Math.floor(e / 2);\n }),\n o = n.map(function (e, t) {\n return e - r[t];\n });\n return n.map(function (e, t) {\n return [r[t], o[t]];\n });\n}\n\nvar maxPool = op({\n maxPool_: maxPool_\n}),\n avgPool = op({\n avgPool_: avgPool_\n}),\n pool = op({\n pool_: pool_\n});\nexports.pool = pool;\nexports.avgPool = avgPool;\nexports.maxPool = maxPool;\n\nfunction slice1d_(e, t, n) {\n var r = convertToTensor(e, \"x\", \"slice1d\");\n return assert(1 === r.rank, \"slice1d expects a rank-1 tensor, but got a rank-\" + r.rank + \" tensor\"), slice(r, [t], [n]);\n}\n\nfunction slice2d_(e, t, n) {\n var r = convertToTensor(e, \"x\", \"slice2d\");\n return assert(2 === r.rank, \"slice2d expects a rank-2 tensor, but got a rank-\" + r.rank + \" tensor\"), slice(r, t, n);\n}\n\nfunction slice3d_(e, t, n) {\n var r = convertToTensor(e, \"x\", \"slice3d\");\n return assert(3 === r.rank, \"slice3d expects a rank-3 tensor, but got a rank-\" + r.rank + \" tensor\"), slice(r, t, n);\n}\n\nfunction slice4d_(e, t, n) {\n var r = convertToTensor(e, \"x\", \"slice4d\");\n return assert(4 === r.rank, \"slice4d expects a rank-4 tensor, but got a rank-\" + r.rank + \" tensor\"), slice(r, t, n);\n}\n\nfunction slice_(e, t, n) {\n var r,\n o,\n a = convertToTensor(e, \"x\", \"slice\");\n if (0 === a.rank) throw new Error(\"Slicing scalar is not possible\");\n r = \"number\" == typeof t ? [t].concat(new Array(a.rank - 1).fill(0)) : t.length < a.rank ? t.concat(new Array(a.rank - t.length).fill(0)) : t.slice(), o = (o = null == n ? new Array(a.rank).fill(-1) : \"number\" == typeof n ? [n].concat(new Array(a.rank - 1).fill(-1)) : n.length < a.rank ? n.concat(new Array(a.rank - n.length).fill(-1)) : n).map(function (e, t) {\n return e >= 0 ? e : (assert(-1 === e, \"Bad value in size\"), a.shape[t] - r[t]);\n }), assertParamsValid(a, r, o);\n var i = a.shape;\n return ENV.engine.runKernel(function (e) {\n return e.slice(a, r, o);\n }, {\n $x: a\n }, function (e) {\n for (var t = [], n = 0; n < e.rank; n++) t.push([r[n], i[n] - r[n] - o[n]]);\n\n return {\n $x: function () {\n return e.pad(t);\n }\n };\n });\n}\n\nvar slice = op({\n slice_: slice_\n}),\n slice1d = op({\n slice1d_: slice1d_\n}),\n slice2d = op({\n slice2d_: slice2d_\n}),\n slice3d = op({\n slice3d_: slice3d_\n}),\n slice4d = op({\n slice4d_: slice4d_\n});\nexports.slice4d = slice4d;\nexports.slice3d = slice3d;\nexports.slice2d = slice2d;\nexports.slice1d = slice1d;\nexports.slice = slice;\n\nfunction logSumExp_(e, t, n) {\n void 0 === t && (t = null), void 0 === n && (n = !1);\n var r = convertToTensor(e, \"x\", \"logSumExp\"),\n o = parseAxisParam(t, r.shape),\n a = r.max(o, !0),\n i = r.sub(a).exp().sum(o).log(),\n s = a.reshape(i.shape).add(i);\n\n if (n) {\n var u = expandShapeToKeepDim(s.shape, o);\n return s.reshape(u);\n }\n\n return s;\n}\n\nfunction sum_(e, t, n) {\n void 0 === t && (t = null), void 0 === n && (n = !1);\n var r = convertToTensor(e, \"x\", \"sum\");\n \"bool\" === r.dtype && (r = r.toInt());\n var o = parseAxisParam(t, r.shape);\n return customGrad(function (e) {\n var t = getAxesPermutation(o, e.rank),\n r = o,\n a = e;\n null != t && (a = e.transpose(t), r = getInnerMostAxes(r.length, e.rank));\n var i = ENV.engine.runKernel(function (e) {\n return e.sum(a, r);\n }, {\n permutedX: a\n });\n\n if (n) {\n var s = expandShapeToKeepDim(i.shape, o);\n i = i.reshape(s);\n }\n\n return {\n value: i,\n gradFunc: function (t) {\n var n = e.shape.slice();\n return o.forEach(function (e) {\n n[e] = 1;\n }), t.reshape(n).mul(ones$1(e.shape, \"float32\"));\n }\n };\n })(r);\n}\n\nfunction prod_(e, t, n) {\n void 0 === t && (t = null), void 0 === n && (n = !1);\n var r = convertToTensor(e, \"x\", \"prod\");\n \"bool\" === r.dtype && (r = r.toInt());\n var o = parseAxisParam(t, r.shape),\n a = getAxesPermutation(o, r.rank),\n i = o,\n s = r;\n null != a && (s = r.transpose(a), i = getInnerMostAxes(i.length, r.rank));\n var u = ENV.engine.runKernel(function (e) {\n return e.prod(s, i);\n }, {\n permutedX: s\n });\n\n if (n) {\n var l = expandShapeToKeepDim(u.shape, o);\n u = u.reshape(l);\n }\n\n return u;\n}\n\nfunction mean_(e, t, n) {\n void 0 === t && (t = null), void 0 === n && (n = !1);\n var r = convertToTensor(e, \"x\", \"mean\"),\n o = parseAxisParam(t, r.shape),\n a = sizeFromShape(computeOutAndReduceShapes(r.shape, o)[1]);\n return customGrad(function (e) {\n var r = scalar(a);\n return {\n value: (r.dtype === e.dtype ? e : e.cast(r.dtype)).div(r).sum(t, n),\n gradFunc: function (t) {\n var n = e.shape.slice();\n return o.forEach(function (e) {\n n[e] = 1;\n }), t.reshape(n).mul(ones$1(e.shape, \"float32\")).div(r);\n }\n };\n })(r);\n}\n\nfunction gradForMinAndMax(e, t, n, r, o) {\n var a = t[0];\n return a.rank < n.rank && (a = a.reshape(expandShapeToKeepDim(a.shape, r))), e.rank < n.rank && (e = e.reshape(expandShapeToKeepDim(e.shape, r))), {\n $x: function () {\n var t = e.mul(n.equal(a).cast(e.dtype));\n return null == o ? t : t.transpose(o);\n }\n };\n}\n\nfunction min_(e, t, n) {\n void 0 === t && (t = null), void 0 === n && (n = !1);\n var r = convertToTensor(e, \"x\", \"min\"),\n o = r,\n a = parseAxisParam(t, r.shape),\n i = a,\n s = getAxesPermutation(i, r.rank);\n null != s && (r = r.transpose(s), i = getInnerMostAxes(i.length, r.rank));\n var u = ENV.engine.runKernel(function (e, t) {\n return t(e.min(r, i));\n }, {\n $x: r\n }, function (e, t) {\n return gradForMinAndMax(e, t, o, a, s);\n });\n\n if (n) {\n var l = expandShapeToKeepDim(u.shape, a);\n u = u.reshape(l);\n }\n\n return u;\n}\n\nfunction max_(e, t, n) {\n void 0 === t && (t = null), void 0 === n && (n = !1);\n var r = convertToTensor(e, \"x\", \"max\"),\n o = r,\n a = parseAxisParam(t, r.shape),\n i = a,\n s = getAxesPermutation(i, r.rank);\n null != s && (r = r.transpose(s), i = getInnerMostAxes(i.length, r.rank));\n var u = ENV.engine.runKernel(function (e, t) {\n return t(e.max(r, i));\n }, {\n $x: r\n }, function (e, t) {\n return gradForMinAndMax(e, t, o, a, s);\n });\n\n if (n) {\n var l = expandShapeToKeepDim(u.shape, a);\n u = u.reshape(l);\n }\n\n return u;\n}\n\nfunction argMin_(e, t) {\n void 0 === t && (t = 0);\n var n = convertToTensor(e, \"x\", \"argMin\");\n null == t && (t = 0);\n var r = parseAxisParam(t, n.shape),\n o = getAxesPermutation(r, n.rank);\n null != o && (n = n.transpose(o), r = getInnerMostAxes(r.length, n.rank));\n return ENV.engine.runKernel(function (e) {\n return e.argMin(n, r[0]);\n }, {\n $x: n\n }, function (e) {\n return {\n $x: function () {\n return zerosLike(n);\n }\n };\n });\n}\n\nfunction argMax_(e, t) {\n void 0 === t && (t = 0);\n var n = convertToTensor(e, \"x\", \"argMax\");\n null == t && (t = 0);\n var r = parseAxisParam(t, n.shape),\n o = getAxesPermutation(r, n.rank);\n null != o && (n = n.transpose(o), r = getInnerMostAxes(r.length, n.rank));\n return ENV.engine.runKernel(function (e) {\n return e.argMax(n, r[0]);\n }, {\n $x: n\n }, function (e) {\n return {\n $x: function () {\n return zerosLike(n);\n }\n };\n });\n}\n\nfunction all_(e, t, n) {\n void 0 === t && (t = null), void 0 === n && (n = !1);\n var r = convertToTensor(e, \"x\", \"all\", \"bool\"),\n o = parseAxisParam(t, r.shape),\n a = o,\n i = getAxesPermutation(a, r.rank);\n null != i && (r = r.transpose(i), a = getInnerMostAxes(a.length, r.rank));\n var s = ENV.engine.runKernel(function (e) {\n return e.all(r, a);\n }, {\n $x: r\n });\n\n if (n) {\n var u = expandShapeToKeepDim(s.shape, o);\n return s.reshape(u);\n }\n\n return s;\n}\n\nfunction any_(e, t, n) {\n void 0 === t && (t = null), void 0 === n && (n = !1);\n var r = convertToTensor(e, \"x\", \"any\", \"bool\"),\n o = parseAxisParam(t, r.shape),\n a = o,\n i = getAxesPermutation(a, r.rank);\n null != i && (r = r.transpose(i), a = getInnerMostAxes(a.length, r.rank));\n var s = ENV.engine.runKernel(function (e) {\n return e.any(r, a);\n }, {\n $x: r\n });\n\n if (n) {\n var u = expandShapeToKeepDim(s.shape, o);\n return s.reshape(u);\n }\n\n return s;\n}\n\nfunction moments_(e, t, n) {\n void 0 === t && (t = null), void 0 === n && (n = !1);\n var r = parseAxisParam(t, (e = convertToTensor(e, \"x\", \"moments\")).shape),\n o = e.mean(r, n),\n a = o.shape;\n return n || (a = expandShapeToKeepDim(o.shape, r)), {\n mean: o,\n variance: e.toFloat().sub(o.reshape(a)).square().mean(r, n)\n };\n}\n\nvar all = op({\n all_: all_\n}),\n any = op({\n any_: any_\n}),\n argMax = op({\n argMax_: argMax_\n}),\n argMin = op({\n argMin_: argMin_\n}),\n logSumExp = op({\n logSumExp_: logSumExp_\n}),\n max = op({\n max_: max_\n}),\n mean = op({\n mean_: mean_\n}),\n min = op({\n min_: min_\n}),\n moments = op({\n moments_: moments_\n}),\n sum$1 = op({\n sum_: sum_\n}),\n prod = op({\n prod_: prod_\n});\nexports.prod = prod;\nexports.sum = sum$1;\nexports.moments = moments;\nexports.min = min;\nexports.mean = mean;\nexports.max = max;\nexports.logSumExp = logSumExp;\nexports.argMin = argMin;\nexports.argMax = argMax;\nexports.any = any;\nexports.all = all;\n\nfunction notEqual_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"notEqual\"),\n o = convertToTensor(t, \"b\", \"notEqual\");\n return n = makeTypesMatch(r, o), r = n[0], o = n[1], assertAndGetBroadcastShape(r.shape, o.shape), ENV.engine.runKernel(function (e) {\n return e.notEqual(r, o);\n }, {\n $a: r,\n $b: o\n });\n}\n\nfunction notEqualStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"notEqualStrict\"),\n r = convertToTensor(t, \"b\", \"notEqualStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in notEqualStrict: \"), n.notEqual(r);\n}\n\nfunction less_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"less\"),\n o = convertToTensor(t, \"b\", \"less\");\n return n = makeTypesMatch(r, o), r = n[0], o = n[1], assertAndGetBroadcastShape(r.shape, o.shape), ENV.engine.runKernel(function (e) {\n return e.less(r, o);\n }, {\n $a: r,\n $b: o\n });\n}\n\nfunction lessStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"lessStrict\"),\n r = convertToTensor(t, \"b\", \"lessStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in lessStrict: \"), n.less(r);\n}\n\nfunction equal_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"equal\"),\n o = convertToTensor(t, \"b\", \"equal\");\n return n = makeTypesMatch(r, o), r = n[0], o = n[1], assertAndGetBroadcastShape(r.shape, o.shape), ENV.engine.runKernel(function (e) {\n return e.equal(r, o);\n }, {\n $a: r,\n $b: o\n });\n}\n\nfunction equalStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"equalStrict\"),\n r = convertToTensor(t, \"b\", \"equalStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in equalStrict: \"), n.equal(r);\n}\n\nfunction lessEqual_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"lessEqual\"),\n o = convertToTensor(t, \"b\", \"lessEqual\");\n return n = makeTypesMatch(r, o), r = n[0], o = n[1], assertAndGetBroadcastShape(r.shape, o.shape), ENV.engine.runKernel(function (e) {\n return e.lessEqual(r, o);\n }, {\n $a: r,\n $b: o\n });\n}\n\nfunction lessEqualStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"lessEqualStrict\"),\n r = convertToTensor(t, \"b\", \"lessEqualStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in lessEqualStrict: \"), n.lessEqual(r);\n}\n\nfunction greater_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"greater\"),\n o = convertToTensor(t, \"b\", \"greater\");\n return n = makeTypesMatch(r, o), r = n[0], o = n[1], assertAndGetBroadcastShape(r.shape, o.shape), ENV.engine.runKernel(function (e) {\n return e.greater(r, o);\n }, {\n $a: r,\n $b: o\n });\n}\n\nfunction greaterStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"greaterStrict\"),\n r = convertToTensor(t, \"b\", \"greaterStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in greaterStrict: \"), n.greater(r);\n}\n\nfunction greaterEqual_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"greaterEqual\"),\n o = convertToTensor(t, \"b\", \"greaterEqual\");\n n = makeTypesMatch(r, o), r = n[0], o = n[1], assertAndGetBroadcastShape(r.shape, o.shape);\n return ENV.engine.runKernel(function (e) {\n return e.greaterEqual(r, o);\n }, {\n $a: r,\n $b: o\n }, function (e) {\n return {\n $a: function () {\n return zerosLike(r);\n },\n $b: function () {\n return zerosLike(o);\n }\n };\n });\n}\n\nfunction greaterEqualStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"greaterEqualStrict\"),\n r = convertToTensor(t, \"b\", \"greaterEqualStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in greaterEqualStrict: \"), n.greaterEqual(r);\n}\n\nvar equal = op({\n equal_: equal_\n}),\n equalStrict = op({\n equalStrict_: equalStrict_\n}),\n greater = op({\n greater_: greater_\n}),\n greaterEqual = op({\n greaterEqual_: greaterEqual_\n}),\n greaterEqualStrict = op({\n greaterEqualStrict_: greaterEqualStrict_\n}),\n greaterStrict = op({\n greaterStrict_: greaterStrict_\n}),\n less = op({\n less_: less_\n}),\n lessEqual = op({\n lessEqual_: lessEqual_\n}),\n lessEqualStrict = op({\n lessEqualStrict_: lessEqualStrict_\n}),\n lessStrict = op({\n lessStrict_: lessStrict_\n}),\n notEqual = op({\n notEqual_: notEqual_\n}),\n notEqualStrict = op({\n notEqualStrict_: notEqualStrict_\n});\nexports.notEqualStrict = notEqualStrict;\nexports.notEqual = notEqual;\nexports.lessStrict = lessStrict;\nexports.lessEqualStrict = lessEqualStrict;\nexports.lessEqual = lessEqual;\nexports.less = less;\nexports.greaterStrict = greaterStrict;\nexports.greaterEqualStrict = greaterEqualStrict;\nexports.greaterEqual = greaterEqual;\nexports.greater = greater;\nexports.equalStrict = equalStrict;\nexports.equal = equal;\n\nfunction add_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"add\"),\n o = convertToTensor(t, \"b\", \"add\");\n n = makeTypesMatch(r, o), r = n[0], o = n[1];\n var a = assertAndGetBroadcastShape(r.shape, o.shape);\n return ENV.engine.runKernel(function (e) {\n return e.add(r, o);\n }, {\n $a: r,\n $b: o\n }, function (e) {\n return {\n $a: function () {\n var t = e,\n n = getReductionAxes(r.shape, a);\n return n.length > 0 && (t = t.sum(n)), t.reshape(r.shape);\n },\n $b: function () {\n var t = e,\n n = getReductionAxes(o.shape, a);\n return n.length > 0 && (t = t.sum(n)), t.reshape(o.shape);\n }\n };\n });\n}\n\nfunction addN_(e) {\n assert(Array.isArray(e), function () {\n return \"The argument passed to tf.addN() must be a list of tensors\";\n }), assert(e.length >= 1, function () {\n return \"Must pass at least one tensor to tf.addN(), but got \" + e.length;\n });\n var t = e.map(function (e, t) {\n return convertToTensor(e, \"tensors\" + t, \"addN\");\n }),\n n = t[0];\n t.forEach(function (e) {\n if (e.dtype !== n.dtype) throw new Error(\"All tensors passed to tf.addN() must have the same dtype\");\n }), t.forEach(function (e) {\n if (!arraysEqual(e.shape, n.shape)) throw new Error(\"All tensors passed to tf.addN() must have the same shape\");\n });\n var r = t;\n return ENV.engine.runKernel(function (e) {\n return e.addN(t);\n }, r, function (e) {\n var n = {};\n return t.forEach(function (t, r) {\n n[r] = function () {\n return e.clone();\n };\n }), n;\n });\n}\n\nfunction addStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"addStrict\"),\n r = convertToTensor(t, \"b\", \"addStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in addStrict: \"), n.add(r);\n}\n\nfunction sub_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"sub\"),\n o = convertToTensor(t, \"b\", \"sub\");\n n = makeTypesMatch(r, o), r = n[0], o = n[1];\n var a = assertAndGetBroadcastShape(r.shape, o.shape);\n return ENV.engine.runKernel(function (e) {\n return e.subtract(r, o);\n }, {\n $a: r,\n $b: o\n }, function (e) {\n return {\n $a: function () {\n var t = e,\n n = getReductionAxes(r.shape, a);\n return n.length > 0 && (t = t.sum(n)), t.reshape(r.shape);\n },\n $b: function () {\n var t = e,\n n = getReductionAxes(o.shape, a);\n return n.length > 0 && (t = t.sum(n)), t.neg().reshape(o.shape);\n }\n };\n });\n}\n\nfunction subStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"subStrict\"),\n r = convertToTensor(t, \"b\", \"subStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in subStrict: \"), n.sub(r);\n}\n\nfunction pow_(e, t) {\n var n = convertToTensor(e, \"base\", \"pow\"),\n r = convertToTensor(t, \"exp\", \"pow\"),\n o = assertAndGetBroadcastShape(n.shape, r.shape);\n e = n.cast(upcastType(n.dtype, r.dtype)), t = r.cast(upcastType(n.dtype, r.dtype));\n return ENV.engine.runKernel(function (e, t) {\n return t(e.pow(n, r));\n }, {\n $base: n,\n $exp: r\n }, function (e, t) {\n var a = t[0];\n return {\n $base: function () {\n var t = r.toFloat(),\n a = e.mul(t.mul(n.pow(t.sub(scalar(1))))),\n i = getReductionAxes(n.shape, o);\n return i.length > 0 && (a = a.sum(i)), a.reshape(n.shape);\n },\n $exp: function () {\n var t = n.greater(0),\n i = n.log().where(t, zerosLike(n)),\n s = e.mul(a.mul(i)),\n u = getReductionAxes(r.shape, o);\n return u.length > 0 && (s = s.sum(u)), s.reshape(r.shape);\n }\n };\n });\n}\n\nfunction powStrict_(e, t) {\n return assertShapesMatch(e.shape, t.shape, \"Error in powStrict: \"), e.pow(t);\n}\n\nfunction mul_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"mul\"),\n o = convertToTensor(t, \"b\", \"mul\");\n n = makeTypesMatch(r, o), r = n[0], o = n[1];\n var a = assertAndGetBroadcastShape(r.shape, o.shape);\n return ENV.engine.runKernel(function (e) {\n return e.multiply(r, o);\n }, {\n $a: r,\n $b: o\n }, function (e) {\n return {\n $a: function () {\n var t = e.mul(o.toFloat()),\n n = getReductionAxes(r.shape, a);\n return n.length > 0 ? t.sum(n).reshape(r.shape) : t;\n },\n $b: function () {\n var t = e.mul(r.toFloat()),\n n = getReductionAxes(o.shape, a);\n return n.length > 0 ? t.sum(n).reshape(o.shape) : t;\n }\n };\n });\n}\n\nfunction mulStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"mul\"),\n r = convertToTensor(t, \"b\", \"mul\");\n return assertShapesMatch(n.shape, r.shape, \"Error in multiplyStrict: \"), n.mul(r);\n}\n\nfunction div_(e, t) {\n var n,\n r,\n o = convertToTensor(e, \"a\", \"div\"),\n a = convertToTensor(t, \"b\", \"div\");\n if (n = makeTypesMatch(o, a), o = n[0], a = n[1], \"int32\" === o.dtype && \"int32\" === a.dtype) return floorDiv(o, a);\n\n r = function (e) {\n return e.realDivide(o, a);\n };\n\n var i = assertAndGetBroadcastShape(o.shape, a.shape);\n return ENV.engine.runKernel(r, {\n $a: o,\n $b: a\n }, function (e) {\n return {\n $a: function () {\n var t = e.div(a.toFloat()),\n n = getReductionAxes(o.shape, i);\n return n.length > 0 ? t.sum(n).reshape(o.shape) : t;\n },\n $b: function () {\n var t = e.mul(o.toFloat()),\n n = getReductionAxes(a.shape, i);\n n.length > 0 && (t = t.sum(n).reshape(a.shape));\n var r = a.square();\n return t.div(r.toFloat()).neg();\n }\n };\n });\n}\n\nfunction floorDiv_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"floorDiv\"),\n o = convertToTensor(t, \"b\", \"floorDiv\");\n n = makeTypesMatch(r, o), r = n[0], o = n[1];\n var a = assertAndGetBroadcastShape(r.shape, o.shape);\n return ENV.engine.runKernel(function (e) {\n return e.floorDiv(r, o);\n }, {\n $a: r,\n $b: o\n }, function (e) {\n return {\n $a: function () {\n var t = e.div(o.toFloat()),\n n = getReductionAxes(r.shape, a);\n return n.length > 0 ? t.sum(n).reshape(r.shape) : t;\n },\n $b: function () {\n var t = e.mul(r.toFloat()),\n n = getReductionAxes(o.shape, a);\n n.length > 0 && (t = t.sum(n).reshape(o.shape));\n var i = o.square();\n return t.div(i.toFloat()).neg();\n }\n };\n });\n}\n\nfunction divStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"div\"),\n r = convertToTensor(t, \"b\", \"div\");\n return assertShapesMatch(n.shape, r.shape, \"Error in divideStrict: \"), n.div(r);\n}\n\nfunction mod_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"mod\"),\n o = convertToTensor(t, \"b\", \"mod\");\n n = makeTypesMatch(r, o), r = n[0], o = n[1];\n var a = assertAndGetBroadcastShape(r.shape, o.shape);\n return ENV.engine.runKernel(function (e) {\n return e.mod(r, o);\n }, {\n $a: r,\n $b: o\n }, function (e) {\n return {\n $a: function () {\n var t = getReductionAxes(r.shape, a);\n return t.length > 0 ? e.sum(t).reshape(r.shape) : e;\n },\n $b: function () {\n var t = e.mul(r.div(o).floor().neg()),\n n = getReductionAxes(o.shape, a);\n return n.length > 0 ? t.sum(n).reshape(o.shape) : t;\n }\n };\n });\n}\n\nfunction modStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"modStrict\"),\n r = convertToTensor(t, \"b\", \"modStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in modStrict: \"), n.mod(r);\n}\n\nfunction minimum_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"minimum\"),\n o = convertToTensor(t, \"b\", \"minimum\");\n n = makeTypesMatch(r, o), r = n[0], o = n[1], \"bool\" === r.dtype && (r = r.toInt(), o = o.toInt()), assertAndGetBroadcastShape(r.shape, o.shape);\n return ENV.engine.runKernel(function (e) {\n return e.minimum(r, o);\n }, {\n $a: r,\n $b: o\n }, function (e) {\n return {\n $a: function () {\n return e.mul(r.lessEqual(o).toFloat());\n },\n $b: function () {\n return e.mul(r.greater(o).toFloat());\n }\n };\n });\n}\n\nfunction minimumStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"minimumStrict\"),\n r = convertToTensor(t, \"b\", \"minimumStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in minimumStrict: \"), n.minimum(r);\n}\n\nfunction maximum_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"maximum\"),\n o = convertToTensor(t, \"b\", \"maximum\");\n n = makeTypesMatch(r, o), r = n[0], o = n[1], \"bool\" === r.dtype && (r = r.toInt(), o = o.toInt()), assertAndGetBroadcastShape(r.shape, o.shape);\n return ENV.engine.runKernel(function (e) {\n return e.maximum(r, o);\n }, {\n $a: r,\n $b: o\n }, function (e) {\n return {\n $a: function () {\n return e.mul(r.greaterEqual(o).toFloat());\n },\n $b: function () {\n return e.mul(r.less(o).toFloat());\n }\n };\n });\n}\n\nfunction maximumStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"maximumStrict\"),\n r = convertToTensor(t, \"b\", \"maximumStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in maximumStrict: \"), n.maximum(r);\n}\n\nfunction squaredDifference_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"squaredDifference\"),\n o = convertToTensor(t, \"b\", \"squaredDifference\");\n n = makeTypesMatch(r, o), r = n[0], o = n[1], assertAndGetBroadcastShape(r.shape, o.shape);\n return ENV.engine.runKernel(function (e) {\n return e.squaredDifference(r, o);\n }, {\n $a: r,\n $b: o\n }, function (e) {\n var t = scalar(2);\n return {\n $a: function () {\n return e.mul(r.sub(o).mul(t));\n },\n $b: function () {\n return e.mul(o.sub(r).mul(t));\n }\n };\n });\n}\n\nfunction squaredDifferenceStrict_(e, t) {\n var n = convertToTensor(e, \"a\", \"squaredDifferenceStrict\"),\n r = convertToTensor(t, \"b\", \"squaredDifferenceStrict\");\n return assertShapesMatch(n.shape, r.shape, \"Error in squaredDifferenceStrict: \"), n.squaredDifference(r);\n}\n\nfunction atan2_(e, t) {\n var n,\n r = convertToTensor(e, \"a\", \"atan2\"),\n o = convertToTensor(t, \"b\", \"atan2\");\n n = makeTypesMatch(r, o), r = n[0], o = n[1];\n var a = assertAndGetBroadcastShape(r.shape, o.shape);\n return ENV.engine.runKernel(function (e) {\n return e.atan2(r, o);\n }, {\n $a: r,\n $b: o\n }, function (e) {\n return {\n $a: function () {\n var t = add(r.square(), o.square()),\n n = e.mul(o.div(t)),\n i = getReductionAxes(r.shape, a);\n return i.length > 0 && (n = n.sum(i)), n.reshape(r.shape);\n },\n $b: function () {\n var t = add(r.square(), o.square()),\n n = neg(e.mul(r.div(t))),\n i = getReductionAxes(o.shape, a);\n return i.length > 0 && (n = n.sum(i)), n.reshape(o.shape);\n }\n };\n });\n}\n\nvar add = op({\n add_: add_\n}),\n addN = op({\n addN_: addN_\n}),\n addStrict = op({\n addStrict_: addStrict_\n}),\n atan2 = op({\n atan2_: atan2_\n}),\n div = op({\n div_: div_\n}),\n divStrict = op({\n divStrict_: divStrict_\n}),\n floorDiv = op({\n floorDiv_: floorDiv_\n}),\n maximum = op({\n maximum_: maximum_\n}),\n maximumStrict = op({\n maximumStrict_: maximumStrict_\n}),\n minimum = op({\n minimum_: minimum_\n}),\n minimumStrict = op({\n minimumStrict_: minimumStrict_\n}),\n mod = op({\n mod_: mod_\n}),\n modStrict = op({\n modStrict_: modStrict_\n}),\n mul = op({\n mul_: mul_\n}),\n mulStrict = op({\n mulStrict_: mulStrict_\n}),\n pow = op({\n pow_: pow_\n}),\n powStrict = op({\n powStrict_: powStrict_\n}),\n squaredDifference = op({\n squaredDifference_: squaredDifference_\n}),\n squaredDifferenceStrict = op({\n squaredDifferenceStrict_: squaredDifferenceStrict_\n}),\n sub = op({\n sub_: sub_\n}),\n subStrict = op({\n subStrict_: subStrict_\n});\nexports.subStrict = subStrict;\nexports.sub = sub;\nexports.squaredDifferenceStrict = squaredDifferenceStrict;\nexports.squaredDifference = squaredDifference;\nexports.powStrict = powStrict;\nexports.pow = pow;\nexports.mulStrict = mulStrict;\nexports.mul = mul;\nexports.modStrict = modStrict;\nexports.mod = mod;\nexports.minimumStrict = minimumStrict;\nexports.minimum = minimum;\nexports.maximumStrict = maximumStrict;\nexports.maximum = maximum;\nexports.floorDiv = floorDiv;\nexports.divStrict = divStrict;\nexports.div = div;\nexports.atan2 = atan2;\nexports.addStrict = addStrict;\nexports.addN = addN;\nexports.add = add;\n\nfunction logicalNot_(e) {\n var t = convertToTensor(e, \"x\", \"logicalNot\", \"bool\");\n return ENV.engine.runKernel(function (e) {\n return e.logicalNot(t);\n }, {\n $x: t\n });\n}\n\nfunction logicalAnd_(e, t) {\n var n = convertToTensor(e, \"a\", \"logicalAnd\", \"bool\"),\n r = convertToTensor(t, \"b\", \"logicalAnd\", \"bool\");\n return assertAndGetBroadcastShape(n.shape, r.shape), ENV.engine.runKernel(function (e) {\n return e.logicalAnd(n, r);\n }, {\n $a: n,\n $b: r\n });\n}\n\nfunction logicalOr_(e, t) {\n var n = convertToTensor(e, \"a\", \"logicalOr\", \"bool\"),\n r = convertToTensor(t, \"b\", \"logicalOr\", \"bool\");\n return assertAndGetBroadcastShape(n.shape, r.shape), ENV.engine.runKernel(function (e) {\n return e.logicalOr(n, r);\n }, {\n $a: n,\n $b: r\n });\n}\n\nfunction logicalXor_(e, t) {\n var n = convertToTensor(e, \"a\", \"logicalXor\", \"bool\"),\n r = convertToTensor(t, \"b\", \"logicalXor\", \"bool\");\n return assertAndGetBroadcastShape(n.shape, r.shape), logicalOr(e, t).logicalAnd(logicalAnd(e, t).logicalNot());\n}\n\nfunction where_(e, t, n) {\n var r = convertToTensor(t, \"a\", \"where\"),\n o = convertToTensor(n, \"b\", \"where\"),\n a = convertToTensor(e, \"condition\", \"where\", \"bool\");\n assertShapesMatch(r.shape, o.shape, \"Error in where: \"), 1 === a.rank ? assert(a.shape[0] === r.shape[0], \"The first dimension of `a` must match the size of `condition`.\") : assertShapesMatch(a.shape, o.shape, \"Error in where: \");\n return ENV.engine.runKernel(function (e) {\n return e.select(a, r, o);\n }, {\n $condition: a,\n $a: r,\n $b: o\n }, function (e) {\n return {\n $condition: function () {\n return zerosLike(a).toFloat();\n },\n $a: function () {\n return e.mul(a.cast(e.dtype));\n },\n $b: function () {\n return e.mul(a.logicalNot().cast(e.dtype));\n }\n };\n });\n}\n\nfunction whereAsync_(e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n, r;\n return __generator(this, function (o) {\n switch (o.label) {\n case 0:\n return [4, (t = convertToTensor(e, \"condition\", \"whereAsync\", \"bool\")).data()];\n\n case 1:\n return n = o.sent(), r = whereImpl(t.shape, n), e !== t && t.dispose(), [2, r];\n }\n });\n });\n}\n\nvar logicalAnd = op({\n logicalAnd_: logicalAnd_\n}),\n logicalNot = op({\n logicalNot_: logicalNot_\n}),\n logicalOr = op({\n logicalOr_: logicalOr_\n}),\n logicalXor = op({\n logicalXor_: logicalXor_\n}),\n where = op({\n where_: where_\n}),\n whereAsync = whereAsync_;\nexports.whereAsync = whereAsync;\nexports.where = where;\nexports.logicalXor = logicalXor;\nexports.logicalOr = logicalOr;\nexports.logicalNot = logicalNot;\nexports.logicalAnd = logicalAnd;\n\nfunction relu_(e) {\n var t = convertToTensor(e, \"x\", \"relu\");\n if (\"bool\" === t.dtype) return t.toInt();\n return ENV.engine.runKernel(function (e) {\n return e.relu(t);\n }, {\n $x: t\n }, function (e) {\n var n = t.step();\n return {\n $x: function () {\n return e.mulStrict(n.toFloat());\n }\n };\n });\n}\n\nfunction elu_(e) {\n var t = convertToTensor(e, \"x\", \"elu\");\n return ENV.engine.runKernel(function (e, n) {\n return n(e.elu(t));\n }, {\n $x: t\n }, function (e, t) {\n var n = t[0];\n return {\n $x: function () {\n return ENV.engine.runKernel(function (t) {\n return t.eluDer(e, n);\n }, {\n dy: e,\n y: n\n });\n }\n };\n });\n}\n\nfunction selu_(e) {\n var t = convertToTensor(e, \"x\", \"selu\");\n return ENV.engine.runKernel(function (e) {\n return e.selu(t);\n }, {\n $x: t\n }, function (e) {\n return {\n $x: function () {\n var n = t.greater(scalar(0)),\n r = scalar(SELU_SCALEALPHA),\n o = scalar(SELU_SCALE),\n a = e.mul(o),\n i = e.mul(r).mul(t.toFloat().exp());\n return where(n, a, i);\n }\n };\n });\n}\n\nfunction leakyRelu_(e, t) {\n void 0 === t && (t = .2);\n var n = convertToTensor(e, \"x\", \"leakyRelu\");\n return maximum(scalar(t).mul(n), n);\n}\n\nfunction prelu_(e, t) {\n var n = convertToTensor(e, \"x\", \"prelu\"),\n r = convertToTensor(t, \"alpha\", \"prelu\");\n return ENV.engine.runKernel(function (e) {\n return e.prelu(n, r);\n }, {\n $x: n,\n $alpha: r\n }, function (e) {\n var t = n.greater(0);\n return {\n $x: function () {\n return where(t, e, e.mul(r));\n },\n $alpha: function () {\n var o = where(t, zerosLike(e), e.mul(n)),\n a = getReductionAxes(r.shape, e.shape);\n return a.length > 0 && (o = o.sum(a)), o.reshape(r.shape);\n }\n };\n });\n}\n\nvar elu = op({\n elu_: elu_\n}),\n leakyRelu = op({\n leakyRelu_: leakyRelu_\n}),\n prelu = op({\n prelu_: prelu_\n}),\n relu = op({\n relu_: relu_\n}),\n selu = op({\n selu_: selu_\n});\nexports.selu = selu;\nexports.relu = relu;\nexports.prelu = prelu;\nexports.leakyRelu = leakyRelu;\nexports.elu = elu;\n\nfunction transpose_(e, t) {\n var n = convertToTensor(e, \"x\", \"transpose\");\n if (null == t && (t = n.shape.map(function (e, t) {\n return t;\n }).reverse()), assert(n.rank === t.length, \"Error in transpose: rank of input \" + n.rank + \" must match length of perm \" + t + \".\"), t.forEach(function (e) {\n assert(e >= 0 && e < n.rank, \"All entries in 'perm' must be between 0 and \" + (n.rank - 1) + \" but got \" + t);\n }), n.rank <= 1) return n.clone();\n return ENV.engine.runKernel(function (e) {\n return e.transpose(n, t);\n }, {\n $x: n\n }, function (e) {\n var n = getUndoAxesPermutation(t);\n return {\n $x: function () {\n return e.transpose(n);\n }\n };\n });\n}\n\nvar transpose = op({\n transpose_: transpose_\n});\nexports.transpose = transpose;\n\nfunction localResponseNormalization_(e, t, n, r, o) {\n void 0 === t && (t = 5), void 0 === n && (n = 1), void 0 === r && (r = 1), void 0 === o && (o = .5);\n var a = convertToTensor(e, \"x\", \"localResponseNormalization\");\n assert(4 === a.rank || 3 === a.rank, \"Error in localResponseNormalization: x must be rank 3 or 4 but got\\n rank \" + a.rank + \".\"), assert(isInt(t), \"Error in localResponseNormalization: depthRadius must be an integer\\n but got depthRadius \" + t + \".\");\n var i = a,\n s = !1;\n 3 === a.rank && (s = !0, i = a.as4D(1, a.shape[0], a.shape[1], a.shape[2]));\n var u = ENV.engine.runKernel(function (e, a) {\n return a(e.localResponseNormalization4D(i, t, n, r, o));\n }, {\n x4D: i\n }, function (e, a) {\n var s = a[0];\n return {\n x4D: function () {\n return ENV.engine.runKernel(function (a) {\n return a.LRNGrad(e, i, s, t, n, r, o);\n }, {});\n }\n };\n });\n return s ? u.as3D(u.shape[1], u.shape[2], u.shape[3]) : u;\n}\n\nvar localResponseNormalization = op({\n localResponseNormalization_: localResponseNormalization_\n});\nexports.localResponseNormalization = localResponseNormalization;\n\nfunction norm_(e, t, n, r) {\n void 0 === t && (t = \"euclidean\"), void 0 === n && (n = null), void 0 === r && (r = !1);\n var o = normImpl(e = convertToTensor(e, \"x\", \"norm\"), t, n),\n a = o.shape;\n\n if (r) {\n var i = parseAxisParam(n, e.shape);\n a = expandShapeToKeepDim(o.shape, i);\n }\n\n return o.reshape(a);\n}\n\nfunction normImpl(e, t, n) {\n if (void 0 === n && (n = null), 0 === e.rank) return e.abs();\n if (1 !== e.rank && null === n) return normImpl(e.reshape([-1]), t, n);\n\n if (1 === e.rank || \"number\" == typeof n || Array.isArray(n) && 1 === n.length) {\n if (1 === t) return e.abs().sum(n);\n if (t === 1 / 0) return e.abs().max(n);\n if (t === -1 / 0) return e.abs().min(n);\n if (\"euclidean\" === t || 2 === t) return e.abs().pow(scalar(2, \"int32\")).sum(n).sqrt();\n throw new Error(\"Error in norm: invalid ord value: \" + t);\n }\n\n if (Array.isArray(n) && 2 === n.length) {\n if (1 === t) return e.abs().sum(n[0]).max(n[1] - 1);\n if (t === 1 / 0) return e.abs().sum(n[1]).max(n[0]);\n if (t === -1 / 0) return e.abs().sum(n[1]).min(n[0]);\n if (\"fro\" === t || \"euclidean\" === t) return e.square().sum(n).sqrt();\n throw new Error(\"Error in norm: invalid ord value: \" + t);\n }\n\n throw new Error(\"Error in norm: invalid axis: \" + n);\n}\n\nvar norm = op({\n norm_: norm_\n});\nexports.norm = norm;\n\nfunction unsortedSegmentSum_(e, t, n) {\n var r = convertToTensor(e, \"x\", \"unsortedSegmentSum\"),\n o = convertToTensor(t, \"segmentIds\", \"unsortedSegmentSum\", \"int32\");\n assert(isInt(n), \"numSegments must be of dtype int\");\n return ENV.engine.runKernel(function (e) {\n return e.unsortedSegmentSum(r, o, n);\n }, {\n $x: r\n }, function (e) {\n return {\n $x: function () {\n return gatherDropNegatives(e, o);\n }\n };\n });\n}\n\nfunction gather_(e, t, n) {\n void 0 === n && (n = 0);\n var r = convertToTensor(e, \"x\", \"gather\"),\n o = convertToTensor(t, \"indices\", \"gather\", \"int32\");\n n = parseAxisParam(n, r.shape)[0];\n var a = collectGatherOpShapeInfo(r, o, n);\n return ENV.engine.runKernel(function (e) {\n return e.gather(r, o.flatten(), n);\n }, {\n $x: r\n }, function (e) {\n return {\n $x: function () {\n var t = r.shape,\n a = o.size,\n i = t.slice(0, n),\n s = i.length,\n u = t.slice(n, t.length).slice(1),\n l = u.length,\n c = arrayRange(0, s),\n p = arrayRange(s + 1, s + 1 + l),\n d = arrayConcat([i, [a], u]),\n h = e.reshape(d),\n f = o.reshape([a]),\n m = arrayConcat([[s], c, p]),\n g = h.transpose(m),\n v = unsortedSegmentSum(g, f, r.shape[n]),\n y = getUndoAxesPermutation(m);\n return v = v.transpose(y);\n }\n };\n }).reshape(a.outputShape);\n}\n\nfunction arrayRange(e, t) {\n for (var n = [], r = e; r < t; ++r) n.push(r);\n\n return n;\n}\n\nfunction arrayConcat(e) {\n for (var t = [], n = 0; n < e.length; ++n) for (var r = 0; r < e[n].length; ++r) t.push(e[n][r]);\n\n return t;\n}\n\nfunction gatherDropNegatives(e, t) {\n for (var n = maximum(t, zerosLike(t)), r = gather(e, n), o = greaterEqual(t, scalar(0, \"int32\")), a = r.rank - o.rank, i = 0; i < a; ++i) o = expandDims(o, i + 1);\n\n o = logicalAnd(o, ones$1(r.shape, \"bool\"));\n var s = zerosLike(r);\n return where(o, r, s);\n}\n\nvar gather = op({\n gather_: gather_\n}),\n unsortedSegmentSum = op({\n unsortedSegmentSum_: unsortedSegmentSum_\n});\nexports.unsortedSegmentSum = unsortedSegmentSum;\nexports.gather = gather;\n\nfunction multiRNNCell_(e, t, n, r) {\n for (var o = convertToTensor(t, \"data\", \"multiRNNCell\"), a = convertToTensorArray(n, \"c\", \"multiRNNCell\"), i = convertToTensorArray(r, \"h\", \"multiRNNCell\"), s = o, u = [], l = 0; l < e.length; l++) {\n var c = e[l](s, a[l], i[l]);\n u.push(c[0]), u.push(c[1]), s = c[1];\n }\n\n var p = [],\n d = [];\n\n for (l = 0; l < u.length; l += 2) p.push(u[l]), d.push(u[l + 1]);\n\n return [p, d];\n}\n\nfunction basicLSTMCell_(e, t, n, r, o, a) {\n var i = convertToTensor(e, \"forgetBias\", \"basicLSTMCell\"),\n s = convertToTensor(t, \"lstmKernel\", \"basicLSTMCell\"),\n u = convertToTensor(n, \"lstmBias\", \"basicLSTMCell\"),\n l = convertToTensor(r, \"data\", \"basicLSTMCell\"),\n c = convertToTensor(o, \"c\", \"basicLSTMCell\"),\n p = convertToTensor(a, \"h\", \"basicLSTMCell\"),\n d = l.concat(p, 1).matMul(s).add(u),\n h = d.shape[0],\n f = d.shape[1] / 4,\n m = [h, f],\n g = d.slice([0, 0], m),\n v = d.slice([0, f], m),\n y = d.slice([0, 2 * f], m),\n x = d.slice([0, 3 * f], m),\n T = g.sigmoid().mulStrict(v.tanh()).addStrict(c.mulStrict(i.add(y).sigmoid()));\n return [T, T.tanh().mulStrict(x.sigmoid())];\n}\n\nvar basicLSTMCell = op({\n basicLSTMCell_: basicLSTMCell_\n}),\n multiRNNCell = op({\n multiRNNCell_: multiRNNCell_\n});\nexports.multiRNNCell = multiRNNCell;\nexports.basicLSTMCell = basicLSTMCell;\n\nfunction movingAverage_(e, t, n, r, o) {\n void 0 === o && (o = !0);\n var a = convertToTensor(e, \"v\", \"movingAverage\"),\n i = convertToTensor(t, \"x\", \"movingAverage\"),\n s = convertToTensor(n, \"decay\", \"movingAverage\");\n assertTypesMatch(a, i), assert(arraysEqual(a.shape, i.shape), \"Shape mismatch in v and x\");\n var u = scalar(1),\n l = u.sub(s),\n c = i.sub(a).mul(l);\n\n if (o) {\n assert(null != r, \"When using zeroDebias: true, step is required.\");\n var p = convertToTensor(r, \"step\", \"movingAverage\");\n c = c.div(u.sub(pow(s, p)));\n }\n\n return a.add(c);\n}\n\nvar movingAverage = op({\n movingAverage_: movingAverage_\n});\nexports.movingAverage = movingAverage;\n\nfunction stridedSlice_(e, t, n, r, o, a, i, s, u) {\n if (void 0 === o && (o = 0), void 0 === a && (a = 0), void 0 === i && (i = 0), void 0 === s && (s = 0), void 0 === u && (u = 0), 0 !== i) throw new Error(\"ellipsis mask is not yet supported\");\n if (0 !== s) throw new Error(\"new axis mask is not yet supported\");\n var l = convertToTensor(e, \"x\", \"stridedSlice\");\n\n if (r.every(function (e) {\n return 1 === e;\n })) {\n var c = getStridedSlicedInfo(l.shape, t, n, r, o, a, i, s, u),\n p = c[0],\n d = c[1],\n h = c[2],\n f = d.filter(function (e, t) {\n return -1 === h.indexOf(t);\n });\n return slice(l, p, d).reshape(f);\n }\n\n return ENV.engine.runKernel(function (e) {\n return e.stridedSlice(l, t, n, r, o, a, i, s, u);\n }, {\n $x: l\n });\n}\n\nvar stridedSlice = op({\n stridedSlice_: stridedSlice_\n});\nexports.stridedSlice = stridedSlice;\n\nfunction topk_(e, t, n) {\n void 0 === t && (t = 1), void 0 === n && (n = !0);\n var r = convertToTensor(e, \"x\", \"topk\");\n if (0 === r.rank) throw new Error(\"topk() expects the input to be of rank 1 or higher\");\n var o = r.shape[r.shape.length - 1];\n if (t > o) throw new Error(\"'k' passed to topk() must be <= the last dimension (\" + o + \") but got \" + t);\n var a = ENV.engine.runKernel(function (e) {\n return e.topk(r, t, n);\n }, {\n $x: r\n });\n return {\n values: a[0],\n indices: a[1]\n };\n}\n\nvar topk = op({\n topk_: topk_\n});\nexports.topk = topk;\n\nfunction scatterND_(e, t, n) {\n var r = convertToTensor(e, \"indices\", \"scatterND\", \"int32\"),\n o = convertToTensor(t, \"updates\", \"scatterND\");\n return validateInput(o, r, n), ENV.engine.runKernel(function (e) {\n return e.scatterND(r, o, n);\n }, {\n $indices: r,\n $updates: o\n });\n}\n\nvar scatterND = op({\n scatterND_: scatterND_\n});\nexports.scatterND = scatterND;\n\nfunction fft_(e) {\n assert(\"complex64\" === e.dtype, \"The dtype for tf.spectral.fft() must be complex64 but got \" + e.dtype + \".\");\n var t = e.shape[e.shape.length - 1],\n n = e.size / t,\n r = e.as2D(n, t);\n return ENV.engine.runKernel(function (e) {\n return e.fft(r);\n }, {\n input: e\n }).reshape(e.shape);\n}\n\nfunction ifft_(e) {\n assert(\"complex64\" === e.dtype, \"The dtype for tf.spectral.ifft() must be complex64 but got \" + e.dtype + \".\");\n var t = e.shape[e.shape.length - 1],\n n = e.size / t,\n r = e.as2D(n, t);\n return ENV.engine.runKernel(function (e) {\n return e.ifft(r);\n }, {\n input: e\n }).reshape(e.shape);\n}\n\nfunction rfft_(e) {\n assert(\"float32\" === e.dtype, \"The dtype for rfft() must be real value but\\n got \" + e.dtype);\n var t = e.shape[e.shape.length - 1],\n n = e.size / t,\n r = e.zerosLike(),\n o = complex(e, r).as2D(n, t),\n a = fft(o),\n i = Math.floor(t / 2) + 1,\n s = real(a),\n u = imag(a),\n l = s.split([i, t - i], s.shape.length - 1),\n c = u.split([i, t - i], u.shape.length - 1),\n p = e.shape.slice();\n return p[e.shape.length - 1] = i, complex(l[0], c[0]).reshape(p);\n}\n\nfunction irfft_(e) {\n var t = e.shape[e.shape.length - 1],\n n = e.size / t;\n\n if (t <= 2) {\n var r = e.as2D(n, t),\n o = ifft(r);\n return real(o);\n }\n\n var a = [n, 2 * (t - 1)],\n i = real(e).as2D(n, t),\n s = imag(e).as2D(n, t),\n u = i.slice([0, 1], [n, t - 2]).reverse(1),\n l = s.slice([0, 1], [n, t - 2]).reverse(1).mul(scalar(-1)),\n c = i.concat(u, 1),\n p = s.concat(l, 1);\n r = complex(c, p).as2D(a[0], a[1]), o = ifft(r);\n return real(o);\n}\n\nvar fft = op({\n fft_: fft_\n}),\n ifft = op({\n ifft_: ifft_\n}),\n rfft = op({\n rfft_: rfft_\n}),\n irfft = op({\n irfft_: irfft_\n}),\n spectral_ops = Object.freeze({\n fft: fft,\n ifft: ifft,\n rfft: rfft,\n irfft: irfft\n});\nexports.spectral = spectral_ops;\nexports.irfft = irfft;\nexports.rfft = rfft;\nexports.ifft = ifft;\nexports.fft = fft;\n\nfunction validateInput$1(e, t, n, r) {\n if (\"int32\" !== e.dtype) throw new Error(\"tf.sparseToDense() expects the indices to be int32 type, but the dtype was \" + e.dtype + \".\");\n if (e.rank > 2) throw new Error(\"sparseIndices should be a scalar, vector, or matrix, but got shape \" + e.shape + \".\");\n var o = e.rank > 0 ? e.shape[0] : 1,\n a = e.rank > 1 ? e.shape[1] : 1;\n if (n.length !== a) throw new Error(\"outputShape has incorrect number of elements:, \" + n.length + \", should be: \" + a + \".\");\n var i = t.size;\n if (0 !== t.rank && (1 !== t.rank || i !== o)) throw new Error(\"sparseValues has incorrect shape \" + t.shape + \", should be [] or [\" + o + \"]\");\n if (t.dtype !== r.dtype) throw new Error(\"sparseValues.dtype must match defaultValues.dtype\");\n}\n\nfunction sparseToDense_(e, t, n, r) {\n var o = convertToTensor(e, \"sparseIndices\", \"sparseToDense\", \"int32\"),\n a = convertToTensor(t, \"sparseValues\", \"sparseToDense\"),\n i = convertToTensor(r, \"defaultValue\", \"sparseToDense\", a.dtype);\n return validateInput$1(o, a, n, i), ENV.engine.runKernel(function (e) {\n return e.sparseToDense(o, a, n, i);\n }, {\n $sparseIndices: o,\n $sparseValues: a,\n $defaultValue: i\n });\n}\n\nvar sparseToDense = op({\n sparseToDense_: sparseToDense_\n});\nexports.sparseToDense = sparseToDense;\n\nfunction gatherND_(e, t) {\n var n = convertToTensor(t, \"indices\", \"gatherND\", \"int32\"),\n r = convertToTensor(e, \"x\", \"gatherND\");\n return ENV.engine.runKernel(function (e) {\n return e.gatherND(r, n);\n }, {\n $x: r,\n $indices: n\n });\n}\n\nvar Reduction,\n gatherND = op({\n gatherND_: gatherND_\n});\nexports.gatherND = gatherND;\nexports.Reduction = Reduction;\n\nfunction computeWeightedLoss_(e, t, n) {\n void 0 === n && (n = Reduction.SUM_BY_NONZERO_WEIGHTS);\n var r = convertToTensor(e, \"losses\", \"computeWeightedLoss\"),\n o = null;\n null != t && (o = convertToTensor(t, \"weights\", \"computeWeightedLoss\"));\n var a = null == o ? r : r.mul(o);\n if (n === Reduction.NONE) return a;\n if (n === Reduction.SUM) return a.sum();\n\n if (n === Reduction.MEAN) {\n if (null == o) return a.mean();\n var i = sizeFromShape(r.shape) / sizeFromShape(o.shape),\n s = a.sum().div(o.sum());\n return i > 1 ? s.div(scalar(i)) : s;\n }\n\n if (n === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n if (null == o) return a.sum().div(scalar(r.size));\n var u = o.mul(ones$1(r.shape)).notEqual(scalar(0)).sum().toFloat();\n return a.sum().div(u);\n }\n\n throw Error(\"Unknown reduction: \" + n);\n}\n\nfunction absoluteDifference_(e, t, n, r) {\n void 0 === r && (r = Reduction.SUM_BY_NONZERO_WEIGHTS);\n var o = convertToTensor(e, \"labels\", \"absoluteDifference\"),\n a = convertToTensor(t, \"predictions\", \"absoluteDifference\"),\n i = null;\n null != n && (i = convertToTensor(n, \"weights\", \"absoluteDifference\")), assertShapesMatch(o.shape, a.shape, \"Error in absoluteDifference: \");\n var s = o.sub(a).abs();\n return computeWeightedLoss(s, i, r);\n}\n\nfunction meanSquaredError_(e, t, n, r) {\n void 0 === r && (r = Reduction.SUM_BY_NONZERO_WEIGHTS);\n var o = convertToTensor(e, \"labels\", \"meanSquaredError\"),\n a = convertToTensor(t, \"predictions\", \"meanSquaredError\"),\n i = null;\n null != n && (i = convertToTensor(n, \"weights\", \"meanSquaredError\")), assertShapesMatch(o.shape, a.shape, \"Error in meanSquaredError: \");\n var s = o.squaredDifference(a);\n return computeWeightedLoss(s, i, r);\n}\n\nfunction cosineDistance_(e, t, n, r, o) {\n void 0 === o && (o = Reduction.SUM_BY_NONZERO_WEIGHTS);\n var a = convertToTensor(e, \"labels\", \"cosineDistance\"),\n i = convertToTensor(t, \"predictions\", \"cosineDistance\"),\n s = null;\n null != r && (s = convertToTensor(r, \"weights\", \"cosineDistance\")), assertShapesMatch(a.shape, i.shape, \"Error in cosineDistance: \");\n var u = scalar(1).sub(a.mul(i).sum(n, !0));\n return computeWeightedLoss(u, s, o);\n}\n\nfunction hingeLoss_(e, t, n, r) {\n void 0 === r && (r = Reduction.SUM_BY_NONZERO_WEIGHTS);\n var o = convertToTensor(e, \"labels\", \"hingeLoss\"),\n a = convertToTensor(t, \"predictions\", \"hingeLoss\"),\n i = null;\n null != n && (i = convertToTensor(n, \"weights\", \"hingeLoss\")), assertShapesMatch(o.shape, a.shape, \"Error in hingeLoss: \");\n var s = scalar(1);\n o = scalar(2).mul(o).sub(s);\n var u = s.sub(o.mul(a)).relu();\n return computeWeightedLoss(u, i, r);\n}\n\nfunction logLoss_(e, t, n, r, o) {\n void 0 === r && (r = 1e-7), void 0 === o && (o = Reduction.SUM_BY_NONZERO_WEIGHTS);\n var a = convertToTensor(e, \"labels\", \"logLoss\"),\n i = convertToTensor(t, \"predictions\", \"logLoss\"),\n s = null;\n null != n && (s = convertToTensor(n, \"weights\", \"logLoss\")), assertShapesMatch(a.shape, i.shape, \"Error in logLoss: \");\n var u = scalar(1),\n l = scalar(r),\n c = a.mul(i.add(l).log()).neg().sub(u.sub(a).mul(u.sub(i).add(l).log()));\n return computeWeightedLoss(c, s, o);\n}\n\nfunction sigmoidCrossEntropyWithLogits_(e, t) {\n var n = convertToTensor(e, \"labels\", \"sigmoidCrossEntropyWithLogits\"),\n r = convertToTensor(t, \"logits\", \"sigmoidCrossEntropyWithLogits\");\n assertShapesMatch(n.shape, r.shape, \"Error in sigmoidCrossEntropyWithLogits: \");\n var o = r.relu(),\n a = r.mul(n),\n i = r.abs().neg().exp().log1p();\n return o.sub(a).add(i);\n}\n\nfunction sigmoidCrossEntropy_(e, t, n, r, o) {\n void 0 === r && (r = 0), void 0 === o && (o = Reduction.SUM_BY_NONZERO_WEIGHTS);\n var a = convertToTensor(e, \"multiClassLabels\", \"sigmoidCrossEntropy\"),\n i = convertToTensor(t, \"logits\", \"sigmoidCrossEntropy\"),\n s = null;\n\n if (null != n && (s = convertToTensor(n, \"weights\", \"sigmoidCrossEntropy\")), assertShapesMatch(a.shape, i.shape, \"Error in sigmoidCrossEntropy: \"), r > 0) {\n var u = scalar(r),\n l = scalar(1),\n c = scalar(.5);\n a = a.mul(l.sub(u)).add(c.mul(u));\n }\n\n var p = sigmoidCrossEntropyWithLogits_(a, i);\n return computeWeightedLoss(p, s, o);\n}\n\nfunction huberLoss_(e, t, n, r, o) {\n void 0 === r && (r = 1), void 0 === o && (o = Reduction.SUM_BY_NONZERO_WEIGHTS);\n var a = convertToTensor(e, \"labels\", \"huberLoss\"),\n i = convertToTensor(t, \"predictions\", \"huberLoss\"),\n s = null;\n null != n && (s = convertToTensor(n, \"weights\", \"huberLoss\")), assertShapesMatch(a.shape, i.shape, \"Error in huberLoss: \");\n var u = scalar(r),\n l = i.sub(a).abs(),\n c = minimum(l, u),\n p = l.sub(c),\n d = scalar(.5).mul(c.square()).add(u.mul(p));\n return computeWeightedLoss(d, s, o);\n}\n\nfunction softmaxCrossEntropyWithLogits_(e, t, n) {\n if (void 0 === n && (n = -1), -1 === n && (n = t.rank - 1), n !== t.rank - 1) throw Error(\"Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank \" + t.rank + \" and dim was \" + n);\n return customGrad(function (e, t) {\n var r = t.logSumExp([n], !0),\n o = t.toFloat().sub(r);\n return {\n value: o.mul(e).neg().sum([n]),\n gradFunc: function (t) {\n var r = expandShapeToKeepDim(t.shape, [n]);\n return [t.reshape(r).mul(e.toFloat().sub(o.exp())), t.reshape(r).mul(o.exp().sub(e.toFloat()))];\n }\n };\n })(e, t);\n}\n\nfunction softmaxCrossEntropy_(e, t, n, r, o) {\n void 0 === r && (r = 0), void 0 === o && (o = Reduction.SUM_BY_NONZERO_WEIGHTS);\n var a = convertToTensor(e, \"onehotLabels\", \"softmaxCrossEntropy\"),\n i = convertToTensor(t, \"logits\", \"softmaxCrossEntropy\"),\n s = null;\n\n if (null != n && (s = convertToTensor(n, \"weights\", \"softmaxCrossEntropy\")), assertShapesMatch(a.shape, i.shape, \"Error in softmaxCrossEntropy: \"), r > 0) {\n var u = scalar(r),\n l = scalar(1),\n c = scalar(a.shape[1]);\n a = a.mul(l.sub(u)).add(u.div(c));\n }\n\n var p = softmaxCrossEntropyWithLogits_(a, i);\n return computeWeightedLoss(p, s, o);\n}\n\n!function (e) {\n e[e.NONE = 0] = \"NONE\", e[e.MEAN = 1] = \"MEAN\", e[e.SUM = 2] = \"SUM\", e[e.SUM_BY_NONZERO_WEIGHTS = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n}(Reduction || (exports.Reduction = Reduction = {}));\nvar absoluteDifference = op({\n absoluteDifference_: absoluteDifference_\n}),\n computeWeightedLoss = op({\n computeWeightedLoss_: computeWeightedLoss_\n}),\n cosineDistance = op({\n cosineDistance_: cosineDistance_\n}),\n hingeLoss = op({\n hingeLoss_: hingeLoss_\n}),\n huberLoss = op({\n huberLoss_: huberLoss_\n}),\n logLoss = op({\n logLoss_: logLoss_\n}),\n meanSquaredError = op({\n meanSquaredError_: meanSquaredError_\n}),\n sigmoidCrossEntropy = op({\n sigmoidCrossEntropy_: sigmoidCrossEntropy_\n}),\n softmaxCrossEntropy = op({\n softmaxCrossEntropy_: softmaxCrossEntropy_\n}),\n loss_ops = Object.freeze({\n get Reduction() {\n return Reduction;\n },\n\n absoluteDifference: absoluteDifference,\n computeWeightedLoss: computeWeightedLoss,\n cosineDistance: cosineDistance,\n hingeLoss: hingeLoss,\n huberLoss: huberLoss,\n logLoss: logLoss,\n meanSquaredError: meanSquaredError,\n sigmoidCrossEntropy: sigmoidCrossEntropy,\n softmaxCrossEntropy: softmaxCrossEntropy\n});\nexports.losses = loss_ops;\n\nfunction gramSchmidt_(e) {\n var t;\n\n if (Array.isArray(e)) {\n t = !1, assert(null != e && e.length > 0, \"Gram-Schmidt process: input must not be null, undefined, or empty\");\n\n for (var n = e[0].shape[0], r = 1; r < e.length; ++r) assert(e[r].shape[0] === n, \"Gram-Schmidt: Non-unique lengths found in the input vectors: (\" + e[r].shape[0] + \" vs. \" + n + \")\");\n } else t = !0, e = split$1(e, e.shape[0], 0).map(function (e) {\n return squeeze(e, [0]);\n });\n\n assert(e.length <= e[0].shape[0], \"Gram-Schmidt: Number of vectors (\" + e.length + \") exceeds number of dimensions (\" + e[0].shape[0] + \").\");\n\n var o = [],\n a = e,\n i = function (e) {\n o.push(ENV.engine.tidy(function () {\n var t = a[e];\n if (e > 0) for (var n = 0; n < e; ++n) {\n var r = sum$1(o[n].mulStrict(t)).mul(o[n]);\n t = t.sub(r);\n }\n return t.div(norm(t, \"euclidean\"));\n }));\n };\n\n for (r = 0; r < e.length; ++r) i(r);\n\n return t ? stack(o, 0) : o;\n}\n\nfunction qr_(e, t) {\n if (void 0 === t && (t = !1), e.rank < 2) throw new Error(\"qr() requires input tensor to have a rank >= 2, but got rank \" + e.rank);\n if (2 === e.rank) return qr2d(e, t);\n var n = e.shape.slice(0, e.shape.length - 2).reduce(function (e, t) {\n return e * t;\n }),\n r = [],\n o = [];\n return unstack(e.reshape([n, e.shape[e.shape.length - 2], e.shape[e.shape.length - 1]]), 0).forEach(function (e) {\n var n = qr2d(e, t),\n a = n[0],\n i = n[1];\n r.push(a), o.push(i);\n }), [stack(r, 0).reshape(e.shape), stack(o, 0).reshape(e.shape)];\n}\n\nfunction qr2d(e, t) {\n return void 0 === t && (t = !1), ENV.engine.tidy(function () {\n if (2 !== e.shape.length) throw new Error(\"qr2d() requires a 2D Tensor, but got a \" + e.shape.length + \"D Tensor.\");\n\n for (var n = e.shape[0], r = e.shape[1], o = eye(n), a = e.clone(), i = tensor2d([[1]], [1, 1]), s = i.clone(), u = n >= r ? r : n, l = function (e) {\n var t,\n u = a,\n l = s,\n c = o;\n t = ENV.engine.tidy(function () {\n var t = a.slice([e, e], [n - e, 1]),\n u = t.norm(),\n l = a.slice([e, e], [1, 1]),\n c = l.sign().neg(),\n p = l.sub(c.mul(u)),\n d = t.div(p);\n s = 1 === d.shape[0] ? i.clone() : i.concat(d.slice([1, 0], [d.shape[0] - 1, d.shape[1]]), 0);\n var h = c.matMul(p).div(u).neg(),\n f = a.slice([e, 0], [n - e, r]),\n m = h.mul(s);\n a = 0 === e ? f.sub(m.matMul(s.transpose().matMul(f))) : a.slice([0, 0], [e, r]).concat(f.sub(m.matMul(s.transpose().matMul(f))), 0);\n var g = o.slice([0, e], [n, o.shape[1] - e]);\n return o = 0 === e ? g.sub(g.matMul(s).matMul(m.transpose())) : o.slice([0, 0], [n, e]).concat(g.sub(g.matMul(s).matMul(m.transpose())), 1), [s, a, o];\n }), s = t[0], a = t[1], o = t[2], dispose([u, l, c]);\n }, c = 0; c < u; ++c) l(c);\n\n return !t && n > r && (o = o.slice([0, 0], [n, r]), a = a.slice([0, 0], [r, r])), [o, a];\n });\n}\n\nvar gramSchmidt = op({\n gramSchmidt_: gramSchmidt_\n}),\n qr = op({\n qr_: qr_\n}),\n linalg_ops = Object.freeze({\n gramSchmidt: gramSchmidt,\n qr: qr\n});\nexports.linalg = linalg_ops;\n\nfunction resizeBilinear_(e, t, n) {\n void 0 === n && (n = !1);\n var r = convertToTensor(e, \"images\", \"resizeBilinear\");\n assert(3 === r.rank || 4 === r.rank, \"Error in resizeBilinear: x must be rank 3 or 4, but got rank \" + r.rank + \".\"), assert(2 === t.length, \"Error in resizeBilinear: new shape must 2D, but got shape \" + t + \".\");\n var o = r,\n a = !1;\n 3 === r.rank && (a = !0, o = r.as4D(1, r.shape[0], r.shape[1], r.shape[2]));\n var i = t[0],\n s = t[1],\n u = ENV.engine.runKernel(function (e, t) {\n return e.resizeBilinear(o, i, s, n);\n }, {\n batchImages: o\n }, function (e, t) {\n return {\n batchImages: function () {\n return ENV.engine.runKernel(function (t) {\n return t.resizeBilinearBackprop(e, o, n);\n }, {});\n }\n };\n });\n return a ? u.as3D(u.shape[1], u.shape[2], u.shape[3]) : u;\n}\n\nfunction resizeNearestNeighbor_(e, t, n) {\n void 0 === n && (n = !1);\n var r = convertToTensor(e, \"images\", \"resizeNearestNeighbor\");\n assert(3 === r.rank || 4 === r.rank, \"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank \" + r.rank + \".\"), assert(2 === t.length, \"Error in resizeNearestNeighbor: new shape must 2D, but got shape \" + t + \".\"), assert(\"float32\" === r.dtype || \"int32\" === r.dtype, \"`images` must have `int32` or `float32` as dtype\");\n var o = r,\n a = !1;\n 3 === r.rank && (a = !0, o = r.as4D(1, r.shape[0], r.shape[1], r.shape[2]));\n var i = t[0],\n s = t[1],\n u = ENV.engine.runKernel(function (e, t) {\n return e.resizeNearestNeighbor(o, i, s, n);\n }, {\n batchImages: o\n }, function (e, t) {\n return {\n batchImages: function () {\n return ENV.engine.runKernel(function (t) {\n return t.resizeNearestNeighborBackprop(e, o, n);\n }, {});\n }\n };\n });\n return a ? u.as3D(u.shape[1], u.shape[2], u.shape[3]) : u;\n}\n\nfunction nonMaxSuppression_(e, t, n, r, o) {\n void 0 === r && (r = .5), void 0 === o && (o = Number.NEGATIVE_INFINITY);\n var a = convertToTensor(e, \"boxes\", \"nonMaxSuppression\"),\n i = convertToTensor(t, \"scores\", \"nonMaxSuppression\"),\n s = nonMaxSuppSanityCheck(a, i, n, r, o);\n return n = s.maxOutputSize, r = s.iouThreshold, o = s.scoreThreshold, ENV.engine.runKernel(function (e) {\n return e.nonMaxSuppression(a, i, n, r, o);\n }, {\n $boxes: a\n });\n}\n\nfunction nonMaxSuppressionAsync_(e, t, n, r, o) {\n return void 0 === r && (r = .5), void 0 === o && (o = Number.NEGATIVE_INFINITY), __awaiter(this, void 0, void 0, function () {\n var a, i, s, u, l, c;\n return __generator(this, function (p) {\n switch (p.label) {\n case 0:\n return a = convertToTensor(e, \"boxes\", \"nonMaxSuppressionAsync\"), i = convertToTensor(t, \"scores\", \"nonMaxSuppressionAsync\"), s = nonMaxSuppSanityCheck(a, i, n, r, o), n = s.maxOutputSize, r = s.iouThreshold, o = s.scoreThreshold, [4, a.data()];\n\n case 1:\n return u = p.sent(), [4, i.data()];\n\n case 2:\n return l = p.sent(), c = nonMaxSuppressionImpl(u, l, n, r, o), a !== e && a.dispose(), i !== t && i.dispose(), [2, c];\n }\n });\n });\n}\n\nfunction nonMaxSuppSanityCheck(e, t, n, r, o) {\n null == r && (r = .5), null == o && (o = Number.NEGATIVE_INFINITY);\n var a = e.shape[0];\n return n = Math.min(n, a), assert(0 <= r && r <= 1, \"iouThreshold must be in [0, 1], but was '\" + r + \"'\"), assert(2 === e.rank, \"boxes must be a 2D tensor, but was of rank '\" + e.rank + \"'\"), assert(4 === e.shape[1], \"boxes must have 4 columns, but 2nd dimension was \" + e.shape[1]), assert(1 === t.rank, \"scores must be a 1D tensor\"), assert(t.shape[0] === a, \"scores has incompatible shape with boxes. Expected \" + a + \", but was \" + t.shape[0]), {\n maxOutputSize: n,\n iouThreshold: r,\n scoreThreshold: o\n };\n}\n\nfunction cropAndResize_(e, t, n, r, o, a) {\n var i = convertToTensor(e, \"image\", \"cropAndResize\", \"float32\"),\n s = convertToTensor(t, \"boxes\", \"cropAndResize\", \"float32\"),\n u = convertToTensor(n, \"boxInd\", \"cropAndResize\", \"int32\");\n o = o || \"bilinear\", a = a || 0;\n var l = s.shape[0];\n assert(4 === i.rank, \"Error in cropAndResize: image must be rank 4,but got rank \" + i.rank + \".\"), assert(2 === s.rank && 4 === s.shape[1], \"Error in cropAndResize: boxes must be have size [\" + l + \",4] but had shape \" + s.shape + \".\"), assert(1 === u.rank && u.shape[0] === l, \"Error in cropAndResize: boxInd must be have size [\" + l + \"] but had shape \" + s.shape + \".\"), assert(2 === r.length, \"Error in cropAndResize: cropSize must be of length 2, but got length \" + r.length + \".\"), assert(r[0] >= 1 && r[1] >= 1, \"cropSize must be atleast [1,1], but was \" + r), assert(\"bilinear\" === o || \"nearest\" === o, \"method must be bilinear or nearest, but was \" + o);\n return ENV.engine.runKernel(function (e, t) {\n return e.cropAndResize(i, s, u, r, o, a);\n }, {\n $image: i,\n $boxes: s\n });\n}\n\nvar resizeBilinear = op({\n resizeBilinear_: resizeBilinear_\n}),\n resizeNearestNeighbor = op({\n resizeNearestNeighbor_: resizeNearestNeighbor_\n}),\n nonMaxSuppression = op({\n nonMaxSuppression_: nonMaxSuppression_\n}),\n nonMaxSuppressionAsync = nonMaxSuppressionAsync_,\n cropAndResize = op({\n cropAndResize_: cropAndResize_\n}),\n image_ops = Object.freeze({\n resizeBilinear: resizeBilinear,\n resizeNearestNeighbor: resizeNearestNeighbor,\n nonMaxSuppression: nonMaxSuppression,\n nonMaxSuppressionAsync: nonMaxSuppressionAsync,\n cropAndResize: cropAndResize\n});\nexports.image = image_ops;\n\nfunction matMul_$1(e, t, n, r, o, a) {\n var i;\n void 0 === n && (n = !1), void 0 === r && (r = !1), void 0 === a && (a = \"linear\");\n var s = convertToTensor(e, \"a\", \"fused matMul\"),\n u = convertToTensor(t, \"b\", \"fused matMul\");\n s = (i = makeTypesMatch(s, u))[0], u = i[1];\n var l = n ? s.shape[s.rank - 2] : s.shape[s.rank - 1],\n c = r ? u.shape[u.rank - 1] : u.shape[u.rank - 2],\n p = n ? s.shape[s.rank - 1] : s.shape[s.rank - 2],\n d = r ? u.shape[u.rank - 2] : u.shape[u.rank - 1],\n h = s.shape.slice(0, -2),\n f = u.shape.slice(0, -2),\n m = sizeFromShape(h),\n g = sizeFromShape(f);\n assert(s.rank >= 2 && u.rank >= 2 && s.rank === u.rank, \"Error in fused matMul: inputs must have the same rank of at least 2, got ranks \" + s.rank + \" and \" + u.rank + \".\"), assert(arraysEqual(h, f), \"Error in fused matMul: outer dimensions (\" + h + \") and (\" + f + \") of Tensors with shapes \" + s.shape + \" and \" + u.shape + \" must match.\"), assert(l === c, \"Error in fused matMul: inner shapes (\" + l + \") and (\" + c + \") of Tensors with shapes \" + s.shape + \" and \" + u.shape + \" and transposeA=\" + n + \" and transposeB=\" + r + \" must match.\");\n var v,\n y = s.shape.slice(0, -2).concat([p, d]),\n x = n ? s.as3D(m, l, p) : s.as3D(m, p, l),\n T = r ? u.as3D(g, d, c) : u.as3D(g, c, d);\n null != o && (v = makeTypesMatch(v = convertToTensor(o, \"bias\", \"fused matMul\"), s)[0], assertAndGetBroadcastShape(y, v.shape));\n var w = {\n $a: x,\n $b: T\n };\n return null != o && (w.$bias = v), ENV.engine.runKernel(function (e, t) {\n return t(e.fusedBatchMatMul(x, T, n, r, v, a));\n }, w, function (e, t) {\n var i,\n s = t[0];\n if (null == a || \"linear\" === a) i = e;else {\n if (\"relu\" !== a) throw new Error(\"Gradient for activation \" + a + \" has not been implemented yet.\");\n i = e.mul(s.step());\n }\n var u = {};\n return null != o && (u = {\n $bias: function () {\n var e = i,\n t = getReductionAxes(v.shape, y);\n return t.length > 0 && (e = e.sum(t)), e.reshape(v.shape);\n }\n }), n || r ? !n && r ? Object.assign({\n $a: function () {\n return i.matMul(T, !1, !1);\n },\n $b: function () {\n return i.matMul(x, !0, !1);\n }\n }, u) : n && !r ? Object.assign({\n $a: function () {\n return T.matMul(i, !1, !0);\n },\n $b: function () {\n return x.matMul(i, !1, !1);\n }\n }, u) : Object.assign({\n $a: function () {\n return T.matMul(i, !0, !0);\n },\n $b: function () {\n return i.matMul(x, !0, !0);\n }\n }, u) : Object.assign({\n $a: function () {\n return i.matMul(T, !1, !0);\n },\n $b: function () {\n return x.matMul(i, !0, !1);\n }\n }, u);\n }).reshape(y);\n}\n\nvar matMul$1 = op({\n matMul_: matMul_$1\n}),\n fused_ops = Object.freeze({\n matMul: matMul$1\n}),\n ops = Object.freeze({\n image: image_ops,\n linalg: linalg_ops,\n losses: loss_ops,\n spectral: spectral_ops,\n fused: fused_ops,\n op: op,\n batchNormalization2d: batchNormalization2d,\n batchNormalization3d: batchNormalization3d,\n batchNormalization4d: batchNormalization4d,\n batchNormalization: batchNormalization,\n batchNorm: batchNorm,\n batchNorm2d: batchNorm2d,\n batchNorm3d: batchNorm3d,\n batchNorm4d: batchNorm4d,\n complex: complex,\n real: real,\n imag: imag,\n concat: concat,\n concat1d: concat1d,\n concat2d: concat2d,\n concat3d: concat3d,\n concat4d: concat4d,\n split: split$1,\n conv1d: conv1d,\n conv2d: conv2d,\n conv3d: conv3d,\n conv2dDerFilter: conv2dDerFilter,\n depthwiseConv2d: depthwiseConv2d,\n separableConv2d: separableConv2d,\n conv2dTranspose: conv2dTranspose,\n matMul: matMul,\n dot: dot,\n outerProduct: outerProduct,\n reverse: reverse,\n reverse1d: reverse1d,\n reverse2d: reverse2d,\n reverse3d: reverse3d,\n reverse4d: reverse4d,\n maxPool: maxPool,\n avgPool: avgPool,\n pool: pool,\n slice: slice,\n slice1d: slice1d,\n slice2d: slice2d,\n slice3d: slice3d,\n slice4d: slice4d,\n abs: abs,\n acos: acos,\n acosh: acosh,\n asin: asin,\n asinh: asinh,\n atan: atan,\n atanh: atanh,\n ceil: ceil,\n clipByValue: clipByValue,\n cos: cos,\n cosh: cosh,\n erf: erf,\n exp: exp,\n expm1: expm1,\n floor: floor,\n log: log$1,\n log1p: log1p,\n logSigmoid: logSigmoid,\n neg: neg,\n reciprocal: reciprocal,\n round: round,\n rsqrt: rsqrt,\n sigmoid: sigmoid,\n sign: sign,\n sin: sin,\n sinh: sinh,\n softplus: softplus,\n sqrt: sqrt,\n square: square,\n step: step,\n tan: tan,\n tanh: tanh$1,\n all: all,\n any: any,\n argMax: argMax,\n argMin: argMin,\n logSumExp: logSumExp,\n max: max,\n mean: mean,\n min: min,\n moments: moments,\n sum: sum$1,\n prod: prod,\n equal: equal,\n equalStrict: equalStrict,\n greater: greater,\n greaterEqual: greaterEqual,\n greaterEqualStrict: greaterEqualStrict,\n greaterStrict: greaterStrict,\n less: less,\n lessEqual: lessEqual,\n lessEqualStrict: lessEqualStrict,\n lessStrict: lessStrict,\n notEqual: notEqual,\n notEqualStrict: notEqualStrict,\n add: add,\n addN: addN,\n addStrict: addStrict,\n atan2: atan2,\n div: div,\n divStrict: divStrict,\n floorDiv: floorDiv,\n maximum: maximum,\n maximumStrict: maximumStrict,\n minimum: minimum,\n minimumStrict: minimumStrict,\n mod: mod,\n modStrict: modStrict,\n mul: mul,\n mulStrict: mulStrict,\n pow: pow,\n powStrict: powStrict,\n squaredDifference: squaredDifference,\n squaredDifferenceStrict: squaredDifferenceStrict,\n sub: sub,\n subStrict: subStrict,\n elu: elu,\n leakyRelu: leakyRelu,\n prelu: prelu,\n relu: relu,\n selu: selu,\n logicalAnd: logicalAnd,\n logicalNot: logicalNot,\n logicalOr: logicalOr,\n logicalXor: logicalXor,\n where: where,\n whereAsync: whereAsync,\n buffer: buffer,\n toPixels: toPixels$1,\n print: print,\n batchToSpaceND: batchToSpaceND,\n cast: cast,\n clone: clone,\n cumsum: cumsum,\n depthToSpace: depthToSpace,\n expandDims: expandDims,\n eye: eye,\n fromPixels: fromPixels$1,\n multinomial: multinomial,\n oneHot: oneHot,\n pad: pad,\n pad1d: pad1d,\n pad2d: pad2d,\n pad3d: pad3d,\n pad4d: pad4d,\n rand: rand,\n randomNormal: randomNormal,\n randomUniform: randomUniform,\n reshape: reshape,\n spaceToBatchND: spaceToBatchND,\n squeeze: squeeze,\n stack: stack,\n tile: tile,\n truncatedNormal: truncatedNormal,\n unstack: unstack,\n setdiff1dAsync: setdiff1dAsync,\n fill: fill,\n linspace: linspace,\n ones: ones$1,\n range: range,\n scalar: scalar,\n tensor: tensor,\n tensor1d: tensor1d,\n tensor2d: tensor2d,\n tensor3d: tensor3d,\n tensor4d: tensor4d,\n tensor5d: tensor5d,\n tensor6d: tensor6d,\n zeros: zeros,\n onesLike: onesLike,\n zerosLike: zerosLike,\n transpose: transpose,\n softmax: softmax,\n logSoftmax: logSoftmax,\n localResponseNormalization: localResponseNormalization,\n norm: norm,\n gather: gather,\n unsortedSegmentSum: unsortedSegmentSum,\n basicLSTMCell: basicLSTMCell,\n multiRNNCell: multiRNNCell,\n movingAverage: movingAverage,\n stridedSlice: stridedSlice,\n topk: topk,\n scatterND: scatterND,\n fft: fft,\n ifft: ifft,\n rfft: rfft,\n irfft: irfft,\n sparseToDense: sparseToDense,\n gatherND: gatherND\n});\nexports.fused = fused_ops;\n\nfunction mapActivation(e, t, n) {\n if (\"linear\" === t) return e.linear(n);\n if (\"relu\" === t) return e.relu(n);\n throw new Error(\"Activation \" + t + \" has not been implemented for the CPU backend.\");\n}\n\nvar MathBackendCPU = function () {\n function e() {\n this.blockSize = 48, this.firstUse = !0, ENV.get(\"IS_BROWSER\") && (this.fromPixels2DContext = document.createElement(\"canvas\").getContext(\"2d\"));\n }\n\n return e.prototype.setDataMover = function (e) {\n this.data = new DataStorage(e);\n }, e.prototype.register = function (e, t, n) {\n if (this.firstUse && (this.firstUse = !1, ENV.get(\"IS_NODE\") && warn(\"\\n============================\\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\\n============================\\n\")), this.data.has(e)) throw new Error(\"Data buffer is already registered\");\n this.data.set(e, {\n dtype: n\n });\n }, e.prototype.write = function (e, t) {\n if (null == t) throw new Error(\"MathBackendCPU.write(): values can not be null\");\n this.data.get(e).values = t;\n }, e.prototype.fromPixels = function (e, t) {\n if (null == e) throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");\n var n, r;\n if (ENV.get(\"IS_NODE\") && null == e.getContext) throw new Error(\"When running in node, pixels must be an HTMLCanvasElement like the one returned by the `canvas` npm package\");\n if (null != e.getContext) n = e.getContext(\"2d\").getImageData(0, 0, e.width, e.height).data;else if (e instanceof ImageData) n = e.data;else {\n if (!(e instanceof HTMLImageElement || e instanceof HTMLVideoElement)) throw new Error(\"pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was \" + e.constructor.name);\n if (null == this.fromPixels2DContext) throw new Error(\"Can't read pixels from HTMLImageElement outside the browser.\");\n this.fromPixels2DContext.canvas.width = e.width, this.fromPixels2DContext.canvas.height = e.height, this.fromPixels2DContext.drawImage(e, 0, 0, e.width, e.height), n = this.fromPixels2DContext.getImageData(0, 0, e.width, e.height).data;\n }\n if (4 === t) r = new Int32Array(n);else {\n var o = e.width * e.height;\n r = new Int32Array(o * t);\n\n for (var a = 0; a < o; a++) for (var i = 0; i < t; ++i) r[a * t + i] = n[4 * a + i];\n }\n return tensor3d(r, [e.height, e.width, t], \"int32\");\n }, e.prototype.read = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (t) {\n return [2, this.readSync(e)];\n });\n });\n }, e.prototype.readSync = function (e) {\n var t = this.data.get(e),\n n = t.dtype,\n r = t.complexTensors;\n return \"complex64\" === n ? mergeRealAndImagArrays(r.real.dataSync(), r.imag.dataSync()) : this.data.get(e).values;\n }, e.prototype.disposeData = function (e) {\n if (this.data.has(e)) {\n var t = this.data.get(e).complexTensors;\n null != t && (t.real.dispose(), t.imag.dispose()), this.data.delete(e);\n }\n }, e.prototype.time = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t;\n return __generator(this, function (n) {\n return t = now(), e(), [2, {\n kernelMs: now() - t\n }];\n });\n });\n }, e.prototype.memory = function () {\n return {\n unreliable: !0,\n reasons: [\"The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less.\"]\n };\n }, e.prototype.complex = function (e, t) {\n var n = Tensor.make(e.shape, {}, \"complex64\");\n return this.data.get(n.dataId).complexTensors = {\n real: ENV.engine.keep(e.clone()),\n imag: ENV.engine.keep(t.clone())\n }, n;\n }, e.prototype.real = function (e) {\n return this.data.get(e.dataId).complexTensors.real.clone();\n }, e.prototype.imag = function (e) {\n return this.data.get(e.dataId).complexTensors.imag.clone();\n }, e.prototype.assertNotComplex = function (e, t) {\n Array.isArray(e) || (e = [e]), e.forEach(function (e) {\n null != e && assert(\"complex64\" !== e.dtype, t + \" does not support complex64 tensors.\");\n });\n }, e.prototype.slice = function (e, t, n) {\n if (this.assertNotComplex(e, \"slice\"), isSliceContinous(e.shape, t, n)) {\n var r = computeFlatOffset(t, e.strides),\n o = sizeFromShape(n);\n return tensor(e.dataSync().subarray(r, r + o), n, e.dtype);\n }\n\n for (var a = buffer(n, e.dtype), i = e.bufferSync(), s = 0; s < a.size; ++s) {\n var u = a.indexToLoc(s).map(function (e, n) {\n return e + t[n];\n });\n a.values[s] = i.get.apply(i, u);\n }\n\n return a.toTensor();\n }, e.prototype.stridedSlice = function (e, t, n, r, o, a, i, s, u) {\n this.assertNotComplex(e, \"stridedSlice\");\n var l = getStridedSlicedInfo(e.shape, t, n, r, o, a, i, s, u),\n c = l[0],\n p = l[1],\n d = l[2],\n h = p.filter(function (e, t) {\n return -1 === d.indexOf(t);\n });\n if (h.some(function (e) {\n return 0 === e;\n })) return tensor([], h);\n\n for (var f = buffer(p, e.dtype), m = e.bufferSync(), g = 0; g < f.size; g++) {\n for (var v = f.indexToLoc(g), y = new Array(v.length), x = 0; x < y.length; x++) y[x] = v[x] * r[x] + c[x];\n\n f.set.apply(f, [m.get.apply(m, y)].concat(v));\n }\n\n return f.toTensor().reshape(h);\n }, e.prototype.unstack = function (e, t) {\n for (var n = e.shape[t], r = new Array(e.rank - 1), o = 0, a = 0; a < e.rank; a++) a !== t && (r[o++] = e.shape[a]);\n\n var i = new Array(e.rank).fill(0),\n s = e.shape.slice();\n s[t] = 1;\n var u = new Array(n);\n\n for (a = 0; a < u.length; a++) i[t] = a, u[a] = this.slice(e, i, s).reshape(r);\n\n return u;\n }, e.prototype.reverse = function (e, t) {\n this.assertNotComplex(e, \"reverse\");\n\n for (var n = buffer(e.shape, e.dtype), r = e.bufferSync(), o = function (o) {\n var a = n.indexToLoc(o),\n i = a.slice();\n t.forEach(function (t) {\n return i[t] = e.shape[t] - 1 - i[t];\n }), n.set.apply(n, [r.get.apply(r, i)].concat(a));\n }, a = 0; a < n.size; a++) o(a);\n\n return n.toTensor();\n }, e.prototype.concat = function (e, t) {\n this.assertNotComplex(e, \"concat\");\n var n = e.map(function (e) {\n var n = sizeFromShape(e.shape.slice(t));\n return e.as2D(-1, n);\n }),\n r = computeOutShape(n.map(function (e) {\n return e.shape;\n }), 1),\n o = buffer(r, e[0].dtype).values;\n\n if (1 === n[0].shape[0]) {\n var a = 0;\n n.forEach(function (e) {\n o.set(e.dataSync(), a), a += e.size;\n });\n } else {\n var i = 0;\n n.forEach(function (e) {\n for (var t = e.dataSync(), n = 0, a = 0; a < e.shape[0]; ++a) for (var s = a * r[1] + i, u = 0; u < e.shape[1]; ++u) o[s + u] = t[n++];\n\n i += e.shape[1];\n });\n }\n\n var s = computeOutShape(e.map(function (e) {\n return e.shape;\n }), t);\n return tensor(o, s, e[0].dtype);\n }, e.prototype.neg = function (e) {\n return this.assertNotComplex(e, \"neg\"), this.multiply(scalar(-1), e);\n }, e.prototype.add = function (e, t) {\n return \"complex64\" === e.dtype || \"complex64\" === t.dtype ? this.broadcastedBinaryComplexOp(e.cast(\"complex64\"), t.cast(\"complex64\"), function (e, t, n, r) {\n return {\n real: e + n,\n imag: t + r\n };\n }) : this.broadcastedBinaryOp(e, t, upcastType(e.dtype, t.dtype), function (e, t) {\n return e + t;\n });\n }, e.prototype.addN = function (e) {\n this.assertNotComplex(e, \"addN\");\n\n for (var t = e.map(function (e) {\n return e.dataSync();\n }), n = buffer(e[0].shape, e[0].dtype), r = n.values, o = 0; o < e.length; o++) for (var a = t[o], i = 0; i < r.length; i++) r[i] += a[i];\n\n return n.toTensor();\n }, e.prototype.subtract = function (e, t) {\n return \"complex64\" === e.dtype || \"complex64\" === t.dtype ? this.broadcastedBinaryComplexOp(e.cast(\"complex64\"), t.cast(\"complex64\"), function (e, t, n, r) {\n return {\n real: e - n,\n imag: t - r\n };\n }) : this.broadcastedBinaryOp(e, t, upcastType(e.dtype, t.dtype), function (e, t) {\n return e - t;\n });\n }, e.prototype.pow = function (e, t) {\n return this.assertNotComplex([e, t], \"pow\"), this.broadcastedBinaryOp(e, t, e.dtype, function (e, t) {\n return Math.pow(e, t);\n });\n }, e.prototype.batchMatMul = function (e, t, n, r) {\n this.assertNotComplex([e, t], \"matMul\");\n\n for (var o = n ? e.shape[1] : e.shape[2], a = n ? e.shape[2] : e.shape[1], i = r ? t.shape[1] : t.shape[2], s = e.shape[0], u = e.dataSync(), l = t.dataSync(), c = n ? [e.strides[0], 1, e.strides[1]] : [e.strides[0], e.strides[1], 1], p = c[0], d = c[1], h = c[2], f = r ? [1, t.strides[1], t.strides[0]] : [t.strides[1], 1, t.strides[0]], m = f[0], g = f[1], v = f[2], y = a * i, x = buffer([s, a, i], e.dtype), T = x.values, w = this.blockSize, E = 0; E < s; E++) for (var b = 0; b < a; b += w) for (var S = 0; S < i; S += w) for (var C = 0; C < o; C += w) for (var A = Math.min(b + w, a), N = Math.min(S + w, i), _ = Math.min(C + w, o), R = b; R < A; R++) for (var I = S; I < N; I++) {\n for (var k = 0, D = C; D < _; D++) k += u[E * p + R * d + D * h] * l[D * m + I * g + E * v];\n\n T[E * y + (R * i + I)] += k;\n }\n\n return x.toTensor();\n }, e.prototype.fusedBatchMatMul = function (e, t, n, r, o, a) {\n var i = this.batchMatMul(e, t, n, r);\n return o && (i = this.add(i, o)), a && (i = mapActivation(this, a, i)), i;\n }, e.prototype.multiply = function (e, t) {\n return \"complex64\" === e.dtype || \"complex64\" === t.dtype ? this.broadcastedBinaryComplexOp(e.cast(\"complex64\"), t.cast(\"complex64\"), function (e, t, n, r) {\n return {\n real: e * n - t * r,\n imag: e * r + t * n\n };\n }) : this.broadcastedBinaryOp(e, t, upcastType(e.dtype, t.dtype), function (e, t) {\n return e * t;\n });\n }, e.prototype.realDivide = function (e, t) {\n this.assertNotComplex([e, t], \"realDivide\");\n return this.broadcastedBinaryOp(e, t, \"float32\", function (e, t) {\n return e / t;\n });\n }, e.prototype.floorDiv = function (e, t) {\n this.assertNotComplex([e, t], \"floorDiv\");\n return this.broadcastedBinaryOp(e, t, \"int32\", function (e, t) {\n return Math.floor(e / t);\n });\n }, e.prototype.sum = function (e, t) {\n this.assertNotComplex(e, \"sum\"), assertAxesAreInnerMostDims(\"sum\", t, e.rank);\n\n for (var n = computeOutAndReduceShapes(e.shape, t), r = n[0], o = n[1], a = zeros(r, upcastType(e.dtype, \"int32\")), i = sizeFromShape(o), s = a.dataSync(), u = e.dataSync(), l = 0; l < s.length; ++l) {\n for (var c = l * i, p = 0, d = 0; d < i; ++d) p += u[c + d];\n\n s[l] = p;\n }\n\n return a;\n }, e.prototype.prod = function (e, t) {\n this.assertNotComplex(e, \"sum\");\n\n for (var n = computeOutAndReduceShapes(e.shape, t), r = n[0], o = n[1], a = zeros(r, upcastType(e.dtype, \"int32\")), i = sizeFromShape(o), s = a.dataSync(), u = e.dataSync(), l = 0; l < s.length; ++l) {\n for (var c = l * i, p = 1, d = 0; d < i; ++d) p *= u[c + d];\n\n s[l] = p;\n }\n\n return a;\n }, e.prototype.unsortedSegmentSum = function (e, t, n) {\n this.assertNotComplex(e, \"unsortedSegmentSum\");\n\n for (var r = [], o = e.rank - t.rank, a = 0; a < o; ++a) t = t.expandDims(a + 1);\n\n for (a = 0; a < n; ++a) {\n var i = scalar(a, \"int32\"),\n s = equal(i, t).asType(\"float32\").mul(e).sum(0);\n r.push(s);\n }\n\n return stack(r);\n }, e.prototype.argMin = function (e, t) {\n this.assertNotComplex(e, \"argMin\");\n var n = [t];\n assertAxesAreInnerMostDims(\"argMin\", n, e.rank);\n\n for (var r = computeOutAndReduceShapes(e.shape, n), o = r[0], a = r[1], i = zeros(o, \"int32\"), s = sizeFromShape(a), u = i.dataSync(), l = e.dataSync(), c = 0; c < u.length; ++c) {\n for (var p = c * s, d = l[p], h = 0, f = 0; f < s; ++f) {\n var m = l[p + f];\n m < d && (d = m, h = f);\n }\n\n u[c] = h;\n }\n\n return i;\n }, e.prototype.argMax = function (e, t) {\n this.assertNotComplex(e, \"argMax\");\n var n = [t];\n assertAxesAreInnerMostDims(\"argMax\", n, e.rank);\n\n for (var r = computeOutAndReduceShapes(e.shape, n), o = r[0], a = r[1], i = zeros(o, \"int32\"), s = sizeFromShape(a), u = i.dataSync(), l = e.dataSync(), c = 0; c < u.length; ++c) {\n for (var p = c * s, d = l[p], h = 0, f = 0; f < s; ++f) {\n var m = l[p + f];\n m > d && (d = m, h = f);\n }\n\n u[c] = h;\n }\n\n return i;\n }, e.prototype.cumsum = function (e, t, n, r) {\n if (this.assertNotComplex(e, \"cumsum\"), t !== e.rank - 1) throw new Error(\"backend.cumsum in CPU expects an inner-most axis=\" + (e.rank - 1) + \" but got axis=\" + t);\n\n for (var o = upcastType(e.dtype, \"int32\"), a = zeros(e.shape, o), i = a.dataSync(), s = e.dataSync(), u = e.shape[e.rank - 1], l = r ? function (e, t) {\n return e + u - t - 1;\n } : function (e, t) {\n return e + t;\n }, c = 0; c < s.length; c += u) for (var p = 0; p < u; p++) {\n var d = l(c, p);\n if (0 === p) i[d] = n ? 0 : s[d];else {\n var h = l(c, p - 1);\n i[d] = n ? s[h] + i[h] : s[d] + i[h];\n }\n }\n\n return a;\n }, e.prototype.equal = function (e, t) {\n return this.assertNotComplex([e, t], \"equal\"), this.broadcastedBinaryOp(e, t, \"bool\", function (e, t) {\n return e === t ? 1 : 0;\n });\n }, e.prototype.notEqual = function (e, t) {\n return this.assertNotComplex([e, t], \"notEqual\"), this.broadcastedBinaryOp(e, t, \"bool\", function (e, t) {\n return e !== t ? 1 : 0;\n });\n }, e.prototype.less = function (e, t) {\n return this.assertNotComplex([e, t], \"less\"), this.broadcastedBinaryOp(e, t, \"bool\", function (e, t) {\n return e < t ? 1 : 0;\n });\n }, e.prototype.lessEqual = function (e, t) {\n return this.assertNotComplex([e, t], \"lessEqual\"), this.broadcastedBinaryOp(e, t, \"bool\", function (e, t) {\n return e <= t ? 1 : 0;\n });\n }, e.prototype.greater = function (e, t) {\n return this.assertNotComplex([e, t], \"greater\"), this.broadcastedBinaryOp(e, t, \"bool\", function (e, t) {\n return e > t ? 1 : 0;\n });\n }, e.prototype.greaterEqual = function (e, t) {\n return this.assertNotComplex([e, t], \"greaterEqual\"), this.broadcastedBinaryOp(e, t, \"bool\", function (e, t) {\n return e >= t ? 1 : 0;\n });\n }, e.prototype.logicalNot = function (e) {\n this.assertNotComplex(e, \"logicalNot\");\n\n for (var t = e.dataSync(), n = new Uint8Array(t.length), r = 0; r < t.length; ++r) n[r] = t[r] ? 0 : 1;\n\n return Tensor.make(e.shape, {\n values: n\n }, \"bool\");\n }, e.prototype.logicalAnd = function (e, t) {\n return this.assertNotComplex([e, t], \"logicalAnd\"), this.broadcastedBinaryOp(e, t, \"bool\", function (e, t) {\n return e && t;\n });\n }, e.prototype.logicalOr = function (e, t) {\n return this.assertNotComplex([e, t], \"logicalOr\"), this.broadcastedBinaryOp(e, t, \"bool\", function (e, t) {\n return e || t;\n });\n }, e.prototype.select = function (e, t, n) {\n this.assertNotComplex([e, t, n], \"select\");\n\n for (var r = e.dataSync(), o = t.dataSync(), a = n.dataSync(), i = zeros(t.shape, upcastType(t.dtype, n.dtype)), s = i.dataSync(), u = 0, l = 0 === e.rank || e.rank > 1 || 1 === t.rank ? 1 : t.shape[1], c = 0; c < r.length; c++) for (var p = 0; p < l; p++) 1 === r[c] ? s[u++] = o[c] : s[u++] = a[c];\n\n return i;\n }, e.prototype.where = function (e) {\n this.assertNotComplex([e], \"where\");\n var t = e.dataSync();\n return whereImpl(e.shape, t);\n }, e.prototype.topk = function (e, t, n) {\n return this.assertNotComplex(e, \"topk\"), topkImpl(e.dataSync(), e.shape, e.dtype, t, n);\n }, e.prototype.min = function (e, t) {\n this.assertNotComplex(e, \"min\"), assertAxesAreInnerMostDims(\"min\", t, e.rank);\n\n for (var n = computeOutAndReduceShapes(e.shape, t), r = n[0], o = n[1], a = zeros(r, e.dtype), i = sizeFromShape(o), s = a.dataSync(), u = e.dataSync(), l = 0; l < s.length; ++l) {\n for (var c = l * i, p = u[c], d = 0; d < i; ++d) {\n var h = u[c + d];\n h < p && (p = h);\n }\n\n s[l] = p;\n }\n\n return a;\n }, e.prototype.minimum = function (e, t) {\n return this.assertNotComplex([e, t], \"minimum\"), this.broadcastedBinaryOp(e, t, e.dtype, function (e, t) {\n return Math.min(e, t);\n });\n }, e.prototype.mod = function (e, t) {\n return this.assertNotComplex([e, t], \"mod\"), this.broadcastedBinaryOp(e, t, e.dtype, function (e, t) {\n var n = e % t;\n return e < 0 && t < 0 || e >= 0 && t >= 0 ? n : (n + t) % t;\n });\n }, e.prototype.max = function (e, t) {\n this.assertNotComplex(e, \"max\"), assertAxesAreInnerMostDims(\"max\", t, e.rank);\n\n for (var n = computeOutAndReduceShapes(e.shape, t), r = n[0], o = n[1], a = zeros(r, e.dtype), i = sizeFromShape(o), s = a.dataSync(), u = e.dataSync(), l = 0; l < s.length; ++l) {\n for (var c = l * i, p = u[c], d = 0; d < i; ++d) {\n var h = u[c + d];\n h > p && (p = h);\n }\n\n s[l] = p;\n }\n\n return a;\n }, e.prototype.maximum = function (e, t) {\n return this.assertNotComplex([e, t], \"maximum\"), this.broadcastedBinaryOp(e, t, e.dtype, function (e, t) {\n return Math.max(e, t);\n });\n }, e.prototype.all = function (e, t) {\n this.assertNotComplex(e, \"all\"), assertAxesAreInnerMostDims(\"all\", t, e.rank);\n\n for (var n = computeOutAndReduceShapes(e.shape, t), r = n[0], o = n[1], a = zeros(r, e.dtype), i = sizeFromShape(o), s = a.dataSync(), u = e.dataSync(), l = 0; l < s.length; ++l) {\n for (var c = l * i, p = u[c], d = 0; d < i; ++d) {\n var h = u[c + d];\n p = p && h;\n }\n\n s[l] = p;\n }\n\n return a;\n }, e.prototype.any = function (e, t) {\n this.assertNotComplex(e, \"any\"), assertAxesAreInnerMostDims(\"any\", t, e.rank);\n\n for (var n = computeOutAndReduceShapes(e.shape, t), r = n[0], o = n[1], a = zeros(r, e.dtype), i = sizeFromShape(o), s = a.dataSync(), u = e.dataSync(), l = 0; l < s.length; ++l) {\n for (var c = l * i, p = u[c], d = 0; d < i; ++d) {\n var h = u[c + d];\n p = p || h;\n }\n\n s[l] = p;\n }\n\n return a;\n }, e.prototype.squaredDifference = function (e, t) {\n return this.assertNotComplex([e, t], \"squaredDifference\"), this.broadcastedBinaryOp(e, t, e.dtype, function (e, t) {\n var n = e - t;\n return n * n;\n });\n }, e.prototype.ceil = function (e) {\n this.assertNotComplex(e, \"ceil\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) n[r] = Math.ceil(t[r]);\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.floor = function (e) {\n this.assertNotComplex(e, \"floor\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) n[r] = Math.floor(t[r]);\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.sign = function (e) {\n this.assertNotComplex(e, \"x\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) t[r] < 0 ? n[r] = -1 : t[r] > 0 ? n[r] = 1 : n[r] = 0;\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.round = function (e) {\n this.assertNotComplex(e, \"round\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) {\n var o = Math.floor(t[r]);\n t[r] - o < .5 ? n[r] = Math.floor(t[r]) : t[r] - o > .5 ? n[r] = Math.ceil(t[r]) : n[r] = o % 2 == 0 ? o : o + 1;\n }\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.exp = function (e) {\n this.assertNotComplex(e, \"exp\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) n[r] = Math.exp(t[r]);\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.expm1 = function (e) {\n this.assertNotComplex(e, \"expm1\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) n[r] = Math.expm1(t[r]);\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.log = function (e) {\n this.assertNotComplex(e, \"log\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) {\n var o = t[r];\n n[r] = Math.log(o);\n }\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.log1p = function (e) {\n this.assertNotComplex(e, \"log1p\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) {\n var o = t[r];\n n[r] = Math.log1p(o);\n }\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.sqrt = function (e) {\n this.assertNotComplex(e, \"sqrt\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) {\n var o = t[r];\n n[r] = Math.sqrt(o);\n }\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.rsqrt = function (e) {\n this.assertNotComplex(e, \"rsqrt\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) {\n var o = t[r];\n n[r] = 1 / Math.sqrt(o);\n }\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.square = function (e) {\n this.assertNotComplex(e, \"square\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) {\n var o = t[r];\n n[r] = o * o;\n }\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.reciprocal = function (e) {\n this.assertNotComplex(e, \"reciprocal\");\n\n for (var t = e.dataSync(), n = new Float32Array(t.length), r = 0; r < t.length; ++r) n[r] = 1 / t[r];\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.linear = function (e) {\n return e;\n }, e.prototype.relu = function (e) {\n this.assertNotComplex(e, \"relu\");\n\n for (var t = zeros(e.shape, e.dtype), n = t.dataSync(), r = e.dataSync(), o = 0; o < r.length; ++o) n[o] = Math.max(0, r[o]);\n\n return t;\n }, e.prototype.prelu = function (e, t) {\n return this.assertNotComplex([e, t], \"prelu\"), this.broadcastedBinaryOp(e, t, e.dtype, function (e, t) {\n return e < 0 ? t * e : e;\n });\n }, e.prototype.elu = function (e) {\n this.assertNotComplex(e, \"elu\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) {\n var o = n[r];\n t[r] = o >= 0 ? o : Math.exp(o) - 1;\n }\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.eluDer = function (e, t) {\n this.assertNotComplex([e, t], \"eluDer\");\n\n for (var n = new Float32Array(t.size), r = t.dataSync(), o = e.dataSync(), a = 0; a < r.length; ++a) {\n var i = r[a];\n n[a] = i >= 1 ? o[a] : o[a] * (i + 1);\n }\n\n return Tensor.make(t.shape, {\n values: n\n });\n }, e.prototype.selu = function (e) {\n this.assertNotComplex(e, \"selu\");\n\n for (var t = SELU_SCALEALPHA, n = SELU_SCALE, r = new Float32Array(e.size), o = e.dataSync(), a = 0; a < o.length; ++a) {\n var i = o[a];\n r[a] = i >= 0 ? n * i : t * (Math.exp(i) - 1);\n }\n\n return Tensor.make(e.shape, {\n values: r\n });\n }, e.prototype.clip = function (e, t, n) {\n this.assertNotComplex(e, \"clip\");\n\n for (var r = new Float32Array(e.size), o = e.dataSync(), a = 0; a < o.length; ++a) {\n var i = o[a];\n r[a] = i > n ? n : i < t ? t : i;\n }\n\n return Tensor.make(e.shape, {\n values: r\n });\n }, e.prototype.abs = function (e) {\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.abs(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.complexAbs = function (e) {\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < e.size; ++r) {\n var o = n[2 * r],\n a = n[2 * r + 1];\n t[r] = Math.hypot(o, a);\n }\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.int = function (e) {\n this.assertNotComplex(e, \"int\");\n\n for (var t = new Int32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = n[r];\n\n return Tensor.make(e.shape, {\n values: t\n }, \"int32\");\n }, e.prototype.sigmoid = function (e) {\n this.assertNotComplex(e, \"sigmoid\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = 1 / (1 + Math.exp(-n[r]));\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.softplus = function (e) {\n this.assertNotComplex(e, \"softplus\");\n\n for (var t = Math.log(1.1920928955078125e-7) + 2, n = new Float32Array(e.size), r = e.dataSync(), o = 0; o < r.length; ++o) {\n var a = r[o] > -t,\n i = r[o] < t,\n s = Math.exp(r[o]),\n u = void 0;\n u = i ? s : a ? r[o] : Math.log(1 + s), n[o] = u;\n }\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.sin = function (e) {\n this.assertNotComplex(e, \"sin\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.sin(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.cos = function (e) {\n this.assertNotComplex(e, \"cos\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.cos(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.tan = function (e) {\n this.assertNotComplex(e, \"tan\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.tan(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.asin = function (e) {\n this.assertNotComplex(e, \"asin\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.asin(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.acos = function (e) {\n this.assertNotComplex(e, \"acos\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.acos(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.atan = function (e) {\n this.assertNotComplex(e, \"atan\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.atan(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.atan2 = function (e, t) {\n return this.assertNotComplex([e, t], \"atan2\"), this.broadcastedBinaryOp(e, t, e.dtype, function (e, t) {\n return Math.atan2(e, t);\n });\n }, e.prototype.sinh = function (e) {\n this.assertNotComplex(e, \"sinh\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.sinh(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.cosh = function (e) {\n this.assertNotComplex(e, \"cosh\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.cosh(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.tanh = function (e) {\n this.assertNotComplex(e, \"tanh\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = tanh(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.asinh = function (e) {\n this.assertNotComplex(e, \"asinh\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.asinh(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.acosh = function (e) {\n this.assertNotComplex(e, \"acosh\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.acosh(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.atanh = function (e) {\n this.assertNotComplex(e, \"atanh\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = 0; r < n.length; ++r) t[r] = Math.atanh(n[r]);\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.erf = function (e) {\n this.assertNotComplex(e, \"erf\");\n\n for (var t = new Float32Array(e.size), n = e.dataSync(), r = ERF_P, o = ERF_A1, a = ERF_A2, i = ERF_A3, s = ERF_A4, u = ERF_A5, l = 0; l < n.length; ++l) {\n var c = n[l],\n p = 1 / (1 + r * c);\n t[l] = 1 - ((((u * p + s) * p + i) * p + a) * p + o) * p * Math.exp(-c * c);\n }\n\n return Tensor.make(e.shape, {\n values: t\n });\n }, e.prototype.step = function (e, t) {\n void 0 === t && (t = 0), this.assertNotComplex(e, \"step\");\n\n for (var n = new Float32Array(e.size), r = e.dataSync(), o = 0; o < r.length; ++o) {\n var a = r[o];\n isNaN(a) ? n[o] = NaN : n[o] = a > 0 ? 1 : t;\n }\n\n return Tensor.make(e.shape, {\n values: n\n });\n }, e.prototype.conv2d = function (e, t, n) {\n this.assertNotComplex([e, t], \"conv2d\");\n\n for (var r = n.filterHeight, o = n.filterWidth, a = n.dilationHeight, i = n.dilationWidth, s = n.padInfo.left, u = n.padInfo.top, l = buffer(n.outShape, e.dtype), c = e.dataSync(), p = t.dataSync(), d = l.values, h = 0; h < n.batchSize; ++h) for (var f = h * e.strides[0], m = h * l.strides[0], g = 0; g < n.outHeight; ++g) for (var v = m + g * l.strides[1], y = g * n.strideHeight - s, x = 0; x < r; x++) {\n var T = y + x * a;\n if (!(T < 0 || T >= n.inHeight)) for (var w = x * t.strides[0], E = f + T * e.strides[1], b = 0; b < n.outWidth; ++b) for (var S = v + b * n.outChannels, C = b * n.strideWidth - u, A = 0; A < o; A++) {\n var N = C + A * i;\n if (!(N < 0 || N >= n.inWidth)) for (var _ = w + A * t.strides[1], R = E + N * n.inChannels, I = _, k = 0; k < n.inChannels; ++k) {\n for (var D = c[R + k], O = 0; O < n.outChannels; ++O) d[S + O] += D * p[I + O];\n\n I += n.outChannels;\n }\n }\n }\n\n return l.toTensor();\n }, e.prototype.conv3d = function (e, t, n) {\n for (var r = n.filterDepth, o = n.filterHeight, a = n.filterWidth, i = n.dilationDepth, s = n.dilationHeight, u = n.dilationWidth, l = n.padInfo.front, c = n.padInfo.left, p = n.padInfo.top, d = buffer(n.outShape, e.dtype), h = e.dataSync(), f = t.dataSync(), m = d.values, g = 0; g < n.batchSize; ++g) for (var v = g * e.strides[0], y = g * d.strides[0], x = 0; x < n.outDepth; ++x) for (var T = y + x * d.strides[1], w = x * n.strideDepth - l, E = 0; E < r; E++) {\n var b = w + E * i;\n if (!(b < 0 || b >= n.inDepth)) for (var S = E * t.strides[0], C = v + b * e.strides[1], A = 0; A < n.outHeight; ++A) for (var N = T + A * d.strides[2], _ = A * n.strideHeight - p, R = 0; R < o; R++) {\n var I = _ + R * s;\n if (!(I < 0 || I >= n.inHeight)) for (var k = S + R * t.strides[1], D = C + I * e.strides[2], O = 0; O < n.outWidth; ++O) for (var M = N + O * n.outChannels, P = O * n.strideWidth - c, F = 0; F < a; F++) {\n var L = P + F * u;\n if (!(L < 0 || L >= n.inWidth)) for (var B = k + F * t.strides[2], U = D + L * n.inChannels, V = B, z = 0; z < n.inChannels; ++z) {\n for (var W = h[U + z], H = 0; H < n.outChannels; ++H) m[M + H] += W * f[V + H];\n\n V += n.outChannels;\n }\n }\n }\n }\n\n return d.toTensor();\n }, e.prototype.conv2dDerInput = function (e, t, n) {\n this.assertNotComplex([e, t], \"conv2dDerInput\");\n\n for (var r = buffer(n.inShape, \"float32\"), o = r.values, a = r.strides, i = a[0], s = a[1], u = a[2], l = e.dataSync(), c = e.strides, p = c[0], d = c[1], h = c[2], f = t.dataSync(), m = t.strides, g = m[0], v = m[1], y = m[2], x = n.batchSize, T = n.filterHeight, w = n.filterWidth, E = n.inChannels, b = n.inHeight, S = n.inWidth, C = n.outChannels, A = n.outHeight, N = n.outWidth, _ = n.strideHeight, R = n.strideWidth, I = T - 1 - n.padInfo.top, k = w - 1 - n.padInfo.left, D = 0; D < x; ++D) for (var O = 0; O < E; ++O) for (var M = 0; M < b; ++M) for (var P = M - I, F = Math.max(0, Math.ceil(P / _)), L = Math.min(A, (T + P) / _), B = 0; B < S; ++B) {\n for (var U = B - k, V = Math.max(0, Math.ceil(U / R)), z = Math.min(N, (w + U) / R), W = 0, H = F; H < L; ++H) for (var G = H * _ - P, q = V; q < z; ++q) for (var $ = p * D + d * H + h * q, X = g * (T - 1 - G) + v * (w - 1 - (q * R - U)) + y * O, K = 0; K < C; ++K) {\n W += l[$ + K] * f[X + K];\n }\n\n o[i * D + s * M + u * B + O] = W;\n }\n\n return r.toTensor();\n }, e.prototype.conv3dDerInput = function (e, t, n) {\n for (var r = buffer(n.inShape, \"float32\"), o = r.values, a = r.strides, i = a[0], s = a[1], u = a[2], l = a[3], c = e.dataSync(), p = e.strides, d = p[0], h = p[1], f = p[2], m = p[3], g = t.dataSync(), v = t.strides, y = v[0], x = v[1], T = v[2], w = v[3], E = n.batchSize, b = n.filterDepth, S = n.filterHeight, C = n.filterWidth, A = n.inChannels, N = n.inDepth, _ = n.inHeight, R = n.inWidth, I = n.outChannels, k = n.outDepth, D = n.outHeight, O = n.outWidth, M = n.strideDepth, P = n.strideHeight, F = n.strideWidth, L = b - 1 - n.padInfo.front, B = S - 1 - n.padInfo.top, U = C - 1 - n.padInfo.left, V = 0; V < E; ++V) for (var z = 0; z < A; ++z) for (var W = 0; W < N; ++W) for (var H = W - L, G = Math.max(0, Math.ceil(H / M)), q = Math.min(k, (b + H) / M), $ = 0; $ < _; ++$) for (var X = $ - B, K = Math.max(0, Math.ceil(X / P)), j = Math.min(D, (S + X) / P), Y = 0; Y < R; ++Y) {\n for (var Q = Y - U, J = Math.max(0, Math.ceil(Q / F)), Z = Math.min(O, (C + Q) / F), ee = 0, te = G; te < q; ++te) for (var ne = te * M - H, re = K; re < j; ++re) for (var oe = re * P - X, ae = J; ae < Z; ++ae) for (var ie = d * V + h * te + f * re + m * ae, se = y * (b - 1 - ne) + x * (S - 1 - oe) + T * (C - 1 - (ae * F - Q)) + w * z, ue = 0; ue < I; ++ue) {\n ee += c[ie + ue] * g[se + ue];\n }\n\n o[i * V + s * W + u * $ + l * Y + z] = ee;\n }\n\n return r.toTensor();\n }, e.prototype.conv2dDerFilter = function (e, t, n) {\n this.assertNotComplex([e, t], \"conv2dDerFilter\");\n\n for (var r = n.strideHeight, o = n.strideWidth, a = n.filterHeight, i = n.filterWidth, s = buffer(n.filterShape, \"float32\"), u = n.padInfo.left, l = n.padInfo.top, c = e.bufferSync(), p = t.bufferSync(), d = 0; d < a; ++d) for (var h = Math.max(0, Math.ceil((l - d) / r)), f = Math.min(n.outHeight, (n.inHeight + l - d) / r), m = 0; m < i; ++m) for (var g = Math.max(0, Math.ceil((u - m) / o)), v = Math.min(n.outWidth, (n.inWidth + u - m) / o), y = 0; y < n.inChannels; ++y) for (var x = 0; x < n.outChannels; ++x) {\n for (var T = 0, w = 0; w < n.batchSize; ++w) for (var E = h; E < f; ++E) for (var b = d + E * r - l, S = g; S < v; ++S) {\n var C = m + S * o - u;\n T += c.get(w, b, C, y) * p.get(w, E, S, x);\n }\n\n s.set(T, d, m, y, x);\n }\n\n return s.toTensor();\n }, e.prototype.conv3dDerFilter = function (e, t, n) {\n for (var r = n.strideDepth, o = n.strideHeight, a = n.strideWidth, i = n.filterDepth, s = n.filterHeight, u = n.filterWidth, l = buffer(n.filterShape, \"float32\"), c = l.values, p = l.strides, d = p[0], h = p[1], f = p[2], m = p[3], g = t.dataSync(), v = t.strides, y = v[0], x = v[1], T = v[2], w = v[3], E = e.dataSync(), b = e.strides, S = b[0], C = b[1], A = b[2], N = b[3], _ = n.padInfo.front, R = n.padInfo.left, I = n.padInfo.top, k = 0; k < i; ++k) for (var D = Math.max(0, Math.ceil((_ - k) / r)), O = Math.min(n.outDepth, (n.inDepth + _ - k) / r), M = k * d, P = 0; P < s; ++P) for (var F = Math.max(0, Math.ceil((I - P) / o)), L = Math.min(n.outHeight, (n.inHeight + I - P) / o), B = P * h + M, U = 0; U < u; ++U) for (var V = Math.max(0, Math.ceil((R - U) / a)), z = Math.min(n.outWidth, (n.inWidth + R - U) / a), W = U * f + B, H = 0; H < n.inChannels; ++H) for (var G = H * m + W, q = 0; q < n.outChannels; ++q) {\n for (var $ = 0, X = 0; X < n.batchSize; ++X) for (var K = X * S, j = X * y, Y = D; Y < O; ++Y) for (var Q = (k + Y * r - _) * C + K, J = Y * x + j, Z = F; Z < L; ++Z) for (var ee = (P + Z * o - I) * A + Q, te = Z * T + J, ne = V; ne < z; ++ne) {\n var re = ne * w + te;\n $ += E[(U + ne * a - R) * N + ee + H] * g[re + q];\n }\n\n c[G + q] = $;\n }\n\n return l.toTensor();\n }, e.prototype.depthwiseConv2D = function (e, t, n) {\n this.assertNotComplex([e, t], \"depthwiseConv2D\");\n\n for (var r = n.filterHeight, o = n.filterWidth, a = n.dilationHeight, i = n.dilationWidth, s = n.padInfo.left, u = n.padInfo.top, l = n.outChannels / n.inChannels, c = buffer(n.outShape, e.dtype), p = e.dataSync(), d = t.dataSync(), h = c.values, f = 0; f < n.batchSize; ++f) for (var m = f * e.strides[0], g = f * c.strides[0], v = 0; v < n.outHeight; ++v) for (var y = g + v * c.strides[1], x = v * n.strideHeight - s, T = 0; T < r; ++T) {\n var w = x + T * a;\n if (!(w < 0 || w >= n.inHeight)) for (var E = T * t.strides[0], b = m + w * e.strides[1], S = 0; S < n.outWidth; ++S) for (var C = y + S * c.strides[2], A = S * n.strideWidth - u, N = 0; N < o; ++N) {\n var _ = A + N * i;\n\n if (!(_ < 0 || _ >= n.inWidth)) for (var R = E + N * t.strides[1], I = b + _ * n.inChannels, k = C, D = R, O = 0; O < n.inChannels; ++O) {\n for (var M = p[I + O], P = 0; P < l; ++P) h[k + P] += M * d[D + P];\n\n k += l, D += l;\n }\n }\n }\n\n return c.toTensor();\n }, e.prototype.depthwiseConv2DDerInput = function (e, t, n) {\n this.assertNotComplex([e, t], \"depthwiseConv2DDerInput\");\n\n for (var r = buffer(n.inShape, \"float32\"), o = r.values, a = r.strides, i = a[0], s = a[1], u = a[2], l = e.dataSync(), c = e.strides, p = c[0], d = c[1], h = c[2], f = t.dataSync(), m = t.strides, g = m[0], v = m[1], y = m[2], x = n.batchSize, T = n.filterHeight, w = n.filterWidth, E = n.inChannels, b = n.inHeight, S = n.inWidth, C = n.outChannels, A = n.outHeight, N = n.outWidth, _ = n.strideHeight, R = n.strideWidth, I = T - 1 - n.padInfo.top, k = w - 1 - n.padInfo.left, D = C / E, O = 0; O < x; ++O) for (var M = 0; M < E; ++M) for (var P = 0; P < b; ++P) for (var F = P - I, L = Math.max(0, Math.ceil(F / _)), B = Math.min(A, (T + F) / _), U = 0; U < S; ++U) {\n for (var V = U - k, z = Math.max(0, Math.ceil(V / R)), W = Math.min(N, (w + V) / R), H = 0, G = L; G < B; ++G) for (var q = G * _ - F, $ = z; $ < W; ++$) for (var X = p * O + d * G + h * $, K = g * (T - 1 - q) + v * (w - 1 - ($ * R - V)) + y * M, j = 0; j < D; ++j) {\n H += l[X + (M * D + j)] * f[K + j];\n }\n\n o[i * O + s * P + u * U + M] = H;\n }\n\n return r.toTensor();\n }, e.prototype.depthwiseConv2DDerFilter = function (e, t, n) {\n this.assertNotComplex([e, t], \"depthwiseConv2DDerFilter\");\n\n for (var r = n.strideHeight, o = n.strideWidth, a = n.filterHeight, i = n.filterWidth, s = buffer(n.filterShape, \"float32\"), u = n.padInfo.left, l = n.padInfo.top, c = n.outChannels / n.inChannels, p = e.bufferSync(), d = t.bufferSync(), h = 0; h < a; ++h) for (var f = Math.max(0, Math.ceil((l - h) / r)), m = Math.min(n.outHeight, (n.inHeight + l - h) / r), g = 0; g < i; ++g) for (var v = Math.max(0, Math.ceil((u - g) / o)), y = Math.min(n.outWidth, (n.inWidth + u - g) / o), x = 0; x < n.outChannels; ++x) {\n for (var T = Math.trunc(x / c), w = x % c, E = 0, b = 0; b < n.batchSize; ++b) for (var S = f; S < m; ++S) for (var C = h + S * r - l, A = v; A < y; ++A) {\n var N = g + A * o - u;\n E += p.get(b, C, N, T) * d.get(b, S, A, x);\n }\n\n s.set(E, h, g, T, w);\n }\n\n return s.toTensor();\n }, e.prototype.tile = function (e, t) {\n this.assertNotComplex(e, \"tile\");\n\n for (var n = new Array(e.rank), r = 0; r < n.length; r++) n[r] = e.shape[r] * t[r];\n\n var o = buffer(n, e.dtype),\n a = e.bufferSync();\n\n for (r = 0; r < o.values.length; ++r) {\n for (var i = o.indexToLoc(r), s = new Array(e.rank), u = 0; u < s.length; u++) s[u] = i[u] % e.shape[u];\n\n var l = a.locToIndex(s);\n o.values[r] = a.values[l];\n }\n\n return o.toTensor();\n }, e.prototype.pad = function (e, t, n) {\n this.assertNotComplex(e, \"pad\");\n var r = t.map(function (t, n) {\n return t[0] + e.shape[n] + t[1];\n }),\n o = t.map(function (e) {\n return e[0];\n }),\n a = e.bufferSync(),\n i = buffer(r, e.dtype);\n 0 !== n && i.values.fill(n);\n\n for (var s = 0; s < e.size; s++) {\n var u = a.indexToLoc(s),\n l = u.map(function (e, t) {\n return e + o[t];\n });\n i.set.apply(i, [a.get.apply(a, u)].concat(l));\n }\n\n return i.toTensor();\n }, e.prototype.transpose = function (e, t) {\n this.assertNotComplex(e, \"transpose\");\n\n for (var n = new Array(e.rank), r = 0; r < n.length; r++) n[r] = e.shape[t[r]];\n\n var o = e.dataSync(),\n a = buffer(n, e.dtype),\n i = e.bufferSync();\n\n for (r = 0; r < e.size; ++r) {\n for (var s = i.indexToLoc(r), u = new Array(s.length), l = 0; l < u.length; l++) u[l] = s[t[l]];\n\n var c = a.locToIndex(u);\n a.values[c] = o[r];\n }\n\n return a.toTensor();\n }, e.prototype.gather = function (e, t, n) {\n this.assertNotComplex([e, t], \"gather\");\n var r = e.shape.slice(),\n o = t.dataSync();\n r[n] = o.length;\n\n for (var a = buffer(r, e.dtype), i = e.bufferSync(), s = 0; s < a.size; ++s) {\n var u = a.indexToLoc(s),\n l = u.slice();\n l[n] = o[u[n]];\n var c = i.locToIndex(l);\n a.values[s] = i.values[c];\n }\n\n return a.toTensor();\n }, e.prototype.batchToSpaceND = function (e, t, n) {\n this.assertNotComplex([e], \"batchToSpaceND\");\n var r = t.reduce(function (e, t) {\n return e * t;\n }),\n o = getReshaped(e.shape, t, r),\n a = getPermuted(o.length, t.length),\n i = getReshapedPermuted(e.shape, t, r),\n s = getSliceBeginCoords(n, t.length),\n u = getSliceSize(i, n, t.length);\n return e.reshape(o).transpose(a).reshape(i).slice(s, u);\n }, e.prototype.spaceToBatchND = function (e, t, n) {\n this.assertNotComplex([e], \"spaceToBatchND\");\n var r = t.reduce(function (e, t) {\n return e * t;\n }),\n o = [[0, 0]];\n o.push.apply(o, n);\n\n for (var a = 1 + t.length; a < e.shape.length; ++a) o.push([0, 0]);\n\n var i = e.pad(o),\n s = getReshaped(i.shape, t, r, !1),\n u = getPermuted(s.length, t.length, !1),\n l = getReshapedPermuted(i.shape, t, r, !1);\n return i.reshape(s).transpose(u).reshape(l);\n }, e.prototype.pool = function (e, t, n) {\n this.assertNotComplex(e, \"pool\");\n\n for (var r = t.strideHeight, o = t.strideWidth, a = t.dilationHeight, i = t.dilationWidth, s = t.effectiveFilterHeight, u = t.effectiveFilterWidth, l = t.padInfo.top, c = t.padInfo.left, p = \"max\" === n ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, d = e.dataSync(), h = buffer(t.outShape, e.dtype), f = h.values, m = t.outShape[1] * t.outShape[2] * t.outShape[3], g = t.outShape[2] * t.outShape[3], v = t.outShape[3], y = 0; y < t.batchSize; ++y) for (var x = y * m, T = y * e.strides[0], w = 0; w < t.inChannels; ++w) for (var E = 0; E < t.outHeight; ++E) for (var b = E * r - l, S = Math.max(0, b), C = Math.min(t.inHeight, s + b), A = x + E * g, N = 0; N < t.outWidth; ++N) {\n for (var _ = N * o - c, R = Math.max(0, _), I = Math.min(t.inWidth, u + _), k = p, D = 0, O = 0, M = S; M < C; M += a) {\n for (var P = T + M * e.strides[1], F = R; F < I; F += i) {\n var L = d[P + F * e.strides[2] + w];\n \"max\" === n && L > k ? k = L : \"avg\" === n && (D += L, O++);\n }\n\n if (isNaN(k)) break;\n }\n\n f[A + N * v + w] = \"avg\" === n ? D / O : k;\n }\n\n return h.toTensor();\n }, e.prototype.maxPool = function (e, t) {\n return this.pool(e, t, \"max\");\n }, e.prototype.maxPoolPositions = function (e, t) {\n for (var n = buffer(t.outShape, \"int32\"), r = t.strideHeight, o = t.strideWidth, a = t.dilationHeight, i = t.dilationWidth, s = t.effectiveFilterHeight, u = t.effectiveFilterWidth, l = t.padInfo.top, c = t.padInfo.left, p = e.bufferSync(), d = 0; d < t.batchSize; ++d) for (var h = 0; h < t.inChannels; ++h) for (var f = 0; f < t.outHeight; ++f) {\n for (var m = f * r - l, g = m; g < 0;) g += a;\n\n for (var v = Math.min(t.inHeight, s + m), y = 0; y < t.outWidth; ++y) {\n for (var x = y * o - c, T = x; T < 0;) T += i;\n\n for (var w = Math.min(t.inWidth, u + x), E = Number.NEGATIVE_INFINITY, b = -1, S = g; S < v; S += a) for (var C = S - m, A = T; A < w; A += i) {\n var N = A - x,\n _ = p.get(d, S, A, h);\n\n _ > E && (E = _, b = C * u + N);\n }\n\n n.set(b, d, f, y, h);\n }\n }\n\n return n.toTensor();\n }, e.prototype.maxPoolBackprop = function (e, t, n, r) {\n this.assertNotComplex([t, n], \"maxPoolBackprop\");\n\n for (var o = this.maxPoolPositions(t, r), a = r.strideHeight, i = r.strideWidth, s = r.dilationHeight, u = r.dilationWidth, l = r.effectiveFilterHeight, c = r.effectiveFilterWidth, p = c - 1 - r.padInfo.left, d = l - 1 - r.padInfo.top, h = buffer(t.shape, \"float32\"), f = o.bufferSync(), m = e.bufferSync(), g = 0; g < r.batchSize; ++g) for (var v = 0; v < r.inChannels; ++v) for (var y = 0; y < r.inHeight; ++y) for (var x = 0; x < r.inWidth; ++x) {\n for (var T = y - d, w = x - p, E = 0, b = 0; b < l; b += s) {\n var S = (T + b) / a;\n if (!(S < 0 || S >= r.outHeight || Math.floor(S) !== S)) for (var C = 0; C < c; C += u) {\n var A = (w + C) / i;\n\n if (!(A < 0 || A >= r.outWidth || Math.floor(A) !== A)) {\n var N = l * c - 1 - f.get(g, S, A, v) === b * c + C ? 1 : 0;\n if (0 !== N) E += m.get(g, S, A, v) * N;\n }\n }\n }\n\n h.set(E, g, y, x, v);\n }\n\n return h.toTensor();\n }, e.prototype.avgPoolBackprop = function (e, t, n) {\n this.assertNotComplex([e, t], \"avgPoolBackprop\");\n\n for (var r = n.strideHeight, o = n.strideWidth, a = n.filterHeight, i = n.filterWidth, s = n.dilationHeight, u = n.dilationWidth, l = n.effectiveFilterHeight, c = n.effectiveFilterWidth, p = c - 1 - n.padInfo.left, d = l - 1 - n.padInfo.top, h = buffer(t.shape, \"float32\"), f = 1 / (a * i), m = e.bufferSync(), g = 0; g < n.batchSize; ++g) for (var v = 0; v < n.inChannels; ++v) for (var y = 0; y < n.inHeight; ++y) for (var x = 0; x < n.inWidth; ++x) {\n for (var T = y - d, w = x - p, E = 0, b = 0; b < l; b += s) {\n var S = (T + b) / r;\n if (!(S < 0 || S >= n.outHeight || Math.floor(S) !== S)) for (var C = 0; C < c; C += u) {\n var A = (w + C) / o;\n if (!(A < 0 || A >= n.outWidth || Math.floor(A) !== A)) E += m.get(g, S, A, v);\n }\n }\n\n h.set(E * f, g, y, x, v);\n }\n\n return h.toTensor();\n }, e.prototype.cast = function (e, t) {\n return castTensor(e, t, this);\n }, e.prototype.reshape = function (e, t) {\n return reshapeTensor(e, t);\n }, e.prototype.avgPool = function (e, t) {\n return this.assertNotComplex(e, \"avgPool\"), this.pool(e, t, \"avg\").toFloat();\n }, e.prototype.resizeBilinear = function (e, t, n, r) {\n this.assertNotComplex(e, \"resizeBilinear\");\n\n for (var o = e.shape, a = o[0], i = o[1], s = o[2], u = o[3], l = e.dataSync(), c = new Float32Array(sizeFromShape([a, t, n, u])), p = [r && t > 1 ? i - 1 : i, r && n > 1 ? s - 1 : s], d = [r && t > 1 ? t - 1 : t, r && n > 1 ? n - 1 : n], h = 0, f = p[0] / d[0], m = p[1] / d[1], g = 0; g < a; g++) for (var v = 0; v < t; v++) for (var y = f * v, x = Math.floor(y), T = y - x, w = Math.min(i - 1, Math.ceil(y)), E = g * e.strides[0] + x * e.strides[1], b = g * e.strides[0] + w * e.strides[1], S = 0; S < n; S++) for (var C = m * S, A = Math.floor(C), N = C - A, _ = Math.min(s - 1, Math.ceil(C)), R = E + A * e.strides[2], I = b + A * e.strides[2], k = E + +_ * e.strides[2], D = b + _ * e.strides[2], O = 0; O < u; O++) {\n var M = l[R + O],\n P = l[I + O],\n F = M + (l[k + O] - M) * N,\n L = F + (P + (l[D + O] - P) * N - F) * T;\n c[h++] = L;\n }\n\n return tensor(c, [a, t, n, u]);\n }, e.prototype.resizeBilinearBackprop = function (e, t, n) {\n this.assertNotComplex([e, t], \"resizeBilinearBackprop\");\n\n for (var r = t.shape, o = r[0], a = r[1], i = r[2], s = r[3], u = e.shape, l = u[1], c = u[2], p = new Float32Array(o * a * i * s), d = [n && l > 1 ? a - 1 : a, n && c > 1 ? i - 1 : i], h = [n && l > 1 ? l - 1 : l, n && c > 1 ? c - 1 : c], f = d[0] / h[0], m = d[1] / h[1], g = e.dataSync(), v = 0, y = 0; y < o; y++) for (var x = y * t.strides[0], T = 0; T < l; T++) for (var w = T * f, E = Math.floor(w), b = Math.min(Math.ceil(w), a - 1), S = x + E * t.strides[1], C = x + b * t.strides[1], A = w - E, N = 1 - A, _ = 0; _ < c; _++) for (var R = _ * m, I = Math.floor(R), k = Math.min(Math.ceil(R), i - 1), D = R - I, O = 1 - D, M = S + I * t.strides[2], P = S + k * t.strides[2], F = C + I * t.strides[2], L = C + k * t.strides[2], B = N * O, U = N * D, V = A * O, z = A * D, W = 0; W < s; W++) {\n var H = g[v++];\n p[M + W] += H * B, p[P + W] += H * U, p[F + W] += H * V, p[L + W] += H * z;\n }\n\n return tensor4d(p, [o, i, a, s], t.dtype);\n }, e.prototype.resizeNearestNeighbor = function (e, t, n, r) {\n this.assertNotComplex(e, \"resizeNearestNeighbor\");\n\n for (var o = e.shape, a = o[0], i = o[1], s = o[2], u = o[3], l = e.dataSync(), c = new Float32Array(a * t * n * u), p = [r && t > 1 ? i - 1 : i, r && n > 1 ? s - 1 : s], d = [r && t > 1 ? t - 1 : t, r && n > 1 ? n - 1 : n], h = p[0] / d[0], f = p[1] / d[1], m = 0, g = 0; g < a; g++) for (var v = g * e.strides[0], y = 0; y < t; y++) for (var x = h * y, T = v + Math.min(i - 1, r ? Math.round(x) : Math.floor(x)) * e.strides[1], w = 0; w < n; w++) for (var E = f * w, b = T + Math.min(s - 1, r ? Math.round(E) : Math.floor(E)) * e.strides[2], S = 0; S < u; S++) {\n var C = l[b + S];\n c[m++] = C;\n }\n\n return tensor(c, [a, t, n, u], e.dtype);\n }, e.prototype.resizeNearestNeighborBackprop = function (e, t, n) {\n this.assertNotComplex([e, t], \"resizeNearestNeighborBackprop\");\n\n for (var r = t.shape, o = r[0], a = r[1], i = r[2], s = r[3], u = e.shape, l = u[1], c = u[2], p = new Float32Array(o * a * i * s), d = e.dataSync(), h = [n && l > 1 ? a - 1 : a, n && c > 1 ? i - 1 : i], f = [n && l > 1 ? l - 1 : l, n && c > 1 ? c - 1 : c], m = h[0] / f[0], g = h[1] / f[1], v = 1 / m, y = 1 / g, x = 2 * Math.ceil(v) + 2, T = 2 * Math.ceil(y) + 2, w = 0; w < o; w++) for (var E = w * t.strides[0], b = 0; b < a; b++) for (var S = E + b * t.strides[1], C = Math.floor(b * v), A = Math.floor(C - x / 2), N = 0; N < i; N++) for (var _ = S + N * t.strides[2], R = Math.floor(N * y), I = Math.floor(R - T / 2), k = 0; k < s; k++) {\n for (var D = 0, O = 0; O < x; O++) {\n var M = O + A;\n\n if (!(M < 0 || M >= l)) {\n var P = E + M * e.strides[1],\n F = M * m;\n if (b === Math.min(a - 1, n ? Math.round(F) : Math.floor(F))) for (var L = 0; L < T; L++) {\n var B = L + I;\n\n if (!(B < 0 || B >= c)) {\n var U = P + B * e.strides[2],\n V = B * g;\n N === Math.min(i - 1, n ? Math.round(V) : Math.floor(V)) && (D += d[U + k]);\n }\n }\n }\n }\n\n p[_ + k] = D;\n }\n\n return tensor4d(p, t.shape, t.dtype);\n }, e.prototype.batchNormalization = function (e, t, n, r, o, a) {\n this.assertNotComplex([e, t, n, o, a], \"batchNorm\");\n\n for (var i = e.dataSync(), s = t.dataSync(), u = n.dataSync(), l = o ? o.dataSync() : new Float32Array([1]), c = a ? a.dataSync() : new Float32Array([0]), p = new Float32Array(i.length), d = c.length, h = l.length, f = u.length, m = s.length, g = 0, v = 0, y = 0, x = 0, T = 0; T < i.length; ++T) p[T] = c[g++] + (i[T] - s[v++]) * l[y++] / Math.sqrt(u[x++] + r), g >= d && (g = 0), v >= m && (v = 0), y >= h && (y = 0), x >= f && (x = 0);\n\n return tensor4d(p, e.shape);\n }, e.prototype.localResponseNormalization4D = function (e, t, n, r, o) {\n this.assertNotComplex(e, \"localResponseNormalization4D\");\n var a = e.shape[3],\n i = a - 1,\n s = e.dataSync(),\n u = sizeFromShape(e.shape),\n l = new Float32Array(u);\n\n function c(e) {\n for (var n = e % a, r = e - n + Math.max(0, n - t), o = e - n + Math.min(n + t, i), u = 0; r <= o; r++) {\n var l = s[r];\n u += l * l;\n }\n\n return u;\n }\n\n for (var p = 0; p < u; p++) {\n var d = c(p),\n h = s[p] * Math.pow(n + r * d, -o);\n l[p] = h;\n }\n\n return tensor4d(l, e.shape);\n }, e.prototype.LRNGrad = function (e, t, n, r, o, a, i) {\n this.assertNotComplex(e, \"LRNGrad\");\n\n for (var s = e.shape[3], u = e.dataSync(), l = t.dataSync(), c = n.dataSync(), p = new Float32Array(sizeFromShape(e.shape)), d = sizeFromShape(e.shape), h = 0; h < d; h++) {\n for (var f = h % s, m = h - f + Math.max(0, f - r), g = h - f + Math.min(s, f + r + 1), v = 0, y = m; y < g; y++) v += Math.pow(l[y], 2);\n\n v = a * v + o;\n\n for (y = m; y < g; y++) {\n var x = -2 * a * i * l[y] * c[h] / v;\n h === y && (x += Math.pow(v, -i)), x *= u[h], p[y] += x;\n }\n }\n\n return tensor4d(p, e.shape);\n }, e.prototype.multinomial = function (e, t, n, r) {\n this.assertNotComplex(e, \"multinomial\");\n\n for (var o = t ? e : softmax(e), a = o.shape[0], i = o.shape[1], s = zeros([a, n], \"int32\"), u = s.dataSync(), l = o.dataSync(), c = 0; c < a; ++c) {\n var p = c * i,\n d = new Float32Array(i - 1);\n d[0] = l[p];\n\n for (var h = 1; h < d.length; ++h) d[h] = d[h - 1] + l[p + h];\n\n for (var f = seedrandom_1(r.toString()), m = c * n, g = 0; g < n; ++g) {\n var v = f();\n u[m + g] = d.length;\n\n for (var y = 0; y < d.length; y++) if (v < d[y]) {\n u[m + g] = y;\n break;\n }\n }\n }\n\n return s;\n }, e.prototype.oneHot = function (e, t, n, r) {\n this.assertNotComplex(e, \"oneHot\");\n var o = new Float32Array(e.size * t);\n o.fill(r);\n\n for (var a = e.dataSync(), i = 0; i < e.size; ++i) a[i] >= 0 && a[i] < t && (o[i * t + a[i]] = n);\n\n return tensor2d(o, [e.size, t], \"int32\");\n }, e.prototype.nonMaxSuppression = function (e, t, n, r, o) {\n return this.assertNotComplex(e, \"nonMaxSuppression\"), nonMaxSuppressionImpl(e.dataSync(), t.dataSync(), n, r, o);\n }, e.prototype.fft = function (e) {\n return this.fftBatch(e, !1);\n }, e.prototype.ifft = function (e) {\n return this.fftBatch(e, !0);\n }, e.prototype.fftBatch = function (e, t) {\n for (var n = e.shape[0], r = e.shape[1], o = buffer(e.shape, \"float32\"), a = buffer(e.shape, \"float32\"), i = real(e).as2D(n, r), s = imag(e).as2D(n, r), u = 0; u < n; u++) for (var l = i.slice([u, 0], [1, r]), c = s.slice([u, 0], [1, r]), p = complex(l, c), d = this.fftImpl(p, t).dataSync(), h = 0; h < r; h++) {\n var f = getComplexWithIndex(d, h);\n o.values[u * r + h] = f.real, a.values[u * r + h] = f.imag;\n }\n\n return complex(o.toTensor(), a.toTensor()).as2D(n, r);\n }, e.prototype.fftImpl = function (e, t) {\n var n = e.as1D(),\n r = n.size;\n\n if (this.isExponentOf2(r)) {\n var o = this.fftRadix2(n, r, t).as2D(e.shape[0], e.shape[1]);\n return t && (o = complex(real(o).div(scalar(r)), imag(o).div(scalar(r)))), o;\n }\n\n var a = e.dataSync(),\n i = splitRealAndImagArrays(this.fourierTransformByMatmul(a, r, t));\n return complex(i.real, i.imag).as2D(e.shape[0], e.shape[1]);\n }, e.prototype.isExponentOf2 = function (e) {\n return 0 == (e & e - 1);\n }, e.prototype.fftRadix2 = function (e, t, n) {\n if (1 === t) return e;\n var r = e.dataSync(),\n o = t / 2,\n a = complexWithEvenIndex(r),\n i = complex(a.real, a.imag).as1D(),\n s = complexWithOddIndex(r),\n u = complex(s.real, s.imag).as1D();\n i = this.fftRadix2(i, o, n), u = this.fftRadix2(u, o, n);\n var l = exponents(t, n),\n c = complex(l.real, l.imag).mul(u),\n p = i.add(c),\n d = i.sub(c),\n h = real(p).concat(real(d)),\n f = imag(p).concat(imag(d));\n return complex(h, f).as1D();\n }, e.prototype.fourierTransformByMatmul = function (e, t, n) {\n for (var r = new Float32Array(2 * t), o = 0; o < t; o++) {\n for (var a = 0, i = 0, s = 0; s < t; s++) {\n var u = exponent(o * s, t, n),\n l = getComplexWithIndex(e, s);\n a += l.real * u.real - l.imag * u.imag, i += l.real * u.imag + l.imag * u.real;\n }\n\n n && (a /= t, i /= t), assignToTypedArray(r, a, i, o);\n }\n\n return r;\n }, e.prototype.depthToSpace = function (e, t, n) {\n assert(\"NHWC\" === n, \"Only NHWC dataFormat supported on CPU for depthToSpace. Got \" + n), assert(t > 1, \"blockSize should be > 1 for depthToSpace, but was: \" + t);\n\n for (var r = e.shape[0], o = e.shape[1], a = e.shape[2], i = e.shape[3], s = o * t, u = a * t, l = i / (t * t), c = e.dataSync(), p = new Float32Array(r * s * u * l), d = 0, h = 0; h < r; ++h) for (var f = 0; f < s; ++f) for (var m = Math.floor(f / t), g = f % t, v = 0; v < u; ++v) for (var y = Math.floor(v / t), x = (g * t + v % t) * l, T = 0; T < l; ++T) {\n var w = T + x + i * (y + a * (m + o * h));\n p[d++] = c[w];\n }\n\n return tensor4d(p, [r, s, u, l]);\n }, e.prototype.broadcastedBinaryOp = function (e, t, n, r) {\n var o = assertAndGetBroadcastShape(e.shape, t.shape),\n a = buffer(o, n),\n i = e.dataSync(),\n s = t.dataSync(),\n u = getBroadcastDims(e.shape, o),\n l = getBroadcastDims(t.shape, o),\n c = a.values;\n if (u.length + l.length === 0) for (var p = 0; p < c.length; ++p) c[p] = r(i[p % i.length], s[p % s.length]);else {\n var d = e.bufferSync(),\n h = t.bufferSync(),\n f = function (n) {\n var o = a.indexToLoc(n),\n p = o.slice(-e.rank);\n u.forEach(function (e) {\n return p[e] = 0;\n });\n var f = d.locToIndex(p),\n m = o.slice(-t.rank);\n l.forEach(function (e) {\n return m[e] = 0;\n });\n var g = h.locToIndex(m);\n c[n] = r(i[f], s[g]);\n };\n\n for (p = 0; p < c.length; ++p) f(p);\n }\n return a.toTensor();\n }, e.prototype.broadcastedBinaryComplexOp = function (e, t, n) {\n var r = assertAndGetBroadcastShape(e.shape, t.shape),\n o = buffer(r, \"float32\"),\n a = buffer(r, \"float32\"),\n i = e.dataSync(),\n s = t.dataSync(),\n u = getBroadcastDims(e.shape, r),\n l = getBroadcastDims(t.shape, r),\n c = o.values,\n p = a.values;\n if (u.length + l.length === 0) for (var d = 0; d < c.length; d++) {\n var h = d % i.length,\n f = d % s.length,\n m = n(i[2 * h], i[2 * h + 1], s[2 * f], s[2 * f + 1]);\n c[d] = m.real, p[d] = m.imag;\n } else {\n var g = this.data.get(e.dataId).complexTensors.real.bufferSync(),\n v = this.data.get(t.dataId).complexTensors.real.bufferSync(),\n y = function (r) {\n var a = o.indexToLoc(r),\n d = a.slice(-e.rank);\n u.forEach(function (e) {\n return d[e] = 0;\n });\n var h = g.locToIndex(d),\n f = a.slice(-t.rank);\n l.forEach(function (e) {\n return f[e] = 0;\n });\n var m = v.locToIndex(f),\n y = n(i[2 * h], i[2 * h + 1], s[2 * m], s[2 * m + 1]);\n c[r] = y.real, p[r] = y.imag;\n };\n\n for (d = 0; d < c.length; d++) y(d);\n }\n return this.complex(o.toTensor(), a.toTensor());\n }, e.prototype.split = function (e, t, n) {\n return split(e, t, n);\n }, e.prototype.dispose = function () {}, e.prototype.floatPrecision = function () {\n return 32;\n }, e.prototype.cropAndResize = function (e, t, n, r, o, a) {\n for (var i = e.shape, s = i[0], u = i[1], l = i[2], c = i[3], p = t.shape[0], d = r[0], h = r[1], f = buffer([p, d, h, c], e.dtype), m = t.dataSync(), g = n.dataSync(), v = e.dataSync(), y = e.strides, x = f.strides, T = 0; T < p; T++) {\n var w = 4 * T,\n E = m[w],\n b = m[w + 1],\n S = m[w + 2],\n C = m[w + 3],\n A = g[T];\n if (!(A >= s)) for (var N = d > 1 ? (S - E) * (u - 1) / (d - 1) : 0, _ = h > 1 ? (C - b) * (l - 1) / (h - 1) : 0, R = 0; R < d; R++) {\n var I = d > 1 ? E * (u - 1) + R * N : .5 * (E + S) * (u - 1);\n if (I < 0 || I > u - 1) for (var k = 0; k < h; k++) for (var D = 0; D < c; D++) {\n var O = D + k * x[2] + R * x[1] + T * x[0];\n f.values[O] = a;\n } else if (\"bilinear\" === o) {\n var M = Math.floor(I),\n P = Math.ceil(I),\n F = I - M;\n\n for (k = 0; k < h; k++) {\n if ((q = h > 1 ? b * (l - 1) + k * _ : .5 * (b + C) * (l - 1)) < 0 || q > l - 1) for (D = 0; D < c; D++) {\n O = D + k * x[2] + R * x[1] + T * x[0];\n f.values[O] = a;\n } else {\n var L = Math.floor(q),\n B = Math.ceil(q),\n U = q - L;\n\n for (D = 0; D < c; D++) {\n var V = v[O = D + L * y[2] + M * y[1] + A * y[0]],\n z = v[O = D + B * y[2] + M * y[1] + A * y[0]],\n W = v[O = D + L * y[2] + P * y[1] + A * y[0]],\n H = V + (z - V) * U,\n G = W + (v[O = D + B * y[2] + P * y[1] + A * y[0]] - W) * U;\n O = D + k * x[2] + R * x[1] + T * x[0], f.values[O] = H + (G - H) * F;\n }\n }\n }\n } else for (k = 0; k < h; ++k) {\n var q;\n if ((q = h > 1 ? b * (l - 1) + k * _ : .5 * (b + C) * (l - 1)) < 0 || q > l - 1) for (D = 0; D < c; D++) {\n O = D + k * x[2] + R * x[1] + T * x[0];\n f.values[O] = a;\n } else {\n var $ = Math.round(q),\n X = Math.round(I);\n\n for (D = 0; D < c; D++) {\n var K = D + $ * y[2] + X * y[1] + A * y[0],\n j = D + k * x[2] + R * x[1] + T * x[0];\n f.values[j] = v[K];\n }\n }\n }\n }\n }\n\n return f.toTensor();\n }, e.prototype.sparseToDense = function (e, t, n, r) {\n var o = calculateShapes(t, e, n),\n a = o.sliceRank,\n i = o.numUpdates,\n s = o.sliceSize,\n u = o.strides,\n l = o.outputSize;\n return this.scatter(e, t, n, l, s, i, a, u, r, !1);\n }, e.prototype.gatherND = function (e, t) {\n var n = t.shape,\n r = n[n.length - 1],\n o = prepareAndValidate(e, t),\n a = o[0],\n i = o[1],\n s = o[2],\n u = o[3];\n if (0 === i) return tensor([], a, e.dtype);\n\n for (var l = new TensorBuffer([i, s], e.dtype), c = t.dataSync(), p = e.dataSync(), d = 0; d < i; d++) {\n for (var h = [], f = 0, m = 0; m < r; m++) {\n var g = c[d * r + m];\n f += g * u[m], h.push(g);\n }\n\n if (f < 0 || f >= e.size / s) throw new Error(\"Invalid indices: \" + h + \" does not index into \" + e.shape);\n\n for (var v = 0; v < s; v++) l.values[d * s + v] = p[f * s + v];\n }\n\n return l.toTensor().reshape(a);\n }, e.prototype.scatterND = function (e, t, n) {\n var r = calculateShapes(t, e, n),\n o = r.sliceRank,\n a = r.numUpdates,\n i = r.sliceSize,\n s = r.strides,\n u = r.outputSize,\n l = scalar(0);\n return this.scatter(e, t, n, u, i, a, o, s, l, !0);\n }, e.prototype.scatter = function (e, t, n, r, o, a, i, s, u, l) {\n var c = [r / o, o],\n p = e.dataSync(),\n d = t.dataSync();\n if (0 === r) return tensor([], n, t.dtype);\n var h = new TensorBuffer(c, t.dtype);\n h.values.fill(u.dataSync()[0]);\n\n for (var f = 0; f < a; f++) {\n for (var m = [], g = 0, v = 0; v < i; v++) {\n var y = p[f * i + v];\n m.push(y), g += y * s[v];\n }\n\n if (g < 0 || g >= r / o) throw new Error(\"Invalid indices: \" + m + \" does not index into \" + n);\n\n for (var x = 0; x < o; x++) l ? h.values[g * o + x] += d[f * o + x] : h.values[g * o + x] = 0 === t.rank ? d[0] : d[f * o + x];\n }\n\n return h.toTensor().reshape(n);\n }, e;\n}();\n\nENV.registerBackend(\"cpu\", function () {\n return new MathBackendCPU();\n}, 1, setTensorTracker);\nvar delayCallback = \"undefined\" != typeof requestAnimationFrame ? requestAnimationFrame : setImmediate;\n\nfunction nextFrame() {\n return new Promise(function (e) {\n return delayCallback(function () {\n return e();\n });\n });\n}\n\nvar DTYPE_VALUE_SIZE_MAP = {\n float32: 4,\n int32: 4,\n uint16: 2,\n uint8: 1,\n bool: 1\n};\n\nfunction encodeWeights(e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n, r, o;\n return __generator(this, function (a) {\n switch (a.label) {\n case 0:\n for (r in t = [], n = [], e) {\n if (\"float32\" !== (o = e[r]).dtype && \"int32\" !== o.dtype && \"bool\" !== o.dtype) throw new Error(\"Unsupported dtype in weight '\" + r + \"': \" + o.dtype);\n t.push({\n name: r,\n shape: o.shape,\n dtype: o.dtype\n }), n.push(o.data());\n }\n\n return [4, Promise.all(n)];\n\n case 1:\n return [2, {\n data: concatenateTypedArrays(a.sent()),\n specs: t\n }];\n }\n });\n });\n}\n\nfunction decodeWeights(e, t) {\n for (var n = {}, r = 0, o = function (t) {\n var o = t.name,\n a = t.dtype,\n i = t.shape,\n s = sizeFromShape(i),\n u = void 0;\n\n if ((\"quantization\" in t)) {\n var l = t.quantization;\n if (\"uint8\" !== l.dtype && \"uint16\" !== l.dtype) throw new Error(\"Weight \" + t.name + \" has unknown quantization dtype \" + l.dtype + \". Supported quantization dtypes are: 'uint8' and 'uint16'.\");\n var c = DTYPE_VALUE_SIZE_MAP[l.dtype],\n p = e.slice(r, r + s * c),\n d = \"uint8\" === l.dtype ? new Uint8Array(p) : new Uint16Array(p);\n if (\"float32\" === a) u = Float32Array.from(d, function (e) {\n return e * l.scale + l.min;\n });else {\n if (\"int32\" !== a) throw new Error(\"Unsupported dtype in weight '\" + o + \"': \" + a);\n u = Int32Array.from(d, function (e) {\n return Math.round(e * l.scale + l.min);\n });\n }\n r += s * c;\n } else {\n var h = DTYPE_VALUE_SIZE_MAP[a];\n p = e.slice(r, r + s * h);\n if (\"float32\" === a) u = new Float32Array(p);else if (\"int32\" === a) u = new Int32Array(p);else {\n if (\"bool\" !== a) throw new Error(\"Unsupported dtype in weight '\" + o + \"': \" + a);\n u = new Uint8Array(p);\n }\n r += s * h;\n }\n\n var f = void 0;\n if (\"float32\" === a) f = tensor(u, i, \"float32\");else if (\"int32\" === a) f = tensor(u, i, \"int32\");else {\n if (\"bool\" !== a) throw new Error(\"Unsupported dtype in weight '\" + o + \"': \" + a);\n f = tensor(u, i, \"bool\");\n }\n n[o] = f;\n }, a = 0, i = t; a < i.length; a++) {\n o(i[a]);\n }\n\n return n;\n}\n\nfunction concatenateTypedArrays(e) {\n if (null === e) throw new Error(\"Invalid input value: \" + JSON.stringify(e));\n var t = 0,\n n = [];\n e.forEach(function (e) {\n if (t += e.byteLength, n.push(e.byteLength === e.buffer.byteLength ? e : new e.constructor(e)), !(e instanceof Float32Array || e instanceof Int32Array || e instanceof Uint8Array)) throw new Error(\"Unsupported TypedArray subtype: \" + e.constructor.name);\n });\n var r = new Uint8Array(t),\n o = 0;\n return n.forEach(function (e) {\n r.set(new Uint8Array(e.buffer), o), o += e.byteLength;\n }), r.buffer;\n}\n\nvar useNodeBuffer = \"undefined\" != typeof Buffer && (\"undefined\" == typeof Blob || \"undefined\" == typeof atob || \"undefined\" == typeof btoa);\n\nfunction stringByteLength(e) {\n return useNodeBuffer ? Buffer.byteLength(e) : new Blob([e]).size;\n}\n\nfunction arrayBufferToBase64String(e) {\n return useNodeBuffer ? Buffer.from(e).toString(\"base64\") : btoa(String.fromCharCode.apply(null, new Uint8Array(e)));\n}\n\nfunction base64StringToArrayBuffer(e) {\n if (useNodeBuffer) {\n var t = Buffer.from(e, \"base64\");\n return t.buffer.slice(t.byteOffset, t.byteOffset + t.byteLength);\n }\n\n for (var n = atob(e), r = new Uint8Array(n.length), o = 0; o < n.length; ++o) r.set([n.charCodeAt(o)], o);\n\n return r.buffer;\n}\n\nfunction concatenateArrayBuffers(e) {\n var t = 0;\n e.forEach(function (e) {\n t += e.byteLength;\n });\n var n = new Uint8Array(t),\n r = 0;\n return e.forEach(function (e) {\n n.set(new Uint8Array(e), r), r += e.byteLength;\n }), n.buffer;\n}\n\nfunction basename(e) {\n for (e = e.trim(); e.endsWith(\"/\");) e = e.slice(0, e.length - 1);\n\n var t = e.split(\"/\");\n return t[t.length - 1];\n}\n\nfunction getModelArtifactsInfoForJSON(e) {\n if (e.modelTopology instanceof ArrayBuffer) throw new Error(\"Expected JSON model topology, received ArrayBuffer.\");\n return {\n dateSaved: new Date(),\n modelTopologyType: \"JSON\",\n modelTopologyBytes: null == e.modelTopology ? 0 : stringByteLength(JSON.stringify(e.modelTopology)),\n weightSpecsBytes: null == e.weightSpecs ? 0 : stringByteLength(JSON.stringify(e.weightSpecs)),\n weightDataBytes: null == e.weightData ? 0 : e.weightData.byteLength\n };\n}\n\nvar IORouterRegistry = function () {\n function e() {\n this.saveRouters = [], this.loadRouters = [];\n }\n\n return e.getInstance = function () {\n return null == e.instance && (e.instance = new e()), e.instance;\n }, e.registerSaveRouter = function (t) {\n e.getInstance().saveRouters.push(t);\n }, e.registerLoadRouter = function (t) {\n e.getInstance().loadRouters.push(t);\n }, e.getSaveHandlers = function (t) {\n return e.getHandlers(t, \"save\");\n }, e.getLoadHandlers = function (t, n) {\n return e.getHandlers(t, \"load\", n);\n }, e.getHandlers = function (e, t, n) {\n var r = [];\n return (\"load\" === t ? this.getInstance().loadRouters : this.getInstance().saveRouters).forEach(function (t) {\n var o = t(e, n);\n null !== o && r.push(o);\n }), r;\n }, e;\n}(),\n registerSaveRouter = function (e) {\n return IORouterRegistry.registerSaveRouter(e);\n},\n registerLoadRouter = function (e) {\n return IORouterRegistry.registerLoadRouter(e);\n},\n getSaveHandlers = function (e) {\n return IORouterRegistry.getSaveHandlers(e);\n},\n getLoadHandlers = function (e, t) {\n return IORouterRegistry.getLoadHandlers(e);\n},\n URL_SCHEME_SUFFIX = \"://\",\n ModelStoreManagerRegistry = function () {\n function e() {\n this.managers = {};\n }\n\n return e.getInstance = function () {\n return null == e.instance && (e.instance = new e()), e.instance;\n }, e.registerManager = function (t, n) {\n assert(null != t, \"scheme must not be undefined or null.\"), t.endsWith(URL_SCHEME_SUFFIX) && (t = t.slice(0, t.indexOf(URL_SCHEME_SUFFIX))), assert(t.length > 0, \"scheme must not be an empty string.\");\n var r = e.getInstance();\n assert(null == r.managers[t], \"A model store manager is already registered for scheme '\" + t + \"'.\"), r.managers[t] = n;\n }, e.getManager = function (e) {\n var t = this.getInstance().managers[e];\n if (null == t) throw new Error(\"Cannot find model manager for scheme '\" + e + \"'\");\n return t;\n }, e.getSchemes = function () {\n return Object.keys(this.getInstance().managers);\n }, e;\n}();\n\nfunction parseURL(e) {\n if (-1 === e.indexOf(URL_SCHEME_SUFFIX)) throw new Error(\"The url string provided does not contain a scheme. Supported schemes are: \" + ModelStoreManagerRegistry.getSchemes().join(\",\"));\n return {\n scheme: e.split(URL_SCHEME_SUFFIX)[0],\n path: e.split(URL_SCHEME_SUFFIX)[1]\n };\n}\n\nfunction cloneModelInternal(e, t, n) {\n return void 0 === n && (n = !1), __awaiter(this, void 0, void 0, function () {\n var r, o, a, i, s, u, l, c, p;\n return __generator(this, function (d) {\n switch (d.label) {\n case 0:\n return assert(e !== t, \"Old path and new path are the same: '\" + e + \"'\"), assert((r = IORouterRegistry.getLoadHandlers(e)).length > 0, \"Copying failed because no load handler is found for source URL \" + e + \".\"), assert(r.length < 2, \"Copying failed because more than one (\" + r.length + \") load handlers for source URL \" + e + \".\"), o = r[0], assert((a = IORouterRegistry.getSaveHandlers(t)).length > 0, \"Copying failed because no save handler is found for destination URL \" + t + \".\"), assert(a.length < 2, \"Copying failed because more than one (\" + r.length + \") save handlers for destination URL \" + t + \".\"), i = a[0], s = parseURL(e).scheme, u = parseURL(e).path, l = s === parseURL(e).scheme, [4, o.load()];\n\n case 1:\n return c = d.sent(), n && l ? [4, ModelStoreManagerRegistry.getManager(s).removeModel(u)] : [3, 3];\n\n case 2:\n d.sent(), d.label = 3;\n\n case 3:\n return [4, i.save(c)];\n\n case 4:\n return p = d.sent(), !n || l ? [3, 6] : [4, ModelStoreManagerRegistry.getManager(s).removeModel(u)];\n\n case 5:\n d.sent(), d.label = 6;\n\n case 6:\n return [2, p.modelArtifactsInfo];\n }\n });\n });\n}\n\nfunction listModels() {\n return __awaiter(this, void 0, void 0, function () {\n var e, t, n, r, o, a, i;\n return __generator(this, function (s) {\n switch (s.label) {\n case 0:\n e = ModelStoreManagerRegistry.getSchemes(), t = {}, n = 0, r = e, s.label = 1;\n\n case 1:\n return n < r.length ? (o = r[n], [4, ModelStoreManagerRegistry.getManager(o).listModels()]) : [3, 4];\n\n case 2:\n for (i in a = s.sent()) t[o + URL_SCHEME_SUFFIX + i] = a[i];\n\n s.label = 3;\n\n case 3:\n return n++, [3, 1];\n\n case 4:\n return [2, t];\n }\n });\n });\n}\n\nfunction removeModel(e) {\n return __awaiter(this, void 0, void 0, function () {\n var t;\n return __generator(this, function (n) {\n switch (n.label) {\n case 0:\n return t = parseURL(e), [4, ModelStoreManagerRegistry.getManager(t.scheme).removeModel(t.path)];\n\n case 1:\n return [2, n.sent()];\n }\n });\n });\n}\n\nfunction copyModel(e, t) {\n return __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (n) {\n switch (n.label) {\n case 0:\n return [4, cloneModelInternal(e, t, !1)];\n\n case 1:\n return [2, n.sent()];\n }\n });\n });\n}\n\nfunction moveModel(e, t) {\n return __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (n) {\n switch (n.label) {\n case 0:\n return [4, cloneModelInternal(e, t, !0)];\n\n case 1:\n return [2, n.sent()];\n }\n });\n });\n}\n\nvar DATABASE_NAME = \"tensorflowjs\",\n DATABASE_VERSION = 1,\n MODEL_STORE_NAME = \"models_store\",\n INFO_STORE_NAME = \"model_info_store\";\n\nfunction getIndexedDBFactory() {\n if (!ENV.get(\"IS_BROWSER\")) throw new Error(\"Failed to obtain IndexedDB factory because the current environmentis not a web browser.\");\n var e = window,\n t = e.indexedDB || e.mozIndexedDB || e.webkitIndexedDB || e.msIndexedDB || e.shimIndexedDB;\n if (null == t) throw new Error(\"The current browser does not appear to support IndexedDB.\");\n return t;\n}\n\nfunction setUpDatabase(e) {\n var t = e.result;\n t.createObjectStore(MODEL_STORE_NAME, {\n keyPath: \"modelPath\"\n }), t.createObjectStore(INFO_STORE_NAME, {\n keyPath: \"modelPath\"\n });\n}\n\nvar BrowserIndexedDB = function () {\n function e(e) {\n if (this.indexedDB = getIndexedDBFactory(), null == e || !e) throw new Error(\"For IndexedDB, modelPath must not be null, undefined or empty.\");\n this.modelPath = e;\n }\n\n return e.prototype.save = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (t) {\n if (e.modelTopology instanceof ArrayBuffer) throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");\n return [2, this.databaseAction(this.modelPath, e)];\n });\n });\n }, e.prototype.load = function () {\n return __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (e) {\n return [2, this.databaseAction(this.modelPath)];\n });\n });\n }, e.prototype.databaseAction = function (e, t) {\n var n = this;\n return new Promise(function (e, r) {\n var o = n.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n o.onupgradeneeded = function () {\n return setUpDatabase(o);\n }, o.onsuccess = function () {\n var a = o.result;\n\n if (null == t) {\n var i = a.transaction(MODEL_STORE_NAME, \"readonly\"),\n s = i.objectStore(MODEL_STORE_NAME).get(n.modelPath);\n s.onsuccess = function () {\n if (null == s.result) return a.close(), r(new Error(\"Cannot find model with path '\" + n.modelPath + \"' in IndexedDB.\"));\n e(s.result.modelArtifacts);\n }, s.onerror = function (e) {\n return a.close(), r(s.error);\n }, i.oncomplete = function () {\n return a.close();\n };\n } else {\n var u,\n l = getModelArtifactsInfoForJSON(t),\n c = a.transaction(INFO_STORE_NAME, \"readwrite\"),\n p = c.objectStore(INFO_STORE_NAME),\n d = p.put({\n modelPath: n.modelPath,\n modelArtifactsInfo: l\n });\n d.onsuccess = function () {\n var o = (u = a.transaction(MODEL_STORE_NAME, \"readwrite\")).objectStore(MODEL_STORE_NAME).put({\n modelPath: n.modelPath,\n modelArtifacts: t,\n modelArtifactsInfo: l\n });\n o.onsuccess = function () {\n return e({\n modelArtifactsInfo: l\n });\n }, o.onerror = function (e) {\n var t = (p = c.objectStore(INFO_STORE_NAME)).delete(n.modelPath);\n t.onsuccess = function () {\n return a.close(), r(o.error);\n }, t.onerror = function (e) {\n return a.close(), r(o.error);\n };\n };\n }, d.onerror = function (e) {\n return a.close(), r(d.error);\n }, c.oncomplete = function () {\n null == u ? a.close() : u.oncomplete = function () {\n return a.close();\n };\n };\n }\n }, o.onerror = function (e) {\n return r(o.error);\n };\n });\n }, e.URL_SCHEME = \"indexeddb://\", e;\n}(),\n indexedDBRouter = function (e) {\n return ENV.get(\"IS_BROWSER\") && !Array.isArray(e) && e.startsWith(BrowserIndexedDB.URL_SCHEME) ? browserIndexedDB(e.slice(BrowserIndexedDB.URL_SCHEME.length)) : null;\n};\n\nfunction browserIndexedDB(e) {\n return new BrowserIndexedDB(e);\n}\n\nfunction maybeStripScheme(e) {\n return e.startsWith(BrowserIndexedDB.URL_SCHEME) ? e.slice(BrowserIndexedDB.URL_SCHEME.length) : e;\n}\n\nIORouterRegistry.registerSaveRouter(indexedDBRouter), IORouterRegistry.registerLoadRouter(indexedDBRouter);\n\nvar BrowserIndexedDBManager = function () {\n function e() {\n this.indexedDB = getIndexedDBFactory();\n }\n\n return e.prototype.listModels = function () {\n return __awaiter(this, void 0, void 0, function () {\n var e = this;\n return __generator(this, function (t) {\n return [2, new Promise(function (t, n) {\n var r = e.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n r.onupgradeneeded = function () {\n return setUpDatabase(r);\n }, r.onsuccess = function () {\n var e = r.result,\n o = e.transaction(INFO_STORE_NAME, \"readonly\"),\n a = o.objectStore(INFO_STORE_NAME).getAll();\n a.onsuccess = function () {\n for (var e = {}, n = 0, r = a.result; n < r.length; n++) {\n var o = r[n];\n e[o.modelPath] = o.modelArtifactsInfo;\n }\n\n t(e);\n }, a.onerror = function (t) {\n return e.close(), n(a.error);\n }, o.oncomplete = function () {\n return e.close();\n };\n }, r.onerror = function (e) {\n return n(r.error);\n };\n })];\n });\n });\n }, e.prototype.removeModel = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t = this;\n return __generator(this, function (n) {\n return e = maybeStripScheme(e), [2, new Promise(function (n, r) {\n var o = t.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n o.onupgradeneeded = function () {\n return setUpDatabase(o);\n }, o.onsuccess = function () {\n var t,\n a = o.result,\n i = a.transaction(INFO_STORE_NAME, \"readwrite\"),\n s = i.objectStore(INFO_STORE_NAME),\n u = s.get(e);\n u.onsuccess = function () {\n if (null == u.result) return a.close(), r(new Error(\"Cannot find model with path '\" + e + \"' in IndexedDB.\"));\n\n var o = s.delete(e),\n i = function () {\n var o = (t = a.transaction(MODEL_STORE_NAME, \"readwrite\")).objectStore(MODEL_STORE_NAME).delete(e);\n o.onsuccess = function () {\n return n(u.result.modelArtifactsInfo);\n }, o.onerror = function (e) {\n return r(u.error);\n };\n };\n\n o.onsuccess = i, o.onerror = function (e) {\n return i(), a.close(), r(u.error);\n };\n }, u.onerror = function (e) {\n return a.close(), r(u.error);\n }, i.oncomplete = function () {\n null == t ? a.close() : t.oncomplete = function () {\n return a.close();\n };\n };\n }, o.onerror = function (e) {\n return r(o.error);\n };\n })];\n });\n });\n }, e;\n}();\n\nif (ENV.get(\"IS_BROWSER\")) try {\n ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n} catch (e) {}\nvar PATH_SEPARATOR = \"/\",\n PATH_PREFIX = \"tensorflowjs_models\",\n INFO_SUFFIX = \"info\",\n MODEL_TOPOLOGY_SUFFIX = \"model_topology\",\n WEIGHT_SPECS_SUFFIX = \"weight_specs\",\n WEIGHT_DATA_SUFFIX = \"weight_data\";\n\nfunction getModelKeys(e) {\n return {\n info: [PATH_PREFIX, e, INFO_SUFFIX].join(PATH_SEPARATOR),\n topology: [PATH_PREFIX, e, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n weightSpecs: [PATH_PREFIX, e, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n weightData: [PATH_PREFIX, e, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)\n };\n}\n\nfunction getModelPathFromKey(e) {\n var t = e.split(PATH_SEPARATOR);\n if (t.length < 3) throw new Error(\"Invalid key format: \" + e);\n return t.slice(1, t.length - 1).join(PATH_SEPARATOR);\n}\n\nfunction maybeStripScheme$1(e) {\n return e.startsWith(BrowserLocalStorage.URL_SCHEME) ? e.slice(BrowserLocalStorage.URL_SCHEME.length) : e;\n}\n\nvar BrowserLocalStorage = function () {\n function e(e) {\n if (!ENV.get(\"IS_BROWSER\") || void 0 === window.localStorage) throw new Error(\"The current environment does not support local storage.\");\n if (this.LS = window.localStorage, null == e || !e) throw new Error(\"For local storage, modelPath must not be null, undefined or empty.\");\n this.modelPath = e, this.keys = getModelKeys(this.modelPath);\n }\n\n return e.prototype.save = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n, r, o;\n return __generator(this, function (a) {\n if (e.modelTopology instanceof ArrayBuffer) throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");\n t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), r = getModelArtifactsInfoForJSON(e);\n\n try {\n return this.LS.setItem(this.keys.info, JSON.stringify(r)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(e.weightData)), [2, {\n modelArtifactsInfo: r\n }];\n } catch (e) {\n for (o in this.keys) this.LS.removeItem(this.keys[o]);\n\n throw new Error(\"Failed to save model '\" + this.modelPath + \"' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=\" + r.modelTopologyBytes + \", weightSpecsBytes=\" + r.weightSpecsBytes + \", weightDataBytes=\" + r.weightDataBytes + \".\");\n }\n\n return [2];\n });\n });\n }, e.prototype.load = function () {\n return __awaiter(this, void 0, void 0, function () {\n var e, t, n, r, o;\n return __generator(this, function (a) {\n if (null == (e = JSON.parse(this.LS.getItem(this.keys.info)))) throw new Error(\"In local storage, there is no model with name '\" + this.modelPath + \"'\");\n if (\"JSON\" !== e.modelTopologyType) throw new Error(\"BrowserLocalStorage does not support loading non-JSON model topology yet.\");\n if (t = {}, null == (n = JSON.parse(this.LS.getItem(this.keys.topology)))) throw new Error(\"In local storage, the topology of model '\" + this.modelPath + \"' is missing.\");\n if (t.modelTopology = n, null == (r = JSON.parse(this.LS.getItem(this.keys.weightSpecs)))) throw new Error(\"In local storage, the weight specs of model '\" + this.modelPath + \"' are missing.\");\n if (t.weightSpecs = r, null == (o = this.LS.getItem(this.keys.weightData))) throw new Error(\"In local storage, the binary weight values of model '\" + this.modelPath + \"' are missing.\");\n return t.weightData = base64StringToArrayBuffer(o), [2, t];\n });\n });\n }, e.URL_SCHEME = \"localstorage://\", e;\n}(),\n localStorageRouter = function (e) {\n return ENV.get(\"IS_BROWSER\") && !Array.isArray(e) && e.startsWith(BrowserLocalStorage.URL_SCHEME) ? browserLocalStorage(e.slice(BrowserLocalStorage.URL_SCHEME.length)) : null;\n};\n\nfunction browserLocalStorage(e) {\n return new BrowserLocalStorage(e);\n}\n\nIORouterRegistry.registerSaveRouter(localStorageRouter), IORouterRegistry.registerLoadRouter(localStorageRouter);\n\nvar BrowserLocalStorageManager = function () {\n function e() {\n assert(ENV.get(\"IS_BROWSER\"), \"Current environment is not a web browser\"), assert(void 0 !== window.localStorage, \"Current browser does not appear to support localStorage\"), this.LS = window.localStorage;\n }\n\n return e.prototype.listModels = function () {\n return __awaiter(this, void 0, void 0, function () {\n var e, t, n, r, o, a;\n return __generator(this, function (i) {\n for (e = {}, t = PATH_PREFIX + PATH_SEPARATOR, n = PATH_SEPARATOR + INFO_SUFFIX, r = 0; r < this.LS.length; ++r) (o = this.LS.key(r)).startsWith(t) && o.endsWith(n) && (a = getModelPathFromKey(o), e[a] = JSON.parse(this.LS.getItem(o)));\n\n return [2, e];\n });\n });\n }, e.prototype.removeModel = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n;\n return __generator(this, function (r) {\n if (e = maybeStripScheme$1(e), t = getModelKeys(e), null == this.LS.getItem(t.info)) throw new Error(\"Cannot find model at path '\" + e + \"'\");\n return n = JSON.parse(this.LS.getItem(t.info)), this.LS.removeItem(t.info), this.LS.removeItem(t.topology), this.LS.removeItem(t.weightSpecs), this.LS.removeItem(t.weightData), [2, n];\n });\n });\n }, e;\n}();\n\nif (ENV.get(\"IS_BROWSER\")) try {\n ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n} catch (e) {}\n\nvar DEFAULT_FILE_NAME_PREFIX = \"model\",\n DEFAULT_JSON_EXTENSION_NAME = \".json\",\n DEFAULT_WEIGHT_DATA_EXTENSION_NAME = \".weights.bin\",\n BrowserDownloads = function () {\n function e(t) {\n if (!ENV.get(\"IS_BROWSER\")) throw new Error(\"browserDownloads() cannot proceed because the current environment is not a browser.\");\n t.startsWith(e.URL_SCHEME) && (t = t.slice(e.URL_SCHEME.length)), null != t && 0 !== t.length || (t = DEFAULT_FILE_NAME_PREFIX), this.modelTopologyFileName = t + DEFAULT_JSON_EXTENSION_NAME, this.weightDataFileName = t + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n }\n\n return e.prototype.save = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n, r, o, a, i;\n return __generator(this, function (s) {\n if (t = window.URL.createObjectURL(new Blob([e.weightData], {\n type: \"application/octet-stream\"\n })), e.modelTopology instanceof ArrayBuffer) throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");\n return n = [{\n paths: [\"./\" + this.weightDataFileName],\n weights: e.weightSpecs\n }], r = {\n modelTopology: e.modelTopology,\n weightsManifest: n\n }, o = window.URL.createObjectURL(new Blob([JSON.stringify(r)], {\n type: \"application/json\"\n })), (a = null == this.jsonAnchor ? document.createElement(\"a\") : this.jsonAnchor).download = this.modelTopologyFileName, a.href = o, a.click(), null != e.weightData && ((i = null == this.weightDataAnchor ? document.createElement(\"a\") : this.weightDataAnchor).download = this.weightDataFileName, i.href = t, i.click()), [2, {\n modelArtifactsInfo: getModelArtifactsInfoForJSON(e)\n }];\n });\n });\n }, e.URL_SCHEME = \"downloads://\", e;\n}(),\n BrowserFiles = function () {\n function e(e) {\n if (null == e || e.length < 1) throw new Error(\"When calling browserFiles, at least 1 file is required, but received \" + e);\n this.files = e;\n }\n\n return e.prototype.load = function () {\n return __awaiter(this, void 0, void 0, function () {\n var e,\n t,\n n = this;\n return __generator(this, function (r) {\n return e = this.files[0], t = this.files.slice(1), [2, new Promise(function (r, o) {\n var a = new FileReader();\n a.onload = function (a) {\n var i = JSON.parse(a.target.result),\n s = i.modelTopology;\n\n if (null != s) {\n 0 === t.length && r({\n modelTopology: s\n });\n var u = i.weightsManifest;\n\n if (null != u) {\n var l;\n\n try {\n l = n.checkManifestAndWeightFiles(u, t);\n } catch (e) {\n return void o(e);\n }\n\n var c = [],\n p = [],\n d = [];\n u.forEach(function (e) {\n e.paths.forEach(function (e) {\n p.push(e), d.push(null);\n }), c.push.apply(c, e.weights);\n }), u.forEach(function (e) {\n e.paths.forEach(function (e) {\n var t = new FileReader();\n t.onload = function (t) {\n var n = t.target.result,\n o = p.indexOf(e);\n d[o] = n, -1 === d.indexOf(null) && r({\n modelTopology: s,\n weightSpecs: c,\n weightData: concatenateArrayBuffers(d)\n });\n }, t.onerror = function (t) {\n return o(\"Failed to weights data from file of path '\" + e + \"'.\");\n }, t.readAsArrayBuffer(l[e]);\n });\n });\n } else o(new Error(\"weightManifest field is missing from file \" + e.name));\n } else o(new Error(\"modelTopology field is missing from file \" + e.name));\n }, a.onerror = function (t) {\n return o(\"Failed to read model topology and weights manifest JSON from file '\" + e.name + \"'. BrowserFiles supports loading Keras-style tf.Model artifacts only.\");\n }, a.readAsText(e);\n })];\n });\n });\n }, e.prototype.checkManifestAndWeightFiles = function (e, t) {\n for (var n = [], r = t.map(function (e) {\n return basename(e.name);\n }), o = {}, a = 0, i = e; a < i.length; a++) {\n i[a].paths.forEach(function (e) {\n var a = basename(e);\n if (-1 !== n.indexOf(a)) throw new Error(\"Duplicate file basename found in weights manifest: '\" + a + \"'\");\n if (n.push(a), -1 === r.indexOf(a)) throw new Error(\"Weight file with basename '\" + a + \"' is not provided.\");\n o[e] = t[r.indexOf(a)];\n });\n }\n\n if (n.length !== t.length) throw new Error(\"Mismatch in the number of files in weights manifest (\" + n.length + \") and the number of weight files provided (\" + t.length + \").\");\n return o;\n }, e;\n}(),\n browserDownloadsRouter = function (e) {\n return ENV.get(\"IS_BROWSER\") && !Array.isArray(e) && e.startsWith(BrowserDownloads.URL_SCHEME) ? browserDownloads(e.slice(BrowserDownloads.URL_SCHEME.length)) : null;\n};\n\nfunction browserDownloads(e) {\n return void 0 === e && (e = \"model\"), new BrowserDownloads(e);\n}\n\nfunction browserFiles(e) {\n return new BrowserFiles(e);\n}\n\nfunction loadWeightsAsArrayBuffer(e, t, n, r) {\n return __awaiter(this, void 0, void 0, function () {\n var o, a, i;\n return __generator(this, function (s) {\n switch (s.label) {\n case 0:\n return null == n && (n = fetch), o = e.map(function (e) {\n return n(e, t);\n }), null != r && monitorPromisesProgress(o, r, 0, .5), [4, Promise.all(o)];\n\n case 1:\n return a = s.sent(), i = a.map(function (e) {\n return e.arrayBuffer();\n }), null != r && monitorPromisesProgress(i, r, .5, 1), [4, Promise.all(i)];\n\n case 2:\n return [2, s.sent()];\n }\n });\n });\n}\n\nfunction loadWeights(e, t, n, r) {\n return void 0 === t && (t = \"\"), __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (o) {\n return [2, weightsLoaderFactory(function (e) {\n return loadWeightsAsArrayBuffer(e, r);\n })(e, t, n)];\n });\n });\n}\n\nfunction weightsLoaderFactory(e) {\n var t = this;\n return function (n, r, o) {\n return void 0 === r && (r = \"\"), __awaiter(t, void 0, void 0, function () {\n var t, a, i, s, u, l, c, p, d, h;\n return __generator(this, function (f) {\n switch (f.label) {\n case 0:\n if (t = n.map(function () {\n return !1;\n }), a = {}, i = null != o ? o.map(function () {\n return !1;\n }) : [], s = [], n.forEach(function (e, n) {\n var r = 0;\n e.weights.forEach(function (e) {\n var u = \"quantization\" in e ? e.quantization.dtype : e.dtype,\n l = DTYPE_VALUE_SIZE_MAP[u] * sizeFromShape(e.shape),\n c = function () {\n t[n] = !0, null == a[n] && (a[n] = []), a[n].push({\n manifestEntry: e,\n groupOffset: r,\n sizeBytes: l\n });\n };\n\n null != o ? o.forEach(function (t, n) {\n t === e.name && (c(), i[n] = !0);\n }) : c(), s.push(e.name), r += l;\n });\n }), !i.every(function (e) {\n return e;\n })) throw u = o.filter(function (e, t) {\n return !i[t];\n }), new Error(\"Could not find weights in manifest with names: \" + u.join(\", \") + \". \\nManifest JSON has weights with names: \" + s.join(\", \") + \".\");\n return l = t.reduce(function (e, t, n) {\n return t && e.push(n), e;\n }, []), c = [], l.forEach(function (e) {\n n[e].paths.forEach(function (e) {\n var t = r + (r.endsWith(\"/\") ? \"\" : \"/\") + e;\n c.push(t);\n });\n }), [4, e(c)];\n\n case 1:\n return p = f.sent(), d = {}, h = 0, l.forEach(function (e) {\n for (var t = n[e].paths.length, r = 0, o = 0; o < t; o++) r += p[h + o].byteLength;\n\n for (var i = new ArrayBuffer(r), s = new Uint8Array(i), u = 0, l = 0; l < t; l++) {\n var c = new Uint8Array(p[h + l]);\n s.set(c, u), u += c.byteLength;\n }\n\n a[e].forEach(function (e) {\n var t = decodeWeights(i.slice(e.groupOffset, e.groupOffset + e.sizeBytes), [e.manifestEntry]);\n\n for (var n in t) d[n] = t[n];\n }), h += t;\n }), [2, d];\n }\n });\n });\n };\n}\n\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n\nvar OCTET_STREAM_MIME_TYPE = \"application/octet-stream\",\n JSON_TYPE = \"application/json\",\n BrowserHTTPRequest = function () {\n function e(e, t, n, r, o) {\n if (this.weightPathPrefix = n, this.onProgress = o, this.DEFAULT_METHOD = \"POST\", null == r) {\n if (\"undefined\" == typeof fetch) throw new Error(\"browserHTTPRequest is not supported outside the web browser without a fetch polyfill.\");\n r = fetch.bind(\"undefined\" == typeof window ? null : window);\n } else assert(\"function\" == typeof r, \"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)\");\n\n if (this.fetchFunc = function (e, t) {\n return r(e, t).catch(function (t) {\n throw new Error(\"Request for \" + e + \" failed due to error: \" + t);\n });\n }, assert(null != e && e.length > 0, \"URL path for browserHTTPRequest must not be null, undefined or empty.\"), Array.isArray(e) && assert(2 === e.length, \"URL paths for browserHTTPRequest must have a length of 2, (actual length is \" + e.length + \").\"), this.path = e, null != t && null != t.body) throw new Error(\"requestInit is expected to have no pre-existing body, but has one.\");\n this.requestInit = t || {};\n }\n\n return e.prototype.save = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n, r, o;\n return __generator(this, function (a) {\n switch (a.label) {\n case 0:\n if (e.modelTopology instanceof ArrayBuffer) throw new Error(\"BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.\");\n return (t = Object.assign({\n method: this.DEFAULT_METHOD\n }, this.requestInit)).body = new FormData(), n = [{\n paths: [\"./model.weights.bin\"],\n weights: e.weightSpecs\n }], r = {\n modelTopology: e.modelTopology,\n weightsManifest: n\n }, t.body.append(\"model.json\", new Blob([JSON.stringify(r)], {\n type: JSON_TYPE\n }), \"model.json\"), null != e.weightData && t.body.append(\"model.weights.bin\", new Blob([e.weightData], {\n type: OCTET_STREAM_MIME_TYPE\n }), \"model.weights.bin\"), [4, this.getFetchFunc()(this.path, t)];\n\n case 1:\n if ((o = a.sent()).ok) return [2, {\n modelArtifactsInfo: getModelArtifactsInfoForJSON(e),\n responses: [o]\n }];\n throw new Error(\"BrowserHTTPRequest.save() failed due to HTTP response status \" + o.status + \".\");\n }\n });\n });\n }, e.prototype.load = function () {\n return __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (e) {\n return [2, Array.isArray(this.path) ? this.loadBinaryModel() : this.loadJSONModel()];\n });\n });\n }, e.prototype.loadBinaryTopology = function () {\n return __awaiter(this, void 0, void 0, function () {\n var e;\n return __generator(this, function (t) {\n switch (t.label) {\n case 0:\n return [4, this.getFetchFunc()(this.path[0], this.requestInit)];\n\n case 1:\n if (!(e = t.sent()).ok) throw new Error(\"Request to \" + this.path[0] + \" failed with error: \" + e.statusText);\n return [4, e.arrayBuffer()];\n\n case 2:\n return [2, t.sent()];\n }\n });\n });\n }, e.prototype.loadBinaryModel = function () {\n return __awaiter(this, void 0, void 0, function () {\n var e, t, n, r, o, a, i, s;\n return __generator(this, function (u) {\n switch (u.label) {\n case 0:\n return e = this.loadBinaryTopology(), [4, this.getFetchFunc()(this.path[1], this.requestInit)];\n\n case 1:\n if (!(t = u.sent()).ok) throw new Error(\"Request to \" + this.path[1] + \" failed with error: \" + t.statusText);\n return [4, Promise.all([e, t])];\n\n case 2:\n return n = u.sent(), r = n[0], [4, n[1].json()];\n\n case 3:\n return null == (o = u.sent()) ? [3, 5] : [4, this.loadWeights(o)];\n\n case 4:\n s = u.sent(), a = s[0], i = s[1], u.label = 5;\n\n case 5:\n return [2, {\n modelTopology: r,\n weightSpecs: a,\n weightData: i\n }];\n }\n });\n });\n }, e.prototype.loadJSONModel = function () {\n return __awaiter(this, void 0, void 0, function () {\n var e, t, n, r, o, a, i, s;\n return __generator(this, function (u) {\n switch (u.label) {\n case 0:\n return [4, this.getFetchFunc()(this.path, this.requestInit)];\n\n case 1:\n if (!(e = u.sent()).ok) throw new Error(\"Request to \" + this.path + \" failed with error: \" + e.statusText);\n return [4, e.json()];\n\n case 2:\n if (t = u.sent(), n = t.modelTopology, r = t.weightsManifest, null == n && null == r) throw new Error(\"The JSON from HTTP path \" + this.path + \" contains neither model topology or manifest for weights.\");\n return null == r ? [3, 4] : (i = t.weightsManifest, [4, this.loadWeights(i)]);\n\n case 3:\n s = u.sent(), o = s[0], a = s[1], u.label = 4;\n\n case 4:\n return [2, {\n modelTopology: n,\n weightSpecs: o,\n weightData: a\n }];\n }\n });\n });\n }, e.prototype.loadWeights = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n var t, n, r, o, a, i, s, u, l, c, p, d;\n return __generator(this, function (h) {\n switch (h.label) {\n case 0:\n for (t = Array.isArray(this.path) ? this.path[1] : this.path, n = parseUrl(t), r = n[0], o = n[1], a = this.weightPathPrefix || r, i = [], s = 0, u = e; s < u.length; s++) l = u[s], i.push.apply(i, l.weights);\n\n return c = [], e.forEach(function (e) {\n e.paths.forEach(function (e) {\n c.push(a + e + o);\n });\n }), p = [i], d = concatenateArrayBuffers, [4, loadWeightsAsArrayBuffer(c, this.requestInit, this.getFetchFunc(), this.onProgress)];\n\n case 1:\n return [2, p.concat([d.apply(void 0, [h.sent()])])];\n }\n });\n });\n }, e.prototype.getFetchFunc = function () {\n return this.fetchFunc;\n }, e.URL_SCHEME_REGEX = /^https?:\\/\\//, e;\n}();\n\nfunction parseUrl(e) {\n var t = e.lastIndexOf(\"/\"),\n n = e.lastIndexOf(\"?\");\n return [e.substring(0, t) + \"/\", n > t ? e.substring(n) : \"\"];\n}\n\nfunction isHTTPScheme(e) {\n return null != e.match(BrowserHTTPRequest.URL_SCHEME_REGEX);\n}\n\nvar httpRequestRouter = function (e, t) {\n if (\"undefined\" == typeof fetch) return null;\n return (Array.isArray(e) ? e.every(function (e) {\n return isHTTPScheme(e);\n }) : isHTTPScheme(e)) ? browserHTTPRequest(e, null, null, null, t) : null;\n};\n\nfunction browserHTTPRequest(e, t, n, r, o) {\n return new BrowserHTTPRequest(e, t, n, r, o);\n}\n\nIORouterRegistry.registerSaveRouter(httpRequestRouter), IORouterRegistry.registerLoadRouter(httpRequestRouter);\n\nvar PassthroughLoader = function () {\n function e(e, t, n) {\n this.modelTopology = e, this.weightSpecs = t, this.weightData = n;\n }\n\n return e.prototype.load = function () {\n return __awaiter(this, void 0, void 0, function () {\n var e;\n return __generator(this, function (t) {\n return e = {}, null != this.modelTopology && (e = __assign({\n modelTopology: this.modelTopology\n }, e)), null != this.weightSpecs && this.weightSpecs.length > 0 && (e = __assign({\n weightSpecs: this.weightSpecs\n }, e)), null != this.weightData && this.weightData.byteLength > 0 && (e = __assign({\n weightData: this.weightData\n }, e)), [2, e];\n });\n });\n }, e;\n}(),\n PassthroughSaver = function () {\n function e(e) {\n this.saveHandler = e;\n }\n\n return e.prototype.save = function (e) {\n return __awaiter(this, void 0, void 0, function () {\n return __generator(this, function (t) {\n return [2, this.saveHandler(e)];\n });\n });\n }, e;\n}();\n\nfunction fromMemory(e, t, n) {\n return new PassthroughLoader(e, t, n);\n}\n\nfunction withSaveHandler(e) {\n return new PassthroughSaver(e);\n}\n\nvar io = Object.freeze({\n browserFiles: browserFiles,\n browserHTTPRequest: browserHTTPRequest,\n concatenateArrayBuffers: concatenateArrayBuffers,\n decodeWeights: decodeWeights,\n encodeWeights: encodeWeights,\n fromMemory: fromMemory,\n getLoadHandlers: getLoadHandlers,\n getModelArtifactsInfoForJSON: getModelArtifactsInfoForJSON,\n getSaveHandlers: getSaveHandlers,\n isHTTPScheme: isHTTPScheme,\n loadWeights: loadWeights,\n registerLoadRouter: registerLoadRouter,\n registerSaveRouter: registerSaveRouter,\n weightsLoaderFactory: weightsLoaderFactory,\n withSaveHandler: withSaveHandler,\n copyModel: copyModel,\n listModels: listModels,\n moveModel: moveModel,\n removeModel: removeModel\n});\nexports.io = io;\n\nfunction confusionMatrix_(e, t, n) {\n var r = convertToTensor(e, \"labels\", \"confusionMatrix\"),\n o = convertToTensor(t, \"predictions\", \"confusionMatrix\");\n assert(null == n || n > 0 && Number.isInteger(n), \"If provided, numClasses must be a positive integer, but got \" + n), assert(1 === r.rank, \"Expected the rank of labels to be 1, but got \" + r.rank), assert(1 === o.rank, \"Expected the rank of predictions to be 1, but got \" + o.rank), assert(r.shape[0] === o.shape[0], \"Mismatch in the number of examples: \" + r.shape[0] + \" vs. \" + o.shape[0] + \". Labels and predictions should have the same number of elements.\"), assert(n > 0 && Number.isInteger(n), \"numClasses is required to be a positive integer, but got \" + n);\n var a = oneHot(r.asType(\"int32\"), n),\n i = oneHot(o.asType(\"int32\"), n);\n return a.transpose().matMul(i).asType(\"int32\");\n}\n\nvar confusionMatrix = op({\n confusionMatrix_: confusionMatrix_\n}),\n math = Object.freeze({\n confusionMatrix: confusionMatrix\n}),\n Serializable = function () {\n function e() {}\n\n return e.prototype.getClassName = function () {\n return this.constructor.className;\n }, e.fromConfig = function (e, t) {\n return new e(t);\n }, e;\n}(),\n SerializationMap = function () {\n function e() {\n this.classNameMap = {};\n }\n\n return e.getMap = function () {\n return null == e.instance && (e.instance = new e()), e.instance;\n }, e.register = function (t) {\n e.getMap().classNameMap[t.className] = [t, t.fromConfig];\n }, e;\n}();\n\nexports.math = math;\n\nfunction registerClass(e) {\n assert(null != e.className, \"Class being registered does not have the static className property defined.\"), assert(\"string\" == typeof e.className, \"className is required to be a string, but got type \" + typeof e.className), assert(e.className.length > 0, \"Class being registered has an empty-string as its className, which is disallowed.\"), SerializationMap.register(e);\n}\n\nvar serialization = Object.freeze({\n Serializable: Serializable,\n SerializationMap: SerializationMap,\n registerClass: registerClass\n}),\n WEBGL_ENVS = {\n HAS_WEBGL: !0\n},\n PACKED_ENVS = {\n WEBGL_PACK: !0\n},\n NODE_ENVS = {\n IS_NODE: !0\n},\n CHROME_ENVS = {\n IS_CHROME: !0\n},\n BROWSER_ENVS = {\n IS_BROWSER: !0\n},\n CPU_ENVS = {\n HAS_WEBGL: !1\n},\n ALL_ENVS = {};\nexports.serialization = serialization;\n\nfunction expectArraysClose(e, t, n) {\n return null == n && (n = ENV.get(\"TEST_EPSILON\")), expectArraysPredicate(e, \"number\" == typeof t || \"boolean\" == typeof t ? [t] : t, function (e, t) {\n return areClose(e, Number(t), n);\n });\n}\n\nfunction expectArraysPredicate(e, t, n) {\n if (e instanceof Tensor || t instanceof Tensor) {\n if (e instanceof Tensor && t instanceof Tensor) {\n if (e.dtype !== t.dtype) throw new Error(\"Arrays are of different type actual: \" + e.dtype + \" vs expected: \" + t.dtype + \".\");\n if (!arraysEqual(e.shape, t.shape)) throw new Error(\"Arrays are of different shape actual: \" + e.shape + \" vs expected: \" + t.shape + \".\");\n }\n } else {\n var r = e.constructor.name,\n o = t.constructor.name;\n if (r !== o) throw new Error(\"Arrays are of different type actual: \" + r + \" vs expected: \" + o);\n }\n\n var a, i;\n if (a = e instanceof Tensor ? e.dataSync() : e, i = t instanceof Tensor ? t.dataSync() : t, a.length !== i.length) throw new Error(\"Arrays have different lengths actual: \" + a.length + \" vs expected: \" + i.length + \".\\nActual: \" + a + \".\\nExpected: \" + i + \".\");\n\n for (var s = 0; s < i.length; ++s) {\n var u = a[s],\n l = i[s];\n if (!n(u, l)) throw new Error(\"Arrays differ: actual[\" + s + \"] = \" + u + \", expected[\" + s + \"] = \" + l + \".\\nActual: \" + a + \".\\nExpected: \" + i + \".\");\n }\n}\n\nfunction expectPromiseToFail(e, t) {\n e().then(function () {\n return t.fail();\n }, function () {\n return t();\n });\n}\n\nfunction expectArraysEqual(e, t) {\n var n = \"string\" == typeof t || \"number\" == typeof t || \"boolean\" == typeof t ? [t] : t;\n return e instanceof Tensor && \"string\" === e.dtype || t instanceof Tensor && \"string\" === t.dtype || Array.isArray(e) && isString(e[0]) || Array.isArray(t) && isString(t[0]) ? expectArraysPredicate(e, n, function (e, t) {\n return e == t;\n }) : expectArraysClose(e, t, 0);\n}\n\nfunction expectNumbersClose(e, t, n) {\n if (null == n && (n = ENV.get(\"TEST_EPSILON\")), !areClose(e, t, n)) throw new Error(\"Numbers differ: actual === \" + e + \", expected === \" + t);\n}\n\nfunction areClose(e, t, n) {\n return !(!isNaN(e) || !isNaN(t)) || !(isNaN(e) || isNaN(t) || Math.abs(e - t) > n);\n}\n\nfunction expectValuesInRange(e, t, n) {\n var r;\n r = e instanceof Tensor ? e.dataSync() : e;\n\n for (var o = 0; o < r.length; o++) if (r[o] < t || r[o] > n) throw new Error(\"Value out of range:\" + r[o] + \" low: \" + t + \", high: \" + n);\n}\n\nfunction expectArrayBuffersEqual(e, t) {\n expect(new Float32Array(e)).toEqual(new Float32Array(t));\n}\n\nvar test_util = Object.freeze({\n WEBGL_ENVS: WEBGL_ENVS,\n PACKED_ENVS: PACKED_ENVS,\n NODE_ENVS: NODE_ENVS,\n CHROME_ENVS: CHROME_ENVS,\n BROWSER_ENVS: BROWSER_ENVS,\n CPU_ENVS: CPU_ENVS,\n ALL_ENVS: ALL_ENVS,\n expectArraysClose: expectArraysClose,\n expectPromiseToFail: expectPromiseToFail,\n expectArraysEqual: expectArraysEqual,\n expectNumbersClose: expectNumbersClose,\n expectValuesInRange: expectValuesInRange,\n expectArrayBuffersEqual: expectArrayBuffersEqual\n}),\n version = \"0.15.4\",\n webgl = Object.freeze({\n gpgpu_util: gpgpu_util,\n webgl_util: webgl_util,\n MathBackendWebGL: MathBackendWebGL,\n GPGPUContext: GPGPUContext\n}),\n Optimizer = function (e) {\n function t() {\n return null !== e && e.apply(this, arguments) || this;\n }\n\n return __extends(t, e), t.prototype.minimize = function (e, t, n) {\n void 0 === t && (t = !1);\n var r = this.computeGradients(e, n),\n o = r.value,\n a = r.grads;\n return this.applyGradients(a), Object.keys(a).forEach(function (e) {\n return a[e].dispose();\n }), t ? o : (o.dispose(), null);\n }, t.prototype.computeGradients = function (e, t) {\n return variableGrads(e, t);\n }, t;\n}(Serializable);\n\nexports.Optimizer = Optimizer;\nexports.webgl = webgl;\nexports.version_core = version;\nexports.test_util = test_util;\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n value: function (e) {\n return null != e.minimize && null != e.computeGradients && null != e.applyGradients;\n }\n});\n\nvar AdadeltaOptimizer = function (e) {\n function t(t, n, r) {\n void 0 === r && (r = null);\n var o = e.call(this) || this;\n return o.learningRate = t, o.rho = n, o.epsilon = r, o.accumulatedGrads = {}, o.accumulatedUpdates = {}, o.c = keep(scalar(-t)), o.rhoScalar = keep(scalar(n)), o.oneMinusRho = keep(scalar(1 - n)), null === r && (r = ENV.get(\"EPSILON\")), o.epsilonScalar = keep(scalar(r)), o;\n }\n\n return __extends(t, e), t.prototype.applyGradients = function (e) {\n var t = this,\n n = function (n) {\n var o = ENV.engine.registeredVariables[n];\n\n if (null == r.accumulatedGrads[n]) {\n tidy(function () {\n t.accumulatedGrads[n] = zerosLike(o).variable(!1);\n });\n }\n\n if (null == r.accumulatedUpdates[n]) {\n tidy(function () {\n t.accumulatedUpdates[n] = zerosLike(o).variable(!1);\n });\n }\n\n var a = e[n],\n i = r.accumulatedGrads[n],\n s = r.accumulatedUpdates[n];\n tidy(function () {\n var e = t.rhoScalar.mul(i).add(t.oneMinusRho.mul(a.square())),\n r = s.add(t.epsilonScalar).sqrt().div(i.add(t.epsilonScalar).sqrt()).mul(a),\n u = t.rhoScalar.mul(s).add(t.oneMinusRho.mul(r.square()));\n t.accumulatedGrads[n].assign(e), t.accumulatedUpdates[n].assign(u);\n var l = t.c.mul(r).add(o);\n o.assign(l);\n });\n },\n r = this;\n\n for (var o in e) n(o);\n }, t.prototype.dispose = function () {\n var e = this;\n this.c.dispose(), this.epsilonScalar.dispose(), this.rhoScalar.dispose(), this.oneMinusRho.dispose(), null != this.accumulatedUpdates && (Object.keys(this.accumulatedUpdates).forEach(function (t) {\n return e.accumulatedUpdates[t].dispose();\n }), Object.keys(this.accumulatedGrads).forEach(function (t) {\n return e.accumulatedGrads[t].dispose();\n }));\n }, t.prototype.getConfig = function () {\n return {\n learningRate: this.learningRate,\n rho: this.rho,\n epsilon: this.epsilon\n };\n }, t.fromConfig = function (e, t) {\n return new e(t.learningRate, t.rho, t.epsilon);\n }, t.className = \"AdadeltaOptimizer\", t;\n}(Optimizer);\n\nexports.AdadeltaOptimizer = AdadeltaOptimizer;\nregisterClass(AdadeltaOptimizer);\n\nvar AdagradOptimizer = function (e) {\n function t(t, n) {\n void 0 === n && (n = .1);\n var r = e.call(this) || this;\n return r.learningRate = t, r.initialAccumulatorValue = n, r.accumulatedGrads = {}, r.c = keep(scalar(-t)), r.epsilon = keep(scalar(ENV.get(\"EPSILON\"))), r;\n }\n\n return __extends(t, e), t.prototype.applyGradients = function (e) {\n var t = this,\n n = function (n) {\n var o = ENV.engine.registeredVariables[n];\n\n if (null == r.accumulatedGrads[n]) {\n tidy(function () {\n t.accumulatedGrads[n] = fill(o.shape, t.initialAccumulatorValue).variable(!1);\n });\n }\n\n var a = e[n],\n i = r.accumulatedGrads[n];\n tidy(function () {\n var e = i.add(a.square());\n t.accumulatedGrads[n].assign(e);\n var r = t.c.mul(a.div(e.add(t.epsilon).sqrt())).add(o);\n o.assign(r);\n });\n },\n r = this;\n\n for (var o in e) n(o);\n }, t.prototype.dispose = function () {\n var e = this;\n this.epsilon.dispose(), this.c.dispose(), null != this.accumulatedGrads && Object.keys(this.accumulatedGrads).forEach(function (t) {\n return e.accumulatedGrads[t].dispose();\n });\n }, t.prototype.getConfig = function () {\n return {\n learningRate: this.learningRate,\n initialAccumulatorValue: this.initialAccumulatorValue\n };\n }, t.fromConfig = function (e, t) {\n return new e(t.learningRate, t.initialAccumulatorValue);\n }, t.className = \"AdagradOptimizer\", t;\n}(Optimizer);\n\nexports.AdagradOptimizer = AdagradOptimizer;\nregisterClass(AdagradOptimizer);\n\nvar AdamOptimizer = function (e) {\n function t(t, n, r, o) {\n void 0 === o && (o = null);\n var a = e.call(this) || this;\n return a.learningRate = t, a.beta1 = n, a.beta2 = r, a.epsilon = o, a.accumulatedFirstMoment = {}, a.accumulatedSecondMoment = {}, a.c = keep(scalar(-t)), a.beta1Scalar = keep(scalar(n)), a.beta2Scalar = keep(scalar(r)), tidy(function () {\n a.accBeta1 = scalar(n).variable(), a.accBeta2 = scalar(r).variable();\n }), a.oneMinusBeta1 = keep(scalar(1 - n)), a.oneMinusBeta2 = keep(scalar(1 - r)), a.one = keep(scalar(1)), null === o && (o = ENV.get(\"EPSILON\")), a.epsScalar = keep(scalar(o)), a;\n }\n\n return __extends(t, e), t.prototype.applyGradients = function (e) {\n var t = this;\n tidy(function () {\n var n = t.one.sub(t.accBeta1),\n r = t.one.sub(t.accBeta2);\n\n for (var o in e) {\n var a = ENV.engine.registeredVariables[o];\n\n if (null == t.accumulatedFirstMoment[o]) {\n var i = !1;\n t.accumulatedFirstMoment[o] = zerosLike(a).variable(i);\n }\n\n if (null == t.accumulatedSecondMoment[o]) {\n i = !1;\n t.accumulatedSecondMoment[o] = zerosLike(a).variable(i);\n }\n\n var s = e[o],\n u = t.accumulatedFirstMoment[o],\n l = t.accumulatedSecondMoment[o],\n c = t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),\n p = t.beta2Scalar.mul(l).add(t.oneMinusBeta2.mul(s.square())),\n d = c.div(n),\n h = p.div(r);\n t.accumulatedFirstMoment[o].assign(c), t.accumulatedSecondMoment[o].assign(p);\n var f = t.c.mul(d.div(t.epsScalar.add(h.sqrt()))).add(a);\n a.assign(f);\n }\n\n t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar)), t.accBeta2.assign(t.accBeta2.mul(t.beta2Scalar));\n });\n }, t.prototype.dispose = function () {\n var e = this;\n this.c.dispose(), this.epsScalar.dispose(), this.beta1Scalar.dispose(), this.beta2Scalar.dispose(), this.accBeta1.dispose(), this.accBeta2.dispose(), this.oneMinusBeta1.dispose(), this.oneMinusBeta2.dispose(), this.one.dispose(), null != this.accumulatedFirstMoment && Object.keys(this.accumulatedFirstMoment).forEach(function (t) {\n return e.accumulatedFirstMoment[t].dispose();\n }), null != this.accumulatedSecondMoment && Object.keys(this.accumulatedSecondMoment).forEach(function (t) {\n return e.accumulatedSecondMoment[t].dispose();\n });\n }, t.prototype.getConfig = function () {\n return {\n learningRate: this.learningRate,\n beta1: this.beta1,\n beta2: this.beta2,\n epsilon: this.epsilon\n };\n }, t.fromConfig = function (e, t) {\n return new e(t.learningRate, t.beta1, t.beta2, t.epsilon);\n }, t.className = \"AdamOptimizer\", t;\n}(Optimizer);\n\nexports.AdamOptimizer = AdamOptimizer;\nregisterClass(AdamOptimizer);\n\nvar AdamaxOptimizer = function (e) {\n function t(t, n, r, o, a) {\n void 0 === o && (o = null), void 0 === a && (a = 0);\n var i = e.call(this) || this;\n return i.learningRate = t, i.beta1 = n, i.beta2 = r, i.epsilon = o, i.decay = a, i.accumulatedFirstMoment = {}, i.accumulatedWeightedInfNorm = {}, i.c = keep(scalar(-t)), i.beta1Scalar = keep(scalar(n)), i.beta2Scalar = keep(scalar(r)), i.decayScalar = keep(scalar(a)), tidy(function () {\n i.iteration = scalar(0).variable(), i.accBeta1 = scalar(n).variable();\n }), i.oneMinusBeta1 = keep(scalar(1 - n)), i.one = keep(scalar(1)), null === o && (o = ENV.get(\"EPSILON\")), i.epsScalar = keep(scalar(o)), i;\n }\n\n return __extends(t, e), t.prototype.applyGradients = function (e) {\n var t = this;\n tidy(function () {\n var n = t.one.sub(t.accBeta1),\n r = t.c.div(t.one.add(t.decayScalar.mul(t.iteration)));\n\n for (var o in e) {\n var a = ENV.engine.registeredVariables[o];\n\n if (null == t.accumulatedFirstMoment[o]) {\n var i = !1;\n t.accumulatedFirstMoment[o] = zerosLike(a).variable(i);\n }\n\n if (null == t.accumulatedWeightedInfNorm[o]) {\n i = !1;\n t.accumulatedWeightedInfNorm[o] = zerosLike(a).variable(i);\n }\n\n var s = e[o],\n u = t.accumulatedFirstMoment[o],\n l = t.accumulatedWeightedInfNorm[o],\n c = t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),\n p = t.beta2Scalar.mul(l),\n d = s.abs(),\n h = p.maximum(d);\n t.accumulatedFirstMoment[o].assign(c), t.accumulatedWeightedInfNorm[o].assign(h);\n var f = r.div(n).mul(c.div(t.epsScalar.add(h))).add(a);\n a.assign(f);\n }\n\n t.iteration.assign(t.iteration.add(t.one)), t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar));\n });\n }, t.prototype.dispose = function () {\n var e = this;\n this.c.dispose(), this.epsScalar.dispose(), this.accBeta1.dispose(), this.beta1Scalar.dispose(), this.beta2Scalar.dispose(), this.oneMinusBeta1.dispose(), this.decayScalar.dispose(), this.iteration.dispose(), this.one.dispose(), null != this.accumulatedFirstMoment && Object.keys(this.accumulatedFirstMoment).forEach(function (t) {\n return e.accumulatedFirstMoment[t].dispose();\n }), null != this.accumulatedWeightedInfNorm && Object.keys(this.accumulatedWeightedInfNorm).forEach(function (t) {\n return e.accumulatedWeightedInfNorm[t].dispose();\n });\n }, t.prototype.getConfig = function () {\n return {\n learningRate: this.learningRate,\n beta1: this.beta1,\n beta2: this.beta2,\n epsilon: this.epsilon,\n decay: this.decay\n };\n }, t.fromConfig = function (e, t) {\n return new e(t.learningRate, t.beta1, t.beta2, t.epsilon, t.decay);\n }, t.className = \"AdamaxOptimizer\", t;\n}(Optimizer);\n\nexports.AdamaxOptimizer = AdamaxOptimizer;\nregisterClass(AdamaxOptimizer);\n\nvar SGDOptimizer = function (e) {\n function t(t) {\n var n = e.call(this) || this;\n return n.learningRate = t, n.setLearningRate(t), n;\n }\n\n return __extends(t, e), t.prototype.applyGradients = function (e) {\n var t = this;\n Object.keys(e).forEach(function (n) {\n var r = e[n],\n o = ENV.engine.registeredVariables[n];\n tidy(function () {\n var e = t.c.mul(r).add(o);\n o.assign(e);\n });\n });\n }, t.prototype.setLearningRate = function (e) {\n this.learningRate = e, null != this.c && this.c.dispose(), this.c = keep(scalar(-e));\n }, t.prototype.dispose = function () {\n this.c.dispose();\n }, t.prototype.getConfig = function () {\n return {\n learningRate: this.learningRate\n };\n }, t.fromConfig = function (e, t) {\n return new e(t.learningRate);\n }, t.className = \"SGDOptimizer\", t;\n}(Optimizer);\n\nexports.SGDOptimizer = SGDOptimizer;\nregisterClass(SGDOptimizer);\n\nvar MomentumOptimizer = function (e) {\n function t(t, n, r) {\n void 0 === r && (r = !1);\n var o = e.call(this, t) || this;\n return o.learningRate = t, o.momentum = n, o.useNesterov = r, o.m = scalar(o.momentum), o.accumulations = {}, o;\n }\n\n return __extends(t, e), t.prototype.applyGradients = function (e) {\n var t = this,\n n = function (n) {\n var o = ENV.engine.registeredVariables[n];\n\n if (null == r.accumulations[n]) {\n tidy(function () {\n t.accumulations[n] = zerosLike(o).variable(!1);\n });\n }\n\n var a = r.accumulations[n],\n i = e[n];\n tidy(function () {\n var e,\n r = t.m.mul(a).add(i);\n e = t.useNesterov ? t.c.mul(i.add(r.mul(t.m))).add(o) : t.c.mul(r).add(o), t.accumulations[n].assign(r), o.assign(e);\n });\n },\n r = this;\n\n for (var o in e) n(o);\n }, t.prototype.dispose = function () {\n if (e.prototype.dispose.call(this), this.m.dispose(), null != this.accumulations) for (var t in this.accumulations) this.accumulations[t].dispose();\n }, t.prototype.setMomentum = function (e) {\n this.momentum = e;\n }, t.prototype.getConfig = function () {\n return {\n learningRate: this.learningRate,\n momentum: this.momentum,\n useNesterov: this.useNesterov\n };\n }, t.fromConfig = function (e, t) {\n return new e(t.learningRate, t.momentum, t.useNesterov);\n }, t.className = \"MomentumOptimizer\", t;\n}(SGDOptimizer);\n\nexports.MomentumOptimizer = MomentumOptimizer;\nregisterClass(MomentumOptimizer);\n\nvar RMSPropOptimizer = function (e) {\n function t(t, n, r, o, a) {\n void 0 === n 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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport function isMobile(): boolean {\n // tslint:disable-next-line:no-any\n const a = navigator.userAgent || navigator.vendor || (window as any).opera;\n // tslint:disable-next-line:max-line-length\n return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n .test(a) ||\n // tslint:disable-next-line:max-line-length\n /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n .test(a.substr(0, 4));\n}\n","../src/util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, DataTypeMap, FlatVector, NumericDataType, RecursiveArray, TensorLike, TypedArray} from './types';\n\n/** Shuffles the array using Fisher-Yates algorithm. */\n// tslint:disable-next-line:no-any\nexport function shuffle(array: any[]|Uint32Array|Int32Array|\n Float32Array): void {\n let counter = array.length;\n let temp = 0;\n let index = 0;\n // While there are elements in the array\n while (counter > 0) {\n // Pick a random index\n index = (Math.random() * counter) | 0;\n // Decrease counter by 1\n counter--;\n // And swap the last element with it\n temp = array[counter];\n array[counter] = array[index];\n array[index] = temp;\n }\n}\n\n/** Clamps a value to a specified range. */\nexport function clamp(min: number, x: number, max: number): number {\n return Math.max(min, Math.min(x, max));\n}\n\nexport function nearestLargerEven(val: number): number {\n return val % 2 === 0 ? val : val + 1;\n}\n\nexport function sum(arr: number[]): number {\n let sum = 0;\n for (let i = 0; i < arr.length; i++) {\n sum += arr[i];\n }\n return sum;\n}\n\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a: number, b: number) {\n const r = Math.random();\n return (b * r) + (1 - r) * a;\n}\n\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a: FlatVector, b: FlatVector): number {\n let result = 0;\n for (let i = 0; i < a.length; i++) {\n const diff = Number(a[i]) - Number(b[i]);\n result += diff * diff;\n }\n return result;\n}\n\nexport function assert(expr: boolean, msg: string|(() => string)) {\n if (!expr) {\n throw new Error(typeof msg === 'string' ? msg : msg());\n }\n}\n\nexport function assertShapesMatch(\n shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {\n assert(\n arraysEqual(shapeA, shapeB),\n errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\n\nexport function assertNonNull(a: TensorLike): void {\n assert(\n a != null,\n `The input to the tensor constructor must be a non-null value.`);\n}\n\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten().\nexport function\nflatten|TypedArray>(\n arr: T|RecursiveArray, ret: T[] = []): T[] {\n if (Array.isArray(arr) || isTypedArray(arr)) {\n for (let i = 0; i < arr.length; ++i) {\n flatten(arr[i], ret);\n }\n } else {\n ret.push(arr as T);\n }\n return ret;\n}\n\nexport function sizeFromShape(shape: number[]): number {\n if (shape.length === 0) {\n // Scalar.\n return 1;\n }\n let size = shape[0];\n for (let i = 1; i < shape.length; i++) {\n size *= shape[i];\n }\n return size;\n}\n\nexport function isScalarShape(shape: number[]): boolean {\n return shape.length === 0;\n}\n\nexport function arraysEqual(n1: FlatVector, n2: FlatVector) {\n if (n1 === n2) {\n return true;\n }\n if (n1 == null || n2 == null) {\n return false;\n }\n\n if (n1.length !== n2.length) {\n return false;\n }\n for (let i = 0; i < n1.length; i++) {\n if (n1[i] !== n2[i]) {\n return false;\n }\n }\n return true;\n}\n\nexport function isInt(a: number): boolean {\n return a % 1 === 0;\n}\n\nexport function tanh(x: number): number {\n // tslint:disable-next-line:no-any\n if ((Math as any).tanh != null) {\n // tslint:disable-next-line:no-any\n return (Math as any).tanh(x);\n }\n if (x === Infinity) {\n return 1;\n } else if (x === -Infinity) {\n return -1;\n } else {\n const e2x = Math.exp(2 * x);\n return (e2x - 1) / (e2x + 1);\n }\n}\n\nexport function sizeToSquarishShape(size: number): [number, number] {\n const width = Math.ceil(Math.sqrt(size));\n return [width, Math.ceil(size / width)];\n}\n\nexport function createShuffledIndices(n: number): Uint32Array {\n const shuffledIndices = new Uint32Array(n);\n for (let i = 0; i < n; ++i) {\n shuffledIndices[i] = i;\n }\n shuffle(shuffledIndices);\n return shuffledIndices;\n}\n\nexport function rightPad(a: string, size: number): string {\n if (size <= a.length) {\n return a;\n }\n return a + ' '.repeat(size - a.length);\n}\n\nexport function repeatedTry(\n checkFn: () => boolean, delayFn = (counter: number) => 0,\n maxCounter?: number): Promise {\n return new Promise((resolve, reject) => {\n let tryCount = 0;\n\n const tryFn = () => {\n if (checkFn()) {\n resolve();\n return;\n }\n\n tryCount++;\n\n const nextBackoff = delayFn(tryCount);\n\n if (maxCounter != null && tryCount >= maxCounter) {\n reject();\n return;\n }\n setTimeout(tryFn, nextBackoff);\n };\n\n tryFn();\n });\n}\n\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(\n shape: number[], size: number): number[] {\n let shapeProd = 1;\n let implicitIdx = -1;\n\n for (let i = 0; i < shape.length; ++i) {\n if (shape[i] >= 0) {\n shapeProd *= shape[i];\n } else if (shape[i] === -1) {\n if (implicitIdx !== -1) {\n throw Error(\n `Shapes can only have 1 implicit size. ` +\n `Found -1 at dim ${implicitIdx} and dim ${i}`);\n }\n implicitIdx = i;\n } else if (shape[i] < 0) {\n throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n }\n }\n\n if (implicitIdx === -1) {\n if (size > 0 && size !== shapeProd) {\n throw Error(`Size(${size}) must match the product of shape ${shape}`);\n }\n return shape;\n }\n\n if (shapeProd === 0) {\n throw Error(\n `Cannot infer the missing size in [${shape}] when ` +\n `there are 0 elements`);\n }\n if (size % shapeProd !== 0) {\n throw Error(\n `The implicit shape can't be a fractional number. ` +\n `Got ${size} / ${shapeProd}`);\n }\n\n const newShape = shape.slice();\n newShape[implicitIdx] = size / shapeProd;\n return newShape;\n}\n\nexport function parseAxisParam(\n axis: number|number[], shape: number[]): number[] {\n const rank = shape.length;\n\n // Normalize input\n axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n\n // Check for valid range\n assert(\n axis.every(ax => ax >= -rank && ax < rank),\n `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n `got axis ${axis}`);\n\n // Check for only integers\n assert(\n axis.every(ax => isInt(ax)),\n `All values in axis param must be integers but ` +\n `got axis ${axis}`);\n\n // Handle negative axis.\n return axis.map(a => a < 0 ? rank + a : a);\n}\n\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape: number[], axis?: number[]):\n {newShape: number[], keptDims: number[]} {\n const newShape: number[] = [];\n const keptDims: number[] = [];\n const axes = axis == null ? null : parseAxisParam(axis, shape).sort();\n let j = 0;\n for (let i = 0; i < shape.length; ++i) {\n if (axes != null) {\n if (axes[j] === i && shape[i] !== 1) {\n throw new Error(\n `Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n }\n if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n if (axes[j] <= i) {\n j++;\n }\n }\n if (shape[i] !== 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n }\n return {newShape, keptDims};\n}\n\nexport function getTypedArrayFromDType(\n dtype: D, size: number): DataTypeMap[D] {\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n } else if (dtype === 'int32') {\n values = new Int32Array(size);\n } else if (dtype === 'bool') {\n values = new Uint8Array(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values;\n}\n\nexport function getArrayFromDType(\n dtype: D, size: number): DataTypeMap[D] {\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n } else if (dtype === 'int32') {\n values = new Int32Array(size);\n } else if (dtype === 'bool') {\n values = new Uint8Array(size);\n } else if (dtype === 'string') {\n values = new Array<'string'>(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values;\n}\n\nexport function checkComputationForErrors(\n vals: DataTypeMap[D], dtype: D, name: string): void {\n if (dtype !== 'float32') {\n // Only floating point computations will generate NaN values\n return;\n }\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i] as number;\n if (isNaN(num) || !isFinite(num)) {\n throw Error(`The result of the '${name}' is ${num}.`);\n }\n }\n}\n\nexport function checkConversionForErrors(\n vals: DataTypeMap[D]|number[], dtype: D): void {\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i] as number;\n if (isNaN(num) || !isFinite(num)) {\n throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n }\n }\n}\n\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType: DataType, newType: DataType): boolean {\n if (newType === 'complex64') {\n return false;\n }\n if (newType === 'float32' && oldType !== 'complex64') {\n return false;\n }\n if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n return false;\n }\n if (newType === 'bool' && oldType === 'bool') {\n return false;\n }\n return true;\n}\n\nexport function isTypedArray(a: {}): a is Float32Array|Int32Array|Uint8Array {\n return a instanceof Float32Array || a instanceof Int32Array ||\n a instanceof Uint8Array;\n}\n\nexport function bytesPerElement(dtype: DataType): number {\n if (dtype === 'float32' || dtype === 'int32') {\n return 4;\n } else if (dtype === 'complex64') {\n return 8;\n } else if (dtype === 'bool') {\n return 1;\n } else {\n throw new Error(`Unknown dtype ${dtype}`);\n }\n}\n\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr: string[]): number {\n if (arr == null) {\n return 0;\n }\n let bytes = 0;\n arr.forEach(x => bytes += x.length * 2);\n return bytes;\n}\n\n/** Returns true if the value is a string. */\nexport function isString(value: {}): value is string {\n return typeof value === 'string' || value instanceof String;\n}\n\nexport function isBoolean(value: {}): boolean {\n return typeof value === 'boolean';\n}\n\nexport function isNumber(value: {}): boolean {\n return typeof value === 'number';\n}\n\nexport function inferDtype(values: TensorLike): DataType {\n if (Array.isArray(values)) {\n return inferDtype(values[0]);\n }\n if (values instanceof Float32Array) {\n return 'float32';\n } else if (values instanceof Int32Array || values instanceof Uint8Array) {\n return 'int32';\n } else if (isNumber(values)) {\n return 'float32';\n } else if (isString(values)) {\n return 'string';\n } else if (isBoolean(values)) {\n return 'bool';\n }\n return 'float32';\n}\n\nexport function isFunction(f: Function) {\n return !!(f && f.constructor && f.call && f.apply);\n}\n\nexport function nearestDivisor(size: number, start: number): number {\n for (let i = start; i < size; ++i) {\n if (size % i === 0) {\n return i;\n }\n }\n return size;\n}\n\nexport function computeStrides(shape: number[]): number[] {\n const rank = shape.length;\n if (rank < 2) {\n return [];\n }\n\n // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n // strides.\n const strides = new Array(rank - 1);\n strides[rank - 2] = shape[rank - 1];\n for (let i = rank - 3; i >= 0; --i) {\n strides[i] = strides[i + 1] * shape[i + 1];\n }\n return strides;\n}\n\nexport function toTypedArray(\n a: TensorLike, dtype: DataType, debugMode: boolean): TypedArray {\n if (dtype === 'string') {\n throw new Error('Cannot convert a string[] to a TypedArray');\n }\n if (Array.isArray(a)) {\n a = flatten(a as number[]);\n }\n if (debugMode) {\n checkConversionForErrors(a as number[], dtype);\n }\n if (noConversionNeeded(a, dtype)) {\n return a as TypedArray;\n }\n if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n return new Float32Array(a as number[]);\n } else if (dtype === 'int32') {\n return new Int32Array(a as number[]);\n } else if (dtype === 'bool') {\n const bool = new Uint8Array((a as number[]).length);\n for (let i = 0; i < bool.length; ++i) {\n if (Math.round((a as number[])[i] as number) !== 0) {\n bool[i] = 1;\n }\n }\n return bool;\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n\nfunction createNestedArray(offset: number, shape: number[], a: TypedArray) {\n const ret = new Array();\n if (shape.length === 1) {\n const d = shape[0];\n for (let i = 0; i < d; i++) {\n ret[i] = a[offset + i];\n }\n } else {\n const d = shape[0];\n const rest = shape.slice(1);\n const len = rest.reduce((acc, c) => acc * c);\n for (let i = 0; i < d; i++) {\n ret[i] = createNestedArray(offset + i * len, rest, a);\n }\n }\n return ret;\n}\n\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape: number[], a: TypedArray) {\n if (shape.length === 0) {\n // Scalar type should return a single number.\n return a[0];\n }\n const size = shape.reduce((acc, c) => acc * c);\n if (size === 0) {\n // A tensor with shape zero should be turned into empty list.\n return [];\n }\n if (size !== a.length) {\n throw new Error(`[${shape}] does not match the input size.`);\n }\n\n return createNestedArray(0, shape, a);\n}\n\nfunction noConversionNeeded(a: TensorLike, dtype: DataType): boolean {\n return (a instanceof Float32Array && dtype === 'float32') ||\n (a instanceof Int32Array && dtype === 'int32') ||\n (a instanceof Uint8Array && dtype === 'bool');\n}\n\nexport function makeOnesTypedArray(\n size: number, dtype: D): DataTypeMap[D] {\n const array = makeZerosTypedArray(size, dtype);\n for (let i = 0; i < array.length; i++) {\n array[i] = 1;\n }\n return array;\n}\n\nexport function makeZerosTypedArray(\n size: number, dtype: D): DataTypeMap[D] {\n if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n return new Float32Array(size);\n } else if (dtype === 'int32') {\n return new Int32Array(size);\n } else if (dtype === 'bool') {\n return new Uint8Array(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n\n/**\n * Returns the current high-resolution real time in milliseconds. It is\n * relative to an arbitrary time in the past.\n */\nexport function now(): number {\n if (typeof performance !== 'undefined') {\n return performance.now();\n } else if (typeof process !== 'undefined') {\n const time = process.hrtime();\n return time[0] * 1000 + time[1] / 1000000;\n } else {\n throw new Error(\n 'Cannot measure time in this environment. You should run tf.js ' +\n 'in the browser or in Node.js');\n }\n}\n\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param {Array>} promises,\n * Promise list going to be monitored\n * @param {Function} onProgress, callback function.\n * Fired when a promise resolved.\n * @param {number} startFraction, Optional fraction start. Default to 0.\n * @param {number} endFraction, Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(\n promises: Array>, onProgress: Function,\n startFraction?: number, endFraction?: number) {\n checkPromises(promises);\n startFraction = startFraction == null ? 0 : startFraction;\n endFraction = endFraction == null ? 1 : endFraction;\n checkFraction(startFraction, endFraction);\n let resolvedPromise = 0;\n\n function registerMonitor(promise: Promise) {\n promise.then((value: D | Function | {} | void) => {\n const fraction = startFraction + ++resolvedPromise / promises.length *\n (endFraction - startFraction);\n // pass fraction as parameter to callback function.\n onProgress(fraction);\n return value;\n });\n return promise;\n }\n\n function checkPromises(\n promises: Array>): void {\n assert(\n promises != null && Array.isArray(promises) && promises.length > 0,\n 'promises must be a none empty array'\n );\n }\n\n function checkFraction(startFraction: number, endFraction: number): void {\n assert(\n startFraction >= 0 && startFraction <= 1,\n `Progress fraction must be in range [0, 1], but ` +\n `got startFraction ${startFraction}`\n );\n assert(\n endFraction >= 0 && endFraction <= 1,\n `Progress fraction must be in range [0, 1], but ` +\n `got endFraction ${endFraction}`\n );\n assert(\n endFraction >= startFraction,\n `startFraction must be no more than endFraction, but ` +\n `got startFraction ${startFraction} and endFraction ${endFraction}`\n );\n }\n\n return Promise.all(promises.map(registerMonitor));\n}\n","../src/profiler.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimer} from './kernels/backend';\nimport {Tensor} from './tensor';\nimport {TypedArray} from './types';\nimport * as util from './util';\n\nexport class Profiler {\n constructor(private backendTimer: BackendTimer, private logger?: Logger) {\n if (logger == null) {\n this.logger = new Logger();\n }\n }\n\n profileKernel(name: string, f: () => T | Tensor[]):\n T {\n let result: T|Tensor[];\n const holdResultWrapperFn = () => {\n result = f();\n };\n const timer = this.backendTimer.time(holdResultWrapperFn);\n\n const results: Tensor[] =\n Array.isArray(result) ? result : [result] as Tensor[];\n results.forEach(r => {\n const vals = r.dataSync();\n util.checkComputationForErrors(vals, r.dtype, name);\n\n timer.then(timing => {\n let extraInfo = '';\n if (timing.getExtraProfileInfo != null) {\n extraInfo = timing.getExtraProfileInfo();\n }\n\n this.logger.logKernelProfile(name, r, vals, timing.kernelMs, extraInfo);\n });\n });\n\n return result as T;\n }\n}\n\nexport class Logger {\n logKernelProfile(\n name: string, result: Tensor, vals: TypedArray, timeMs: number,\n extraInfo?: string) {\n const time = util.rightPad(`${timeMs}ms`, 9);\n const paddedName = util.rightPad(name, 25);\n const rank = result.rank;\n const size = result.size;\n const shape = util.rightPad(result.shape.toString(), 14);\n\n console.log(\n `%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${\n extraInfo}`,\n 'font-weight:bold', 'color:red', 'color:blue', 'color: orange',\n 'color: green');\n }\n}\n","../src/tensor_format.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, TypedArray} from './types';\nimport {computeStrides, isString, rightPad, sizeFromShape} from './util';\n\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\n\nexport function tensorToString(\n vals: TypedArray|string[], shape: number[], dtype: DataType,\n verbose: boolean) {\n const strides = computeStrides(shape);\n const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n const rank = shape.length;\n const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n const lines = ['Tensor'];\n if (verbose) {\n lines.push(` dtype: ${dtype}`);\n lines.push(` rank: ${rank}`);\n lines.push(` shape: [${shape}]`);\n lines.push(` values:`);\n }\n lines.push(valsLines.map(l => ' ' + l).join('\\n'));\n return lines.join('\\n');\n}\n\nfunction computeMaxSizePerColumn(\n vals: TypedArray|string[], shape: number[], dtype: DataType,\n strides: number[]): number[] {\n const n = sizeFromShape(shape);\n const numCols = strides[strides.length - 1];\n const padPerCol = new Array(numCols).fill(0);\n const rank = shape.length;\n const valuesOrTuples =\n dtype === 'complex64' ? createComplexTuples(vals) : vals;\n\n if (rank > 1) {\n for (let row = 0; row < n / numCols; row++) {\n const offset = row * numCols;\n for (let j = 0; j < numCols; j++) {\n padPerCol[j] = Math.max(\n padPerCol[j], valToString(valuesOrTuples[offset + j], 0).length);\n }\n }\n }\n return padPerCol;\n}\n\nfunction valToString(val: number|string|[number, number], pad: number) {\n let valStr: string;\n if (Array.isArray(val)) {\n valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n } else if (isString(val)) {\n valStr = `'${val}'`;\n } else {\n valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n }\n\n return rightPad(valStr, pad);\n}\n\nfunction subTensorToString(\n vals: TypedArray|string[], shape: number[], dtype: DataType,\n strides: number[], padPerCol: number[], isLast = true): string[] {\n const storagePerElement = dtype === 'complex64' ? 2 : 1;\n\n const size = shape[0];\n const rank = shape.length;\n if (rank === 0) {\n if (dtype === 'complex64') {\n const complexTuple = createComplexTuples(vals);\n return [valToString(complexTuple[0], 0)];\n }\n return [vals[0].toString()];\n }\n\n if (rank === 1) {\n if (size > FORMAT_LIMIT_NUM_VALS) {\n const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n\n let firstVals = Array.from(\n vals.slice(0, firstValsSize));\n let lastVals = Array.from(vals.slice(\n size - FORMAT_NUM_FIRST_LAST_VALS * storagePerElement, size));\n if (dtype === 'complex64') {\n firstVals = createComplexTuples(firstVals);\n lastVals = createComplexTuples(lastVals);\n }\n return [\n '[' + firstVals.map((x, i) => valToString(x, padPerCol[i])).join(', ') +\n ', ..., ' +\n lastVals\n .map(\n (x, i) => valToString(\n x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i]))\n .join(', ') +\n ']'\n ];\n }\n const displayVals: Array =\n dtype === 'complex64' ? createComplexTuples(vals) :\n Array.from(vals);\n\n return [\n '[' + displayVals.map((x, i) => valToString(x, padPerCol[i])).join(', ') +\n ']'\n ];\n }\n\n // The array is rank 2 or more.\n const subshape = shape.slice(1);\n const substrides = strides.slice(1);\n const stride = strides[0] * storagePerElement;\n const lines: string[] = [];\n if (size > FORMAT_LIMIT_NUM_VALS) {\n for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n false /* isLast */));\n }\n lines.push('...');\n for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n i === size - 1 /* isLast */));\n }\n } else {\n for (let i = 0; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n i === size - 1 /* isLast */));\n }\n }\n const sep = rank === 2 ? ',' : '';\n lines[0] = '[' + lines[0] + sep;\n for (let i = 1; i < lines.length - 1; i++) {\n lines[i] = ' ' + lines[i] + sep;\n }\n let newLineSep = ',\\n';\n for (let i = 2; i < rank; i++) {\n newLineSep += '\\n';\n }\n lines[lines.length - 1] =\n ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n return lines;\n}\n\nfunction createComplexTuples(vals: Array<{}>|\n TypedArray): Array<[number, number]> {\n const complexTuples: Array<[number, number]> = [];\n for (let i = 0; i < vals.length; i += 2) {\n complexTuples.push([vals[i], vals[i + 1]] as [number, number]);\n }\n return complexTuples;\n}\n","../src/tensor.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tensorToString} from './tensor_format';\nimport {ArrayMap, DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TensorLike, TensorLike1D, TensorLike3D, TensorLike4D, TypedArray} from './types';\nimport * as util from './util';\nimport {computeStrides, toNestedArray} from './util';\n\nexport interface TensorData {\n dataId?: DataId;\n values?: DataTypeMap[D];\n}\n\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class TensorBuffer {\n size: number;\n shape: ShapeMap[R];\n strides: number[];\n values: DataTypeMap[D];\n\n constructor(shape: ShapeMap[R], public dtype: D, values?: DataTypeMap[D]) {\n this.shape = shape.slice();\n this.size = util.sizeFromShape(shape);\n\n if (values != null) {\n const n = values.length;\n util.assert(\n n === this.size,\n `Length of values '${n}' does not match the size ` +\n `inferred by the shape '${this.size}'.`);\n }\n if (dtype === 'complex64') {\n throw new Error(\n `complex64 dtype TensorBuffers are not supported. Please create ` +\n `a TensorBuffer for the real and imaginary parts separately and ` +\n `call tf.complex(real, imag).`);\n }\n this.values =\n values || util.getArrayFromDType(dtype, util.sizeFromShape(this.shape));\n this.strides = computeStrides(shape);\n }\n\n /**\n * Sets a value in the buffer at a given location.\n *\n * @param value The value to set.\n * @param locs The location indices.\n */\n /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n set(value: SingleValueMap[D], ...locs: number[]): void {\n if (locs.length === 0) {\n locs = [0];\n }\n util.assert(\n locs.length === this.rank,\n `The number of provided coordinates (${locs.length}) must ` +\n `match the rank (${this.rank})`);\n\n const index = this.locToIndex(locs);\n this.values[index] = value as number;\n }\n\n /**\n * Returns the value in the buffer at the provided location.\n *\n * @param locs The location indices.\n */\n /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n get(...locs: number[]): SingleValueMap[D] {\n if (locs.length === 0) {\n locs = [0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return this.values[index];\n }\n\n locToIndex(locs: number[]): number {\n if (this.rank === 0) {\n return 0;\n } else if (this.rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return index;\n }\n\n indexToLoc(index: number): number[] {\n if (this.rank === 0) {\n return [];\n } else if (this.rank === 1) {\n return [index];\n }\n const locs: number[] = new Array(this.shape.length);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / this.strides[i]);\n index -= locs[i] * this.strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n }\n\n get rank() {\n return this.shape.length;\n }\n\n /**\n * Creates an immutable `tf.Tensor` object from the buffer.\n */\n /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n toTensor(): Tensor {\n return Tensor.make(this.shape, {values: this.values}, this.dtype);\n }\n}\n\nexport interface TensorTracker {\n registerTensor(t: Tensor): void;\n disposeTensor(t: Tensor): void;\n write(dataId: DataId, values: DataValues): void;\n read(dataId: DataId): Promise;\n readSync(dataId: DataId): DataValues;\n registerVariable(v: Variable): void;\n nextTensorId(): number;\n nextVariableId(): number;\n}\n\n/**\n * The Tensor class calls into this handler to delegate chaining operations.\n */\nexport interface OpHandler {\n cast(x: T, dtype: DataType): T;\n buffer(\n shape: ShapeMap[R], dtype: D,\n values?: DataTypeMap[D]): TensorBuffer;\n print(x: T, verbose: boolean): void;\n reshape(x: Tensor, shape: ShapeMap[R2]): Tensor;\n expandDims(x: Tensor, axis: number): Tensor;\n cumsum(\n x: Tensor, axis: number, exclusive: boolean, reverse: boolean): T;\n squeeze(x: Tensor, axis?: number[]): T;\n clone(x: T): T;\n oneHot(\n x: Tensor|TensorLike, depth: number, onValue?: number,\n offValue?: number): Tensor;\n tile(x: T, reps: number[]): T;\n gather(x: T, indices: Tensor|TensorLike, axis: number): T;\n matMul(\n a: T, b: T|TensorLike, transposeA: boolean, transposeB: boolean): T;\n dot(t1: Tensor, t2: Tensor|TensorLike): Tensor;\n norm(\n x: Tensor, ord: number|'euclidean'|'fro', axis: number|number[],\n keepDims: boolean): Tensor;\n slice>(\n x: T, begin: number|number[], size?: number|number[]): T;\n split(\n x: T, numOrSizeSplits: number[]|number, axis?: number): T[];\n reverse(x: T, axis?: number|number[]): T;\n concat(tensors: Array, axis: number): T;\n stack(tensors: Array, axis: number): Tensor;\n unstack(value: T, axis: number): Tensor[];\n pad(\n x: T, paddings: Array<[number, number]>, constantValue: number): T;\n batchNorm(\n x: Tensor, mean: Tensor|Tensor1D|TensorLike,\n variance: Tensor|Tensor1D|TensorLike,\n offset?: Tensor|Tensor1D|TensorLike,\n scale?: Tensor|Tensor1D|TensorLike,\n varianceEpsilon?: number): Tensor;\n all(x: Tensor, axis: number|number[], keepDims: boolean): T;\n any(x: Tensor, axis: number|number[], keepDims: boolean): T;\n logSumExp(\n x: Tensor, axis: number|number[], keepDims: boolean): T;\n sum(x: Tensor, axis: number|number[], keepDims: boolean): T;\n prod(x: Tensor, axis: number|number[], keepDims: boolean):\n T;\n mean(x: Tensor, axis: number|number[], keepDims: boolean):\n T;\n min(x: Tensor, axis: number|number[], keepDims: boolean): T;\n max(x: Tensor, axis: number|number[], keepDims: boolean): T;\n argMin(x: Tensor, axis: number): T;\n argMax(x: Tensor, axis: number): T;\n add(a: Tensor, b: Tensor|TensorLike): T;\n addStrict(a: T, b: T|TensorLike): T;\n atan2(a: Tensor, b: Tensor|TensorLike): T;\n sub(a: Tensor, b: Tensor|TensorLike): T;\n subStrict(a: T, b: T|TensorLike): T;\n pow(base: T, exp: Tensor|TensorLike): T;\n powStrict(base: T, exp: Tensor|TensorLike): T;\n mul(a: Tensor, b: Tensor|TensorLike): T;\n mulStrict(a: T, b: T|TensorLike): T;\n div(a: Tensor, b: Tensor|TensorLike): T;\n floorDiv(a: Tensor, b: Tensor|TensorLike): T;\n divStrict(a: T, b: T|TensorLike): T;\n mod(a: Tensor, b: Tensor|TensorLike): T;\n modStrict(a: T, b: T|TensorLike): T;\n minimum(a: Tensor, b: Tensor|TensorLike): T;\n minimumStrict(a: T, b: T|TensorLike): T;\n maximum(a: Tensor, b: Tensor|TensorLike): T;\n maximumStrict(a: T, b: T|TensorLike): T;\n squaredDifference(a: Tensor, b: Tensor|TensorLike): T;\n squaredDifferenceStrict(a: T, b: T|TensorLike): T;\n transpose(x: T, perm?: number[]): T;\n logicalNot(x: T): T;\n logicalAnd(a: Tensor, b: Tensor|TensorLike): T;\n logicalOr(a: Tensor, b: Tensor|TensorLike): T;\n logicalXor(a: Tensor, b: Tensor|TensorLike): T;\n where(condition: Tensor|TensorLike, a: T, b: T|TensorLike):\n T;\n notEqual(a: Tensor, b: Tensor|TensorLike): T;\n notEqualStrict(a: T, b: T|TensorLike): T;\n less(a: Tensor, b: Tensor|TensorLike): T;\n lessStrict(a: T, b: T|TensorLike): T;\n equal(a: Tensor, b: Tensor|TensorLike): T;\n equalStrict(a: T, b: T|TensorLike): T;\n lessEqual(a: Tensor, b: Tensor|TensorLike): T;\n lessEqualStrict(a: T, b: T|TensorLike): T;\n greater(a: Tensor, b: Tensor|TensorLike): T;\n greaterStrict(a: T, b: T|TensorLike): T;\n greaterEqual(a: Tensor, b: Tensor|TensorLike): T;\n greaterEqualStrict(a: T, b: T|TensorLike): T;\n neg(x: T): T;\n ceil(x: T): T;\n floor(x: T): T;\n sign(x: T): T;\n round(x: T): T;\n exp(x: T): T;\n expm1(x: T): T;\n log(x: T): T;\n log1p(x: T): T;\n sqrt(x: T): T;\n rsqrt(x: T): T;\n square(x: T): T;\n reciprocal(x: T): T;\n abs(x: T): T;\n clipByValue(\n x: T, clipValueMin: number, clipValueMax: number): T;\n sigmoid(x: T): T;\n logSigmoid(x: T): T;\n softplus(x: T): T;\n zerosLike(x: T): T;\n onesLike(x: T): T;\n sin(x: T): T;\n cos(x: T): T;\n tan(x: T): T;\n asin(x: T): T;\n acos(x: T): T;\n atan(x: T): T;\n sinh(x: T): T;\n cosh(x: T): T;\n tanh(x: T): T;\n asinh(x: T): T;\n acosh(x: T): T;\n atanh(x: T): T;\n erf(x: T): T;\n step(x: T, alpha: number): T;\n relu(x: T): T;\n elu(x: T): T;\n selu(x: T): T;\n leakyRelu(x: T, alpha: number): T;\n prelu(x: T, alpha: T|TensorLike): T;\n softmax(logits: T, dim: number): T;\n logSoftmax(logits: T, axis: number): T;\n image: {\n resizeBilinear(\n images: T, size: [number, number], alignCorners: boolean): T;\n resizeNearestNeighbor(\n images: T, size: [number, number], alignCorners: boolean): T;\n };\n conv1d(\n x: T, filter: Tensor3D|TensorLike3D, stride: number,\n pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW', dilation: number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n conv2d(\n x: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW',\n dilations: [number, number]|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n conv2dTranspose(\n x: T, filter: Tensor4D|TensorLike4D,\n outputShape: [number, number, number, number]|[number, number, number],\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n depthwiseConv2d(\n x: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW',\n dilations: [number, number]|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n separableConv2d(\n x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike4D,\n pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n pad: 'valid'|'same', dilation: [number, number]|number,\n dataFormat: 'NHWC'|'NCHW'): T;\n maxPool(\n x: T, filterSize: [number, number]|number,\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n avgPool(\n x: T, filterSize: [number, number]|number,\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n pool(\n input: T, windowShape: [number, number]|number, poolingType: 'avg'|'max',\n padding: 'valid'|'same'|number, diationRate?: [number, number]|number,\n strides?: [number, number]|number): T;\n localResponseNormalization(\n x: T, depthRadius: number, bias: number, alpha: number, beta: number): T;\n unsortedSegmentSum(\n x: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T;\n batchToSpaceND(\n x: T, blockShape: number[], crops: number[][]): T;\n spaceToBatchND(\n x: T, blockShape: number[], paddings: number[][]): T;\n topk(x: T, k: number, sorted: boolean):\n {values: T, indices: T};\n stridedSlice(\n x: Tensor, begin: number[], end: number[], strides: number[],\n beginMask: number, endMask: number, ellipsisMask: number,\n newAxisMask: number, shrinkAxisMask: number): Tensor;\n depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D;\n spectral: {\n fft(x: Tensor): Tensor; ifft(x: Tensor): Tensor; rfft(x: Tensor): Tensor;\n irfft(x: Tensor): Tensor\n };\n}\n\n// For tracking tensor creation and disposal.\nlet trackerFn: () => TensorTracker = null;\n// Used by chaining methods to call into ops.\nlet opHandler: OpHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn: (msg: string) => void = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn: () => TensorTracker) {\n trackerFn = fn;\n}\n\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op handler.\n */\nexport function setOpHandler(handler: OpHandler) {\n opHandler = handler;\n}\n\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn: (msg: string) => void) {\n deprecationWarningFn = fn;\n}\n\n/**\n * We wrap data id since we use weak map to avoid memory leaks.\n * Since we have our own memory management, we have a reference counter\n * mapping a tensor to its data, so there is always a pointer (even if that\n * data is otherwise garbage collectable).\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/\n * Global_Objects/WeakMap\n */\nexport type DataId = object; // object instead of {} to force non-primitive.\n\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class Tensor {\n /** Unique id of this tensor. */\n readonly id: number;\n /**\n * Id of the bucket holding the data for this tensor. Multiple arrays can\n * point to the same bucket (e.g. when calling array.reshape()).\n */\n dataId: DataId;\n /** The shape of the tensor. */\n readonly shape: ShapeMap[R];\n /** Number of elements in the tensor. */\n readonly size: number;\n /** The data type for the array. */\n readonly dtype: DataType;\n /** The rank type for the array (see `Rank` enum). */\n readonly rankType: R;\n\n /**\n * Number of elements to skip in each dimension when indexing. See\n * https://docs.scipy.org/doc/numpy/reference/generated/\\\n * numpy.ndarray.strides.html\n */\n readonly strides: number[];\n\n protected constructor(\n shape: ShapeMap[R], dtype: DataType, values?: DataValues,\n dataId?: DataId) {\n this.shape = shape.slice();\n this.dtype = dtype || 'float32';\n this.size = util.sizeFromShape(shape);\n this.strides = computeStrides(shape);\n this.dataId = dataId != null ? dataId : {};\n this.id = trackerFn().nextTensorId();\n this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher') as R;\n trackerFn().registerTensor(this);\n if (values != null) {\n trackerFn().write(this.dataId, values);\n }\n }\n\n /**\n * Makes a new tensor with the provided shape and values. Values should be in\n * a flat array.\n */\n static make, D extends DataType = 'float32',\n R extends Rank = Rank>(\n shape: ShapeMap[R], data: TensorData, dtype?: D): T {\n return new Tensor(shape, dtype, data.values, data.dataId) as T;\n }\n\n /** Flatten a Tensor to a 1D array. */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n flatten(): Tensor1D {\n this.throwIfDisposed();\n return this.as1D();\n }\n\n /** Converts a size-1 `tf.Tensor` to a `tf.Scalar`. */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n asScalar(): Scalar {\n this.throwIfDisposed();\n util.assert(this.size === 1, 'The array must have only 1 element.');\n return this.reshape([]);\n }\n\n /** Converts a `tf.Tensor` to a `tf.Tensor1D`. */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n as1D(): Tensor1D {\n this.throwIfDisposed();\n return this.reshape([this.size]);\n }\n\n /**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n as2D(rows: number, columns: number): Tensor2D {\n this.throwIfDisposed();\n return this.reshape([rows, columns]);\n }\n\n /**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n as3D(rows: number, columns: number, depth: number): Tensor3D {\n this.throwIfDisposed();\n return this.reshape([rows, columns, depth]);\n }\n\n /**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n as4D(rows: number, columns: number, depth: number, depth2: number): Tensor4D {\n this.throwIfDisposed();\n return this.reshape([rows, columns, depth, depth2]);\n }\n\n /**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n as5D(\n rows: number, columns: number, depth: number, depth2: number,\n depth3: number): Tensor5D {\n this.throwIfDisposed();\n return this.reshape([rows, columns, depth, depth2, depth3]);\n }\n\n /**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n asType(this: T, dtype: DataType): T {\n this.throwIfDisposed();\n return opHandler.cast(this, dtype) as T;\n }\n\n get rank(): number {\n return this.shape.length;\n }\n\n /**\n * Returns the value in the tensor at the provided location.\n * If using WebGL backend, this is a blocking call.\n * Prefer calling the `async data()[flatIndex]` method instead.\n *\n * @param locs The location indices.\n */\n get(...locs: number[]) {\n deprecationWarningFn(\n `Tensor.get() is deprecated. Use Tensor.array() and native array ` +\n `indexing instead.`);\n\n util.assert(\n locs.length === this.rank,\n 'Number of coordinates in get() must match the rank of the tensor');\n util.assert(\n this.dtype !== 'complex64',\n 'Tensor.get() is not supported for complex64 tensors yet.');\n this.throwIfDisposed();\n if (locs.length === 0) {\n locs = [0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return this.dataSync()[index];\n }\n\n /** Returns a `tf.TensorBuffer` that holds the underlying data. */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n buffer(): TensorBuffer {\n deprecationWarningFn(\n `Tensor.buffer() is renamed to Tensor.bufferSync() in TensorFlow.js ` +\n `1.0 and Tensor.buffer() will become an async function.`);\n\n return opHandler.buffer(this.shape, this.dtype as D, this.dataSync());\n }\n\n /** Returns a `tf.TensorBuffer` that holds the underlying data. */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n bufferSync(): TensorBuffer {\n return opHandler.buffer(this.shape, this.dtype as D, this.dataSync());\n }\n\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * asynchronously.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n // tslint:disable-next-line:no-any\n async array(): Promise {\n return toNestedArray(this.shape, await this.data());\n }\n\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * synchronously.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n // tslint:disable-next-line:no-any\n arraySync(): ArrayMap[R] {\n return toNestedArray(this.shape, this.dataSync());\n }\n\n /**\n * Asynchronously downloads the values from the `tf.Tensor`. Returns a promise\n * of `TypedArray` that resolves when the computation has finished.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n async data(): Promise {\n this.throwIfDisposed();\n return trackerFn().read(this.dataId);\n }\n\n /**\n * Synchronously downloads the values from the `tf.Tensor`. This blocks the UI\n * thread until the values are ready, which can cause performance issues.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n dataSync(): DataTypeMap[D] {\n this.throwIfDisposed();\n return trackerFn().readSync(this.dataId);\n }\n\n /**\n * Disposes `tf.Tensor` from memory.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n dispose(): void {\n if (this.isDisposed) {\n return;\n }\n trackerFn().disposeTensor(this);\n this.isDisposedInternal = true;\n }\n\n private isDisposedInternal = false;\n get isDisposed(): boolean {\n return this.isDisposedInternal;\n }\n\n private throwIfDisposed() {\n if (this.isDisposed) {\n throw new Error(`Tensor is disposed.`);\n }\n }\n\n /** Casts the array to type `float32` */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n toFloat(this: T): T {\n return this.asType('float32');\n }\n\n /** Casts the array to type `int32` */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n toInt() {\n return this.asType('int32');\n }\n\n /** Casts the array to type `bool` */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n toBool() {\n return this.asType('bool');\n }\n\n /**\n * Prints the `tf.Tensor`. See `tf.print` for details.\n *\n * @param verbose Whether to print verbose information about the tensor,\n * including dtype and size.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n print(verbose = false): void {\n return opHandler.print(this, verbose);\n }\n\n /**\n * Reshapes the tensor into the provided shape.\n * See `tf.reshape` for more details.\n *\n * @param newShape An array of integers defining the output tensor shape.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n reshape(newShape: ShapeMap[R2]): Tensor {\n this.throwIfDisposed();\n return opHandler.reshape(this, newShape);\n }\n\n /**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n reshapeAs(x: T): T {\n this.throwIfDisposed();\n return this.reshape(x.shape) as T;\n }\n\n /**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape. See `tf.expandDims` for details.\n *\n * @param axis The dimension index at which to insert shape of 1. Defaults to\n * 0 (the first dimension).\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n expandDims(axis = 0): Tensor {\n return opHandler.expandDims(this, axis);\n }\n\n /**\n * Returns the cumulative sum of the `tf.Tensor` along `axis`.\n *\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Defaults to\n * false. If set to true then the sum of each tensor entry does not include\n * its own value, but only the values previous to it along the specified\n * axis.\n * @param reverse Whether to sum in the opposite direction. Defaults to\n * false.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n cumsum(axis = 0, exclusive = false, reverse = false): T {\n return opHandler.cumsum(this, axis, exclusive, reverse);\n }\n\n /**\n * Returns a `tf.Tensor` with dimensions of size 1 removed from the shape.\n * See `tf.squeeze` for more details.\n *\n * @param axis A list of numbers. If specified, only squeezes the\n * dimensions listed. The dimension index starts at 0. It is an error to\n * squeeze a dimension that is not 1.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n squeeze(axis?: number[]): T {\n this.throwIfDisposed();\n return opHandler.squeeze(this, axis);\n }\n\n /** Returns a copy of the tensor. See `tf.clone` for details. */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n clone(this: T): T {\n this.throwIfDisposed();\n return opHandler.clone(this);\n }\n\n oneHot(this: Tensor, depth: number, onValue?: number, offValue?: number):\n Tensor {\n this.throwIfDisposed();\n return opHandler.oneHot(this, depth, onValue, offValue);\n }\n\n /** Returns a human-readable description of the tensor. Useful for logging. */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n toString(verbose = false): string {\n const vals = this.dataSync();\n return tensorToString(vals, this.shape, this.dtype, verbose);\n }\n\n // Below is chain API that is not exposed to docs to avoid repetition. To\n // expose a method, move it above this comment and add @doc and jsdoc.\n\n tile(this: T, reps: number[]): T {\n this.throwIfDisposed();\n return opHandler.tile(this, reps) as T;\n }\n\n gather(this: T, indices: Tensor|TensorLike, axis = 0): T {\n this.throwIfDisposed();\n return opHandler.gather(this, indices, axis) as T;\n }\n\n matMul(\n this: T, b: T|TensorLike, transposeA = false, transposeB = false): T {\n this.throwIfDisposed();\n return opHandler.matMul(this, b, transposeA, transposeB);\n }\n dot(b: Tensor|TensorLike): Tensor {\n this.throwIfDisposed();\n return opHandler.dot(this, b);\n }\n norm(\n ord: number|'euclidean'|'fro' = 'euclidean', axis: number|number[] = null,\n keepDims = false): Tensor {\n this.throwIfDisposed();\n return opHandler.norm(this, ord, axis, keepDims);\n }\n slice>(\n this: T, begin: number|number[], size?: number|number[]): T {\n this.throwIfDisposed();\n return opHandler.slice(this, begin, size);\n }\n reverse(this: T, axis?: number|number[]): T {\n this.throwIfDisposed();\n return opHandler.reverse(this, axis);\n }\n concat(this: T, x: T|Array, axis = 0): T {\n this.throwIfDisposed();\n if (x instanceof Tensor) {\n x = [x];\n }\n return opHandler.concat([this, ...x], axis);\n }\n split(this: T, numOrSizeSplits: number[]|number, axis = 0):\n T[] {\n this.throwIfDisposed();\n return opHandler.split(this, numOrSizeSplits, axis);\n }\n stack(x: Tensor, axis = 0): Tensor {\n return opHandler.stack([this, x], axis);\n }\n unstack(axis = 0): Tensor[] {\n return opHandler.unstack(this, axis);\n }\n pad(\n this: T, paddings: Array<[number, number]>, constantValue = 0): T {\n return opHandler.pad(this, paddings, constantValue);\n }\n /**\n * @deprecated Use `tf.batchNorm` instead, and note the positional argument\n * change of scale, offset, and varianceEpsilon.\n */\n batchNormalization(\n mean: Tensor|Tensor1D|TensorLike,\n variance: Tensor|Tensor1D|TensorLike, varianceEpsilon = .001,\n scale?: Tensor|Tensor1D|TensorLike,\n offset?: Tensor|Tensor1D|TensorLike): Tensor {\n deprecationWarningFn(\n 'tf.batchNormalization() is going away. ' +\n 'Use tf.batchNorm() instead, and note the positional argument change ' +\n 'of scale, offset, and varianceEpsilon');\n return this.batchNorm(mean, variance, offset, scale, varianceEpsilon);\n }\n\n batchNorm(\n mean: Tensor|Tensor1D|TensorLike,\n variance: Tensor|Tensor1D|TensorLike,\n offset?: Tensor|Tensor1D|TensorLike,\n scale?: Tensor|Tensor1D|TensorLike,\n varianceEpsilon = .001,\n ): Tensor {\n this.throwIfDisposed();\n return opHandler.batchNorm(\n this, mean, variance, offset, scale, varianceEpsilon);\n }\n // Reduction ops.\n all(axis: number|number[] = null, keepDims = false): T {\n this.throwIfDisposed();\n return opHandler.all(this, axis, keepDims);\n }\n any(axis: number|number[] = null, keepDims = false): T {\n this.throwIfDisposed();\n return opHandler.any(this, axis, keepDims);\n }\n logSumExp(axis: number|number[] = null, keepDims = false):\n T {\n this.throwIfDisposed();\n return opHandler.logSumExp(this, axis, keepDims);\n }\n sum(axis: number|number[] = null, keepDims = false): T {\n this.throwIfDisposed();\n return opHandler.sum(this, axis, keepDims);\n }\n prod(axis: number|number[] = null, keepDims = false): T {\n this.throwIfDisposed();\n return opHandler.prod(this, axis, keepDims);\n }\n mean(axis: number|number[] = null, keepDims = false): T {\n this.throwIfDisposed();\n return opHandler.mean(this, axis, keepDims);\n }\n min(axis: number|number[] = null, keepDims = false): T {\n this.throwIfDisposed();\n return opHandler.min(this, axis, keepDims);\n }\n max(axis: number|number[] = null, keepDims = false): T {\n this.throwIfDisposed();\n return opHandler.max(this, axis, keepDims);\n }\n argMin(axis: number = null): T {\n this.throwIfDisposed();\n return opHandler.argMin(this, axis);\n }\n argMax(axis: number = null): T {\n this.throwIfDisposed();\n return opHandler.argMax(this, axis);\n }\n\n // Transformations\n cast(dtype: DataType): T {\n this.throwIfDisposed();\n return opHandler.cast(this as T, dtype) as T;\n }\n\n // Binary ops.\n\n add(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.add(this, x);\n }\n addStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.addStrict(this, x) as T;\n }\n atan2(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.atan2(this, x) as T;\n }\n sub(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.sub(this, x);\n }\n subStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.subStrict(this, x) as T;\n }\n pow(this: T, exp: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.pow(this, exp);\n }\n powStrict(exp: Tensor|TensorLike): Tensor {\n this.throwIfDisposed();\n return opHandler.powStrict(this, exp);\n }\n mul(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.mul(this, x);\n }\n mulStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.mulStrict(this, x) as T;\n }\n div(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.div(this, x);\n }\n floorDiv(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.floorDiv(this, x);\n }\n divStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.divStrict(this, x) as T;\n }\n minimum(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.minimum(this, x);\n }\n minimumStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.minimumStrict(this, x) as T;\n }\n maximum(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.maximum(this, x);\n }\n maximumStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.maximumStrict(this, x) as T;\n }\n mod(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.mod(this, x);\n }\n modStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.modStrict(this, x) as T;\n }\n squaredDifference(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.squaredDifference(this, x);\n }\n squaredDifferenceStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.squaredDifferenceStrict(this, x) as T;\n }\n transpose(this: T, perm?: number[]): T {\n this.throwIfDisposed();\n return opHandler.transpose(this, perm);\n }\n\n // Compare ops.\n\n notEqual(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.notEqual(this, x);\n }\n notEqualStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.notEqualStrict(this, x) as T;\n }\n less(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.less(this, x);\n }\n lessStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.lessStrict(this, x) as T;\n }\n equal(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.equal(this, x);\n }\n equalStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.equalStrict(this, x) as T;\n }\n lessEqual(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.lessEqual(this, x);\n }\n lessEqualStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.lessEqualStrict(this, x) as T;\n }\n greater(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.greater(this, x);\n }\n greaterStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.greaterStrict(this, x) as T;\n }\n greaterEqual(x: Tensor|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.greaterEqual(this, x);\n }\n greaterEqualStrict(this: T, x: T|TensorLike): T {\n this.throwIfDisposed();\n return opHandler.greaterEqualStrict(this, x) as T;\n }\n\n // Compare ops.\n logicalAnd(x: Tensor|TensorLike): Tensor {\n this.throwIfDisposed();\n return opHandler.logicalAnd(this, x);\n }\n logicalOr(x: Tensor|TensorLike): Tensor {\n this.throwIfDisposed();\n return opHandler.logicalOr(this, x);\n }\n logicalNot(this: T): T {\n this.throwIfDisposed();\n return opHandler.logicalNot(this);\n }\n logicalXor(x: Tensor|TensorLike): Tensor {\n this.throwIfDisposed();\n return opHandler.logicalXor(this, x);\n }\n where(condition: Tensor|TensorLike, x: Tensor|TensorLike): Tensor {\n this.throwIfDisposed();\n return opHandler.where(condition, this, x);\n }\n\n // Unary ops.\n neg(this: T): T {\n this.throwIfDisposed();\n return opHandler.neg(this);\n }\n ceil(this: T): T {\n this.throwIfDisposed();\n return opHandler.ceil(this);\n }\n floor(this: T): T {\n this.throwIfDisposed();\n return opHandler.floor(this);\n }\n sign(this: T): T {\n this.throwIfDisposed();\n return opHandler.sign(this);\n }\n exp(this: T): T {\n this.throwIfDisposed();\n return opHandler.exp(this);\n }\n expm1(this: T): T {\n this.throwIfDisposed();\n return opHandler.expm1(this);\n }\n log(this: T): T {\n this.throwIfDisposed();\n return opHandler.log(this);\n }\n log1p(this: T): T {\n this.throwIfDisposed();\n return opHandler.log1p(this);\n }\n sqrt(this: T): T {\n this.throwIfDisposed();\n return opHandler.sqrt(this);\n }\n rsqrt(this: T): T {\n this.throwIfDisposed();\n return opHandler.rsqrt(this);\n }\n square(this: T): T {\n this.throwIfDisposed();\n return opHandler.square(this);\n }\n reciprocal(this: T): T {\n this.throwIfDisposed();\n return opHandler.reciprocal(this);\n }\n abs(this: T): T {\n this.throwIfDisposed();\n return opHandler.abs(this);\n }\n clipByValue(min: number, max: number): Tensor {\n this.throwIfDisposed();\n return opHandler.clipByValue(this, min, max);\n }\n relu(this: T): T {\n this.throwIfDisposed();\n return opHandler.relu(this);\n }\n elu(this: T): T {\n this.throwIfDisposed();\n return opHandler.elu(this);\n }\n selu(this: T): T {\n this.throwIfDisposed();\n return opHandler.selu(this);\n }\n leakyRelu(alpha = 0.2): Tensor {\n this.throwIfDisposed();\n return opHandler.leakyRelu(this, alpha);\n }\n prelu(alpha: Tensor|TensorLike): Tensor {\n this.throwIfDisposed();\n return opHandler.prelu(this, alpha);\n }\n sigmoid(this: T): T {\n this.throwIfDisposed();\n return opHandler.sigmoid(this);\n }\n logSigmoid(this: T): T {\n this.throwIfDisposed();\n return opHandler.logSigmoid(this);\n }\n softplus(this: T): T {\n this.throwIfDisposed();\n return opHandler.softplus(this);\n }\n zerosLike(this: T): T {\n this.throwIfDisposed();\n return opHandler.zerosLike(this);\n }\n onesLike(this: T): T {\n this.throwIfDisposed();\n return opHandler.onesLike(this);\n }\n sin(this: T): T {\n this.throwIfDisposed();\n return opHandler.sin(this);\n }\n cos(this: T): T {\n this.throwIfDisposed();\n return opHandler.cos(this);\n }\n tan(this: T): T {\n this.throwIfDisposed();\n return opHandler.tan(this);\n }\n asin(this: T): T {\n this.throwIfDisposed();\n return opHandler.asin(this);\n }\n acos(this: T): T {\n this.throwIfDisposed();\n return opHandler.acos(this);\n }\n atan(this: T): T {\n this.throwIfDisposed();\n return opHandler.atan(this);\n }\n sinh(this: T): T {\n this.throwIfDisposed();\n return opHandler.sinh(this);\n }\n cosh(this: T): T {\n this.throwIfDisposed();\n return opHandler.cosh(this);\n }\n tanh(this: T): T {\n this.throwIfDisposed();\n return opHandler.tanh(this);\n }\n asinh(this: T): T {\n this.throwIfDisposed();\n return opHandler.asinh(this);\n }\n acosh(this: T): T {\n this.throwIfDisposed();\n return opHandler.acosh(this);\n }\n atanh(this: T): T {\n this.throwIfDisposed();\n return opHandler.atanh(this);\n }\n erf(this: T): T {\n this.throwIfDisposed();\n return opHandler.erf(this);\n }\n round(this: T): T {\n this.throwIfDisposed();\n return opHandler.round(this);\n }\n step(this: T, alpha = 0.0): T {\n this.throwIfDisposed();\n return opHandler.step(this, alpha);\n }\n softmax(this: T, dim = -1): T {\n this.throwIfDisposed();\n return opHandler.softmax(this, dim) as T;\n }\n logSoftmax(this: T, axis = -1): T {\n this.throwIfDisposed();\n return opHandler.logSoftmax(this, axis) as T;\n }\n\n // Image ops.\n resizeBilinear(\n this: T, newShape2D: [number, number], alignCorners = false): T {\n (this as Tensor).throwIfDisposed();\n return opHandler.image.resizeBilinear(this, newShape2D, alignCorners);\n }\n\n resizeNearestNeighbor(\n this: T, newShape2D: [number, number], alignCorners = false): T {\n (this as Tensor).throwIfDisposed();\n return opHandler.image.resizeNearestNeighbor(\n this, newShape2D, alignCorners);\n }\n\n // Convolutions.\n conv1d(\n this: T, filter: Tensor3D|TensorLike3D, stride: number,\n pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n (this as Tensor).throwIfDisposed();\n return opHandler.conv1d(\n this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n }\n conv2d(\n this: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n dilations: [number, number]|number = [1, 1],\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n (this as Tensor).throwIfDisposed();\n return opHandler.conv2d(\n this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n }\n conv2dTranspose(\n this: T, filter: Tensor4D|TensorLike4D,\n outputShape: [number, number, number, number]|[number, number, number],\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n (this as Tensor).throwIfDisposed();\n return opHandler.conv2dTranspose(\n this, filter, outputShape, strides, pad, dimRoundingMode);\n }\n depthwiseConv2D(\n this: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n dilations: [number, number]|number = [1, 1],\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n (this as Tensor).throwIfDisposed();\n return opHandler.depthwiseConv2d(\n this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n }\n\n separableConv2d(\n this: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike4D,\n pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n (this as Tensor).throwIfDisposed();\n return opHandler.separableConv2d(\n this, depthwiseFilter, pointwiseFilter, strides, pad, dilation,\n dataFormat);\n }\n\n // Pooling.\n avgPool(\n this: T, filterSize: [number, number]|number,\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n (this as Tensor).throwIfDisposed();\n return opHandler.avgPool(this, filterSize, strides, pad, dimRoundingMode);\n }\n maxPool(\n this: T, filterSize: [number, number]|number,\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n (this as Tensor).throwIfDisposed();\n return opHandler.maxPool(this, filterSize, strides, pad, dimRoundingMode);\n }\n localResponseNormalization(\n this: T, radius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n return opHandler.localResponseNormalization(\n this, radius, bias, alpha, beta);\n }\n pool(\n this: T, windowShape: [number, number]|number, poolingType: 'max'|'avg',\n padding: 'valid'|'same'|number, dilationRate?: [number, number]|number,\n strides?: [number, number]|number): T {\n (this as Tensor).throwIfDisposed();\n return opHandler.pool(\n this, windowShape, poolingType, padding, dilationRate, strides);\n }\n\n variable(trainable = true, name?: string, dtype?: DataType): Variable {\n this.throwIfDisposed();\n return Variable.variable(this, trainable, name, dtype);\n }\n\n unsortedSegmentSum(\n this: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T {\n this.throwIfDisposed();\n return opHandler.unsortedSegmentSum(this, segmentIds, numSegments);\n }\n\n batchToSpaceND(\n this: T, blockShape: number[], crops: number[][]): T {\n this.throwIfDisposed();\n return opHandler.batchToSpaceND(this, blockShape, crops);\n }\n\n spaceToBatchND(\n this: T, blockShape: number[], paddings: number[][]): T {\n this.throwIfDisposed();\n return opHandler.spaceToBatchND(this, blockShape, paddings);\n }\n\n topk(this: T, k = 1, sorted = true):\n {values: T, indices: T} {\n this.throwIfDisposed();\n return opHandler.topk(this, k, sorted);\n }\n\n stridedSlice(\n this: Tensor, begin: number[], end: number[], strides: number[],\n beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n shrinkAxisMask = 0): Tensor {\n this.throwIfDisposed();\n return opHandler.stridedSlice(\n this, begin, end, strides, beginMask, endMask, ellipsisMask,\n newAxisMask, shrinkAxisMask);\n }\n\n depthToSpace(this: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n Tensor4D {\n this.throwIfDisposed();\n return opHandler.depthToSpace(this, blockSize, dataFormat);\n }\n\n fft(this: Tensor): Tensor {\n this.throwIfDisposed();\n return opHandler.spectral.fft(this);\n }\n\n ifft(this: Tensor): Tensor {\n this.throwIfDisposed();\n return opHandler.spectral.ifft(this);\n }\n\n rfft(this: Tensor): Tensor {\n this.throwIfDisposed();\n return opHandler.spectral.rfft(this);\n }\n\n irfft(this: Tensor): Tensor {\n this.throwIfDisposed();\n return opHandler.spectral.irfft(this);\n }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n value: (instance: Tensor) => {\n return !!instance && instance.dataId != null && instance.shape != null &&\n instance.dtype != null;\n }\n});\n\nexport interface NumericTensor extends Tensor {\n dtype: NumericDataType;\n data(): Promise;\n dataSync(): TypedArray;\n}\n\nexport interface StringTensor extends Tensor {\n dtype: 'string';\n dataSync(): string[];\n data(): Promise;\n}\n\n/** @doclink Tensor */\nexport type Scalar = Tensor;\n/** @doclink Tensor */\nexport type Tensor1D = Tensor;\n/** @doclink Tensor */\nexport type Tensor2D = Tensor;\n/** @doclink Tensor */\nexport type Tensor3D = Tensor;\n/** @doclink Tensor */\nexport type Tensor4D = Tensor;\n/** @doclink Tensor */\nexport type Tensor5D = Tensor;\n/** @doclink Tensor */\nexport type Tensor6D = Tensor;\n\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class Variable extends Tensor {\n name: string;\n\n /**\n * Private constructor since we cannot add logic before calling `super()`.\n * Instead, we expose static `Variable.variable` method below, which will be\n * added to global namespace.\n */\n private constructor(\n initialValue: Tensor, public trainable = true, name?: string) {\n super(\n initialValue.shape, initialValue.dtype, null /* values */,\n initialValue.dataId);\n this.name = name;\n if (this.name == null) {\n this.name = trackerFn().nextVariableId().toString();\n }\n try {\n trackerFn().registerVariable(this);\n } catch (ex) {\n trackerFn().disposeTensor(this);\n throw ex;\n }\n }\n\n /**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n */\n /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n static variable(\n initialValue: Tensor, trainable = true, name?: string,\n dtype?: DataType): Variable {\n if (dtype != null && dtype !== initialValue.dtype) {\n initialValue = initialValue.asType(dtype) as Tensor;\n }\n return new Variable(initialValue, trainable, name);\n }\n\n /**\n * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n * the same shape and dtype as the old `tf.Tensor`.\n *\n * @param newValue New tensor to be assigned to this variable.\n */\n /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n assign(newValue: Tensor): void {\n if (newValue.dtype !== this.dtype) {\n throw new Error(\n `dtype of the new value (${newValue.dtype}) and ` +\n `previous value (${this.dtype}) must match`);\n }\n if (!util.arraysEqual(newValue.shape, this.shape)) {\n throw new Error(\n `shape of the new value (${newValue.shape}) and ` +\n `previous value (${this.shape}) must match`);\n }\n trackerFn().disposeTensor(this);\n this.dataId = newValue.dataId;\n trackerFn().registerTensor(this);\n }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n value: (instance: Variable) => {\n return instance instanceof Tensor && instance.assign != null &&\n instance.assign instanceof Function;\n }\n});\n\nconst variable = Variable.variable;\nexport {variable};\n","../src/types.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** @docalias number[] */\nexport interface ShapeMap {\n R0: number[];\n R1: [number];\n R2: [number, number];\n R3: [number, number, number];\n R4: [number, number, number, number];\n R5: [number, number, number, number, number];\n R6: [number, number, number, number, number, number];\n}\n\n/** @docalias number[] */\nexport interface ArrayMap {\n R0: number;\n R1: number[];\n R2: number[][];\n R3: number[][][];\n R4: number[][][][];\n R5: number[][][][][];\n R6: number[][][][][][];\n}\n\nexport interface DataTypeMap {\n float32: Float32Array;\n int32: Int32Array;\n bool: Uint8Array;\n complex64: Float32Array;\n string: string[];\n}\n\nexport interface SingleValueMap {\n bool: boolean;\n int32: number;\n float32: number;\n complex64: number;\n string: string;\n}\n\n/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */\nexport type DataType = keyof DataTypeMap;\nexport type NumericDataType = 'float32'|'int32'|'bool'|'complex64';\nexport type TypedArray = Float32Array|Int32Array|Uint8Array;\nexport type DataValues = DataTypeMap[DataType];\n\nexport enum Rank {\n R0 = 'R0',\n R1 = 'R1',\n R2 = 'R2',\n R3 = 'R3',\n R4 = 'R4',\n R5 = 'R5',\n R6 = 'R6'\n}\n\nexport type FlatVector = boolean[]|number[]|TypedArray;\nexport type RegularArray =\n T[]|T[][]|T[][][]|T[][][][]|T[][][][][]|T[][][][][][];\n\n// tslint:disable-next-line:no-any\nexport interface RecursiveArray {\n [index: number]: T|RecursiveArray;\n}\n\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nenum UpcastInt32AndMap {\n 'float32' = 'float32',\n 'int32' = 'int32',\n 'bool' = 'int32',\n 'complex64' = 'complex64'\n}\n\nenum UpcastBoolAndMap {\n 'float32' = 'float32',\n 'int32' = 'int32',\n 'bool' = 'bool',\n 'complex64' = 'complex64'\n}\n\nenum UpcastFloat32AndMap {\n 'float32' = 'float32',\n 'int32' = 'float32',\n 'bool' = 'float32',\n 'complex64' = 'complex64'\n}\n\nenum UpcastComplex64AndMap {\n 'float32' = 'complex64',\n 'int32' = 'complex64',\n 'bool' = 'complex64',\n 'complex64' = 'complex64'\n}\n\nconst upcastTypeMap = {\n 'float32': UpcastFloat32AndMap,\n 'int32': UpcastInt32AndMap,\n 'bool': UpcastBoolAndMap,\n 'complex64': UpcastComplex64AndMap\n};\n\nexport function upcastType(typeA: DataType, typeB: DataType): DataType {\n if (typeA === 'string' || typeB === 'string') {\n if (typeA === 'string' && typeB === 'string') {\n return 'string';\n }\n throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n }\n return upcastTypeMap[typeA][typeB];\n}\n\n/** Returns the output type after summation. */\nexport function sumOutType(type: DataType): DataType {\n return upcastType(type, 'int32');\n}\n\n/** @docalias TypedArray|Array */\nexport type TensorLike =\n TypedArray|number|boolean|string|RegularArray|\n RegularArray|RegularArray;\nexport type ScalarLike = number|boolean|string;\n/** @docalias TypedArray|Array */\nexport type TensorLike1D = TypedArray|number[]|boolean[]|string[];\n/** @docalias TypedArray|Array */\nexport type TensorLike2D =\n TypedArray|number[]|number[][]|boolean[]|boolean[][]|string[]|string[][];\n/** @docalias TypedArray|Array */\nexport type TensorLike3D = TypedArray|number[]|number[][][]|boolean[]|\n boolean[][][]|string[]|string[][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike4D = TypedArray|number[]|number[][][][]|boolean[]|\n boolean[][][][]|string[]|string[][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike5D = TypedArray|number[]|number[][][][][]|boolean[]|\n boolean[][][][][]|string[]|string[][][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike6D = TypedArray|number[]|number[][][][][][]|boolean[]|\n boolean[][][][][][]|string[]|string[][][][][][];\n","../src/tape.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport * as util from './util';\n\nexport interface TapeNode {\n id: number;\n name: string;\n outputs: Tensor[];\n inputs: NamedTensorMap;\n // Optional params, defined only for ops with gradient impl.\n gradient?: (dy: Tensor|Tensor[]) => NamedGradientMap;\n}\n\nexport type NamedGradientMap = {\n [inputName: string]: () => Tensor;\n};\n\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(\n tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[] {\n // Forward pass to compute all the nodes and Tensors that are transitively a\n // function of x.\n const tensorsFromX: {[tensorId: number]: boolean} = {};\n const nodesFromX: {[nodeId: number]: boolean} = {};\n for (let i = 0; i < xs.length; i++) {\n tensorsFromX[xs[i].id] = true;\n }\n\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n for (const inputName in nodeInputs) {\n const input = nodeInputs[inputName];\n\n let anyInputFromX = false;\n for (let j = 0; j < xs.length; j++) {\n if (tensorsFromX[input.id]) {\n node.outputs.forEach(output => tensorsFromX[output.id] = true);\n anyInputFromX = true;\n nodesFromX[node.id] = true;\n break;\n }\n }\n\n if (anyInputFromX) {\n break;\n }\n }\n }\n\n // Backward pass to find all of the nodes and Tensors that lead to y.\n const tensorsLeadToY: {[tensorId: number]: boolean} = {};\n tensorsLeadToY[y.id] = true;\n const nodesToY: {[nodeId: number]: boolean} = {};\n\n for (let i = tape.length - 1; i >= 0; i--) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n\n // If any of the outputs lead to y, mark all of the inputs as leading to y.\n for (let j = 0; j < node.outputs.length; j++) {\n if (tensorsLeadToY[node.outputs[j].id]) {\n for (const inputName in nodeInputs) {\n tensorsLeadToY[nodeInputs[inputName].id] = true;\n nodesToY[node.id] = true;\n }\n break;\n }\n }\n }\n\n // Return the paths that come from x and lead to y.\n const filteredTape: TapeNode[] = [];\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n\n if (nodesFromX[node.id] && nodesToY[node.id]) {\n // Prune the inputs from the node that aren't a function of x.\n const prunedInputs: {[inputName: string]: Tensor} = {};\n for (const inputName in node.inputs) {\n const nodeInput = node.inputs[inputName];\n if (tensorsFromX[nodeInput.id]) {\n prunedInputs[inputName] = nodeInput;\n }\n }\n\n // Copy the node and overwrite inputsAndArgs to the pruned version.\n const prunedNode = Object.assign({}, node) as TapeNode;\n prunedNode.inputs = prunedInputs;\n prunedNode.outputs = node.outputs;\n\n filteredTape.push(prunedNode);\n }\n }\n\n return filteredTape;\n}\n\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(\n tensorAccumulatedGradientMap: {[tensorId: number]: Tensor},\n filteredTape: TapeNode[]) {\n // Walk the tape backward and keep a map of Tensor to its gradient.\n for (let i = filteredTape.length - 1; i >= 0; i--) {\n const node = filteredTape[i];\n\n const dys: Tensor[] = [];\n node.outputs.forEach(o => {\n const gradTensor = tensorAccumulatedGradientMap[o.id];\n if (gradTensor != null) {\n dys.push(gradTensor);\n } else {\n // This particular output is not in the back-propagation subgraph, so it\n // does not affect the final output, thus we put zeros for its dy.\n const dy = Tensor.make(\n o.shape, {values: util.makeZerosTypedArray(o.size, o.dtype)},\n o.dtype);\n dys.push(dy);\n }\n });\n\n if (node.gradient == null) {\n throw new Error(\n `Cannot compute gradient: gradient function not found ` +\n `for ${node.name}.`);\n }\n\n // Backprop dy through this node and accumulate gradients over the inputs.\n const inputGradients =\n // Grad functions of ops with single outputs expect a dy, while ops\n // with multiple outputs expect dys (array of dy).\n node.gradient(node.outputs.length === 1 ? dys[0] : dys);\n for (const inputName in node.inputs) {\n if (!(inputName in inputGradients)) {\n throw new Error(\n `Cannot backprop through input ${inputName}. ` +\n `Available gradients found: ${Object.keys(inputGradients)}.`);\n }\n\n // Call the gradient function.\n const dx = inputGradients[inputName]();\n if (dx.dtype !== 'float32') {\n throw new Error(\n `Error in gradient for op ${node.name}. The gradient of input ` +\n `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n }\n const x = node.inputs[inputName];\n if (!util.arraysEqual(dx.shape, x.shape)) {\n throw new Error(\n `Error in gradient for op ${node.name}. The gradient of input ` +\n `'${inputName}' has shape '${dx.shape}', which does not match ` +\n `the shape of the input '${x.shape}'`);\n }\n\n if (tensorAccumulatedGradientMap[x.id] == null) {\n tensorAccumulatedGradientMap[x.id] = dx;\n } else {\n const curGradient = tensorAccumulatedGradientMap[x.id];\n tensorAccumulatedGradientMap[x.id] = curGradient.add(dx);\n curGradient.dispose();\n }\n }\n }\n}\n","../src/tensor_util.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap, TensorContainer, TensorContainerArray} from './tensor_types';\nimport {upcastType} from './types';\nimport {assert} from './util';\n\nexport function makeTypesMatch(a: T, b: T): [T, T] {\n if (a.dtype === b.dtype) {\n return [a, b];\n }\n const dtype = upcastType(a.dtype, b.dtype);\n return [a.cast(dtype), b.cast(dtype)];\n}\n\nexport function assertTypesMatch(a: Tensor, b: Tensor): void {\n assert(\n a.dtype === b.dtype,\n `The dtypes of the first(${a.dtype}) and` +\n ` second(${b.dtype}) input must match`);\n}\n\nexport function isTensorInList(tensor: Tensor, tensorList: Tensor[]): boolean {\n for (let i = 0; i < tensorList.length; i++) {\n if (tensorList[i].id === tensor.id) {\n return true;\n }\n }\n return false;\n}\n\nexport function flattenNameArrayMap(\n nameArrayMap: Tensor|NamedTensorMap, keys?: string[]): Tensor[] {\n const xs: Tensor[] = [];\n if (nameArrayMap instanceof Tensor) {\n xs.push(nameArrayMap);\n } else {\n const xMap = nameArrayMap as {[xName: string]: Tensor};\n for (let i = 0; i < keys.length; i++) {\n xs.push(xMap[keys[i]]);\n }\n }\n return xs;\n}\n\nexport function unflattenToNameArrayMap(\n keys: string[], flatArrays: Tensor[]): NamedTensorMap {\n if (keys.length !== flatArrays.length) {\n throw new Error(\n `Cannot unflatten Tensor[], keys and arrays are not of same length.`);\n }\n const result: NamedTensorMap = {};\n for (let i = 0; i < keys.length; i++) {\n result[keys[i]] = flatArrays[i];\n }\n return result;\n}\n\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n * `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n * is safe to pass any object here, except that `Promise`s are not\n * supported.\n * @returns An array of `Tensors` found within the passed object. If the\n * argument is simply a `Tensor', a list containing that `Tensor` is\n * returned. If the object is not a `Tensor` or does not\n * contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result: TensorContainer): Tensor[] {\n const list: Tensor[] = [];\n const seen = new Set<{}|void>();\n walkTensorContainer(result, list, seen);\n return list;\n}\n\nfunction walkTensorContainer(\n container: TensorContainer, list: Tensor[], seen: Set<{}|void>): void {\n if (container == null) {\n return;\n }\n if (container instanceof Tensor) {\n list.push(container);\n return;\n }\n if (!isIterable(container)) {\n return;\n }\n // Iteration over keys works also for arrays.\n const iterable = container as TensorContainerArray;\n for (const k in iterable) {\n const val = iterable[k];\n if (!seen.has(val)) {\n seen.add(val);\n walkTensorContainer(val, list, seen);\n }\n }\n}\n\n// tslint:disable-next-line:no-any\nfunction isIterable(obj: any): boolean {\n return Array.isArray(obj) || typeof obj === 'object';\n}\n","../src/engine.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimingInfo, DataMover, KernelBackend} from './kernels/backend';\nimport {Profiler} from './profiler';\nimport {backpropagateGradients, getFilteredNodesXToY, NamedGradientMap, TapeNode} from './tape';\nimport {DataId, Tensor, Tensor3D, TensorTracker, Variable} from './tensor';\nimport {NamedTensorMap, NamedVariableMap, TensorContainer} from './tensor_types';\nimport {getTensorsInContainer, isTensorInList} from './tensor_util';\nimport {DataType, DataValues} from './types';\nimport * as util from './util';\nimport {bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape} from './util';\n\n/**\n * A function that computes an output. The save function is for saving tensors\n * computed in the forward pass, that we need in the backward pass.\n */\nexport type ForwardFunc =\n (backend: KernelBackend, save?: (tensor: S) => S) => T;\n\n/**\n * @docalias (a: Tensor, b: Tensor,...) => {\n * value: Tensor, * gradFunc: (dy: Tensor) => Tensor | Tensor[] * }\n */\nexport type CustomGradientFunc = (...args: Tensor[]) => {\n value: T, gradFunc: (dy: T) => Tensor | Tensor[];\n};\n\nexport type MemoryInfo = {\n numTensors: number; numDataBuffers: number; numBytes: number;\n unreliable?: boolean; reasons: string[];\n};\n\ntype KernelProfile = {\n name: string; bytesAdded: number; totalBytesSnapshot: number;\n tensorsAdded: number;\n totalTensorsSnapshot: number;\n inputShapes: number[][];\n outputShape: number[] | number[][];\n};\n\nexport type ProfileInfo = {\n newBytes: number; newTensors: number; peakBytes: number;\n kernels: KernelProfile[];\n result: TensorContainer;\n};\n\nexport interface TimingInfo extends BackendTimingInfo {\n wallMs: number;\n}\n\n/** @docalias Function */\nexport type ScopeFn = () => T;\n\nexport interface TensorManager {\n registerTensor(a: Tensor): void;\n registerVariable(v: Variable): void;\n disposeTensor(a: Tensor): void;\n memory(): {numDataBuffers: number; numBytes: number;};\n}\n\ninterface ScopeState {\n track: Tensor[];\n name: string;\n}\n\nexport class Engine implements TensorManager, TensorTracker, DataMover {\n // Public since optimizers will use it.\n registeredVariables: NamedVariableMap = {};\n\n private nextTapeNodeId = 0;\n private numBytes = 0;\n private numTensors = 0;\n private numStringTensors = 0;\n private numDataBuffers = 0;\n\n private profiling = false;\n private activeProfile: ProfileInfo;\n\n private activeTape: TapeNode[];\n private gradientScopeCount = 0;\n private customGradientDepth = 0;\n\n // Keep Tensors that parallel the tapes.\n private activeScope: ScopeState;\n private scopeStack: ScopeState[] = [];\n private keepTensors: Set = new Set();\n private profiler: Profiler;\n\n private tensorInfo = new WeakMap();\n\n constructor(\n public backend: KernelBackend, public safeMode: boolean,\n private debugMode: () => boolean) {\n this.profiler = new Profiler(backend);\n this.activeProfile =\n {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};\n }\n\n moveData(dataId: DataId) {\n this.write(dataId, this.readSync(dataId));\n }\n\n tidy(\n nameOrFn: string|ScopeFn, fn?: ScopeFn, gradMode = false): T {\n // gradMode Primarily for internal use during backprop\n // If true, will start a tape if it is the outermost tidy.\n\n let name: string = null;\n if (fn == null) {\n // Called with only 1 argument.\n if (typeof nameOrFn !== 'function') {\n throw new Error('Please provide a function to tidy()');\n }\n fn = nameOrFn;\n } else {\n // Called with 2 arguments.\n if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n throw new Error(\n 'When calling with two arguments, the first argument ' +\n 'to tidy() must be a string');\n }\n if (typeof fn !== 'function') {\n throw new Error(\n 'When calling with two arguments, the 2nd argument ' +\n 'to tidy() must be a function');\n }\n name = nameOrFn as string;\n // TODO(nsthorat,smilkov): Do operation logging and performance\n // profiling.\n }\n let result: T;\n return this.scopedRun(\n () => this.startScope(name, gradMode),\n () => this.endScope(result, gradMode), () => {\n result = fn();\n if (result instanceof Promise) {\n console.error('Cannot return a Promise inside of tidy.');\n }\n return result;\n });\n }\n\n private scopedRun(start: () => void, end: () => void, f: () => T): T {\n start();\n try {\n const res = f();\n end();\n return res;\n } catch (ex) {\n end();\n throw ex;\n }\n }\n\n private static nextTensorId = 0;\n nextTensorId(): number {\n return Engine.nextTensorId++;\n }\n\n private static nextVariableId = 0;\n nextVariableId(): number {\n return Engine.nextVariableId++;\n }\n\n runKernel(\n forwardFunc: ForwardFunc,\n inputs: I,\n backwardsFunc?: (dy: T, saved: Tensor[]) => {[P in keyof I]: () => I[P]},\n ): T {\n let result: T;\n const saved: Tensor[] = [];\n const saveFunc = (x: T): T => {\n saved.push(x);\n return x;\n };\n const scopeName = this.activeScope != null ? this.activeScope.name : '';\n const startingBytecount = this.numBytes;\n const startingNumTensors = this.numTensors;\n\n // Stop recording to a tape when running a kernel.\n this.scopedRun(\n () => this.customGradientDepth++, () => this.customGradientDepth--,\n () => {\n if (!this.debugMode()) {\n result = forwardFunc(this.backend, saveFunc);\n } else {\n result = this.profiler.profileKernel(\n scopeName, () => forwardFunc(this.backend, saveFunc));\n }\n });\n\n if (this.shouldRecord()) {\n const tapeNode: TapeNode = {\n id: this.nextTapeNodeId++,\n name: scopeName,\n inputs,\n outputs: Array.isArray(result) ? result : [result] as Tensor[]\n };\n if (backwardsFunc != null) {\n tapeNode.gradient =\n ((dy: T) => backwardsFunc(dy, saved)) as (dy: Tensor) =>\n NamedGradientMap;\n }\n this.activeTape.push(tapeNode);\n }\n\n if (this.profiling) {\n this.activeProfile.kernels.push({\n name: scopeName,\n bytesAdded: this.numBytes - startingBytecount,\n totalBytesSnapshot: this.numBytes,\n tensorsAdded: this.numTensors - startingNumTensors,\n totalTensorsSnapshot: this.numTensors,\n inputShapes: Object.keys(inputs).map(key => inputs[key].shape),\n outputShape: Array.isArray(result) ?\n (result as Tensor[]).map(item => (item as Tensor).shape) :\n (result as Tensor).shape\n });\n }\n\n return result;\n }\n\n // TensorManager implementation.\n\n registerTensor(a: Tensor|Variable): void {\n const refCount = this.tensorInfo.has(a.dataId) ?\n this.tensorInfo.get(a.dataId).refCount :\n 0;\n this.numTensors++;\n if (a.dtype === 'string') {\n this.numStringTensors++;\n }\n if (refCount === 0) {\n this.numDataBuffers++;\n\n // Bytes for complex numbers are counted by their components. Bytes for\n // string tensors are counted when writing values.\n let bytes = 0;\n if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n bytes = util.sizeFromShape(a.shape) * util.bytesPerElement(a.dtype);\n }\n this.tensorInfo.set(a.dataId, {\n backend: this.backend,\n dtype: a.dtype,\n shape: a.shape,\n bytes,\n refCount: 0\n });\n this.numBytes += bytes;\n this.backend.register(a.dataId, a.shape, a.dtype);\n }\n this.tensorInfo.get(a.dataId).refCount++;\n if (!(a instanceof Variable)) {\n this.track(a);\n }\n }\n\n registerVariable(v: Variable) {\n if (this.registeredVariables[v.name] != null) {\n throw new Error(`Variable with name ${v.name} was already registered`);\n }\n this.registeredVariables[v.name] = v;\n }\n\n disposeTensor(a: Tensor): void {\n if (!this.tensorInfo.has(a.dataId)) {\n return;\n }\n if (this.keepTensors.has(a.id)) {\n this.keepTensors.delete(a.id);\n }\n this.numTensors--;\n if (a.dtype === 'string') {\n this.numStringTensors--;\n }\n const info = this.tensorInfo.get(a.dataId);\n const refCount = info.refCount;\n if (refCount <= 1) {\n // Don't count bytes for complex numbers as they are counted by their\n // components.\n if (a.dtype !== 'complex64') {\n this.numBytes -= info.bytes;\n }\n this.numDataBuffers--;\n info.backend.disposeData(a.dataId);\n this.tensorInfo.delete(a.dataId);\n } else {\n this.tensorInfo.get(a.dataId).refCount--;\n }\n // TODO(nsthorat): Construct an error and save the stack trace for\n // debugging when in debug mode. Creating a stack trace is too expensive\n // to do unconditionally.\n }\n\n disposeVariables(): void {\n for (const varName in this.registeredVariables) {\n const v = this.registeredVariables[varName];\n this.disposeTensor(v);\n delete this.registeredVariables[varName];\n }\n }\n\n memory(): MemoryInfo {\n const info = this.backend.memory() as MemoryInfo;\n info.numTensors = this.numTensors;\n info.numDataBuffers = this.numDataBuffers;\n info.numBytes = this.numBytes;\n if (this.numStringTensors > 0) {\n info.unreliable = true;\n if (info.reasons == null) {\n info.reasons = [];\n }\n info.reasons.push(\n 'Memory usage by string tensors is approximate ' +\n '(2 bytes per character)');\n }\n return info;\n }\n\n async profile(query: () => TensorContainer): Promise {\n this.profiling = true;\n\n const startBytes = this.numBytes;\n const startNumTensors = this.numTensors;\n\n this.activeProfile.kernels = [];\n this.activeProfile.result = query();\n\n this.profiling = false;\n\n this.activeProfile.peakBytes =\n Math.max(...this.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n this.activeProfile.newBytes = this.numBytes - startBytes;\n this.activeProfile.newTensors = this.numTensors - startNumTensors;\n return this.activeProfile;\n }\n\n private shouldRecord(): boolean {\n return this.activeTape != null && this.customGradientDepth === 0;\n }\n\n private addTapeNode(\n inputs: Tensor[], result: Tensor,\n gradientsFunc: (dy: Tensor) => Tensor[]): void {\n const inputsMap: NamedTensorMap = {};\n inputs.forEach((input, idx) => {\n inputsMap[idx] = input;\n });\n\n const gradient = (dy: Tensor) => {\n const res = gradientsFunc(dy);\n const resMap: NamedGradientMap = {};\n res.forEach((r, idx) => {\n resMap[idx] = () => r;\n });\n return resMap;\n };\n\n const tapeNode: TapeNode = {\n id: this.nextTapeNodeId++,\n name: this.activeScope.name,\n inputs: inputsMap,\n outputs: [result],\n gradient\n };\n this.activeTape.push(tapeNode);\n }\n\n keep(result: T): T {\n if (this.scopeStack.length === 1 && this.safeMode) {\n throw new Error(\n 'Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +\n 'tf.tidy(() => {...}) to avoid memory leaks.');\n }\n this.keepTensors.add(result.id);\n return result;\n }\n\n /**\n * Start a scope. Use this with endScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n startScope(name?: string, gradientsMode = false) {\n if (gradientsMode && this.gradientScopeCount === 0) {\n this.activeTape = [];\n }\n if (gradientsMode) {\n this.gradientScopeCount++;\n }\n\n const scopeInfo: ScopeState = {track: [], name: 'unnamed scope'};\n if (name) {\n scopeInfo.name = name;\n }\n this.scopeStack.push(scopeInfo);\n this.activeScope = scopeInfo;\n }\n\n /**\n * End a scope. Use this with startScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n endScope(result?: TensorContainer, gradientsMode = false) {\n if (gradientsMode) {\n this.gradientScopeCount--;\n if (this.gradientScopeCount === 0) {\n this.activeTape = null;\n }\n }\n\n const tensorsToKeep = new Set(this.keepTensors);\n\n const tensorsToTrackInParent = getTensorsInContainer(result);\n tensorsToTrackInParent.forEach(tensor => tensorsToKeep.add(tensor.id));\n\n // Dispose the arrays tracked in this scope.\n for (let i = 0; i < this.activeScope.track.length; i++) {\n const tensor = this.activeScope.track[i];\n if (tensorsToKeep.has(tensor.id)) {\n continue;\n }\n\n if (this.activeTape != null) {\n tensorsToTrackInParent.push(tensor);\n } else {\n tensor.dispose();\n }\n }\n\n const oldScope = this.scopeStack.pop();\n this.activeScope = this.scopeStack.length === 0 ?\n null :\n this.scopeStack[this.scopeStack.length - 1];\n\n // Track the current result in the parent scope.\n tensorsToTrackInParent.forEach(tensor => {\n // Only track the tensor if was allocated in the inner scope and is not\n // globally kept.\n if (!this.keepTensors.has(tensor.id) &&\n isTensorInList(tensor, oldScope.track)) {\n this.track(tensor);\n }\n });\n }\n\n /**\n * Returns gradients of `f` with respect to each of the `xs`. The gradients\n * returned are of the same length as `xs`, but some might be null if `f` was\n * not a function of that `x`. It also takes optional dy to multiply the\n * gradient, which defaults to `1`.\n */\n gradients(\n f: () => T, xs: Tensor[], dy?: T,\n allowNoGradients = false): {value: T, grads: Tensor[]} {\n util.assert(xs.length > 0, 'gradients() received an empty list of xs.');\n if (dy != null && dy.dtype !== 'float32') {\n throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n }\n\n return this.tidy('gradients', () => {\n const y = f();\n util.assert(\n y instanceof Tensor,\n 'The result y returned by f() must be a tensor.');\n // Filter out the nodes that don't connect x => y.\n const filteredTape = getFilteredNodesXToY(this.activeTape, xs, y);\n if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n throw new Error(\n 'Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n 'that the f you passed encloses all operations that lead from x ' +\n 'to y.');\n }\n\n const accumulatedGradientMap: {[tensorId: number]: Tensor} = {};\n accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n\n // Backprop gradients through the filtered nodes.\n backpropagateGradients(accumulatedGradientMap, filteredTape);\n\n const grads = xs.map(x => accumulatedGradientMap[x.id]);\n return {value: y, grads};\n }, true /* gradientsMode */);\n }\n\n customGrad(f: CustomGradientFunc):\n (...args: Tensor[]) => T {\n util.assert(\n util.isFunction(f),\n 'The f passed in customGrad(f) must be a function.');\n return (...inputs: Tensor[]): T => {\n util.assert(\n inputs.every(t => t instanceof Tensor),\n 'The args passed in customGrad(f)(x1, x2,...) must all be tensors');\n\n let gradientsFunc: (dy: T) => Tensor | Tensor[];\n let result: T;\n this.scopedRun(\n () => this.customGradientDepth++, () => this.customGradientDepth--,\n () => {\n const gradientsMode = true;\n result = this.tidy(f.name, () => {\n const {value, gradFunc} = f(...inputs);\n util.assert(\n value instanceof Tensor,\n 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.value` is a tensor');\n util.assert(\n util.isFunction(gradFunc),\n 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function.');\n gradientsFunc = gradFunc;\n return value;\n }, gradientsMode);\n });\n\n if (this.shouldRecord()) {\n const gradFunc = (dy: T): Tensor[] => {\n const res = gradientsFunc(dy);\n const grads: Tensor[] = Array.isArray(res) ? res : [res];\n util.assert(\n grads.length === inputs.length,\n 'The function f passed in customGrad(f) must return an object ' +\n 'where `obj.gradFunc` is a function that returns the same ' +\n 'number of tensors as inputs passed to f(...).');\n util.assert(\n grads.every(t => t instanceof Tensor),\n 'The function f passed in customGrad(f) must return an object ' +\n 'where `obj.gradFunc` is a function that returns a list of ' +\n 'only tensors.');\n return grads;\n };\n this.addTapeNode(inputs, result, gradFunc);\n }\n return result;\n };\n }\n\n // Forwarding to backend.\n write(dataId: DataId, values: DataValues): void {\n const info = this.tensorInfo.get(dataId);\n // Bytes for string tensors are counted when writing.\n if (info.dtype === 'string') {\n const newBytes = bytesFromStringArray(values as string[]);\n this.numBytes += newBytes - info.bytes;\n info.bytes = newBytes;\n }\n\n if (this.backend !== info.backend) {\n // Delete the tensor from the old backend and move it to the new backend.\n info.backend.disposeData(dataId);\n info.backend = this.backend;\n this.backend.register(dataId, info.shape, info.dtype);\n }\n this.backend.write(dataId, values);\n }\n readSync(dataId: DataId): DataValues {\n // Route the read to the correct backend.\n const info = this.tensorInfo.get(dataId);\n return info.backend.readSync(dataId);\n }\n read(dataId: DataId): Promise {\n // Route the read to the correct backend.\n const info = this.tensorInfo.get(dataId);\n return info.backend.read(dataId);\n }\n fromPixels(\n pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n numChannels: number): Tensor3D {\n return this.backend.fromPixels(pixels, numChannels);\n }\n async time(query: () => void): Promise {\n const start = now();\n const timingInfo = await this.backend.time(query) as TimingInfo;\n timingInfo.wallMs = now() - start;\n return timingInfo;\n }\n\n /**\n * Tracks a Tensor in the current scope to be automatically cleaned up\n * when the current scope ends, and returns the value.\n *\n * @param result The Tensor to track in the current scope.\n */\n private track(result: T): T {\n if (this.scopeStack.length === 1 && this.safeMode) {\n throw new Error(\n 'Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +\n 'tf.tidy(() => {op();...}); to avoid memory leaks.');\n }\n if (this.activeScope != null) {\n this.activeScope.track.push(result);\n }\n return result;\n }\n}\n\nfunction ones(shape: number[]): Tensor {\n const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n return Tensor.make(shape, {values});\n}\n","../src/environment_util.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from './canvas_util';\n\nexport interface Features {\n // Whether to enable debug mode.\n 'DEBUG'?: boolean;\n // Whether we are in a browser (as versus, say, node.js) environment.\n 'IS_BROWSER'?: boolean;\n // Whether we are in the Node.js environment.\n 'IS_NODE'?: boolean;\n // Whether packed WebGL kernels lazily unpack their outputs.\n 'WEBGL_LAZILY_UNPACK'?: boolean;\n // Whether the WebGL backend will sometimes forward ops to the CPU.\n 'WEBGL_CPU_FORWARD'?: boolean;\n // Whether to turn all packing related flags on.\n 'WEBGL_PACK'?: boolean;\n // Whether we will pack the batchnormalization op.\n 'WEBGL_PACK_BATCHNORMALIZATION'?: boolean;\n // Whether we will pack the clipping op.\n 'WEBGL_PACK_CLIP'?: boolean;\n // Whether we pack the depthwise convolution op.\n 'WEBGL_PACK_DEPTHWISECONV'?: boolean;\n // Whether we will pack binary operations.\n 'WEBGL_PACK_BINARY_OPERATIONS'?: boolean;\n // Whether we will pack SpaceToBatchND, BatchToSpaceND, slice, pad, transpose.\n 'WEBGL_PACK_ARRAY_OPERATIONS'?: boolean;\n // Whether we will use the im2col algorithm to speed up convolutions.\n 'WEBGL_CONV_IM2COL'?: boolean;\n // The maximum texture dimension.\n 'WEBGL_MAX_TEXTURE_SIZE'?: number;\n // The maximum number of textures in a single shader program.\n 'WEBGL_MAX_TEXTURES_IN_SHADER'?: number;\n // The disjoint_query_timer extension version.\n // 0: disabled, 1: EXT_disjoint_timer_query, 2:\n // EXT_disjoint_timer_query_webgl2.\n // In Firefox with WebGL 2.0,\n // EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n // WebGL 1.0 extension.\n 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'?: number;\n // Whether the timer object from the disjoint_query_timer extension gives\n // timing information that is reliable.\n 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'?: boolean;\n // 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0.\n 'WEBGL_VERSION'?: number;\n // True if WebGL is supported.\n 'HAS_WEBGL'?: boolean;\n // Whether rendering to float32 textures is enabled. If disabled, renders to\n // float16 textures.\n 'WEBGL_RENDER_FLOAT32_ENABLED'?: boolean;\n // Whether downloading float textures is enabled. If disabled, uses IEEE 754\n // encoding of the float32 values to 4 uint8 when downloading.\n 'WEBGL_DOWNLOAD_FLOAT_ENABLED'?: boolean;\n // Whether the fence API is available.\n 'WEBGL_FENCE_API_ENABLED'?: boolean;\n // Tensors with size <= than this will be uploaded as uniforms, not textures.\n 'WEBGL_SIZE_UPLOAD_UNIFORM'?: number;\n /**\n * Number of megabytes bytes allocated on the GPU before we start moving data\n * to cpu. Moving avoids gpu memory leaks and relies on JS's garbage\n * collector.\n */\n 'WEBGL_NUM_MB_BEFORE_PAGING'?: number;\n 'BACKEND'?: string;\n // Test precision for unit tests. This is decreased when we can't render\n // float32 textures.\n 'TEST_EPSILON'?: number;\n 'IS_CHROME'?: boolean;\n // True if running unit tests.\n 'IS_TEST'?: boolean;\n // Smallest positive value used to make ops like division and log numerically\n // stable.\n 'EPSILON'?: number;\n // True when the environment is \"production\" where we disable safety checks\n // to gain performance.\n 'PROD'?: boolean;\n // Whether to do sanity checks when inferring a shape from user-provided\n // values, used when creating a new tensor.\n 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY'?: boolean;\n // Whether deprecation warnings are enabled.\n 'DEPRECATION_WARNINGS_ENABLED'?: boolean;\n}\n\nexport enum Type {\n NUMBER,\n BOOLEAN,\n STRING\n}\n\nexport const URL_PROPERTIES: URLProperty[] = [\n {name: 'DEBUG', type: Type.BOOLEAN},\n {name: 'IS_BROWSER', type: Type.BOOLEAN},\n {name: 'WEBGL_LAZILY_UNPACK', type: Type.BOOLEAN},\n {name: 'WEBGL_CPU_FORWARD', type: Type.BOOLEAN},\n {name: 'WEBGL_PACK', type: Type.BOOLEAN},\n {name: 'WEBGL_PACK_BATCHNORMALIZATION', type: Type.BOOLEAN},\n {name: 'WEBGL_PACK_CLIP', type: Type.BOOLEAN},\n {name: 'WEBGL_PACK_DEPTHWISECONV', type: Type.BOOLEAN},\n {name: 'WEBGL_PACK_BINARY_OPERATIONS', type: Type.BOOLEAN},\n {name: 'WEBGL_PACK_ARRAY_OPERATIONS', type: Type.BOOLEAN},\n {name: 'WEBGL_CONV_IM2COL', type: Type.BOOLEAN},\n {name: 'WEBGL_MAX_TEXTURE_SIZE', type: Type.NUMBER},\n {name: 'WEBGL_NUM_MB_BEFORE_PAGING', type: Type.NUMBER},\n {name: 'WEBGL_MAX_TEXTURES_IN_SHADER', type: Type.NUMBER},\n {name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', type: Type.NUMBER},\n {name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', type: Type.BOOLEAN},\n {name: 'WEBGL_VERSION', type: Type.NUMBER},\n {name: 'WEBGL_RENDER_FLOAT32_ENABLED', type: Type.BOOLEAN},\n {name: 'WEBGL_DOWNLOAD_FLOAT_ENABLED', type: Type.BOOLEAN},\n {name: 'WEBGL_FENCE_API_ENABLED', type: Type.BOOLEAN},\n {name: 'WEBGL_SIZE_UPLOAD_UNIFORM', type: Type.NUMBER},\n {name: 'BACKEND', type: Type.STRING},\n {name: 'EPSILON', type: Type.NUMBER},\n {name: 'PROD', type: Type.BOOLEAN},\n {name: 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', type: Type.BOOLEAN},\n {name: 'DEPRECATION_WARNINGS_ENABLED', type: Type.BOOLEAN},\n];\n\nexport interface URLProperty {\n name: keyof Features;\n type: Type;\n}\n\nexport function isWebGLVersionEnabled(webGLVersion: 1|2) {\n try {\n const gl = getWebGLContext(webGLVersion);\n if (gl != null) {\n return true;\n }\n } catch (e) {\n return false;\n }\n return false;\n}\n\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE: number;\nlet MAX_TEXTURES_IN_SHADER: number;\n\nexport function getWebGLMaxTextureSize(webGLVersion: number): number {\n if (MAX_TEXTURE_SIZE == null) {\n const gl = getWebGLContext(webGLVersion);\n MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n }\n return MAX_TEXTURE_SIZE;\n}\n\nexport function getMaxTexturesInShader(webGLVersion: number): number {\n if (MAX_TEXTURES_IN_SHADER == null) {\n const gl = getWebGLContext(webGLVersion);\n MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n }\n // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\n\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion: number):\n number {\n if (webGLVersion === 0) {\n return 0;\n }\n\n let queryTimerVersion: number;\n const gl = getWebGLContext(webGLVersion);\n\n if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n webGLVersion === 2) {\n queryTimerVersion = 2;\n } else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n queryTimerVersion = 1;\n } else {\n queryTimerVersion = 0;\n }\n return queryTimerVersion;\n}\n\nexport function isRenderToFloatTextureEnabled(webGLVersion: number): boolean {\n if (webGLVersion === 0) {\n return false;\n }\n\n const gl = getWebGLContext(webGLVersion);\n\n if (webGLVersion === 1) {\n if (!hasExtension(gl, 'OES_texture_float')) {\n return false;\n }\n } else {\n if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n return false;\n }\n }\n\n const isFrameBufferComplete =\n createFloatTextureAndBindToFramebuffer(gl, webGLVersion);\n return isFrameBufferComplete;\n}\n\nexport function isDownloadFloatTextureEnabled(webGLVersion: number): boolean {\n if (webGLVersion === 0) {\n return false;\n }\n\n const gl = getWebGLContext(webGLVersion);\n\n if (webGLVersion === 1) {\n if (!hasExtension(gl, 'OES_texture_float')) {\n return false;\n }\n if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n return false;\n }\n } else {\n if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n return false;\n }\n }\n\n const isFrameBufferComplete =\n createFloatTextureAndBindToFramebuffer(gl, webGLVersion);\n return isFrameBufferComplete;\n}\n\nexport function isWebGLFenceEnabled(webGLVersion: number) {\n if (webGLVersion !== 2) {\n return false;\n }\n const gl = getWebGLContext(webGLVersion);\n\n // tslint:disable-next-line:no-any\n const isEnabled = (gl as any).fenceSync != null;\n return isEnabled;\n}\n\nexport function isChrome() {\n return typeof navigator !== 'undefined' && navigator != null &&\n navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n /Google Inc/.test(navigator.vendor);\n}\n\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\nexport function getFeaturesFromURL(): Features {\n const features: Features = {};\n\n if (typeof window === 'undefined' || typeof window.location === 'undefined' ||\n typeof window.location.search === 'undefined') {\n return features;\n }\n\n const urlParams = getQueryParams(window.location.search);\n if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n const urlFlags: {[key: string]: string} = {};\n\n const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n keyValues.forEach(keyValue => {\n const [key, value] = keyValue.split(':') as [string, string];\n urlFlags[key] = value;\n });\n\n URL_PROPERTIES.forEach(urlProperty => {\n if (urlProperty.name in urlFlags) {\n console.log(\n `Setting feature override from URL ${urlProperty.name}: ` +\n `${urlFlags[urlProperty.name]}`);\n if (urlProperty.type === Type.NUMBER) {\n features[urlProperty.name] = +urlFlags[urlProperty.name];\n } else if (urlProperty.type === Type.BOOLEAN) {\n features[urlProperty.name] = urlFlags[urlProperty.name] === 'true';\n } else if (urlProperty.type === Type.STRING) {\n // tslint:disable-next-line:no-any\n features[urlProperty.name] = urlFlags[urlProperty.name] as any;\n } else {\n console.warn(`Unknown URL param: ${urlProperty.name}.`);\n }\n }\n });\n }\n\n return features;\n}\n\nfunction hasExtension(gl: WebGLRenderingContext, extensionName: string) {\n const ext = gl.getExtension(extensionName);\n return ext != null;\n}\n\nfunction createFloatTextureAndBindToFramebuffer(\n gl: WebGLRenderingContext, webGLVersion: number): boolean {\n const frameBuffer = gl.createFramebuffer();\n const texture = gl.createTexture();\n\n gl.bindTexture(gl.TEXTURE_2D, texture);\n\n // tslint:disable-next-line:no-any\n const internalFormat = webGLVersion === 2 ? (gl as any).RGBA32F : gl.RGBA;\n gl.texImage2D(\n gl.TEXTURE_2D, 0, internalFormat, 1, 1, 0, gl.RGBA, gl.FLOAT, null);\n\n gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n gl.framebufferTexture2D(\n gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n const isFrameBufferComplete =\n gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n gl.deleteTexture(texture);\n gl.deleteFramebuffer(frameBuffer);\n\n return isFrameBufferComplete;\n}\n\nexport function getQueryParams(queryString: string): {[key: string]: string} {\n const params = {};\n queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n decodeParam(params, t[0], t[1]);\n return t.join('=');\n });\n return params;\n}\n\nfunction decodeParam(\n params: {[key: string]: string}, name: string, value?: string) {\n params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\n\n// Empirically determined constant used to decide the number of bytes on GPU\n// before we start paging. The MB are this constant * screen area * dpi / 1024.\nexport const BEFORE_PAGING_CONSTANT = 600;\nexport function getNumMBBeforePaging(): number {\n return (window.screen.height * window.screen.width *\n window.devicePixelRatio) *\n BEFORE_PAGING_CONSTANT / 1024;\n}\n","../src/environment.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as device_util from './device_util';\nimport {Engine, MemoryInfo, ProfileInfo, ScopeFn, TimingInfo} from './engine';\nimport {Features, getFeaturesFromURL, getMaxTexturesInShader, getNumMBBeforePaging, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isChrome, isDownloadFloatTextureEnabled, isRenderToFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled} from './environment_util';\nimport {KernelBackend} from './kernels/backend';\nimport {DataId, setDeprecationWarningFn, setTensorTracker, Tensor, TensorTracker} from './tensor';\nimport {TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\n\nexport const EPSILON_FLOAT16 = 1e-4;\nconst TEST_EPSILON_FLOAT16 = 1e-1;\n\nexport const EPSILON_FLOAT32 = 1e-7;\nconst TEST_EPSILON_FLOAT32 = 1e-3;\n\nexport class Environment {\n private features: Features = {};\n private globalEngine: Engine;\n private registry:\n {[id: string]: {backend: KernelBackend, priority: number}} = {};\n backendName: string;\n\n constructor(features?: Features) {\n if (features != null) {\n this.features = features;\n }\n\n if (this.get('DEBUG')) {\n console.warn(\n 'Debugging mode is ON. The output of every math call will ' +\n 'be downloaded to CPU and checked for NaNs. ' +\n 'This significantly impacts performance.');\n }\n }\n\n /**\n * Sets the backend (cpu, webgl, etc) responsible for creating tensors and\n * executing operations on those tensors.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n * `'webgl'|'cpu'` in the browser, and `'tensorflow'` under node.js\n * (requires tfjs-node).\n * @param safeMode Defaults to false. In safe mode, you are forced to\n * construct tensors and call math operations inside a `tidy()` which\n * will automatically clean up intermediate tensors.\n */\n /** @doc {heading: 'Environment'} */\n static setBackend(backendName: string, safeMode = false) {\n if (!(backendName in ENV.registry)) {\n throw new Error(`Backend name '${backendName}' not found in registry`);\n }\n ENV.engine.backend = ENV.findBackend(backendName);\n ENV.backendName = backendName;\n }\n\n /**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n */\n /** @doc {heading: 'Environment'} */\n static getBackend(): string {\n ENV.initEngine();\n return ENV.backendName;\n }\n\n /**\n * Dispose all variables kept in backend engine.\n */\n /** @doc {heading: 'Environment'} */\n static disposeVariables(): void {\n ENV.engine.disposeVariables();\n }\n\n /**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n * (undisposed) at this time, which is ≤ the number of tensors\n * (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n * data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n * `unrealible` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n * `unreliable` is true.\n */\n /** @doc {heading: 'Performance', subheading: 'Memory'} */\n static memory(): MemoryInfo {\n return ENV.engine.memory();\n }\n\n /**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: tne number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n * const x = tf.tensor1d([1, 2, 3]);\n * let x2 = x.square();\n * x2.dispose();\n * x2 = x.square();\n * x2.dispose();\n * return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n */\n /** @doc {heading: 'Performance', subheading: 'Profile'} */\n static profile(f: () => TensorContainer): Promise {\n return ENV.engine.profile(f);\n }\n\n /**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * When in safe mode, you must enclose all `tf.Tensor` creation and ops\n * inside a `tf.tidy` to prevent memory leaks.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n * // a, b, and one will be cleaned up when the tidy ends.\n * const one = tf.scalar(1);\n * const a = tf.scalar(2);\n * const b = a.square();\n *\n * console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n * // The value returned inside the tidy function will return\n * // through the tidy, in this case to the variable y.\n * return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n * If a name is provided, the 2nd argument should be the function.\n * If debug mode is on, the timing and the memory usage of the function\n * will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n */\n /** @doc {heading: 'Performance', subheading: 'Memory'} */\n static tidy(\n nameOrFn: string|ScopeFn, fn?: ScopeFn): T {\n return ENV.engine.tidy(nameOrFn, fn);\n }\n\n /**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n * contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n * the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n * happens. In general it is safe to pass any object here, except that\n * `Promise`s are not supported.\n */\n /** @doc {heading: 'Performance', subheading: 'Memory'} */\n static dispose(container: TensorContainer) {\n const tensors = getTensorsInContainer(container);\n tensors.forEach(tensor => tensor.dispose());\n }\n\n /**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n * const one = tf.scalar(1);\n * const a = tf.scalar(2);\n *\n * // b will not be cleaned up by the tidy. a and one will be cleaned up\n * // when the tidy ends.\n * b = tf.keep(a.square());\n *\n * console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n * // The value returned inside the tidy function will return\n * // through the tidy, in this case to the variable y.\n * return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n */\n /** @doc {heading: 'Performance', subheading: 'Memory'} */\n static keep(result: T): T {\n return ENV.engine.keep(result);\n }\n\n /**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer.\n * - On `WebGL` The following additional properties exist:\n * - `uploadWaitMs`: CPU blocking time on texture uploads.\n * - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n */\n /** @doc {heading: 'Performance', subheading: 'Timing'} */\n static time(f: () => void): Promise {\n return ENV.engine.time(f);\n }\n\n get(feature: K): Features[K] {\n if (feature in this.features) {\n return this.features[feature];\n }\n\n this.features[feature] = this.evaluateFeature(feature);\n\n return this.features[feature];\n }\n\n getFeatures(): Features {\n return this.features;\n }\n\n set(feature: K, value: Features[K]): void {\n this.features[feature] = value;\n }\n\n private getBestBackendName(): string {\n if (Object.keys(this.registry).length === 0) {\n throw new Error('No backend found in registry.');\n }\n const sortedBackends = Object.keys(this.registry)\n .map(name => {\n return {name, entry: this.registry[name]};\n })\n .sort((a, b) => {\n // Highest priority comes first.\n return b.entry.priority - a.entry.priority;\n });\n return sortedBackends[0].name;\n }\n\n private evaluateFeature(feature: K): Features[K] {\n if (feature === 'DEBUG') {\n return false;\n } else if (feature === 'IS_BROWSER') {\n return typeof window !== 'undefined';\n } else if (feature === 'IS_NODE') {\n return (typeof process !== 'undefined') &&\n (typeof process.versions !== 'undefined') &&\n (typeof process.versions.node !== 'undefined');\n } else if (feature === 'IS_CHROME') {\n return isChrome();\n } else if (feature === 'WEBGL_CPU_FORWARD') {\n return true;\n } else if (feature === 'WEBGL_PACK') {\n return false;\n } else if (feature === 'WEBGL_PACK_BATCHNORMALIZATION') {\n return this.get('WEBGL_PACK');\n } else if (feature === 'WEBGL_PACK_CLIP') {\n return this.get('WEBGL_PACK');\n } else if (feature === 'WEBGL_PACK_DEPTHWISECONV') {\n return this.get('WEBGL_PACK');\n } else if (feature === 'WEBGL_PACK_BINARY_OPERATIONS') {\n return this.get('WEBGL_PACK');\n } else if (feature === 'WEBGL_PACK_ARRAY_OPERATIONS') {\n return this.get('WEBGL_PACK');\n } else if (feature === 'WEBGL_LAZILY_UNPACK') {\n return this.get('WEBGL_PACK');\n } else if (feature === 'WEBGL_CONV_IM2COL') {\n return this.get('WEBGL_PACK');\n } else if (feature === 'WEBGL_NUM_MB_BEFORE_PAGING') {\n if (this.get('PROD') || !this.get('IS_BROWSER')) {\n return Number.POSITIVE_INFINITY;\n }\n return getNumMBBeforePaging();\n } else if (feature === 'WEBGL_MAX_TEXTURE_SIZE') {\n return getWebGLMaxTextureSize(this.get('WEBGL_VERSION'));\n } else if (feature === 'WEBGL_MAX_TEXTURES_IN_SHADER') {\n return getMaxTexturesInShader(this.get('WEBGL_VERSION'));\n } else if (feature === 'IS_TEST') {\n return false;\n } else if (feature === 'BACKEND') {\n return this.getBestBackendName();\n } else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') {\n const webGLVersion = this.get('WEBGL_VERSION');\n\n if (webGLVersion === 0) {\n return 0;\n }\n return getWebGLDisjointQueryTimerVersion(webGLVersion);\n } else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') {\n return this.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n !device_util.isMobile();\n } else if (feature === 'HAS_WEBGL') {\n return this.get('WEBGL_VERSION') > 0;\n } else if (feature === 'WEBGL_VERSION') {\n if (isWebGLVersionEnabled(2)) {\n return 2;\n } else if (isWebGLVersionEnabled(1)) {\n return 1;\n }\n return 0;\n } else if (feature === 'WEBGL_RENDER_FLOAT32_ENABLED') {\n return isRenderToFloatTextureEnabled(this.get('WEBGL_VERSION'));\n } else if (feature === 'WEBGL_DOWNLOAD_FLOAT_ENABLED') {\n return isDownloadFloatTextureEnabled(this.get('WEBGL_VERSION'));\n } else if (feature === 'WEBGL_FENCE_API_ENABLED') {\n return isWebGLFenceEnabled(this.get('WEBGL_VERSION'));\n } else if (feature === 'WEBGL_SIZE_UPLOAD_UNIFORM') {\n // Use uniform uploads only when 32bit floats are supported. In 16bit\n // environments there are problems with comparing a 16bit texture value\n // with a 32bit uniform value.\n const useUniforms = this.get('WEBGL_RENDER_FLOAT32_ENABLED');\n return useUniforms ? 4 : 0;\n } else if (feature === 'TEST_EPSILON') {\n return this.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n TEST_EPSILON_FLOAT16;\n } else if (feature === 'EPSILON') {\n return this.backend.floatPrecision() === 32 ? EPSILON_FLOAT32 :\n EPSILON_FLOAT16;\n } else if (feature === 'PROD') {\n return false;\n } else if (feature === 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY') {\n return !this.get('PROD');\n } else if (feature === 'DEPRECATION_WARNINGS_ENABLED') {\n return true;\n }\n throw new Error(`Unknown feature ${feature}.`);\n }\n\n setFeatures(features: Features) {\n this.features = Object.assign({}, features);\n }\n\n reset() {\n this.features = getFeaturesFromURL();\n if (this.globalEngine != null) {\n this.globalEngine = null;\n }\n }\n\n get backend(): KernelBackend {\n return this.engine.backend;\n }\n\n findBackend(name: string): KernelBackend {\n if (!(name in this.registry)) {\n return null;\n }\n return this.registry[name].backend;\n }\n\n /**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return an instance of the backend.\n * @param priority The priority of the backend (higher = more important).\n * In case multiple backends are registered, the priority is used to find\n * the best backend. Defaults to 1.\n * @return False if the creation/registration failed. True otherwise.\n */\n registerBackend(\n name: string, factory: () => KernelBackend, priority = 1,\n setTensorTrackerFn?: (f: () => TensorTracker) => void): boolean {\n if (name in this.registry) {\n console.warn(\n `${name} backend was already registered. Reusing existing backend`);\n if (setTensorTrackerFn != null) {\n setTensorTrackerFn(() => this.engine);\n }\n return false;\n }\n try {\n const backend = factory();\n backend.setDataMover(\n {moveData: (dataId: DataId) => this.engine.moveData(dataId)});\n this.registry[name] = {backend, priority};\n return true;\n } catch (err) {\n console.warn(`Registration of backend ${name} failed`);\n console.warn(err.stack || err.message);\n return false;\n }\n }\n\n removeBackend(name: string): void {\n if (!(name in this.registry)) {\n throw new Error(`${name} backend not found in registry`);\n }\n this.registry[name].backend.dispose();\n delete this.registry[name];\n }\n\n get engine(): Engine {\n this.initEngine();\n return this.globalEngine;\n }\n\n private initEngine() {\n if (this.globalEngine == null) {\n this.backendName = this.get('BACKEND');\n const backend = this.findBackend(this.backendName);\n this.globalEngine =\n new Engine(backend, false /* safeMode */, () => this.get('DEBUG'));\n }\n }\n}\n\nfunction getGlobalNamespace(): {ENV: Environment} {\n // tslint:disable-next-line:no-any\n let ns: any;\n if (typeof (window) !== 'undefined') {\n ns = window;\n } else if (typeof (process) !== 'undefined') {\n ns = process;\n } else {\n throw new Error('Could not find a global object');\n }\n return ns;\n}\n\nfunction getOrMakeEnvironment(): Environment {\n const ns = getGlobalNamespace();\n if (ns.ENV == null) {\n ns.ENV = new Environment(getFeaturesFromURL());\n setTensorTracker(() => ns.ENV.engine);\n }\n return ns.ENV;\n}\n\n/** Enables production mode which disables safety checks to gain performance */\nexport function enableProdMode(): void {\n ENV.set('PROD', true);\n}\n\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings(): void {\n ENV.set('DEPRECATION_WARNINGS_ENABLED', false);\n console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg: string) {\n if (ENV.get('DEPRECATION_WARNINGS_ENABLED')) {\n console.warn(\n msg + ' You can disable deprecation warnings with ' +\n 'tf.disableDeprecationWarnings().');\n }\n}\nsetDeprecationWarningFn(deprecationWarn);\n\nexport let ENV = getOrMakeEnvironment();\n","../src/gradients.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {CustomGradientFunc, ScopeFn} from './engine';\nimport {ENV} from './environment';\nimport {Scalar, Tensor, Variable} from './tensor';\nimport {NamedTensorMap, TensorContainer} from './tensor_types';\nimport * as util from './util';\n\n/**\n * Create a new gradient scope. Similar to scope, but forces all inner scopes\n * to not clean up so that gradient operations can be used inside of this\n * scope.\n * @param nameOrScopeFn The name of the scope, or the function to execute.\n * If a name is provided, the 2nd argument should be the function.\n * If a name is provided, and debug mode is on, the timing and the memory\n * usage of the function will be tracked and displayed on the console\n * using the provided name.\n * @param scopeFn The function to execute.\n */\nfunction gradScope(\n nameOrScopeFn: string|ScopeFn, scopeFn?: ScopeFn): T {\n return ENV.engine.tidy(nameOrScopeFn, scopeFn, true /* gradScope */);\n}\n\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction grad(f: (x: I) => O): (\n x: I, dy?: O) => I {\n util.assert(util.isFunction(f), 'The f passed in grad(f) must be a function');\n return (x: I, dy?: O): I => {\n util.assert(\n x instanceof Tensor, 'The x passed in grad(f)(x) must be a tensor');\n util.assert(\n dy == null || dy instanceof Tensor,\n 'The dy passed in grad(f)(x, dy) must be a tensor');\n return ENV.engine.tidy(() => {\n const {value, grads} = ENV.engine.gradients(() => f(x), [x], dy);\n if (dy != null) {\n util.assertShapesMatch(\n value.shape, dy.shape,\n 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n 'returned by f(x)');\n }\n checkGrads(grads);\n return grads[0] as I;\n });\n };\n}\n\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction grads(f: (...args: Tensor[]) => O): (\n args: Tensor[], dy?: O) => Tensor[] {\n util.assert(\n util.isFunction(f), 'The f passed in grads(f) must be a function');\n return (args: Tensor[], dy?: O): Tensor[] => {\n util.assert(\n Array.isArray(args) && args.every(arg => arg instanceof Tensor),\n 'The args passed in grads(f)(args) must be an array of tensors');\n util.assert(\n dy == null || dy instanceof Tensor,\n 'The dy passed in grads(f)(args, dy) must be a tensor');\n return ENV.engine.tidy(() => {\n const {value, grads} = ENV.engine.gradients(() => f(...args), args, dy);\n if (dy != null) {\n util.assertShapesMatch(\n value.shape, dy.shape,\n 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n 'match the shape returned by f([x1,...])');\n }\n checkGrads(grads);\n return grads;\n });\n };\n}\n\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction valueAndGrad(f: (x: I) => O): (\n x: I, dy?: O) => {\n value: O;\n grad: I;\n} {\n util.assert(\n util.isFunction(f), 'The f passed in valueAndGrad(f) must be a function');\n return (x: I, dy?: O) => {\n util.assert(\n x instanceof Tensor,\n 'The x passed in valueAndGrad(f)(x) must be a tensor');\n util.assert(\n dy == null || dy instanceof Tensor,\n 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n const {grads, value} = ENV.engine.gradients(() => f(x), [x], dy);\n checkGrads(grads);\n return {grad: grads[0] as I, value: value as O};\n };\n}\n\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction valueAndGrads(f: (...args: Tensor[]) => O): (\n args: Tensor[], dy?: O) => {\n grads: Tensor[];\n value: O;\n} {\n util.assert(\n util.isFunction(f),\n 'The f passed in valueAndGrads(f) must be a function');\n return (args: Tensor[], dy?: O) => {\n util.assert(\n Array.isArray(args) && args.every(arg => arg instanceof Tensor),\n 'The args passed in valueAndGrads(f)(args) must be array of tensors');\n util.assert(\n dy == null || dy instanceof Tensor,\n 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n const res = ENV.engine.gradients(() => f(...args), args, dy);\n if (dy != null) {\n util.assertShapesMatch(\n res.value.shape, dy.shape,\n 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n 'match the shape returned by f([x1,...])');\n }\n checkGrads(res.grads);\n return res;\n };\n}\n\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of trainable variables. Defaults to all variables.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction variableGrads(f: () => Scalar, varList?: Variable[]):\n {value: Scalar, grads: NamedTensorMap} {\n util.assert(\n util.isFunction(f),\n 'The f passed in variableGrads(f) must be a function');\n util.assert(\n varList == null ||\n Array.isArray(varList) && varList.every(v => v instanceof Variable),\n 'The varList passed in variableGrads(f, varList) must be an array ' +\n 'of variables');\n if (varList == null) {\n // Get all of the trainable variables.\n varList = [];\n for (const varName in ENV.engine.registeredVariables) {\n varList.push(ENV.engine.registeredVariables[varName]);\n }\n }\n // Prune non-trainable variables.\n const originalVarCount = varList.length;\n varList = varList.filter(variable => variable.trainable);\n util.assert(\n varList.length > 0,\n `variableGrads() expects at least one of the input variables to be ` +\n `trainable, but none of the ${originalVarCount} variables is ` +\n `trainable.`);\n\n const allowNoGradients = true;\n const {value, grads} =\n ENV.engine.gradients(f, varList, null, allowNoGradients);\n\n util.assert(\n grads.some(g => g != null),\n 'Cannot find a connection between any variable and the result of the ' +\n 'loss function y=f(x). Please make sure the operations that use ' +\n 'variables are inside the function f passed to minimize().');\n util.assert(\n value.rank === 0,\n `The f passed in variableGrads(f) must return a scalar, but it ` +\n `returned a rank-${value.rank} tensor`);\n\n const namedGrads: NamedTensorMap = {};\n varList.forEach((v, i) => {\n if (grads[i] != null) {\n namedGrads[v.name] = grads[i];\n }\n });\n return {value, grads: namedGrads};\n}\n\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs) => {value: Tensor, gradFunc: dy => Tensor[]}` and returns\n * another function `g(...inputs)` which takes the same inputs as `f`. When\n * called, `g` returns `f().value`. In backward mode, custom gradients with\n * respect to each input of `f` are computed using `f().gradFunc`.\n *\n * ```js\n * const customOp = tf.customGrad(x => {\n * // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n * return {value: x.square(), gradFunc: dy => [dy.mul(x.abs())]};\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n * `{value: Tensor, gradFunc: (dy) => Tensor[]}`, where `gradFunc` returns\n * the custom gradients of `f` with respect to its inputs.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction customGrad(f: CustomGradientFunc):\n (...args: Tensor[]) => T {\n return ENV.engine.customGrad(f);\n}\n\nfunction checkGrads(grads: Tensor[]) {\n const numNullGradients = grads.filter(g => g == null).length;\n if (numNullGradients > 0) {\n throw new Error(\n `Cannot compute gradient of y=f(x) with respect to x. Make sure that\n the f you passed encloses all operations that lead from x to y.`);\n }\n}\n\nexport {\n gradScope,\n customGrad,\n variableGrads,\n valueAndGrad,\n valueAndGrads,\n grad,\n grads,\n};\n","../src/globals.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Environment} from './environment';\nexport {customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads} from './gradients';\n\nexport const tidy = Environment.tidy;\nexport const keep = Environment.keep;\nexport const dispose = Environment.dispose;\nexport const time = Environment.time;\nexport const profile = Environment.profile;","../src/log.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\n\nexport function warn(...msg: Array<{}>): void {\n if (!ENV.get('IS_TEST')) {\n console.warn(...msg);\n }\n}\n\nexport function log(...msg: Array<{}>): void {\n if (!ENV.get('IS_TEST')) {\n console.log(...msg);\n }\n}\n","../src/ops/array_ops_util.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(\n inputShape: number[], blockShape: number[], prod: number,\n batchToSpace = true): number[] {\n let reshaped: number[] = [];\n if (batchToSpace) {\n reshaped = reshaped.concat(blockShape.slice(0));\n reshaped.push(inputShape[0] / prod);\n reshaped = reshaped.concat(inputShape.slice(1));\n } else {\n reshaped = reshaped.concat(inputShape[0]);\n const spatialLength = blockShape.length;\n for (let i = 0; i < spatialLength; ++i) {\n reshaped =\n reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n }\n reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n }\n return reshaped;\n}\n\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(\n reshapedRank: number, blockShapeRank: number,\n batchToSpace = true): number[] {\n const permuted = [];\n if (batchToSpace) {\n permuted.push(blockShapeRank);\n for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n if (i <= 2 * blockShapeRank) {\n permuted.push(i);\n permuted.push(i - (blockShapeRank + 1));\n } else {\n permuted.push(i);\n }\n }\n } else {\n const permutedBeforeBatch = [];\n const permutedAfterBatch = [];\n for (let i = 1; i < reshapedRank; ++i) {\n if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n permutedAfterBatch.push(i);\n } else {\n permutedBeforeBatch.push(i);\n }\n }\n permuted.push(...permutedBeforeBatch);\n permuted.push(0);\n permuted.push(...permutedAfterBatch);\n }\n return permuted;\n}\n\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied. The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(\n inputShape: number[], blockShape: number[], prod: number,\n batchToSpace = true): number[] {\n const reshapedPermuted = [];\n\n if (batchToSpace) {\n reshapedPermuted.push(inputShape[0] / prod);\n } else {\n reshapedPermuted.push(inputShape[0] * prod);\n }\n\n for (let i = 1; i < inputShape.length; ++i) {\n if (i <= blockShape.length) {\n if (batchToSpace) {\n reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n } else {\n reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n }\n } else {\n reshapedPermuted.push(inputShape[i]);\n }\n }\n\n return reshapedPermuted;\n}\n\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(\n crops: number[][], blockShape: number): number[] {\n const sliceBeginCoords = [0];\n for (let i = 0; i < blockShape; ++i) {\n sliceBeginCoords.push(crops[i][0]);\n }\n return sliceBeginCoords;\n}\n\n/**\n * Converts the crops argument into the size of a slice operation. When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(\n uncroppedShape: number[], crops: number[][], blockShape: number): number[] {\n const sliceSize = uncroppedShape.slice(0, 1);\n for (let i = 0; i < blockShape; ++i) {\n sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n }\n\n return sliceSize;\n}\n","../src/ops/axis_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes: number[], rank: number): boolean {\n for (let i = 0; i < axes.length; ++i) {\n if (axes[axes.length - i - 1] !== rank - 1 - i) {\n return false;\n }\n }\n return true;\n}\n\nexport function combineLocations(\n outputLoc: number[], reduceLoc: number[], axes: number[]): number[] {\n const rank = outputLoc.length + reduceLoc.length;\n const loc = [];\n let outIdx = 0;\n let reduceIdx = 0;\n   for (let dim = 0; dim < rank; dim++) {\n if (axes.indexOf(dim) === -1) {\n loc.push(outputLoc[outIdx++]);\n } else {\n loc.push(reduceLoc[reduceIdx++]);\n }\n }\n return loc;\n}\n\nexport function computeOutAndReduceShapes(\n aShape: number[], axes: number[]): [number[], number[]] {\n const outShape = [];\n const rank = aShape.length;\n for (let dim = 0; dim < rank; dim++) {\n if (axes.indexOf(dim) === -1) {\n outShape.push(aShape[dim]);\n }\n }\n const reduceShape = axes.map(dim => aShape[dim]);\n return [outShape, reduceShape];\n}\n\nexport function expandShapeToKeepDim(\n shape: number[], axes: number[]): number[] {\n const reduceSubShape = axes.map(x => 1);\n return combineLocations(shape, reduceSubShape, axes);\n}\n\nexport function assertAxesAreInnerMostDims(\n msg: string, axes: number[], rank: number): void {\n util.assert(\n axesAreInnerMostDims(axes, rank),\n `${msg} supports only inner-most axes for now. ` +\n `Got axes ${axes} and rank-${rank} input.`);\n}\n\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes: number[], rank: number): number[]|\n null {\n if (axesAreInnerMostDims(axes, rank)) {\n return null;\n }\n const result: number[] = [];\n for (let i = 0; i < rank; ++i) {\n if (axes.indexOf(i) === -1) {\n result.push(i);\n }\n }\n axes.forEach(axis => result.push(axis));\n return result;\n}\n\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes: number[]): number[] {\n return axes.map((axis, i) => [i, axis])\n .sort((a, b) => a[1] - b[1])\n .map(x => x[0]);\n}\n\nexport function getInnerMostAxes(numAxes: number, rank: number): number[] {\n const res: number[] = [];\n for (let i = rank - numAxes; i < rank; ++i) {\n res.push(i);\n }\n return res;\n}\n","../src/ops/concat_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\nexport function assertParamsConsistent(shapes: number[][], axis: number) {\n const rank = shapes[0].length;\n shapes.forEach((shape, i) => {\n util.assert(\n shape.length === rank,\n `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n `as the rank of the rest (${rank})`);\n });\n\n util.assert(\n axis >= 0 && axis < rank,\n `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n\n const firstShape = shapes[0];\n shapes.forEach((shape, i) => {\n for (let r = 0; r < rank; r++) {\n util.assert(\n (r === axis) || (shape[r] === firstShape[r]),\n `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n `does not match the shape of the rest (${firstShape}) ` +\n `along the non-concatenated axis ${i}.`);\n }\n });\n}\n\nexport function computeOutShape(shapes: number[][], axis: number): number[] {\n const outputShape = shapes[0].slice();\n for (let i = 1; i < shapes.length; i++) {\n outputShape[axis] += shapes[i][axis];\n }\n return outputShape;\n}\n","../src/ops/gather_nd_util.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\nimport {computeStrides} from '../util';\n\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(\n tensor: Tensor, indices: Tensor): [number[], number, number, number[]] {\n if (tensor.rank < 1) {\n throw new Error(\n 'tf.gatherND() expects the input to be rank 1 or higher,' +\n ` but the rank was ${tensor.rank}.`);\n }\n if (indices.rank < 1) {\n throw new Error(\n 'tf.gatherND() expects the indices to be rank 1 or higher,' +\n ` but the rank was ${indices.rank}.`);\n }\n if (indices.dtype !== 'int32') {\n throw new Error(\n 'tf.gatherND() expects the indices to be int32 type,' +\n ` but the dtype was ${indices.dtype}.`);\n }\n if (indices.shape[indices.rank - 1] > tensor.rank) {\n throw new Error(\n 'index innermost dimension length must be <= tensor rank; saw: ' +\n `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n }\n\n if (tensor.size === 0) {\n throw new Error(\n 'Requested more than 0 entries, but input is empty.' +\n ` Input shape: ${tensor.shape}.`);\n }\n\n const indicesShape = indices.shape;\n const sliceRank = indicesShape[indicesShape.length - 1];\n\n // The result shape is\n // indices.shape[:-1] + params.shape[indices.shape[-1]:]\n let nResult = 1;\n for (let i = 0; i < indicesShape.length - 1; ++i) {\n nResult *= indicesShape[i];\n }\n\n const inputShape = tensor.shape;\n\n const resultShape = indicesShape.slice();\n resultShape.pop();\n\n let sliceSize = 1;\n for (let i = sliceRank; i < tensor.rank; ++i) {\n sliceSize *= inputShape[i];\n resultShape.push(inputShape[i]);\n }\n\n const strides =\n [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n 1].slice(0, sliceRank);\n\n return [resultShape, nResult, sliceSize, strides];\n}\n","../src/ops/reduce_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport {nearestDivisor} from '../util';\n\nexport const PARALLELIZE_THRESHOLD = 30;\n\nexport interface ReduceInfo {\n windowSize: number;\n batchSize: number;\n inSize: number;\n}\n\nexport function computeOptimalWindowSize(inSize: number): number {\n if (inSize <= PARALLELIZE_THRESHOLD) {\n return inSize;\n }\n return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n","../src/ops/scatter_nd_util.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\nimport {computeStrides, sizeFromShape} from '../util';\n\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(\n shape: number[], indices: Tensor, updates: Tensor) {\n const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n\n const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n `, indices.shape: ${indices.shape}, shape: ${shape}` +\n `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n\n if (updates.rank < batchDim) {\n throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n }\n if (shape.length < sliceDim + (updates.rank - batchDim)) {\n throw new Error(\n shapeError +\n ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n }\n if (updates.rank !== batchDim + shape.length - sliceDim) {\n throw new Error(\n shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n }\n for (let d = 0; d < batchDim; ++d) {\n if (updates.shape[d] !== indices.shape[d]) {\n throw new Error(\n shapeError +\n ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${\n indices.shape[d]}).`);\n }\n }\n for (let d = 0; d < updates.rank - batchDim; ++d) {\n if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n throw new Error(\n shapeError +\n ` updates.shape[${d + batchDim}] (${\n updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${\n shape[d + batchDim]})`);\n }\n }\n}\n\nexport interface ScatterShapeInfo {\n sliceRank: number;\n numUpdates: number;\n sliceSize: number;\n strides: number[];\n outputSize: number;\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(\n updates: Tensor, indices: Tensor, shape: number[]) {\n if (indices.rank < 1) {\n throw new Error(\n 'tf.scatterND() expects the indices to be rank 1 or higher,' +\n ` but the rank was ${indices.rank}.`);\n }\n if (updates.rank < 1) {\n throw new Error(\n 'tf.scatterND() expects the updates to be rank 1 or higher,' +\n ` but the rank was ${updates.rank}.`);\n }\n if (indices.dtype !== 'int32') {\n throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${\n indices.dtype}`);\n }\n if (shape.length < 1) {\n throw new Error(\n `Output rank must be greater or equal to 1, but got shape: ${shape}`);\n }\n\n if (shape.length === 0) {\n if (indices.size === 0) {\n throw new Error(`Indices specified for empty output. indices shape: ${\n indices.shape}`);\n }\n if (updates.size === 0) {\n throw new Error(`Updates specified for empty output. updates shape: ${\n updates.shape}`);\n }\n }\n\n validateUpdateShape(shape, indices, updates);\n}\n\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(\n updates: Tensor, indices: Tensor, shape: number[]): ScatterShapeInfo {\n // Calculate the number of dimensions in indices\n const sliceRank = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n\n // Calculate the number of elements that make up each slice of our updated\n // tensor. This allows us to work with flattened tensors and copy over whole\n // slices at a time.\n const totalNd = shape.length;\n\n let sliceSize = 1;\n for (let i = sliceRank; i < totalNd; ++i) {\n sliceSize *= shape[i];\n }\n\n const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n const numUpdates = indices.size / safeSliceDim;\n\n const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n const outputSize = sizeFromShape(shape);\n return {sliceRank, numUpdates, sliceSize, strides, outputSize};\n}\n","../src/ops/segment_util.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {nearestDivisor} from '../util';\n\nimport {PARALLELIZE_THRESHOLD} from './reduce_util';\n\nexport interface SegOpInfo {\n windowSize: number;\n batchSize: number;\n inSize: number;\n numSegments: number;\n}\n\nexport function segOpComputeOptimalWindowSize(\n inSize: number, numSegments: number): number {\n let done = false;\n let res;\n\n if (inSize <= PARALLELIZE_THRESHOLD) {\n res = inSize;\n done = true;\n } else {\n res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n }\n\n while (!done) {\n if (res > numSegments || res === inSize) {\n done = true;\n break;\n } else {\n res = nearestDivisor(inSize, res + 1);\n }\n }\n return res;\n}\n\nexport function computeOutShape(\n aShape: number[], axis: number, numSegments: number): number[] {\n const outShape = [];\n const rank = aShape.length;\n for (let dim = 0; dim < rank; dim++) {\n if (dim !== axis) {\n outShape.push(aShape[dim]);\n } else {\n outShape.push(numSegments);\n }\n }\n return outShape;\n}\n\nexport interface GatherOpShapeInfo {\n batchSize: number;\n sliceSize: number;\n dimSize: number;\n outputShape: number[];\n}\nexport function collectGatherOpShapeInfo(\n x: Tensor, indices: Tensor, axis: number): GatherOpShapeInfo {\n const dimSize = x.shape[axis];\n\n const outputShape: number[] = [];\n let batchSize = 1;\n let sliceSize = 1;\n for (let i = 0; i < axis; i++) {\n outputShape.push(x.shape[i]);\n batchSize *= x.shape[i];\n }\n\n for (let i = 0; i < indices.rank; i++) {\n outputShape.push(indices.shape[i]);\n }\n\n for (let i = axis + 1; i < x.rank; i++) {\n outputShape.push(x.shape[i]);\n sliceSize *= x.shape[i];\n }\n\n return {batchSize, sliceSize, dimSize, outputShape};\n}\n","../src/ops/slice_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport * as util from '../util';\n\nexport function assertParamsValid(\n input: Tensor, begin: number[], size: number[]): void {\n util.assert(\n input.rank === begin.length,\n `Error in slice${input.rank}D: Length of begin ${begin} must ` +\n `match the rank of the array (${input.rank}).`);\n util.assert(\n input.rank === size.length,\n `Error in slice${input.rank}D: Length of size ${size} must ` +\n `match the rank of the array (${input.rank}).`);\n\n for (let i = 0; i < input.rank; ++i) {\n util.assert(\n begin[i] + size[i] <= input.shape[i],\n `Error in slice${input.rank}D: begin[${i}] + size[${i}] ` +\n `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${\n input.shape[i]})`);\n }\n}\n\n/**\n * Calculate the start index and output tensor shape for strided slice op.\n * @returns array of [startIndex, size, shrinkAxis]\n */\nexport function getStridedSlicedInfo(\n shape: number[], begin: number[], end: number[], strides: number[],\n beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n shrinkAxisMask = 0): [number[], number[], number[]] {\n if (ellipsisMask !== 0) {\n throw new Error('ellipsis mask is not yet supported');\n }\n if (newAxisMask !== 0) {\n throw new Error('new axis mask is not yet supported');\n }\n // Note that the axis orders are reversed for runtime ops, so the indices,\n // strides and masks must be as well too.\n const startIndex: number[] = [];\n const endIndex: number[] = [];\n const shrinkAxis: number[] = [];\n for (let i = 0; i < shape.length; i++) {\n startIndex[i] = startForAxis(beginMask, begin, strides, shape, i);\n endIndex[i] = stopForAxis(endMask, end, strides, shape, i);\n // When shrinking an axis, use startIndex + 1 for endIndex.\n // Check the axis bit from right of shrinkAxisMask\n if (shrinkAxisMask & 1 << i) {\n endIndex[i] = startIndex[i] + 1;\n shrinkAxis.push(i);\n }\n }\n\n let size = new Array(shape.length).fill(0);\n size = size.map((d, i) => {\n let count = 0;\n for (let start = startIndex[i];\n !(strides[i] > 0 ? start >= endIndex[i] : start <= endIndex[i]);\n start += strides[i]) {\n count += 1;\n }\n return count;\n });\n\n return [startIndex, size, shrinkAxis];\n}\n\nexport function startForAxis(\n beginMask: number, startIndices: number[], strides: number[],\n inputShape: number[], axis: number): number {\n // Begin with the specified index\n let start = startIndices[axis];\n\n // Check the axis bit from right of beginMask\n if (beginMask & 1 << axis) {\n if (strides[axis] > 0) {\n // Forward iteration - use the first element. These values will get\n // clamped below (Note: We could have set them to 0 and axis_size-1, but\n // use lowest() and max() to maintain symmetry with StopForAxis())\n start = Number.MIN_SAFE_INTEGER;\n } else {\n // Backward iteration - use the last element.\n start = Number.MAX_SAFE_INTEGER;\n }\n }\n\n // Handle negative indices\n const axisSize = inputShape[axis];\n if (start < 0) {\n start += axisSize;\n }\n\n // Clamping\n start = util.clamp(0, start, axisSize - 1);\n\n return start;\n}\n\nexport function stopForAxis(\n endMask: number, stopIndices: number[], strides: number[],\n inputShape: number[], axis: number): number {\n // Begin with the specified index\n let stop = stopIndices[axis];\n\n // Check the axis bit from right of endMask\n if (endMask & (1 << axis)) {\n if (strides[axis] > 0) {\n // Forward iteration - use the last element. These values will get\n // clamped below\n stop = Number.MAX_SAFE_INTEGER;\n } else {\n // Backward iteration - use the first element.\n stop = Number.MIN_SAFE_INTEGER;\n }\n }\n\n // Handle negative indices\n const axisSize = inputShape[axis];\n if (stop < 0) {\n stop += axisSize;\n }\n\n // Clamping\n // Because the end index points one past the last element, we need slightly\n // different clamping ranges depending on the direction.\n if (strides[axis] > 0) {\n // Forward iteration\n stop = util.clamp(0, stop, axisSize);\n } else {\n // Backward iteration\n stop = util.clamp(-1, stop, axisSize - 1);\n }\n\n return stop;\n}\n\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(\n shape: number[], begin: number[], size: number[]) {\n // Index of the first axis that has size > 1.\n let firstNonOneAxis = size.length;\n for (let i = 0; i < size.length; i++) {\n if (size[i] > 1) {\n firstNonOneAxis = i;\n break;\n }\n }\n\n for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n if (begin[i] > 0 || size[i] !== shape[i]) {\n return false;\n }\n }\n return true;\n}\n\nexport function computeFlatOffset(begin: number[], strides: number[]): number {\n let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n for (let i = 0; i < begin.length - 1; i++) {\n flatOffset += begin[i] * strides[i];\n }\n return flatOffset;\n}\n","../src/tensor_util_env.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\nimport {Tensor} from './tensor';\nimport {DataType, TensorLike, TypedArray} from './types';\nimport {assert, flatten, inferDtype, isTypedArray, toTypedArray} from './util';\n\nexport function inferShape(val: TensorLike): number[] {\n let firstElem: typeof val = val;\n\n if (isTypedArray(val)) {\n return [(val as TypedArray).length];\n }\n if (!Array.isArray(val)) {\n return []; // Scalar.\n }\n const shape: number[] = [];\n\n while (Array.isArray(firstElem) || isTypedArray(firstElem)) {\n shape.push(firstElem.length);\n firstElem = firstElem[0];\n }\n if (Array.isArray(val) && ENV.get('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n deepAssertShapeConsistency(val, shape, []);\n }\n\n return shape;\n}\n\nfunction deepAssertShapeConsistency(\n val: TensorLike, shape: number[], indices: number[]) {\n indices = indices || [];\n if (!(Array.isArray(val)) && !isTypedArray(val)) {\n assert(\n shape.length === 0,\n () => `Element arr[${indices.join('][')}] is a primitive, ` +\n `but should be an array/TypedArray of ${shape[0]} elements`);\n return;\n }\n assert(\n shape.length > 0,\n () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n `but is an array of ${val.length} elements`);\n assert(\n val.length === shape[0],\n () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n `elements, but has ${val.length} elements`);\n const subShape = shape.slice(1);\n for (let i = 0; i < val.length; ++i) {\n deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n }\n}\n\nfunction assertDtype(\n expectedDtype: DataType|'numeric', actualDType: DataType, argName: string,\n functionName: string) {\n if (expectedDtype == null) {\n return;\n }\n if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n expectedDtype === 'numeric' && actualDType === 'string') {\n throw new Error(\n `Argument '${argName}' passed to '${functionName}' must ` +\n `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n }\n}\n\nexport function convertToTensor(\n x: T|TensorLike, argName: string, functionName: string,\n parseAsDtype: DataType|'numeric' = 'numeric'): T {\n if (x instanceof Tensor) {\n assertDtype(parseAsDtype, x.dtype, argName, functionName);\n return x;\n }\n let inferredDtype = inferDtype(x);\n // If the user expects a bool/int/float, use that info to update the\n // inferredDtype when it is not a string.\n if (inferredDtype !== 'string' &&\n ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n inferredDtype = parseAsDtype as DataType;\n }\n assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n\n if (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n typeof x !== 'boolean' && typeof x !== 'string') {\n throw new Error(\n `Argument '${argName}' passed to '${functionName}' must be a ` +\n `Tensor or TensorLike, but got '${(x as {}).constructor.name}'`);\n }\n const inferredShape = inferShape(x);\n if (!isTypedArray(x) && !Array.isArray(x)) {\n x = [x] as number[];\n }\n const values = inferredDtype !== 'string' ?\n toTypedArray(x, inferredDtype as DataType, ENV.get('DEBUG')) :\n flatten(x as string[]) as string[];\n return Tensor.make(inferredShape, {values}, inferredDtype);\n}\n\nexport function convertToTensorArray(\n arg: Array, argName: string, functionName: string): T[] {\n if (!Array.isArray(arg)) {\n throw new Error(\n `Argument ${argName} passed to ${functionName} must be a ` +\n '`Tensor[]` or `TensorLike[]`');\n }\n const tensors = arg as T[];\n return tensors.map(\n (t, i) => convertToTensor(t, `${argName}[${i}]`, functionName));\n}\n","../src/ops/operation.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\n\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f: {[name: string]: T}): T {\n const keys = Object.keys(f);\n if (keys.length !== 1) {\n throw new Error(\n `Please provide an object with a single key ` +\n `(operation name) mapping to a function. Got an object with ` +\n `${keys.length} keys.`);\n }\n\n let opName = keys[0];\n const fn = f[opName];\n\n // Strip the underscore from the end of the function name.\n if (opName.endsWith('_')) {\n opName = opName.substring(0, opName.length - 1);\n }\n\n // tslint:disable-next-line:no-any\n const f2 = (...args: any[]) => {\n ENV.engine.startScope(opName);\n try {\n const result = fn(...args);\n if (result instanceof Promise) {\n console.error('Cannot return a Promise inside of tidy.');\n }\n ENV.engine.endScope(result);\n return result;\n } catch (ex) {\n ENV.engine.endScope(null);\n throw ex;\n }\n };\n Object.defineProperty(f2, 'name', {value: opName, configurable: true});\n\n // tslint:disable-next-line:no-any\n return f2 as any as T;\n}\n","../src/ops/softmax.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../gradients';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print(); // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print(); // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n * which indicates the last dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction softmax_(logits: T|TensorLike, dim = -1): T {\n const $logits = convertToTensor(logits, 'logits', 'softmax');\n\n if (dim === -1) {\n dim = $logits.rank - 1;\n }\n if (dim !== $logits.rank - 1) {\n throw Error(\n 'Softmax along a non-last dimension is not yet supported. ' +\n `Logits was rank ${$logits.rank} and dim was ${dim}`);\n }\n\n const customOp = customGrad(logits => {\n // Do it in log space for numerical stability.\n // exp(X - logSumExp(X))\n const keepDims = true;\n const lse = logits.logSumExp([dim], keepDims);\n const logResult = logits.toFloat().sub(lse);\n const y = logResult.exp() as T;\n\n const gradFunc = (dy: T) => {\n const dyTimesY = dy.mul(y);\n const keepDims = true;\n return dyTimesY.sub(dyTimesY.sum([dim], keepDims).mul(y));\n };\n\n return {value: y, gradFunc};\n });\n\n return customOp($logits);\n}\n\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print(); // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print(); // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n * which indicates the last dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction logSoftmax_(logits: T|TensorLike, axis = -1): T {\n const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n\n if (axis === -1) {\n axis = $logits.rank - 1;\n }\n if (axis !== $logits.rank - 1) {\n throw Error(\n 'Log Softmax along a non-last dimension is not yet supported. ' +\n `Logits was rank ${$logits.rank} and axis was ${axis}`);\n }\n\n const customOp = customGrad(logits => {\n const keepDims = true;\n const xMax = logits.max(axis, true);\n const shifted = logits.sub(xMax);\n const value =\n shifted.toFloat().sub(shifted.exp().sum(axis, keepDims).log()) as T;\n\n const gradFunc = (dy: T) => {\n const softmax = value.exp();\n return dy.sub(dy.sum(axis, keepDims).mul(softmax));\n };\n\n return {value, gradFunc};\n });\n\n return customOp($logits);\n}\n\nexport const softmax = op({softmax_});\nexport const logSoftmax = op({logSoftmax_});\n","../src/ops/complex_ops.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction complex_(real: T|TensorLike, imag: T|TensorLike): T {\n const $real = convertToTensor(real, 'real', 'complex');\n const $imag = convertToTensor(imag, 'imag', 'complex');\n util.assertShapesMatch(\n $real.shape, $imag.shape,\n `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n `must match in call to tf.complex().`);\n\n return ENV.engine.runKernel(\n backend => backend.complex($real, $imag), {$real, $imag}) as T;\n}\n\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction real_(input: T|TensorLike): T {\n const $input = convertToTensor(input, 'input', 'real');\n\n return ENV.engine.runKernel(backend => backend.real($input), {$input}) as T;\n}\n\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction imag_(input: T|TensorLike): T {\n const $input = convertToTensor(input, 'input', 'imag');\n\n return ENV.engine.runKernel(backend => backend.imag($input), {$input}) as T;\n}\n\nexport const complex = op({complex_});\nexport const real = op({real_});\nexport const imag = op({imag_});\n","../src/ops/tensor_ops.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, Tensor6D} from '../tensor';\nimport {convertToTensor, inferShape} from '../tensor_util_env';\nimport {TensorLike, TensorLike1D, TensorLike2D, TensorLike3D, TensorLike4D, TensorLike5D, TensorLike6D, TypedArray} from '../types';\nimport {DataType, Rank, ShapeMap} from '../types';\nimport {assert, assertNonNull, flatten, getArrayFromDType, inferDtype, isTypedArray, makeOnesTypedArray, makeZerosTypedArray, sizeFromShape, toTypedArray} from '../util';\n\nimport {complex} from './complex_ops';\nimport {op} from './operation';\n\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor(\n values: TensorLike, shape?: ShapeMap[R], dtype?: DataType): Tensor {\n if (dtype == null) {\n dtype = inferDtype(values);\n }\n if (dtype === 'complex64') {\n throw new Error(\n `Cannot construct a complex64 tensor directly. ` +\n `Please use tf.complex(real, imag).`);\n }\n if (!isTypedArray(values) && !Array.isArray(values) &&\n typeof values !== 'number' && typeof values !== 'boolean' &&\n typeof values !== 'string') {\n throw new Error(\n 'values passed to tensor(values) must be a number/boolean/string or ' +\n 'an array of numbers/booleans/strings, or a TypedArray');\n }\n const inferredShape = inferShape(values);\n if (shape != null) {\n const providedSize = sizeFromShape(shape);\n const inferredSize = sizeFromShape(inferredShape);\n assert(\n providedSize === inferredSize,\n () =>\n `Based on the provided shape, [${shape}], the tensor should have ` +\n `${providedSize} values but has ${inferredSize}`);\n\n for (let i = 0; i < inferredShape.length; ++i) {\n const inferred = inferredShape[i];\n const flatDimsDontMatch = i === inferredShape.length - 1 ?\n inferred !== sizeFromShape(shape.slice(i)) :\n true;\n assert(\n inferredShape[i] === shape[i] || !flatDimsDontMatch,\n () => `Error creating a new Tensor. Inferred shape ` +\n `(${inferredShape}) does not match the provided ` +\n `shape (${shape}). `);\n }\n }\n\n if (!isTypedArray(values) && !Array.isArray(values)) {\n values = [values] as number[];\n }\n\n shape = shape || inferredShape;\n values = dtype !== 'string' ? toTypedArray(values, dtype, ENV.get('DEBUG')) :\n flatten(values as string[]) as string[];\n return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction scalar(value: number|boolean|string, dtype?: DataType): Scalar {\n if ((isTypedArray(value) || Array.isArray(value)) && dtype !== 'complex64') {\n throw new Error(\n 'Error creating a new Scalar: value must be a primitive ' +\n '(number|boolean|string)');\n }\n return tensor(value, [], dtype);\n}\n\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n * or a `TypedArray`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor1d(values: TensorLike1D, dtype?: DataType): Tensor1D {\n assertNonNull(values);\n const inferredShape = inferShape(values);\n if (inferredShape.length !== 1) {\n throw new Error('tensor1d() requires values to be a flat/TypedArray');\n }\n return tensor(values, inferredShape as [number], dtype);\n}\n\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n * `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor2d(\n values: TensorLike2D, shape?: [number, number],\n dtype?: DataType): Tensor2D {\n assertNonNull(values);\n if (shape != null && shape.length !== 2) {\n throw new Error('tensor2d() requires shape to have two numbers');\n }\n const inferredShape = inferShape(values);\n if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n throw new Error(\n 'tensor2d() requires values to be number[][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\n 'tensor2d() requires shape to be provided when `values` ' +\n 'are a flat/TypedArray');\n }\n shape = shape || inferredShape as [number, number];\n return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n * `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor3d(\n values: TensorLike3D, shape?: [number, number, number],\n dtype?: DataType): Tensor3D {\n assertNonNull(values);\n if (shape != null && shape.length !== 3) {\n throw new Error('tensor3d() requires shape to have three numbers');\n }\n const inferredShape = inferShape(values);\n if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n throw new Error(\n 'tensor3d() requires values to be number[][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\n 'tensor3d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n shape = shape || inferredShape as [number, number, number];\n return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor4d(\n values: TensorLike4D, shape?: [number, number, number, number],\n dtype?: DataType): Tensor4D {\n assertNonNull(values);\n if (shape != null && shape.length !== 4) {\n throw new Error('tensor4d() requires shape to have four numbers');\n }\n const inferredShape = inferShape(values);\n if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n throw new Error(\n 'tensor4d() requires values to be number[][][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\n 'tensor4d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n shape = shape || inferredShape as [number, number, number, number];\n return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor5d(\n values: TensorLike5D, shape?: [number, number, number, number, number],\n dtype?: DataType): Tensor5D {\n assertNonNull(values);\n if (shape != null && shape.length !== 5) {\n throw new Error('tensor5d() requires shape to have five numbers');\n }\n const inferredShape = inferShape(values);\n if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n throw new Error(\n 'tensor5d() requires values to be ' +\n 'number[][][][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\n 'tensor5d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n shape = shape || inferredShape as [number, number, number, number, number];\n return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor6d(\n values: TensorLike6D,\n shape?: [number, number, number, number, number, number],\n dtype?: DataType): Tensor6D {\n assertNonNull(values);\n if (shape != null && shape.length !== 6) {\n throw new Error('tensor6d() requires shape to have six numbers');\n }\n const inferredShape = inferShape(values);\n if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n throw new Error(\n 'tensor6d() requires values to be number[][][][][][] or ' +\n 'flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error(\n 'tensor6d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n shape = shape ||\n inferredShape as [number, number, number, number, number, number];\n return tensor(values, shape, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction ones(\n shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor {\n if (dtype === 'complex64') {\n const real = ones(shape, 'float32');\n const imag = ones(shape, 'float32');\n return complex(real, imag);\n }\n const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n * be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction zeros(\n shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor {\n if (dtype === 'complex64') {\n const real = zeros(shape, 'float32');\n const imag = zeros(shape, 'float32');\n return complex(real, imag);\n }\n const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction fill(\n shape: ShapeMap[R], value: number|string, dtype?: DataType): Tensor {\n dtype = dtype || inferDtype(value);\n const values = getArrayFromDType(dtype, sizeFromShape(shape)) as TypedArray;\n values.fill(value as number);\n return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction onesLike_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'onesLike');\n return ones($x.shape, $x.dtype) as T;\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction zerosLike_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'zerosLike');\n return zeros($x.shape, $x.dtype) as T;\n}\n\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction linspace(start: number, stop: number, num: number): Tensor1D {\n if (num === 0) {\n throw new Error('Cannot request zero samples');\n }\n\n const step = (stop - start) / (num - 1);\n\n const values = makeZerosTypedArray(num, 'float32');\n values[0] = start;\n for (let i = 1; i < values.length; i++) {\n values[i] = values[i - 1] + step;\n }\n\n return tensor1d(values, 'float32');\n}\n\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction range(\n start: number, stop: number, step = 1,\n dtype: 'float32'|'int32' = 'float32'): Tensor1D {\n if (step === 0) {\n throw new Error('Cannot have a step of zero');\n }\n\n const sameStartStop = start === stop;\n const increasingRangeNegativeStep = start < stop && step < 0;\n const decreasingRangePositiveStep = stop < start && step > 1;\n\n if (sameStartStop || increasingRangeNegativeStep ||\n decreasingRangePositiveStep) {\n return zeros([0], dtype);\n }\n\n const numElements = Math.abs(Math.ceil((stop - start) / step));\n const values = makeZerosTypedArray(numElements, dtype);\n\n if (stop < start && step === 1) {\n // Auto adjust the step's sign if it hasn't been set\n // (or was set to 1)\n step = -1;\n }\n\n values[0] = start;\n for (let i = 1; i < values.length; i++) {\n values[i] = values[i - 1] + step;\n }\n\n return tensor1d(values, dtype);\n}\n\nexport {\n fill,\n linspace,\n ones,\n range,\n scalar,\n tensor,\n tensor1d,\n tensor2d,\n tensor3d,\n tensor4d,\n tensor5d,\n tensor6d,\n zeros\n};\n\nexport const onesLike = op({onesLike_});\nexport const zerosLike = op({zerosLike_});\n","../src/kernels/backend.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport {Activation} from '../ops/fused_util';\nimport {DataId, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {DataType, DataValues, Rank, ShapeMap} from '../types';\n\n// Required information for all backends.\nexport interface BackendTimingInfo {\n kernelMs: number;\n getExtraProfileInfo?(): string; // a field for additional timing information\n // e.g. packing / unpacking for WebGL backend\n}\n\nexport interface TensorStorage {\n read(dataId: DataId): Promise;\n readSync(dataId: DataId): DataValues;\n disposeData(dataId: DataId): void;\n write(dataId: DataId, values: DataValues): void;\n fromPixels(\n pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n numChannels: number): Tensor3D;\n register(dataId: DataId, shape: number[], dtype: DataType): void;\n memory(): {unreliable: boolean;}; // Backend-specific information.\n}\n\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n private data = new WeakMap();\n\n constructor(private dataMover: DataMover) {}\n\n get(dataId: DataId) {\n if (!this.data.has(dataId)) {\n this.dataMover.moveData(dataId);\n }\n return this.data.get(dataId);\n }\n\n set(dataId: DataId, value: T): void {\n this.data.set(dataId, value);\n }\n\n has(dataId: DataId): boolean {\n return this.data.has(dataId);\n }\n\n delete(dataId: DataId): boolean {\n return this.data.delete(dataId);\n }\n}\n\nexport interface DataMover {\n /**\n * To be called by backends whenever they see a dataId that they don't own.\n * Upon calling this method, the mover will fetch the tensor from another\n * backend and register it with the current active backend.\n */\n moveData(dataId: DataId): void;\n}\n\nexport interface BackendTimer {\n time(f: () => void): Promise;\n}\n\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend implements TensorStorage, BackendTimer {\n time(f: () => void): Promise {\n throw new Error('Not yet implemented.');\n }\n read(dataId: object): Promise {\n throw new Error('Not yet implemented.');\n }\n readSync(dataId: object): DataValues {\n throw new Error('Not yet implemented.');\n }\n disposeData(dataId: object): void {\n throw new Error('Not yet implemented.');\n }\n write(dataId: object, values: DataValues): void {\n throw new Error('Not yet implemented.');\n }\n fromPixels(\n pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n numChannels: number): Tensor {\n throw new Error('Not yet implemented.');\n }\n register(dataId: object, shape: number[], dtype: DataType): void {\n throw new Error('Not yet implemented.');\n }\n memory(): {unreliable: boolean; reasons?: string[]} {\n throw new Error('Not yet implemented.');\n }\n /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n floatPrecision(): number {\n throw new Error('Not yet implemented');\n }\n\n batchMatMul(\n a: Tensor3D, b: Tensor3D, transposeA: boolean,\n transposeB: boolean): Tensor3D {\n throw new Error('Not yet implemented');\n }\n\n fusedBatchMatMul(\n a: Tensor3D, b: Tensor3D, transposeA: boolean, transposeB: boolean,\n bias?: Tensor, activation?: Activation): Tensor3D {\n throw new Error('Not yet implemented');\n }\n\n slice(x: T, begin: number[], size: number[]): T {\n throw new Error('Not yet implemented');\n }\n stridedSlice(\n x: T, begin: number[], end: number[], strides: number[],\n beginMask: number, endMask: number, ellipsisMask: number,\n newAxisMask: number, shrinkAxisMask: number): T {\n throw new Error('Not yet implemented');\n }\n unstack(x: Tensor, axis: number): Tensor[] {\n throw new Error('Not yet implemented');\n }\n reverse(a: T, axis: number[]): T {\n throw new Error('Not yet implemented');\n }\n\n concat(tensors: Tensor[], axis: number): Tensor {\n throw new Error('Not yet implemented');\n }\n\n neg(a: T): T {\n throw new Error('Not yet implemented');\n }\n\n add(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n addN(tensors: T[]): T {\n throw new Error('Not yet implemented');\n }\n subtract(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n multiply(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n realDivide(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n floorDiv(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n sum(x: Tensor, axes: number[]): Tensor {\n throw new Error('Not yet implemented');\n }\n prod(x: Tensor, axes: number[]): Tensor {\n throw new Error('Not yet implemented');\n }\n\n unsortedSegmentSum(\n x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n throw new Error('Not yet implemented');\n }\n\n argMin(x: Tensor, axis: number): Tensor {\n throw new Error('Not yet implemented');\n }\n argMax(x: Tensor, axis: number): Tensor {\n throw new Error('Not yet implemented');\n }\n\n equal(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n notEqual(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n less(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n lessEqual(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n greater(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n greaterEqual(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n logicalNot(a: T): T {\n throw new Error('Not yet implemented');\n }\n logicalAnd(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n logicalOr(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n where(condition: Tensor): Tensor2D {\n throw new Error('Not yet implemented');\n }\n select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n topk(x: T, k: number, sorted: boolean): [T, T] {\n throw new Error('Not yet implemented');\n }\n\n min(x: Tensor, axes: number[]): Tensor {\n throw new Error('Not yet implemented');\n }\n minimum(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n mod(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n max(x: Tensor, axes: number[]): Tensor {\n throw new Error('Not yet implemented');\n }\n maximum(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n all(x: Tensor, axes: number[]): Tensor {\n throw new Error('Not yet implemented');\n }\n any(x: Tensor, axes: number[]): Tensor {\n throw new Error('Not yet implemented');\n }\n\n squaredDifference(a: Tensor, b: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n ceil(x: T): T {\n throw new Error('Not yet implemented');\n }\n floor(x: T): T {\n throw new Error('Not yet implemented');\n }\n round(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n sign(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n pow(a: T, b: Tensor): T {\n throw new Error('Not yet implemented');\n }\n exp(x: T): T {\n throw new Error('Not yet implemented');\n }\n expm1(x: T): T {\n throw new Error('Not yet implemented');\n }\n log(x: T): T {\n throw new Error('Not yet implemented');\n }\n log1p(x: T): T {\n throw new Error('Not yet implemented');\n }\n sqrt(x: T): T {\n throw new Error('Not yet implemented');\n }\n rsqrt(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n square(x: T): T {\n throw new Error('Not yet implemented');\n }\n reciprocal(x: T): T {\n throw new Error('Not yet implemented');\n }\n relu(x: T): T {\n throw new Error('Not yet implemented');\n }\n prelu(x: T, a: T): T {\n throw new Error('Not yet implemented');\n }\n elu(x: T): T {\n throw new Error('Not yet implemented');\n }\n eluDer(dy: T, y: T): T {\n throw new Error('Not yet implemented');\n }\n selu(x: T): T {\n throw new Error('Not yet implemented');\n }\n int(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n clip(x: T, min: number, max: number): T {\n throw new Error('Not yet implemented');\n }\n\n abs(x: T): T {\n throw new Error('Not yet implemented');\n }\n complexAbs(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n sigmoid(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n softplus(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n sin(x: T): T {\n throw new Error('Not yet implemented');\n }\n cos(x: T): T {\n throw new Error('Not yet implemented');\n }\n tan(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n asin(x: T): T {\n throw new Error('Not yet implemented');\n }\n acos(x: T): T {\n throw new Error('Not yet implemented');\n }\n atan(x: T): T {\n throw new Error('Not yet implemented');\n }\n atan2(a: T, b: T): T {\n throw new Error('Not yet implemented');\n }\n\n sinh(x: T): T {\n throw new Error('Not yet implemented');\n }\n cosh(x: T): T {\n throw new Error('Not yet implemented');\n }\n tanh(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n asinh(x: T): T {\n throw new Error('Not yet implemented');\n }\n acosh(x: T): T {\n throw new Error('Not yet implemented');\n }\n atanh(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n erf(x: T): T {\n throw new Error('Not yet implemented');\n }\n\n step(x: T, alpha: number): T {\n throw new Error('Not yet implemented');\n }\n\n conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n throw new Error('Not yet implemented');\n }\n conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n throw new Error('Not yet implemented');\n }\n conv2dDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n depthwiseConv2D(input: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n throw new Error('Not yet implemented');\n }\n depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n throw new Error('Not yet implemented');\n }\n depthwiseConv2DDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n throw new Error('Not yet implemented');\n }\n conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n throw new Error('Not yet implemented');\n }\n conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n Tensor5D {\n throw new Error('Not yet implemented');\n }\n conv3dDerFilter(x: Tensor5D, dY: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n throw new Error('Not yet implemented');\n }\n maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n throw new Error('Not yet implemented');\n }\n maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n throw new Error('Not yet implemented');\n }\n avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n throw new Error('Not yet implemented');\n }\n avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n reshape(x: T, shape: ShapeMap[R]):\n Tensor {\n throw new Error('Not yet implemented');\n }\n cast(x: T, dtype: DataType): T {\n throw new Error('Not yet implemented');\n }\n\n tile(x: T, reps: number[]): T {\n throw new Error('Not yet implemented');\n }\n\n pad(\n x: T, paddings: Array<[number, number]>, constantValue: number): T {\n throw new Error('Not yet implemented');\n }\n\n transpose(x: T, perm: number[]): T {\n throw new Error('Not yet implemented');\n }\n\n gather(x: T, indices: Tensor1D, axis: number): T {\n throw new Error('Not yet implemented');\n }\n\n gatherND(x: Tensor, indices: Tensor): Tensor {\n throw new Error('Not yet implemented');\n }\n\n scatterND(\n indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor {\n throw new Error('Not yet implemented');\n }\n\n batchToSpaceND(\n x: T, blockShape: number[], crops: number[][]): T {\n throw new Error('Not yet implemented');\n }\n\n spaceToBatchND(\n x: T, blockShape: number[], paddings: number[][]): T {\n throw new Error('Not yet implemented');\n }\n\n resizeBilinear(\n x: Tensor4D, newHeight: number, newWidth: number,\n alignCorners: boolean): Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):\n Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n resizeNearestNeighbor(\n x: Tensor4D, newHEight: number, newWidth: number,\n alignCorners: boolean): Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n resizeNearestNeighborBackprop(\n dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n batchNormalization(\n x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n offset?: Tensor4D|Tensor1D): Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n localResponseNormalization4D(\n x: Tensor4D, radius: number, bias: number, alpha: number,\n beta: number): Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n LRNGrad(\n dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, radius: number,\n bias: number, alpha: number, beta: number): Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n multinomial(\n logits: Tensor2D, normalized: boolean, numSamples: number,\n seed: number): Tensor2D {\n throw new Error('Not yet implemented');\n }\n\n oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n Tensor2D {\n throw new Error('Not yet implemented');\n }\n\n cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n Tensor {\n throw new Error('Not yet implemented');\n }\n\n nonMaxSuppression(\n boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n iouThreshold: number, scoreThreshold?: number): Tensor1D {\n throw new Error('Not yet implemented');\n }\n\n fft(x: Tensor2D): Tensor2D {\n throw new Error('Not yet implemented');\n }\n ifft(x: Tensor2D): Tensor2D {\n throw new Error('Not yet implemented');\n }\n complex(real: T, imag: T): T {\n throw new Error('Not yet implemented');\n }\n real(input: T): T {\n throw new Error('Not yet implemented');\n }\n imag(input: T): T {\n throw new Error('Not yet implemented');\n }\n\n cropAndResize(\n image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D,\n cropSize: [number, number], method: 'bilinear'|'nearest',\n extrapolationValue: number): Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D {\n throw new Error('Not yet implemented');\n }\n\n // Aligns with the \"SplitV\" kernel in TensorFlow.\n split(value: T, sizeSplits: number[], axis: number): T[] {\n throw new Error('Not yet implemented');\n }\n\n sparseToDense(\n sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n defaultValue: Scalar): Tensor {\n throw new Error('Not yet implemented');\n }\n /**\n * Sets the data mover for this backend. Backends should use the mover to\n * move data from other backends to this backend.\n */\n setDataMover(dataMover: DataMover): void {\n throw new Error('Not yet implemented');\n }\n\n dispose(): void {\n throw new Error('Not yet implemented');\n }\n}\n","../src/kernels/backend_util.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {scalar, zeros} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {Rank} from '../types';\nimport {DataType, ShapeMap} from '../types';\nimport {hasEncodingLoss} from '../util';\nimport {KernelBackend} from './backend';\n\nexport function castTensor(\n x: T, dtype: DataType, backend: KernelBackend): T {\n if (dtype === 'complex64') {\n if (x.dtype === 'complex64') {\n return x.clone();\n }\n const zerosTensor = zeros(x.shape);\n const floatX = x.toFloat();\n const result = backend.complex(floatX, zerosTensor);\n zerosTensor.dispose();\n floatX.dispose();\n return result as T;\n }\n\n if (!hasEncodingLoss(x.dtype, dtype)) {\n // We don't change the underlying data, since we cast to higher\n // precision.\n return Tensor.make(x.shape, {dataId: x.dataId}, dtype) as T;\n }\n if (x.dtype === 'complex64') {\n const real = backend.real(x);\n const result = real.cast(dtype);\n real.dispose();\n return result;\n }\n if (dtype === 'int32') {\n return backend.int(x);\n } else if (dtype === 'bool') {\n const zero = scalar(0, x.dtype);\n const result = backend.notEqual(x, zero) as T;\n zero.dispose();\n return result;\n } else {\n throw new Error(`Error in Cast: unknown dtype argument (${dtype})`);\n }\n}\n\nexport function reshapeTensor(\n x: T, shape: ShapeMap[R]): Tensor {\n return Tensor.make(shape, {dataId: x.dataId}, x.dtype);\n}\n","../src/kernels/complex_util.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray} from '../types';\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(\n real: Float32Array, imag: Float32Array): Float32Array {\n if (real.length !== imag.length) {\n throw new Error(\n `Cannot merge real and imag arrays of different lengths. real:` +\n `${real.length}, imag: ${imag.length}.`);\n }\n const result = new Float32Array(real.length * 2);\n for (let i = 0; i < result.length; i += 2) {\n result[i] = real[i / 2];\n result[i + 1] = imag[i / 2];\n }\n return result;\n}\n\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n * tensor.\n */\nexport function splitRealAndImagArrays(complex: Float32Array):\n {real: Float32Array, imag: Float32Array} {\n const real = new Float32Array(complex.length / 2);\n const imag = new Float32Array(complex.length / 2);\n for (let i = 0; i < complex.length; i += 2) {\n real[i / 2] = complex[i];\n imag[i / 2] = complex[i + 1];\n }\n return {real, imag};\n}\n\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex: Float32Array):\n {real: Float32Array, imag: Float32Array} {\n const len = Math.ceil(complex.length / 4);\n const real = new Float32Array(len);\n const imag = new Float32Array(len);\n for (let i = 0; i < complex.length; i += 4) {\n real[Math.floor(i / 4)] = complex[i];\n imag[Math.floor(i / 4)] = complex[i + 1];\n }\n return {real, imag};\n}\n\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex: Float32Array):\n {real: Float32Array, imag: Float32Array} {\n const len = Math.floor(complex.length / 4);\n const real = new Float32Array(len);\n const imag = new Float32Array(len);\n for (let i = 2; i < complex.length; i += 4) {\n real[Math.floor(i / 4)] = complex[i];\n imag[Math.floor(i / 4)] = complex[i + 1];\n }\n return {real, imag};\n}\n\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(\n complex: Float32Array, index: number): {real: number, imag: number} {\n const real = complex[index * 2];\n const imag = complex[index * 2 + 1];\n return {real, imag};\n}\n\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(\n data: TypedArray, real: number, imag: number, index: number) {\n data[index * 2] = real;\n data[index * 2 + 1] = imag;\n}\n\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(\n n: number, inverse: boolean): {real: Float32Array, imag: Float32Array} {\n const real = new Float32Array(n / 2);\n const imag = new Float32Array(n / 2);\n for (let i = 0; i < Math.ceil(n / 2); i++) {\n const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n real[i] = Math.cos(x);\n imag[i] = Math.sin(x);\n }\n return {real, imag};\n}\n\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(\n k: number, n: number, inverse: boolean): {real: number, imag: number} {\n const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n const real = Math.cos(x);\n const imag = Math.sin(x);\n return {real, imag};\n}\n","../src/kernels/non_max_suppression_impl.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\n\nimport {tensor1d} from '../ops/tensor_ops';\nimport {Tensor1D} from '../tensor';\nimport {TypedArray} from '../types';\n\nexport function nonMaxSuppressionImpl(\n boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n iouThreshold: number, scoreThreshold: number): Tensor1D {\n const candidates = Array.from(scores)\n .map((score, boxIndex) => ({score, boxIndex}))\n .filter(c => c.score > scoreThreshold)\n .sort((c1, c2) => c2.score - c1.score);\n\n const selected: number[] = [];\n\n for (let i = 0; i < candidates.length; i++) {\n const {score, boxIndex} = candidates[i];\n if (score < scoreThreshold) {\n break;\n }\n\n let ignoreCandidate = false;\n for (let j = selected.length - 1; j >= 0; --j) {\n const iou = intersectionOverUnion(boxes, boxIndex, selected[j]);\n if (iou >= iouThreshold) {\n ignoreCandidate = true;\n break;\n }\n }\n\n if (!ignoreCandidate) {\n selected.push(boxIndex);\n if (selected.length >= maxOutputSize) {\n break;\n }\n }\n }\n\n return tensor1d(selected, 'int32');\n}\n\nfunction intersectionOverUnion(boxes: TypedArray, i: number, j: number) {\n const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n const yminI = Math.min(iCoord[0], iCoord[2]);\n const xminI = Math.min(iCoord[1], iCoord[3]);\n const ymaxI = Math.max(iCoord[0], iCoord[2]);\n const xmaxI = Math.max(iCoord[1], iCoord[3]);\n const yminJ = Math.min(jCoord[0], jCoord[2]);\n const xminJ = Math.min(jCoord[1], jCoord[3]);\n const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) {\n return 0.0;\n }\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n","../src/kernels/split_shared.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\n\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split(\n x: T, sizeSplits: number[], axis: number): T[] {\n const begin = new Array(x.rank).fill(0);\n const size = x.shape.slice();\n return sizeSplits.map(s => {\n size[axis] = s;\n const slice = x.slice(begin, size);\n begin[axis] += s;\n return slice;\n });\n}\n","../src/kernels/topk_impl.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the TopK kernel shared between webgl and cpu. */\n\nimport {tensor} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {NumericDataType, TypedArray} from '../types';\nimport {getTypedArrayFromDType} from '../util';\n\nexport function topkImpl(\n x: TypedArray, xShape: number[], xDtype: NumericDataType, k: number,\n sorted: boolean): [T, T] {\n // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n const lastDim = xShape[xShape.length - 1];\n const [batch, size] = [x.length / lastDim, lastDim];\n const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n\n for (let b = 0; b < batch; b++) {\n const offset = b * size;\n const vals = x.subarray(offset, offset + size);\n const valAndInd: Array<{value: number, index: number}> = [];\n for (let i = 0; i < vals.length; i++) {\n valAndInd.push({value: vals[i], index: i});\n }\n valAndInd.sort((a, b) => b.value - a.value);\n\n const outOffset = b * k;\n const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n for (let i = 0; i < k; i++) {\n topKVals[i] = valAndInd[i].value;\n topKIndices[i] = valAndInd[i].index;\n }\n }\n // Reshape back to the original input shape, except that the last\n // dimension is k.\n const outputShape = xShape.slice();\n outputShape[outputShape.length - 1] = k;\n return [\n tensor(allTopKVals, outputShape, xDtype) as T,\n tensor(allTopKIndices, outputShape, 'int32') as T\n ];\n}\n","../src/kernels/webgl/argminmax_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ReduceInfo} from '../../ops/reduce_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ArgMinMaxProgram implements GPGPUProgram {\n variableNames = ['A'];\n outputShape: number[];\n userCode: string;\n\n constructor(reduceInfo: ReduceInfo, op: 'max'|'min', firstPass: boolean) {\n const windowSize = reduceInfo.windowSize;\n const batchSize = reduceInfo.batchSize;\n const inSize = reduceInfo.inSize;\n const outSize = Math.ceil(inSize / windowSize);\n if (!firstPass) {\n this.variableNames.push('bestIndicesA');\n }\n this.outputShape = [batchSize, outSize];\n const compOp = (op === 'max') ? '>' : '<';\n const indexSnippet = firstPass ?\n 'inOffset + i;' :\n 'round(getBestIndicesA(batch, inOffset + i));';\n\n this.userCode = `\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${windowSize};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${windowSize}; i++) {\n int inIdx = ${indexSnippet};\n float candidate = getA(batch, inIdx);\n if (candidate ${compOp} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `;\n }\n}\n","../src/kernels/webgl/avg_pool_backprop_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class AvgPool2DBackpropProgram implements GPGPUProgram {\n variableNames = ['dy'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv2DInfo) {\n this.outputShape = convInfo.inShape;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n const avgMultiplier = 1 / (filterHeight * filterWidth);\n\n this.userCode = `\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n const float avgMultiplier = float(${avgMultiplier});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${effectiveFilterWidth};\n wC+= ${dilationWidth}) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n","../src/ops/broadcast_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(\n inShape: number[], outShape: number[]): number[] {\n const inRank = inShape.length;\n const dims: number[] = [];\n for (let i = 0; i < inRank; i++) {\n const dim = inRank - 1 - i;\n const a = inShape[dim] || 1;\n const b = outShape[outShape.length - 1 - i] || 1;\n if (b > 1 && a === 1) {\n dims.unshift(dim);\n }\n }\n return dims;\n}\n\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(\n inShape: number[], outShape: number[]): number[] {\n const result: number[] = [];\n for (let i = 0; i < outShape.length; i++) {\n const inDim = inShape[inShape.length - i - 1];\n const outAxis = outShape.length - i - 1;\n const outDim = outShape[outAxis];\n if (inDim == null || (inDim === 1 && outDim > 1)) {\n result.unshift(outAxis);\n }\n }\n return result;\n}\n\nexport function assertAndGetBroadcastShape(\n shapeA: number[], shapeB: number[]): number[] {\n const result: number[] = [];\n const l = Math.max(shapeA.length, shapeB.length);\n\n for (let i = 0; i < l; i++) {\n let a = shapeA[shapeA.length - i - 1];\n if (a == null) {\n a = 1;\n }\n let b = shapeB[shapeB.length - i - 1];\n if (b == null) {\n b = 1;\n }\n if (a === 1) {\n result.unshift(b);\n } else if (b === 1) {\n result.unshift(a);\n } else if (a !== b) {\n const errMsg = `Operands could not be broadcast together with shapes ` +\n `${shapeA} and ${shapeB}.`;\n throw Error(errMsg);\n } else {\n result.unshift(a);\n }\n }\n return result;\n}\n","../src/kernels/webgl/batchnorm_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormProgram implements GPGPUProgram {\n variableNames: string[];\n outputShape: number[] = [];\n userCode: string;\n\n constructor(\n xShape: number[], meanShape: number[], varianceShape: number[],\n offsetShape: number[]|null, scaleShape: number[]|null,\n varianceEpsilon: number) {\n this.variableNames = ['x', 'mean', 'variance'];\n broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);\n broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n let offsetSnippet = '0.0';\n if (offsetShape != null) {\n broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);\n this.variableNames.push('offset');\n offsetSnippet = 'getOffsetAtOutCoords()';\n }\n\n let scaleSnippet = '1.0';\n if (scaleShape != null) {\n broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);\n this.variableNames.push('scale');\n scaleSnippet = 'getScaleAtOutCoords()';\n }\n\n this.outputShape = xShape;\n this.userCode = `\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${offsetSnippet};\n float scale = ${scaleSnippet};\n float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `;\n }\n}\n","../src/kernels/webgl/batchnorm_packed_gpu.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormPackedProgram implements GPGPUProgram {\n variableNames: string[];\n outputShape: number[];\n userCode: string;\n usesPackedTextures = true;\n\n constructor(\n xShape: number[], meanShape: number[], varianceShape: number[],\n offsetShape: number[]|null, scaleShape: number[]|null,\n varianceEpsilon: number) {\n this.variableNames = ['x', 'mean', 'variance'];\n broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);\n broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n let offsetSnippet = 'vec4(0.0)';\n if (offsetShape != null) {\n broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);\n this.variableNames.push('offset');\n offsetSnippet = 'getOffsetAtOutCoords()';\n }\n\n let scaleSnippet = 'vec4(1.0)';\n if (scaleShape != null) {\n broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);\n this.variableNames.push('scale');\n scaleSnippet = 'getScaleAtOutCoords()';\n }\n\n this.outputShape = xShape;\n this.userCode = `\n void main() {\n vec4 offset = ${offsetSnippet};\n vec4 scale = ${scaleSnippet};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n setOutput((x - mean) * inv + offset);\n }\n `;\n }\n}\n","../src/kernels/webgl/binaryop_complex_gpu.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n REAL: 'return areal * breal - aimag * bimag;',\n IMAG: 'return areal * bimag + aimag * breal;'\n};\n\nexport class BinaryOpComplexProgram implements GPGPUProgram {\n variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n userCode: string;\n outputShape: number[];\n\n constructor(op: string, aShape: number[], bShape: number[]) {\n this.outputShape =\n broadcast_util.assertAndGetBroadcastShape(aShape, bShape);\n\n this.userCode = `\n float binaryOpComplex(\n float areal, float aimag, float breal, float bimag) {\n ${op}\n }\n\n void main() {\n float areal = getARealAtOutCoords();\n float aimag = getAImagAtOutCoords();\n float breal = getBRealAtOutCoords();\n float bimag = getBImagAtOutCoords();\n setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n }\n `;\n }\n}\n","../src/kernels/webgl/binaryop_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nconst CHECK_NAN_SNIPPET = `\n if (isNaN(a)) return a;\n if (isNaN(b)) return b;\n`;\n\nexport const ADD = 'return a + b;';\nexport const SUB = 'return a - b;';\nexport const MUL = 'return a * b;';\nexport const DIV = `if (a == b) return 1.0;\n return a / b;`;\n\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n float resultSign = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n int result = ia / ib;\n int amodb = ia - ib * result;\n\n if (resultSign < 0.0 && amodb != 0) {\n result -= 1;\n }\n return float(result);\n`;\n\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n return NAN;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\n\nexport const EQUAL = `return float(a == b);`;\n\nexport const NOT_EQUAL = `return float(a != b);`;\n\nexport const LESS = `return float(a < b);`;\n\nexport const LESS_EQUAL = `return float(a <= b);`;\n\nexport const GREATER = `return float(a > b);`;\n\nexport const GREATER_EQUAL = `return float(a >= b);`;\n\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\n\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\n\nexport const MAX = CHECK_NAN_SNIPPET + `\n return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n return mod(a, b);`;\n\nexport const ATAN2 = CHECK_NAN_SNIPPET + `\n return atan(a, b);\n`;\n\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\n\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\n\nexport class BinaryOpProgram implements GPGPUProgram {\n variableNames = ['A', 'B'];\n outputShape: number[];\n userCode: string;\n\n // Caching uniform location for speed.\n startLoc: WebGLUniformLocation;\n\n constructor(op: string, aShape: number[], bShape: number[]) {\n this.outputShape =\n broadcast_util.assertAndGetBroadcastShape(aShape, bShape);\n this.userCode = `\n uniform float NAN;\n float binaryOperation(float a, float b) {\n ${op}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `;\n }\n\n getCustomSetupFunc() {\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.startLoc == null) {\n this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');\n if (this.startLoc == null) {\n // This means the compiler has optimized and realized it doesn't need\n // the uniform.\n return;\n }\n }\n gpgpu.gl.uniform1f(this.startLoc, NaN);\n };\n }\n}\n","../src/kernels/webgl/binaryop_packed_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// TODO(https://github.com/tensorflow/tfjs/issues/1141): Understand why\n// vectorized implementation causes layers tests to break.\nexport const PACKED_DIV = `\n // vec4 one = vec4(equal(a, b));\n // return one + (vec4(1.0) - one) * a / b;\n vec4 result = a / b;\n result.x = a.x == b.x ? 1. : result.x;\n result.y = a.y == b.y ? 1. : result.y;\n result.z = a.z == b.z ? 1. : result.z;\n result.w = a.w == b.w ? 1. : result.w;\n return result;\n`;\n\nexport const PACKED_INT_DIV = `\n vec4 resultSign = sign(a) * sign(b);\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n ivec4 result = ia / ib;\n ivec4 amodb = ia - ib * result;\n\n // Vectorize INT_DIV\n // if (resultSign < 0.0 && amodb != 0) result -= 1;\n // return float(result);\n return vec4(result -\n ivec4(lessThan(resultSign, vec4(0.0))) * ivec4(notEqual(amodb, ivec4(0))));\n`;\n\nexport const PACKED_POW = `\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n result.r = isNaN.r == 1.0 ? NAN : result.r;\n result.g = isNaN.g == 1.0 ? NAN : result.g;\n result.b = isNaN.b == 1.0 ? NAN : result.b;\n result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n return result;\n`;\n\nexport class BinaryOpPackedProgram implements GPGPUProgram {\n variableNames = ['A', 'B'];\n outputShape: number[];\n userCode: string;\n supportsBroadcasting = true;\n usesPackedTextures = true;\n\n // Caching uniform location for speed.\n startLoc: WebGLUniformLocation;\n\n constructor(op: string, aShape: number[], bShape: number[]) {\n this.outputShape =\n broadcast_util.assertAndGetBroadcastShape(aShape, bShape);\n this.userCode = `\n uniform float NAN;\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${op}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `;\n }\n\n getCustomSetupFunc() {\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.startLoc == null) {\n this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');\n if (this.startLoc == null) {\n // This means the compiler has optimized and realized it doesn't need\n // the uniform.\n return;\n }\n }\n gpgpu.gl.uniform1f(this.startLoc, NaN);\n };\n }\n}\n","../src/kernels/webgl/clip_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n outputShape: number[];\n\n // Caching uniform locations for speed.\n minLoc: WebGLUniformLocation;\n maxLoc: WebGLUniformLocation;\n\n constructor(aShape: number[]) {\n this.outputShape = aShape;\n this.userCode = `\n uniform float min;\n uniform float max;\n\n void main() {\n float value = getAAtOutCoords();\n if (isNaN(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, min, max));\n }\n `;\n }\n\n getCustomSetupFunc(min: number, max: number) {\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.minLoc == null) {\n this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'min');\n this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'max');\n }\n gpgpu.gl.uniform1f(this.minLoc, min);\n gpgpu.gl.uniform1f(this.maxLoc, max);\n };\n }\n}\n","../src/kernels/webgl/clip_packed_gpu.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipPackedProgram implements GPGPUProgram {\n variableNames = ['A'];\n usesPackedTextures = true;\n userCode: string;\n outputShape: number[];\n\n // Caching uniform locations for speed.\n minLoc: WebGLUniformLocation;\n maxLoc: WebGLUniformLocation;\n\n constructor(aShape: number[]) {\n this.outputShape = aShape;\n this.userCode = `\n uniform float min;\n uniform float max;\n\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (hasNaN(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(min), vec4(max)));\n }\n `;\n }\n\n getCustomSetupFunc(min: number, max: number) {\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.minLoc == null) {\n this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'min');\n this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'max');\n }\n gpgpu.gl.uniform1f(this.minLoc, min);\n gpgpu.gl.uniform1f(this.maxLoc, max);\n };\n }\n}\n","../src/kernels/webgl/complex_abs_gpu.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ComplexAbsProgram implements GPGPUProgram {\n variableNames = ['real', 'imag'];\n userCode: string;\n outputShape: number[];\n\n constructor(shape: number[]) {\n this.outputShape = shape;\n this.userCode = `\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `;\n }\n}\n","../src/kernels/webgl/concat_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as concat_util from '../../ops/concat_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ConcatProgram implements GPGPUProgram {\n variableNames: string[];\n outputShape: number[] = [];\n userCode: string;\n\n // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n constructor(shapes: Array<[number, number]>) {\n this.outputShape = concat_util.computeOutShape(shapes, 1 /* axis */);\n this.variableNames = shapes.map((_, i) => `T${i}`);\n\n const offsets: number[] = new Array(shapes.length - 1);\n offsets[0] = shapes[0][1];\n for (let i = 1; i < offsets.length; i++) {\n offsets[i] = offsets[i - 1] + shapes[i][1];\n }\n\n const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n for (let i = 1; i < offsets.length; i++) {\n const shift = offsets[i - 1];\n snippets.push(\n `else if (yC < ${offsets[i]}) ` +\n `setOutput(getT${i}(yR, yC-${shift}));`);\n }\n const lastIndex = offsets.length;\n const lastShift = offsets[offsets.length - 1];\n snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n\n this.userCode = `\n void main() {\n ivec2 coords = getOutputCoords();\n int yR = coords.x;\n int yC = coords.y;\n\n ${snippets.join('\\n ')}\n }\n `;\n }\n}\n","../src/kernels/webgl/conv_backprop_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Conv2DDerFilterProgram implements GPGPUProgram {\n variableNames = ['x', 'dy'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv2DInfo) {\n this.outputShape = convInfo.filterShape;\n\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n\n this.userCode = `\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${convInfo.batchSize}; b++) {\n for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n int xR = wR + yR * ${strideHeight} - ${padTop};\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n\nexport class Conv2DDerInputProgram implements GPGPUProgram {\n variableNames = ['dy', 'W'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv2DInfo) {\n this.outputShape = convInfo.inShape;\n\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n\n const padTop = filterHeight - 1 - convInfo.padInfo.top;\n const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n\n this.userCode = `\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${filterHeight}; wR++) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${filterHeight} - 1 - wR;\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${filterWidth} - 1 - wC;\n\n for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n\nexport class Conv3DDerFilterProgram implements GPGPUProgram {\n variableNames = ['x', 'dy'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv3DInfo) {\n this.outputShape = convInfo.filterShape;\n\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const padFront = convInfo.padInfo.front;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n\n this.userCode = `\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${convInfo.batchSize}; b++) {\n for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n int xF = wF + yF * ${strideDepth} - ${padFront};\n\n if (xF < 0 || xF >= ${convInfo.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n int xR = wR + yR * ${strideHeight} - ${padTop};\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n\nexport class Conv3DDerInputProgram implements GPGPUProgram {\n variableNames = ['dy', 'W'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv3DInfo) {\n this.outputShape = convInfo.inShape;\n\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n\n const padFront = filterDepth - 1 - convInfo.padInfo.front;\n const padTop = filterHeight - 1 - convInfo.padInfo.top;\n const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n\n this.userCode = `\n const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${filterDepth}; wF++) {\n float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${filterDepth} - 1 - wF;\n\n for (int wR = 0; wR < ${filterHeight}; wR++) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${filterHeight} - 1 - wR;\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${filterWidth} - 1 - wC;\n\n for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n","../src/kernels/webgl/conv_backprop_gpu_depthwise.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DDerFilterProgram implements GPGPUProgram {\n variableNames = ['x', 'dy'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv2DInfo) {\n this.outputShape = convInfo.filterShape;\n\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n this.userCode = `\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${channelMul} + dm;\n\n float dotProd = 0.0;\n\n // TODO: Vec4 over the batch size\n for (int b = 0; b < ${convInfo.batchSize}; b++) {\n for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n int xR = wR + yR * ${strideHeight} - ${padTop};\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n\nexport class DepthwiseConv2DDerInputProgram implements GPGPUProgram {\n variableNames = ['dy', 'W'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv2DInfo) {\n this.outputShape = convInfo.inShape;\n\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n\n const padTop = filterHeight - 1 - convInfo.padInfo.top;\n const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n this.userCode = `\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${filterHeight}; wR++) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${filterHeight} - 1 - wR;\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${filterWidth} - 1 - wC;\n\n // TODO: Vec4 over the channelMul\n for (int dm = 0; dm < ${channelMul}; dm++) {\n int d2 = d1 * ${channelMul} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n","../src/kernels/webgl/conv_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Conv2DProgram implements GPGPUProgram {\n variableNames = ['x', 'W'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv2DInfo) {\n this.outputShape = convInfo.outShape;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n\n const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n const inputDepthVec4Remainder = convInfo.inChannels % 4;\n\n this.userCode = `\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${filterHeight}; wR++) {\n int xR = xRCorner + wR * ${dilationHeight};\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n int xC = xCCorner + wC * ${dilationWidth};\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${inputDepthVec4Remainder === 1}) {\n dotProd +=\n getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n getW(wR, wC, ${inputDepthNearestVec4}, d2);\n } else if (${inputDepthVec4Remainder === 2}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${inputDepthNearestVec4}),\n getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n );\n vec2 wValues = vec2(\n getW(wR, wC, ${inputDepthNearestVec4}, d2),\n getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${inputDepthVec4Remainder === 3}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${inputDepthNearestVec4}),\n getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n );\n vec3 wValues = vec3(\n getW(wR, wC, ${inputDepthNearestVec4}, d2),\n getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n\nexport class Conv3DProgram implements GPGPUProgram {\n variableNames = ['x', 'W'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv3DInfo) {\n this.outputShape = convInfo.outShape;\n const padFront = convInfo.padInfo.front;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n\n const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n const inputDepthVec4Remainder = convInfo.inChannels % 4;\n\n this.userCode = `\n const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${\n strideWidth});\n const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${filterDepth}; wF++) {\n int xF = xFCorner + wF * ${dilationDepth};\n\n if (xF < 0 || xF >= ${convInfo.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${filterHeight}; wR++) {\n int xR = xRCorner + wR * ${dilationHeight};\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n int xC = xCCorner + wC * ${dilationWidth};\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${inputDepthVec4Remainder === 1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n } else if (${inputDepthVec4Remainder === 2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${inputDepthVec4Remainder === 3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n","../src/kernels/webgl/conv_gpu_depthwise.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DProgram implements GPGPUProgram {\n variableNames = ['x', 'W'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv2DInfo) {\n this.outputShape = convInfo.outShape;\n\n const xNumRows = convInfo.inHeight;\n const xNumCols = convInfo.inWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n this.userCode = `\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${channelMul};\n int q = d2 - d1 * ${channelMul};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${filterHeight}; wR++) {\n int xR = xRCorner + wR * ${dilationHeight};\n\n if (xR < 0 || xR >= ${xNumRows}) {\n continue;\n }\n\n for (int wC = 0; wC < ${filterWidth}; wC++) {\n int xC = xCCorner + wC * ${dilationWidth};\n\n if (xC < 0 || xC >= ${xNumCols}) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n","../src/kernels/webgl/conv_packed_gpu_depthwise.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport * as util from '../../util';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConvPacked2DProgram implements GPGPUProgram {\n variableNames = ['x', 'W'];\n usesPackedTextures = true;\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv2DInfo) {\n this.outputShape = convInfo.outShape;\n\n const xNumRows = convInfo.inHeight;\n const xNumCols = convInfo.inWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const texelsAcross = filterWidth;\n\n let mainLoop = `int xR; int xC; int xCOffset;`;\n\n for (let r = 0; r < filterHeight; r++) {\n for (let c = 0; c < filterWidth; c++) {\n mainLoop += `\n vec4 xTexelR${r}C${c * 2} = vec4(0.);\n vec4 wR${r}C${c} = vec4(0.);\n vec4 xR${r}C${c} = vec4(0.);`;\n }\n }\n\n /**\n * This vectorized implementation works by gathering the values needed for\n * each output channel's dot product into vec4's and then multiplying them\n * all together (this happens in the final double for-loop below). Most of\n * the main loop consists of constructing these vec4's with the minimum\n * number of texture2D calls, which means making use of all four returned\n * values from a texture2D call at once.\n */\n for (let r = 0; r < filterHeight; r++) {\n for (let texelC = 0; texelC < texelsAcross; texelC++) {\n const c = texelC * 2;\n\n mainLoop += `\n xR = xRCorner + ${r * dilationHeight};\n xC = xCCorner + ${c * dilationWidth};\n `;\n\n if (strideWidth === 1) {\n if (c < filterWidth) {\n // If padding is odd, the outer texels have to be composed.\n if (padLeft % 2 === 1) {\n // TODO: Ensure vec4 previous does not result in redundant sample,\n // and avoid setting xTexelRC's that exceed the boundary in the\n // first place rather than resetting them to vec4(0)).\n\n // To compute xCOffset:\n // - If padding is odd, we must add 1 to ensure we ask for an\n // even-numbered row.\n // - We subtract 2 to access the previous texel.\n\n mainLoop += `\n xCOffset = xC + 1;\n if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${\n xNumCols}) {\n xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR${r}C${c} = vec4(0.);\n }\n\n xCOffset = xC + 1 - 2;\n if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${\n xNumCols}) {\n vec4 previous = getX(batch, xR, xCOffset, d1);\n xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);\n } else {\n xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);\n }\n `;\n } else {\n // Padding is even, so xRC corresponds to a single texel.\n mainLoop += `\n if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n } else {\n xTexelR${r}C${c} = vec4(0.);\n }\n\n xR${r}C${c} = xTexelR${r}C${c};\n `;\n }\n\n if (c + 1 < filterWidth) {\n // If dilation is even, the second entry should match the first\n // (either both are composed or both are single samples). But if\n // dilation is odd, then the second entry should be the opposite\n // of the first (if the first is composed, the second is a single\n // sample, and vice versa.)\n\n const nextTexelOffset = padLeft % 2 === 0 ?\n util.nearestLargerEven(dilationWidth) :\n dilationWidth;\n\n if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n mainLoop += `\n xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n if(xR >= 0 && xR < ${xNumRows} &&\n xCOffset >= 0 && xCOffset < ${xNumCols}) {\n xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n }\n `;\n\n // If dilation > 1 then the xRC's will not be able to share any\n // values, so each xRC will require two unique calls to getX.\n if (dilationWidth > 1) {\n mainLoop += `\n xCOffset -= 2;\n if(xR >= 0 && xR < ${xNumRows} &&\n xCOffset >= 0 && xCOffset < ${xNumCols}) {\n xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR${r}C${c} = vec4(0.);\n }\n `;\n }\n\n mainLoop += `\n xR${r}C${c + 1} = vec4(\n xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);\n `;\n } else {\n mainLoop += `\n xCOffset = xC + ${nextTexelOffset};\n\n if(xR >= 0 && xR < ${xNumRows} &&\n xCOffset >= 0 && xCOffset < ${xNumCols}) {\n xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n }\n\n xR${r}C${c + 1} = xTexelR${r}C${c + 2};\n `;\n }\n }\n }\n } else { // stride > 1\n if (c < filterWidth) {\n mainLoop += `\n if(xR >= 0 && xR < ${xNumRows}) {\n `;\n\n // Depending on whether padLeft is even or odd, we want either the\n // xy or zw channels from X texels for xR${r}C${c}. If padLeft is\n // even, xR${r}C${c + 1} is simply the zw channels of texels we've\n // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will\n // need to come from the xy channels of a new texel, hence the `vec4\n // final` initialized below.\n if (padLeft % 2 === 1) {\n mainLoop += `\n xCOffset = xC + 1 - ${strideWidth};\n if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR${r}C${c} = vec4(0.);\n }\n\n if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {\n xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);\n } else {\n xTexelR${r}C${c + 2} = vec4(0.);\n }\n\n xR${r}C${c} = vec4(\n xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n `;\n\n if (c + 1 < filterWidth) {\n mainLoop += `\n vec4 final = vec4(0.);\n xCOffset = xC + 1 + ${strideWidth};\n if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);\n `;\n }\n } else {\n mainLoop += `\n if(xC >= 0 && xC < ${xNumCols}) {\n xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n } else {\n xTexelR${r}C${c} = vec4(0.);\n }\n\n xCOffset = xC + ${strideWidth};\n if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n } else {\n xTexelR${r}C${c + 2} = vec4(0.);\n }\n\n xR${r}C${c} = vec4(\n xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);\n `;\n\n if (c + 1 < filterWidth) {\n mainLoop += `\n xR${r}C${c + 1} = vec4(\n xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n `;\n }\n }\n\n mainLoop += `}`;\n }\n }\n\n if (c < filterWidth) {\n mainLoop += `\n vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);\n wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);\n `;\n\n if (c + 1 < filterWidth) {\n mainLoop += `\n vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);\n wR${r}C${c + 1} =\n vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;\n }\n }\n }\n }\n\n for (let r = 0; r < filterHeight; r++) {\n for (let c = 0; c < filterWidth; c++) {\n mainLoop += `result += xR${r}C${c} * wR${r}C${c};`;\n }\n }\n\n this.userCode = `\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n void main() {\n\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2;\n int q = 0;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n vec4 result = vec4(0.);\n\n ${mainLoop}\n\n setOutput(result);\n }\n `;\n }\n}\n","../src/kernels/webgl/crop_and_resize_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport { GPGPUProgram } from './gpgpu_math';\n\nexport class CropAndResizeProgram implements GPGPUProgram {\n variableNames = ['Image', 'Boxes', 'BoxInd'];\n outputShape: number[] = [];\n userCode: string;\n\n constructor(\n imageShape: [number, number, number, number], boxShape: [number, number],\n cropSize: [number, number], method: 'bilinear' | 'nearest',\n extrapolationValue: number) {\n const [batch, imageHeight, imageWidth, depth] = imageShape;\n const [numBoxes,] = boxShape;\n const [cropHeight, cropWidth] = cropSize;\n this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n const methodId = method === 'bilinear' ? 1 : 0;\n\n const [inputHeightFloat, inputWidthFloat] =\n [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n\n const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n [\n `${(imageHeight-1)/(cropHeight-1)}`,\n '(y2-y1) * height_ratio',\n `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n ] :\n [\n '0.0',\n '0.0',\n `0.5 * (y1+y2) * ${inputHeightFloat}`,\n ];\n const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n [\n `${(imageWidth-1)/(cropWidth-1)}`,\n '(x2-x1) * width_ratio',\n `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n ] :\n [\n '0.0',\n '0.0',\n `0.5 * (x1+x2) * ${inputWidthFloat}`,\n ];\n\n // Reference implementation\n // tslint:disable-next-line:max-line-length\n // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n this.userCode = `\n const float height_ratio = float(${heightRatio});\n const float width_ratio = float(${widthRatio});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${batch}) {\n return;\n }\n\n float height_scale = ${heightScale};\n float width_scale = ${widthScale};\n\n float in_y = ${inY};\n if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n setOutput(float(${extrapolationValue}));\n return;\n }\n float in_x = ${inX};\n if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n setOutput(float(${extrapolationValue}));\n return;\n }\n\n vec2 sourceFracIndexRC = vec2(in_y,in_x);\n if(${methodId} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(floor(\n sourceFracIndexRC + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n setOutput(newValue);\n }\n }\n `;\n }\n}\n","../src/kernels/webgl/glsl_version.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\n\nexport type GLSL = {\n version: string,\n attribute: string,\n varyingVs: string,\n varyingFs: string,\n texture2D: string,\n output: string,\n defineOutput: string,\n defineRound: string\n};\n\nexport function getGlslDifferences(): GLSL {\n let version: string;\n let attribute: string;\n let varyingVs: string;\n let varyingFs: string;\n let texture2D: string;\n let output: string;\n let defineOutput: string;\n let defineRound: string;\n\n if (ENV.get('WEBGL_VERSION') === 2) {\n version = '#version 300 es';\n attribute = 'in';\n varyingVs = 'out';\n varyingFs = 'in';\n texture2D = 'texture';\n output = 'outputColor';\n defineOutput = 'out vec4 outputColor;';\n defineRound = `\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `;\n } else {\n version = '';\n attribute = 'attribute';\n varyingVs = 'varying';\n varyingFs = 'varying';\n texture2D = 'texture2D';\n output = 'gl_FragColor';\n defineOutput = '';\n defineRound = `\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `;\n }\n\n return {\n version,\n attribute,\n varyingVs,\n varyingFs,\n texture2D,\n output,\n defineOutput,\n defineRound\n };\n}\n","../src/kernels/webgl/shader_compiler_util.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../../util';\n\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(\n coords: string[], shape: number[], index = 'index'): string {\n const strides = util.computeStrides(shape);\n return strides\n .map((stride, i) => {\n const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n const line2 = i === strides.length - 1 ?\n `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n `index -= ${coords[i]} * ${stride}`;\n return `${line1}; ${line2};`;\n })\n .join('');\n}\n\nfunction buildVec(x: string[]): string {\n if (x.length === 1) {\n return `${x[0]}`;\n }\n return `vec${x.length}(${x.join(',')})`;\n}\n\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x: string[], y: string[]): string {\n if (x.length !== y.length) {\n throw new Error(\n `Vectors to be dotted must be of the same length -` +\n `got ${x.length} and ${y.length}`);\n }\n\n const slices: string[] = [];\n const nearestVec4 = Math.floor(x.length / 4);\n const nearestVec4Remainder = x.length % 4;\n\n for (let i = 0; i < nearestVec4; i++) {\n const xSlice = x.slice(i * 4, i * 4 + 4);\n const ySlice = y.slice(i * 4, i * 4 + 4);\n slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n }\n\n if (nearestVec4Remainder !== 0) {\n let xSlice = x.slice(nearestVec4 * 4);\n let ySlice = y.slice(nearestVec4 * 4);\n if (xSlice.length === 1) {\n xSlice = xSlice.map(d => `float(${d})`);\n ySlice = ySlice.map(d => `float(${d})`);\n }\n slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n }\n\n return slices.map((d, i) => `dot(${d})`).join('+');\n}\n","../src/kernels/webgl/shader_compiler.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\nimport {getBroadcastDims} from '../../ops/broadcast_util';\nimport * as util from '../../util';\nimport {getGlslDifferences, GLSL} from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n\nexport type ShapeInfo = {\n logicalShape: number[],\n texShape: [number, number],\n isUniform: boolean,\n isPacked: boolean,\n flatOffset: number\n};\n\nexport type InputInfo = {\n name: string,\n shapeInfo: ShapeInfo\n};\n\nexport function makeShader(\n inputsInfo: InputInfo[], outputShape: ShapeInfo, userCode: string,\n usesPackedTextures: boolean): string {\n const prefixSnippets: string[] = [];\n inputsInfo.forEach(x => {\n const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n\n // Snippet when we decided to upload the values as uniform.\n if (x.shapeInfo.isUniform) {\n prefixSnippets.push(\n `uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n } else {\n prefixSnippets.push(`uniform sampler2D ${x.name};`);\n prefixSnippets.push(`uniform int offset${x.name};`);\n }\n });\n const inputPrefixSnippet = prefixSnippets.join('\\n');\n\n const inputSamplingSnippet =\n inputsInfo\n .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n .join('\\n');\n const outTexShape = outputShape.texShape;\n const glsl = getGlslDifferences();\n const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n let outputSamplingSnippet: string;\n let floatTextureSetOutputSnippet: string;\n let shaderPrefix = getShaderPrefix(glsl);\n\n if (outputShape.isPacked) {\n outputSamplingSnippet =\n getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n } else {\n outputSamplingSnippet =\n getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n }\n\n if (usesPackedTextures) {\n shaderPrefix += SHADER_PACKED_PREFIX;\n }\n\n const source = [\n shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n ].join('\\n');\n return source;\n}\n\nfunction getSamplerFromInInfo(inInfo: InputInfo): string {\n const shape = inInfo.shapeInfo.logicalShape;\n switch (shape.length) {\n case 0:\n return getSamplerScalar(inInfo);\n case 1:\n return getSampler1D(inInfo);\n case 2:\n return getSampler2D(inInfo);\n case 3:\n return getSampler3D(inInfo);\n case 4:\n return getSampler4D(inInfo);\n case 5:\n return getSampler5D(inInfo);\n case 6:\n return getSampler6D(inInfo);\n default:\n throw new Error(\n `${shape.length}-D input sampling` +\n ` is not yet supported`);\n }\n}\n\nfunction getPackedSamplerFromInInfo(inInfo: InputInfo): string {\n const shape = inInfo.shapeInfo.logicalShape;\n switch (shape.length) {\n case 0:\n return getPackedSamplerScalar(inInfo);\n case 1:\n return getPackedSampler1D(inInfo);\n case 2:\n return getPackedSampler2D(inInfo);\n case 3:\n return getPackedSampler3D(inInfo);\n default:\n return getPackedSamplerND(inInfo);\n }\n}\n\nfunction getInputSamplingSnippet(\n inInfo: InputInfo, outShapeInfo: ShapeInfo,\n usesPackedTextures = false): string {\n let res = '';\n if (usesPackedTextures) {\n res += getPackedSamplerFromInInfo(inInfo);\n } else {\n res += getSamplerFromInInfo(inInfo);\n }\n\n const inShape = inInfo.shapeInfo.logicalShape;\n const outShape = outShapeInfo.logicalShape;\n if (inShape.length <= outShape.length) {\n if (usesPackedTextures) {\n res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n } else {\n res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n }\n }\n return res;\n}\n\nfunction getPackedOutputSamplingSnippet(\n outShape: number[], outTexShape: [number, number]): string {\n switch (outShape.length) {\n case 0:\n return getOutputScalarCoords();\n case 1:\n return getOutputPacked1DCoords(outShape as [number], outTexShape);\n case 2:\n return getOutputPacked2DCoords(outShape as [number, number], outTexShape);\n case 3:\n return getOutputPacked3DCoords(\n outShape as [number, number, number], outTexShape);\n default:\n return getOutputPackedNDCoords(outShape, outTexShape);\n }\n}\n\nfunction getOutputSamplingSnippet(\n outShape: number[], outTexShape: [number, number]): string {\n switch (outShape.length) {\n case 0:\n return getOutputScalarCoords();\n case 1:\n return getOutput1DCoords(outShape as [number], outTexShape);\n case 2:\n return getOutput2DCoords(outShape as [number, number], outTexShape);\n case 3:\n return getOutput3DCoords(\n outShape as [number, number, number], outTexShape);\n case 4:\n return getOutput4DCoords(\n outShape as [number, number, number, number], outTexShape);\n case 5:\n return getOutput5DCoords(\n outShape as [number, number, number, number, number], outTexShape);\n case 6:\n return getOutput6DCoords(\n outShape as [number, number, number, number, number, number],\n outTexShape);\n default:\n throw new Error(\n `${outShape.length}-D output sampling is not yet supported`);\n }\n}\n\nfunction getFloatTextureSampleSnippet(glsl: GLSL): string {\n return `\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${glsl.texture2D}(textureSampler, uv).r;\n }\n `;\n}\n\nfunction getFloatTextureSetRSnippet(glsl: GLSL): string {\n return `\n void setOutput(float val) {\n ${glsl.output} = vec4(val, 0, 0, 0);\n }\n `;\n}\n\nfunction getFloatTextureSetRGBASnippet(glsl: GLSL): string {\n return `\n void setOutput(vec4 val) {\n ${glsl.output} = val;\n }\n `;\n}\n\nfunction getShaderPrefix(glsl: GLSL): string {\n let NAN_CHECKS = '';\n if (ENV.get('PROD')) {\n NAN_CHECKS = `\n bool isNaN(float val) {\n return false;\n }\n\n bool hasNaN(vec4 values) {\n return false;\n }\n `;\n } else {\n /**\n * Previous NaN check '(val < 0.0 || 0.0 < val || val == 0.0) ? false :\n * true' does not work on iOS 12\n */\n NAN_CHECKS = `\n bool isNaN(float val) {\n return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n }\n\n bool hasNaN(vec4 values) {\n return any(bvec4(\n isNaN(values.x),\n isNaN(values.y),\n isNaN(values.z),\n isNaN(values.w)\n ));\n }\n `;\n }\n\n const SHADER_PREFIX = `${glsl.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${glsl.varyingFs} vec2 resultUV;\n ${glsl.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n ${NAN_CHECKS}\n\n float getNaN(vec4 values) {\n return dot(vec4(1), values);\n }\n\n ${glsl.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${SAMPLE_1D_SNIPPET}\n ${SAMPLE_2D_SNIPPET}\n ${SAMPLE_3D_SNIPPET}\n ${SAMPLE_5D_SNIPPET}\n ${SAMPLE_6D_SNIPPET}\n `;\n\n return SHADER_PREFIX;\n}\n\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_5D_SNIPPET = `\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n int stride1, int stride2, int stride3, int row, int col, int depth,\n int depth2, int depth3) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * stride0 + col * stride1 +\n depth * stride2 + depth2 * stride3 + depth3;\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_6D_SNIPPET = `\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n int stride1, int stride2, int stride3, int stride4,\n int row, int col, int depth, int depth2, int depth3, int depth4) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n stride3 + depth3 * stride4 + depth4;\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SHADER_PACKED_PREFIX = `\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;\n\nfunction getOutputScalarCoords() {\n return `\n int getOutputCoords() {\n return 0;\n }\n `;\n}\n\nfunction getOutputPacked1DCoords(\n shape: [number], texShape: [number, number]): string {\n const packedTexShape =\n [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n if (packedTexShape[0] === 1) {\n return `\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n }\n `;\n }\n\n if (packedTexShape[1] === 1) {\n return `\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n }\n `;\n }\n\n return `\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n return resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n }\n `;\n}\n\nfunction getOutput1DCoords(\n shape: [number], texShape: [number, number]): string {\n if (texShape[0] === 1) {\n return `\n int getOutputCoords() {\n return int(resultUV.x * ${texShape[1]}.0);\n }\n `;\n }\n if (texShape[1] === 1) {\n return `\n int getOutputCoords() {\n return int(resultUV.y * ${texShape[0]}.0);\n }\n `;\n }\n return `\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n return resTexRC.x * ${texShape[1]} + resTexRC.y;\n }\n `;\n}\n\nfunction getOutputPacked3DCoords(\n shape: [number, number, number], texShape: [number, number]): string {\n const packedTexShape =\n [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n\n return `\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n int b = index / ${texelsInBatch};\n index -= b * ${texelsInBatch};\n\n int r = 2 * (index / ${texelsInLogicalRow});\n int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n return ivec3(b, r, c);\n }\n `;\n}\n\nfunction getOutput3DCoords(\n shape: [number, number, number], texShape: [number, number]): string {\n const coordsFromIndexSnippet =\n shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n\n return `\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n ${coordsFromIndexSnippet}\n return ivec3(r, c, d);\n }\n `;\n}\n\nfunction getOutputPackedNDCoords(\n shape: number[], texShape: [number, number]): string {\n const packedTexShape =\n [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n const texelsInBatch =\n texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n let texelsInBatchN = texelsInBatch;\n let batches = ``;\n let coords = 'b, r, c';\n\n for (let b = 2; b < shape.length - 1; b++) {\n texelsInBatchN *= shape[shape.length - b - 1];\n batches = `\n int b${b} = index / ${texelsInBatchN};\n index -= b${b} * ${texelsInBatchN};\n ` + batches;\n coords = `b${b}, ` + coords;\n }\n\n return `\n ivec${shape.length} getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n ${batches}\n\n int b = index / ${texelsInBatch};\n index -= b * ${texelsInBatch};\n\n int r = 2 * (index / ${texelsInLogicalRow});\n int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n return ivec${shape.length}(${coords});\n }\n `;\n}\n\nfunction getOutput4DCoords(\n shape: [number, number, number, number],\n texShape: [number, number]): string {\n const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n ['r', 'c', 'd', 'd2'], shape);\n\n return `\n ivec4 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n ${coordsFromIndexSnippet}\n return ivec4(r, c, d, d2);\n }\n `;\n}\n\nfunction getOutput5DCoords(\n shape: [number, number, number, number, number],\n texShape: [number, number]): string {\n const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n ['r', 'c', 'd', 'd2', 'd3'], shape);\n\n return `\n ivec5 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n ${texShape[1]}));\n\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n ${coordsFromIndexSnippet}\n\n ivec5 outShape = ivec5(r, c, d, d2, d3);\n return outShape;\n }\n `;\n}\n\nfunction getOutput6DCoords(\n shape: [number, number, number, number, number, number],\n texShape: [number, number]): string {\n const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n ['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n\n return `\n ivec6 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n ${coordsFromIndexSnippet}\n\n ivec6 result = ivec6(r, c, d, d2, d3, d4);\n return result;\n }\n `;\n}\n\nfunction getOutputPacked2DCoords(\n shape: [number, number], texShape: [number, number]): string {\n const packedTexShape =\n [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n if (util.arraysEqual(shape, texShape)) {\n return `\n ivec2 getOutputCoords() {\n return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${\n packedTexShape[1]}));\n }\n `;\n }\n\n // texels needed to accommodate a logical row\n const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n\n /**\n * getOutputCoords\n *\n * resTexRC: The rows and columns of the texels. If you move over one\n * texel to the right in the packed texture, you are moving over one column\n * (not two).\n *\n * index: The texel index\n */\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n int r = 2 * (index / ${texelsInLogicalRow});\n int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n return ivec2(r, c);\n }\n `;\n}\n\nfunction getOutput2DCoords(\n shape: [number, number], texShape: [number, number]): string {\n if (util.arraysEqual(shape, texShape)) {\n return `\n ivec2 getOutputCoords() {\n return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n }\n `;\n }\n if (shape[1] === 1) {\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n return ivec2(index, 0);\n }\n `;\n }\n if (shape[0] === 1) {\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n return ivec2(0, index);\n }\n `;\n }\n return `\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${texShape[0]}, ${texShape[1]}));\n int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n int r = index / ${shape[1]};\n int c = index - r * ${shape[1]};\n return ivec2(r, c);\n }\n `;\n}\n\nfunction getFlatOffsetUniformName(texName: string): string {\n return `offset${texName}`;\n}\n\nfunction getPackedSamplerScalar(inputInfo: InputInfo): string {\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n const glsl = getGlslDifferences();\n return `\n vec4 ${funcName}() {\n return ${glsl.texture2D}(${texName}, halfCR);\n }\n `;\n}\n\nfunction getSamplerScalar(inputInfo: InputInfo): string {\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n if (inputInfo.shapeInfo.isUniform) {\n return `float ${funcName}() {return ${texName};}`;\n }\n const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n if (texNumR === 1 && texNumC === 1) {\n return `\n float ${funcName}() {\n return sampleTexture(${texName}, halfCR);\n }\n `;\n }\n\n const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n const offset = getFlatOffsetUniformName(texName);\n return `\n float ${funcName}() {\n vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n return sampleTexture(${texName}, uv);\n }\n `;\n}\n\nfunction getPackedSampler1D(inputInfo: InputInfo): string {\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n const texShape = inputInfo.shapeInfo.texShape;\n const packedTexShape =\n [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n const glsl = getGlslDifferences();\n\n return `\n vec4 ${funcName}(int index) {\n vec2 uv = packedUVfrom1D(\n ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n}\n\nfunction getSampler1D(inputInfo: InputInfo): string {\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n\n if (inputInfo.shapeInfo.isUniform) {\n // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n return `\n float ${funcName}(int index) {\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n\n const texShape = inputInfo.shapeInfo.texShape;\n const tNumR = texShape[0];\n const tNumC = texShape[1];\n\n if (tNumC === 1 && tNumR === 1) {\n return `\n float ${funcName}(int index) {\n return sampleTexture(${texName}, halfCR);\n }\n `;\n }\n const offset = getFlatOffsetUniformName(texName);\n if (tNumC === 1) {\n return `\n float ${funcName}(int index) {\n vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (tNumR === 1) {\n return `\n float ${funcName}(int index) {\n vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n return `\n float ${funcName}(int index) {\n vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n return sampleTexture(${texName}, uv);\n }\n `;\n}\n\nfunction getPackedSampler2D(inputInfo: InputInfo): string {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n const texShape = inputInfo.shapeInfo.texShape;\n\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n const glsl = getGlslDifferences();\n if (texShape != null && util.arraysEqual(shape, texShape)) {\n return `\n vec4 ${funcName}(int row, int col) {\n vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n }\n\n const packedTexShape =\n [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n const valuesPerRow = Math.ceil(shape[1] / 2);\n\n return `\n vec4 ${funcName}(int row, int col) {\n vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${\n packedTexShape[1]}, row, col);\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n}\n\nfunction getSampler2D(inputInfo: InputInfo): string {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n const texShape = inputInfo.shapeInfo.texShape;\n\n if (texShape != null && util.arraysEqual(shape, texShape)) {\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n return `\n float ${funcName}(int row, int col) {\n vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n\n const {newShape, keptDims} = util.squeezeShape(shape);\n const squeezedShape = newShape;\n if (squeezedShape.length < shape.length) {\n const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n const params = ['row', 'col'];\n return `\n ${getSamplerFromInInfo(newInputInfo)}\n float ${funcName}(int row, int col) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n\n if (inputInfo.shapeInfo.isUniform) {\n // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col) {\n int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n const offset = getFlatOffsetUniformName(texName);\n if (texNumC === 1) {\n // index is used directly as physical (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col) {\n float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (texNumR === 1) {\n // index is used directly as physical (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col) {\n float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n\n return `\n float ${funcName}(int row, int col) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${shape[1]} + col + ${offset};\n vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n return sampleTexture(${texName}, uv);\n }\n`;\n}\n\nfunction getPackedSampler3D(inputInfo: InputInfo): string {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n const texShape = inputInfo.shapeInfo.texShape;\n const packedTexShape =\n [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n if (shape[0] === 1) {\n const squeezedShape = shape.slice(1);\n const keptDims = [1, 2];\n const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n const params = ['b', 'row', 'col'];\n return `\n ${getPackedSamplerFromInInfo(newInputInfo)}\n vec4 ${funcName}(int b, int row, int col) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n\n const texNumR = packedTexShape[0];\n const texNumC = packedTexShape[1];\n\n const valuesPerRow = Math.ceil(shape[2] / 2);\n const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n const glsl = getGlslDifferences();\n\n return `\n vec4 ${funcName}(int b, int row, int col) {\n vec2 uv = packedUVfrom3D(\n ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n}\n\nfunction getSampler3D(inputInfo: InputInfo): string {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n const stride0 = shape[1] * shape[2];\n const stride1 = shape[2];\n\n const {newShape, keptDims} = util.squeezeShape(shape);\n const squeezedShape = newShape;\n if (squeezedShape.length < shape.length) {\n const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n const params = ['row', 'col', 'depth'];\n return `\n ${getSamplerFromInInfo(newInputInfo)}\n float ${funcName}(int row, int col, int depth) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n\n if (inputInfo.shapeInfo.isUniform) {\n // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth) {\n int index = round(dot(vec3(row, col, depth),\n vec3(${stride0}, ${stride1}, 1)));\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n\n const texShape = inputInfo.shapeInfo.texShape;\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n const flatOffset = inputInfo.shapeInfo.flatOffset;\n if (texNumC === stride0 && flatOffset == null) {\n // texC is used directly as physical (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth) {\n float texR = float(row);\n float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n\n if (texNumC === stride1 && flatOffset == null) {\n // texR is used directly as physical (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth) {\n float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n float texC = float(depth);\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n\n const offset = getFlatOffsetUniformName(texName);\n return `\n float ${funcName}(int row, int col, int depth) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n return sampleTexture(${texName}, uv);\n }\n `;\n}\n\nfunction getPackedSamplerND(inputInfo: InputInfo): string {\n const shape = inputInfo.shapeInfo.logicalShape;\n const rank = shape.length;\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n const texShape = inputInfo.shapeInfo.texShape;\n const packedTexShape =\n [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n const texNumR = packedTexShape[0];\n const texNumC = packedTexShape[1];\n\n const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n let params = `int b, int row, int col`;\n let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n for (let b = 2; b < rank - 1; b++) {\n params = `int b${b}, ` + params;\n texelsInBatch *= shape[rank - b - 1];\n index = `b${b} * ${texelsInBatch} + ` + index;\n }\n const glsl = getGlslDifferences();\n return `\n vec4 ${funcName}(${params}) {\n int index = ${index};\n int texR = index / ${texNumC};\n int texC = index - texR * ${texNumC};\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n return ${glsl.texture2D}(${texName}, uv);\n }\n `;\n}\n\nfunction getSampler4D(inputInfo: InputInfo): string {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n const stride2 = shape[3];\n const stride1 = shape[2] * stride2;\n const stride0 = shape[1] * stride1;\n\n const {newShape, keptDims} = util.squeezeShape(shape);\n if (newShape.length < shape.length) {\n const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n const params = ['row', 'col', 'depth', 'depth2'];\n return `\n ${getSamplerFromInInfo(newInputInfo)}\n float ${funcName}(int row, int col, int depth, int depth2) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n\n if (inputInfo.shapeInfo.isUniform) {\n // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n int index = round(dot(vec4(row, col, depth, depth2),\n vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n\n const flatOffset = inputInfo.shapeInfo.flatOffset;\n const texShape = inputInfo.shapeInfo.texShape;\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n\n if (texNumC === stride0 && flatOffset == null) {\n // texC is used directly as physical (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n float texR = float(row);\n float texC =\n dot(vec3(col, depth, depth2),\n vec3(${stride1}, ${stride2}, 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (texNumC === stride2 && flatOffset == null) {\n // texR is used directly as physical (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n float texR = dot(vec3(row, col, depth),\n vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n float texC = float(depth2);\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n\n const offset = getFlatOffsetUniformName(texName);\n return `\n float ${funcName}(int row, int col, int depth, int depth2) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${stride0} + col * ${stride1} +\n depth * ${stride2} + depth2;\n vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n return sampleTexture(${texName}, uv);\n }\n `;\n}\n\nfunction getSampler5D(inputInfo: InputInfo): string {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n const stride3 = shape[4];\n const stride2 = shape[3] * stride3;\n const stride1 = shape[2] * stride2;\n const stride0 = shape[1] * stride1;\n\n const {newShape, keptDims} = util.squeezeShape(shape);\n if (newShape.length < shape.length) {\n const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n return `\n ${getSamplerFromInInfo(newInputInfo)}\n float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n\n if (inputInfo.shapeInfo.isUniform) {\n // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n float index = dot(\n vec4(row, col, depth, depth2),\n vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n depth3;\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n\n const flatOffset = inputInfo.shapeInfo.flatOffset;\n const texShape = inputInfo.shapeInfo.texShape;\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n\n if (texNumC === stride0 && flatOffset == null) {\n // texC is used directly as physical (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n int texR = row;\n float texC = dot(vec4(col, depth, depth2, depth3),\n vec4(${stride1}, ${stride2}, ${stride3}, 1));\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n\n if (texNumC === stride3 && flatOffset == null) {\n // texR is used directly as physical (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n float texR = dot(\n vec4(row, col, depth, depth2),\n vec4(${shape[1] * shape[2] * shape[3]},\n ${shape[2] * shape[3]}, ${shape[3]}, 1));\n int texC = depth3;\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n\n const offset = getFlatOffsetUniformName(texName);\n return `\n float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n depth2 * ${stride3} + depth3 + ${offset};\n vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n return sampleTexture(${texName}, uv);\n }\n `;\n}\n\nfunction getSampler6D(inputInfo: InputInfo): string {\n const shape = inputInfo.shapeInfo.logicalShape;\n const texName = inputInfo.name;\n const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n\n const {newShape, keptDims} = util.squeezeShape(shape);\n if (newShape.length < shape.length) {\n const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n return `\n ${getSamplerFromInInfo(newInputInfo)}\n float ${funcName}(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n return ${funcName}(${getSqueezedParams(params, keptDims)});\n }\n `;\n }\n\n const stride4 = shape[5];\n const stride3 = shape[4] * stride4;\n const stride2 = shape[3] * stride3;\n const stride1 = shape[2] * stride2;\n const stride0 = shape[1] * stride1;\n\n if (inputInfo.shapeInfo.isUniform) {\n // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n int index = round(dot(\n vec4(row, col, depth, depth2),\n vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n dot(\n vec2(depth3, depth4),\n vec2(${stride4}, 1)));\n ${getUniformSampler(inputInfo)}\n }\n `;\n }\n\n const flatOffset = inputInfo.shapeInfo.flatOffset;\n const texShape = inputInfo.shapeInfo.texShape;\n const texNumR = texShape[0];\n const texNumC = texShape[1];\n if (texNumC === stride0 && flatOffset == null) {\n // texC is used directly as physical (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n int texR = row;\n float texC = dot(vec4(col, depth, depth2, depth3),\n vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n float(depth4);\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n if (texNumC === stride4 && flatOffset == null) {\n // texR is used directly as physical (no risk of float16 overflow).\n return `\n float ${funcName}(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n float texR = dot(vec4(row, col, depth, depth2),\n vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n ${shape[2] * shape[3] * shape[4]},\n ${shape[3] * shape[4]},\n ${shape[4]})) + float(depth3);\n int texC = depth4;\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${texNumC}.0, ${texNumR}.0);\n return sampleTexture(${texName}, uv);\n }\n `;\n }\n const offset = getFlatOffsetUniformName(texName);\n return `\n float ${funcName}(int row, int col, int depth,\n int depth2, int depth3, int depth4) {\n // Explicitly use integer operations as dot() only works on floats.\n int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n return sampleTexture(${texName}, uv);\n }\n `;\n}\n\nfunction getUniformSampler(inputInfo: InputInfo): string {\n const texName = inputInfo.name;\n const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n\n if (inSize === 1) {\n return `return ${texName};`;\n }\n return `\n for (int i = 0; i < ${inSize}; i++) {\n if (i == index) {\n return ${texName}[i];\n }\n }\n `;\n}\n\nfunction getPackedSamplerAtOutputCoords(\n inputInfo: InputInfo, outShapeInfo: ShapeInfo) {\n const texName = inputInfo.name;\n const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n const inRank = inputInfo.shapeInfo.logicalShape.length;\n const outRank = outShapeInfo.logicalShape.length;\n\n const broadcastDims = getBroadcastDims(\n inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n\n const type = getCoordsDataType(outRank);\n const rankDiff = outRank - inRank;\n let coordsSnippet: string;\n const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n\n if (inRank === 0) {\n coordsSnippet = '';\n } else if (outRank < 2 && broadcastDims.length >= 1) {\n coordsSnippet = 'coords = 0;';\n } else {\n coordsSnippet =\n broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n .join('\\n');\n }\n let unpackedCoordsSnippet = '';\n if (outRank < 2 && inRank > 0) {\n unpackedCoordsSnippet = 'coords';\n } else {\n unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n .map((s, i) => `coords.${fields[i + rankDiff]}`)\n .join(', ');\n }\n\n let output = `return outputValue;`;\n\n if (inRank === 1 && outRank > 1) {\n output = `\n return vec4(outputValue.xy, outputValue.xy);\n `;\n } else if (inRank === 0 && outRank > 0) {\n if (outRank === 1) {\n output = `\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n `;\n } else {\n output = `\n return vec4(outputValue.x);\n `;\n }\n } else if (broadcastDims.length) {\n const rows = inRank - 2;\n const cols = inRank - 1;\n\n if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n output = `return vec4(outputValue.x);`;\n } else if (broadcastDims.indexOf(rows) > -1) {\n output = `return vec4(outputValue.x, outputValue.y, ` +\n `outputValue.x, outputValue.y);`;\n } else if (broadcastDims.indexOf(cols) > -1) {\n output = `return vec4(outputValue.xx, outputValue.zz);`;\n }\n }\n\n return `\n vec4 ${funcName}() {\n ${type} coords = getOutputCoords();\n ${coordsSnippet}\n vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n ${output}\n }\n `;\n}\n\nfunction getSamplerAtOutputCoords(\n inputInfo: InputInfo, outShapeInfo: ShapeInfo) {\n const texName = inputInfo.name;\n const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n const outTexShape = outShapeInfo.texShape;\n const inTexShape = inputInfo.shapeInfo.texShape;\n const inRank = inputInfo.shapeInfo.logicalShape.length;\n const outRank = outShapeInfo.logicalShape.length;\n\n if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n inputInfo.shapeInfo.flatOffset == null &&\n util.arraysEqual(inTexShape, outTexShape)) {\n return `\n float ${funcName}() {\n return sampleTexture(${texName}, resultUV);\n }\n `;\n }\n\n const type = getCoordsDataType(outRank);\n const broadcastDims = getBroadcastDims(\n inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n const rankDiff = outRank - inRank;\n let coordsSnippet: string;\n const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n\n if (inRank === 0) {\n coordsSnippet = '';\n } else if (outRank < 2 && broadcastDims.length >= 1) {\n coordsSnippet = 'coords = 0;';\n } else {\n coordsSnippet =\n broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n .join('\\n');\n }\n let unpackedCoordsSnippet = '';\n if (outRank < 2 && inRank > 0) {\n unpackedCoordsSnippet = 'coords';\n } else {\n unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n .map((s, i) => `coords.${fields[i + rankDiff]}`)\n .join(', ');\n }\n\n return `\n float ${funcName}() {\n ${type} coords = getOutputCoords();\n ${coordsSnippet}\n return get${texFuncSnippet}(${unpackedCoordsSnippet});\n }\n `;\n}\n\nexport function getCoordsDataType(rank: number): string {\n if (rank <= 1) {\n return 'int';\n } else if (rank === 2) {\n return 'ivec2';\n } else if (rank === 3) {\n return 'ivec3';\n } else if (rank === 4) {\n return 'ivec4';\n } else if (rank === 5) {\n return 'ivec5';\n } else if (rank === 6) {\n return 'ivec6';\n } else {\n throw Error(`GPU for rank ${rank} is not yet supported`);\n }\n}\n\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(\n inInfo: InputInfo, squeezedShape: number[]): InputInfo {\n // Deep copy.\n const newInputInfo: InputInfo = JSON.parse(JSON.stringify(inInfo));\n newInputInfo.shapeInfo.logicalShape = squeezedShape;\n return newInputInfo;\n}\n\nfunction getSqueezedParams(params: string[], keptDims: number[]): string {\n return keptDims.map(d => params[d]).join(', ');\n}\n","../src/kernels/webgl/cumsum_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class CumSumProgram implements GPGPUProgram {\n variableNames = ['x'];\n outputShape: number[];\n userCode: string;\n\n constructor(shape: number[], exclusive: boolean, reverse: boolean) {\n this.outputShape = shape;\n const rank = shape.length;\n const finalDim = shape[shape.length - 1];\n const comparator = reverse ? '<' : '>';\n\n this.userCode = `\n int getIndex(int i) {\n ${reverse ? `return ${finalDim} -i - 1;` : 'return i;'}\n }\n\n void main() {\n ${getCoordsDataType(rank)} coords = getOutputCoords();\n int end = ${getFinalCoord(rank, 'coords')};\n float val = 0.0;\n for (int i = ${finalDim} - 1; i >= 0; i -= 1) {\n int idx = getIndex(i);\n if (idx ${comparator} end) {\n continue;\n }\n if (idx == end && ${exclusive}) {\n continue;\n }\n ${getFinalCoord(rank, 'coords')} = idx;\n val += getX(${getCoords(rank, 'coords')});\n }\n setOutput(val);\n }\n `;\n }\n}\n\nfunction getCoords(rank: number, name: string): string {\n if (rank === 1) {\n return `${name}`;\n } else if (rank === 2) {\n return `${name}.x, ${name}.y`;\n } else if (rank === 3) {\n return `${name}.x, ${name}.y, ${name}.z`;\n } else if (rank === 4) {\n return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n } else {\n throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n }\n}\n\nfunction getFinalCoord(rank: number, name: string): string {\n if (rank === 1) {\n return `${name}`;\n } else if (rank === 2) {\n return `${name}.y`;\n } else if (rank === 3) {\n return `${name}.z`;\n } else if (rank === 4) {\n return `${name}.w`;\n } else {\n throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n }\n}\n","../src/kernels/webgl/depth_to_space_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthToSpaceProgram implements GPGPUProgram {\n variableNames = ['x'];\n outputShape: number[] = [];\n userCode: string;\n blockSize: number;\n dataFormat: string;\n\n constructor(\n outputShape: number[], blockSize: number, dataFormat: 'NHWC'|'NCHW') {\n this.outputShape = outputShape;\n this.blockSize = blockSize;\n this.dataFormat = dataFormat;\n this.userCode = `\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${blockSize};\n int offset_h = imod(h, ${blockSize});\n int in_w = w / ${blockSize};\n int offset_w = imod(w, ${blockSize});\n int offset_d = (offset_h * ${blockSize} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `;\n }\n\n private getHeightCoordString(): string {\n if (this.dataFormat === 'NHWC') {\n return `coords[1]`;\n } else {\n return `coords[2]`;\n }\n }\n\n private getWidthCoordString(): string {\n if (this.dataFormat === 'NHWC') {\n return `coords[2]`;\n } else {\n return `coords[3]`;\n }\n }\n\n private getDepthCoordString(): string {\n if (this.dataFormat === 'NHWC') {\n return `coords[3]`;\n } else {\n return `coords[1]`;\n }\n }\n\n private getOutputDepthSize(): number {\n if (this.dataFormat === 'NHWC') {\n return this.outputShape[3];\n } else {\n return this.outputShape[1];\n }\n }\n\n private getInputSamplingString(): string {\n if (this.dataFormat === 'NHWC') {\n return `getX(b, in_h, in_w, in_d)`;\n } else {\n return `getX(b, in_d, in_h, in_w)`;\n }\n }\n}\n","../src/kernels/webgl/encode_float_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class EncodeFloatProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n outputShape: number[];\n\n constructor(outputShape: number[]) {\n const glsl = getGlslDifferences();\n this.outputShape = outputShape;\n this.userCode = `\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isNaN(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n\n void main() {\n float x = getAAtOutCoords();\n ${glsl.output} = encode_float(x);\n }\n `;\n }\n}\n","../src/kernels/webgl/fft_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport const COMPLEX_FFT = {\n REAL: 'return real * expR - imag * expI;',\n IMAG: 'return real * expI + imag * expR;'\n};\n\nexport class FFTProgram implements GPGPUProgram {\n variableNames = ['real', 'imag'];\n outputShape: number[];\n userCode: string;\n\n constructor(op: string, inputShape: [number, number], inverse: boolean) {\n const innerDim = inputShape[1];\n this.outputShape = inputShape;\n\n const exponentMultiplierSnippet =\n inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n\n this.userCode = `\n const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${op}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${innerDim});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${innerDim}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `;\n }\n}\n","../src/kernels/webgl/from_pixels_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class FromPixelsProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n outputShape: number[];\n\n constructor(outputShape: number[]) {\n const glsl = getGlslDifferences();\n const [height, width, ] = outputShape;\n this.outputShape = outputShape;\n this.userCode = `\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n vec4 values = ${glsl.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `;\n }\n}\n","../src/kernels/webgl/gather_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherProgram implements GPGPUProgram {\n variableNames = ['A', 'indices'];\n outputShape: number[];\n userCode: string;\n rank: number;\n\n constructor(aShape: number[], indicesLength: number, axis: number) {\n const outputShape: number[] = aShape.slice();\n outputShape[axis] = indicesLength;\n this.outputShape = outputShape;\n this.rank = outputShape.length;\n const dtype = getCoordsDataType(this.rank);\n const sourceCoords = getSourceCoords(aShape, axis);\n\n this.userCode = `\n void main() {\n ${dtype} resRC = getOutputCoords();\n setOutput(getA(${sourceCoords}));\n }\n `;\n }\n}\n\nfunction getSourceCoords(aShape: number[], axis: number): string {\n const rank = aShape.length;\n if (rank > 4) {\n throw Error(`Gather for rank ${rank} is not yet supported`);\n }\n if (rank === 1) {\n return `int(getIndices(resRC))`;\n }\n\n const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n\n const sourceCoords = [];\n for (let i = 0; i < aShape.length; i++) {\n if (i === axis) {\n sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n } else {\n sourceCoords.push(`${currentCoords[i]}`);\n }\n }\n return sourceCoords.join();\n}\n","../src/kernels/webgl/gather_nd_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherNDProgram implements GPGPUProgram {\n variableNames = ['x', 'indices'];\n outputShape: number[];\n userCode: string;\n constructor(\n private sliceDim: number, private strides: number[], shape: number[]) {\n this.outputShape = shape;\n const stridesType = getCoordsDataType(strides.length);\n const dtype = getCoordsDataType(shape.length);\n const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n this.userCode = `\n ${stridesType} strides = ${stridesType}(${this.strides});\n void main() {\n ${dtype} coords = getOutputCoords();\n int flattenIndex = 0;\n for (int j = 0; j < ${this.sliceDim}; j++) {\n int index = round(getIndices(coords[0], j));\n flattenIndex += index * ${strideString};\n }\n setOutput(getX(flattenIndex, coords[1]));\n }\n `;\n }\n}\n","../src/kernels/webgl/tex_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataId, Tensor} from '../../tensor';\nimport {DataType, DataValues} from '../../types';\nimport * as util from '../../util';\n\nexport enum TextureUsage {\n RENDER,\n UPLOAD,\n PIXELS,\n DOWNLOAD\n}\n\nexport enum PhysicalTextureType {\n UNPACKED_FLOAT16,\n UNPACKED_FLOAT32,\n PACKED_4X1_UNSIGNED_BYTE,\n PACKED_2X2_FLOAT32,\n PACKED_2X2_FLOAT16\n}\n\nexport interface TextureData {\n // Required.\n shape: number[];\n dtype: DataType;\n\n // Optional.\n values?: DataValues;\n texture?: WebGLTexture;\n // For complex numbers, the real and imaginary parts are stored as their own\n // individual tensors, with a parent joining the two with the\n // complexTensors field. When this is defined, texture will be null.\n complexTensors?: {real: Tensor, imag: Tensor};\n /** [rows, columns] shape of the texture. */\n texShape?: [number, number];\n usage?: TextureUsage;\n isPacked?: boolean;\n\n // Available when the tensor has been sliced.\n slice?: {\n // Offset in the 'flat index' space.\n flatOffset: number;\n // Used for counting how many sliced tensors point to the same texture.\n origDataId: DataId;\n };\n}\n\nexport function getUnpackedMatrixTextureShapeWidthHeight(\n rows: number, columns: number): [number, number] {\n return [columns, rows];\n}\n\nexport function getUnpackedArraySizeFromMatrixSize(\n matrixSize: number, channelsPerTexture: number): number {\n return matrixSize * channelsPerTexture;\n}\n\nexport function getColorMatrixTextureShapeWidthHeight(\n rows: number, columns: number): [number, number] {\n return [columns * 4, rows];\n}\n\nexport function getMatrixSizeFromUnpackedArraySize(\n unpackedSize: number, channelsPerTexture: number): number {\n if (unpackedSize % channelsPerTexture !== 0) {\n throw new Error(\n `unpackedSize (${unpackedSize}) must be a multiple of ` +\n `${channelsPerTexture}`);\n }\n return unpackedSize / channelsPerTexture;\n}\n\nexport function encodeMatrixToUnpackedArray(\n matrix: Float32Array|Uint8Array, unpackedArray: Float32Array|Uint8Array,\n channelsPerTexture: number) {\n const requiredSize =\n getUnpackedArraySizeFromMatrixSize(matrix.length, channelsPerTexture);\n if (unpackedArray.length < requiredSize) {\n throw new Error(\n `unpackedArray length (${unpackedArray.length}) must be >= ` +\n `${requiredSize}`);\n }\n let dst = 0;\n for (let src = 0; src < matrix.length; ++src) {\n unpackedArray[dst] = matrix[src];\n dst += channelsPerTexture;\n }\n}\n\nexport function decodeMatrixFromUnpackedArray(\n unpackedArray: Float32Array, matrix: Float32Array,\n channelsPerTexture: number) {\n const requiredSize = getMatrixSizeFromUnpackedArraySize(\n unpackedArray.length, channelsPerTexture);\n if (matrix.length < requiredSize) {\n throw new Error(\n `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n }\n let dst = 0;\n for (let src = 0; src < unpackedArray.length; src += channelsPerTexture) {\n matrix[dst++] = unpackedArray[src];\n }\n}\n\nexport function decodeMatrixFromUnpackedColorRGBAArray(\n unpackedArray: Float32Array, matrix: Float32Array, channels: number) {\n const requiredSize = unpackedArray.length * channels / 4;\n if (matrix.length < requiredSize) {\n throw new Error(\n `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n }\n let dst = 0;\n for (let src = 0; src < unpackedArray.length; src += 4) {\n for (let c = 0; c < channels; c++) {\n matrix[dst++] = unpackedArray[src + c];\n }\n }\n}\n\nexport function getPackedMatrixTextureShapeWidthHeight(\n rows: number, columns: number): [number, number] {\n return [\n Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n ];\n}\n\nexport function getPackedRGBAArraySizeFromMatrixShape(\n rows: number, columns: number): number {\n const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n return w * h * 4;\n}\n\n/*\nThis is how encodeMatrixToPackedRGBA encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001 002|003 004|xxx 020|021 022|023 024|xxx\n------- ------- ------- ------- ------- -------\n010|011 012|013 014|xxx xxx|xxx xxx|xxx xxx|xxx\n\n100|101 102|103 104|xxx 120|121 122|123 124|xxx\n------- ------- ------- ------- ------- -------\n110|111 112|113 114|xxx xxx|xxx xxx|xxx xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n\nNote the batch dimension is needed so xxx's are inserted below 020, 021, 022,\n023, and 024.\n */\n\nexport function encodeMatrixToPackedRGBA(\n matrix: Float32Array, batches: number, rows: number, columns: number,\n packedRGBA: Float32Array) {\n const oddWidth = (columns % 2) === 1;\n const oddHeight = (rows % 2) === 1;\n const widthInFullBlocks = Math.floor(columns / 2);\n const heightInFullBlocks = Math.floor(rows / 2);\n\n const texelsPerRow = Math.ceil(columns / 2);\n const texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);\n\n const flattenedMatrixSize =\n util.nearestLargerEven(rows) * util.nearestLargerEven(columns);\n\n for (let batch = 0; batch < batches; batch++) {\n const sourceOffset = batch * rows * columns;\n const batchOffset = batch * flattenedMatrixSize;\n\n // loop over full 2x2 blocks\n {\n const dstStride = (oddWidth ? 4 : 0);\n const oneRow = columns;\n let dst = batchOffset;\n for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n const matrixSrcRow = (blockY * 2 * columns);\n for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n const matrixSrcCol = blockX * 2;\n const src = sourceOffset + matrixSrcRow + matrixSrcCol;\n packedRGBA[dst] = matrix[src];\n packedRGBA[dst + 1] = matrix[src + 1];\n packedRGBA[dst + 2] = matrix[src + oneRow];\n packedRGBA[dst + 3] = matrix[src + oneRow + 1];\n dst += 4;\n }\n dst += dstStride;\n }\n }\n\n // loop down final odd column\n if (oddWidth) {\n let src = sourceOffset + columns - 1;\n let dst = batchOffset + (texelsPerRow - 1) * 4;\n const srcStride = 2 * columns;\n const dstStride = texelsPerRow * 4;\n for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n packedRGBA[dst] = matrix[src];\n packedRGBA[dst + 2] = matrix[src + columns];\n src += srcStride;\n dst += dstStride;\n }\n }\n\n // loop across final row\n if (oddHeight) {\n let src = sourceOffset + (rows - 1) * columns;\n let dst = batchOffset + (texelsPerBatch - texelsPerRow) * 4;\n for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n packedRGBA[dst++] = matrix[src++];\n packedRGBA[dst++] = matrix[src++];\n dst += 2;\n }\n\n // fill in bottom-right texel\n if (oddWidth && oddHeight) {\n packedRGBA[batchOffset + flattenedMatrixSize - 4] = matrix[src];\n }\n }\n }\n\n return packedRGBA;\n}\n\nexport function decodeMatrixFromPackedRGBA(\n packedRGBA: Float32Array, batches: number, rows: number, columns: number,\n matrix: Float32Array): Float32Array {\n const requiredSize = rows * columns;\n if (matrix.length < requiredSize) {\n throw new Error(\n `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n }\n\n const oddWidth = (columns % 2) === 1;\n const oddHeight = (rows % 2) === 1;\n const widthInFullBlocks = Math.floor(columns / 2);\n const heightInFullBlocks = Math.floor(rows / 2);\n\n const texelsPerRow = Math.ceil(columns / 2);\n const texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);\n\n const flattenedMatrixSize =\n util.nearestLargerEven(rows) * util.nearestLargerEven(columns);\n\n for (let batch = 0; batch < batches; batch++) {\n const batchOffset = batch * rows * columns;\n const sourceOffset = batch * flattenedMatrixSize;\n\n // loop over full 2x2 blocks\n {\n const srcStride = oddWidth ? 4 : 0;\n const dstStride = columns + (oddWidth ? 1 : 0);\n let src = sourceOffset;\n let dstRow1 = batchOffset;\n let dstRow2 = batchOffset + columns;\n for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n matrix[dstRow1++] = packedRGBA[src++];\n matrix[dstRow1++] = packedRGBA[src++];\n matrix[dstRow2++] = packedRGBA[src++];\n matrix[dstRow2++] = packedRGBA[src++];\n }\n src += srcStride;\n dstRow1 += dstStride;\n dstRow2 += dstStride;\n }\n }\n\n // loop down final column\n if (oddWidth) {\n let src = sourceOffset + (texelsPerRow - 1) * 4;\n let dst = batchOffset + columns - 1;\n const srcStride = texelsPerRow * 4;\n const dstStride = 2 * columns;\n for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n matrix[dst] = packedRGBA[src];\n matrix[dst + columns] = packedRGBA[src + 2];\n src += srcStride;\n dst += dstStride;\n }\n }\n\n // loop across final row\n if (oddHeight) {\n let src = sourceOffset + (texelsPerBatch - texelsPerRow) * 4;\n let dst = batchOffset + (rows - 1) * columns;\n for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n matrix[dst++] = packedRGBA[src++];\n matrix[dst++] = packedRGBA[src++];\n src += 2;\n }\n\n // fill in bottom-right cell\n if (oddWidth) {\n matrix[batchOffset + (rows * columns) - 1] = packedRGBA[src];\n }\n }\n }\n\n return matrix;\n}\n","../src/kernels/webgl/webgl_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\n\nexport function callAndCheck(gl: WebGLRenderingContext, func: () => T): T {\n const returnValue = func();\n checkWebGLError(gl);\n return returnValue;\n}\n\nlet webGLDebugErrorCheckingEnabled = false;\n\nexport function enableDebugWebGLErrorChecking(enabled: boolean) {\n webGLDebugErrorCheckingEnabled = enabled;\n}\n\nexport function checkWebGLError(gl: WebGLRenderingContext) {\n if (webGLDebugErrorCheckingEnabled) {\n const error = gl.getError();\n if (error !== gl.NO_ERROR) {\n throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n }\n }\n}\n\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\n\nexport function canBeRepresented(num: number): boolean {\n if (ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n return true;\n }\n return false;\n}\n\nexport function getWebGLErrorMessage(\n gl: WebGLRenderingContext, status: number): string {\n switch (status) {\n case gl.NO_ERROR:\n return 'NO_ERROR';\n case gl.INVALID_ENUM:\n return 'INVALID_ENUM';\n case gl.INVALID_VALUE:\n return 'INVALID_VALUE';\n case gl.INVALID_OPERATION:\n return 'INVALID_OPERATION';\n case gl.INVALID_FRAMEBUFFER_OPERATION:\n return 'INVALID_FRAMEBUFFER_OPERATION';\n case gl.OUT_OF_MEMORY:\n return 'OUT_OF_MEMORY';\n case gl.CONTEXT_LOST_WEBGL:\n return 'CONTEXT_LOST_WEBGL';\n default:\n return `Unknown error code ${status}`;\n }\n}\n\nexport function getExtensionOrThrow(\n gl: WebGLRenderingContext, extensionName: string): {} {\n return throwIfNull<{}>(\n gl, () => gl.getExtension(extensionName),\n 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\n\nexport function createVertexShader(\n gl: WebGLRenderingContext, vertexShaderSource: string): WebGLShader {\n const vertexShader: WebGLShader = throwIfNull(\n gl, () => gl.createShader(gl.VERTEX_SHADER),\n 'Unable to create vertex WebGLShader.');\n callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n callAndCheck(gl, () => gl.compileShader(vertexShader));\n if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n console.log(gl.getShaderInfoLog(vertexShader));\n throw new Error('Failed to compile vertex shader.');\n }\n return vertexShader;\n}\n\nexport function createFragmentShader(\n gl: WebGLRenderingContext, fragmentShaderSource: string): WebGLShader {\n const fragmentShader: WebGLShader = throwIfNull(\n gl, () => gl.createShader(gl.FRAGMENT_SHADER),\n 'Unable to create fragment WebGLShader.');\n callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n callAndCheck(gl, () => gl.compileShader(fragmentShader));\n if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n logShaderSourceAndInfoLog(\n fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n throw new Error('Failed to compile fragment shader.');\n }\n return fragmentShader;\n}\n\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(\n shaderSource: string, shaderInfoLog: string) {\n const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n if (lineNumberRegexResult == null) {\n console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n console.log(shaderSource);\n return;\n }\n\n const lineNumber = +lineNumberRegexResult[1];\n\n const shaderLines = shaderSource.split('\\n');\n const pad = shaderLines.length.toString().length + 2;\n const linesWithLineNumbers = shaderLines.map(\n (line, lineNumber) =>\n util.rightPad((lineNumber + 1).toString(), pad) + line);\n let maxLineLength = 0;\n for (let i = 0; i < linesWithLineNumbers.length; i++) {\n maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n }\n\n const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n\n console.log(beforeErrorLines.join('\\n'));\n console.log(shaderInfoLog.split('\\n')[0]);\n console.log(\n `%c ${util.rightPad(errorLine[0], maxLineLength)}`,\n 'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n console.log(afterErrorLines.join('\\n'));\n}\n\nexport function createProgram(gl: WebGLRenderingContext): WebGLProgram {\n return throwIfNull(\n gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\n\nexport function linkProgram(gl: WebGLRenderingContext, program: WebGLProgram) {\n callAndCheck(gl, () => gl.linkProgram(program));\n if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n console.log(gl.getProgramInfoLog(program));\n throw new Error('Failed to link vertex and fragment shaders.');\n }\n}\n\nexport function validateProgram(\n gl: WebGLRenderingContext, program: WebGLProgram) {\n callAndCheck(gl, () => gl.validateProgram(program));\n if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n console.log(gl.getProgramInfoLog(program));\n throw new Error('Shader program validation failed.');\n }\n}\n\nexport function createStaticVertexBuffer(\n gl: WebGLRenderingContext, data: Float32Array): WebGLBuffer {\n const buffer: WebGLBuffer = throwIfNull(\n gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n return buffer;\n}\n\nexport function createStaticIndexBuffer(\n gl: WebGLRenderingContext, data: Uint16Array): WebGLBuffer {\n const buffer: WebGLBuffer = throwIfNull(\n gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n callAndCheck(\n gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n return buffer;\n}\n\nexport function getNumChannels(): number {\n if (ENV.get('WEBGL_VERSION') === 2) {\n return 1;\n }\n return 4;\n}\n\nexport function createTexture(gl: WebGLRenderingContext): WebGLTexture {\n return throwIfNull(\n gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\n\nexport function validateTextureSize(width: number, height: number) {\n const maxTextureSize = ENV.get('WEBGL_MAX_TEXTURE_SIZE');\n if ((width <= 0) || (height <= 0)) {\n const requested = `[${width}x${height}]`;\n throw new Error('Requested texture size ' + requested + ' is invalid.');\n }\n if ((width > maxTextureSize) || (height > maxTextureSize)) {\n const requested = `[${width}x${height}]`;\n const max = `[${maxTextureSize}x${maxTextureSize}]`;\n throw new Error(\n 'Requested texture size ' + requested +\n ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n }\n}\n\nexport function createFramebuffer(gl: WebGLRenderingContext): WebGLFramebuffer {\n return throwIfNull(\n gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\n\nexport function bindVertexBufferToProgramAttribute(\n gl: WebGLRenderingContext, program: WebGLProgram, attribute: string,\n buffer: WebGLBuffer, arrayEntriesPerItem: number, itemStrideInBytes: number,\n itemOffsetInBytes: number): boolean {\n const loc = gl.getAttribLocation(program, attribute);\n if (loc === -1) {\n // The GPU compiler decided to strip out this attribute because it's unused,\n // thus no need to bind.\n return false;\n }\n callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n callAndCheck(\n gl,\n () => gl.vertexAttribPointer(\n loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes,\n itemOffsetInBytes));\n callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n return true;\n}\n\nexport function bindTextureUnit(\n gl: WebGLRenderingContext, texture: WebGLTexture, textureUnit: number) {\n validateTextureUnit(gl, textureUnit);\n callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\n\nexport function unbindTextureUnit(\n gl: WebGLRenderingContext, textureUnit: number) {\n validateTextureUnit(gl, textureUnit);\n callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function getProgramUniformLocationOrThrow(\n gl: WebGLRenderingContext, program: WebGLProgram,\n uniformName: string): WebGLUniformLocation {\n return throwIfNull(\n gl, () => gl.getUniformLocation(program, uniformName),\n 'uniform \"' + uniformName + '\" not present in program.');\n}\n\nexport function getProgramUniformLocation(\n gl: WebGLRenderingContext, program: WebGLProgram,\n uniformName: string): WebGLUniformLocation {\n return gl.getUniformLocation(program, uniformName);\n}\n\nexport function bindTextureToProgramUniformSampler(\n gl: WebGLRenderingContext, program: WebGLProgram, texture: WebGLTexture,\n uniformSamplerLocation: WebGLUniformLocation, textureUnit: number) {\n callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\n\nexport function bindCanvasToFramebuffer(gl: WebGLRenderingContext) {\n callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\n\nexport function bindColorTextureToFramebuffer(\n gl: WebGLRenderingContext, texture: WebGLTexture,\n framebuffer: WebGLFramebuffer) {\n callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n callAndCheck(\n gl,\n () => gl.framebufferTexture2D(\n gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\n\nexport function unbindColorTextureFromFramebuffer(\n gl: WebGLRenderingContext, framebuffer: WebGLFramebuffer) {\n callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n callAndCheck(\n gl,\n () => gl.framebufferTexture2D(\n gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\n\nexport function validateFramebuffer(gl: WebGLRenderingContext) {\n const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n if (status !== gl.FRAMEBUFFER_COMPLETE) {\n throw new Error(\n 'Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n }\n}\n\nexport function getFramebufferErrorMessage(\n gl: WebGLRenderingContext, status: number): string {\n switch (status) {\n case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n case gl.FRAMEBUFFER_UNSUPPORTED:\n return 'FRAMEBUFFER_UNSUPPORTED';\n default:\n return `unknown error ${status}`;\n }\n}\n\nfunction throwIfNull(\n gl: WebGLRenderingContext, returnTOrNull: () => T | null,\n failureMessage: string): T {\n const tOrNull: T|null = callAndCheck(gl, () => returnTOrNull());\n if (tOrNull == null) {\n throw new Error(failureMessage);\n }\n return tOrNull as T;\n}\n\nfunction validateTextureUnit(gl: WebGLRenderingContext, textureUnit: number) {\n const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n const glTextureUnit = textureUnit + gl.TEXTURE0;\n if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n }\n}\n\nexport function getBatchDim(shape: number[], dimsToSkip = 2): number {\n return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\n\nexport function getRowsCols(shape: number[]): [number, number] {\n if (shape.length === 0) {\n throw Error('Cannot get rows and columns of an empty shape array.');\n }\n\n return [\n shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n ];\n}\n\nexport function getTextureShapeFromLogicalShape(\n logShape: number[], isPacked = false): [number, number] {\n let maxTexSize = ENV.get('WEBGL_MAX_TEXTURE_SIZE');\n if (isPacked) {\n maxTexSize = maxTexSize * 2;\n\n // This logic ensures we accurately count the number of packed texels needed\n // to accommodate the tensor. We can only pack values in the same texel if\n // they are from adjacent pairs of rows/cols within the same batch. So if a\n // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n // fact that the texels containing the third row are half empty.\n logShape = logShape.map(\n (d, i) => i >= logShape.length - 2 ?\n util.nearestLargerEven(logShape[i]) :\n logShape[i]);\n\n // Packed texture height is at least 2 (the channel height of a single\n // texel).\n if (logShape.length === 1) {\n logShape = [2, logShape[0]];\n }\n }\n\n // If logical shape is 2, we don't squeeze, since we want to match physical.\n if (logShape.length !== 2) {\n const squeezeResult = util.squeezeShape(logShape);\n logShape = squeezeResult.newShape;\n }\n\n let size = util.sizeFromShape(logShape);\n if (logShape.length <= 1 && size <= maxTexSize) {\n return [1, size];\n } else if (\n logShape.length === 2 && logShape[0] <= maxTexSize &&\n logShape[1] <= maxTexSize) {\n return logShape as [number, number];\n } else if (\n logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n logShape[2] <= maxTexSize) {\n return [logShape[0] * logShape[1], logShape[2]];\n } else if (\n logShape.length === 3 && logShape[0] <= maxTexSize &&\n logShape[1] * logShape[2] <= maxTexSize) {\n return [logShape[0], logShape[1] * logShape[2]];\n } else if (\n logShape.length === 4 &&\n logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n logShape[3] <= maxTexSize) {\n return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n } else if (\n logShape.length === 4 && logShape[0] <= maxTexSize &&\n logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n } else {\n if (isPacked) {\n // For packed textures size equals the number of channels required to\n // accommodate the texture data. However in order to squarify such that\n // inner dimensions stay even, we rewrite size to equal the number of\n // texels. Then in the return statement we rehydrate the squarified\n // dimensions to channel units.\n\n const batchDim = getBatchDim(logShape);\n let rows = 2, cols = 2;\n if (logShape.length) {\n [rows, cols] = getRowsCols(logShape);\n }\n size = batchDim * (rows / 2) * (cols / 2);\n return util.sizeToSquarishShape(size).map(d => d * 2) as [number, number];\n }\n return util.sizeToSquarishShape(size);\n }\n}\n\nfunction isEven(n: number): boolean {\n return n % 2 === 0;\n}\n\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1: number[], shape2: number[]): boolean {\n shape1 = shape1.slice(-2);\n shape2 = shape2.slice(-2);\n\n if (util.arraysEqual(shape1, shape2)) {\n return true;\n }\n\n if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.\n return true;\n }\n\n if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n shape2[1] === 0) {\n return true;\n }\n\n if (shape1.length !== shape2.length) { // One of the shapes is a vector.\n const shape1Cols = shape1.slice(-1)[0];\n const shape2Cols = shape2.slice(-1)[0];\n if (shape1Cols === shape2Cols) {\n return true;\n }\n\n if (isEven(shape1Cols) && isEven(shape2Cols) &&\n (shape1[0] === 1 || shape2[0] === 1)) {\n return true;\n }\n }\n return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n","../src/kernels/webgl/gpgpu_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\nimport {getGlslDifferences} from './glsl_version';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\n\nexport interface TextureConfig {\n internalFormatFloat: number;\n textureFormatFloat: number;\n internalFormatHalfFloat: number;\n internalFormatPackedFloat: number;\n\n // The format to use during a gl.readPixels call.\n downloadTextureFormat: number;\n // How many channels need to be unpacked after a gl.readPixels call.\n downloadUnpackNumChannels: number;\n\n defaultNumChannels: number;\n textureTypeHalfFloat: number;\n}\n\nexport function createVertexShader(gl: WebGLRenderingContext): WebGLShader {\n const glsl = getGlslDifferences();\n const vertexShaderSource = `${glsl.version}\n precision highp float;\n ${glsl.attribute} vec3 clipSpacePos;\n ${glsl.attribute} vec2 uv;\n ${glsl.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = vec4(clipSpacePos, 1);\n resultUV = uv;\n }`;\n return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\n\nexport function createVertexBuffer(gl: WebGLRenderingContext): WebGLBuffer {\n // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n const vertexArray = new Float32Array(\n [-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\n\nexport function createIndexBuffer(gl: WebGLRenderingContext): WebGLBuffer {\n // OpenGL (and WebGL) have \"CCW == front\" winding\n const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\n\nexport function getTextureConfig(\n // tslint:disable-next-line:no-any\n gl: WebGLRenderingContext, textureHalfFloatExtension?: any): TextureConfig {\n // tslint:disable-next-line:no-any\n const glany = gl as any;\n\n let internalFormatFloat: number;\n let internalFormatHalfFloat: number;\n let internalFormatPackedFloat: number;\n let textureFormatFloat: number;\n\n let downloadTextureFormat: number;\n let downloadUnpackNumChannels: number;\n\n let defaultNumChannels: number;\n let textureTypeHalfFloat: number;\n\n if (ENV.get('WEBGL_VERSION') === 2) {\n internalFormatFloat = glany.R32F;\n internalFormatHalfFloat = glany.R16F;\n internalFormatPackedFloat = glany.RGBA32F;\n textureFormatFloat = glany.RED;\n downloadUnpackNumChannels = 4;\n defaultNumChannels = 1;\n textureTypeHalfFloat = glany.HALF_FLOAT;\n } else {\n internalFormatFloat = gl.RGBA;\n internalFormatHalfFloat = gl.RGBA;\n internalFormatPackedFloat = glany.RGBA;\n textureFormatFloat = gl.RGBA;\n downloadUnpackNumChannels = 4;\n defaultNumChannels = 4;\n textureTypeHalfFloat = textureHalfFloatExtension != null ?\n textureHalfFloatExtension.HALF_FLOAT_OES :\n null;\n }\n downloadTextureFormat = gl.RGBA;\n\n return {\n internalFormatFloat,\n internalFormatHalfFloat,\n internalFormatPackedFloat,\n textureFormatFloat,\n downloadTextureFormat,\n downloadUnpackNumChannels,\n defaultNumChannels,\n textureTypeHalfFloat\n };\n}\n\nfunction createAndConfigureTexture(\n gl: WebGLRenderingContext, width: number, height: number,\n internalFormat: number, textureFormat: number,\n textureType: number): WebGLTexture {\n webgl_util.validateTextureSize(width, height);\n const texture = webgl_util.createTexture(gl);\n\n const tex2d = gl.TEXTURE_2D;\n webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n webgl_util.callAndCheck(\n gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n webgl_util.callAndCheck(\n gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n webgl_util.callAndCheck(\n gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n webgl_util.callAndCheck(\n gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n webgl_util.callAndCheck(\n gl,\n () => gl.texImage2D(\n tex2d, 0, internalFormat, width, height, 0, textureFormat,\n textureType, null));\n webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n return texture;\n}\n\nexport function createFloat32MatrixTexture(\n gl: WebGLRenderingContext, rows: number, columns: number,\n textureConfig: TextureConfig): WebGLTexture {\n const [width, height] =\n tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n return createAndConfigureTexture(\n gl, width, height, textureConfig.internalFormatFloat,\n textureConfig.textureFormatFloat, gl.FLOAT);\n}\n\nexport function createFloat16MatrixTexture(\n gl: WebGLRenderingContext, rows: number, columns: number,\n textureConfig: TextureConfig): WebGLTexture {\n const [width, height] =\n tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n return createAndConfigureTexture(\n gl, width, height, textureConfig.internalFormatFloat,\n textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\n\nexport function createUnsignedBytesMatrixTexture(\n gl: WebGLRenderingContext, rows: number, columns: number,\n textureConfig: TextureConfig): WebGLTexture {\n const [width, height] =\n tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n return createAndConfigureTexture(\n gl, width, height, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE);\n}\n\nexport function createPackedMatrixTexture(\n gl: WebGLRenderingContext, rows: number, columns: number,\n textureConfig: TextureConfig): WebGLTexture {\n const [width, height] =\n tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n return createAndConfigureTexture(\n gl, width, height, textureConfig.internalFormatPackedFloat, gl.RGBA,\n gl.FLOAT);\n}\n\nexport function createFloat16PackedMatrixTexture(\n gl: WebGLRenderingContext, rows: number, columns: number,\n textureConfig: TextureConfig): WebGLTexture {\n const [width, height] =\n tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n return createAndConfigureTexture(\n gl, width, height, textureConfig.internalFormatHalfFloat, gl.RGBA,\n textureConfig.textureTypeHalfFloat);\n}\n\nexport function bindVertexProgramAttributeStreams(\n gl: WebGLRenderingContext, program: WebGLProgram,\n vertexBuffer: WebGLBuffer): boolean {\n const posOffset = 0; // x is the first buffer element\n const uvOffset = 3 * 4; // uv comes after [x y z]\n const stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.\n webgl_util.callAndCheck(\n gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n const success = webgl_util.bindVertexBufferToProgramAttribute(\n gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n return success &&\n webgl_util.bindVertexBufferToProgramAttribute(\n gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\n\nexport function uploadPixelDataToTexture(\n gl: WebGLRenderingContext, texture: WebGLTexture,\n pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement) {\n webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n webgl_util.callAndCheck(\n gl,\n () => gl.texImage2D(\n gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nfunction uploadDataToTexture(\n gl: WebGLRenderingContext, texture: WebGLTexture, width: number,\n height: number, data: Float32Array, textureFormat: number) {\n webgl_util.validateTextureSize(width, height);\n webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n webgl_util.callAndCheck(\n gl,\n () => gl.texSubImage2D(\n gl.TEXTURE_2D, 0, 0, 0, width, height, textureFormat, gl.FLOAT,\n data));\n\n webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function uploadMatrixToTexture(\n gl: WebGLRenderingContext, texture: WebGLTexture, rows: number,\n columns: number, matrix: Float32Array, numChannels: number,\n textureConfig: TextureConfig) {\n const [w, h] =\n tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n let unpackedArray: Float32Array;\n const numTexels = rows * columns;\n if (textureConfig.defaultNumChannels === 1 && numTexels === matrix.length) {\n // No need to allocate a temporary array.\n unpackedArray = matrix;\n } else {\n unpackedArray = new Float32Array(numTexels * numChannels);\n tex_util.encodeMatrixToUnpackedArray(matrix, unpackedArray, numChannels);\n }\n\n uploadDataToTexture(\n gl, texture, w, h, unpackedArray, textureConfig.textureFormatFloat);\n}\n\n/**\n * This method writes a tensor to a packed texture in a way that respects how we\n * represent data using each texel's r,g,b,a channels. Specifically, we lay\n * out the four channels in two rows each containing two channels, so a single\n * texel can represent up to four values from the tensor. That means a texture\n * that has a channel width of 11 and channel height of 4 will have a texel\n * width of 6 and texel height of 2.\n *\n * rows, columns: Logical number of rows and columns in the tensor to be\n * uploaded.\n *\n * physicalRows, physicalCols: Channel dimensions of the texture that will hold\n * the tensor.\n *\n * width, height (internal parameters): Texel dimensions of the texture.\n */\nexport function uploadMatrixToPackedTexture(\n gl: WebGLRenderingContext, texture: WebGLTexture, batch: number,\n rows: number, columns: number, physicalRows: number, physicalCols: number,\n matrix: Float32Array, textureConfig: TextureConfig) {\n const [w, h] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n physicalRows, physicalCols);\n const packedRGBA =\n new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(\n physicalRows, physicalCols));\n tex_util.encodeMatrixToPackedRGBA(matrix, batch, rows, columns, packedRGBA);\n uploadDataToTexture(gl, texture, w, h, packedRGBA, gl.RGBA);\n}\n\nexport function maybeCreateBufferFromOutputTexture(\n gl: WebGLRenderingContext, texture: WebGLTexture, rows: number,\n columns: number, textureConfig: TextureConfig): WebGLBuffer|WebGLTexture {\n let bufferOrTexture: WebGLBuffer|WebGLTexture = texture;\n\n if (ENV.get('WEBGL_VERSION') === 2) {\n const gl2 = gl as WebGL2RenderingContext;\n\n // Create and bind the buffer.\n const buffer = gl2.createBuffer();\n webgl_util.callAndCheck(\n gl, () => gl.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n\n // Initialize the buffer to the size of the texture in bytes.\n const bytesPerFloat = 4;\n const bufferSizeBytes = bytesPerFloat *\n tex_util.getUnpackedArraySizeFromMatrixSize(\n rows * columns, textureConfig.downloadUnpackNumChannels);\n\n webgl_util.callAndCheck(\n gl,\n () => gl.bufferData(\n gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n\n // Enqueue a command on the GPU command queue to copy of texture into the\n // buffer.\n webgl_util.callAndCheck(\n gl, () => gl2.readPixels(0, 0, columns, rows, gl.RGBA, gl.FLOAT, 0));\n\n webgl_util.callAndCheck(\n gl, () => gl.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n\n bufferOrTexture = buffer;\n }\n\n return bufferOrTexture;\n}\n\nexport function downloadFloat32MatrixFromBuffer(\n gl: WebGLRenderingContext, buffer: WebGLBuffer, rows: number,\n columns: number, textureConfig: TextureConfig): Float32Array {\n const gl2 = gl as WebGL2RenderingContext;\n\n const downloadTarget =\n new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n rows * columns, textureConfig.downloadUnpackNumChannels));\n\n gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n\n const matrix = new Float32Array(rows * columns);\n tex_util.decodeMatrixFromUnpackedArray(\n downloadTarget as Float32Array, matrix,\n textureConfig.downloadUnpackNumChannels);\n\n return matrix;\n}\n\nexport function downloadFloat32MatrixFromOutputTexture(\n gl: WebGLRenderingContext, rows: number, columns: number,\n textureConfig: TextureConfig): Float32Array {\n const [w, h] =\n tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n const downloadTarget =\n new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n rows * columns, textureConfig.downloadUnpackNumChannels));\n\n webgl_util.callAndCheck(\n gl,\n () => gl.readPixels(\n 0, 0, w, h, textureConfig.downloadTextureFormat, gl.FLOAT,\n downloadTarget));\n\n const matrix = new Float32Array(rows * columns);\n tex_util.decodeMatrixFromUnpackedArray(\n downloadTarget as Float32Array, matrix,\n textureConfig.downloadUnpackNumChannels);\n return matrix;\n}\n\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(\n gl: WebGLRenderingContext, rows: number, columns: number,\n textureConfig: TextureConfig) {\n const [w, h] =\n tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n const numChannels = 4;\n const downloadTarget = new Uint8Array(\n tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n\n webgl_util.callAndCheck(\n gl,\n () => gl.readPixels(\n 0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE,\n downloadTarget));\n\n // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n // decoding of the 4 bytes that back each 32 bit float.\n return new Float32Array(downloadTarget.buffer);\n}\n\nexport function downloadPackedMatrixFromBuffer(\n gl: WebGLRenderingContext, buffer: WebGLBuffer, batch: number, rows: number,\n cols: number, physicalRows: number, physicalCols: number,\n textureConfig: TextureConfig): Float32Array {\n const gl2 = gl as WebGL2RenderingContext;\n\n const downloadTarget =\n new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(\n physicalRows, physicalCols));\n\n gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n\n const matrix = new Float32Array(util.sizeFromShape([batch, rows, cols]));\n tex_util.decodeMatrixFromPackedRGBA(\n downloadTarget, batch, rows, cols, matrix);\n return matrix;\n}\n\nexport function downloadMatrixFromPackedOutputTexture(\n gl: WebGLRenderingContext, batch: number, rows: number, cols: number,\n physicalRows: number, physicalCols: number,\n textureConfig: TextureConfig): Float32Array {\n const [w, h] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n physicalRows, physicalCols);\n\n const packedRGBA =\n new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(\n physicalRows, physicalCols));\n webgl_util.callAndCheck(\n gl, () => gl.readPixels(0, 0, w, h, gl.RGBA, gl.FLOAT, packedRGBA));\n const matrix = new Float32Array(util.sizeFromShape([batch, rows, cols]));\n return tex_util.decodeMatrixFromPackedRGBA(\n packedRGBA, batch, rows, cols, matrix);\n}\n","../src/kernels/webgl/gpgpu_context.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from '../../canvas_util';\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\nimport * as gpgpu_util from './gpgpu_util';\nimport {TextureConfig} from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport {WebGL1DisjointQueryTimerExtension, WebGL2DisjointQueryTimerExtension} from './webgl_types';\nimport * as webgl_util from './webgl_util';\n\nexport interface FenceContext {\n query: WebGLQuery|WebGLSync;\n isFencePassed(): boolean;\n}\n\nexport class GPGPUContext {\n gl: WebGLRenderingContext;\n textureFloatExtension: {};\n textureHalfFloatExtension: {};\n colorBufferFloatExtension: {};\n colorBufferHalfFloatExtension: {};\n disjointQueryTimerExtension: WebGL2DisjointQueryTimerExtension|\n WebGL1DisjointQueryTimerExtension;\n vertexBuffer: WebGLBuffer;\n indexBuffer: WebGLBuffer;\n framebuffer: WebGLFramebuffer;\n outputTexture: WebGLTexture|null = null;\n program: WebGLProgram|null = null;\n private disposed = false;\n private autoDebugValidate = false;\n private disjoint: boolean;\n private textureConfig: TextureConfig;\n\n constructor(gl?: WebGLRenderingContext) {\n if (gl != null) {\n this.gl = gl;\n } else {\n this.gl = getWebGLContext(ENV.get('WEBGL_VERSION'));\n }\n // WebGL 2.0 enables texture floats without an extension.\n if (ENV.get('WEBGL_VERSION') === 1) {\n this.textureFloatExtension =\n webgl_util.getExtensionOrThrow(this.gl, 'OES_texture_float');\n this.colorBufferFloatExtension =\n this.gl.getExtension('WEBGL_color_buffer_float');\n\n if (!ENV.get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n this.textureHalfFloatExtension =\n webgl_util.getExtensionOrThrow(this.gl, 'OES_texture_half_float');\n this.colorBufferHalfFloatExtension =\n this.gl.getExtension('EXT_color_buffer_half_float');\n }\n } else {\n this.colorBufferFloatExtension =\n webgl_util.getExtensionOrThrow(this.gl, 'EXT_color_buffer_float');\n }\n\n this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n this.framebuffer = webgl_util.createFramebuffer(this.gl);\n\n this.textureConfig =\n gpgpu_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n }\n\n public dispose() {\n if (this.disposed) {\n return;\n }\n if (this.program != null) {\n console.warn(\n 'Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n ' This is probably a resource leak, delete the program with ' +\n 'GPGPUContext.deleteProgram before disposing.');\n }\n if (this.outputTexture != null) {\n console.warn(\n 'Disposing a GPGPUContext that still has a bound output matrix ' +\n 'texture. This is probably a resource leak, delete the output ' +\n 'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n 'disposing.');\n }\n const gl = this.gl;\n webgl_util.callAndCheck(gl, () => gl.finish());\n webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n webgl_util.callAndCheck(\n gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n this.disposed = true;\n }\n\n public enableAutomaticDebugValidation(enabled: boolean) {\n this.autoDebugValidate = enabled;\n webgl_util.enableDebugWebGLErrorChecking(enabled);\n }\n\n public createFloat32MatrixTexture(rows: number, columns: number):\n WebGLTexture {\n this.throwIfDisposed();\n return gpgpu_util.createFloat32MatrixTexture(\n this.gl, rows, columns, this.textureConfig);\n }\n\n public createFloat16MatrixTexture(rows: number, columns: number):\n WebGLTexture {\n this.throwIfDisposed();\n return gpgpu_util.createFloat16MatrixTexture(\n this.gl, rows, columns, this.textureConfig);\n }\n\n public createUnsignedBytesMatrixTexture(rows: number, columns: number):\n WebGLTexture {\n this.throwIfDisposed();\n return gpgpu_util.createUnsignedBytesMatrixTexture(\n this.gl, rows, columns, this.textureConfig);\n }\n\n public uploadPixelDataToTexture(\n texture: WebGLTexture,\n pixels: ImageData|HTMLImageElement|HTMLCanvasElement) {\n this.throwIfDisposed();\n gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n }\n\n public createFloat16PackedMatrixTexture(rows: number, columns: number):\n WebGLTexture {\n this.throwIfDisposed();\n return gpgpu_util.createFloat16PackedMatrixTexture(\n this.gl, rows, columns, this.textureConfig);\n }\n\n public createPackedMatrixTexture(rows: number, columns: number):\n WebGLTexture {\n this.throwIfDisposed();\n return gpgpu_util.createPackedMatrixTexture(\n this.gl, rows, columns, this.textureConfig);\n }\n\n public deleteMatrixTexture(texture: WebGLTexture) {\n this.throwIfDisposed();\n if (this.outputTexture === texture) {\n webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n this.outputTexture = null;\n }\n webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n }\n\n public uploadMatrixToTexture(\n texture: WebGLTexture, rows: number, columns: number,\n matrix: Float32Array) {\n this.throwIfDisposed();\n const numChannels = webgl_util.getNumChannels();\n return gpgpu_util.uploadMatrixToTexture(\n this.gl, texture, rows, columns, matrix, numChannels,\n this.textureConfig);\n }\n\n public uploadMatrixToPackedTexture(\n texture: WebGLTexture, batch: number, rows: number, columns: number,\n physicalRows: number, physicalCols: number, matrix: Float32Array) {\n this.throwIfDisposed();\n return gpgpu_util.uploadMatrixToPackedTexture(\n this.gl, texture, batch, rows, columns, physicalRows, physicalCols,\n matrix, this.textureConfig);\n }\n\n public downloadFloat32MatrixFromOutputTexture(\n texture: WebGLTexture, rows: number, columns: number): Float32Array {\n return this.downloadMatrixDriver(\n texture,\n () => gpgpu_util.downloadFloat32MatrixFromOutputTexture(\n this.gl, rows, columns, this.textureConfig));\n }\n\n public downloadByteEncodedFloatMatrixFromOutputTexture(\n texture: WebGLTexture, rows: number, columns: number): Float32Array {\n return this.downloadMatrixDriver(\n texture,\n () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(\n this.gl, rows, columns, this.textureConfig));\n }\n\n public downloadPackedMatrixFromBuffer(\n buffer: WebGLBuffer, batch: number, rows: number, columns: number,\n physicalRows: number, physicalCols: number): Float32Array {\n return gpgpu_util.downloadPackedMatrixFromBuffer(\n this.gl, buffer, batch, rows, columns, physicalRows, physicalCols,\n this.textureConfig);\n }\n\n public downloadFloat32MatrixFromBuffer(\n buffer: WebGLBuffer, rows: number, columns: number): Float32Array {\n return gpgpu_util.downloadFloat32MatrixFromBuffer(\n this.gl, buffer, rows, columns, this.textureConfig);\n }\n\n public maybeCreateBufferFromTexture(\n texture: WebGLTexture, rows: number, columns: number): WebGLBuffer\n |WebGLTexture {\n this.bindTextureToFrameBuffer(texture);\n const result = gpgpu_util.maybeCreateBufferFromOutputTexture(\n this.gl, texture, rows, columns, this.textureConfig);\n this.unbindTextureToFrameBuffer();\n return result;\n }\n\n public createAndWaitForFence(): Promise {\n const fenceContext = this.createFence(this.gl);\n return this.pollFence(fenceContext);\n }\n\n private createFence(gl: WebGLRenderingContext): FenceContext {\n let query: WebGLQuery|WebGLSync;\n let isFencePassed: () => boolean;\n\n if (ENV.get('WEBGL_FENCE_API_ENABLED')) {\n const gl2 = gl as WebGL2RenderingContext;\n\n const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n gl.flush();\n\n isFencePassed = () => {\n const status = gl2.clientWaitSync(sync, 0, 0);\n return status === gl2.ALREADY_SIGNALED ||\n status === gl2.CONDITION_SATISFIED;\n };\n\n query = sync;\n } else if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n query = this.beginQuery();\n this.endQuery();\n isFencePassed = () => this.isQueryAvailable(\n query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n } else {\n // If we have no way to fence, return true immediately. This will fire in\n // WebGL 1.0 when there is no disjoint query timer. In this case, because\n // the fence passes immediately, we'll immediately ask for a download of\n // the texture, which will cause the UI thread to hang.\n isFencePassed = () => true;\n }\n\n return {query, isFencePassed};\n }\n\n public downloadMatrixFromPackedTexture(\n texture: WebGLTexture, batch: number, rows: number, columns: number,\n physicalRows: number, physicalCols: number): Float32Array {\n return this.downloadMatrixDriver(\n texture,\n () => gpgpu_util.downloadMatrixFromPackedOutputTexture(\n this.gl, batch, rows, columns, physicalRows, physicalCols,\n this.textureConfig));\n }\n\n private vertexAttrsAreBound = false;\n\n public createProgram(fragmentShaderSource: string): WebGLProgram {\n this.throwIfDisposed();\n const gl = this.gl;\n const fragmentShader: WebGLShader =\n webgl_util.createFragmentShader(gl, fragmentShaderSource);\n const vertexShader: WebGLShader = gpgpu_util.createVertexShader(gl);\n const program: WebGLProgram = webgl_util.createProgram(gl);\n webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n webgl_util.linkProgram(gl, program);\n if (this.autoDebugValidate) {\n webgl_util.validateProgram(gl, program);\n }\n if (!this.vertexAttrsAreBound) {\n this.setProgram(program);\n this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(\n gl, this.program, this.vertexBuffer);\n }\n return program;\n }\n\n public deleteProgram(program: WebGLProgram) {\n this.throwIfDisposed();\n if (program === this.program) {\n this.program = null;\n }\n if (program != null) {\n webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n }\n }\n\n public setProgram(program: WebGLProgram|null) {\n this.throwIfDisposed();\n this.program = program;\n if ((this.program != null) && this.autoDebugValidate) {\n webgl_util.validateProgram(this.gl, this.program);\n }\n webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n }\n\n public getUniformLocation(\n program: WebGLProgram, uniformName: string,\n shouldThrow = true): WebGLUniformLocation {\n this.throwIfDisposed();\n if (shouldThrow) {\n return webgl_util.getProgramUniformLocationOrThrow(\n this.gl, program, uniformName);\n } else {\n return webgl_util.getProgramUniformLocation(\n this.gl, program, uniformName);\n }\n }\n\n public getAttributeLocation(program: WebGLProgram, attribute: string):\n number {\n this.throwIfDisposed();\n return webgl_util.callAndCheck(\n this.gl, () => this.gl.getAttribLocation(program, attribute));\n }\n\n public getUniformLocationNoThrow(program: WebGLProgram, uniformName: string):\n WebGLUniformLocation {\n this.throwIfDisposed();\n return this.gl.getUniformLocation(program, uniformName);\n }\n\n public setInputMatrixTexture(\n inputMatrixTexture: WebGLTexture, uniformLocation: WebGLUniformLocation,\n textureUnit: number) {\n this.throwIfDisposed();\n this.throwIfNoProgram();\n webgl_util.bindTextureToProgramUniformSampler(\n this.gl, this.program, inputMatrixTexture, uniformLocation,\n textureUnit);\n }\n\n public setOutputMatrixTexture(\n outputMatrixTexture: WebGLTexture, rows: number, columns: number) {\n this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n }\n\n public setOutputPackedMatrixTexture(\n outputPackedMatrixTexture: WebGLTexture, rows: number, columns: number) {\n this.throwIfDisposed();\n const [width, height] =\n tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n }\n\n public setOutputMatrixWriteRegion(\n startRow: number, numRows: number, startColumn: number,\n numColumns: number) {\n this.setOutputMatrixWriteRegionDriver(\n startColumn, startRow, numColumns, numRows);\n }\n\n public setOutputPackedMatrixWriteRegion(\n startRow: number, numRows: number, startColumn: number,\n numColumns: number) {\n throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n }\n\n public debugValidate() {\n if (this.program != null) {\n webgl_util.validateProgram(this.gl, this.program);\n }\n webgl_util.validateFramebuffer(this.gl);\n }\n\n public executeProgram() {\n this.throwIfDisposed();\n this.throwIfNoProgram();\n const gl = this.gl;\n if (this.autoDebugValidate) {\n this.debugValidate();\n }\n webgl_util.callAndCheck(\n gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n }\n\n public blockUntilAllProgramsCompleted() {\n this.throwIfDisposed();\n webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n }\n\n private getQueryTimerExtension(): WebGL1DisjointQueryTimerExtension\n |WebGL2DisjointQueryTimerExtension {\n if (this.disjointQueryTimerExtension == null) {\n this.disjointQueryTimerExtension =\n webgl_util.getExtensionOrThrow(\n this.gl,\n ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n 'EXT_disjoint_timer_query_webgl2' :\n 'EXT_disjoint_timer_query') as\n WebGL1DisjointQueryTimerExtension |\n WebGL2DisjointQueryTimerExtension;\n }\n return this.disjointQueryTimerExtension;\n }\n\n private getQueryTimerExtensionWebGL2(): WebGL2DisjointQueryTimerExtension {\n return this.getQueryTimerExtension();\n }\n\n private getQueryTimerExtensionWebGL1(): WebGL1DisjointQueryTimerExtension {\n return this.getQueryTimerExtension() as WebGL1DisjointQueryTimerExtension;\n }\n\n beginQuery(): WebGLQuery {\n if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n const gl2 = this.gl as WebGL2RenderingContext;\n const ext = this.getQueryTimerExtensionWebGL2();\n\n const query = gl2.createQuery();\n gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n return query;\n }\n const ext = this.getQueryTimerExtensionWebGL1();\n const query = ext.createQueryEXT() as WebGLQuery;\n ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n return query;\n }\n\n endQuery() {\n if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n const gl2 = this.gl as WebGL2RenderingContext;\n const ext = this.getQueryTimerExtensionWebGL2();\n gl2.endQuery(ext.TIME_ELAPSED_EXT);\n return;\n }\n const ext = this.getQueryTimerExtensionWebGL1();\n ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n }\n\n public async waitForQueryAndGetTime(query: WebGLQuery): Promise {\n await util.repeatedTry(\n () => this.disposed || // while testing contexts are created / disposed\n // in rapid succession, so without this check we\n // may poll for the query timer indefinitely\n this.isQueryAvailable(\n query,\n ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n return this.getQueryTime(\n query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n }\n\n private getQueryTime(query: WebGLQuery, queryTimerVersion: number): number {\n if (queryTimerVersion === 0) {\n return null;\n }\n\n if (queryTimerVersion === 2) {\n const gl2 = this.gl as WebGL2RenderingContext;\n\n const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n // Return milliseconds.\n return timeElapsedNanos / 1000000;\n } else {\n const ext = this.getQueryTimerExtensionWebGL1();\n\n const timeElapsedNanos =\n ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n // Return milliseconds.\n return timeElapsedNanos / 1000000;\n }\n }\n\n private isQueryAvailable(query: WebGLQuery, queryTimerVersion: number):\n boolean {\n if (queryTimerVersion === 0) {\n return true;\n }\n\n if (queryTimerVersion === 2) {\n const gl2 = this.gl as WebGL2RenderingContext;\n const ext = this.getQueryTimerExtensionWebGL2();\n\n const available =\n gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n if (this.disjoint == null) {\n this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n }\n\n return available && !this.disjoint;\n } else {\n const ext = this.getQueryTimerExtensionWebGL1();\n\n const available =\n ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n if (this.disjoint == null) {\n this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n }\n\n return available && !this.disjoint;\n }\n }\n\n pollFence(fenceContext: FenceContext) {\n return new Promise(resolve => {\n this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n });\n }\n\n private itemsToPoll: PollItem[] = [];\n\n pollItems(): void {\n // Find the last query that has finished.\n const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n for (let i = 0; i <= index; ++i) {\n const {resolveFn} = this.itemsToPoll[i];\n resolveFn();\n }\n this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n }\n\n private addItemToPoll(isDoneFn: () => boolean, resolveFn: () => void) {\n this.itemsToPoll.push({isDoneFn, resolveFn});\n if (this.itemsToPoll.length > 1) {\n // We already have a running loop that polls.\n return;\n }\n // Start a new loop that polls.\n util.repeatedTry(() => {\n this.pollItems();\n // End the loop if no more items to poll.\n return this.itemsToPoll.length === 0;\n });\n }\n\n private bindTextureToFrameBuffer(texture: WebGLTexture) {\n this.throwIfDisposed();\n webgl_util.bindColorTextureToFramebuffer(\n this.gl, texture, this.framebuffer);\n if (this.autoDebugValidate) {\n webgl_util.validateFramebuffer(this.gl);\n }\n }\n\n private unbindTextureToFrameBuffer() {\n if (this.outputTexture != null) {\n webgl_util.bindColorTextureToFramebuffer(\n this.gl, this.outputTexture, this.framebuffer);\n if (this.autoDebugValidate) {\n webgl_util.validateFramebuffer(this.gl);\n }\n } else {\n webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n }\n }\n\n private downloadMatrixDriver(\n texture: WebGLTexture,\n downloadAndDecode: () => Float32Array): Float32Array {\n this.bindTextureToFrameBuffer(texture);\n const result = downloadAndDecode();\n this.unbindTextureToFrameBuffer();\n\n return result;\n }\n\n private setOutputMatrixTextureDriver(\n outputMatrixTextureMaybePacked: WebGLTexture, width: number,\n height: number) {\n this.throwIfDisposed();\n const gl = this.gl;\n webgl_util.bindColorTextureToFramebuffer(\n gl, outputMatrixTextureMaybePacked, this.framebuffer);\n if (this.autoDebugValidate) {\n webgl_util.validateFramebuffer(gl);\n }\n this.outputTexture = outputMatrixTextureMaybePacked;\n webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n }\n\n private setOutputMatrixWriteRegionDriver(\n x: number, y: number, width: number, height: number) {\n this.throwIfDisposed();\n webgl_util.callAndCheck(\n this.gl, () => this.gl.scissor(x, y, width, height));\n }\n\n private throwIfDisposed() {\n if (this.disposed) {\n throw new Error('Attempted to use disposed GPGPUContext.');\n }\n }\n\n private throwIfNoProgram() {\n if (this.program == null) {\n throw new Error('No GPU program is currently set.');\n }\n }\n}\n\ntype PollItem = {\n isDoneFn: () => boolean,\n resolveFn: () => void\n};\n\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr: Array<() => boolean>): number {\n let i = 0;\n for (; i < arr.length; ++i) {\n const isDone = arr[i]();\n if (!isDone) {\n break;\n }\n }\n return i - 1;\n}\n","../src/kernels/webgl/gpgpu_math.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {TypedArray} from '../../types';\nimport * as util from '../../util';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport * as shader_compiler from './shader_compiler';\nimport {InputInfo, ShapeInfo} from './shader_compiler';\nimport {TextureData} from './tex_util';\n\nexport interface GPGPUProgram {\n variableNames: string[];\n outputShape: number[];\n userCode: string;\n usesPackedTextures?: boolean;\n isPackShader?: boolean; // This property is used to single out the packing\n // shader so its output does not get eagerly unpacked\n // by backend_webgl.compileAndRun.\n}\n\nexport interface GPGPUBinary {\n webGLProgram: WebGLProgram;\n program: GPGPUProgram;\n uniformLocations: {[name: string]: WebGLUniformLocation};\n gpgpu: GPGPUContext;\n source: string;\n inShapeInfos: ShapeInfo[];\n outShapeInfo: ShapeInfo;\n}\n\nexport interface TensorData {\n shape: number[];\n texData: TextureData;\n isUniform: boolean;\n // Available when we decide to upload as uniform instead of texture.\n uniformValues?: TypedArray;\n}\n\nexport function compileProgram(\n gpgpu: GPGPUContext, program: GPGPUProgram, inputs: TensorData[],\n output: TensorData): GPGPUBinary {\n const userCode = program.userCode;\n const inputInfos: InputInfo[] = inputs.map((input, i) => {\n const shapeInfo: ShapeInfo = {\n logicalShape: input.shape,\n texShape: input.isUniform ? null : input.texData.texShape,\n isUniform: input.isUniform,\n isPacked: input.isUniform ? false : input.texData.isPacked,\n flatOffset: null\n };\n if (input.texData != null && input.texData.slice != null &&\n input.texData.slice.flatOffset > 0) {\n shapeInfo.flatOffset = input.texData.slice.flatOffset;\n }\n return {name: program.variableNames[i], shapeInfo};\n });\n const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n const outShapeInfo: ShapeInfo = {\n logicalShape: output.shape,\n texShape: output.texData.texShape,\n isUniform: false,\n isPacked: output.texData.isPacked,\n flatOffset: null\n };\n const source = shader_compiler.makeShader(\n inputInfos, outShapeInfo, userCode, program.usesPackedTextures);\n\n const webGLProgram = gpgpu.createProgram(source);\n\n const uniformLocations: {[name: string]: WebGLUniformLocation} = {};\n for (let i = 0; i < program.variableNames.length; i++) {\n const varName = program.variableNames[i];\n const shouldThrow = false;\n uniformLocations[varName] =\n gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n uniformLocations[`offset${varName}`] =\n gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n }\n\n return {\n program,\n source,\n webGLProgram,\n uniformLocations,\n gpgpu,\n inShapeInfos,\n outShapeInfo\n };\n}\n\nfunction validateBinaryAndProgram(\n shapeInfos: ShapeInfo[], inputs: TensorData[]) {\n if (shapeInfos.length !== inputs.length) {\n throw Error(\n `Binary was compiled with ${shapeInfos.length} inputs, but ` +\n `was executed with ${inputs.length} inputs`);\n }\n\n shapeInfos.forEach((s, i) => {\n const shapeA = s.logicalShape;\n const input = inputs[i];\n const shapeB = input.shape;\n\n if (!util.arraysEqual(shapeA, shapeB)) {\n throw Error(\n `Binary was compiled with different shapes than ` +\n `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n }\n // The input is uploaded as uniform.\n if (s.isUniform && input.isUniform) {\n return;\n }\n\n const texShapeA = s.texShape;\n const texShapeB = input.isUniform ? null : input.texData.texShape;\n if (!util.arraysEqual(texShapeA, texShapeB)) {\n throw Error(\n `Binary was compiled with different texture shapes than the` +\n ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n }\n });\n}\n\nexport function runProgram(\n binary: GPGPUBinary, inputs: TensorData[], output: TensorData,\n customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) =>\n void): void {\n validateBinaryAndProgram(binary.inShapeInfos, inputs);\n validateBinaryAndProgram([binary.outShapeInfo], [output]);\n\n const outTex = output.texData.texture;\n const outTexShape = output.texData.texShape;\n const gpgpu = binary.gpgpu;\n if (output.texData.isPacked) {\n gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n } else {\n gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n }\n gpgpu.setProgram(binary.webGLProgram);\n inputs.forEach((input, i) => {\n const varName = binary.program.variableNames[i];\n const varLoc = binary.uniformLocations[varName];\n const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n\n if (varLoc == null) {\n // The compiler inferred that this variable is not used in this shader.\n return;\n }\n\n if (input.isUniform) {\n // Upload the values of the tensor as uniform.\n if (util.sizeFromShape(input.shape) === 1) {\n gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n } else {\n let vals = input.uniformValues;\n if (!(vals instanceof Float32Array)) {\n vals = new Float32Array(vals);\n }\n gpgpu.gl.uniform1fv(varLoc, vals);\n }\n return;\n }\n\n // If the input was sliced, upload the flat offset index.\n if (input.texData.slice != null && varOffsetLoc != null) {\n gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n }\n\n gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n });\n\n if (customSetup != null) {\n customSetup(gpgpu, binary.webGLProgram);\n }\n gpgpu.executeProgram();\n}\n\nexport function makeShaderKey(\n program: GPGPUProgram, inputs: TensorData[], output: TensorData): string {\n let keyInputs = '';\n inputs.concat(output).forEach(x => {\n const hasOffset = x.texData != null && x.texData.slice != null &&\n x.texData.slice.flatOffset > 0;\n const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n });\n const keyUserCode = program.userCode;\n let key = program.constructor.name;\n // Fast string concat. See https://jsperf.com/string-concatenation/14.\n key += '_' + keyInputs + '_' + keyUserCode;\n return key;\n}\n","../src/kernels/webgl/im2col_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {getGlslDifferences} from './glsl_version';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Im2ColProgram implements GPGPUProgram {\n variableNames = ['A'];\n outputShape: number[];\n userCode: string;\n\n constructor(\n outputShape: number[], inputShape: number[], convInfo: Conv2DInfo) {\n this.outputShape = outputShape;\n\n const {\n filterWidth,\n inChannels,\n strideWidth,\n strideHeight,\n padInfo,\n outWidth,\n dilationWidth,\n dilationHeight\n } = convInfo;\n const {left, top} = padInfo;\n const itemsPerBlockRow = inChannels * filterWidth;\n const glsl = getGlslDifferences();\n\n this.userCode = `\n void main() {\n ivec2 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n int blockIndex = rc.y + col;\n int pos = rc.x + row;\n\n if(blockIndex >= ${outputShape[1]} || pos >= ${\n outputShape[0]}) continue;\n\n int offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${\n top};\n int d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n if(d0 >= ${inputShape[0]} || d0 < 0) continue;\n\n int offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${\n strideWidth}. - ${left}.);\n int d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${\n itemsPerBlockRow}.) / ${inChannels}.));\n\n if(d1 >= ${inputShape[1]} || d1 < 0) continue;\n\n result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), ${\n inChannels}.)));\n }\n }\n\n ${glsl.output} = result;\n }\n `;\n }\n}\n","../src/kernels/webgl/lrn_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNProgram implements GPGPUProgram {\n variableNames = ['x'];\n outputShape: number[] = [];\n userCode: string;\n\n constructor(\n xShape: number[], radius: number, bias: number, alpha: number,\n beta: number) {\n const rad = radius;\n const maxD = xShape[3] - 1;\n this.outputShape = xShape;\n\n // optimize pow(bias + alpha * sum, -beta)\n // src: https://github.com/tensorflow/tensorflow/..\n // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n let powOperator;\n const basis = `float(${bias}) + float(${alpha}) * sum`;\n if (beta === 0.5) {\n powOperator = `inversesqrt(${basis})`;\n } else if (beta === 1.0) {\n powOperator = `1.0/(${basis})`;\n } else {\n powOperator = `exp(log(${basis}) * float(-${beta}));`;\n }\n\n this.userCode = `\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${rad}; j <= ${rad}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${maxD}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${powOperator};\n setOutput(val);\n }\n `;\n }\n}\n","../src/kernels/webgl/lrn_grad_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNGradProgram implements GPGPUProgram {\n variableNames = ['inputImage', 'outputImage', 'dy'];\n outputShape: number[] = [];\n userCode: string;\n depthRadius: number;\n bias: number;\n alpha: number;\n beta: number;\n depth: number;\n\n constructor(\n inputShape: number[], depthRadius: number, bias: number, alpha: number,\n beta: number) {\n this.outputShape = inputShape;\n this.depth = inputShape[3];\n this.depthRadius = depthRadius;\n this.bias = bias;\n this.alpha = alpha;\n this.beta = beta;\n this.userCode = `\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${depthRadius} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${alpha}) * norm + float(${bias});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${alpha})\n * float(${beta})\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${beta});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `;\n }\n}\n","../src/kernels/webgl/max_pool_backprop_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MaxPool2DBackpropProgram implements GPGPUProgram {\n variableNames = ['dy', 'maxPos'];\n outputShape: number[];\n userCode: string;\n\n constructor(convInfo: Conv2DInfo) {\n this.outputShape = convInfo.inShape;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n this.userCode = `\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${effectiveFilterWidth} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `;\n }\n}\n","../src/kernels/webgl/mulmat_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MatMulProgram implements GPGPUProgram {\n variableNames = ['matrixA', 'matrixB'];\n outputShape: number[];\n userCode: string;\n\n constructor(\n aShape: [number, number, number], bShape: [number, number, number],\n transposeA = false, transposeB = false, addBias = false,\n activation: string = null) {\n const batchSize = aShape[0];\n const outerShapeA = transposeA ? aShape[2] : aShape[1];\n const outerShapeB = transposeB ? bShape[1] : bShape[2];\n const sharedDim = transposeA ? aShape[1] : aShape[2];\n this.outputShape = [batchSize, outerShapeA, outerShapeB];\n\n const aSnippetFromOffset = (vec4Offset: number, indexVar: string|number) =>\n transposeA ? `batch, ${indexVar} + ${vec4Offset}, aRow` :\n `batch, aRow, ${indexVar} + ${vec4Offset}`;\n const bSnippetFromOffset = (vec4Offset: number, indexVar: string|number) =>\n transposeB ? `batch, bCol, ${indexVar} + ${vec4Offset}` :\n `batch, ${indexVar} + ${vec4Offset}, bCol`;\n\n const sharedDimNearestVec4 = Math.floor(sharedDim / 4) * 4;\n const sharedDimVec4Remainder = sharedDim % 4;\n\n let activationSnippet = '', applyActivationSnippet = '';\n if (activation) {\n activationSnippet = `float activation(float x) {\n ${activation}\n }`;\n\n applyActivationSnippet = `result = activation(result);`;\n }\n\n const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n if (addBias) {\n this.variableNames.push('bias');\n }\n\n this.userCode = `\n ${activationSnippet}\n\n float dotARowBCol(int batch, int aRow, int bCol) {\n float result = 0.0;\n for (int i = 0; i < ${sharedDimNearestVec4}; i += 4) {\n vec4 a = vec4(\n getMatrixA(${aSnippetFromOffset(0, 'i')}),\n getMatrixA(${aSnippetFromOffset(1, 'i')}),\n getMatrixA(${aSnippetFromOffset(2, 'i')}),\n getMatrixA(${aSnippetFromOffset(3, 'i')})\n );\n vec4 b = vec4(\n getMatrixB(${bSnippetFromOffset(0, 'i')}),\n getMatrixB(${bSnippetFromOffset(1, 'i')}),\n getMatrixB(${bSnippetFromOffset(2, 'i')}),\n getMatrixB(${bSnippetFromOffset(3, 'i')})\n );\n\n result += dot(a, b);\n }\n\n if (${sharedDimVec4Remainder === 1}) {\n result += getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}) *\n getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)});\n } else if (${sharedDimVec4Remainder === 2}) {\n vec2 a = vec2(\n getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}),\n getMatrixA(${aSnippetFromOffset(1, sharedDimNearestVec4)})\n );\n vec2 b = vec2(\n getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)}),\n getMatrixB(${bSnippetFromOffset(1, sharedDimNearestVec4)})\n );\n result += dot(a, b);\n } else if (${sharedDimVec4Remainder === 3}) {\n vec3 a = vec3(\n getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}),\n getMatrixA(${aSnippetFromOffset(1, sharedDimNearestVec4)}),\n getMatrixA(${aSnippetFromOffset(2, sharedDimNearestVec4)})\n );\n vec3 b = vec3(\n getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)}),\n getMatrixB(${bSnippetFromOffset(1, sharedDimNearestVec4)}),\n getMatrixB(${bSnippetFromOffset(2, sharedDimNearestVec4)})\n );\n result += dot(a, b);\n }\n\n return result;\n }\n\n void main() {\n ivec3 resBRC = getOutputCoords();\n float result = dotARowBCol(resBRC.x, resBRC.y, resBRC.z);\n\n ${addBiasSnippet}\n\n ${applyActivationSnippet}\n\n setOutput(result);\n }\n `;\n }\n}\n","../src/kernels/webgl/mulmat_packed_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MatMulPackedProgram implements GPGPUProgram {\n variableNames = ['matrixA', 'matrixB'];\n usesPackedTextures = true;\n outputShape: number[];\n userCode: string;\n\n constructor(\n aShape: [number, number], bShape: [number, number],\n outputShape: [number, number], transposeA = false, transposeB = false,\n addBias = false, activation: string = null) {\n this.outputShape = outputShape;\n\n const sharedDim = transposeA ? aShape[0] : aShape[1];\n const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n\n const aSample = transposeA ? 'i * 2, rc.x' : 'rc.x, i * 2';\n const bSample = transposeB ? 'rc.y, i * 2' : 'i * 2, rc.y';\n const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n\n let activationSnippet = '', applyActivationSnippet = '';\n if (activation) {\n activationSnippet = `vec4 activation(vec4 x) {\n ${activation}\n }`;\n\n applyActivationSnippet = `result = activation(result);`;\n }\n\n const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n if (addBias) {\n this.variableNames.push('bias');\n }\n\n this.userCode = `\n ${activationSnippet}\n\n const float sharedDimension = ${sharedDimensionPacked}.0;\n\n vec4 dot2x2ARowBCol(ivec2 rc) {\n vec4 result = vec4(0);\n for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n vec4 a = getMatrixA(${aSample});\n vec4 b = getMatrixB(${bSample});\n\n result += (${aSwizzle[0]} * ${bSwizzle[0]}) + (${aSwizzle[1]} * ${\n bSwizzle[1]});\n }\n return result;\n }\n\n void main() {\n ivec2 rc = getOutputCoords();\n vec4 result = dot2x2ARowBCol(rc);\n\n ${addBiasSnippet}\n\n ${applyActivationSnippet}\n\n setOutput(result);\n }\n `;\n }\n}","../src/kernels/webgl/multinomial_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MultinomialProgram implements GPGPUProgram {\n variableNames = ['probs'];\n outputShape: number[];\n userCode: string;\n\n // Caching uniform location for speed.\n seedLoc: WebGLUniformLocation;\n\n constructor(batchSize: number, numOutcomes: number, numSamples: number) {\n this.outputShape = [batchSize, numSamples];\n\n this.userCode = `\n uniform float seed;\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < ${numOutcomes - 1}; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float(${numOutcomes - 1}));\n }\n `;\n }\n\n getCustomSetupFunc(seed: number) {\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.seedLoc == null) {\n this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n }\n gpgpu.gl.uniform1f(this.seedLoc, seed);\n };\n }\n}\n","../src/kernels/webgl/onehot_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class OneHotProgram implements GPGPUProgram {\n variableNames = ['indices'];\n outputShape: number[];\n userCode: string;\n\n // Caching uniform location for speed.\n seedLoc: WebGLUniformLocation;\n\n constructor(\n numIndices: number, depth: number, onValue: number, offValue: number) {\n this.outputShape = [numIndices, depth];\n\n this.userCode = `\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float(${offValue}), float(${onValue}),\n float(index == coords.y)));\n }\n `;\n }\n}\n","../src/kernels/packing_util.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport function getVecChannels(name: string, rank: number): string[] {\n return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\n\nexport function getChannels(name: string, rank: number): string[] {\n if (rank === 1) {\n return [name];\n }\n return getVecChannels(name, rank);\n}\n\nexport function getSourceCoords(rank: number, dims: string[]): string {\n if (rank === 1) {\n return 'rc';\n }\n\n let coords = '';\n for (let i = 0; i < rank; i++) {\n coords += dims[i];\n if (i < rank - 1) {\n coords += ',';\n }\n }\n return coords;\n}","../src/kernels/webgl/pack_gpu.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getChannels} from '../packing_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PackProgram implements GPGPUProgram {\n variableNames = ['A'];\n isPackShader = true;\n outputShape: number[];\n userCode: string;\n\n constructor(\n outputShape:\n number[]) { // TODO(https://github.com/tensorflow/tfjs/issues/893):\n // Only input / output 3D tensors.\n this.outputShape = outputShape;\n const rank = outputShape.length;\n\n if (rank === 0) {\n this.userCode = `\n void main() {\n setOutput(vec4(getA(), 0., 0., 0.));\n }\n `;\n } else {\n const channels = getChannels('rc', rank);\n const dtype = getCoordsDataType(rank);\n const outOfBoundsCondition =\n getOutOfBoundsCondition(rank, outputShape, channels);\n const setup = getSetup(\n rank, outputShape[outputShape.length - 1],\n outputShape[outputShape.length - 2], channels);\n const output = getOutput(outputShape, channels);\n\n this.userCode = `\n void main() {\n ${dtype} rc = getOutputCoords();\n\n if(${outOfBoundsCondition}) {\n setOutput(vec4(0));\n } else {\n ${setup}\n\n setOutput(vec4(${output}));\n }\n }\n `;\n }\n }\n}\n\nfunction getSourceCoordsArr(rank: number, dims: string[]): string[] {\n const coords = [];\n\n for (let row = 0; row <= 1; row++) {\n for (let col = 0; col <= 1; col++) {\n let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n\n for (let d = 2; d < rank; d++) {\n coord = `${dims[dims.length - 1 - d]},` + coord;\n }\n\n coords.push(coord);\n }\n }\n return coords;\n}\n\nfunction getOutOfBoundsCondition(\n rank: number, shape: number[], dims: string[]): string {\n if (rank === 1) {\n return `rc > ${shape[0]}`;\n }\n\n let cond = '';\n for (let i = rank - 2; i < rank; i++) {\n cond += `${dims[i]} >= ${shape[i]}`;\n if (i < rank - 1) {\n cond += '||';\n }\n }\n\n return cond;\n}\n\nfunction getSetup(\n rank: number, cols: number, rows: number, dims: string[]): string {\n if (rank === 1) {\n return '';\n }\n\n const innerDims = dims.slice(-2);\n\n return `\n int r = ${innerDims[0]};\n int c = ${innerDims[1]};\n int rp1 = r + 1;\n int cp1 = c + 1;\n\n bool cEdge = cp1 >= ${cols};\n bool rEdge = rp1 >= ${rows};\n `;\n}\n\nfunction getOutput(shape: number[], dims: string[]): string {\n const rank = shape.length;\n const sourceCoords = getSourceCoordsArr(rank, dims);\n if (rank === 1) {\n return `getA(rc),\n rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n 0, 0`;\n }\n\n return `getA(${sourceCoords[0]}),\n cEdge ? 0. : getA(${sourceCoords[1]}),\n rEdge ? 0. : getA(${sourceCoords[2]}),\n rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}","../src/kernels/webgl/pad_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PadProgram implements GPGPUProgram {\n variableNames = ['x'];\n outputShape: number[];\n userCode: string;\n\n constructor(\n xShape: number[], paddings: Array<[number, number]>,\n constantValue: number) {\n this.outputShape = paddings.map(\n (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n const rank = xShape.length;\n const type = getCoordsDataType(rank);\n\n const start = paddings.map(p => p[0]).join(',');\n const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n const unpackedCoords =\n ['coords[0]', 'coords[1]', 'coords[2]','coords[3]'].slice(0, rank);\n\n if (rank === 1) {\n this.userCode = `\n int start = ${start};\n int end = ${end};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(float(${constantValue}));\n } else {\n setOutput(getX(outC - start));\n }\n }\n `;\n return;\n }\n this.userCode = `\n ${type} start = ${type}(${start});\n ${type} end = ${type}(${end});\n\n void main() {\n ${type} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(float(${constantValue}));\n } else {\n ${type} coords = outC - start;\n setOutput(getX(${unpackedCoords}));\n }\n }\n `;\n }\n}\n","../src/kernels/webgl/pad_packed_gpu.ts":"/**\n * @license\n * Copyright 2019 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\nimport {getChannels} from '../packing_util';\n\nexport class PadPackedProgram implements GPGPUProgram {\n variableNames = ['x'];\n usesPackedTextures = true;\n outputShape: number[];\n userCode: string;\n\n constructor(\n xShape: number[], paddings: Array<[number, number]>,\n constantValue: number) {\n this.outputShape = paddings.map(\n (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n const rank = xShape.length;\n const dtype = getCoordsDataType(rank);\n\n const start = paddings.map(p => p[0]).join(',');\n const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n const coords = getChannels('rc', rank);\n const source = getChannels('source', rank);\n const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n const innerDims =\n rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n\n const componentSetup = [\n `${dtype} rc = outputLoc;`,\n `${coords[rank - 1]} += 1;\n if(${cLimit}) {\n `,\n rank === 1 ? '' : \n `}\n rc = outputLoc;\n ${coords[rank - 2]} += 1;\n if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n rank === 1 ? '' :\n ` ${coords[rank - 1]} += 1;\n if(${cLimit}) {`\n ];\n \n const paddingArea = rank === 1 ?\n 'rc < start || rc >= end' :\n 'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n let mainLoop = '';\n for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n mainLoop += `\n ${componentSetup[i]}\n if (${paddingArea}) {\n result[${i}] = float(${constantValue});\n } else {\n ${dtype} source = rc - start;\n result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n }\n `;\n }\n mainLoop += (rank === 1 ? `} ` : `}}`);\n\n this.userCode = `\n const ${dtype} start = ${dtype}(${start});\n const ${dtype} end = ${dtype}(${end});\n\n void main() {\n ${dtype} outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n ${mainLoop}\n setOutput(result);\n }\n `;\n }\n}\n","../src/kernels/webgl/pool_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Pool2DProgram implements GPGPUProgram {\n variableNames = ['x'];\n outputShape: number[];\n userCode: string;\n\n constructor(\n convInfo: Conv2DInfo, poolType: 'max'|'avg', computePositions: boolean) {\n if (poolType === 'avg' && computePositions) {\n throw new Error('Cannot compute positions for average pool.');\n }\n\n const filterWidth = convInfo.filterWidth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n this.outputShape = convInfo.outShape;\n\n const isAvgPool = poolType === 'avg';\n\n let initializationValue = '0.0';\n if (!isAvgPool) {\n // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n initializationValue = '-1.0 / 1e-20';\n }\n\n if (computePositions) {\n const compareOp = '>=';\n\n this.userCode = `\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${effectiveFilterWidth};\n wC += ${dilationWidth}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${compareOp} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = wR * ${effectiveFilterWidth} + wC;\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;\n return;\n }\n\n const compareOp = 'max';\n\n let returnValue = `${poolType}(${poolType}(${poolType}(` +\n 'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n if (poolType === 'avg') {\n returnValue = `avgValue / count`;\n }\n\n const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n const filterWidthVec4Remainder = filterWidth % 4;\n\n const updateSnippet = `\n if (${isAvgPool}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${compareOp}(values, minMaxValue);\n }\n `;\n\n this.userCode = `\n const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n const ivec2 pads = ivec2(${padTop}, ${padLeft});\n const float initializationValue = ${initializationValue};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${convInfo.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${initializationValue});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${effectiveFilterHeight};\n wR += ${dilationHeight}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${convInfo.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n int xC = xCCorner + wC * ${dilationWidth};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${dilationWidth}, d),\n getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n );\n\n ${updateSnippet}\n }\n\n int xC = xCCorner + ${filterWidthNearestVec4};\n if (${filterWidthVec4Remainder === 1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${updateSnippet}\n } else if (${filterWidthVec4Remainder === 2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${dilationWidth}, d),\n initializationValue,\n initializationValue\n );\n\n ${updateSnippet}\n } else if (${filterWidthVec4Remainder === 3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${dilationWidth}, d),\n getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n initializationValue\n );\n\n ${updateSnippet}\n }\n }\n setOutput(${returnValue});\n }\n `;\n }\n}\n","../src/kernels/webgl/reduce_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ReduceInfo} from '../../ops/reduce_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ReduceProgram implements GPGPUProgram {\n variableNames = ['x'];\n outputShape: number[];\n userCode: string;\n\n constructor(\n reduceInfo: ReduceInfo,\n reduceType: 'all'|'any'|'max'|'min'|'sum'|'prod') {\n const windowSize = reduceInfo.windowSize;\n const batchSize = reduceInfo.batchSize;\n const inSize = reduceInfo.inSize;\n const outSize = Math.ceil(inSize / windowSize);\n this.outputShape = [batchSize, outSize];\n\n let initializationValue = '0.0';\n let compareOp = ``;\n\n if (reduceType === 'prod') {\n initializationValue = '1.0';\n } else if (reduceType === 'min') {\n // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n initializationValue = '1.0 / 1e-20';\n compareOp = `min`;\n } else if (reduceType === 'max') {\n // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n initializationValue = '-1.0 / 1e-20';\n compareOp = `max`;\n }\n\n let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n 'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n\n if (reduceType === 'sum') {\n returnValue = `sumValue`;\n } else if (reduceType === 'prod') {\n returnValue = `prodValue`;\n } else if (reduceType === 'all') {\n returnValue = `allValue`;\n } else if (reduceType === 'any') {\n returnValue = `anyValue`;\n }\n\n const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n const windowSizeVec4Remainder = windowSize % 4;\n\n let updateSnippet = `\n if (${reduceType === 'sum'}) {\n sumValue += dot(values, ones);\n } else if (${reduceType === 'prod'}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${compareOp}(values, minMaxValue);\n }\n `;\n\n let vecType = `vec4`;\n\n if (reduceType === 'all') {\n initializationValue = '1.0';\n updateSnippet = `\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `;\n vecType = `bvec4`;\n } else if (reduceType === 'any') {\n initializationValue = '0.0';\n updateSnippet = `\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `;\n vecType = `bvec4`;\n }\n\n let checkOutOfBounds = '';\n if (inSize % windowSize > 0) {\n checkOutOfBounds = `\n if (inIdx < 0 || inIdx >= ${inSize}) {\n return initializationValue;\n }\n `;\n }\n this.userCode = `\n const float initializationValue = ${initializationValue};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${checkOutOfBounds}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${windowSize};\n\n vec4 minMaxValue = vec4(${initializationValue});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n int inIdx = inOffset + i;\n ${vecType} values = ${vecType}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${updateSnippet}\n }\n\n int inIdx = inOffset + ${windowSizeNearestVec4};\n if (${windowSizeVec4Remainder === 1}) {\n ${vecType} values = ${vecType}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${updateSnippet}\n } else if (${windowSizeVec4Remainder === 2}) {\n ${vecType} values = ${vecType}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${updateSnippet}\n } else if (${windowSizeVec4Remainder === 3}) {\n ${vecType} values = ${vecType}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${updateSnippet}\n }\n setOutput(${returnValue});\n }\n `;\n }\n}\n","../src/kernels/webgl/reshape_packed_gpu.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../../util';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\n\nexport class ReshapePackedProgram implements GPGPUProgram {\n variableNames = ['A'];\n usesPackedTextures = true;\n outputShape: number[];\n userCode: string;\n\n constructor(outputShape: [number, number, number], inputShape: [\n number, number, number\n ]) {\n this.outputShape = outputShape;\n\n let mainLoop = ``;\n for (let i = 0; i < 4; i++) {\n let thisRC = `thisRC = rc;`;\n if (i % 2 === 1) {\n thisRC += `thisRC.z += 1;`;\n }\n if (i > 1) {\n thisRC += `thisRC.y += 1;`;\n }\n\n mainLoop += `\n ${thisRC}\n ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${i}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${i > 0 ? '}' : ''}\n `;\n }\n\n this.userCode = `\n ${getReshapedInputCoords(inputShape)}\n ${getFlatIndex(outputShape)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${outputShape[1]};\n int cols = ${outputShape[2]};\n\n ${mainLoop}\n\n setOutput(result);\n }\n `;\n }\n}\n\nfunction getFlatIndex(shape: [number, number, number]): string {\n const dotCoordsWithStrides = shader_util.dotify(\n ['coords.x', 'coords.y', 'coords.z'],\n util.computeStrides(shape).map(d => d.toString()).concat(['1.']));\n\n return `\n int getFlatIndex(ivec3 coords) {\n return round(${dotCoordsWithStrides});\n }\n `;\n}\n\nfunction getReshapedInputCoords(shape: [number, number, number]): string {\n const coordsFromIndexSnippet =\n shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n\n return `\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${coordsFromIndexSnippet}\n return ivec3(r, c, d);\n }\n `;\n}","../src/kernels/webgl/resize_bilinear_backprop_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../../tensor';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearBackpropProgram implements GPGPUProgram {\n variableNames = ['dy'];\n outputShape: number[] = [];\n userCode: string;\n\n constructor(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n this.outputShape = x.shape;\n const [, xHeight, xWidth, ] = x.shape;\n const [, yHeight, yWidth] = dy.shape;\n\n // In the backwards pass, we want to find the pixels that were generated for\n // each pixel in the input image the forward pass and add the corresponding\n // coefficient from dy to the gradient (with some interpolation).\n\n const effectiveXSize: [number, number] = [\n (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n ];\n\n const effectiveYSize: [number, number] = [\n (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n ];\n\n const heightScale = effectiveXSize[0] / effectiveYSize[0];\n const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n const invHeightScale = 1 / heightScale;\n const invWidthScale = 1 / widthScale;\n\n // This defines the size of the window of values around a particular\n // index in dy that we want to search for contributions to dx.\n const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n this.userCode = `\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${heightScale});\n const float widthScale = float(${widthScale});\n\n const float invHeightScale = float(${invHeightScale});\n const float invWidthScale = float(${invWidthScale});\n\n const int winHeight = int(${winHeight});\n const int winWidth = int(${winWidth});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${yHeight}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${yWidth}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `;\n }\n}\n","../src/kernels/webgl/resize_bilinear_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearProgram implements GPGPUProgram {\n variableNames = ['A'];\n outputShape: number[] = [];\n userCode: string;\n\n constructor(\n inputShape: [number, number, number, number], newHeight: number,\n newWidth: number, alignCorners: boolean) {\n const [batch, oldHeight, oldWidth, depth] = inputShape;\n this.outputShape = [batch, newHeight, newWidth, depth];\n\n const effectiveInSize: [number, number] = [\n (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n ];\n\n const effectiveOutSize: [number, number] = [\n (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n ];\n\n this.userCode = `\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${effectiveInSize[0] / effectiveOutSize[0]},\n ${effectiveInSize[1] / effectiveOutSize[1]});\n const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `;\n }\n}\n","../src/kernels/webgl/resize_nearest_neighbor_backprop_gpu.ts":"/**\n * @license\n * Copyright 2018 Google LLC All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../../tensor';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeigborBackpropProgram implements GPGPUProgram {\n variableNames = ['dy'];\n outputShape: number[] = [];\n userCode: string;\n\n constructor(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n this.outputShape = x.shape;\n const [, xHeight, xWidth, ] = x.shape;\n const [, yHeight, yWidth] = dy.shape;\n\n // In the backwards pass, we want to find the pixels that were generated for\n // each pixel in the input image the forward pass and add the corresponding\n // coefficient from dy to the gradient (with some interpolation).\n\n const effectiveXSize: [number, number] = [\n (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n ];\n\n const effectiveYSize: [number, number] = [\n (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n ];\n\n const heightScale = effectiveXSize[0] / effectiveYSize[0];\n const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n const invHeightScale = 1 / heightScale;\n const invWidthScale = 1 / widthScale;\n\n // This defines the size of the window of values around a particular\n // index in dy that we want to search for contributions to dx.\n const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n this.userCode = `\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${heightScale});\n const float widthScale = float(${widthScale});\n\n const float invHeightScale = float(${invHeightScale});\n const float invWidthScale = float(${invWidthScale});\n\n const int winHeight = int(${winHeight});\n const int winWidth = int(${winWidth});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${yHeight}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${yWidth}) {\n continue;\n }\n\n float sourceFracRow =\n float(${effectiveXSize[0]}) *\n (float(dyR) / float(${effectiveYSize[0]}));\n\n float sourceFracCol =\n float(${effectiveXSize[1]}) *\n (float(dyC) / float(${effectiveYSize[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${xHeight}) - 1),\n ${alignCorners} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${xWidth}) - 1),\n ${alignCorners} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `;\n }\n}\n","../src/kernels/webgl/resize_nearest_neighbor_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeighborProgram implements GPGPUProgram {\n variableNames = ['A'];\n outputShape: number[] = [];\n userCode: string;\n\n constructor(\n inputShape: [number, number, number, number], newHeight: number,\n newWidth: number, alignCorners: boolean) {\n const [batch, oldHeight, oldWidth, depth] = inputShape;\n this.outputShape = [batch, newHeight, newWidth, depth];\n\n const effectiveInSize: [number, number] = [\n (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n ];\n\n const effectiveOutSize: [number, number] = [\n (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n ];\n\n // When align corners is false, we rounds the value with floor.\n const roundBase = alignCorners ? '0.5' : '0.0';\n\n this.userCode = `\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${effectiveInSize[0] / effectiveOutSize[0]},\n ${effectiveInSize[1] / effectiveOutSize[1]});\n const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `;\n }\n}\n","../src/kernels/webgl/reverse_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ReverseProgram implements GPGPUProgram {\n variableNames = ['x'];\n outputShape: number[];\n userCode: string;\n\n constructor(xShape: number[], axis: number[]) {\n const rank = xShape.length;\n if (rank > 4) {\n throw new Error(\n `WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n }\n this.outputShape = xShape;\n\n if (rank === 1) {\n this.userCode = `\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${xShape[0]} - coord - 1));\n }\n `;\n return;\n }\n const getInCoord = (i: number) => {\n if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n return `${xShape[i]} - coords[${i}] - 1`;\n }\n return `coords[${i}]`;\n };\n const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n const type = getCoordsDataType(rank);\n\n this.userCode = `\n void main() {\n ${type} coords = getOutputCoords();\n setOutput(getX(${inCoords}));\n }\n `;\n }\n}\n","../src/kernels/webgl/scatter_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ScatterProgram implements GPGPUProgram {\n variableNames = ['updates', 'indices', 'defaultValue'];\n outputShape: number[];\n userCode: string;\n\n constructor(\n updateSize: number, sliceDim: number, indicesRank: number,\n updatesRank: number, strides: number[], shape: number[],\n summingDupeIndex = true) {\n this.outputShape = shape;\n const stridesType = getCoordsDataType(strides.length);\n const dtype = getCoordsDataType(shape.length);\n let indicesString = '';\n if (indicesRank === 1) {\n indicesString = 'i';\n } else if (indicesRank === 2) {\n indicesString = 'i, j';\n }\n const indicesSnippet = `getIndices(${indicesString})`;\n\n let updatesString = '';\n if (updatesRank === 1) {\n updatesString = 'i';\n } else if (updatesRank === 2) {\n updatesString = 'i, coords[1]';\n }\n const updatesSnippet = `getUpdates(${updatesString})`;\n\n const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n this.userCode = `\n ${stridesType} strides = ${stridesType}(${strides});\n\n void main() {\n ${dtype} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${updateSize}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${sliceDim}; j++) {\n int index = round(${indicesSnippet});\n flattenedIndex += index * ${strideString};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${updatesSnippet};\n found = true;\n }\n }\n setOutput(mix(getDefaultValue(), sum, float(found)));\n }\n `;\n }\n}\n","../src/kernels/webgl/segment_gpu.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {SegOpInfo} from '../../ops/segment_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class SegmentOpProgram implements GPGPUProgram {\n variableNames = ['x', 'segmentIds'];\n outputShape: number[];\n userCode: string;\n\n constructor(segOpInfo: SegOpInfo, segOpType: 'unsortedSegmentSum') {\n const windowSize = segOpInfo.windowSize;\n const batchSize = segOpInfo.batchSize;\n const inSize = segOpInfo.inSize;\n const numSegments = segOpInfo.numSegments;\n const outSize = numSegments * Math.ceil(inSize / windowSize);\n this.outputShape = [batchSize, outSize];\n\n const initializationValue = '0.0';\n const returnValue = `sumValue`;\n\n const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n const windowSizeVec4Remainder = windowSize % 4;\n\n const updateSnippet = `\n sumValue += dot(values, segFilter);\n `;\n\n let checkValueOutOfBounds = '';\n if (inSize % windowSize > 0) {\n checkValueOutOfBounds = `\n if (inIdx < 0 || inIdx >= ${inSize}) {\n return initializationValue;\n }\n `;\n }\n\n let checkSegmentIdOutOfBounds = '';\n if (inSize % windowSize > 0) {\n checkSegmentIdOutOfBounds = `\n if (inIdx < 0 || inIdx >= ${inSize}) {\n return -1.0;\n }\n `;\n }\n\n this.userCode = `\n const float initializationValue = ${initializationValue};\n\n float getValue(int batch, int inIdx) {\n ${checkValueOutOfBounds}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${checkSegmentIdOutOfBounds}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${numSegments})) * float(${windowSize}));\n int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${updateSnippet}\n }\n\n int inIdx = inOffset + ${windowSizeNearestVec4};\n if (${windowSizeVec4Remainder === 1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${updateSnippet}\n } else if (${windowSizeVec4Remainder === 2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${updateSnippet}\n } else if (${windowSizeVec4Remainder === 3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${updateSnippet}\n }\n setOutput(${returnValue});\n }\n `;\n }\n}\n","../src/kernels/webgl/select_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SelectProgram implements GPGPUProgram {\n variableNames = ['c', 'a', 'b'];\n outputShape: number[];\n userCode: string;\n\n constructor(cRank: number, shape: number[], rank: number) {\n this.outputShape = shape;\n\n let cCoords;\n let abCoords;\n if (rank > 4) {\n throw Error(`Where for rank ${rank} is not yet supported`);\n }\n\n if (rank === 1) {\n abCoords = `resRC`;\n cCoords = `resRC`;\n } else {\n const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n const cCoordVars = [];\n const abCoordVars = [];\n for (let i = 0; i < shape.length; i++) {\n abCoordVars.push(`${currentCoords[i]}`);\n if (i < cRank) {\n cCoordVars.push(`${currentCoords[i]}`);\n }\n }\n cCoords = cCoordVars.join();\n abCoords = abCoordVars.join();\n }\n\n const dtype = getCoordsDataType(rank);\n\n this.userCode = `\n void main() {\n ${dtype} resRC = getOutputCoords();\n float cVal = getC(${cCoords});\n if (cVal >= 1.0) {\n setOutput(getA(${abCoords}));\n } else {\n setOutput(getB(${abCoords}));\n }\n }\n `;\n }\n}\n","../src/kernels/webgl/slice_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SliceProgram implements GPGPUProgram {\n variableNames = ['source'];\n outputShape: number[];\n userCode: string;\n rank: number;\n\n // Caching uniform location for speed.\n startLoc: WebGLUniformLocation;\n\n constructor(destSize: number[]) {\n this.outputShape = destSize;\n this.rank = destSize.length;\n\n const dtype = getCoordsDataType(this.rank);\n const uniformPart = `uniform int start[${this.rank}];`;\n const sourceCoords = getCoords(this.rank);\n\n let body: string;\n const coordSum = destSize.map((_, i) => {\n return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n });\n body = `\n ${dtype} sourceLoc;\n ${dtype} coords = getOutputCoords();\n ${coordSum.join('\\n')}\n `;\n this.userCode = `\n ${uniformPart}\n void main() {\n ${body}\n setOutput(getSource(${sourceCoords}));\n }\n `;\n }\n\n getCustomSetupFunc(start: number[]) {\n if (start.length !== this.rank) {\n throw Error(\n `The rank (${this.rank}) of the program must match the ` +\n `length of start (${start.length})`);\n }\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.startLoc == null) {\n this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n if (this.startLoc == null) {\n // This means the compiler has optimized and realized it doesn't need\n // the uniform.\n return;\n }\n }\n gpgpu.gl.uniform1iv(this.startLoc, start);\n };\n }\n}\n\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\n\nfunction getCoords(rank: number): string {\n if (rank === 1) {\n return 'sourceLoc';\n } else if (rank <= 6) {\n return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n } else {\n throw Error(`Slicing for rank ${rank} is not yet supported`);\n }\n}\n","../src/kernels/webgl/strided_slice_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class StridedSliceProgram implements GPGPUProgram {\n variableNames = ['x'];\n outputShape: number[];\n userCode: string;\n\n constructor(\n begin: number[], strides: number[], size: number[],\n shrinkAxis: number[]) {\n const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n this.outputShape = shape;\n const rank = size.length;\n const inputDtype = getCoordsDataType(size.length);\n const dtype = getCoordsDataType(shape.length);\n\n let newCoords = '';\n if (rank === 1) {\n newCoords = 'coords * strides + begin';\n } else {\n let outputAxis = 0;\n newCoords =\n size.map((_, i) => {\n if (shrinkAxis.indexOf(i) === -1) {\n outputAxis++;\n return shape.length === 1 ?\n `coords * strides[${i}] + begin[${i}]` :\n `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n } else {\n return `begin[${i}]`;\n }\n })\n .join(',');\n }\n\n this.userCode = `\n ${inputDtype} begin = ${inputDtype}(${begin});\n ${inputDtype} strides = ${inputDtype}(${strides});\n\n void main() {\n ${dtype} coords = getOutputCoords();\n setOutput(getX(${newCoords}));\n }\n `;\n }\n}\n","../src/kernels/webgl/texture_manager.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {PhysicalTextureType, TextureUsage} from './tex_util';\n\nexport class TextureManager {\n private numUsedTextures = 0;\n private numFreeTextures = 0;\n private freeTextures: {[shape: string]: WebGLTexture[]} = {};\n private logEnabled = false;\n private usedTextures: {[shape: string]: WebGLTexture[]} = {};\n\n constructor(private gpgpu: GPGPUContext) {}\n\n acquireTexture(\n shapeRC: [number, number], usage: TextureUsage,\n isPacked: boolean): WebGLTexture {\n const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n\n const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n if (!(shapeKey in this.freeTextures)) {\n this.freeTextures[shapeKey] = [];\n }\n if (!(shapeKey in this.usedTextures)) {\n this.usedTextures[shapeKey] = [];\n }\n\n if (this.freeTextures[shapeKey].length > 0) {\n this.numFreeTextures--;\n this.numUsedTextures++;\n this.log();\n const newTexture = this.freeTextures[shapeKey].shift();\n this.usedTextures[shapeKey].push(newTexture);\n return newTexture;\n }\n this.numUsedTextures++;\n this.log();\n\n let newTexture: WebGLTexture;\n if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n newTexture =\n this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n newTexture =\n this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n newTexture =\n this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n\n } else if (\n physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n newTexture =\n this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n }\n this.usedTextures[shapeKey].push(newTexture);\n\n return newTexture;\n }\n\n releaseTexture(\n texture: WebGLTexture, shape: [number, number],\n logicalTexType: TextureUsage, isPacked: boolean): void {\n if (this.freeTextures == null) {\n // Already disposed.\n return;\n }\n const physicalTexType =\n getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n if (!(shapeKey in this.freeTextures)) {\n this.freeTextures[shapeKey] = [];\n }\n this.freeTextures[shapeKey].push(texture);\n this.numFreeTextures++;\n this.numUsedTextures--;\n const texList = this.usedTextures[shapeKey];\n const texIndex = texList.indexOf(texture);\n if (texIndex < 0) {\n throw new Error(\n 'Cannot release a texture that was never provided by this ' +\n 'texture manager');\n }\n texList.splice(texIndex, 1);\n this.log();\n }\n\n private log() {\n if (!this.logEnabled) {\n return;\n }\n const total = this.numFreeTextures + this.numUsedTextures;\n console.log(\n 'Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`,\n `(${total})`);\n }\n\n getNumUsedTextures(): number {\n return this.numUsedTextures;\n }\n\n getNumFreeTextures(): number {\n return this.numFreeTextures;\n }\n\n dispose() {\n if (this.freeTextures == null) {\n // Already disposed.\n return;\n }\n for (const texShape in this.freeTextures) {\n this.freeTextures[texShape].forEach(tex => {\n this.gpgpu.deleteMatrixTexture(tex);\n });\n }\n for (const texShape in this.usedTextures) {\n this.usedTextures[texShape].forEach(tex => {\n this.gpgpu.deleteMatrixTexture(tex);\n });\n }\n this.freeTextures = null;\n this.usedTextures = null;\n this.numUsedTextures = 0;\n this.numFreeTextures = 0;\n }\n}\n\nfunction getPhysicalFromLogicalTextureType(\n logicalTexType: TextureUsage, isPacked: boolean): PhysicalTextureType {\n if (logicalTexType === TextureUsage.UPLOAD) {\n return isPacked ? PhysicalTextureType.PACKED_2X2_FLOAT32 :\n PhysicalTextureType.UNPACKED_FLOAT32;\n } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n if (isPacked) {\n return ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?\n PhysicalTextureType.PACKED_2X2_FLOAT32 :\n PhysicalTextureType.PACKED_2X2_FLOAT16;\n }\n return ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?\n PhysicalTextureType.UNPACKED_FLOAT32 :\n PhysicalTextureType.UNPACKED_FLOAT16;\n } else if (\n logicalTexType === TextureUsage.DOWNLOAD ||\n logicalTexType === TextureUsage.PIXELS) {\n return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n }\n throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\n\nfunction getKeyFromTextureShape(\n shapeRowsCol: [number, number], physicalTexType: PhysicalTextureType,\n isPacked: boolean): string {\n return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n","../src/kernels/webgl/tile_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TileProgram implements GPGPUProgram {\n variableNames = ['A'];\n outputShape: number[];\n userCode: string;\n rank: number;\n\n constructor(aShape: number[], reps: number[]) {\n const outputShape: number[] = new Array(aShape.length);\n for (let i = 0; i < outputShape.length; i++) {\n outputShape[i] = aShape[i] * reps[i];\n }\n this.outputShape = outputShape;\n this.rank = outputShape.length;\n const dtype = getCoordsDataType(this.rank);\n const sourceCoords = getSourceCoords(aShape);\n\n this.userCode = `\n void main() {\n ${dtype} resRC = getOutputCoords();\n setOutput(getA(${sourceCoords}));\n }\n `;\n }\n}\n\nfunction getSourceCoords(aShape: number[]): string {\n const rank = aShape.length;\n if (rank > 5) {\n throw Error(`Tile for rank ${rank} is not yet supported`);\n }\n if (rank === 1) {\n return `imod(resRC, ${aShape[0]})`;\n }\n\n const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n\n const sourceCoords = [];\n for (let i = 0; i < aShape.length; i++) {\n sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n }\n return sourceCoords.join();\n}\n","../src/kernels/webgl/transpose_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TransposeProgram implements GPGPUProgram {\n variableNames = ['A'];\n outputShape: number[];\n userCode: string;\n rank: number;\n\n constructor(aShape: number[], newDim: number[]) {\n const outputShape: number[] = new Array(aShape.length);\n for (let i = 0; i < outputShape.length; i++) {\n outputShape[i] = aShape[newDim[i]];\n }\n this.outputShape = outputShape;\n this.rank = outputShape.length;\n const dtype = getCoordsDataType(this.rank);\n const switched = getSwitchedCoords(newDim);\n\n this.userCode = `\n void main() {\n ${dtype} resRC = getOutputCoords();\n setOutput(getA(${switched}));\n }\n `;\n }\n}\n\nfunction getSwitchedCoords(newDim: number[]): string {\n const rank = newDim.length;\n if (rank > 6) {\n throw Error(`Transpose for rank ${rank} is not yet supported`);\n }\n const originalOrder =\n ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n const switchedCoords = new Array(rank);\n for (let i = 0; i < newDim.length; i++) {\n switchedCoords[newDim[i]] = originalOrder[i];\n }\n return switchedCoords.join();\n}\n","../src/ops/erf_util.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n","../src/ops/selu_util.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n","../src/kernels/webgl/unaryop_gpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as erf_util from '../../ops/erf_util';\nimport * as selu_util from '../../ops/selu_util';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class UnaryOpProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n outputShape: number[];\n\n // Caching uniform location for speed.\n startLoc: WebGLUniformLocation;\n\n constructor(aShape: number[], opSnippet: string) {\n this.outputShape = aShape;\n this.userCode = `\n uniform float NAN;\n float unaryOperation(float x) {\n ${opSnippet}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `;\n }\n\n getCustomSetupFunc() {\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.startLoc == null) {\n this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');\n if (this.startLoc == null) {\n // This means the compiler has optimized and realized it doesn't need\n // the uniform.\n return;\n }\n }\n gpgpu.gl.uniform1f(this.startLoc, NaN);\n };\n }\n}\n\nconst CHECK_NAN_SNIPPET = `if (isNaN(x)) return x;`;\n\nexport const LINEAR = `return x;`;\n\nexport const ABS = `return abs(x);`;\n\nexport const RELU = CHECK_NAN_SNIPPET + `\n return (x < 0.0) ? 0.0 : x;\n`;\n\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\n\nexport const SELU = `\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${selu_util.SELU_SCALEALPHA};\n float scale = ${selu_util.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\n\nexport function STEP(alpha = 0.0) {\n return CHECK_NAN_SNIPPET + `\n return x > 0.0 ? 1.0 : float(${alpha});\n `;\n}\n\nexport const NEG = `return -x;`;\n\nexport const CEIL = `return ceil(x);`;\n\nexport const FLOOR = `return floor(x);`;\n\nexport const SIGN = `\n if (isNaN(x)) { return 0.0; }\n return sign(x);\n`;\n\nexport const ROUND = `\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`;\n\nexport const EXP = `return exp(x);`;\n\nexport const EXPM1 = `return exp(x) - 1.0;`;\n\nexport const LOG = `if (x < 0.0) return NAN;\n return log(x);`;\n\nexport const LOG1P = `return log(1.0 + x);`;\n\nexport const SQRT = `return sqrt(x);`;\n\nexport const RSQRT = `return inversesqrt(x);`;\n\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`;\n\nexport const SIN = CHECK_NAN_SNIPPET + `\n return sin(x);\n`;\n\nexport const COS = CHECK_NAN_SNIPPET + `\n return cos(x);\n`;\n\nexport const TAN = `return tan(x);`;\n\nexport const ASIN = `return asin(x);`;\n\nexport const ACOS = `return acos(x);`;\n\nexport const ATAN = CHECK_NAN_SNIPPET + `\n return atan(x);\n`;\n\nexport const SINH = `\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`;\n\nexport const COSH = `\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`;\n\nexport const TANH = `\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\n\nexport const ASINH = `return log(x + sqrt(x * x + 1.0));`;\n\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n if (x < 1.0) return NAN;\n return log(x + sqrt(x * x - 1.0));`;\n\nexport const ATANH = CHECK_NAN_SNIPPET + `\n if ((x < -1.0) || (x > 1.0)) return NAN;\n return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\n\nexport const ERF = `\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${erf_util.ERF_P};\n float a1 = ${erf_util.ERF_A1};\n float a2 = ${erf_util.ERF_A2};\n float a3 = ${erf_util.ERF_A3};\n float a4 = ${erf_util.ERF_A4};\n float a5 = ${erf_util.ERF_A5};\n\n float t = 1.0 / (1.0 + p * x);\n return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\n`;\n\nexport const SQUARE = `return x * x;`;\n\nexport const RECIPROCAL = `return 1.0 / x;`;\n\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\n\nexport const TO_INT = `return float(int(x));`;\n\nexport const CLONE = 'return x;';\n","../src/kernels/webgl/unaryop_packed_gpu.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport const LINEAR = `return x;`;\n\nexport const LOG = `\n vec4 result = log(x);\n vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n result.r = isNaN.r == 1.0 ? NAN : result.r;\n result.g = isNaN.g == 1.0 ? NAN : result.g;\n result.b = isNaN.b == 1.0 ? NAN : result.b;\n result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n return result;\n`;\n\nexport const RELU = `\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n\n result.r = isNaN(x.r) ? x.r : result.r;\n result.g = isNaN(x.g) ? x.g : result.g;\n result.b = isNaN(x.b) ? x.b : result.b;\n result.a = isNaN(x.a) ? x.a : result.a;\n\n return result;\n`;\n\nexport class UnaryOpPackedProgram implements GPGPUProgram {\n variableNames = ['A'];\n userCode: string;\n outputShape: number[];\n usesPackedTextures = true;\n\n // Caching uniform location for speed.\n startLoc: WebGLUniformLocation;\n\n constructor(aShape: number[], opSnippet: string) {\n this.outputShape = aShape;\n this.userCode = `\n uniform float NAN;\n vec4 unaryOperation(vec4 x) {\n ${opSnippet}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `;\n }\n\n getCustomSetupFunc() {\n return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n if (this.startLoc == null) {\n this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');\n if (this.startLoc == null) {\n // This means the compiler has optimized and realized it doesn't need\n // the uniform.\n return;\n }\n }\n gpgpu.gl.uniform1f(this.startLoc, NaN);\n };\n }\n}\n","../src/kernels/webgl/unpack_gpu.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getChannels, getSourceCoords} from '../packing_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class UnpackProgram implements GPGPUProgram {\n variableNames = ['A'];\n usesPackedTextures = true;\n outputShape: number[];\n userCode: string;\n\n constructor(outputShape: number[]) {\n this.outputShape = outputShape;\n const rank = outputShape.length;\n\n const channels = getChannels('rc', rank);\n const dtype = getCoordsDataType(rank);\n const sourceCoords = getSourceCoords(rank, channels);\n const innerDims = channels.slice(-2);\n const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n\n this.userCode = `\n void main() {\n ${dtype} rc = getOutputCoords();\n vec4 packedInput = getA(${sourceCoords});\n\n setOutput(getChannel(packedInput, ${coords}));\n }\n `;\n }\n}","../src/ops/browser.ts":"/**\n * @license\n * Copyright 2019 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor2D, Tensor3D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel.\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n */\n/** @doc {heading: 'Browser', namespace: 'browser'} */\nfunction fromPixels_(\n pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n numChannels = 3): Tensor3D {\n if (numChannels > 4) {\n throw new Error(\n 'Cannot construct Tensor with more than 4 channels from pixels.');\n }\n return ENV.engine.fromPixels(pixels, numChannels);\n}\n\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n * rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n */\n/** @doc {heading: 'Browser', namespace: 'browser'} */\nexport async function toPixels(\n img: Tensor2D|Tensor3D|TensorLike,\n canvas?: HTMLCanvasElement): Promise {\n let $img = convertToTensor(img, 'img', 'toPixels');\n if (!(img instanceof Tensor)) {\n // Assume int32 if user passed a native array.\n $img = $img.toInt();\n }\n if ($img.rank !== 2 && $img.rank !== 3) {\n throw new Error(\n `toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n }\n const [height, width] = $img.shape.slice(0, 2);\n const depth = $img.rank === 2 ? 1 : $img.shape[2];\n\n if (depth > 4 || depth === 2) {\n throw new Error(\n `toPixels only supports depth of size ` +\n `1, 3 or 4 but got ${depth}`);\n }\n\n const minTensor = $img.min();\n const maxTensor = $img.max();\n const min = (await minTensor.data())[0];\n const max = (await maxTensor.data())[0];\n minTensor.dispose();\n maxTensor.dispose();\n if ($img.dtype === 'float32') {\n if (min < 0 || max > 1) {\n throw new Error(\n `Tensor values for a float32 Tensor must be in the ` +\n `range [0 - 1] but got range [${min} - ${max}].`);\n }\n } else if ($img.dtype === 'int32') {\n if (min < 0 || max > 255) {\n throw new Error(\n `Tensor values for a int32 Tensor must be in the ` +\n `range [0 - 255] but got range [${min} - ${max}].`);\n }\n } else {\n throw new Error(\n `Unsupported type for toPixels: ${$img.dtype}.` +\n ` Please use float32 or int32 tensors.`);\n }\n\n const data = await $img.data();\n const multiplier = $img.dtype === 'float32' ? 255 : 1;\n const bytes = new Uint8ClampedArray(width * height * 4);\n\n for (let i = 0; i < height * width; ++i) {\n let r, g, b, a;\n if (depth === 1) {\n r = data[i] * multiplier;\n g = data[i] * multiplier;\n b = data[i] * multiplier;\n a = 255;\n } else if (depth === 3) {\n r = data[i * 3] * multiplier;\n g = data[i * 3 + 1] * multiplier;\n b = data[i * 3 + 2] * multiplier;\n a = 255;\n } else if (depth === 4) {\n r = data[i * 4] * multiplier;\n g = data[i * 4 + 1] * multiplier;\n b = data[i * 4 + 2] * multiplier;\n a = data[i * 4 + 3] * multiplier;\n }\n\n const j = i * 4;\n bytes[j + 0] = Math.round(r);\n bytes[j + 1] = Math.round(g);\n bytes[j + 2] = Math.round(b);\n bytes[j + 3] = Math.round(a);\n }\n\n if (canvas != null) {\n canvas.width = width;\n canvas.height = height;\n const ctx = canvas.getContext('2d');\n const imageData = new ImageData(bytes, width, height);\n ctx.putImageData(imageData, 0, 0);\n }\n if ($img !== img) {\n $img.dispose();\n }\n return bytes;\n}\n\nexport const fromPixels = op({fromPixels_});\n","../src/ops/concat_split.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, sizeFromShape} from '../util';\nimport {parseAxisParam} from '../util';\nimport {assertParamsConsistent, computeOutShape} from './concat_util';\nimport {op} from './operation';\nimport {tensor} from './tensor_ops';\n\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors: Array): Tensor1D {\n return concat(tensors, 0 /* axis */);\n}\n\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n * | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n * | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(\n tensors: Array, axis: number): Tensor2D {\n return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor3D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n * | r2, g2, b2 |\n * | r3, g3, b3 |\n * | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n * | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n * | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(\n tensors: Array, axis: number): Tensor3D {\n return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor4D`s along an axis. See `concat` for details.\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(\n tensors: Array, axis: number): Tensor4D {\n return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n * - `tf.concat1d`\n * - `tf.concat2d`\n * - `tf.concat3d`\n * - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print(); // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction concat_(tensors: Array, axis = 0): T {\n assert(tensors.length >= 1, 'Pass at least one tensor to concat');\n let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n axis = parseAxisParam(axis, $tensors[0].shape)[0];\n const outShape = computeOutShape($tensors.map(t => t.shape), axis);\n if (sizeFromShape(outShape) === 0) {\n return tensor([], outShape) as T;\n }\n // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n $tensors = $tensors.filter(t => t.size > 0);\n if ($tensors.length === 1) {\n return $tensors[0];\n }\n\n const shapes = $tensors.map(t => t.shape);\n assertParamsConsistent(shapes, axis);\n const der = (dy: T) => {\n const sizeSplits = shapes.map(s => s[axis]);\n const derTensors = split(dy, sizeSplits, axis);\n return derTensors.map(t => () => t) as {};\n };\n const inputs = $tensors as {};\n return ENV.engine.runKernel(\n backend => backend.concat($tensors, axis) as T, inputs, der);\n}\n\n/**\n * Splits a`tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `(numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction split_(\n x: T|TensorLike, numOrSizeSplits: number[]|number, axis = 0): T[] {\n const $x = convertToTensor(x, 'x', 'split');\n\n axis = parseAxisParam(axis, $x.shape)[0];\n let splitSizes: number[];\n if (typeof (numOrSizeSplits) === 'number') {\n assert(\n $x.shape[axis] % numOrSizeSplits === 0,\n 'Number of splits must evenly divide the axis.');\n splitSizes =\n new Array(numOrSizeSplits).fill($x.shape[axis] / numOrSizeSplits);\n } else {\n assert(\n $x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b),\n 'The sum of sizes must match the size of the axis dimension.');\n splitSizes = numOrSizeSplits;\n }\n const der = (dy: T[]) => ({$x: () => concat(dy, axis)});\n return ENV.engine.runKernel(\n backend => backend.split($x, splitSizes, axis), {$x}, der);\n}\n\nexport const concat = op({concat_});\nexport const concat1d = op({concat1d_});\nexport const concat2d = op({concat2d_});\nexport const concat3d = op({concat3d_});\nexport const concat4d = op({concat4d_});\nexport const split = op({split_});\n","../node_modules/seedrandom/lib/alea.js":"// A port of an algorithm by Johannes Baagøe , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baagøe \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n","../node_modules/seedrandom/lib/xor128.js":"// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n","../node_modules/seedrandom/lib/xorwow.js":"// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n","../node_modules/seedrandom/lib/xorshift7.js":"// A Javascript implementaion of the \"xorshift7\" algorithm by\n// François Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n","../node_modules/seedrandom/lib/xor4096.js":"// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n","../node_modules/seedrandom/lib/tychei.js":"// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n","../node_modules/seedrandom/seedrandom.js":"/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n","../node_modules/seedrandom/index.js":"// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by François Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n","../src/ops/rand.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\n\nexport interface RandGauss {\n nextValue(): number;\n}\n\nexport interface RandNormalDataTypes {\n float32: Float32Array;\n int32: Int32Array;\n}\n\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss implements RandGauss {\n private mean: number;\n private stdDev: number;\n private nextVal: number;\n private dtype?: keyof RandNormalDataTypes;\n private truncated?: boolean;\n private upper?: number;\n private lower?: number;\n private random: seedrandom.prng;\n\n constructor(\n mean: number, stdDeviation: number, dtype?: keyof RandNormalDataTypes,\n truncated?: boolean, seed?: number) {\n this.mean = mean;\n this.stdDev = stdDeviation;\n this.dtype = dtype;\n this.nextVal = NaN;\n this.truncated = truncated;\n if (this.truncated) {\n this.upper = this.mean + this.stdDev * 2;\n this.lower = this.mean - this.stdDev * 2;\n }\n const seedValue = seed ? seed : Math.random();\n this.random = seedrandom.alea(seedValue.toString());\n }\n\n /** Returns next sample from a gaussian distribution. */\n public nextValue(): number {\n if (!isNaN(this.nextVal)) {\n const value = this.nextVal;\n this.nextVal = NaN;\n return value;\n }\n\n let resultX: number, resultY: number;\n let isValid = false;\n while (!isValid) {\n let v1: number, v2: number, s: number;\n do {\n v1 = 2 * this.random() - 1;\n v2 = 2 * this.random() - 1;\n s = v1 * v1 + v2 * v2;\n } while (s >= 1 || s === 0);\n\n const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n resultX = this.mean + this.stdDev * v1 * mul;\n resultY = this.mean + this.stdDev * v2 * mul;\n\n if (!this.truncated || this.isValidTruncated(resultX)) {\n isValid = true;\n }\n }\n\n if (!this.truncated || this.isValidTruncated(resultY)) {\n this.nextVal = this.convertValue(resultY);\n }\n return this.convertValue(resultX);\n }\n\n /** Handles proper rounding for non floating point numbers. */\n private convertValue(value: number): number {\n if (this.dtype == null || this.dtype === 'float32') {\n return value;\n }\n return Math.round(value);\n }\n\n /** Returns true if less than 2-standard-deviations from the mean. */\n private isValidTruncated(value: number): boolean {\n return value <= this.upper && value >= this.lower;\n }\n}\n","../src/ops/array_ops.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {deprecationWarn, ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {DataType, DataTypeMap, Rank, ShapeMap, TensorLike, TensorLike4D} from '../types';\nimport * as util from '../util';\n\nimport {getAxesPermutation, getInnerMostAxes} from './axis_util';\nimport {fromPixels as browserFromPixels, toPixels as browserToPixels} from './browser';\nimport {concat} from './concat_split';\nimport {op} from './operation';\nimport {MPRandGauss} from './rand';\nimport {zeros, zerosLike} from './tensor_ops';\n\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction clone_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'clone', null);\n const der = (dy: T) => {\n return {$x: () => dy.toFloat()};\n };\n\n return ENV.engine.runKernel(\n backend =>\n Tensor.make($x.shape, {dataId: $x.dataId}, $x.dtype) as T,\n {$x}, der) as T;\n}\n\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n * of the shape of the returned `tf.Tensor` by repeating the identity\n * matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n * with batch repetition if `batchShape` is specified.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction eye_(\n numRows: number, numColumns?: number,\n batchShape?:\n [\n number\n ]|[number,\n number]|[number, number, number]|[number, number, number, number],\n dtype: DataType = 'float32'): Tensor2D {\n if (numColumns == null) {\n numColumns = numRows;\n }\n const buff = buffer([numRows, numColumns], dtype);\n const n = numRows <= numColumns ? numRows : numColumns;\n for (let i = 0; i < n; ++i) {\n buff.set(1, i, i);\n }\n const out = buff.toTensor().as2D(numRows, numColumns);\n if (batchShape == null) {\n return out;\n } else {\n if (batchShape.length === 1) {\n return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n } else if (batchShape.length === 2) {\n return tile(\n expandDims(expandDims(out, 0), 0),\n [batchShape[0], batchShape[1], 1, 1]);\n } else if (batchShape.length === 3) {\n return tile(\n expandDims(expandDims(expandDims(out, 0), 0), 0),\n [batchShape[0], batchShape[1], batchShape[2], 1, 1]);\n } else {\n throw new Error(\n `eye() currently supports only 1D and 2D ` +\n // tslint:disable-next-line:no-any\n `batchShapes, but received ${(batchShape as any).length}D.`);\n }\n }\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction randomNormal_(\n shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n seed?: number): Tensor {\n if (dtype != null && (dtype as DataType) === 'bool') {\n throw new Error(`Unsupported data type ${dtype}`);\n }\n const randGauss =\n new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = randGauss.nextValue();\n }\n return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction truncatedNormal_(\n shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n seed?: number): Tensor {\n if (dtype != null && (dtype as DataType) === 'bool') {\n throw new Error(`Unsupported data type ${dtype}`);\n }\n const randGauss =\n new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = randGauss.nextValue();\n }\n return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n * Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n * Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction randomUniform_(\n shape: ShapeMap[R], minval = 0, maxval = 1,\n dtype: DataType = 'float32'): Tensor {\n const res = buffer(shape, dtype);\n for (let i = 0; i < res.values.length; i++) {\n res.values[i] = util.randUniform(minval, maxval);\n }\n return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_(\n shape: ShapeMap[R], randFunction: () => number,\n dtype?: DataType): Tensor {\n const size = util.sizeFromShape(shape);\n\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n } else if (dtype === 'int32') {\n values = new Int32Array(size);\n } else if (dtype === 'bool') {\n values = new Uint8Array(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n\n for (let i = 0; i < size; i++) {\n values[i] = randFunction();\n }\n return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n * 2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n * parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n * probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n * `[batchSize, numSamples]`, depending on the rank of the input.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction multinomial_(\n logits: Tensor1D|Tensor2D|TensorLike, numSamples: number, seed?: number,\n normalized = false): Tensor1D|Tensor2D {\n const $logits = convertToTensor(logits, 'logits', 'multinomial');\n const numOutcomes = $logits.size;\n const origRank = $logits.rank;\n if (numOutcomes < 2) {\n throw new Error(\n `Error in multinomial: you need at least 2 outcomes, but got ` +\n `${numOutcomes}.`);\n }\n if (origRank > 2) {\n throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n }\n seed = seed || Math.random();\n const logits2D = origRank === 1 ? $logits.as2D(1, -1) : $logits as Tensor2D;\n const res = ENV.engine.runKernel(\n backend => backend.multinomial(logits2D, normalized, numSamples, seed),\n {logits2D});\n\n return origRank === 1 ? res.as1D() : res;\n}\n\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n * not match the location.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction oneHot_(\n indices: Tensor|TensorLike, depth: number, onValue = 1,\n offValue = 0): Tensor {\n if (depth < 2) {\n throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n }\n let $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n const outShape = [...$indices.shape, depth];\n $indices = $indices.flatten();\n\n const grad = (dy: Tensor2D) => {\n return {$indices: () => zeros($indices.shape, 'float32')};\n };\n const result = ENV.engine.runKernel(\n backend => backend.oneHot($indices as Tensor1D, depth, onValue, offValue),\n {$indices}, grad);\n return result.reshape(outShape);\n}\n\n/**\n * Deprecated. Use `tf.browser.fromPixels`.\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel.\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction fromPixels_(\n pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n numChannels = 3): Tensor3D {\n deprecationWarn(\n `tf.fromPixels() is renamed to tf.browser.fromPixels(), please switch ` +\n `to the new method as the old method will be removed in TensorFlow.js ` +\n `1.0.`);\n\n return browserFromPixels(pixels, numChannels);\n}\n\n/**\n * Deprecated. Use `tf.browser.toPixels`.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n * rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n */\n/** @doc {heading: 'Visualization'} */\nasync function toPixels(\n img: Tensor2D|Tensor3D|TensorLike,\n canvas?: HTMLCanvasElement): Promise {\n deprecationWarn(\n `tf.toPixels() is renamed to tf.browser.toPixels(), please switch to ` +\n `the new method as the old method will be removed in TensorFlow.js 1.0.`);\n\n return browserToPixels(img, canvas);\n}\n\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction reshape_(\n x: Tensor|TensorLike, shape: ShapeMap[R2]): Tensor {\n const $x = convertToTensor(x, 'x', 'reshape', null);\n shape = util.inferFromImplicitShape(shape, $x.size);\n util.assert(\n $x.size === util.sizeFromShape(shape),\n 'new shape and old shape must have the same number of elements.');\n\n const grad = (dy: Tensor) => {\n return {$x: () => dy.reshape($x.shape)};\n };\n return ENV.engine.runKernel(\n backend => backend.reshape($x, shape), {$x}, grad);\n}\n\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n * squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction squeeze_(x: Tensor|TensorLike, axis?: number[]): T {\n const $x = convertToTensor(x, 'x', 'squeeze');\n return reshape($x, util.squeezeShape($x.shape, axis).newShape) as T;\n}\n\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction cast_(x: T|TensorLike, dtype: DataType): T {\n const $x = convertToTensor(x, 'x', 'cast');\n\n const grad = (dy: T) => {\n return {$x: () => dy.clone()};\n };\n return ENV.engine.runKernel(backend => backend.cast($x, dtype), {$x}, grad) as\n T;\n}\n\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print(); // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print(); // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction tile_(x: T|TensorLike, reps: number[]): T {\n const $x = convertToTensor(x, 'x', 'tile');\n\n util.assert(\n $x.rank === reps.length,\n `Error in transpose: rank of input ${$x.rank} ` +\n `must match length of reps ${reps}.`);\n const grad = (dy: T) => {\n const derX = () => {\n let xGrad = zerosLike($x);\n // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n // slicing.\n if ($x.rank === 1) {\n for (let i = 0; i < reps[0]; ++i) {\n xGrad = xGrad.add(dy.slice([i * $x.shape[0]], [$x.shape[0]]));\n }\n } else if ($x.rank === 2) {\n for (let i = 0; i < reps[0]; ++i) {\n for (let j = 0; j < reps[1]; ++j) {\n xGrad = xGrad.add(dy.slice(\n [i * $x.shape[0], j * $x.shape[1]],\n [$x.shape[0], $x.shape[1]]));\n }\n }\n } else if ($x.rank === 3) {\n for (let i = 0; i < reps[0]; ++i) {\n for (let j = 0; j < reps[1]; ++j) {\n for (let k = 0; k < reps[2]; ++k) {\n xGrad = xGrad.add(dy.slice(\n [i * $x.shape[0], j * $x.shape[1], k * $x.shape[2]],\n [$x.shape[0], $x.shape[1], $x.shape[2]]));\n }\n }\n }\n } else if ($x.rank === 4) {\n for (let i = 0; i < reps[0]; ++i) {\n for (let j = 0; j < reps[1]; ++j) {\n for (let k = 0; k < reps[2]; ++k) {\n for (let l = 0; l < reps[3]; ++l) {\n xGrad = xGrad.add(dy.slice(\n [\n i * $x.shape[0], j * $x.shape[1], k * $x.shape[2],\n l * $x.shape[3]\n ],\n [$x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]));\n }\n }\n }\n }\n } else {\n throw new Error(\n `Gradient for tile operation is not implemented for rank-` +\n `${$x.rank} tensors yet.`);\n }\n return xGrad;\n };\n return {$x: derX};\n };\n return ENV.engine.runKernel(backend => backend.tile($x, reps), {$x}, grad);\n}\n\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(\n x: Tensor1D|TensorLike, paddings: [number, number],\n constantValue = 0): Tensor1D {\n util.assert(\n paddings.length === 2,\n 'Invalid number of paddings. Must be length of 2.');\n return pad(x, [paddings], constantValue);\n}\n\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(\n x: Tensor2D|TensorLike, paddings: [[number, number], [number, number]],\n constantValue = 0): Tensor2D {\n util.assert(\n paddings.length === 2 && paddings[0].length === 2 &&\n paddings[1].length === 2,\n 'Invalid number of paddings. Must be length of 2 each.');\n return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(\n x: Tensor3D|TensorLike,\n paddings: [[number, number], [number, number], [number, number]],\n constantValue = 0): Tensor3D {\n util.assert(\n paddings.length === 3 && paddings[0].length === 2 &&\n paddings[1].length === 2 && paddings[2].length === 2,\n 'Invalid number of paddings. Must be length of 2 each.');\n return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(\n x: Tensor4D|TensorLike,\n paddings:\n [\n [number, number], [number, number], [number, number], [number, number]\n ],\n constantValue = 0): Tensor4D {\n util.assert(\n paddings.length === 4 && paddings[0].length === 2 &&\n paddings[1].length === 2 && paddings[2].length === 2 &&\n paddings[3].length === 2,\n 'Invalid number of paddings. Must be length of 2 each.');\n return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation currently only implements the `CONSTANT` mode.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n * - `tf.pad1d`\n * - `tf.pad2d`\n * - `tf.pad3d`\n * - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction pad_(\n x: T|TensorLike, paddings: Array<[number, number]>, constantValue = 0): T {\n const $x = convertToTensor(x, 'x', 'pad');\n\n if ($x.rank === 0) {\n throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n }\n // Pad introduces values around the original tensor, so the gradient\n // slices the original shape out of the gradient.\n const begin = paddings.map(p => p[0]);\n const grad = (dy: T) => {\n return {$x: () => dy.slice(begin, $x.shape)};\n };\n return ENV.engine.runKernel(\n backend => backend.pad($x, paddings, constantValue), {$x}, grad) as\n T;\n}\n\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction stack_(\n tensors: Array, axis = 0): Tensor {\n const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n\n util.assert($tensors.length >= 1, 'Pass at least one tensor to tf.stack');\n if ($tensors.length === 1) {\n return $tensors[0].expandDims(axis);\n }\n const rank = $tensors[0].rank;\n const shape = $tensors[0].shape;\n const dtype = $tensors[0].dtype;\n\n util.assert(axis <= rank, 'Axis must be <= rank of the tensor');\n\n $tensors.forEach(t => {\n util.assertShapesMatch(\n shape, t.shape,\n 'All tensors passed to stack must have matching shapes');\n });\n\n $tensors.forEach(t => {\n util.assert(\n dtype === t.dtype,\n 'All tensors passed to stack must have matching dtypes');\n });\n const expandedTensors = $tensors.map(t => t.expandDims(axis));\n return concat(expandedTensors, axis);\n}\n\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array. Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction batchToSpaceND_(\n x: T|TensorLike, blockShape: number[], crops: number[][]): T {\n const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n const prod = blockShape.reduce((a, b) => a * b);\n\n util.assert(\n $x.rank >= 1 + blockShape.length,\n `input rank is ${$x.rank} but should be > than blockShape.length ${\n blockShape.length}`);\n\n util.assert(\n crops.length === blockShape.length,\n `crops.length is ${\n crops.length} but should be equal to blockShape.length ${\n blockShape.length}`);\n\n util.assert(\n $x.shape[0] % prod === 0,\n `input tensor batch is ${\n $x.shape[0]} but is not divisible by the product of ` +\n `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n\n const grad = (dy: T) => {\n return {$x: () => dy.spaceToBatchND(blockShape, crops)};\n };\n\n return ENV.engine.runKernel(\n backend => backend.batchToSpaceND($x, blockShape, crops), {$x}, grad);\n}\n\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n * 0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction spaceToBatchND_(\n x: T|TensorLike, blockShape: number[], paddings: number[][]): T {\n const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n\n util.assert(\n $x.rank >= 1 + blockShape.length,\n `input rank ${$x.rank} should be > than [blockShape] ${\n blockShape.length}`);\n\n util.assert(\n paddings.length === blockShape.length,\n `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${\n blockShape.length}`);\n\n util.assert(\n $x.shape.reduce(\n (a, b, i) => {\n if (i > 0 && i <= blockShape.length) {\n return a &&\n ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n blockShape[i - 1] ===\n 0);\n }\n return a;\n },\n true),\n `input spatial dimensions ${$x.shape.slice(1)} with paddings ${\n paddings.toString()} must be divisible by blockShapes ${\n blockShape.toString()}`);\n\n const grad = (dy: T) => {\n return {$x: () => dy.batchToSpaceND(blockShape, paddings)};\n };\n\n return ENV.engine.runKernel(\n backend => backend.spaceToBatchND($x, blockShape, paddings), {$x}, grad);\n}\n\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction unstack_(x: Tensor|TensorLike, axis = 0): Tensor[] {\n axis = axis || 0;\n const $x = convertToTensor(x, 'x', 'unstack');\n util.assert(\n axis < $x.shape.length,\n `Axis ${axis} is >= to tensor shape length ${$x.shape.length}`);\n const grad = (dy: Tensor[]) => {\n return {$x: () => stack(dy, axis)};\n };\n return ENV.engine.runKernel(backend => backend.unstack($x, axis), {$x}, grad);\n}\n\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n * Defaults to false. If set to true then the sum of each tensor entry\n * does not include its own value, but only the values previous to it\n * along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n * Defaults to false.\n */\n/** @doc {heading: 'Operations', subheading: 'Scan'} */\nfunction cumsum_(\n x: Tensor|TensorLike, axis = 0, exclusive = false, reverse = false): T {\n const $x = convertToTensor(x, 'x', 'cumsum');\n\n axis = axis | 0;\n const permutation = getAxesPermutation([axis], $x.rank);\n let permutedX = $x;\n if (permutation != null) {\n permutedX = $x.transpose(permutation);\n }\n const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n\n const grad = (dy: T) => {\n return {permutedX: () => dy.cumsum(axis, exclusive, !reverse)};\n };\n let value = ENV.engine.runKernel(\n backend => backend.cumsum(\n permutedX, permutedAxis, exclusive, reverse),\n {permutedX}, grad) as T;\n\n if (permutation != null) {\n value = value.transpose(permutation);\n }\n return value;\n}\n\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n * to 0 (the first dimension).\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction expandDims_(\n x: Tensor|TensorLike, axis = 0): Tensor {\n const $x = convertToTensor(x, 'x', 'expandDims');\n\n util.assert(axis <= $x.rank, 'Axis must be <= rank of the tensor');\n const newShape = $x.shape.slice();\n if (axis < 0) {\n // Negative value is counted from the tail of rank.\n util.assert(\n -($x.rank + 1) <= axis,\n `Axis must be in the interval [${- ($x.rank + 1)}, ${$x.rank}]`);\n axis = $x.rank + axis + 1;\n }\n newShape.splice(axis, 0, 1);\n return reshape($x, newShape);\n}\n\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n * - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n * - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n * - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n * - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = 'NHWC';\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction depthToSpace_(\n x: Tensor4D|TensorLike4D, blockSize: number,\n dataFormat: 'NHWC'|'NCHW' = 'NHWC'): Tensor4D {\n const $x = convertToTensor(x, 'x', 'depthToSpace') as Tensor4D;\n\n const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n\n util.assert(\n inputHeight * blockSize >= 0,\n `Negative dimension size caused by overflow when multiplying\n ${inputHeight} and ${blockSize} for depthToSpace with input shape\n ${$x.shape}`);\n\n util.assert(\n inputWidth * blockSize >= 0,\n `Negative dimension size caused by overflow when multiplying\n ${inputWidth} and ${blockSize} for depthToSpace with input shape\n ${$x.shape}`);\n\n util.assert(\n (inputDepth % (blockSize * blockSize) === 0),\n `Dimension size must be evenly divisible by ${\n blockSize * blockSize} but is ${\n inputDepth} for depthToSpace with input shape ${$x.shape}`);\n\n return ENV.engine.runKernel(\n backend => backend.depthToSpace($x, blockSize, dataFormat), {$x});\n}\n\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n * output.\n * @returns Promise of Tensor tuple [out, indices].\n * out: Tensor with the same type as x.\n * indices: A Tensor of type int32.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nasync function setdiff1dAsync_(\n x: Tensor|TensorLike, y: Tensor|TensorLike): Promise<[Tensor, Tensor]> {\n const $x = convertToTensor(x, 'x', 'setdiff1d');\n const $y = convertToTensor(y, 'y', 'setdiff1d');\n\n util.assert(\n $x.dtype === $y.dtype,\n `x and y should have the same dtype, but got x (${$x.dtype}) and y (${\n $y.dtype}).`);\n\n util.assert($x.rank === 1, `x should be 1D tensor, but got x (${$x.shape}).`);\n\n util.assert($y.rank === 1, `y should be 1D tensor, but got y (${$y.shape}).`);\n\n const xVals = await $x.data();\n const yVals = await $y.data();\n const ySet = new Set(yVals);\n\n let outputSize = 0;\n for (let i = 0; i < xVals.length; i++) {\n if (!ySet.has(xVals[i])) {\n outputSize++;\n }\n }\n\n const buffer = new TensorBuffer([outputSize], $x.dtype);\n const indices = new TensorBuffer([outputSize], 'int32');\n for (let i = 0, p = 0; i < xVals.length; i++) {\n if (!ySet.has(xVals[i])) {\n buffer.values[p] = xVals[i];\n indices.values[p] = i;\n p++;\n }\n }\n return [buffer.toTensor(), indices.toTensor()];\n}\n\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction buffer(\n shape: ShapeMap[R], dtype: D = 'float32' as D,\n values?: DataTypeMap[D]): TensorBuffer {\n dtype = dtype || 'float32' as D;\n return new TensorBuffer(shape, dtype, values);\n}\n\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction print(x: T, verbose = false): void {\n console.log(x.toString(verbose));\n}\n\nexport {\n buffer, // Not wrapped in op() since no tensors.\n toPixels, // Not wrapped in op() since async.\n print // Not wrapped in op() since no need to increase stack trace.\n};\n\nexport const batchToSpaceND = op({batchToSpaceND_});\nexport const cast = op({cast_});\nexport const clone = op({clone_});\nexport const cumsum = op({cumsum_});\nexport const depthToSpace = op({depthToSpace_});\nexport const expandDims = op({expandDims_});\nexport const eye = op({eye_});\nexport const fromPixels = fromPixels_;\nexport const multinomial = op({multinomial_});\nexport const oneHot = op({oneHot_});\nexport const pad = op({pad_});\nexport const pad1d = op({pad1d_});\nexport const pad2d = op({pad2d_});\nexport const pad3d = op({pad3d_});\nexport const pad4d = op({pad4d_});\nexport const rand = op({rand_});\nexport const randomNormal = op({randomNormal_});\nexport const randomUniform = op({randomUniform_});\nexport const reshape = op({reshape_});\nexport const spaceToBatchND = op({spaceToBatchND_});\nexport const squeeze = op({squeeze_});\nexport const stack = op({stack_});\nexport const tile = op({tile_});\nexport const truncatedNormal = op({truncatedNormal_});\nexport const unstack = op({unstack_});\nexport const setdiff1dAsync = setdiff1dAsync_;\n","../src/kernels/where_impl.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the Where kernel shared between cpu and webgl */\n\nimport {buffer} from '../ops/array_ops';\nimport {Tensor2D} from '../tensor';\nimport {TypedArray} from '../types';\n\nexport function whereImpl(condShape: number[], condVals: TypedArray): Tensor2D {\n const indices = [];\n for (let i = 0; i < condVals.length; i++) {\n if (condVals[i]) {\n indices.push(i);\n }\n }\n\n const inBuffer = buffer(condShape, 'int32');\n\n const out = buffer([indices.length, condShape.length], 'int32');\n for (let i = 0; i < indices.length; i++) {\n const loc = inBuffer.indexToLoc(indices[i]);\n const offset = i * condShape.length;\n out.values.set(loc, offset);\n }\n return out.toTensor() as Tensor2D;\n}\n","../src/kernels/backend_webgl.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from '../canvas_util';\nimport {MemoryInfo, TimingInfo} from '../engine';\nimport {ENV} from '../environment';\nimport {tidy} from '../globals';\nimport {warn} from '../log';\nimport * as array_ops_util from '../ops/array_ops_util';\nimport * as axis_util from '../ops/axis_util';\nimport {computeOutShape} from '../ops/concat_util';\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport {Activation} from '../ops/fused_util';\nimport * as gather_nd_util from '../ops/gather_nd_util';\nimport * as reduce_util from '../ops/reduce_util';\nimport * as scatter_nd_util from '../ops/scatter_nd_util';\nimport * as segment_util from '../ops/segment_util';\nimport {computeFlatOffset, getStridedSlicedInfo, isSliceContinous} from '../ops/slice_util';\nimport {softmax} from '../ops/softmax';\nimport {range, scalar, tensor} from '../ops/tensor_ops';\nimport {DataId, Scalar, setTensorTracker, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, RecursiveArray, ShapeMap, sumOutType, TypedArray, upcastType} from '../types';\nimport * as util from '../util';\nimport {getTypedArrayFromDType, sizeFromShape} from '../util';\n\nimport {DataMover, DataStorage, KernelBackend} from './backend';\nimport * as backend_util from './backend_util';\nimport {mergeRealAndImagArrays} from './complex_util';\nimport {nonMaxSuppressionImpl} from './non_max_suppression_impl';\nimport {split} from './split_shared';\nimport {topkImpl} from './topk_impl';\nimport {ArgMinMaxProgram} from './webgl/argminmax_gpu';\nimport {AvgPool2DBackpropProgram} from './webgl/avg_pool_backprop_gpu';\nimport {BatchNormProgram} from './webgl/batchnorm_gpu';\nimport {BatchNormPackedProgram} from './webgl/batchnorm_packed_gpu';\nimport * as binaryop_complex_gpu from './webgl/binaryop_complex_gpu';\nimport {BinaryOpComplexProgram} from './webgl/binaryop_complex_gpu';\nimport * as binaryop_gpu from './webgl/binaryop_gpu';\nimport {BinaryOpProgram} from './webgl/binaryop_gpu';\nimport {BinaryOpPackedProgram, PACKED_DIV, PACKED_INT_DIV, PACKED_POW} from './webgl/binaryop_packed_gpu';\nimport {ClipProgram} from './webgl/clip_gpu';\nimport {ClipPackedProgram} from './webgl/clip_packed_gpu';\nimport {ComplexAbsProgram} from './webgl/complex_abs_gpu';\nimport {ConcatProgram} from './webgl/concat_gpu';\nimport {Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram} from './webgl/conv_backprop_gpu';\nimport {DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram} from './webgl/conv_backprop_gpu_depthwise';\nimport {Conv2DProgram, Conv3DProgram} from './webgl/conv_gpu';\nimport {DepthwiseConv2DProgram} from './webgl/conv_gpu_depthwise';\nimport {DepthwiseConvPacked2DProgram} from './webgl/conv_packed_gpu_depthwise';\nimport {CropAndResizeProgram} from './webgl/crop_and_resize_gpu';\nimport {CumSumProgram} from './webgl/cumsum_gpu';\nimport {DepthToSpaceProgram} from './webgl/depth_to_space_gpu';\nimport {EncodeFloatProgram} from './webgl/encode_float_gpu';\nimport * as fft_gpu from './webgl/fft_gpu';\nimport {FFTProgram} from './webgl/fft_gpu';\nimport {FromPixelsProgram} from './webgl/from_pixels_gpu';\nimport {GatherProgram} from './webgl/gather_gpu';\nimport {GatherNDProgram} from './webgl/gather_nd_gpu';\nimport {GPGPUContext} from './webgl/gpgpu_context';\nimport * as gpgpu_math from './webgl/gpgpu_math';\nimport {GPGPUBinary, GPGPUProgram, TensorData} from './webgl/gpgpu_math';\nimport {Im2ColProgram} from './webgl/im2col_gpu';\nimport {LRNProgram} from './webgl/lrn_gpu';\nimport {LRNGradProgram} from './webgl/lrn_grad_gpu';\nimport {MaxPool2DBackpropProgram} from './webgl/max_pool_backprop_gpu';\nimport {MatMulProgram} from './webgl/mulmat_gpu';\nimport {MatMulPackedProgram} from './webgl/mulmat_packed_gpu';\nimport {MultinomialProgram} from './webgl/multinomial_gpu';\nimport {OneHotProgram} from './webgl/onehot_gpu';\nimport {PackProgram} from './webgl/pack_gpu';\nimport {PadProgram} from './webgl/pad_gpu';\nimport {PadPackedProgram} from './webgl/pad_packed_gpu';\nimport {Pool2DProgram} from './webgl/pool_gpu';\nimport {ReduceProgram} from './webgl/reduce_gpu';\nimport {ReshapePackedProgram} from './webgl/reshape_packed_gpu';\nimport {ResizeBilinearBackpropProgram} from './webgl/resize_bilinear_backprop_gpu';\nimport {ResizeBilinearProgram} from './webgl/resize_bilinear_gpu';\nimport {ResizeNearestNeigborBackpropProgram} from './webgl/resize_nearest_neighbor_backprop_gpu';\nimport {ResizeNearestNeighborProgram} from './webgl/resize_nearest_neighbor_gpu';\nimport {ReverseProgram} from './webgl/reverse_gpu';\nimport {ScatterProgram} from './webgl/scatter_gpu';\nimport {SegmentOpProgram} from './webgl/segment_gpu';\nimport {SelectProgram} from './webgl/select_gpu';\nimport {SliceProgram} from './webgl/slice_gpu';\nimport {StridedSliceProgram} from './webgl/strided_slice_gpu';\nimport * as tex_util from './webgl/tex_util';\nimport {TextureData, TextureUsage} from './webgl/tex_util';\nimport {TextureManager} from './webgl/texture_manager';\nimport {TileProgram} from './webgl/tile_gpu';\nimport {TransposeProgram} from './webgl/transpose_gpu';\nimport * as unary_op from './webgl/unaryop_gpu';\nimport {UnaryOpProgram} from './webgl/unaryop_gpu';\nimport * as unary_packed_op from './webgl/unaryop_packed_gpu';\nimport {UnaryOpPackedProgram} from './webgl/unaryop_packed_gpu';\nimport {UnpackProgram} from './webgl/unpack_gpu';\nimport * as webgl_util from './webgl/webgl_util';\nimport {whereImpl} from './where_impl';\n\ntype KernelInfo = {\n name: string; query: Promise;\n};\n\nexport type TimerNode = RecursiveArray|KernelInfo;\nexport interface CPUTimerQuery {\n startMs: number;\n endMs?: number;\n}\n\nexport interface WebGLMemoryInfo extends MemoryInfo {\n numBytesInGPU: number;\n unreliable: boolean;\n}\n\nexport interface WebGLTimingInfo extends TimingInfo {\n uploadWaitMs: number;\n downloadWaitMs: number;\n}\n\nfunction mapActivationToShaderProgram(\n activation: Activation, packed = false): string {\n if (activation === 'linear') {\n if (packed) {\n return unary_packed_op.LINEAR;\n }\n return unary_op.LINEAR;\n } else if (activation === 'relu') {\n if (packed) {\n return unary_packed_op.RELU;\n }\n return unary_op.RELU;\n }\n throw new Error(`Activation ${\n activation} has not been implemented for the WebGL backend.`);\n}\n\n// Combines a dataId, a shape, and a dtype without a Tensor object so that\n// programs can be executed without a full Tensor object.\nexport interface TensorHandle {\n dataId: DataId;\n shape: number[];\n dtype: DataType;\n}\n\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 10;\n\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\n\nexport class MathBackendWebGL implements KernelBackend {\n private texData: DataStorage;\n // Maps data ids that have a pending read operation, to list of subscribers.\n private pendingRead = new WeakMap void>>();\n // List of data ids that are scheduled for disposal, but are waiting on a\n // pending read operation.\n private pendingDisposal = new WeakSet();\n // Used to count the number of 'shallow' sliced tensors that point to the\n // same data id.\n private dataRefCount = new WeakMap();\n // List of data ids that are currently residing on gpu memory. Sorted with\n // least recently used being first.\n private lruDataGPU: DataId[] = [];\n private numBytesInGPU = 0;\n\n private canvas: HTMLCanvasElement;\n private fromPixels2DContext: CanvasRenderingContext2D;\n\n private programTimersStack: TimerNode[];\n private activeTimers: TimerNode[];\n // Accumulated time spent (including blocking) in uploading data to webgl.\n private uploadWaitMs = 0;\n // Accumulated time spent (including blocking in downloading data from webgl.\n private downloadWaitMs = 0;\n private cpuBackend: KernelBackend;\n\n register(dataId: DataId, shape: number[], dtype: DataType): void {\n if (this.texData.has(dataId)) {\n throw new Error('Data buffer is already registered');\n }\n this.texData.set(dataId, {shape, dtype});\n }\n\n setDataMover(dataMover: DataMover): void {\n this.texData = new DataStorage(dataMover);\n }\n\n fromPixels(\n pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n numChannels: number): Tensor3D {\n if (pixels == null) {\n throw new Error(\n 'pixels passed to tf.browser.fromPixels() can not be null');\n }\n const texShape: [number, number] = [pixels.height, pixels.width];\n const outShape = [pixels.height, pixels.width, numChannels];\n\n if (!(pixels instanceof HTMLVideoElement) &&\n !(pixels instanceof HTMLImageElement) &&\n !(pixels instanceof HTMLCanvasElement) &&\n !(pixels instanceof ImageData)) {\n throw new Error(\n 'pixels passed to tf.browser.fromPixels() must be either an ' +\n `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ` +\n `ImageData, but was ${(pixels as {}).constructor.name}`);\n }\n if (pixels instanceof HTMLVideoElement) {\n if (this.fromPixels2DContext == null) {\n if (!ENV.get('IS_BROWSER')) {\n throw new Error(\n 'Can\\'t read pixels from HTMLImageElement outside the browser.');\n }\n if (document.readyState !== 'complete') {\n throw new Error(\n 'The DOM is not ready yet. Please call tf.fromPixels() ' +\n 'once the DOM is ready. One way to do that is to add an event ' +\n 'listener for `DOMContentLoaded` on the document object');\n }\n this.fromPixels2DContext =\n document.createElement('canvas').getContext('2d');\n }\n this.fromPixels2DContext.canvas.width = pixels.width;\n this.fromPixels2DContext.canvas.height = pixels.height;\n this.fromPixels2DContext.drawImage(\n pixels, 0, 0, pixels.width, pixels.height);\n pixels = this.fromPixels2DContext.canvas;\n }\n const tempPixelHandle = this.makeTensorHandle(texShape, 'int32');\n // This is a byte texture with pixels.\n this.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n this.gpgpu.uploadPixelDataToTexture(\n this.getTexture(tempPixelHandle.dataId), pixels);\n const program = new FromPixelsProgram(outShape);\n const res = this.compileAndRun(program, [tempPixelHandle]);\n\n this.disposeData(tempPixelHandle.dataId);\n\n return res as Tensor3D;\n }\n\n private makeTensorHandle(shape: number[], dtype: DataType): TensorHandle {\n const dataId = {};\n this.register(dataId, shape, dtype);\n return {dataId, shape, dtype};\n }\n\n write(dataId: DataId, values: DataValues): void {\n if (values == null) {\n throw new Error('MathBackendWebGL.write(): values can not be null');\n }\n\n if (ENV.get('DEBUG')) {\n for (let i = 0; i < values.length; i++) {\n const num = values[i] as number;\n if (!webgl_util.canBeRepresented(num)) {\n throw Error(`The value ${num} cannot be represented on this device.`);\n }\n }\n }\n\n const texData = this.texData.get(dataId);\n const {texture, texShape, usage, dtype, isPacked} = texData;\n if (dtype === 'complex64') {\n throw new Error(\n `Cannot write to a complex64 dtype. ` +\n `Please use tf.complex(real, imag).`);\n }\n\n if (texture != null) {\n // Release the old texture.\n this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n texData.texture = null;\n texData.texShape = null;\n }\n texData.usage = TextureUsage.UPLOAD;\n texData.values = values;\n\n if (!this.delayedStorage) {\n this.uploadToGPU(dataId);\n }\n }\n readSync(dataId: DataId): DataValues {\n const texData = this.texData.get(dataId);\n const {values, dtype, complexTensors, slice, shape} = texData;\n if (slice != null) {\n const program = new UnaryOpProgram(shape, unary_op.CLONE);\n const res = this.compileAndRun(program, [{dataId, shape, dtype}]);\n const data = this.readSync(res.dataId);\n (res as Tensor).dispose();\n return data;\n }\n if (values != null) {\n return this.convertAndCacheOnCPU(dataId);\n }\n if (dtype === 'string') {\n return values;\n }\n const shouldTimeProgram = this.activeTimers != null;\n let start: number;\n if (shouldTimeProgram) {\n start = performance.now();\n }\n\n let result: Float32Array;\n if (dtype === 'complex64') {\n const realValues = complexTensors.real.dataSync() as Float32Array;\n const imagValues = complexTensors.imag.dataSync() as Float32Array;\n result = mergeRealAndImagArrays(realValues, imagValues);\n } else {\n result = this.getValuesFromTexture(dataId);\n }\n\n if (shouldTimeProgram) {\n this.downloadWaitMs += performance.now() - start;\n }\n return this.convertAndCacheOnCPU(dataId, result);\n }\n\n async read(dataId: DataId): Promise {\n if (this.pendingRead.has(dataId)) {\n const subscribers = this.pendingRead.get(dataId);\n return new Promise(resolve => subscribers.push(resolve));\n }\n const texData = this.texData.get(dataId);\n const {texture, values, texShape, isPacked, shape, slice, dtype} = texData;\n\n if (slice != null) {\n const program = new UnaryOpProgram(shape, unary_op.CLONE);\n const res = this.compileAndRun(program, [{dataId, shape, dtype}]);\n const data = this.read(res.dataId);\n (res as Tensor).dispose();\n return data;\n }\n\n if (values != null) {\n return this.convertAndCacheOnCPU(dataId);\n }\n\n this.pendingRead.set(dataId, []);\n\n if (!ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n ENV.get('WEBGL_VERSION') === 2) {\n throw new Error(\n `tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n `WEBGL_VERSION=2 not yet supported.`);\n }\n\n // Possibly copy the texture into a buffer before inserting a fence.\n let width = texShape[1];\n let height = texShape[0];\n if (isPacked) {\n [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n texShape[0], texShape[1]);\n }\n const bufferOrTexture =\n this.gpgpu.maybeCreateBufferFromTexture(texture, height, width);\n\n // Create a fence and wait for it to resolve.\n await this.gpgpu.createAndWaitForFence();\n\n // Download the values from the GPU.\n let vals: Float32Array;\n if (bufferOrTexture instanceof WebGLTexture) {\n vals = this.getValuesFromTexture(dataId);\n } else {\n const size = util.sizeFromShape(shape);\n if (isPacked) {\n const batch = webgl_util.getBatchDim(shape);\n let rows = 1, cols = 1;\n if (shape.length) {\n [rows, cols] = webgl_util.getRowsCols(shape);\n }\n vals = this.gpgpu\n .downloadPackedMatrixFromBuffer(\n bufferOrTexture, batch, rows, cols, texShape[0],\n texShape[1])\n .subarray(0, size);\n } else {\n vals = this.gpgpu\n .downloadFloat32MatrixFromBuffer(\n bufferOrTexture, texShape[0], texShape[1])\n .subarray(0, size);\n }\n }\n const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n\n const subscribers = this.pendingRead.get(dataId);\n this.pendingRead.delete(dataId);\n\n // Notify all pending reads.\n subscribers.forEach(resolve => resolve(dTypeVals));\n if (this.pendingDisposal.has(dataId)) {\n this.pendingDisposal.delete(dataId);\n this.disposeData(dataId);\n }\n return dTypeVals;\n }\n\n private getValuesFromTexture(dataId: DataId): Float32Array {\n const {shape, dtype, texture, texShape} = this.texData.get(dataId);\n const size = util.sizeFromShape(shape);\n if (ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n if (this.texData.get(dataId).isPacked) {\n const batch = webgl_util.getBatchDim(shape);\n let rows = 1, cols = 1;\n if (shape.length) {\n [rows, cols] = webgl_util.getRowsCols(shape);\n }\n return this.gpgpu\n .downloadMatrixFromPackedTexture(\n texture, batch, rows, cols, texShape[0], texShape[1])\n .subarray(0, size);\n } else {\n return this.gpgpu\n .downloadFloat32MatrixFromOutputTexture(\n texture, texShape[0], texShape[1])\n .subarray(0, size);\n }\n }\n\n const tmpTarget = this.makeTensorHandle(shape, 'float32') as TensorHandle &\n {size: number};\n tmpTarget.size = sizeFromShape(shape);\n this.texData.get(tmpTarget.dataId).usage = TextureUsage.DOWNLOAD;\n const program = new EncodeFloatProgram(shape);\n const pageToCpu = false;\n this.compileAndRun(\n program, [{shape, dtype, dataId}], tmpTarget, null, pageToCpu);\n const tmpData = this.texData.get(tmpTarget.dataId);\n const vals =\n this.gpgpu\n .downloadByteEncodedFloatMatrixFromOutputTexture(\n tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n .subarray(0, size);\n this.disposeData(tmpTarget.dataId);\n\n return vals;\n }\n\n async time(f: () => void): Promise {\n const oldActiveTimers = this.activeTimers;\n const newActiveTimers: TimerNode[] = [];\n\n let outerMostTime = false;\n if (this.programTimersStack == null) {\n this.programTimersStack = newActiveTimers;\n outerMostTime = true;\n } else {\n this.activeTimers.push(newActiveTimers);\n }\n this.activeTimers = newActiveTimers;\n\n f();\n\n // needing to split these up because util.flatten only accepts certain types\n const flattenedActiveTimerQueries =\n util.flatten(this.activeTimers.map((d: KernelInfo) => d.query))\n .filter(d => d != null);\n const flattenedActiveTimerNames =\n util.flatten(this.activeTimers.map((d: KernelInfo) => d.name))\n .filter(d => d != null);\n\n this.activeTimers = oldActiveTimers;\n\n if (outerMostTime) {\n this.programTimersStack = null;\n }\n\n const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n\n const res: WebGLTimingInfo = {\n uploadWaitMs: this.uploadWaitMs,\n downloadWaitMs: this.downloadWaitMs,\n kernelMs: util.sum(kernelMs),\n getExtraProfileInfo: () =>\n kernelMs.map((d, i) => ({name: flattenedActiveTimerNames[i], ms: d}))\n .map(d => `${d.name}: ${d.ms}`)\n .join(', '),\n wallMs: null // will be filled by the engine\n };\n this.uploadWaitMs = 0;\n this.downloadWaitMs = 0;\n return res;\n }\n memory(): WebGLMemoryInfo {\n return {unreliable: false, numBytesInGPU: this.numBytesInGPU} as\n WebGLMemoryInfo;\n }\n\n private startTimer(): WebGLQuery|CPUTimerQuery {\n if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n return this.gpgpu.beginQuery();\n }\n return {startMs: performance.now(), endMs: null};\n }\n\n private endTimer(query: WebGLQuery|CPUTimerQuery): WebGLQuery|CPUTimerQuery {\n if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n this.gpgpu.endQuery();\n return query;\n }\n (query as CPUTimerQuery).endMs = performance.now();\n return query;\n }\n\n private async getQueryTime(query: WebGLQuery|CPUTimerQuery): Promise {\n if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n return this.gpgpu.waitForQueryAndGetTime(query as WebGLQuery);\n }\n const timerQuery = query as CPUTimerQuery;\n return timerQuery.endMs - timerQuery.startMs;\n }\n\n disposeData(dataId: DataId): void {\n if (this.pendingDisposal.has(dataId)) {\n return;\n }\n if (this.pendingRead.has(dataId)) {\n this.pendingDisposal.add(dataId);\n return;\n }\n if (this.texData.has(dataId)) {\n const {texture, texShape, usage, complexTensors, isPacked, slice} =\n this.texData.get(dataId);\n if (texture != null) {\n const key = slice && slice.origDataId || dataId;\n const refCount = this.dataRefCount.get(key);\n if (refCount > 1) {\n this.dataRefCount.set(key, refCount - 1);\n } else {\n this.dataRefCount.delete(key);\n this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n this.texData.delete(dataId);\n }\n }\n if (complexTensors != null) {\n complexTensors.real.dispose();\n complexTensors.imag.dispose();\n }\n }\n }\n\n getTexture(dataId: DataId): WebGLTexture {\n this.uploadToGPU(dataId);\n return this.texData.get(dataId).texture;\n }\n\n private textureManager: TextureManager;\n private binaryCache: {[key: string]: GPGPUBinary} = {};\n private gpgpuCreatedLocally: boolean;\n\n constructor(private gpgpu?: GPGPUContext, private delayedStorage = true) {\n if (ENV.get('WEBGL_VERSION') < 1) {\n throw new Error('WebGL is not supported on this device');\n }\n\n if (gpgpu == null) {\n const gl = getWebGLContext(ENV.get('WEBGL_VERSION'));\n this.gpgpu = new GPGPUContext(gl);\n this.canvas = gl.canvas;\n this.gpgpuCreatedLocally = true;\n } else {\n this.gpgpuCreatedLocally = false;\n this.canvas = gpgpu.gl.canvas;\n }\n this.textureManager = new TextureManager(this.gpgpu);\n }\n\n private getCPUBackend(): KernelBackend|null {\n if (!ENV.get('WEBGL_CPU_FORWARD')) {\n return null;\n }\n\n if (this.cpuBackend == null) {\n this.cpuBackend = ENV.findBackend('cpu');\n }\n\n return this.cpuBackend;\n }\n\n /*\n Tests whether all the inputs to an op are small and on the CPU. This heuristic\n determines when it would be faster to execute a kernel on the CPU. WebGL\n kernels opt into running this check and forwarding when appropriate.\n TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n sustainable strategy for optimizing backend execution of ops.\n */\n private shouldExecuteOnCPU(\n inputs: Tensor[], sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD): boolean {\n return this.getCPUBackend() != null &&\n inputs.every(\n input => this.texData.get(input.dataId).texture == null &&\n input.size < sizeThreshold);\n }\n\n getGPGPUContext(): GPGPUContext {\n return this.gpgpu;\n }\n getCanvas(): HTMLCanvasElement {\n return this.canvas;\n }\n\n complex(real: T, imag: T): T {\n const result = this.makeOutputArray(real.shape, 'complex64') as T;\n const resultData = this.texData.get(result.dataId);\n // The backend owns the reference to the underlying real and imaginary\n // clones. These will explicitly get disposed when the complex tensor is\n // disposed.\n resultData.complexTensors = {\n real: ENV.engine.keep(real.clone()),\n imag: ENV.engine.keep(imag.clone())\n };\n\n return result;\n }\n real(input: T): T {\n const resultData = this.texData.get(input.dataId);\n return resultData.complexTensors.real.clone() as T;\n }\n imag(input: T): T {\n const resultData = this.texData.get(input.dataId);\n return resultData.complexTensors.imag.clone() as T;\n }\n\n slice(x: T, begin: number[], size: number[]): T {\n if (this.shouldExecuteOnCPU([x])) {\n return this.cpuBackend.slice(x, begin, size);\n }\n const {isPacked} = this.texData.get(x.dataId);\n const isContinous = isSliceContinous(x.shape, begin, size);\n if (isPacked || !isContinous) {\n const program = new SliceProgram(size);\n const customSetup = program.getCustomSetupFunc(begin);\n return this.compileAndRun(program, [x], null, customSetup);\n }\n this.uploadToGPU(x.dataId);\n return this.shallowSlice(x, begin, size) as T;\n }\n\n private shallowSlice(x: Tensor, begin: number[], size: number[]): Tensor {\n const xTexData = this.texData.get(x.dataId);\n const t = Tensor.make(size, {}, x.dtype);\n const newTexData = this.texData.get(t.dataId);\n // Copy texture data from the original tensor.\n Object.assign(newTexData, xTexData);\n newTexData.shape = size;\n newTexData.dtype = x.dtype;\n let flatOffset = computeFlatOffset(begin, x.strides);\n if (xTexData.slice) {\n // We are slicing an already sliced tensor, so we have to accumulate\n // the offset.\n flatOffset += xTexData.slice.flatOffset;\n }\n newTexData.slice = {\n flatOffset,\n // Point to the original dataId, which is used to do ref counting.\n origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n };\n\n // Increase the ref count for that data bucket.\n const refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;\n this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n\n return t;\n }\n\n stridedSlice(\n x: T, begin: number[], end: number[], strides: number[],\n beginMask: number, endMask: number, ellipsisMask: number,\n newAxisMask: number, shrinkAxisMask: number): T {\n if (this.shouldExecuteOnCPU([x])) {\n return this.cpuBackend.stridedSlice(\n x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask,\n shrinkAxisMask);\n }\n\n const [beginIndex, size, shrinkAxis] = getStridedSlicedInfo(\n x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n newAxisMask, shrinkAxisMask);\n\n const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n if (shape.some(axis => axis === 0)) {\n return tensor([], shape) as T;\n }\n\n const program =\n new StridedSliceProgram(beginIndex, strides, size, shrinkAxis);\n return this.compileAndRun(program, [x]);\n }\n\n reverse(x: T, axis: number[]): T {\n const program = new ReverseProgram(x.shape, axis);\n return this.compileAndRun(program, [x]);\n }\n\n concat(tensors: Tensor[], axis: number): Tensor {\n if (this.shouldExecuteOnCPU(tensors)) {\n return this.cpuBackend.concat(tensors, axis);\n }\n\n if (tensors.length === 1) {\n return tensors[0];\n }\n if (tensors.length > ENV.get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n const midIndex = Math.floor(tensors.length / 2);\n const leftSide = this.concat(tensors.slice(0, midIndex), axis);\n const rightSide = this.concat(tensors.slice(midIndex), axis);\n return this.concat([leftSide, rightSide], axis);\n }\n // Any concat of n-dimensional tensors across any axis can be reduced to\n // a concatenation of two-dimensional tensors across the axis 1 by first\n // partitioning the axes of the original tensors into those less than the\n // axis to be concatenated and the rest. Then reshape the tensors\n // into a two-dimensional tensor by collapsing these two sets of axes and\n // concatenate the resulting matrices across the axis 1, finally reshaping\n // the result to have the proper shape.\n const outShape = computeOutShape(tensors.map(t => t.shape), axis);\n const tensors2D =\n tensors.map(t => t.as2D(-1, sizeFromShape(t.shape.slice(axis))));\n const program = new ConcatProgram(tensors2D.map(t => t.shape));\n const res = this.compileAndRun(program, tensors2D) as Tensor;\n return res.reshape(outShape);\n }\n\n neg(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n return this.compileAndRun(program, [x]) as T;\n }\n\n batchMatMul(\n a: Tensor3D, b: Tensor3D, transposeA: boolean,\n transposeB: boolean): Tensor3D {\n const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n const [batch, , ] = a.shape;\n\n // Since the matrices are vectors, it is faster to call mul().sum()\n // because sum() is O(sqrt(N)) due to divide-and-conquer.\n if ((outerShapeA === 1 || outerShapeB === 1) &&\n sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n if (transposeA) {\n a = a.transpose([0, 2, 1]);\n }\n if (transposeB) {\n b = b.transpose([0, 2, 1]);\n }\n\n const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n const axis = outerShapeB === 1 ? 2 : 1;\n const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);\n }\n\n const dtype = upcastType(a.dtype, b.dtype);\n\n // TODO(https://github.com/tensorflow/tfjs/issues/693): Support 3D tensors\n if (batch === 1) {\n const aSqueezed = a.as2D(a.shape[1], a.shape[2]);\n const bSqueezed = b.as2D(b.shape[1], b.shape[2]);\n\n const program = new MatMulPackedProgram(\n aSqueezed.shape, bSqueezed.shape, [outerShapeA, outerShapeB],\n transposeA, transposeB);\n const output =\n this.makePackedTensor(program.outputShape, dtype) as Tensor2D;\n const result =\n this.compileAndRun(program, [aSqueezed, bSqueezed], output);\n return result.reshape([1, result.shape[0], result.shape[1]]);\n } else {\n const program =\n new MatMulProgram(a.shape, b.shape, transposeA, transposeB);\n const output =\n this.makeOutputArray(program.outputShape, dtype) as Tensor3D;\n return this.compileAndRun(program, [a, b], output);\n }\n }\n\n fusedBatchMatMul(\n a: Tensor3D, b: Tensor3D, transposeA: boolean, transposeB: boolean,\n bias?: Tensor, activation?: Activation): Tensor3D {\n const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n const [batch, , ] = a.shape;\n\n const dtype = upcastType(a.dtype, b.dtype);\n\n // TODO(https://github.com/tensorflow/tfjs/issues/693): Support 3D tensors\n if (batch === 1) {\n const aSqueezed = a.as2D(a.shape[1], a.shape[2]);\n const bSqueezed = b.as2D(b.shape[1], b.shape[2]);\n\n const program = new MatMulPackedProgram(\n aSqueezed.shape, bSqueezed.shape, [outerShapeA, outerShapeB],\n transposeA, transposeB, !!bias,\n activation ? mapActivationToShaderProgram(activation, true) : null);\n const output =\n this.makePackedTensor(program.outputShape, dtype) as Tensor2D;\n const inputs: TensorHandle[] = [aSqueezed, bSqueezed];\n if (bias) {\n inputs.push(bias);\n }\n const result = this.compileAndRun(program, inputs, output);\n return result.reshape([1, result.shape[0], result.shape[1]]);\n } else {\n const program = new MatMulProgram(\n a.shape, b.shape, transposeA, transposeB, !!bias,\n activation ? mapActivationToShaderProgram(activation) : null);\n const inputs: TensorHandle[] = [a, b];\n if (bias) {\n inputs.push(bias);\n }\n const output =\n this.makeOutputArray(program.outputShape, dtype) as Tensor3D;\n return this.compileAndRun(program, inputs, output);\n }\n }\n\n multiply(a: Tensor, b: Tensor): Tensor {\n if (a.dtype === 'complex64') {\n const aData = this.texData.get(a.dataId);\n const bData = this.texData.get(b.dataId);\n\n const realProgram = new BinaryOpComplexProgram(\n binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n const imagProgram = new BinaryOpComplexProgram(\n binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n\n const inputs = [\n this.makeComplexComponentTensorHandle(a, aData.complexTensors.real),\n this.makeComplexComponentTensorHandle(a, aData.complexTensors.imag),\n this.makeComplexComponentTensorHandle(b, bData.complexTensors.real),\n this.makeComplexComponentTensorHandle(b, bData.complexTensors.imag)\n ];\n const real = this.compileAndRun(realProgram, inputs);\n const imag = this.compileAndRun(imagProgram, inputs);\n\n const complex = this.complex(real, imag);\n real.dispose();\n imag.dispose();\n return complex;\n }\n\n if (this.shouldExecuteOnCPU([a, b])) {\n return this.cpuBackend.multiply(a, b);\n }\n if (ENV.get('WEBGL_PACK_BINARY_OPERATIONS')) {\n return this.packedBinaryOp(a, b, binaryop_gpu.MUL, a.dtype);\n }\n const program = new BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, a.dtype) as Tensor;\n return this.compileAndRun(program, [a, b], output) as Tensor;\n }\n\n batchNormalization(\n x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n offset?: Tensor4D|Tensor1D): Tensor4D {\n const inputs = [x, mean, variance];\n\n let offsetShape = null;\n if (offset != null) {\n offsetShape = offset.shape;\n inputs.push(offset);\n }\n\n let scaleShape = null;\n if (scale != null) {\n scaleShape = scale.shape;\n inputs.push(scale);\n }\n\n if (ENV.get('WEBGL_PACK_BATCHNORMALIZATION')) {\n const batchNormPackedProgram = new BatchNormPackedProgram(\n x.shape, mean.shape, variance.shape, offsetShape, scaleShape,\n varianceEpsilon);\n return this.compileAndRun(batchNormPackedProgram, inputs);\n }\n\n const batchNormProgram = new BatchNormProgram(\n x.shape, mean.shape, variance.shape, offsetShape, scaleShape,\n varianceEpsilon);\n return this.compileAndRun(batchNormProgram, inputs);\n }\n\n localResponseNormalization4D(\n x: Tensor4D, radius: number, bias: number, alpha: number,\n beta: number): Tensor4D {\n const program = new LRNProgram(x.shape, radius, bias, alpha, beta);\n return this.compileAndRun(program, [x]);\n }\n\n LRNGrad(\n dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D,\n depthRadius: number, bias: number, alpha: number,\n beta: number): Tensor4D {\n const program =\n new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n return this.compileAndRun(program, [inputImage, outputImage, dy]);\n }\n\n tile(x: T, reps: number[]): T {\n const program = new TileProgram(x.shape, reps);\n return this.compileAndRun(program, [x]);\n }\n\n pad(\n x: T, paddings: Array<[number, number]>, constantValue: number): T {\n const program = ENV.get('WEBGL_PACK_ARRAY_OPERATIONS') ?\n new PadPackedProgram(x.shape, paddings, constantValue) :\n new PadProgram(x.shape, paddings, constantValue);\n return this.compileAndRun(program, [x]);\n }\n\n transpose(x: T, perm: number[]): T {\n const program = new TransposeProgram(x.shape, perm);\n return this.compileAndRun(program, [x]);\n }\n\n gather(x: T, indices: Tensor1D, axis: number): T {\n const program = new GatherProgram(x.shape, indices.size, axis);\n return this.compileAndRun(program, [x, indices]);\n }\n\n batchToSpaceND(\n x: T, blockShape: number[], crops: number[][]): T {\n util.assert(\n x.rank <= 4,\n 'batchToSpaceND for rank > 4 with a WebGL backend not implemented yet');\n const prod = blockShape.reduce((a, b) => a * b);\n\n const reshaped = array_ops_util.getReshaped(x.shape, blockShape, prod);\n const permuted =\n array_ops_util.getPermuted(reshaped.length, blockShape.length);\n const reshapedPermuted =\n array_ops_util.getReshapedPermuted(x.shape, blockShape, prod);\n const sliceBeginCoords =\n array_ops_util.getSliceBeginCoords(crops, blockShape.length);\n const sliceSize =\n array_ops_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n\n return x.reshape(reshaped)\n .transpose(permuted)\n .reshape(reshapedPermuted)\n .slice(sliceBeginCoords, sliceSize) as T;\n }\n\n spaceToBatchND(\n x: T, blockShape: number[], paddings: Array<[number, number]>): T {\n util.assert(\n x.rank <= 4,\n 'spaceToBatchND for rank > 4 with a WebGL backend not implemented yet');\n\n const prod = blockShape.reduce((a, b) => a * b);\n\n const completePaddings: Array<[number, number]> = [[0, 0]];\n completePaddings.push(...paddings);\n for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n completePaddings.push([0, 0]);\n }\n\n const paddedX = x.pad(completePaddings);\n\n const reshapedPaddedShape =\n array_ops_util.getReshaped(paddedX.shape, blockShape, prod, false);\n\n const permutedReshapedPaddedPermutation = array_ops_util.getPermuted(\n reshapedPaddedShape.length, blockShape.length, false);\n\n const flattenShape = array_ops_util.getReshapedPermuted(\n paddedX.shape, blockShape, prod, false);\n\n return paddedX.reshape(reshapedPaddedShape)\n .transpose(permutedReshapedPaddedPermutation)\n .reshape(flattenShape) as T;\n }\n\n private reduce(\n x: Tensor2D, reduceType: 'all'|'any'|'max'|'min'|'sum'|'prod',\n dtype: DataType): Tensor2D {\n const batchSize = x.shape[0];\n const inSize = x.shape[1];\n const windowSize = reduce_util.computeOptimalWindowSize(inSize);\n const reduceInfo = {windowSize, inSize, batchSize};\n const program = new ReduceProgram(reduceInfo, reduceType);\n const [rows, cols] = program.outputShape;\n const output = this.makeOutputArray([rows, cols], dtype);\n\n this.compileAndRun(program, [x], output);\n // No need to run another GPGPU program.\n if (output.shape[1] === 1) {\n return output;\n }\n return this.reduce(output, reduceType, dtype);\n }\n\n private argReduce(\n x: Tensor2D, reduceType: 'max'|'min',\n bestIndicesA: Tensor2D = null): Tensor2D {\n let batchSize = x.shape[0];\n let inSize = x.shape[1];\n if (bestIndicesA != null) {\n batchSize = bestIndicesA.shape[0];\n inSize = bestIndicesA.shape[1];\n }\n const windowSize = reduce_util.computeOptimalWindowSize(inSize);\n const reduceInfo = {windowSize, inSize, batchSize};\n const program =\n new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n const [rows, cols] = program.outputShape;\n const output = this.makeOutputArray([rows, cols], 'int32');\n const inputs = [x];\n if (bestIndicesA != null) {\n inputs.push(bestIndicesA);\n }\n this.compileAndRun(program, inputs, output);\n // No need to run another GPGPU program.\n if (output.shape[1] === 1) {\n return output;\n }\n return this.argReduce(x, reduceType, output);\n }\n\n sum(x: Tensor, axes: number[]): Tensor {\n axis_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const inSize = util.sizeFromShape(reduceShape);\n const a2D = x.as2D(-1, inSize);\n const outputDType = sumOutType(x.dtype);\n return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n }\n\n prod(x: Tensor, axes: number[]): Tensor {\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const inSize = util.sizeFromShape(reduceShape);\n const a2D = x.as2D(-1, inSize);\n const outputDType = sumOutType(x.dtype);\n return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n }\n\n unsortedSegmentSum(\n x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n let axis = 0;\n const permutation = axis_util.getAxesPermutation([axis], x.rank);\n let permutedX = x;\n if (permutation != null) {\n permutedX = x.transpose(permutation);\n axis = axis_util.getInnerMostAxes(1, x.rank)[0];\n }\n\n const outShape =\n segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n const a2D = permutedX.as2D(-1, inSize);\n const outputDType = sumOutType(x.dtype);\n let result =\n this.segOpCompute(\n a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n .reshape(outShape);\n if (permutation != null) {\n result = result.transpose(axis_util.getUndoAxesPermutation(permutation));\n }\n return result;\n }\n\n private segOpCompute(\n x: Tensor2D, segOpType: 'unsortedSegmentSum', segmentIds: Tensor1D,\n dtype: DataType, numSegments: number): Tensor2D {\n const batchSize = x.shape[0];\n const inSize = x.shape[1];\n const windowSize =\n segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n const segOpInfo = {windowSize, inSize, batchSize, numSegments};\n const program = new SegmentOpProgram(segOpInfo, segOpType);\n const [rows, cols] = program.outputShape;\n const output = this.makeOutputArray([rows, cols], dtype);\n this.compileAndRun(program, [x, segmentIds], output);\n // No need to run another GPGPU program.\n if (output.shape[1] === numSegments) {\n return output;\n }\n segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n }\n\n argMin(x: Tensor, axis: number): Tensor {\n const axes = [axis];\n axis_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const inSize = util.sizeFromShape(reduceShape);\n const a2D = x.as2D(-1, inSize);\n return this.argReduce(a2D, 'min').reshape(outShape);\n }\n\n argMax(x: Tensor, axis: number): Tensor {\n const axes = [axis];\n axis_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const inSize = util.sizeFromShape(reduceShape);\n const a2D = x.as2D(-1, inSize);\n return this.argReduce(a2D, 'max').reshape(outShape);\n }\n\n cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n Tensor {\n if (axis !== x.rank - 1) {\n throw new Error(\n `WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n `but got axis=${axis}`);\n }\n const program = new CumSumProgram(x.shape, exclusive, reverse);\n return this.compileAndRun(program, [x]);\n }\n\n equal(a: Tensor, b: Tensor): Tensor {\n const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, 'bool');\n return this.compileAndRun(program, [a, b], output);\n }\n\n notEqual(a: Tensor, b: Tensor): Tensor {\n const program =\n new BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, 'bool');\n return this.compileAndRun(program, [a, b], output);\n }\n\n less(a: Tensor, b: Tensor): Tensor {\n if (this.shouldExecuteOnCPU([a, b])) {\n return this.cpuBackend.less(a, b);\n }\n\n const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, 'bool');\n return this.compileAndRun(program, [a, b], output);\n }\n\n lessEqual(a: Tensor, b: Tensor): Tensor {\n const program =\n new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, 'bool');\n return this.compileAndRun(program, [a, b], output);\n }\n\n greater(a: Tensor, b: Tensor): Tensor {\n if (this.shouldExecuteOnCPU([a, b])) {\n return this.cpuBackend.greater(a, b);\n }\n\n const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, 'bool');\n return this.compileAndRun(program, [a, b], output);\n }\n\n greaterEqual(a: Tensor, b: Tensor): Tensor {\n const program =\n new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, 'bool');\n return this.compileAndRun(program, [a, b], output);\n }\n\n logicalNot(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n return this.compileAndRun(program, [x]) as T;\n }\n\n logicalAnd(a: Tensor, b: Tensor): Tensor {\n const program =\n new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, 'bool');\n return this.compileAndRun(program, [a, b], output);\n }\n\n logicalOr(a: Tensor, b: Tensor): Tensor {\n const program =\n new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, 'bool');\n return this.compileAndRun(program, [a, b], output);\n }\n\n select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n const program = new SelectProgram(condition.rank, a.shape, a.rank);\n const output =\n this.makeOutputArray(program.outputShape, upcastType(a.dtype, b.dtype));\n return this.compileAndRun(program, [condition, a, b], output);\n }\n\n where(condition: Tensor): Tensor2D {\n warn(\n 'tf.where() in webgl locks the UI thread. ' +\n 'Call tf.whereAsync() instead');\n const condVals = condition.dataSync();\n return whereImpl(condition.shape, condVals);\n }\n\n topk(x: T, k: number, sorted: boolean): [T, T] {\n const xVals = x.dataSync();\n return topkImpl(xVals, x.shape, x.dtype as NumericDataType, k, sorted);\n }\n\n min(x: Tensor, axes: number[]): Tensor {\n axis_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const inSize = util.sizeFromShape(reduceShape);\n const a2D = x.as2D(-1, inSize);\n return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n }\n\n minimum(a: Tensor, b: Tensor): Tensor {\n if (this.shouldExecuteOnCPU([a, b])) {\n return this.cpuBackend.minimum(a, b);\n }\n\n const program = new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n return this.compileAndRun(program, [a, b]);\n }\n\n mod(a: Tensor, b: Tensor): Tensor {\n const program = new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n const customSetup = program.getCustomSetupFunc();\n return this.compileAndRun(program, [a, b], null, customSetup);\n }\n\n max(x: Tensor, axes: number[]): Tensor {\n axis_util.assertAxesAreInnerMostDims('max', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const inSize = util.sizeFromShape(reduceShape);\n const a2D = x.as2D(-1, inSize);\n return this.reduce(a2D, 'max', a2D.dtype).reshape(outShape);\n }\n\n maximum(a: Tensor, b: Tensor): Tensor {\n if (this.shouldExecuteOnCPU([a, b])) {\n return this.cpuBackend.maximum(a, b);\n }\n\n const program = new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n return this.compileAndRun(program, [a, b]);\n }\n\n all(x: Tensor, axes: number[]): Tensor {\n axis_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const inSize = util.sizeFromShape(reduceShape);\n const a2D = x.as2D(-1, inSize);\n return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n }\n\n any(x: Tensor, axes: number[]): Tensor {\n axis_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const inSize = util.sizeFromShape(reduceShape);\n const a2D = x.as2D(-1, inSize);\n return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n }\n\n squaredDifference(a: Tensor, b: Tensor): Tensor {\n const program =\n new BinaryOpProgram(binaryop_gpu.SQUARED_DIFFERENCE, a.shape, b.shape);\n return this.compileAndRun(program, [a, b]);\n }\n\n realDivide(a: Tensor, b: Tensor): Tensor {\n const op = binaryop_gpu.DIV;\n const outputDtype = 'float32';\n if (ENV.get('WEBGL_PACK_BINARY_OPERATIONS')) {\n return this.packedBinaryOp(a, b, PACKED_DIV, outputDtype);\n }\n const program = new BinaryOpProgram(op, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, outputDtype);\n return this.compileAndRun(program, [a, b], output);\n }\n\n floorDiv(a: Tensor, b: Tensor): Tensor {\n const op = binaryop_gpu.INT_DIV;\n const outputDtype = 'int32';\n if (ENV.get('WEBGL_PACK_BINARY_OPERATIONS')) {\n return this.packedBinaryOp(a, b, PACKED_INT_DIV, outputDtype);\n }\n const program = new BinaryOpProgram(op, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, outputDtype);\n return this.compileAndRun(program, [a, b], output);\n }\n\n add(a: Tensor, b: Tensor): Tensor {\n if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);\n }\n const dtype = upcastType(a.dtype, b.dtype);\n if (ENV.get('WEBGL_PACK_BINARY_OPERATIONS')) {\n return this.packedBinaryOp(a, b, binaryop_gpu.ADD, dtype);\n }\n const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, dtype) as Tensor;\n return this.compileAndRun(program, [a, b], output);\n }\n\n private packedBinaryOp(a: Tensor, b: Tensor, op: string, dtype: DataType) {\n const program = new BinaryOpPackedProgram(op, a.shape, b.shape);\n const output = this.makePackedTensor(program.outputShape, dtype) as Tensor;\n return this.compileAndRun(program, [a, b], output);\n }\n\n /**\n * Computes a complex binary operation that can be decomposed into a simple\n * binary operation on both the real and imagary parts.\n */\n private complexSeparableBinaryOp(a: Tensor, b: Tensor, op: string): Tensor {\n const aData = this.texData.get(a.dataId);\n const bData = this.texData.get(b.dataId);\n\n const [real, imag] = [\n [aData.complexTensors.real, bData.complexTensors.real],\n [aData.complexTensors.imag, bData.complexTensors.imag]\n ].map(complexParts => {\n const [aPart, bPart] = complexParts;\n\n const program = new BinaryOpProgram(op, a.shape, b.shape);\n const output = this.makeOutputArray(\n program.outputShape,\n upcastType(aPart.dtype, bPart.dtype)) as Tensor;\n\n const aHandle = this.makeComplexComponentTensorHandle(a, aPart);\n const bHandle = this.makeComplexComponentTensorHandle(b, bPart);\n\n return this.compileAndRun(program, [aHandle, bHandle], output);\n });\n\n const complex = this.complex(real, imag);\n real.dispose();\n imag.dispose();\n return complex;\n }\n\n // Returns a TensorHandle with the complex shape and the dataId of the\n // underlying part. We need to do this because a reshaped complex tensor is\n // not reflected in its parts.\n private makeComplexComponentTensorHandle(\n complexTensor: Tensor, complexPart: Tensor): TensorHandle {\n return {\n dataId: complexPart.dataId,\n dtype: complexPart.dtype,\n shape: complexTensor.shape\n };\n }\n\n addN(tensors: T[]): T {\n let res = tensors[0];\n for (let i = 1; i < tensors.length; i++) {\n res = this.add(res, tensors[i]) as T;\n }\n return res;\n }\n\n subtract(a: Tensor, b: Tensor): Tensor {\n if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);\n }\n\n if (this.shouldExecuteOnCPU([a, b])) {\n return this.cpuBackend.subtract(a, b);\n }\n const dtype = upcastType(a.dtype, b.dtype);\n if (ENV.get('WEBGL_PACK_BINARY_OPERATIONS')) {\n return this.packedBinaryOp(a, b, binaryop_gpu.SUB, a.dtype);\n }\n const program = new BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);\n const output = this.makeOutputArray(program.outputShape, dtype) as Tensor;\n return this.compileAndRun(program, [a, b], output);\n }\n\n pow(a: T, b: Tensor): T {\n const usePackedOp = ENV.get('WEBGL_PACK_BINARY_OPERATIONS');\n const program = usePackedOp ?\n new BinaryOpPackedProgram(PACKED_POW, a.shape, b.shape) :\n new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n const dtype = upcastType(a.dtype, b.dtype);\n const output = usePackedOp ?\n this.makePackedTensor(program.outputShape, dtype) as T :\n this.makeOutputArray(program.outputShape, dtype) as T;\n const customSetup = program.getCustomSetupFunc();\n return this.compileAndRun(program, [a, b], output, customSetup);\n }\n\n ceil(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n return this.compileAndRun(program, [x]) as T;\n }\n\n floor(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n return this.compileAndRun(program, [x]) as T;\n }\n\n sign(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n return this.compileAndRun(program, [x]) as T;\n }\n\n round(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n return this.compileAndRun(program, [x]) as T;\n }\n\n exp(x: T): T {\n let program: UnaryOpProgram|UnaryOpPackedProgram;\n if (ENV.get('WEBGL_PACK')) {\n program = new UnaryOpPackedProgram(x.shape, unary_op.EXP);\n } else {\n program = new UnaryOpProgram(x.shape, unary_op.EXP);\n }\n return this.compileAndRun(program, [x]) as T;\n }\n\n expm1(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n return this.compileAndRun(program, [x]) as T;\n }\n\n log(x: T): T {\n let program: UnaryOpProgram|UnaryOpPackedProgram;\n if (ENV.get('WEBGL_PACK')) {\n program = new UnaryOpPackedProgram(x.shape, unary_packed_op.LOG);\n } else {\n program = new UnaryOpProgram(x.shape, unary_op.LOG);\n }\n const customSetup = program.getCustomSetupFunc();\n return this.compileAndRun(program, [x], null, customSetup) as T;\n }\n\n log1p(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n return this.compileAndRun(program, [x]) as T;\n }\n\n sqrt(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n return this.compileAndRun(program, [x]) as T;\n }\n\n rsqrt(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n return this.compileAndRun(program, [x]) as T;\n }\n\n square(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.SQUARE);\n return this.compileAndRun(program, [x]) as T;\n }\n\n reciprocal(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n return this.compileAndRun(program, [x]) as T;\n }\n\n relu(x: T): T {\n let program: UnaryOpProgram|UnaryOpPackedProgram;\n if (ENV.get('WEBGL_PACK')) {\n program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU);\n } else {\n program = new UnaryOpProgram(x.shape, unary_op.RELU);\n }\n return this.compileAndRun(program, [x]) as T;\n }\n\n prelu(x: T, alpha: T): T {\n const program =\n new BinaryOpProgram(binaryop_gpu.PRELU, x.shape, alpha.shape);\n return this.compileAndRun(program, [x, alpha]) as T;\n }\n\n elu(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n return this.compileAndRun(program, [x]) as T;\n }\n\n eluDer(dy: T, y: T): T {\n const program =\n new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n return this.compileAndRun(program, [dy, y]) as T;\n }\n\n selu(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n return this.compileAndRun(program, [x]) as T;\n }\n\n int(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.TO_INT);\n const output = this.makeOutputArray(program.outputShape, 'int32');\n return this.compileAndRun(program, [x], output) as T;\n }\n\n clip(x: T, min: number, max: number): T {\n let program;\n if (ENV.get('WEBGL_PACK_CLIP')) {\n program = new ClipPackedProgram(x.shape);\n } else {\n program = new ClipProgram(x.shape);\n }\n const customSetup = program.getCustomSetupFunc(min, max);\n return this.compileAndRun(program, [x], null, customSetup) as T;\n }\n\n abs(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n return this.compileAndRun(program, [x]) as T;\n }\n\n complexAbs(x: T): T {\n const xData = this.texData.get(x.dataId);\n\n const program = new ComplexAbsProgram(x.shape);\n const inputs = [\n this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),\n this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),\n ];\n\n return this.compileAndRun(program, inputs) as T;\n }\n\n sigmoid(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n return this.compileAndRun(program, [x]) as T;\n }\n\n softplus(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n return this.compileAndRun(program, [x]) as T;\n }\n\n sin(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.SIN);\n return this.compileAndRun(program, [x]) as T;\n }\n\n cos(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.COS);\n return this.compileAndRun(program, [x]) as T;\n }\n\n tan(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.TAN);\n return this.compileAndRun(program, [x]) as T;\n }\n\n asin(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n return this.compileAndRun(program, [x]) as T;\n }\n\n acos(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n return this.compileAndRun(program, [x]) as T;\n }\n\n atan(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n return this.compileAndRun(program, [x]) as T;\n }\n\n atan2(a: T, b: T): T {\n const program = new BinaryOpProgram(binaryop_gpu.ATAN2, a.shape, b.shape);\n return this.compileAndRun(program, [a, b]) as T;\n }\n\n sinh(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n return this.compileAndRun(program, [x]) as T;\n }\n\n cosh(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n return this.compileAndRun(program, [x]) as T;\n }\n\n tanh(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n return this.compileAndRun(program, [x]) as T;\n }\n\n asinh(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n return this.compileAndRun(program, [x]) as T;\n }\n\n acosh(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n const customSetup = program.getCustomSetupFunc();\n return this.compileAndRun(program, [x], null, customSetup) as T;\n }\n\n atanh(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n const customSetup = program.getCustomSetupFunc();\n return this.compileAndRun(program, [x], null, customSetup) as T;\n }\n\n erf(x: T): T {\n const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n return this.compileAndRun(program, [x]) as T;\n }\n\n step(x: T, alpha: number): T {\n const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n return this.compileAndRun(program, [x]) as T;\n }\n\n conv2dWithIm2Row(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n // Rearranges conv2d input so each block to be convolved over forms the\n // column of a new matrix with shape [filterWidth * filterHeight *\n // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n // output channel forms a row of a new matrix with shape [outChannels,\n // filterWidth * filterHeight * inChannels]. The convolution is then\n // computed by multiplying these matrices and reshaping the result.\n const {\n filterWidth,\n filterHeight,\n inChannels,\n outWidth,\n outHeight,\n } = convInfo;\n\n const sharedDim = filterWidth * filterHeight * inChannels;\n const numCols = outHeight * outWidth;\n const x2ColShape = [sharedDim, numCols];\n\n const xSqueezed = x.squeeze([0]);\n const w2Row = filter.reshape([sharedDim, -1]) as Tensor2D;\n\n const im2ColProgram =\n new Im2ColProgram(x2ColShape, xSqueezed.shape, convInfo);\n const im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]);\n\n const matmulProgram = new MatMulPackedProgram(\n im2Col.shape, w2Row.shape, [numCols, convInfo.outChannels], true,\n false);\n const product =\n this.compileAndRun(matmulProgram, [im2Col, w2Row]);\n\n return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n }\n\n conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n if (ENV.get('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n return this.conv2dWithIm2Row(x, filter, convInfo);\n }\n const program = new Conv2DProgram(convInfo);\n return this.compileAndRun(program, [x, filter]);\n }\n\n conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n const program = new Conv2DDerInputProgram(convInfo);\n return this.compileAndRun(program, [dy, filter]);\n }\n\n conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n const program = new Conv2DDerFilterProgram(convInfo);\n return this.compileAndRun(program, [x, dy]);\n }\n\n depthwiseConv2D(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n let program: DepthwiseConv2DProgram|DepthwiseConvPacked2DProgram;\n if (ENV.get('WEBGL_PACK_DEPTHWISECONV') && convInfo.strideWidth <= 2 &&\n convInfo.outChannels / convInfo.inChannels === 1) {\n program = new DepthwiseConvPacked2DProgram(convInfo);\n return this.compileAndRun(\n program, [x, filter],\n this.makePackedTensor(convInfo.outShape, x.dtype));\n }\n\n program = new DepthwiseConv2DProgram(convInfo);\n return this.compileAndRun(program, [x, filter]);\n }\n\n depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n const program = new DepthwiseConv2DDerInputProgram(convInfo);\n return this.compileAndRun(program, [dy, filter]);\n }\n\n depthwiseConv2DDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n return this.compileAndRun(program, [x, dy]);\n }\n\n conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n const program = new Conv3DProgram(convInfo);\n return this.compileAndRun(program, [x, filter]);\n }\n\n conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n Tensor5D {\n const program = new Conv3DDerInputProgram(convInfo);\n return this.compileAndRun(program, [dy, filter]);\n }\n\n conv3dDerFilter(x: Tensor5D, dy: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n const program = new Conv3DDerFilterProgram(convInfo);\n return this.compileAndRun(program, [x, dy]);\n }\n\n maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n const program = new Pool2DProgram(convInfo, 'max', false);\n const output =\n this.makeOutputArray(program.outputShape, x.dtype) as Tensor4D;\n return this.compileAndRun(program, [x], output);\n }\n\n avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n const program = new Pool2DProgram(convInfo, 'avg', false);\n const output = this.makeOutputArray(program.outputShape, 'float32');\n return this.compileAndRun(program, [x], output) as Tensor4D;\n }\n\n maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n const getPositions = true;\n const maxPoolPositionsProgram =\n new Pool2DProgram(convInfo, 'max', getPositions);\n const maxPoolPositions: Tensor4D =\n this.compileAndRun(maxPoolPositionsProgram, [x]);\n\n const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n const output =\n this.makeOutputArray(maxPoolBackPropProgram.outputShape, x.dtype);\n const result = this.compileAndRun(\n maxPoolBackPropProgram, [dy, maxPoolPositions], output);\n maxPoolPositions.dispose();\n return result as Tensor4D;\n }\n\n avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n const output =\n this.makeOutputArray(avgPoolBackpropProgram.outputShape, x.dtype);\n return this.compileAndRun(avgPoolBackpropProgram, [dy], output) as Tensor4D;\n }\n\n cast(x: T, dtype: DataType): T {\n return backend_util.castTensor(x, dtype, this);\n }\n\n unstack(x: Tensor, axis: number): Tensor[] {\n const num = x.shape[axis];\n const outShape: number[] = new Array(x.rank - 1);\n let outIndex = 0;\n for (let i = 0; i < x.rank; i++) {\n if (i !== axis) {\n outShape[outIndex++] = x.shape[i];\n }\n }\n\n const begin = new Array(x.rank).fill(0);\n const size = x.shape.slice();\n size[axis] = 1;\n const res = new Array(num);\n for (let i = 0; i < res.length; i++) {\n begin[axis] = i;\n res[i] = this.slice(x, begin, size).reshape(outShape);\n }\n return res;\n }\n\n reshape(x: Tensor, shape: ShapeMap[R]): Tensor {\n const {isPacked} = this.texData.get(x.dataId);\n if (isPacked && !webgl_util.isReshapeFree(x.shape, shape)) {\n return this.packedReshape(x, shape);\n }\n return backend_util.reshapeTensor(x, shape);\n }\n\n resizeBilinear(\n x: Tensor4D, newHeight: number, newWidth: number,\n alignCorners: boolean): Tensor4D {\n const program =\n new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n return this.compileAndRun(program, [x]);\n }\n\n resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):\n Tensor4D {\n const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n\n return this.compileAndRun(program, [dy]);\n }\n\n resizeNearestNeighbor(\n x: Tensor4D, newHeight: number, newWidth: number,\n alignCorners: boolean): Tensor4D {\n const program = new ResizeNearestNeighborProgram(\n x.shape, newHeight, newWidth, alignCorners);\n return this.compileAndRun(program, [x]);\n }\n\n resizeNearestNeighborBackprop(\n dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {\n const program =\n new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n return this.compileAndRun(program, [dy]);\n }\n\n multinomial(\n logits: Tensor2D, normalized: boolean, numSamples: number,\n seed: number): Tensor2D {\n const probs = normalized ? logits : softmax(logits);\n const batchSize = probs.shape[0];\n const numOutcomes = probs.shape[1];\n const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n const output =\n this.makeOutputArray(program.outputShape, 'int32') as Tensor2D;\n const customSetup = program.getCustomSetupFunc(seed);\n return this.compileAndRun(program, [probs], output, customSetup);\n }\n\n oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n Tensor2D {\n const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n return this.compileAndRun(program, [indices]);\n }\n\n nonMaxSuppression(\n boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n iouThreshold: number, scoreThreshold: number): Tensor1D {\n warn(\n 'tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n 'Call tf.nonMaxSuppressionAsync() instead');\n const boxesVals = boxes.dataSync();\n const scoresVals = scores.dataSync();\n return nonMaxSuppressionImpl(\n boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n }\n\n cropAndResize(\n image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D,\n cropSize: [number, number], method: 'bilinear'|'nearest',\n extrapolationValue: number): Tensor4D {\n const program = new CropAndResizeProgram(\n image.shape, boxes.shape, cropSize, method, extrapolationValue);\n return this.compileAndRun(program, [image, boxes, boxIndex]);\n }\n\n depthToSpace(x: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n Tensor4D {\n util.assert(\n blockSize > 1,\n `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n\n const batchSize = x.shape[0];\n const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n\n const outputHeight = inputHeight * blockSize;\n const outputWidth = inputWidth * blockSize;\n const outputDepth = inputDepth / (blockSize * blockSize);\n\n const outputShape = (dataFormat === 'NHWC') ?\n [batchSize, outputHeight, outputWidth, outputDepth] :\n [batchSize, outputDepth, outputHeight, outputWidth];\n\n const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n return this.compileAndRun(program, [x]);\n }\n\n split(x: T, sizeSplits: number[], axis: number): T[] {\n return split(x, sizeSplits, axis);\n }\n\n scatterND(\n indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor {\n const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n scatter_nd_util.calculateShapes(updates, indices, shape);\n\n const flattenShape = [outputSize / sliceSize, sliceSize];\n const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n const flattenX = updates.reshape([numUpdates, sliceSize]);\n\n if (outputSize === 0) {\n return backend_util.reshapeTensor(tensor([]), shape);\n }\n const defaultValue = scalar(0);\n const program = new ScatterProgram(\n numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides,\n flattenShape);\n return (this.compileAndRun(\n program, [flattenX, flattenIndices, defaultValue]) as Tensor)\n .reshape(shape);\n }\n\n sparseToDense(\n sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n defaultValue: Scalar): Tensor {\n const {sliceRank, numUpdates, strides, outputSize} =\n scatter_nd_util.calculateShapes(\n sparseValues, sparseIndices, outputShape);\n\n const sumDupeIndices = false;\n const program = new ScatterProgram(\n numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides,\n [outputSize, 1], sumDupeIndices);\n return (this.compileAndRun(\n program, [sparseValues, sparseIndices, defaultValue]) as Tensor)\n .reshape(outputShape);\n }\n\n fft(x: Tensor2D): Tensor2D {\n const inverse = false;\n return this.fftImpl(x, inverse);\n }\n\n ifft(x: Tensor2D): Tensor2D {\n const inverse = true;\n return this.fftImpl(x, inverse);\n }\n\n private fftImpl(x: Tensor2D, inverse: boolean): Tensor2D {\n const xData = this.texData.get(x.dataId);\n\n const realProgram =\n new FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);\n const imagProgram =\n new FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);\n const inputs = [\n this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),\n this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),\n ];\n\n const real = this.compileAndRun(realProgram, inputs);\n const imag = this.compileAndRun(imagProgram, inputs);\n const complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);\n real.dispose();\n imag.dispose();\n return complex;\n }\n\n gatherND(x: Tensor, indices: Tensor): Tensor {\n const indicesShape = indices.shape;\n const sliceRank = indicesShape[indicesShape.length - 1];\n\n const [resultShape, numSlices, sliceSize, strides] =\n gather_nd_util.prepareAndValidate(x, indices);\n\n const flattenIndices = indices.reshape([numSlices, sliceRank]);\n const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n const program =\n new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n return (this.compileAndRun(program, [flattenX, flattenIndices]) as Tensor)\n .reshape(resultShape);\n }\n\n private makeOutputArray(shape: number[], dtype: DataType):\n T {\n return Tensor.make(shape, {}, dtype) as T;\n }\n\n private makePackedTensor(\n shape: number[], dtype?: D): T {\n const packedTensor = Tensor.make(shape, {}, dtype);\n this.texData.get(packedTensor.dataId).isPacked = true;\n return packedTensor as T;\n }\n\n private unpackTensor(input: T): T {\n const program = new UnpackProgram(input.shape);\n return this.compileAndRun(\n program, [input], Tensor.make(program.outputShape, {}, input.dtype));\n }\n\n private packedReshape(input: Tensor, afterShape: ShapeMap[R]):\n Tensor {\n const inputAs3D = input.reshape([\n webgl_util.getBatchDim(input.shape),\n ...webgl_util.getRowsCols(input.shape)\n ]);\n const afterShapeAs3D = [\n webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n ];\n const program = new ReshapePackedProgram(\n afterShapeAs3D as [number, number, number],\n inputAs3D.shape as [number, number, number]);\n return this.compileAndRun>(program, [inputAs3D])\n .reshape(afterShape);\n }\n\n public compileAndRun<\n K extends {dtype: DataType, size: number, dataId: {}, shape: number[]}>(\n program: GPGPUProgram, inputs: TensorHandle[], output?: K,\n customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void,\n pageToCpu = true): K {\n if (output == null) {\n if (program.usesPackedTextures) {\n output = this.makePackedTensor(program.outputShape, inputs[0].dtype) as\n {} as K;\n } else {\n output = this.makeOutputArray(program.outputShape, inputs[0].dtype) as\n {} as K;\n }\n }\n if (output.size === 0) {\n // Short-circuit the computation since the result is empty (has 0 in its\n // shape).\n this.texData.get(output.dataId).values =\n getTypedArrayFromDType(output.dtype as 'float32', 0);\n return output;\n }\n\n const inputsData: TensorData[] = inputs.map(input => {\n if (input.dtype === 'complex64') {\n throw new Error(\n `GPGPUProgram does not support complex64 input. For complex64 ` +\n `dtypes, please separate the program into real and imaginary ` +\n `parts.`);\n }\n\n let texData = this.texData.get(input.dataId);\n\n if (texData.texture == null) {\n if (!program.usesPackedTextures &&\n util.sizeFromShape(input.shape) <=\n ENV.get('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n // Upload small tensors that live on the CPU as uniforms, not as\n // textures. Do this only when the environment supports 32bit floats\n // due to problems when comparing 16bit floats with 32bit floats.\n // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n // possible for packed shaders to sample from uniforms.\n return {\n shape: input.shape,\n texData: null,\n isUniform: true,\n uniformValues: this.readSync(input.dataId) as TypedArray\n };\n }\n\n // This ensures that if a packed program's inputs have not yet been\n // uploaded to the GPU, they get uploaded as packed right off the bat.\n if (program.usesPackedTextures) {\n texData.isPacked = true;\n texData.shape = input.shape;\n }\n } else if (!!texData.isPacked !== !!program.usesPackedTextures) {\n let preProcessProgram: UnpackProgram|PackProgram;\n let processedInput: Tensor;\n\n // Explicitly specifying output tensors because compileAndRun assumes\n // that programs that take packed inputs produce a packed output.\n if (texData.isPacked) {\n preProcessProgram = new UnpackProgram(input.shape);\n processedInput = this.compileAndRun(\n preProcessProgram, [input],\n Tensor.make(preProcessProgram.outputShape, {}, input.dtype));\n } else {\n preProcessProgram = new PackProgram(input.shape);\n processedInput = this.compileAndRun(\n preProcessProgram, [input],\n this.makePackedTensor(input.shape, input.dtype));\n }\n\n texData = this.texData.get(processedInput.dataId);\n input = processedInput;\n } else if (\n texData.isPacked &&\n !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n // This is a special, temporary case where a texture exists for a tensor\n // but the shapes are incompatible (due to packing constraints) because\n // the tensor did not have a chance to go through the packed reshape\n // shader. This only happens when we reshape the *same* tensor to form\n // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n // case will disappear once packed uploading is the default.\n\n // Temporarily disable delayedStorage so the texture isn't removed from\n // the original input\n this.delayedStorage = false;\n const inputValues = (input as Tensor).dataSync();\n this.delayedStorage = true;\n\n input = Tensor.make(input.shape, {values: inputValues}, input.dtype);\n texData = this.texData.get(input.dataId);\n texData.isPacked = true;\n }\n\n this.uploadToGPU(input.dataId);\n return {shape: input.shape, texData, isUniform: false};\n });\n\n this.uploadToGPU(output.dataId);\n const outputData: TensorData = {\n shape: output.shape,\n texData: this.texData.get(output.dataId),\n isUniform: false\n };\n const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n const binary = this.getAndSaveBinary(key, () => {\n return gpgpu_math.compileProgram(\n this.gpgpu, program, inputsData, outputData);\n });\n const shouldTimeProgram = this.activeTimers != null;\n let query: WebGLQuery|CPUTimerQuery;\n if (shouldTimeProgram) {\n query = this.startTimer();\n }\n\n gpgpu_math.runProgram(binary, inputsData, outputData, customSetup);\n\n const numBytesBeforePaging = ENV.get('WEBGL_NUM_MB_BEFORE_PAGING') * 1024;\n if (pageToCpu && this.numBytesInGPU > numBytesBeforePaging) {\n let numBytesToPage = this.numBytesInGPU - numBytesBeforePaging;\n while (numBytesToPage > 0 && this.lruDataGPU.length > 0) {\n const dataId = this.lruDataGPU.shift();\n const {shape, dtype} = this.texData.get(dataId);\n numBytesToPage -= this.computeBytes(shape, dtype);\n this.read(dataId);\n }\n }\n\n if (shouldTimeProgram) {\n query = this.endTimer(query);\n this.activeTimers.push(\n {name: program.constructor.name, query: this.getQueryTime(query)});\n }\n\n if (!ENV.get('WEBGL_LAZILY_UNPACK') &&\n this.texData.get(output.dataId).isPacked && !program.isPackShader) {\n return this.unpackTensor(output as {} as Tensor) as {} as K;\n }\n return output;\n }\n\n private getAndSaveBinary(key: string, getBinary: () => GPGPUBinary):\n GPGPUBinary {\n if (!(key in this.binaryCache)) {\n this.binaryCache[key] = getBinary();\n }\n return this.binaryCache[key];\n }\n\n getTextureManager(): TextureManager {\n return this.textureManager;\n }\n\n private disposed = false;\n\n dispose() {\n if (this.disposed) {\n return;\n }\n for (const key in this.binaryCache) {\n this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n }\n this.textureManager.dispose();\n this.canvas.remove();\n if (this.fromPixels2DContext != null) {\n this.fromPixels2DContext.canvas.remove();\n }\n if (this.gpgpuCreatedLocally) {\n this.gpgpu.dispose();\n }\n this.disposed = true;\n }\n\n floatPrecision(): number {\n return tidy(() => {\n // Momentarily switching DEBUG flag to false so we don't throw an error\n // trying to upload a small value.\n const debugFlag = ENV.get('DEBUG');\n ENV.set('DEBUG', false);\n const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n ENV.set('DEBUG', debugFlag);\n\n if (underflowCheckValue > 0) {\n return 32;\n }\n return 16;\n });\n }\n\n private uploadToGPU(dataId: DataId): void {\n const texData = this.texData.get(dataId);\n const {shape, values, texture, usage, isPacked} = texData;\n if (texture != null) {\n // Array is already on GPU. No-op.\n // Touching the texture.\n if (ENV.get('WEBGL_NUM_MB_BEFORE_PAGING') < Number.POSITIVE_INFINITY) {\n const index = this.lruDataGPU.indexOf(dataId);\n if (index >= 0) {\n this.lruDataGPU.splice(this.lruDataGPU.indexOf(dataId), 1);\n this.lruDataGPU.push(dataId);\n }\n }\n return;\n }\n const shouldTimeProgram = this.activeTimers != null;\n let start: number;\n if (shouldTimeProgram) {\n start = performance.now();\n }\n const texShape =\n webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n texData.texShape = texShape;\n const newTexture = this.acquireTexture(dataId, texShape, usage, isPacked);\n texData.texture = newTexture;\n if (values != null) {\n // TODO(smilkov): Propagate the original typed array to gpgpu.\n if (isPacked) {\n const batch = webgl_util.getBatchDim(shape);\n let rows = 1, cols = 1;\n if (shape.length) {\n [rows, cols] = webgl_util.getRowsCols(shape);\n }\n this.gpgpu.uploadMatrixToPackedTexture(\n newTexture, batch, rows, cols, texShape[0], texShape[1],\n typedArrayToFloat32(values as Float32Array));\n } else {\n this.gpgpu.uploadMatrixToTexture(\n newTexture, texShape[0], texShape[1],\n typedArrayToFloat32(values as Float32Array));\n }\n // Once uploaded, don't store the values on cpu.\n texData.values = null;\n if (shouldTimeProgram) {\n this.uploadWaitMs += performance.now() - start;\n }\n }\n }\n\n private convertAndCacheOnCPU(dataId: DataId, float32Values?: Float32Array):\n TypedArray {\n // In delayed storage mode, when the user reads data, we don't keep a\n // copy on the gpu, to minimize likelihood of memory leak. We re-upload\n // to gpu the next time a gpgpu program needs the texture.\n const dontKeepCopyOnGPU = this.delayedStorage;\n const texData = this.texData.get(dataId);\n const {texture, texShape, dtype, usage, isPacked} = texData;\n if (dontKeepCopyOnGPU && texture != null) {\n this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n texData.texture = null;\n texData.texShape = null;\n texData.isPacked = false;\n }\n texData.usage = TextureUsage.UPLOAD;\n if (float32Values != null) {\n texData.values = float32ToTypedArray(float32Values, dtype as 'float32');\n }\n return texData.values as TypedArray;\n }\n\n private releaseTexture(\n dataId: DataId, texture: WebGLTexture, texShape: [number, number],\n texType: TextureUsage, isPacked: boolean) {\n const {shape, dtype} = this.texData.get(dataId);\n\n if (ENV.get('WEBGL_NUM_MB_BEFORE_PAGING') < Number.POSITIVE_INFINITY) {\n const idx = this.lruDataGPU.indexOf(dataId);\n if (idx >= 0) {\n this.lruDataGPU.splice(idx, 1);\n }\n }\n this.numBytesInGPU -= this.computeBytes(shape, dtype);\n this.textureManager.releaseTexture(texture, texShape, texType, isPacked);\n }\n\n private acquireTexture(\n dataId: DataId, texShape: [number, number], texType: TextureUsage,\n isPacked: boolean): WebGLTexture {\n const {shape, dtype} = this.texData.get(dataId);\n if (ENV.get('WEBGL_NUM_MB_BEFORE_PAGING') < Number.POSITIVE_INFINITY) {\n this.lruDataGPU.push(dataId);\n }\n this.numBytesInGPU += this.computeBytes(shape, dtype);\n return this.textureManager.acquireTexture(texShape, texType, isPacked);\n }\n\n private computeBytes(shape: number[], dtype: DataType) {\n return util.sizeFromShape(shape) * util.bytesPerElement(dtype);\n }\n}\n\nif (ENV.get('IS_BROWSER')) {\n ENV.registerBackend(\n 'webgl', () => new MathBackendWebGL(), 2 /* priority */,\n setTensorTracker);\n}\n\nfunction float32ToTypedArray(\n a: Float32Array, dtype: D): DataTypeMap[D] {\n if (dtype === 'float32' || dtype === 'complex64') {\n return a;\n } else if (dtype === 'int32' || dtype === 'bool') {\n const result = (dtype === 'int32') ? new Int32Array(a.length) :\n new Uint8Array(a.length);\n for (let i = 0; i < result.length; ++i) {\n result[i] = Math.round(a[i]);\n }\n return result;\n } else {\n throw new Error(`Unknown dtype ${dtype}`);\n }\n}\n\nfunction typedArrayToFloat32(a: TypedArray): Float32Array {\n return (a instanceof Float32Array) ? a : new Float32Array(a);\n}\n","../src/ops/unary_ops.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print(); // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction neg_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'neg');\n\n const grad = (dy: T) => {\n return {$x: () => dy.neg()};\n };\n return ENV.engine.runKernel(backend => backend.neg($x), {$x}, grad);\n}\n\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print(); // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction ceil_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'ceil');\n\n // TODO(manrajgrover): Return null for gradients when backprop supports it.\n const grad = (dy: T) => {\n return {$x: () => zerosLike(dy)};\n };\n return ENV.engine.runKernel(backend => backend.ceil($x), {$x}, grad);\n}\n\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print(); // or tf.floor(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction floor_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'floor');\n\n // TODO(nsthorat): Let gradients be null for cases where we want to stop\n // backpropgation.\n const grad = (dy: T) => {\n return {$x: () => zerosLike(dy)};\n };\n return ENV.engine.runKernel(backend => backend.floor($x), {$x}, grad);\n}\n\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print(); // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sign_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'sign');\n\n const grad = (dy: T) => {\n return {$x: () => zerosLike(dy)};\n };\n return ENV.engine.runKernel(backend => backend.sign($x), {$x}, grad);\n}\n\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print(); // or tf.round(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction round_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'round');\n\n // TODO(nsthorat): Let gradients be null for cases where we want to stop\n // backpropgation.\n const grad = (dy: T) => {\n return {$x: () => zerosLike(dy)};\n };\n return ENV.engine.runKernel(backend => backend.round($x), {$x}, grad);\n}\n\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print(); // or tf.exp(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction exp_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'exp');\n\n const bck = (dy: T, saved: Tensor[]) => {\n const [y] = saved;\n return {$x: () => dy.mulStrict(y as T)};\n };\n return ENV.engine.runKernel(\n (backend, save) => save(backend.exp($x)), {$x}, bck);\n}\n\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print(); // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction expm1_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'expm1');\n\n const grad = (dy: T) => {\n return {$x: () => dy.mulStrict($x.exp())};\n };\n return ENV.engine.runKernel(backend => backend.expm1($x), {$x}, grad);\n}\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print(); // or tf.log(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction log_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'log');\n\n const grad = (dy: T) => {\n return {$x: () => dy.divStrict($x.toFloat())};\n };\n return ENV.engine.runKernel(backend => backend.log($x), {$x}, grad);\n}\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print(); // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction log1p_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'log1p');\n\n const grad = (dy: T) => {\n return {$x: () => dy.div($x.add(1)) as T};\n };\n return ENV.engine.runKernel(backend => backend.log1p($x), {$x}, grad);\n}\n\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print(); // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sqrt_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'sqrt');\n\n const grad = (dy: T) => {\n return {$x: () => dy.div($x.toFloat().sqrt().mul(2)) as T};\n };\n return ENV.engine.runKernel(backend => backend.sqrt($x), {$x}, grad);\n}\n\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print(); // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction rsqrt_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'rsqrt');\n\n const grad = (dy: T) => {\n return {$x: () => dy.div($x.pow(1.5).mul(2)).neg() as T};\n };\n return ENV.engine.runKernel(backend => backend.rsqrt($x), {$x}, grad);\n}\n\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print(); // or tf.square(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction square_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'square');\n\n const grad = (dy: T) => {\n return {$x: () => dy.mul($x.toFloat().mul(2)) as T};\n };\n return ENV.engine.runKernel(backend => backend.square($x), {$x}, grad);\n}\n\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print(); // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction reciprocal_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'reciprocal');\n\n const grad = (dy: T) => {\n return {$x: () => dy.divStrict($x.square().neg())};\n };\n return ENV.engine.runKernel(backend => backend.reciprocal($x), {$x}, grad);\n}\n\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print(); // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction abs_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'abs');\n\n if ($x.dtype === 'complex64') {\n return ENV.engine.runKernel(backend => backend.complexAbs($x), {$x});\n }\n\n const grad = (dy: T) => {\n return {$x: () => dy.mulStrict($x.toFloat().step(-1))};\n };\n return ENV.engine.runKernel(backend => backend.abs($x), {$x}, grad);\n}\n\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print(); // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction clipByValue_(\n x: T|TensorLike, clipValueMin: number, clipValueMax: number): T {\n const $x = convertToTensor(x, 'x', 'clipByValue');\n util.assert(\n (clipValueMin <= clipValueMax),\n `Error in clip: min (${clipValueMin}) must be ` +\n `less than or equal to max (${clipValueMax}).`);\n\n const grad = (dy: T) => {\n return {\n $x: () => dy.where(\n $x.greaterEqual(clipValueMin)\n .logicalAnd($x.lessEqual(clipValueMax)),\n zerosLike(dy)) as T,\n };\n };\n return ENV.engine.runKernel(\n backend => backend.clip($x, clipValueMin, clipValueMax), {$x}, grad);\n}\n\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print(); // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sigmoid_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'sigmoid');\n\n const grad = (dy: T, saved: Tensor[]) => {\n const [y] = saved;\n return {$x: () => dy.mul(y.mul(scalar(1).sub(y))) as T};\n };\n return ENV.engine.runKernel(\n (backend, save) => save(backend.sigmoid($x)), {$x}, grad);\n}\n\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print(); // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction logSigmoid_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'logSigmoid');\n\n const grad = (dy: T) => {\n return {$x: () => dy.mulStrict($x.neg().sigmoid())};\n };\n return ENV.engine.runKernel(\n backend => backend.softplus($x.neg()).neg(), {$x}, grad);\n}\n\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print(); // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction softplus_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'softplus');\n\n const grad = (dy: T) => {\n return {$x: () => dy.mulStrict($x.sigmoid())};\n };\n return ENV.engine.runKernel(backend => backend.softplus($x), {$x}, grad);\n}\n\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print(); // or tf.sin(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sin_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'sin');\n\n const grad = (dy: T) => {\n return {$x: () => $x.toFloat().cos().mulStrict(dy)};\n };\n return ENV.engine.runKernel(backend => backend.sin($x), {$x}, grad);\n}\n\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print(); // or tf.cos(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction cos_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'cos');\n\n const grad = (dy: T) => {\n return {$x: () => $x.toFloat().sin().neg().mulStrict(dy)};\n };\n return ENV.engine.runKernel(backend => backend.cos($x), {$x}, grad);\n}\n\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print(); // or tf.tan(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction tan_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'tan');\n\n const grad = (dy: T) => {\n return {$x: () => dy.divStrict($x.cos().square())};\n };\n return ENV.engine.runKernel(backend => backend.tan($x), {$x}, grad);\n}\n\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print(); // or tf.asin(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction asin_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'asin');\n\n const grad = (dy: T) => {\n return {\n $x: () => dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt() as T)\n };\n };\n return ENV.engine.runKernel(backend => backend.asin($x), {$x}, grad);\n}\n\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print(); // or tf.acos(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction acos_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'acos');\n\n const grad = (dy: T) => {\n return {\n $x: () =>\n dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt() as T).neg()\n };\n };\n return ENV.engine.runKernel(backend => backend.acos($x), {$x}, grad);\n}\n\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print(); // or tf.atan(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atan_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'atan');\n\n const grad = (dy: T) => {\n return {$x: () => dy.div($x.toFloat().square().add(1)) as T};\n };\n return ENV.engine.runKernel(backend => backend.atan($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print(); // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sinh_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'sinh');\n\n const grad = (dy: T) => {\n return {$x: () => $x.toFloat().cosh().mulStrict(dy)};\n };\n return ENV.engine.runKernel(backend => backend.sinh($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print(); // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction cosh_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'cosh');\n\n const grad = (dy: T) => {\n return {$x: () => $x.toFloat().sinh().mulStrict(dy)};\n };\n return ENV.engine.runKernel(backend => backend.cosh($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print(); // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction tanh_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'tanh');\n\n const grad = (dy: T, saved: Tensor[]) => {\n const [y] = saved;\n return {$x: () => scalar(1).sub(y.square()).mulStrict(dy) as T};\n };\n return ENV.engine.runKernel(\n (backend, save) => save(backend.tanh($x)), {$x}, grad);\n}\n\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print(); // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction asinh_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'asinh');\n\n const grad = (dy: T) => {\n return {\n $x: () => dy.divStrict(scalar(1).add($x.toFloat().square()).sqrt() as T)\n };\n };\n return ENV.engine.runKernel(backend => backend.asinh($x), {$x}, grad);\n}\n\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print(); // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction acosh_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'acosh');\n\n const grad = (dy: T) => {\n return {$x: () => dy.divStrict($x.toFloat().square().sub(1).sqrt() as T)};\n };\n return ENV.engine.runKernel(backend => backend.acosh($x), {$x}, grad);\n}\n\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print(); // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atanh_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'atanh');\n\n const grad = (dy: T) => {\n return {$x: () => dy.div(scalar(1).sub($x.toFloat().square())) as T};\n };\n return ENV.engine.runKernel(backend => backend.atanh($x), {$x}, grad);\n}\n\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction erf_(x: T|TensorLike): T {\n let $x = convertToTensor(x, 'x', 'erf');\n util.assert(\n $x.dtype === 'int32' || $x.dtype === 'float32',\n 'Input dtype must be `int32` or `float32`.');\n\n if ($x.dtype === 'int32') {\n $x = $x.toFloat();\n }\n\n const grad = (dy: T) => {\n return {\n $x: () => dy.mul($x.square().neg().exp().mul(2 / Math.sqrt(Math.PI))) as T\n };\n };\n return ENV.engine.runKernel(backend => backend.erf($x), {$x}, grad);\n}\n\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print(); // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction step_(x: T|TensorLike, alpha = 0.0): T {\n const $x = convertToTensor(x, 'x', 'step');\n\n // TODO(manrajgrover): Return null for gradients when backprop supports\n // it.\n const grad = (dy: T) => {\n return {$x: () => zerosLike(dy)};\n };\n return ENV.engine.runKernel(backend => backend.step($x, alpha), {$x}, grad);\n}\n\nexport const abs = op({abs_});\nexport const acos = op({acos_});\nexport const acosh = op({acosh_});\nexport const asin = op({asin_});\nexport const asinh = op({asinh_});\nexport const atan = op({atan_});\nexport const atanh = op({atanh_});\nexport const ceil = op({ceil_});\nexport const clipByValue = op({clipByValue_});\nexport const cos = op({cos_});\nexport const cosh = op({cosh_});\nexport const erf = op({erf_});\nexport const exp = op({exp_});\nexport const expm1 = op({expm1_});\nexport const floor = op({floor_});\nexport const log = op({log_});\nexport const log1p = op({log1p_});\nexport const logSigmoid = op({logSigmoid_});\nexport const neg = op({neg_});\nexport const reciprocal = op({reciprocal_});\nexport const round = op({round_});\nexport const rsqrt = op({rsqrt_});\nexport const sigmoid = op({sigmoid_});\nexport const sign = op({sign_});\nexport const sin = op({sin_});\nexport const sinh = op({sinh_});\nexport const softplus = op({softplus_});\nexport const sqrt = op({sqrt_});\nexport const square = op({square_});\nexport const step = op({step_});\nexport const tan = op({tan_});\nexport const tanh = op({tanh_});\n","../src/ops/batchnorm.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {deprecationWarn, ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {tile} from './array_ops';\nimport {getReductionAxes} from './broadcast_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\nimport {rsqrt} from './unary_ops';\n\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(\n x: Tensor2D|TensorLike, mean: Tensor2D|Tensor1D|TensorLike,\n variance: Tensor2D|Tensor1D|TensorLike,\n offset?: Tensor2D|Tensor1D|TensorLike, scale?: Tensor2D|Tensor1D|TensorLike,\n varianceEpsilon = .001): Tensor2D {\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale: Tensor2D|Tensor1D;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset: Tensor2D|Tensor1D;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n util.assert(\n $x.rank === 2,\n `Error in batchNorm3D: x must be rank 3 but got rank ` +\n `${$x.rank}.`);\n util.assert(\n $mean.rank === 2 || $mean.rank === 1,\n `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n `got rank ${$mean.rank}.`);\n util.assert(\n $variance.rank === 2 || $variance.rank === 1,\n `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n `but got rank ${$variance.rank}.`);\n if ($scale != null) {\n util.assert(\n $scale.rank === 2 || $scale.rank === 1,\n `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n `but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n util.assert(\n $offset.rank === 2 || $offset.rank === 1,\n `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n `but got rank ${$offset.rank}.`);\n }\n\n return batchNorm_($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\n\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(\n x: Tensor3D|TensorLike, mean: Tensor3D|Tensor1D|TensorLike,\n variance: Tensor3D|Tensor1D|TensorLike,\n offset?: Tensor3D|Tensor1D|TensorLike, scale?: Tensor3D|Tensor1D|TensorLike,\n varianceEpsilon = .001): Tensor3D {\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale: Tensor3D|Tensor1D;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset: Tensor3D|Tensor1D;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n util.assert(\n $x.rank === 3,\n `Error in batchNorm3D: x must be rank 3 but got rank ` +\n `${$x.rank}.`);\n util.assert(\n $mean.rank === 3 || $mean.rank === 1,\n `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n `got rank ${$mean.rank}.`);\n util.assert(\n $variance.rank === 3 || $variance.rank === 1,\n `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n `but got rank ${$variance.rank}.`);\n if ($scale != null) {\n util.assert(\n $scale.rank === 3 || $scale.rank === 1,\n `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n `but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n util.assert(\n $offset.rank === 3 || $offset.rank === 1,\n `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n `but got rank ${$offset.rank}.`);\n }\n\n return batchNorm_($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\n\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(\n x: Tensor4D|TensorLike, mean: Tensor4D|Tensor1D|TensorLike,\n variance: Tensor4D|Tensor1D|TensorLike,\n offset?: Tensor4D|Tensor1D|TensorLike, scale?: Tensor4D|Tensor1D|TensorLike,\n varianceEpsilon = .001): Tensor4D {\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale: Tensor4D|Tensor1D;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset: Tensor4D|Tensor1D;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n util.assert(\n $x.rank === 4,\n `Error in batchNorm4D: x must be rank 4 but got rank ` +\n `${$x.rank}.`);\n util.assert(\n $mean.rank === 4 || $mean.rank === 1,\n `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n `got rank ${$mean.rank}.`);\n util.assert(\n $variance.rank === 4 || $variance.rank === 1,\n `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n `but got rank ${$variance.rank}.`);\n if ($scale != null) {\n util.assert(\n $scale.rank === 4 || $scale.rank === 1,\n `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n `but got rank ${$scale.rank}.`);\n }\n if ($offset != null) {\n util.assert(\n $offset.rank === 4 || $offset.rank === 1,\n `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n `but got rank ${$offset.rank}.`);\n }\n return batchNorm_($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\n\n/**\n * @deprecated Please use `tf.batchNorm` instead and note the positional\n * argument change of scale, offset, and varianceEpsilon.\n */\nfunction batchNormalization_(\n x: Tensor|Tensor1D|TensorLike, mean: Tensor|Tensor1D|TensorLike,\n variance: Tensor|Tensor1D|TensorLike, varianceEpsilon = .001,\n scale?: Tensor|Tensor1D|TensorLike,\n offset?: Tensor|Tensor1D|TensorLike): Tensor {\n warnDeprecation();\n return batchNorm_(x, mean, variance, offset, scale, varianceEpsilon);\n}\n\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n * - The same shape as the input.\n * - In the common case, the depth dimension is the last dimension of x, so\n * the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n * - `tf.batchNorm2d`\n * - `tf.batchNorm3d`\n * - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction batchNorm_(\n x: Tensor|Tensor1D|TensorLike, mean: Tensor|Tensor1D|TensorLike,\n variance: Tensor|Tensor1D|TensorLike,\n offset?: Tensor|Tensor1D|TensorLike,\n scale?: Tensor|Tensor1D|TensorLike, varianceEpsilon = .001): Tensor {\n const $x = convertToTensor(x, 'x', 'batchNorm');\n const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n let $scale: Tensor|Tensor1D;\n if (scale != null) {\n $scale = convertToTensor(scale, 'scale', 'batchNorm');\n }\n let $offset: Tensor|Tensor1D;\n if (offset != null) {\n $offset = convertToTensor(offset, 'offset', 'batchNorm');\n }\n\n util.assert(\n $mean.rank === $variance.rank,\n 'Batch normalization gradient requires mean and variance to have ' +\n 'equal ranks.');\n util.assert(\n $offset == null || $mean.rank === $offset.rank,\n 'Batch normalization gradient requires mean and offset to have ' +\n 'equal ranks.');\n util.assert(\n $scale == null || $mean.rank === $scale.rank,\n 'Batch normalization gradient requires mean and scale to have ' +\n 'equal ranks.');\n\n let x4D: Tensor4D;\n if ($x.rank === 0 || $x.rank === 1) {\n x4D = $x.as4D(1, 1, 1, $x.size);\n } else if ($x.rank === 2) {\n x4D = $x.as4D(1, 1, $x.shape[0], $x.shape[1]);\n } else if ($x.rank === 3) {\n x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]) as Tensor4D;\n } else {\n x4D = $x as Tensor4D;\n }\n\n const der = (dy: Tensor) => {\n const scaleValue = $scale == null ? scalar(1) : $scale;\n const reductionAxes = getReductionAxes($mean.shape, x4D.shape);\n const tileShape: number[] = [];\n if ($mean.rank === 1) {\n for (let i = 0; i < x4D.shape.length - 1; ++i) {\n tileShape.push(x4D.shape[i]);\n }\n tileShape.push(1);\n }\n\n const xMinusMean = $x.sub($mean);\n const dyTimesScaleValue = dy.mul(scaleValue);\n const oneOverSqrtVariance = rsqrt($variance.add(scalar(varianceEpsilon)));\n const minusHalfRCube = oneOverSqrtVariance.mul(oneOverSqrtVariance)\n .mul(oneOverSqrtVariance)\n .mul(scalar(-0.5));\n const derX = () => {\n if ($mean.rank === 1) {\n return dy\n .mul(tile(\n oneOverSqrtVariance.as4D(1, 1, 1, $mean.shape[0]), tileShape))\n .mul(scaleValue)\n .reshape($x.shape);\n } else {\n return dy.mul(oneOverSqrtVariance).mul(scaleValue).reshape($x.shape);\n }\n };\n const derMean = () => {\n let meanDer = oneOverSqrtVariance.mul(scalar(-1)).mul(dyTimesScaleValue);\n if ($mean.rank === 1) {\n meanDer = meanDer.sum(reductionAxes);\n }\n return meanDer.reshape($mean.shape);\n };\n const derVariance = () => {\n let varianceDer = minusHalfRCube.mul(xMinusMean).mul(dyTimesScaleValue);\n if ($mean.rank === 1) {\n varianceDer = varianceDer.sum(reductionAxes);\n }\n return varianceDer.reshape($mean.shape);\n };\n const derScale = () => {\n const xMinusMean2TimesRsqrt = xMinusMean.mul(oneOverSqrtVariance);\n let scaleDer = dy.mul(xMinusMean2TimesRsqrt);\n if ($mean.rank === 1) {\n scaleDer = scaleDer.sum(reductionAxes);\n }\n return scaleDer.reshape($mean.shape);\n };\n const derOffset = () => {\n let offsetDer = dy;\n if ($mean.rank === 1) {\n offsetDer = offsetDer.sum(reductionAxes);\n }\n return offsetDer.reshape($mean.shape);\n };\n return {\n $x: derX,\n $mean: derMean,\n $variance: derVariance,\n $scale: derScale,\n $offset: derOffset\n };\n };\n\n const res = ENV.engine.runKernel(\n backend => backend.batchNormalization(\n x4D, batchnormReshape4D($mean), batchnormReshape4D($variance),\n varianceEpsilon, batchnormReshape4D($scale),\n batchnormReshape4D($offset)),\n {$x, $mean, $variance, $scale, $offset}, der);\n return res.reshape($x.shape);\n}\n\nfunction batchnormReshape4D(x: Tensor): Tensor4D|Tensor1D {\n if (x == null) {\n return null;\n }\n if (x.rank === 0) {\n return x.as1D();\n } else if (x.rank === 1) {\n return x as Tensor1D;\n } else if (x.rank === 2) {\n return x.as4D(1, 1, x.shape[0], x.shape[1]);\n } else if (x.rank === 3) {\n return x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n }\n return x as Tensor4D;\n}\n\n/**\n * @deprecated Please use `tf.batchNorm2d` instead and note the positional\n * argument change of scale, offset, and varianceEpsilon.\n */\nfunction batchNormalization2d_(\n x: Tensor2D|TensorLike, mean: Tensor2D|Tensor1D|TensorLike,\n variance: Tensor2D|Tensor1D|TensorLike, varianceEpsilon = .001,\n scale?: Tensor2D|Tensor1D|TensorLike,\n offset?: Tensor2D|Tensor1D|TensorLike): Tensor2D {\n warnDeprecation();\n return batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon);\n}\n\n/**\n * @deprecated Please use `tf.batchNorm3d` instead and note the positional\n * argument change of scale, offset, and varianceEpsilon.\n */\nfunction batchNormalization3d_(\n x: Tensor3D|TensorLike, mean: Tensor3D|Tensor1D|TensorLike,\n variance: Tensor3D|Tensor1D|TensorLike, varianceEpsilon = .001,\n scale?: Tensor3D|Tensor1D|TensorLike,\n offset?: Tensor3D|Tensor1D|TensorLike): Tensor3D {\n warnDeprecation();\n return batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon);\n}\n\n/**\n * @deprecated Please use `tf.batchNorm4d` instead and note the positional\n * argument change of scale, offset, and varianceEpsilon.\n */\nfunction batchNormalization4d_(\n x: Tensor4D|TensorLike, mean: Tensor4D|Tensor1D|TensorLike,\n variance: Tensor4D|Tensor1D|TensorLike, varianceEpsilon = .001,\n scale?: Tensor4D|Tensor1D|TensorLike,\n offset?: Tensor4D|Tensor1D|TensorLike): Tensor4D {\n warnDeprecation();\n return batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon);\n}\n\nfunction warnDeprecation() {\n deprecationWarn(\n 'tf.batchNormalization() is going away. ' +\n 'Use tf.batchNorm() instead, and note the positional argument change ' +\n 'of scale, offset, and varianceEpsilon');\n}\n\nexport const batchNormalization2d = op({batchNormalization2d_});\nexport const batchNormalization3d = op({batchNormalization3d_});\nexport const batchNormalization4d = op({batchNormalization4d_});\nexport const batchNormalization = op({batchNormalization_});\n\nexport const batchNorm = op({batchNorm_});\nexport const batchNorm2d = op({batchNorm2d_});\nexport const batchNorm3d = op({batchNorm3d_});\nexport const batchNorm4d = op({batchNorm4d_});\n","../src/ops/conv_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\nexport type PadInfo = {\n top: number,\n left: number,\n right: number,\n bottom: number,\n type: string\n};\n\nexport type PadInfo3D = {\n top: number,\n left: number,\n right: number,\n bottom: number,\n front: number,\n back: number,\n type: string\n};\n\n/**\n * Information about the forward pass of a convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv2DInfo = {\n batchSize: number,\n inHeight: number,\n inWidth: number,\n inChannels: number,\n outHeight: number,\n outWidth: number,\n outChannels: number,\n dataFormat: 'channelsFirst'|'channelsLast',\n strideHeight: number,\n strideWidth: number,\n dilationHeight: number,\n dilationWidth: number,\n filterHeight: number,\n filterWidth: number,\n effectiveFilterHeight: number,\n effectiveFilterWidth: number,\n padInfo: PadInfo,\n inShape: [number, number, number, number],\n outShape: [number, number, number, number],\n filterShape: [number, number, number, number]\n};\n\nexport function computePool2DInfo(\n inShape: [number, number, number, number],\n filterSize: [number, number]|number, strides: number|[number, number],\n dilations: number|[number, number], pad: 'same'|'valid'|number,\n roundingMode?: 'floor'|'round'|'ceil',\n dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n\n let filterShape: [number, number, number, number];\n if (dataFormat === 'channelsLast') {\n filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n } else if (dataFormat === 'channelsFirst') {\n filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n } else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n\n return computeConv2DInfo(\n inShape, filterShape, strides, dilations, pad, roundingMode, false,\n dataFormat);\n}\n\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(\n inShape: [number, number, number, number],\n filterShape: [number, number, number, number],\n strides: number|[number, number], dilations: number|[number, number],\n pad: 'same'|'valid'|number, roundingMode?: 'floor'|'round'|'ceil',\n depthwise = false,\n dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n if (dataFormat === 'channelsLast') {\n [batchSize, inHeight, inWidth, inChannels] = inShape;\n } else if (dataFormat === 'channelsFirst') {\n [batchSize, inChannels, inHeight, inWidth] = inShape;\n } else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n\n const [filterHeight, filterWidth, , filterChannels] = filterShape;\n const [strideHeight, strideWidth] = parseTupleParam(strides);\n const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n\n const effectiveFilterHeight =\n getEffectiveFilterSize(filterHeight, dilationHeight);\n const effectiveFilterWidth =\n getEffectiveFilterSize(filterWidth, dilationWidth);\n const {padInfo, outHeight, outWidth} = getPadAndOutInfo(\n pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight,\n effectiveFilterWidth, roundingMode);\n\n const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n let outShape: [number, number, number, number];\n if (dataFormat === 'channelsFirst') {\n outShape = [batchSize, outChannels, outHeight, outWidth];\n } else if (dataFormat === 'channelsLast') {\n outShape = [batchSize, outHeight, outWidth, outChannels];\n }\n\n return {\n batchSize,\n dataFormat,\n inHeight,\n inWidth,\n inChannels,\n outHeight,\n outWidth,\n outChannels,\n padInfo,\n strideHeight,\n strideWidth,\n filterHeight,\n filterWidth,\n effectiveFilterHeight,\n effectiveFilterWidth,\n dilationHeight,\n dilationWidth,\n inShape,\n outShape,\n filterShape\n };\n}\n\n/**\n * Information about the forward pass of a 3D convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv3DInfo = {\n batchSize: number,\n inDepth: number,\n inHeight: number,\n inWidth: number,\n inChannels: number,\n outDepth: number,\n outHeight: number,\n outWidth: number,\n outChannels: number,\n dataFormat: 'channelsFirst'|'channelsLast',\n strideDepth: number,\n strideHeight: number,\n strideWidth: number,\n dilationDepth: number,\n dilationHeight: number,\n dilationWidth: number,\n filterDepth: number,\n filterHeight: number,\n filterWidth: number,\n padInfo: PadInfo3D,\n inShape: [number, number, number, number, number],\n outShape: [number, number, number, number, number],\n filterShape: [number, number, number, number, number]\n};\n\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(\n inShape: [number, number, number, number, number],\n filterShape: [number, number, number, number, number],\n strides: number|[number, number, number],\n dilations: number|[number, number, number], pad: 'same'|'valid',\n depthwise = false,\n dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv3DInfo {\n let [batchSize, inDepth, inHeight, inWidth, inChannels] =\n [-1, -1, -1, -1, -1];\n if (dataFormat === 'channelsLast') {\n [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n } else if (dataFormat === 'channelsFirst') {\n [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n } else {\n throw new Error(`Unknown dataFormat ${dataFormat}`);\n }\n\n const [filterDepth, filterHeight, filterWidth, , filterChannels] =\n filterShape;\n const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n const [dilationDepth, dilationHeight, dilationWidth] =\n parse3TupleParam(dilations);\n\n const effectiveFilterDepth =\n getEffectiveFilterSize(filterDepth, dilationDepth);\n const effectiveFilterHeight =\n getEffectiveFilterSize(filterHeight, dilationHeight);\n const effectiveFilterWidth =\n getEffectiveFilterSize(filterWidth, dilationWidth);\n const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(\n pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth,\n effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth);\n\n const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n let outShape: [number, number, number, number, number];\n if (dataFormat === 'channelsFirst') {\n outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n } else if (dataFormat === 'channelsLast') {\n outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n }\n\n return {\n batchSize,\n dataFormat,\n inDepth,\n inHeight,\n inWidth,\n inChannels,\n outDepth,\n outHeight,\n outWidth,\n outChannels,\n padInfo,\n strideDepth,\n strideHeight,\n strideWidth,\n filterDepth,\n filterHeight,\n filterWidth,\n dilationDepth,\n dilationHeight,\n dilationWidth,\n inShape,\n outShape,\n filterShape\n };\n}\n\nfunction computeOutputShape3D(\n inShape: [number, number, number], fieldSize: number, outDepth: number,\n stride: number, zeroPad?: number,\n roundingMode?: 'floor'|'round'|'ceil'): [number, number, number] {\n if (zeroPad == null) {\n zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n }\n const inputRows = inShape[0];\n const inputCols = inShape[1];\n\n const outputRows = conditionalRound(\n (inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n util.assert(\n util.isInt(outputRows),\n `The output # of rows (${outputRows}) must be an integer. Change the ` +\n `stride and/or zero pad parameters`);\n\n const outputCols = conditionalRound(\n (inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n util.assert(\n util.isInt(outputCols),\n `The output # of columns (${outputCols}) must be an integer. Change ` +\n `the stride and/or zero pad parameters`);\n\n return [outputRows, outputCols, outDepth];\n}\n\nexport function computeDefaultPad(\n inputShape: [number, number, number], fieldSize: number, stride: number,\n dilation = 1): number {\n const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n return Math.floor(\n (inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\n\nfunction parseTupleParam(param: number|[number, number]): [number, number] {\n return typeof param === 'number' ? [param, param] : param;\n}\n\nfunction parse3TupleParam(param: number|[number, number, number]):\n [number, number, number] {\n return typeof param === 'number' ? [param, param, param] : param;\n}\n\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize: number, dilation: number) {\n if (dilation <= 1) {\n return filterSize;\n }\n\n return filterSize + (filterSize - 1) * (dilation - 1);\n}\n\nfunction getPadAndOutInfo(\n pad: 'same'|'valid'|number, inHeight: number, inWidth: number,\n strideHeight: number, strideWidth: number, filterHeight: number,\n filterWidth: number, roundingMode?: 'floor'|'round'|'ceil'):\n {padInfo: PadInfo, outHeight: number, outWidth: number} {\n let padInfo: PadInfo;\n let outHeight: number;\n let outWidth: number;\n\n if (typeof pad === 'number') {\n const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n padInfo = {top: pad, bottom: pad, left: pad, right: pad, type: padType};\n const outShape = computeOutputShape3D(\n [inHeight, inWidth, 1], filterHeight, 1, strideHeight, pad,\n roundingMode);\n outHeight = outShape[0];\n outWidth = outShape[1];\n } else if (pad === 'same') {\n outHeight = Math.ceil(inHeight / strideHeight);\n outWidth = Math.ceil(inWidth / strideWidth);\n const padAlongHeight =\n Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n const padAlongWidth =\n Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n const top = Math.floor(padAlongHeight / 2);\n const bottom = padAlongHeight - top;\n const left = Math.floor(padAlongWidth / 2);\n const right = padAlongWidth - left;\n padInfo = {top, bottom, left, right, type: 'SAME'};\n } else if (pad === 'valid') {\n padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: 'VALID'};\n outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n } else {\n throw Error(`Unknown padding parameter: ${pad}`);\n }\n return {padInfo, outHeight, outWidth};\n}\n\nfunction get3DPadAndOutInfo(\n pad: 'same'|'valid', inDepth: number, inHeight: number, inWidth: number,\n strideDepth: number, strideHeight: number, strideWidth: number,\n filterDepth: number, filterHeight: number, filterWidth: number): {\n padInfo: PadInfo3D,\n outDepth: number,\n outHeight: number,\n outWidth: number\n} {\n let padInfo: PadInfo3D;\n let outDepth: number;\n let outHeight: number;\n let outWidth: number;\n\n if (pad === 'same') {\n outDepth = Math.ceil(inDepth / strideDepth);\n outHeight = Math.ceil(inHeight / strideHeight);\n outWidth = Math.ceil(inWidth / strideWidth);\n const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n const padAlongHeight =\n (outHeight - 1) * strideHeight + filterHeight - inHeight;\n const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n const front = Math.floor(padAlongDepth / 2);\n const back = padAlongDepth - front;\n const top = Math.floor(padAlongHeight / 2);\n const bottom = padAlongHeight - top;\n const left = Math.floor(padAlongWidth / 2);\n const right = padAlongWidth - left;\n\n padInfo = {top, bottom, left, right, front, back, type: 'SAME'};\n } else if (pad === 'valid') {\n padInfo = {\n top: 0,\n bottom: 0,\n left: 0,\n right: 0,\n front: 0,\n back: 0,\n type: 'VALID'\n };\n outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n } else {\n throw Error(`Unknown padding parameter: ${pad}`);\n }\n return {padInfo, outDepth, outHeight, outWidth};\n}\n\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(\n value: number, roundingMode?: 'floor'|'round'|'ceil') {\n if (!roundingMode) {\n return value;\n }\n switch (roundingMode) {\n case 'round':\n // used for Caffe Conv\n return Math.round(value);\n case 'ceil':\n // used for Caffe Pool\n return Math.ceil(value);\n case 'floor':\n return Math.floor(value);\n default:\n throw new Error(`Unknown roundingMode ${roundingMode}`);\n }\n}\n\nexport function tupleValuesAreOne(param: number|[number, number]): boolean {\n const [dimA, dimB] = parseTupleParam(param);\n return dimA === 1 && dimB === 1;\n}\n\nexport function eitherStridesOrDilationsAreOne(\n strides: number|[number, number],\n dilations: number|[number, number]): boolean {\n return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n","../src/ops/matmul.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print(); // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction matMul_(\n a: T|TensorLike, b: T|TensorLike, transposeA = false,\n transposeB = false): T {\n let $a = convertToTensor(a, 'a', 'matMul');\n let $b = convertToTensor(b, 'b', 'matMul');\n [$a, $b] = makeTypesMatch($a, $b);\n\n const innerShapeA =\n transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n const innerShapeB =\n transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n const outerShapeA =\n transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n const outerShapeB =\n transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n const outerDimsA = $a.shape.slice(0, -2);\n const outerDimsB = $b.shape.slice(0, -2);\n const batchDimA = util.sizeFromShape(outerDimsA);\n const batchDimB = util.sizeFromShape(outerDimsB);\n\n util.assert(\n $a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank,\n `Error in matMul: inputs must have the same rank of at least 2, ` +\n `got ranks ${$a.rank} and ${$b.rank}.`);\n\n util.assert(\n util.arraysEqual(outerDimsA, outerDimsB),\n `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n `${$b.shape} must match.`);\n\n util.assert(\n innerShapeA === innerShapeB,\n `Error in matMul: inner shapes (${innerShapeA}) and (` +\n `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n `${$b.shape} and transposeA=${transposeA}` +\n ` and transposeB=${transposeB} must match.`);\n\n const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n\n const a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :\n $a.as3D(batchDimA, outerShapeA, innerShapeA);\n const b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :\n $b.as3D(batchDimB, innerShapeB, outerShapeB);\n\n const grad = (dy: Tensor3D) => {\n if (!transposeA && !transposeB) {\n return {\n $a: () => dy.matMul(b3D, false, true),\n $b: () => a3D.matMul(dy, true, false)\n };\n } else if (!transposeA && transposeB) {\n return {\n $a: () => dy.matMul(b3D, false, false),\n $b: () => dy.matMul(a3D, true, false)\n };\n } else if (transposeA && !transposeB) {\n return {\n $a: () => b3D.matMul(dy, false, true),\n $b: () => a3D.matMul(dy, false, false)\n };\n } else {\n return {\n $a: () => b3D.matMul(dy, true, true),\n $b: () => dy.matMul(a3D, true, true)\n };\n }\n };\n\n const res = ENV.engine.runKernel(\n backend => backend.batchMatMul(a3D, b3D, transposeA, transposeB),\n {$a: a3D, $b: b3D}, grad);\n return res.reshape(outShape) as T;\n}\n\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction outerProduct_(\n v1: Tensor1D|TensorLike, v2: Tensor1D|TensorLike): Tensor2D {\n const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n\n util.assert(\n $v1.rank === 1 && $v2.rank === 1,\n `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n `${$v1.rank} and ${$v2.rank}.`);\n\n return $v1.as2D(-1, 1).matMul($v2.as2D(1, -1));\n}\n\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print(); // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction dot_(t1: Tensor|TensorLike, t2: Tensor|TensorLike): Tensor {\n const $t1 = convertToTensor(t1, 't1', 'dot');\n const $t2 = convertToTensor(t2, 't2', 'dot');\n util.assert(\n ($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2),\n `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n `${$t1.rank} and ${$t2.rank}.`);\n\n const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n\n util.assert(\n t1Inner === t2Inner,\n `Error in dot: inner dimensions of inputs must match, but got ` +\n `${t1Inner} and ${t2Inner}.`);\n\n if ($t1.rank === 1 && $t2.rank === 1) {\n return $t1.as2D(1, -1).matMul($t2.as2D(-1, 1)).asScalar();\n } else if ($t1.rank === 1 && $t2.rank === 2) {\n return $t1.as2D(1, -1).matMul($t2.as2D($t2.shape[0], $t2.shape[1])).as1D();\n } else if ($t1.rank === 2 && $t2.rank === 1) {\n return $t1.matMul($t2.as2D(-1, 1)).as1D();\n } else {\n return $t1.matMul($t2.as2D($t2.shape[0], $t2.shape[1]));\n }\n}\n\nexport const matMul = op({matMul_});\nexport const dot = op({dot_});\nexport const outerProduct = op({outerProduct_});\n","../src/ops/conv.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport {matMul} from './matmul';\nimport {op} from './operation';\n\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n * `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n * `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n * each step.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n * the data is stored in the order of [batch, in_width, in_channels]. Only\n * \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n * atrous convolution. Defaults to `1`. If it is greater than 1, then\n * stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n * dimensions if pad is a number. If none is provided, it will not round\n * and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv1d_(\n x: T|TensorLike, filter: Tensor3D|TensorLike, stride: number,\n pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n const $x = convertToTensor(x, 'x', 'conv1d');\n const $filter = convertToTensor(filter, 'filter', 'conv1d');\n\n let x3D = $x as Tensor3D;\n let reshapedTo3D = false;\n if ($x.rank === 2) {\n reshapedTo3D = true;\n x3D = $x.as3D(1, $x.shape[0], $x.shape[1]);\n }\n\n util.assert(\n x3D.rank === 3,\n `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n util.assert(\n $filter.rank === 3,\n `Error in conv1d: filter must be rank 3, but got rank ` +\n `${$filter.rank}.`);\n if (dimRoundingMode != null) {\n util.assert(\n util.isInt(pad as number),\n `Error in conv1d: pad must be an integer when using, ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n }\n\n util.assert(\n x3D.shape[2] === $filter.shape[1],\n `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n `input depth for filter ${$filter.shape[1]}.`);\n util.assert(\n conv_util.eitherStridesOrDilationsAreOne(stride, dilation),\n 'Error in conv1D: Either stride or dilation must be 1. ' +\n `Got stride ${stride} and dilation '${dilation}'`);\n util.assert(\n dataFormat === 'NWC',\n `Error in conv1d: got dataFormat of ${\n dataFormat} but only NWC is currently supported.`);\n\n const filter4D =\n $filter.as4D(1, $filter.shape[0], $filter.shape[1], $filter.shape[2]);\n const input4D = x3D.as4D(x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]);\n const strides: [number, number] = [1, stride];\n const dilations: [number, number] = [1, dilation];\n\n const conv2dDataFormat = 'NHWC';\n\n const res = conv2d(\n input4D, filter4D, strides, pad, conv2dDataFormat, dilations,\n dimRoundingMode);\n\n if (reshapedTo3D) {\n return res.as2D(res.shape[2], res.shape[3]) as T;\n }\n return res.as3D(res.shape[0], res.shape[2], res.shape[3]) as T;\n}\n\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n * dimensions if pad is a number. If none is provided, it will not round\n * and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv2d_(\n x: T|TensorLike, filter: Tensor4D|TensorLike,\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n dilations: [number, number]|number = [1, 1],\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n const $x = convertToTensor(x, 'x', 'conv2d');\n const $filter = convertToTensor(filter, 'filter', 'conv2d');\n\n let x4D = $x as Tensor4D;\n let reshapedTo4D = false;\n\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n }\n util.assert(\n x4D.rank === 4,\n `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n util.assert(\n $filter.rank === 4,\n `Error in conv2d: filter must be rank 4, but got rank ` +\n `${$filter.rank}.`);\n if (dimRoundingMode != null) {\n util.assert(\n util.isInt(pad as number),\n `Error in conv2d: pad must be an integer when using, ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n }\n\n util.assert(\n x4D.shape[3] === $filter.shape[2],\n `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n `input depth for filter ${$filter.shape[2]}.`);\n util.assert(\n conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n 'Error in conv2D: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n util.assert(\n dataFormat === 'NHWC',\n `Error in conv2d: got dataFormat of ${\n dataFormat} but only NHWC is currently supported.`);\n\n const convInfo = conv_util.computeConv2DInfo(\n x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n\n let res: Tensor3D|Tensor4D;\n if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {\n const x2d = x4D.reshape([-1, convInfo.inChannels]) as Tensor2D;\n const w2d = $filter.reshape([convInfo.inChannels, convInfo.outChannels]) as\n Tensor2D;\n\n res = matMul(x2d, w2d).reshape(convInfo.outShape);\n } else {\n const grad = (dy: Tensor4D) => {\n util.assert(\n conv_util.tupleValuesAreOne(dilations),\n 'Error in gradient of conv2D: dilation rates greater than 1 are not' +\n `yet supported in gradients. Got dilations '${dilations}'`);\n\n return {\n x: () => conv2dDerInput_(x4D.shape, dy, $filter, strides, pad),\n $filter: () => conv2dDerFilter_(x4D, dy, $filter.shape, strides, pad)\n };\n };\n\n res = ENV.engine.runKernel(\n backend => backend.conv2d(x4D, $filter, convInfo), {x: x4D, $filter},\n grad);\n }\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n * `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * @param dimRoundingMode The rounding mode used when computing output\n * dimensions if pad is a number. If none is provided, it will not round\n * and error if the output is of fractional size.\n */\nfunction conv2dDerInput_(\n xShape: [number, number, number, number]|[number, number, number], dy: T,\n filter: Tensor4D, strides: [number, number]|number,\n pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n util.assert(\n xShape.length === dy.rank,\n `Length of inShape ` +\n `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n let xShape4D = xShape as [number, number, number, number];\n let dy4D = dy as Tensor4D;\n let reshapedTo4D = false;\n if (dy.rank === 3) {\n reshapedTo4D = true;\n dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n }\n\n const inDepth = xShape4D[3];\n const outDepth = dy4D.shape[3];\n util.assert(\n xShape4D.length === 4,\n `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n `${xShape4D.length}.`);\n util.assert(\n dy4D.rank === 4,\n `Error in conv2dDerInput: dy must be rank 4, but got ` +\n `rank ${dy4D.rank}`);\n util.assert(\n filter.rank === 4,\n `Error in conv2dDerInput: filter must be rank 4, but got ` +\n `rank ${filter.rank}`);\n util.assert(\n inDepth === filter.shape[2],\n `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n `match input depth for filter ${filter.shape[2]}.`);\n util.assert(\n outDepth === filter.shape[3],\n `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n `match output depth for filter ${filter.shape[3]}.`);\n if (dimRoundingMode != null) {\n util.assert(\n util.isInt(pad as number),\n `Error in conv2dDerInput: pad must be an integer when using, ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n }\n\n const dilations = 1;\n\n const grad = (ddx: Tensor4D) => {\n const dataFormat = 'NHWC';\n return {\n dy4D: () => conv2d(\n ddx, filter, strides, pad, dataFormat, dilations, dimRoundingMode),\n filter: () => conv2dDerFilter(\n ddx, dy4D, filter.shape, strides, pad, dimRoundingMode)\n };\n };\n\n const convInfo = conv_util.computeConv2DInfo(\n xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode);\n const res = ENV.engine.runKernel(\n backend => backend.conv2dDerInput(dy4D, filter, convInfo), {dy4D, filter},\n grad);\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n * [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n * [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n * used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n * rounding mode used when computing output dimensions if pad is a\n * number. If none is provided, it will not round and error if the output\n * is of fractional size.\n */\nfunction conv2dDerFilter_(\n x: T, dy: T, filterShape: [number, number, number, number],\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n let x4D = x as Tensor4D;\n if (x.rank === 3) {\n x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n }\n let dy4D = dy as Tensor4D;\n if (dy4D.rank === 3) {\n dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n }\n util.assert(\n x4D.rank === 4,\n `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n `${x4D.shape}.`);\n util.assert(\n dy4D.rank === 4,\n `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n `${dy4D.shape}.`);\n util.assert(\n filterShape.length === 4,\n `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n `${filterShape}.`);\n util.assert(\n x4D.shape[3] === filterShape[2],\n `Error in conv2dDerFilter: depth of input ${x4D.shape[3]}) must ` +\n `match input depth in filter (${filterShape[2]}.`);\n util.assert(\n dy4D.shape[3] === filterShape[3],\n `Error in conv2dDerFilter: depth of dy (${dy4D.shape[3]}) must ` +\n `match output depth for filter (${filterShape[3]}).`);\n if (dimRoundingMode != null) {\n util.assert(\n util.isInt(pad as number),\n `Error in conv2dDerFilter: pad must be an integer when using, ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n }\n\n const dilations = 1;\n\n const convInfo = conv_util.computeConv2DInfo(\n x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode);\n return ENV.engine.runKernel(\n backend => backend.conv2dDerFilter(x4D, dy4D, convInfo), {x4D, dy4D});\n}\n\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n * `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n * `[filterHeight, filterWidth, outDepth, inDepth]`.\n * `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n * `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n * `[strideHeight, strideWidth]`.\n * @param pad The type of padding algorithm used in the non-transpose version\n * of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n * dimensions if pad is a number. If none is provided, it will not round\n * and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv2dTranspose_(\n x: T|TensorLike, filter: Tensor4D|TensorLike,\n outputShape: [number, number, number, number]|[number, number, number],\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n\n return conv2dDerInput_(\n outputShape, $x, $filter, strides, pad, dimRoundingMode);\n}\n\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n * https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n * dimensions if pad is a number. If none is provided, it will not round\n * and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction depthwiseConv2d_(\n x: T|TensorLike, filter: Tensor4D|TensorLike,\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n dilations: [number, number]|number = [1, 1],\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n\n let x4D = $x as Tensor4D;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n }\n util.assert(\n x4D.rank === 4,\n `Error in depthwiseConv2d: input must be rank 4, but got ` +\n `rank ${x4D.rank}.`);\n util.assert(\n $filter.rank === 4,\n `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n `${$filter.rank}.`);\n util.assert(\n x4D.shape[3] === $filter.shape[2],\n `Error in depthwiseConv2d: number of input channels ` +\n `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n `filter ${$filter.shape[2]}.`);\n if (dilations == null) {\n dilations = [1, 1];\n }\n util.assert(\n conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n 'Error in depthwiseConv2d: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n\n if (dimRoundingMode != null) {\n util.assert(\n util.isInt(pad as number),\n `Error in depthwiseConv2d: pad must be an integer when using, ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n }\n\n const convInfo = conv_util.computeConv2DInfo(\n x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,\n true /* depthwise */);\n\n const grad = (dy: Tensor4D) => {\n util.assert(\n conv_util.tupleValuesAreOne(dilations),\n 'Error in gradient of depthwiseConv2d: dilation rates greater than ' +\n `1 are not yet supported. Got dilations '${dilations}'`);\n return {\n x: () => depthwiseConv2dDerInput(x4D.shape, dy, $filter, convInfo),\n $filter: () => depthwiseConv2dDerFilter(x4D, dy, $filter.shape, convInfo),\n };\n };\n\n const res = ENV.engine.runKernel(\n backend => backend.depthwiseConv2D(x4D, $filter, convInfo),\n {x: x4D, $filter}, grad);\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n * https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n * the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n * `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n * the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * height, width, channels]. Only \"NHWC\" is currently supported.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction separableConv2d_(\n x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike,\n pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n const $x = convertToTensor(x, 'x', 'separableConv2d');\n const $depthwiseFilter =\n convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n const $pointwiseFilter =\n convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n\n let x4D = $x as Tensor4D;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n }\n\n if (dataFormat === 'NCHW') {\n throw new Error(\n 'separableConv2d currently does not support dataFormat NCHW; only ' +\n 'NHWC is supported');\n }\n\n util.assert(\n x4D.rank === 4,\n `Error in separableConv2d: input must be rank 4, but got ` +\n `rank ${x4D.rank}.`);\n util.assert(\n $depthwiseFilter.rank === 4,\n `Error in separableConv2d: depthwise filter must be rank 4, but got ` +\n `rank ${$depthwiseFilter.rank}.`);\n util.assert(\n $pointwiseFilter.rank === 4,\n `Error in separableConv2d: pointwise filter must be rank 4, but got ` +\n `rank ${$depthwiseFilter.rank}.`);\n util.assert(\n $pointwiseFilter.shape[0] === 1,\n `Error in separableConv2d: the first dimension of pointwise filter ` +\n ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n util.assert(\n $pointwiseFilter.shape[1] === 1,\n `Error in separableConv2d: the second dimension of pointwise filter ` +\n ` must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n\n const inChannels = $depthwiseFilter.shape[2];\n const channelMultiplier = $depthwiseFilter.shape[3];\n util.assert(\n $pointwiseFilter.shape[2] === inChannels * channelMultiplier,\n `Error in separableConv2d: the third dimension of pointwise filter ` +\n `must be ${inChannels * channelMultiplier}, ` +\n `but got ${$pointwiseFilter.shape[2]}.`);\n\n const depthwise = depthwiseConv2d(\n x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n const pointwiseStride = 1;\n const res =\n conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\nfunction parseTupleParam(\n param: number|[number, number]|[number, number, number]):\n [number, number, number] {\n if (typeof param === 'number') {\n return [param, param, param];\n }\n if (param.length === 2) {\n return [param[0], param[1], 1];\n }\n return param;\n}\n\nfunction tupleValuesAreOne(\n param: number|[number, number]|[number, number, number]): boolean {\n const [dimA, dimB, dimC] = parseTupleParam(param);\n return dimA === 1 && dimB === 1 && dimC === 1;\n}\n\nfunction eitherStridesOrDilationsAreOne(\n strides: number|[number, number]|[number, number, number],\n dilations: number|[number, number]|[number, number, number]): boolean {\n return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n\nfunction depthwiseConv2dDerInput(\n xShape: [number, number, number, number]|[number, number, number], dy: T,\n filter: Tensor4D, convInfo: conv_util.Conv2DInfo): T {\n let dy4D = dy as Tensor4D;\n let reshapedTo4D = false;\n if (dy.rank === 3) {\n reshapedTo4D = true;\n dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n }\n const res = ENV.engine.runKernel(\n backend => backend.depthwiseConv2DDerInput(dy4D, filter, convInfo),\n {dy4D});\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\nfunction depthwiseConv2dDerFilter(\n x: T, dy: T, filterShape: [number, number, number, number],\n convInfo: conv_util.Conv2DInfo): Tensor4D {\n let x4D = x as Tensor4D;\n if (x.rank === 3) {\n x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n }\n let dy4D = dy as Tensor4D;\n if (dy4D.rank === 3) {\n dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n }\n return ENV.engine.runKernel(\n backend => backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo),\n {x4D, dy4D});\n}\n\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n * `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n * `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n * inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n * \"NHWC\". Specify the data format of the input and output data. With the\n * default format \"NHWC\", the data is stored in the order of: [batch,\n * depth, height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n * dilationWidth]` in which we sample input values across the height\n * and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n * If `dilations` is a single number, then\n * `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n * than 1, then all values of `strides` must be 1.\n */\n\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv3d_(\n x: T|TensorLike, filter: Tensor5D|TensorLike,\n strides: [number, number, number]|number, pad: 'valid'|'same',\n dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n dilations: [number, number, number]|number = [1, 1, 1]): T {\n const $x = convertToTensor(x, 'x', 'conv3d');\n const $filter = convertToTensor(filter, 'filter', 'conv3d');\n\n let x5D = $x as Tensor5D;\n let reshapedTo5D = false;\n\n if ($x.rank === 4) {\n reshapedTo5D = true;\n x5D = $x.as5D(1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]);\n }\n util.assert(\n x5D.rank === 5,\n `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n util.assert(\n $filter.rank === 5,\n `Error in conv3d: filter must be rank 5, but got rank ` +\n `${$filter.rank}.`);\n util.assert(\n x5D.shape[4] === $filter.shape[3],\n `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n `input depth for filter ${$filter.shape[3]}.`);\n util.assert(\n eitherStridesOrDilationsAreOne(strides, dilations),\n 'Error in conv3D: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n util.assert(\n dataFormat === 'NHWC',\n `Error in conv3d: got dataFormat of ${\n dataFormat} but only NHWC is currently supported.`);\n\n const convInfo = conv_util.computeConv3DInfo(\n x5D.shape, $filter.shape, strides, dilations, pad);\n\n const grad = (dy: Tensor5D) => {\n util.assert(\n tupleValuesAreOne(dilations),\n 'Error in gradient of conv3D: dilation rates greater than 1 are not' +\n `yet supported in gradients. Got dilations '${dilations}'`);\n\n return {\n x: () => conv3dDerInput_(x5D.shape, dy, $filter, strides, pad),\n $filter: () => conv3dDerFilter_(x5D, dy, $filter.shape, strides, pad)\n };\n };\n\n const res = ENV.engine.runKernel(\n backend => backend.conv3d(x5D, $filter, convInfo), {x: x5D, $filter},\n grad);\n if (reshapedTo5D) {\n return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]) as\n T;\n }\n return res as T;\n}\n\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n * `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n * `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n */\nfunction conv3dDerInput_(\n xShape:\n [number, number, number, number,\n number]|[number, number, number, number],\n dy: T, filter: Tensor5D, strides: [number, number, number]|number,\n pad: 'valid'|'same'): T {\n util.assert(\n xShape.length === dy.rank,\n `Length of inShape ` +\n `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n let xShape5D = xShape as [number, number, number, number, number];\n let dy5D = dy as Tensor5D;\n let reshapedTo5D = false;\n if (dy.rank === 4) {\n reshapedTo5D = true;\n dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);\n xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n }\n\n const inDepth = xShape5D[4];\n const outDepth = dy5D.shape[4];\n util.assert(\n xShape5D.length === 5,\n `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n `${xShape5D.length}.`);\n util.assert(\n dy5D.rank === 5,\n `Error in conv3dDerInput: dy must be rank 5, but got ` +\n `rank ${dy5D.rank}`);\n util.assert(\n filter.rank === 5,\n `Error in conv3dDerInput: filter must be rank 5, but got ` +\n `rank ${filter.rank}`);\n util.assert(\n inDepth === filter.shape[3],\n `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n `match input depth for filter ${filter.shape[3]}.`);\n util.assert(\n outDepth === filter.shape[4],\n `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n `match output depth for filter ${filter.shape[4]}.`);\n\n const dilations = 1;\n\n const convInfo = conv_util.computeConv3DInfo(\n xShape5D, filter.shape, strides, dilations, pad);\n const res = ENV.engine.runKernel(\n backend => backend.conv3dDerInput(dy5D, filter, convInfo), {dy5D});\n if (reshapedTo5D) {\n return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]) as\n T;\n }\n return res as T;\n}\n\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n * [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n * assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n * [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n * assumed.\n * @param filterShape The shape of the filter, length 5,\n * [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n * used in the forward prop of the op.\n */\nfunction conv3dDerFilter_(\n x: T, dy: T, filterShape: [number, number, number, number, number],\n strides: [number, number, number]|number, pad: 'valid'|'same'): Tensor5D {\n let x5D = x as Tensor5D;\n if (x.rank === 4) {\n x5D = x.as5D(1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]);\n }\n let dy5D = dy as Tensor5D;\n if (dy5D.rank === 4) {\n dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);\n }\n util.assert(\n x5D.rank === 5,\n `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n `${x5D.shape}.`);\n util.assert(\n dy5D.rank === 5,\n `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n `${dy5D.shape}.`);\n util.assert(\n filterShape.length === 5,\n `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n `${filterShape}.`);\n util.assert(\n x5D.shape[4] === filterShape[3],\n `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n `match input depth in filter (${filterShape[3]}.`);\n util.assert(\n dy5D.shape[4] === filterShape[4],\n `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n `match output depth for filter (${filterShape[4]}).`);\n\n const dilations = 1;\n\n const convInfo = conv_util.computeConv3DInfo(\n x5D.shape, filterShape, strides, dilations, pad);\n return ENV.engine.runKernel(\n backend => backend.conv3dDerFilter(x5D, dy5D, convInfo), {x5D, dy5D});\n}\n\nexport const conv1d = op({conv1d_});\nexport const conv2d = op({conv2d_});\nexport const conv3d = op({conv3d_});\nexport const conv2dDerFilter = op({conv2dDerFilter_});\nexport const depthwiseConv2d = op({depthwiseConv2d_});\nexport const separableConv2d = op({separableConv2d_});\nexport const conv2dTranspose = op({conv2dTranspose_});\n","../src/ops/reverse.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x: Tensor1D|TensorLike): Tensor1D {\n const $x = convertToTensor(x, 'x', 'reverse');\n util.assert($x.rank === 1, `Error in reverse1D: x must be rank 1 but got\n rank ${$x.rank}.`);\n return reverse($x, 0);\n}\n\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x: Tensor2D|TensorLike, axis?: number|number[]): Tensor2D {\n const $x = convertToTensor(x, 'x', 'reverse');\n util.assert($x.rank === 2, `Error in reverse2D: x must be rank 2 but got\n rank ${$x.rank}.`);\n return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x: Tensor3D|TensorLike, axis?: number|number[]): Tensor3D {\n const $x = convertToTensor(x, 'x', 'reverse');\n util.assert($x.rank === 3, `Error in reverse3D: x must be rank 3 but got\n rank ${$x.rank}.`);\n return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x: Tensor4D|TensorLike, axis?: number|number[]): Tensor4D {\n const $x = convertToTensor(x, 'x', 'reverse');\n util.assert($x.rank === 4, `Error in reverse4D: x must be rank 4 but got\n rank ${$x.rank}.`);\n return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n * - `tf.reverse1d`\n * - `tf.reverse2d`\n * - `tf.reverse3d`\n * - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n * range [-rank(x), rank(x)). Defaults to all axes.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction reverse_(\n x: T|TensorLike, axis?: number|number[]): T {\n const $x = convertToTensor(x, 'x', 'reverse');\n\n if ($x.rank === 0) {\n return $x.clone();\n }\n const axes = util.parseAxisParam(axis, $x.shape);\n const grad = (dy: T) => {\n return {$x: () => dy.reverse(axes)};\n };\n const res =\n ENV.engine.runKernel(backend => backend.reverse($x, axes), {$x}, grad);\n return res.reshapeAs($x);\n}\n\nexport const reverse = op({reverse_});\nexport const reverse1d = op({reverse1d_});\nexport const reverse2d = op({reverse2d_});\nexport const reverse3d = op({reverse3d_});\nexport const reverse4d = op({reverse4d_});\n","../src/ops/pool.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {batchToSpaceND, spaceToBatchND} from './array_ops';\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\n\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n * dimensions if pad is a number. If none is provided, it will not round\n * and error if the output is of fractional size.\n */\nfunction maxPoolImpl_(\n x: T|TensorLike, filterSize: [number, number]|number,\n strides: [number, number]|number, dilations: [number, number]|number,\n pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n const $x = convertToTensor(x, 'x', 'maxPool');\n\n let x4D = $x as Tensor4D;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n }\n if (dilations == null) {\n dilations = [1, 1];\n }\n util.assert(\n x4D.rank === 4,\n `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n util.assert(\n conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n 'Error in maxPool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n if (dimRoundingMode != null) {\n util.assert(\n util.isInt(pad as number),\n `Error in maxPool: pad must be an integer when using, ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n }\n const convInfo = conv_util.computePool2DInfo(\n x4D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n\n const grad = (dy: Tensor4D, saved: Tensor[]) => {\n const [y4D] = saved;\n return {\n x: () => maxPoolBackprop(\n dy, x4D, y4D as Tensor4D, filterSize, strides, dilations, pad)\n };\n };\n\n const res = ENV.engine.runKernel(\n (backend, save) => save(backend.maxPool(x4D, convInfo)), {x: x4D}, grad);\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n * dimensions if pad is a number. If none is provided, it will not round\n * and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction maxPool_(\n x: T|TensorLike, filterSize: [number, number]|number,\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n return maxPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);\n}\n\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n * dimensions if pad is a number. If none is provided, it will not round\n * and error if the output is of fractional size.\n */\nfunction avgPoolImpl_(\n x: T|TensorLike, filterSize: [number, number]|number,\n strides: [number, number]|number, dilations: [number, number]|number,\n pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n if (dilations == null) {\n dilations = [1, 1];\n }\n util.assert(\n conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n 'Error in avgPool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n let x4D = $x as Tensor4D;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n }\n util.assert(\n x4D.rank === 4,\n `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n if (dimRoundingMode != null) {\n util.assert(\n util.isInt(pad as number),\n `Error in avgPool: pad must be an integer when using, ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n }\n\n const convInfo = conv_util.computePool2DInfo(\n x4D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n\n const grad = (dy: Tensor4D) => {\n return {\n x: () => avgPoolBackprop(dy, x4D, filterSize, strides, dilations, pad)\n };\n };\n let res = ENV.engine.runKernel(\n backend => backend.avgPool(x4D, convInfo), {x: x4D}, grad);\n res = res.cast($x.dtype);\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n * dimensions if pad is a number. If none is provided, it will not round\n * and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction avgPool_(\n x: T|TensorLike, filterSize: [number, number]|number,\n strides: [number, number]|number, pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n return avgPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);\n}\n\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n * - `same` and stride 1: output will be of same size as input,\n * regardless of filter size.\n * - `valid`: output will be smaller than input if filter is larger\n * than 1x1.\n * - For more info, see this guide:\n * [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n * https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n * in which we sample input values across the height and width dimensions\n * in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n * number, then `dilationHeight == dilationWidth`. If it is greater than\n * 1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction pool_(\n input: T|TensorLike, windowShape: [number, number]|number,\n poolingType: 'avg'|'max', pad: 'valid'|'same'|number,\n dilations?: [number, number]|number, strides?: [number, number]|number) {\n if (dilations == null) {\n dilations = [1, 1];\n }\n if (strides == null) {\n strides = 1;\n }\n if (pad === 0) {\n pad = 'valid';\n }\n const $x = convertToTensor(input, 'x', 'maxPool');\n let x4D = $x as Tensor4D;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n }\n util.assert(\n conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n 'Error in pool: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n const convInfo = conv_util.computePool2DInfo(\n x4D.shape, windowShape, strides, dilations, pad);\n const dilation: [number, number] =\n [convInfo.dilationHeight, convInfo.dilationWidth];\n\n // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n // tslint:disable-next-line:max-line-length\n // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n\n let basePadding: number[][];\n if (pad === 'same') {\n basePadding = withSpaceToBatchBasePaddings(\n [convInfo.filterHeight, convInfo.filterWidth], dilation);\n } else {\n basePadding = [[0, 0], [0, 0]];\n }\n const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings(\n [convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n const convertedPad = isDilationOne ? pad : 'valid';\n const convertedX =\n isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n const forwardOp = poolingType === 'avg' ?\n () => avgPoolImpl_(\n convertedX, windowShape, strides, 1 /* dilation */, convertedPad) :\n () => maxPoolImpl_(\n convertedX, windowShape, strides, 1 /* dilation */, convertedPad);\n const y = forwardOp();\n const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\n/**\n * Computes the backprop of a max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n * [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n * [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n * [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n * used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n * rounding mode used when computing output dimensions if pad is a\n * number. If none is provided, it will not round and error if the output\n * is of fractional size.\n */\nfunction maxPoolBackprop(\n dy: Tensor4D|TensorLike, input: Tensor4D|TensorLike,\n output: Tensor4D|TensorLike, filterSize: [number, number]|number,\n strides: [number, number]|number, dilations: [number, number]|number,\n pad: 'valid'|'same'|number,\n dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n util.assert(\n $input.rank === $dy.rank,\n `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n if (dilations == null) {\n dilations = [1, 1];\n }\n util.assert(\n conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n 'Error in maxPoolBackProp: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n\n util.assert(\n $dy.rank === 4,\n `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n `${$dy.rank}.`);\n util.assert(\n $input.rank === 4,\n `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n `${$input.rank}.`);\n if (dimRoundingMode != null) {\n util.assert(\n util.isInt(pad as number),\n `Error in maxPoolBackprop: pad must be an integer when using, ` +\n `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n }\n\n const convInfo = conv_util.computePool2DInfo(\n $input.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n const res = ENV.engine.runKernel(\n backend => backend.maxPoolBackprop($dy, $input, $output, convInfo),\n {$dy, $input});\n return res;\n}\n\n/**\n * Computes the backprop of an avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n * [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n * [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n * `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n * `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n * used in the forward prop of the op.\n */\nfunction avgPoolBackprop(\n dy: T|TensorLike, input: T|TensorLike, filterSize: [number, number]|number,\n strides: [number, number]|number, dilations: [number, number]|number,\n pad: 'valid'|'same'|number): T {\n const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n util.assert(\n $input.rank === $dy.rank,\n `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n if (dilations == null) {\n dilations = [1, 1];\n }\n util.assert(\n conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n 'Error in avgPoolBackprop: Either strides or dilations must be 1. ' +\n `Got strides ${strides} and dilations '${dilations}'`);\n\n let input4D = $input as Tensor4D;\n let dy4D = $dy as Tensor4D;\n let reshapedTo4D = false;\n if ($input.rank === 3) {\n reshapedTo4D = true;\n input4D = $input.as4D(1, $input.shape[0], $input.shape[1], $input.shape[2]);\n dy4D = $dy.as4D(1, $dy.shape[0], $dy.shape[1], $dy.shape[2]);\n }\n\n util.assert(\n dy4D.rank === 4,\n `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n `${dy4D.rank}.`);\n util.assert(\n input4D.rank === 4,\n `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n `${input4D.rank}.`);\n\n const convInfo = conv_util.computePool2DInfo(\n input4D.shape, filterSize, strides, dilations, pad);\n const res = ENV.engine.runKernel(\n backend => backend.avgPoolBackprop(dy4D, input4D, convInfo),\n {dy4D, input4D});\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(\n inputShape: [number, number], blockShape: [number, number],\n basePadding: number[][]) {\n const padStart = basePadding.map(b => b[0]);\n const origPadEnd = basePadding.map(b => b[1]);\n const fullInputShape = inputShape.concat(padStart, origPadEnd);\n const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n return [paddings, crops];\n}\n\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(\n filterShape: [number, number], dilation: [number, number]) {\n // Spatial dimensions of the filters and the upsampled filters in which we\n // introduce (rate - 1) zeros between consecutive filter values.\n const dilatedFilterShape = filterShape.map((s, i) => {\n return s + (s - 1) * (dilation[i] - 1);\n });\n const padExtraShape = dilatedFilterShape.map(s => s - 1);\n\n // When padding is odd, we pad more at end, following the same\n // convention as conv2d.\n const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n return padExtraShape.map((_, i) => {\n return [padExtraStart[i], padExtraEnd[i]];\n });\n}\n\nexport const maxPool = op({maxPool_});\nexport const avgPool = op({avgPool_});\nexport const pool = op({pool_});\n","../src/ops/slice.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport * as slice_util from './slice_util';\n\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(\n x: Tensor1D|TensorLike, begin: number, size: number): Tensor1D {\n const $x = convertToTensor(x, 'x', 'slice1d');\n util.assert(\n $x.rank === 1,\n `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, [begin], [size]);\n}\n\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(\n x: Tensor2D|TensorLike, begin: [number, number],\n size: [number, number]): Tensor2D {\n const $x = convertToTensor(x, 'x', 'slice2d');\n util.assert(\n $x.rank === 2,\n `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\n\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(\n x: Tensor3D|TensorLike, begin: [number, number, number],\n size: [number, number, number]): Tensor3D {\n const $x = convertToTensor(x, 'x', 'slice3d');\n util.assert(\n $x.rank === 3,\n `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\n\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(\n x: Tensor4D|TensorLike, begin: [number, number, number, number],\n size: [number, number, number, number]): Tensor4D {\n const $x = convertToTensor(x, 'x', 'slice4d');\n util.assert(\n $x.rank === 4,\n `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n return slice($x, begin, size);\n}\n\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n * - `tf.slice1d`\n * - `tf.slice2d`\n * - `tf.slice3d`\n * - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n * less than the rank of x - the rest of the axes will have implicit 0 as\n * start. Can also be a single number, in which case it specifies the\n * first axis.\n * @param size The size of the slice. The length can be less than the rank of\n * x - the rest of the axes will have implicit -1. A value of -1 requests\n * the rest of the dimensions in the axis. Can also be a single number,\n * in which case it specifies the size of the first axis.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction slice_>(\n x: T|TensorLike, begin: number|number[], size?: number|number[]): T {\n const $x = convertToTensor(x, 'x', 'slice');\n\n if ($x.rank === 0) {\n throw new Error('Slicing scalar is not possible');\n }\n // The following logic allows for more ergonomic calls.\n let begin_: number[];\n if (typeof begin === 'number') {\n begin_ = [begin, ...new Array($x.rank - 1).fill(0)];\n } else if (begin.length < $x.rank) {\n begin_ = begin.concat(new Array($x.rank - begin.length).fill(0));\n } else {\n begin_ = begin.slice();\n }\n let size_: number[];\n if (size == null) {\n size_ = new Array($x.rank).fill(-1);\n } else if (typeof size === 'number') {\n size_ = [size, ...new Array($x.rank - 1).fill(-1)];\n } else if (size.length < $x.rank) {\n size_ = size.concat(new Array($x.rank - size.length).fill(-1));\n } else {\n size_ = size;\n }\n size_ = size_.map((d, i) => {\n if (d >= 0) {\n return d;\n } else {\n util.assert(d === -1, 'Bad value in size');\n return $x.shape[i] - begin_[i];\n }\n });\n slice_util.assertParamsValid($x, begin_, size_);\n const inputShape = $x.shape;\n const grad = (dy: T) => {\n // Create an Nx2 padding where the first column represents how many\n // zeros are prepended (at start) for each dimension, and the second\n // column indicates how many zeros are appended (at end).\n\n // The number of zeros to append is the shape of the input\n // elementwise-subtracted by both the begin vector and sizes vector.\n const paddings: Array<[number, number]> = [];\n for (let i = 0; i < dy.rank; i++) {\n paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n }\n return {$x: () => dy.pad(paddings)};\n };\n return ENV.engine.runKernel(\n backend => backend.slice($x, begin_, size_), {$x}, grad) as T;\n}\n\nexport const slice = op({slice_});\nexport const slice1d = op({slice1d_});\nexport const slice2d = op({slice2d_});\nexport const slice3d = op({slice3d_});\nexport const slice4d = op({slice4d_});\n","../src/ops/reduction_ops.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {customGrad} from '../globals';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {ones, scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print(); // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print(); // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n * reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n * of 1. Defaults to false.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction logSumExp_(\n x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n const $x = convertToTensor(x, 'x', 'logSumExp');\n\n const axes = util.parseAxisParam(axis, $x.shape);\n const xMax = $x.max(axes, true /* keepDims */);\n const a = $x.sub(xMax);\n const b = a.exp();\n const c = b.sum(axes);\n const d = c.log();\n const res = xMax.reshape(d.shape).add(d);\n\n if (keepDims) {\n const newShape = axis_util.expandShapeToKeepDim(res.shape, axes);\n return res.reshape(newShape) as T;\n }\n return res as T;\n}\n\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print(); // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print(); // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n * it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction sum_(\n x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n let $x = convertToTensor(x, 'x', 'sum');\n\n if ($x.dtype === 'bool') {\n $x = $x.toInt();\n }\n const axes = util.parseAxisParam(axis, $x.shape);\n\n // Use a custom gradient to bypass 2 gradient backprops since sum is used\n // extremely often.\n const customOp = customGrad(x => {\n const permutation = axis_util.getAxesPermutation(axes, x.rank);\n let reductionAxes = axes;\n let permutedX = x;\n if (permutation != null) {\n permutedX = x.transpose(permutation);\n reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, x.rank);\n }\n let value = ENV.engine.runKernel(\n backend => backend.sum(permutedX, reductionAxes), {permutedX});\n if (keepDims) {\n const newShape = axis_util.expandShapeToKeepDim(value.shape, axes);\n value = value.reshape(newShape);\n }\n\n const gradFunc = (dy: Tensor) => {\n const expandedDyShape = x.shape.slice();\n axes.forEach(axis => {\n expandedDyShape[axis] = 1;\n });\n const expandedDy = dy.reshape(expandedDyShape);\n const derX = expandedDy.mul(ones(x.shape, 'float32'));\n return derX;\n };\n return {value, gradFunc};\n });\n\n return customOp($x) as T;\n}\n\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print(); // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print(); // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n * it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction prod_(\n x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n let $x = convertToTensor(x, 'x', 'prod');\n\n if ($x.dtype === 'bool') {\n $x = $x.toInt();\n }\n const axes = util.parseAxisParam(axis, $x.shape);\n\n const permutation = axis_util.getAxesPermutation(axes, $x.rank);\n let reductionAxes = axes;\n let permutedX = $x;\n if (permutation != null) {\n permutedX = $x.transpose(permutation);\n reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, $x.rank);\n }\n let value = ENV.engine.runKernel(\n backend => backend.prod(permutedX, reductionAxes), {permutedX});\n if (keepDims) {\n const newShape = axis_util.expandShapeToKeepDim(value.shape, axes);\n value = value.reshape(newShape);\n }\n\n return value as T;\n}\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print(); // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print(); // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction mean_(\n x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n const $x = convertToTensor(x, 'x', 'mean');\n\n const axes = util.parseAxisParam(axis, $x.shape);\n const shapes = axis_util.computeOutAndReduceShapes($x.shape, axes);\n const reduceShape = shapes[1];\n const reduceSize = util.sizeFromShape(reduceShape);\n\n // Use a custom gradient to bypass 2 gradient backprops since mean is used\n // extremely often.\n const customOp = customGrad(x => {\n const reduceSizeScalar = scalar(reduceSize);\n // Cast if needed.\n const xReduce =\n reduceSizeScalar.dtype === x.dtype ? x : x.cast(reduceSizeScalar.dtype);\n const res = xReduce.div(reduceSizeScalar);\n const value = res.sum(axis, keepDims);\n\n const gradFunc = (dy: Tensor) => {\n const expandedDyShape = x.shape.slice();\n axes.forEach(axis => {\n expandedDyShape[axis] = 1;\n });\n const expandedDy = dy.reshape(expandedDyShape);\n const derX =\n expandedDy.mul(ones(x.shape, 'float32')).div(reduceSizeScalar);\n return derX;\n };\n return {value, gradFunc};\n });\n\n return customOp($x) as T;\n}\n\n/**\n * Gradient helper function for the min and max operations.\n */\nfunction gradForMinAndMax(\n dy: T, saved: Tensor[], xOrig: Tensor, origAxes: number[],\n permutedAxes: number[]) {\n let [y] = saved;\n if (y.rank < xOrig.rank) {\n y = y.reshape(axis_util.expandShapeToKeepDim(y.shape, origAxes)) as T;\n }\n if (dy.rank < xOrig.rank) {\n dy = dy.reshape(axis_util.expandShapeToKeepDim(dy.shape, origAxes)) as T;\n }\n return {\n $x: () => {\n const dx = dy.mul(xOrig.equal(y).cast(dy.dtype));\n return permutedAxes == null ? dx : dx.transpose(permutedAxes);\n }\n };\n}\n\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print(); // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print(); // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction min_(\n x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n let $x = convertToTensor(x, 'x', 'min');\n const xOrig = $x;\n\n const origAxes = util.parseAxisParam(axis, $x.shape);\n let axes = origAxes;\n const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n if (permutedAxes != null) {\n $x = $x.transpose(permutedAxes);\n axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n }\n\n const grad = (dy: T, saved: Tensor[]) =>\n gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);\n let res = ENV.engine.runKernel(\n (backend, save) => save(backend.min($x, axes)), {$x}, grad);\n if (keepDims) {\n const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n res = res.reshape(newShape) as T;\n }\n return res as T;\n}\n\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print(); // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print(); // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction max_(\n x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n let $x = convertToTensor(x, 'x', 'max');\n const xOrig = $x;\n\n const origAxes = util.parseAxisParam(axis, $x.shape);\n let axes = origAxes;\n const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n if (permutedAxes != null) {\n $x = $x.transpose(permutedAxes);\n axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n }\n\n const grad = (dy: T, saved: Tensor[]) =>\n gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);\n let res = ENV.engine.runKernel(\n (backend, save) => save(backend.max($x, axes)), {$x}, grad);\n if (keepDims) {\n const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n res = res.reshape(newShape) as T;\n }\n return res as T;\n}\n\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print(); // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print(); // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction argMin_(x: Tensor|TensorLike, axis = 0): T {\n let $x = convertToTensor(x, 'x', 'argMin');\n\n if (axis == null) {\n axis = 0;\n }\n let axes = util.parseAxisParam(axis, $x.shape);\n const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n if (permutedAxes != null) {\n $x = $x.transpose(permutedAxes);\n axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n }\n const grad = (dy: T) => {\n return {$x: () => zerosLike($x)};\n };\n return ENV.engine.runKernel(\n backend => backend.argMin($x, axes[0]), {$x}, grad) as T;\n}\n\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print(); // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print(); // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction argMax_(x: Tensor|TensorLike, axis = 0): T {\n let $x = convertToTensor(x, 'x', 'argMax');\n\n if (axis == null) {\n axis = 0;\n }\n let axes = util.parseAxisParam(axis, $x.shape);\n const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n if (permutedAxes != null) {\n $x = $x.transpose(permutedAxes);\n axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n }\n const grad = (dy: T) => {\n return {$x: () => zerosLike($x)};\n };\n return ENV.engine.runKernel(\n backend => backend.argMax($x, axes[0]), {$x}, grad) as T;\n}\n\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print(); // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print(); // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction all_(\n x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n let $x = convertToTensor(x, 'x', 'all', 'bool');\n\n const origAxes = util.parseAxisParam(axis, $x.shape);\n let axes = origAxes;\n const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n if (permutedAxes != null) {\n $x = $x.transpose(permutedAxes);\n axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n }\n const res = ENV.engine.runKernel(backend => backend.all($x, axes), {$x});\n if (keepDims) {\n const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n return res.reshape(newShape) as T;\n }\n return res as T;\n}\n\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print(); // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print(); // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n * all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction any_(\n x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n let $x = convertToTensor(x, 'x', 'any', 'bool');\n\n const origAxes = util.parseAxisParam(axis, $x.shape);\n let axes = origAxes;\n const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n if (permutedAxes != null) {\n $x = $x.transpose(permutedAxes);\n axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n }\n const res = ENV.engine.runKernel(backend => backend.any($x, axes), {$x});\n if (keepDims) {\n const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n return res.reshape(newShape) as T;\n }\n return res as T;\n}\n\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n * variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n * input.\n * @return An object with two keys: `mean` and `variance`.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction moments_(\n x: Tensor|TensorLike, axis: number|number[] = null,\n keepDims = false): {mean: Tensor, variance: Tensor} {\n x = convertToTensor(x, 'x', 'moments');\n const axes = util.parseAxisParam(axis, x.shape);\n const mean = x.mean(axes, keepDims);\n let keepDimsShape = mean.shape;\n if (!keepDims) {\n keepDimsShape = axis_util.expandShapeToKeepDim(mean.shape, axes);\n }\n const devSquared = x.toFloat().sub(mean.reshape(keepDimsShape)).square();\n const variance = devSquared.mean(axes, keepDims);\n return {mean, variance};\n}\n\nexport const all = op({all_});\n// tslint:disable-next-line:variable-name\nexport const any = op({any_});\nexport const argMax = op({argMax_});\nexport const argMin = op({argMin_});\nexport const logSumExp = op({logSumExp_});\nexport const max = op({max_});\nexport const mean = op({mean_});\nexport const min = op({min_});\nexport const moments = op({moments_});\nexport const sum = op({sum_});\nexport const prod = op({prod_});\n","../src/ops/compare.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertShapesMatch} from '../util';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\nimport {zerosLike} from './tensor_ops';\n\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.notEqualStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction notEqual_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'notEqual');\n let $b = convertToTensor(b, 'b', 'notEqual');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n return ENV.engine.runKernel(backend => backend.notEqual($a, $b), {$a, $b}) as\n T;\n}\n\n/**\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n * `a`.\n */\nfunction notEqualStrict_(\n a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'notEqualStrict');\n const $b = convertToTensor(b, 'b', 'notEqualStrict');\n assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n return $a.notEqual($b);\n}\n\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.lessStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction less_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'less');\n let $b = convertToTensor(b, 'b', 'less');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n\n return ENV.engine.runKernel(backend => backend.less($a, $b), {$a, $b}) as T;\n}\n\n/**\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n * `a`.\n */\nfunction lessStrict_(a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'lessStrict');\n const $b = convertToTensor(b, 'b', 'lessStrict');\n assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n return $a.less($b);\n}\n\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.equalStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction equal_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'equal');\n let $b = convertToTensor(b, 'b', 'equal');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n\n return ENV.engine.runKernel(backend => backend.equal($a, $b), {$a, $b}) as T;\n}\n\nfunction equalStrict_(a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'equalStrict');\n const $b = convertToTensor(b, 'b', 'equalStrict');\n assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n return $a.equal($b);\n}\n\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.lessEqualStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction lessEqual_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'lessEqual');\n let $b = convertToTensor(b, 'b', 'lessEqual');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n\n return ENV.engine.runKernel(backend => backend.lessEqual($a, $b), {$a, $b}) as\n T;\n}\n\nfunction lessEqualStrict_(\n a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n return $a.lessEqual($b);\n}\n\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.greaterStrict` which has the same signature as this\n * op and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction greater_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'greater');\n let $b = convertToTensor(b, 'b', 'greater');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n\n return ENV.engine.runKernel(backend => backend.greater($a, $b), {$a, $b}) as\n T;\n}\n\nfunction greaterStrict_(a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'greaterStrict');\n const $b = convertToTensor(b, 'b', 'greaterStrict');\n assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n return $a.greater($b);\n}\n\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.greaterEqualStrict` which has the same signature as this\n * op and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction greaterEqual_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'greaterEqual');\n let $b = convertToTensor(b, 'b', 'greaterEqual');\n [$a, $b] = makeTypesMatch($a, $b);\n assertAndGetBroadcastShape($a.shape, $b.shape);\n\n const grad = (dy: T) => {\n return {$a: () => zerosLike($a), $b: () => zerosLike($b)};\n };\n return ENV.engine.runKernel(\n backend => backend.greaterEqual($a, $b), {$a, $b}, grad) as T;\n}\n\nfunction greaterEqualStrict_(\n a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n return $a.greaterEqual($b);\n}\n\nexport const equal = op({equal_});\nexport const equalStrict = op({equalStrict_});\nexport const greater = op({greater_});\nexport const greaterEqual = op({greaterEqual_});\nexport const greaterEqualStrict = op({greaterEqualStrict_});\nexport const greaterStrict = op({greaterStrict_});\nexport const less = op({less_});\nexport const lessEqual = op({lessEqual_});\nexport const lessEqualStrict = op({lessEqualStrict_});\nexport const lessStrict = op({lessStrict_});\nexport const notEqual = op({notEqual_});\nexport const notEqualStrict = op({notEqualStrict_});\n","../src/ops/binary_ops.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {KernelBackend} from '../kernels/backend';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike, upcastType} from '../types';\nimport * as util from '../util';\nimport * as broadcast_util from './broadcast_util';\nimport {op} from './operation';\nimport {scalar, zerosLike} from './tensor_ops';\nimport {neg} from './unary_ops';\n\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n * We also expose `tf.addStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print(); // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print(); // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction add_(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'add');\n let $b = convertToTensor(b, 'b', 'add');\n [$a, $b] = makeTypesMatch($a, $b);\n\n const outShape =\n broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n const der = (dy: Tensor) => {\n const derA = () => {\n let res = dy;\n const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes);\n }\n return res.reshape($a.shape);\n };\n const derB = () => {\n let res = dy;\n const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes);\n }\n return res.reshape($b.shape);\n };\n return {$a: derA, $b: derB};\n };\n return ENV.engine.runKernel(backend => backend.add($a, $b), {$a, $b}, der) as\n T;\n}\n\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction addN_(tensors: Array): T {\n util.assert(\n Array.isArray(tensors),\n () => 'The argument passed to tf.addN() must be a list of tensors');\n util.assert(\n tensors.length >= 1,\n () => `Must pass at least one tensor to tf.addN(), but got ` +\n `${tensors.length}`);\n const $tensors =\n tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n const firstTensor = $tensors[0];\n $tensors.forEach(t => {\n if (t.dtype !== firstTensor.dtype) {\n throw new Error(\n 'All tensors passed to tf.addN() must have the same dtype');\n }\n });\n $tensors.forEach(t => {\n if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n throw new Error(\n 'All tensors passed to tf.addN() must have the same shape');\n }\n });\n\n const der = (dy: T) => {\n const ders: {[key: string]: () => Tensor} = {};\n $tensors.forEach((t, i) => {\n ders[i] = () => dy.clone();\n });\n return ders;\n };\n const inputs: NamedTensorMap = $tensors as {} as NamedTensorMap;\n return ENV.engine.runKernel(backend => backend.addN($tensors), inputs, der);\n}\n\n/**\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_(a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'addStrict');\n const $b = convertToTensor(b, 'b', 'addStrict');\n util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n return $a.add($b);\n}\n\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * We also expose `tf.subStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print(); // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print(); // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction sub_(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'sub');\n let $b = convertToTensor(b, 'b', 'sub');\n [$a, $b] = makeTypesMatch($a, $b);\n\n const outShape =\n broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n const der = (dy: Tensor) => {\n const derA = () => {\n let res = dy;\n const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes);\n }\n return res.reshape($a.shape);\n };\n const derB = () => {\n let res = dy;\n const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes);\n }\n return res.neg().reshape($b.shape);\n };\n return {$a: derA, $b: derB};\n };\n return ENV.engine.runKernel(\n backend => backend.subtract($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_(a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'subStrict');\n const $b = convertToTensor(b, 'b', 'subStrict');\n util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n return $a.sub($b);\n}\n\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print(); // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print(); // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction pow_(base: T|TensorLike, exp: Tensor|TensorLike): T {\n const $base = convertToTensor(base, 'base', 'pow');\n const $exp = convertToTensor(exp, 'exp', 'pow');\n\n const outShape =\n broadcast_util.assertAndGetBroadcastShape($base.shape, $exp.shape);\n base = $base.cast(upcastType($base.dtype, $exp.dtype));\n exp = $exp.cast(upcastType($base.dtype, $exp.dtype));\n const grad = (dy: Tensor, saved: Tensor[]) => {\n const [y] = saved;\n const derBase = () => {\n const expFloat = $exp.toFloat();\n let res = dy.mul(expFloat.mul($base.pow(expFloat.sub(scalar(1)))));\n const reduceAxes = broadcast_util.getReductionAxes($base.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes);\n }\n return res.reshape($base.shape) as T;\n };\n const derExp = () => {\n const condition = $base.greater(0);\n const logBase = $base.log().where(condition, zerosLike($base));\n let res = dy.mul(y.mul(logBase));\n const reduceAxes = broadcast_util.getReductionAxes($exp.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes);\n }\n return res.reshape($exp.shape);\n };\n return {$base: derBase, $exp: derExp};\n };\n return ENV.engine.runKernel(\n (backend, save) => save(backend.pow($base, $exp)), {$base, $exp},\n grad) as T;\n}\n\n/**\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_(base: T, exp: Tensor): T {\n util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n return base.pow(exp);\n}\n\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print(); // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print(); // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction mul_(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'mul');\n let $b = convertToTensor(b, 'b', 'mul');\n [$a, $b] = makeTypesMatch($a, $b);\n\n const outShape =\n broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n const der = (dy: Tensor) => {\n const derA = () => {\n const res = dy.mul($b.toFloat());\n const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n if (reduceAxes.length > 0) {\n return res.sum(reduceAxes).reshape($a.shape);\n }\n return res;\n };\n const derB = () => {\n const res = dy.mul($a.toFloat());\n const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n if (reduceAxes.length > 0) {\n return res.sum(reduceAxes).reshape($b.shape);\n }\n return res;\n };\n return {$a: derA, $b: derB};\n };\n return ENV.engine.runKernel(\n backend => backend.multiply($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n * dtype as `a`.\n */\nfunction mulStrict_(a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'mul');\n const $b = convertToTensor(b, 'b', 'mul');\n util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n return $a.mul($b) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * We also expose `tf.divStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print(); // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print(); // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction div_(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'div');\n let $b = convertToTensor(b, 'b', 'div');\n [$a, $b] = makeTypesMatch($a, $b);\n\n let forwardFunc: (backend: KernelBackend) => Tensor;\n if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n return floorDiv($a, $b);\n } else {\n forwardFunc = (backend: KernelBackend) => backend.realDivide($a, $b);\n }\n\n const outShape =\n broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n const der = (dy: Tensor) => {\n const derA = () => {\n const res = dy.div($b.toFloat());\n const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n if (reduceAxes.length > 0) {\n return res.sum(reduceAxes).reshape($a.shape);\n }\n return res;\n };\n const derB = () => {\n let res = dy.mul($a.toFloat());\n const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes).reshape($b.shape);\n }\n const tmp = $b.square() as Tensor;\n return res.div(tmp.toFloat()).neg() as Tensor;\n };\n return {$a: derA, $b: derB};\n };\n return ENV.engine.runKernel(forwardFunc, {$a, $b}, der) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print(); // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print(); // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction floorDiv_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'floorDiv');\n let $b = convertToTensor(b, 'b', 'floorDiv');\n [$a, $b] = makeTypesMatch($a, $b);\n\n const forwardFunc = (backend: KernelBackend) => backend.floorDiv($a, $b);\n const outShape =\n broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n const der = (dy: Tensor) => {\n const derA = () => {\n const res = dy.div($b.toFloat());\n const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n if (reduceAxes.length > 0) {\n return res.sum(reduceAxes).reshape($a.shape);\n }\n return res;\n };\n const derB = () => {\n let res = dy.mul($a.toFloat());\n const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes).reshape($b.shape);\n }\n const tmp = $b.square() as Tensor;\n return res.div(tmp.toFloat()).neg() as Tensor;\n };\n return {$a: derA, $b: derB};\n };\n return ENV.engine.runKernel(forwardFunc, {$a, $b}, der) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_(a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'div');\n const $b = convertToTensor(b, 'b', 'div');\n util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n return $a.div($b) as T;\n}\n\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print(); // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print(); // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction mod_(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'mod');\n let $b = convertToTensor(b, 'b', 'mod');\n [$a, $b] = makeTypesMatch($a, $b);\n\n const outShape =\n broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n const der = (dy: Tensor) => {\n const derA = () => {\n const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n if (reduceAxes.length > 0) {\n return dy.sum(reduceAxes).reshape($a.shape);\n }\n return dy;\n };\n const derB = () => {\n const res = dy.mul($a.div($b).floor().neg());\n const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n if (reduceAxes.length > 0) {\n return res.sum(reduceAxes).reshape($b.shape);\n }\n return res;\n };\n return {$a: derA, $b: derB};\n };\n return ENV.engine.runKernel(backend => backend.mod($a, $b), {$a, $b}, der) as\n T;\n}\n\n/**\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_(a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'modStrict');\n const $b = convertToTensor(b, 'b', 'modStrict');\n util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n return $a.mod($b);\n}\n\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print(); // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print(); // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction minimum_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'minimum');\n let $b = convertToTensor(b, 'b', 'minimum');\n [$a, $b] = makeTypesMatch($a, $b);\n\n if ($a.dtype === 'bool') {\n $a = $a.toInt();\n $b = $b.toInt();\n }\n\n broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n const der = (dy: Tensor) => {\n const derA = () => dy.mul($a.lessEqual($b).toFloat());\n const derB = () => dy.mul($a.greater($b).toFloat());\n return {$a: derA, $b: derB};\n };\n return ENV.engine.runKernel(\n backend => backend.minimum($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_(a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'minimumStrict');\n const $b = convertToTensor(b, 'b', 'minimumStrict');\n util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n return $a.minimum($b);\n}\n\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print(); // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print(); // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction maximum_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'maximum');\n let $b = convertToTensor(b, 'b', 'maximum');\n [$a, $b] = makeTypesMatch($a, $b);\n\n if ($a.dtype === 'bool') {\n $a = $a.toInt();\n $b = $b.toInt();\n }\n\n broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n const der = (dy: Tensor) => {\n const derA = () => dy.mul($a.greaterEqual($b).toFloat());\n const derB = () => dy.mul($a.less($b).toFloat());\n return {$a: derA, $b: derB};\n };\n return ENV.engine.runKernel(\n backend => backend.maximum($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_(a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'maximumStrict');\n const $b = convertToTensor(b, 'b', 'maximumStrict');\n util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n return $a.maximum($b);\n}\n\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.squaredDifferenceStrict` which has the same signature as\n * this op and asserts that `a` and `b` are the same shape (does not\n * broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print(); // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print(); // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction squaredDifference_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'squaredDifference');\n let $b = convertToTensor(b, 'b', 'squaredDifference');\n [$a, $b] = makeTypesMatch($a, $b);\n\n broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n const der = (dy: Tensor) => {\n const two = scalar(2);\n const derA = () => dy.mul($a.sub($b).mul(two));\n const derB = () => dy.mul($b.sub($a).mul(two));\n return {$a: derA, $b: derB};\n };\n return ENV.engine.runKernel(\n backend => backend.squaredDifference($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_(\n a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n util.assertShapesMatch(\n $a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n return $a.squaredDifference($b);\n}\n\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atan2_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n let $a = convertToTensor(a, 'a', 'atan2');\n let $b = convertToTensor(b, 'b', 'atan2');\n [$a, $b] = makeTypesMatch($a, $b);\n\n const outShape =\n broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n const der = (dy: Tensor) => {\n const derA = () => {\n const d = add($a.square(), $b.square());\n let res = dy.mul($b.div(d));\n const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes);\n }\n return res.reshape($a.shape);\n };\n const derB = () => {\n const d = add($a.square(), $b.square()) as T;\n let res = neg(dy.mul($a.div(d)));\n const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes);\n }\n return res.reshape($b.shape);\n };\n return {$a: derA, $b: derB};\n };\n return ENV.engine.runKernel(\n backend => backend.atan2($a, $b), {$a, $b}, der) as T;\n}\n\nexport const add = op({add_});\nexport const addN = op({addN_});\nexport const addStrict = op({addStrict_});\nexport const atan2 = op({atan2_});\nexport const div = op({div_});\nexport const divStrict = op({divStrict_});\nexport const floorDiv = op({floorDiv_});\nexport const maximum = op({maximum_});\nexport const maximumStrict = op({maximumStrict_});\nexport const minimum = op({minimum_});\nexport const minimumStrict = op({minimumStrict_});\nexport const mod = op({mod_});\nexport const modStrict = op({modStrict_});\nexport const mul = op({mul_});\nexport const mulStrict = op({mulStrict_});\nexport const pow = op({pow_});\nexport const powStrict = op({powStrict_});\nexport const squaredDifference = op({squaredDifference_});\nexport const squaredDifferenceStrict = op({squaredDifferenceStrict_});\nexport const sub = op({sub_});\nexport const subStrict = op({subStrict_});\n","../src/ops/logical_ops.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {whereImpl} from '../kernels/where_impl';\nimport {Tensor, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, assertShapesMatch} from '../util';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\nimport {zerosLike} from './tensor_ops';\n\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalNot_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n return ENV.engine.runKernel(backend => backend.logicalNot($x), {$x});\n}\n\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalAnd_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n assertAndGetBroadcastShape($a.shape, $b.shape);\n\n return ENV.engine.runKernel(\n backend => backend.logicalAnd($a, $b), {$a, $b}) as T;\n}\n\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalOr_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n assertAndGetBroadcastShape($a.shape, $b.shape);\n\n return ENV.engine.runKernel(backend => backend.logicalOr($a, $b), {$a, $b}) as\n T;\n}\n\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalXor_(\n a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n assertAndGetBroadcastShape($a.shape, $b.shape);\n\n // x ^ y = (x | y) & ~(x & y)\n return logicalOr(a, b).logicalAnd(logicalAnd(a, b).logicalNot()) as T;\n}\n\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n * its first dimension must match the size of `condition`.\n * @param b A tensor with the same shape and type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction where_(\n condition: Tensor|TensorLike, a: T|TensorLike, b: T|TensorLike): T {\n const $a = convertToTensor(a, 'a', 'where');\n const $b = convertToTensor(b, 'b', 'where');\n const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n\n assertShapesMatch($a.shape, $b.shape, 'Error in where: ');\n\n if ($condition.rank === 1) {\n // If condition rank is 1, then the first dimension must match the size of\n // condition.\n assert(\n $condition.shape[0] === $a.shape[0],\n 'The first dimension of `a` must match the size of `condition`.');\n } else {\n // A must have the same shape as condition.\n assertShapesMatch($condition.shape, $b.shape, 'Error in where: ');\n }\n\n // TODO(julianoks): Return null for condition gradient\n // when backprop supports it.\n const grad = (dy: T) => ({\n $condition: () => zerosLike($condition).toFloat(),\n $a: () => dy.mul($condition.cast(dy.dtype)) as T,\n $b: () => dy.mul($condition.logicalNot().cast(dy.dtype)) as T\n });\n\n return ENV.engine.runKernel(\n backend => backend.select($condition, $a, $b),\n {$condition, $a, $b}, grad) as T;\n}\n\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * tf.whereAsync(cond).then(result => result.print());\n * ```\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nasync function whereAsync_(condition: Tensor|TensorLike): Promise {\n const $condition =\n convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n const vals = await $condition.data();\n const res = whereImpl($condition.shape, vals);\n if (condition !== $condition) {\n $condition.dispose();\n }\n return res;\n}\n\nexport const logicalAnd = op({logicalAnd_});\nexport const logicalNot = op({logicalNot_});\nexport const logicalOr = op({logicalOr_});\nexport const logicalXor = op({logicalXor_});\nexport const where = op({where_});\nexport const whereAsync = whereAsync_;\n","../src/ops/relu_ops.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {maximum} from './binary_ops';\nimport {getReductionAxes} from './broadcast_util';\nimport {where} from './logical_ops';\nimport {op} from './operation';\nimport {SELU_SCALE, SELU_SCALEALPHA} from './selu_util';\nimport {scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print(); // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n * `int32'.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction relu_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'relu');\n\n if ($x.dtype === 'bool') {\n return $x.toInt();\n }\n const grad = (dy: T) => {\n const stepRes = $x.step();\n return {$x: () => dy.mulStrict(stepRes.toFloat())};\n };\n return ENV.engine.runKernel(backend => backend.relu($x), {$x}, grad);\n}\n\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print(); // or tf.elu(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction elu_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'elu');\n\n const grad = (dy: T, saved: Tensor[]) => {\n const [y] = saved;\n return {\n $x: () =>\n ENV.engine.runKernel(backend => backend.eluDer(dy, y), {dy, y}) as T\n };\n };\n return ENV.engine.runKernel(\n (backend, save) => save(backend.elu($x)), {$x}, grad);\n}\n\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print(); // or tf.selu(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction selu_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'selu');\n\n const grad = (dy: T) => {\n return {\n $x: () => {\n const mask = $x.greater(scalar(0));\n\n const scaleAlpha = scalar(SELU_SCALEALPHA);\n const scale = scalar(SELU_SCALE);\n\n const greaterThanZeroDer = dy.mul(scale);\n const lessEqualZeroDer = dy.mul(scaleAlpha).mul($x.toFloat().exp());\n\n return where(mask, greaterThanZeroDer, lessEqualZeroDer) as T;\n }\n };\n };\n return ENV.engine.runKernel(backend => backend.selu($x), {$x}, grad);\n}\n\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n * http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print(); // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction leakyRelu_(x: T|TensorLike, alpha = 0.2): T {\n const $x = convertToTensor(x, 'x', 'leakyRelu');\n return maximum(scalar(alpha).mul($x), $x);\n}\n\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print(); // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction prelu_(x: T|TensorLike, alpha: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'prelu');\n const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n\n const grad = (dy: Tensor) => {\n const mask = $x.greater(0);\n\n return {\n $x: () => where(mask, dy, dy.mul($alpha)) as T,\n $alpha: () => {\n let res = where(mask, zerosLike(dy), dy.mul($x));\n const reduceAxes = getReductionAxes($alpha.shape, dy.shape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes);\n }\n return res.reshape($alpha.shape) as T;\n }\n };\n };\n\n return ENV.engine.runKernel(\n backend => backend.prelu($x, $alpha), {$x, $alpha}, grad) as T;\n}\n\nexport const elu = op({elu_});\nexport const leakyRelu = op({leakyRelu_});\nexport const prelu = op({prelu_});\nexport const relu = op({relu_});\nexport const selu = op({selu_});\n","../src/ops/transpose.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\n\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print(); // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction transpose_(x: T|TensorLike, perm?: number[]): T {\n const $x = convertToTensor(x, 'x', 'transpose');\n\n if (perm == null) {\n perm = $x.shape.map((s, i) => i).reverse();\n }\n util.assert(\n $x.rank === perm.length,\n `Error in transpose: rank of input ${$x.rank} ` +\n `must match length of perm ${perm}.`);\n perm.forEach(axis => {\n util.assert(\n axis >= 0 && axis < $x.rank,\n `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n ` but got ${perm}`);\n });\n\n if ($x.rank <= 1) {\n return $x.clone();\n }\n\n const der = (dy: T) => {\n const undoPerm = axis_util.getUndoAxesPermutation(perm);\n return {$x: () => dy.transpose(undoPerm)};\n };\n return ENV.engine.runKernel(\n backend => backend.transpose($x, perm), {$x}, der);\n}\n\nexport const transpose = op({transpose_});\n","../src/ops/lrn.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n * of 1D vectors (along the last dimension), and each vector is\n * normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n * window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction localResponseNormalization_(\n x: T|TensorLike, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n util.assert(\n $x.rank === 4 || $x.rank === 3,\n `Error in localResponseNormalization: x must be rank 3 or 4 but got\n rank ${$x.rank}.`);\n util.assert(\n util.isInt(depthRadius),\n `Error in localResponseNormalization: depthRadius must be an integer\n but got depthRadius ${depthRadius}.`);\n\n let x4D = $x as Tensor4D;\n let reshapedTo4D = false;\n if ($x.rank === 3) {\n reshapedTo4D = true;\n x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n }\n const backward = (dy: Tensor4D, saved: Tensor[]) => {\n const [outputImage] = saved;\n return {\n x4D: () => ENV.engine.runKernel(\n backend => backend.LRNGrad(\n dy, x4D, outputImage as Tensor4D, depthRadius, bias, alpha, beta),\n {})\n };\n };\n const res = ENV.engine.runKernel(\n (backend, save) => save(backend.localResponseNormalization4D(\n x4D, depthRadius, bias, alpha, beta)),\n {x4D}, backward);\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n } else {\n return res as T;\n }\n}\n\nexport const localResponseNormalization = op({localResponseNormalization_});","../src/ops/norm.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\nimport {parseAxisParam} from '../util';\n\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print(); // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n * | ord | norm for matrices | norm for vectors\n * |------------|---------------------------|---------------------\n * |'euclidean' |Frobenius norm |2-norm\n * |'fro' |Frobenius norm\t |\n * |Infinity |max(sum(abs(x), axis=1)) |max(abs(x))\n * |-Infinity |min(sum(abs(x), axis=1)) |min(abs(x))\n * |1 |max(sum(abs(x), axis=0)) |sum(abs(x))\n * |2 | |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction norm_(\n x: Tensor|TensorLike, ord: number|'euclidean'|'fro' = 'euclidean',\n axis: number|number[] = null, keepDims = false): Tensor {\n x = convertToTensor(x, 'x', 'norm');\n\n const norm = normImpl(x, ord, axis);\n let keepDimsShape = norm.shape;\n if (keepDims) {\n const axes = parseAxisParam(axis, x.shape);\n keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n }\n return norm.reshape(keepDimsShape);\n}\n\nfunction normImpl(\n x: Tensor, p: number|string, axis: number|number[] = null): Tensor {\n if (x.rank === 0) {\n return x.abs();\n }\n\n // consider vector when no axis is specified\n if (x.rank !== 1 && axis === null) {\n return normImpl(x.reshape([-1]), p, axis);\n }\n\n // vector\n if (x.rank === 1 || typeof axis === 'number' ||\n Array.isArray(axis) && axis.length === 1) {\n if (p === 1) {\n return x.abs().sum(axis);\n }\n if (p === Infinity) {\n return x.abs().max(axis);\n }\n if (p === -Infinity) {\n return x.abs().min(axis);\n }\n if (p === 'euclidean' || p === 2) {\n // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n return x.abs().pow(scalar(2, 'int32')).sum(axis).sqrt() as Tensor;\n }\n\n throw new Error(`Error in norm: invalid ord value: ${p}`);\n }\n\n // matrix (assumption axis[0] < axis[1])\n if (Array.isArray(axis) && axis.length === 2) {\n if (p === 1) {\n return x.abs().sum(axis[0]).max(axis[1] - 1);\n }\n if (p === Infinity) {\n return x.abs().sum(axis[1]).max(axis[0]);\n }\n if (p === -Infinity) {\n return x.abs().sum(axis[1]).min(axis[0]);\n }\n if (p === 'fro' || p === 'euclidean') {\n // norm(x) = sqrt(sum(pow(x, 2)))\n return x.square().sum(axis).sqrt();\n }\n\n throw new Error(`Error in norm: invalid ord value: ${p}`);\n }\n\n throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\n\nexport const norm = op({norm_});\n","../src/ops/segment_ops.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, isInt, parseAxisParam} from '../util';\nimport {expandDims} from './array_ops';\nimport {getUndoAxesPermutation} from './axis_util';\nimport {maximum} from './binary_ops';\nimport {greaterEqual} from './compare';\nimport {logicalAnd, where} from './logical_ops';\nimport {op} from './operation';\nimport {collectGatherOpShapeInfo} from './segment_util';\nimport {ones, scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`. Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n */\n/** @doc {heading: 'Operations', subheading: 'Segment'} */\nfunction unsortedSegmentSum_(\n x: T|TensorLike, segmentIds: Tensor1D|TensorLike, numSegments: number): T {\n const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n const $segmentIds =\n convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n assert(isInt(numSegments), 'numSegments must be of dtype int');\n\n const gradFunc = (dy: T) => {\n const derX = () => {\n return gatherDropNegatives(dy, $segmentIds);\n };\n return {$x: derX};\n };\n return ENV.engine.runKernel(\n backend =>\n backend.unsortedSegmentSum($x, $segmentIds, numSegments),\n {$x}, gradFunc) as T;\n}\n\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction gather_(\n x: T|TensorLike, indices: Tensor|TensorLike, axis = 0): T {\n const $x = convertToTensor(x, 'x', 'gather');\n const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n axis = parseAxisParam(axis, $x.shape)[0];\n const shapeInfo = collectGatherOpShapeInfo($x, $indices, axis);\n\n const grad = (dy: T) => {\n const derX = () => {\n const paramsShape = $x.shape;\n const indicesSize = $indices.size;\n\n const outerShape = paramsShape.slice(0, axis);\n const outerDims = outerShape.length;\n const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n const innerDims = innerShape.length;\n\n const outerAxesIndices = arrayRange(0, outerDims);\n const innerAxesIndices =\n arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n\n const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n\n const values = dy.reshape(valuesShape);\n const reshapedIndices = $indices.reshape([indicesSize]);\n\n const transposeDims =\n arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n const valuesTranspose = values.transpose(transposeDims);\n let paramsGrad = unsortedSegmentSum(\n valuesTranspose, reshapedIndices as Tensor1D, $x.shape[axis]);\n\n const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n paramsGrad = paramsGrad.transpose(invertTransposeDims);\n\n return paramsGrad as T;\n };\n return {$x: derX};\n };\n return (ENV.engine.runKernel(\n backend => backend.gather($x, $indices.flatten(), axis), {$x},\n grad))\n .reshape(shapeInfo.outputShape) as T;\n}\n\nfunction arrayRange(start: number, stop: number): number[] {\n const result = [];\n for (let i = start; i < stop; ++i) {\n result.push(i);\n }\n return result;\n}\n\nfunction arrayConcat(arrays: number[][]): number[] {\n const result = [];\n for (let i = 0; i < arrays.length; ++i) {\n for (let j = 0; j < arrays[i].length; ++j) {\n result.push(arrays[i][j]);\n }\n }\n return result;\n}\n\nfunction gatherDropNegatives(x: T, indices: Tensor1D) {\n // Helper function for unsorted segment ops. Gathers params for\n // positive segment ids and gathers 0 for inputs with negative segment id.\n // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n const zeroClippedIndices = maximum(indices, zerosLike(indices));\n const gathered = gather(x, zeroClippedIndices as Tensor1D);\n let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n const numIters = gathered.rank - isPositive.rank;\n for (let i = 0; i < numIters; ++i) {\n isPositive = expandDims(isPositive, i + 1);\n }\n isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n const zeroSlice = zerosLike(gathered);\n return where(isPositive, gathered, zeroSlice);\n}\n\nexport const gather = op({gather_});\nexport const unsortedSegmentSum = op({unsortedSegmentSum_});\n","../src/ops/lstm.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * @docalias (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D]\n */\nexport type LSTMCellFunc = {\n (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D];\n};\n\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n */\n/** @doc {heading: 'Operations', subheading: 'RNN'} */\nfunction multiRNNCell_(\n lstmCells: LSTMCellFunc[], data: Tensor2D|TensorLike,\n c: Array,\n h: Array): [Tensor2D[], Tensor2D[]] {\n const $data = convertToTensor(data, 'data', 'multiRNNCell');\n const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n\n let input = $data;\n const newStates = [];\n for (let i = 0; i < lstmCells.length; i++) {\n const output = lstmCells[i](input, $c[i], $h[i]);\n newStates.push(output[0]);\n newStates.push(output[1]);\n input = output[1];\n }\n const newC: Tensor2D[] = [];\n const newH: Tensor2D[] = [];\n for (let i = 0; i < newStates.length; i += 2) {\n newC.push(newStates[i]);\n newH.push(newStates[i + 1]);\n }\n return [newC, newH];\n}\n\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n */\n/** @doc {heading: 'Operations', subheading: 'RNN'} */\nfunction basicLSTMCell_(\n forgetBias: Scalar|TensorLike, lstmKernel: Tensor2D|TensorLike,\n lstmBias: Tensor1D|TensorLike, data: Tensor2D|TensorLike,\n c: Tensor2D|TensorLike, h: Tensor2D|TensorLike): [Tensor2D, Tensor2D] {\n const $forgetBias =\n convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n const $lstmKernel =\n convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n\n const combined = $data.concat($h, 1);\n const weighted = combined.matMul($lstmKernel);\n const res = weighted.add($lstmBias) as Tensor2D;\n\n // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n const batchSize = res.shape[0];\n const sliceCols = res.shape[1] / 4;\n const sliceSize: [number, number] = [batchSize, sliceCols];\n const i = res.slice([0, 0], sliceSize);\n const j = res.slice([0, sliceCols], sliceSize);\n const f = res.slice([0, sliceCols * 2], sliceSize);\n const o = res.slice([0, sliceCols * 3], sliceSize);\n\n const newC = i.sigmoid().mulStrict(j.tanh()).addStrict(\n $c.mulStrict($forgetBias.add(f).sigmoid() as Tensor2D));\n const newH = newC.tanh().mulStrict(o.sigmoid());\n return [newC, newH];\n}\n\nexport const basicLSTMCell = op({basicLSTMCell_});\nexport const multiRNNCell = op({multiRNNCell_});\n","../src/ops/moving_average.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor} from '../tensor';\nimport {assertTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {pow} from './binary_ops';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\n\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n * `v += delta`\n * where\n * `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n * `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n * https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n */\n/** @doc {heading: 'Operations', subheading: 'Moving Average'} */\nfunction movingAverage_(\n v: T|TensorLike, x: T|TensorLike, decay: number|Scalar,\n step?: number|Scalar, zeroDebias = true): T {\n const $v = convertToTensor(v, 'v', 'movingAverage');\n const $x = convertToTensor(x, 'x', 'movingAverage');\n const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n\n assertTypesMatch($v, $x);\n util.assert(\n util.arraysEqual($v.shape, $x.shape), 'Shape mismatch in v and x');\n\n const one = scalar(1);\n const oneMinusDecay = one.sub($decay);\n\n let update = $x.sub($v).mul(oneMinusDecay);\n if (zeroDebias) {\n util.assert(step != null, 'When using zeroDebias: true, step is required.');\n const $step = convertToTensor(step, 'step', 'movingAverage');\n update = update.div(one.sub(pow($decay, $step)));\n }\n return $v.add(update);\n}\n\nexport const movingAverage = op({movingAverage_});\n","../src/ops/strided_slice.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\nimport {slice} from './slice';\nimport {getStridedSlicedInfo} from './slice_util';\n\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input_ tensor. Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n * [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print() // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print() // [[[3, 3, 3],\n * // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n * // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of begin_mask is set, begin[i] is ignored\n * and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of end_mask is set, end[i] is ignored\n * and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction stridedSlice_(\n x: Tensor|TensorLike, begin: number[], end: number[], strides: number[],\n beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n shrinkAxisMask = 0): Tensor {\n if (ellipsisMask !== 0) {\n throw new Error('ellipsis mask is not yet supported');\n }\n if (newAxisMask !== 0) {\n throw new Error('new axis mask is not yet supported');\n }\n const $x = convertToTensor(x, 'x', 'stridedSlice');\n const nonStrided = strides.every(v => v === 1);\n if (nonStrided) {\n const [beginIndex, size, shrinkAxis] = getStridedSlicedInfo(\n $x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n newAxisMask, shrinkAxisMask);\n const outShape =\n size.filter((_, index) => shrinkAxis.indexOf(index) === -1);\n return slice($x, beginIndex, size).reshape(outShape);\n }\n return ENV.engine.runKernel(\n backend => backend.stridedSlice(\n $x, begin, end, strides, beginMask, endMask, ellipsisMask,\n newAxisMask, shrinkAxisMask),\n {$x});\n}\n\nexport const stridedSlice = op({stridedSlice_});\n","../src/ops/topk.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {NumericTensor, Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n * values in descending order.\n */\n/** @doc {heading: 'Operations', subheading: 'Evaluation'} */\nfunction topk_(\n x: T|TensorLike, k = 1, sorted = true): {values: T, indices: T} {\n const $x = convertToTensor(x, 'x', 'topk');\n if ($x.rank === 0) {\n throw new Error('topk() expects the input to be of rank 1 or higher');\n }\n const lastDim = $x.shape[$x.shape.length - 1];\n if (k > lastDim) {\n throw new Error(\n `'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n `but got ${k}`);\n }\n\n const [values, indices] =\n ENV.engine.runKernel(b => b.topk($x as NumericTensor, k, sorted), {$x});\n return {values, indices} as {values: T, indices: T};\n}\n\nexport const topk = op({topk_});\n","../src/ops/scatter_nd.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([[4], [3], [1], [7]]);\n * const updates = tf.tensor2d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape]).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction scatterND_(\n indices: Tensor|TensorLike, updates: Tensor|TensorLike,\n shape: ShapeMap[R]): Tensor {\n const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n const $updates = convertToTensor(updates, 'updates', 'scatterND');\n scatter_nd_util.validateInput($updates, $indices, shape);\n\n return ENV.engine.runKernel(\n backend => backend.scatterND($indices, $updates, shape),\n {$indices, $updates}) as Tensor;\n}\n\nexport const scatterND = op({scatterND_});\n","../src/ops/spectral_ops.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {complex, imag, real} from '../ops/complex_ops';\nimport {op} from '../ops/operation';\nimport {Tensor, Tensor2D} from '../tensor';\nimport {assert} from '../util';\nimport {scalar} from './tensor_ops';\n\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print(); // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input: Tensor): Tensor {\n assert(\n input.dtype === 'complex64',\n `The dtype for tf.spectral.fft() must be complex64 ` +\n `but got ${input.dtype}.`);\n\n // Collapse all outer dimensions to a single batch dimension.\n const innerDimensionSize = input.shape[input.shape.length - 1];\n const batch = input.size / innerDimensionSize;\n const input2D = input.as2D(batch, innerDimensionSize);\n\n const ret = ENV.engine.runKernel(backend => backend.fft(input2D), {input});\n\n return ret.reshape(input.shape);\n}\n\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print(); // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input: Tensor): Tensor {\n assert(\n input.dtype === 'complex64',\n `The dtype for tf.spectral.ifft() must be complex64 ` +\n `but got ${input.dtype}.`);\n\n // Collapse all outer dimensions to a single batch dimension.\n const innerDimensionSize = input.shape[input.shape.length - 1];\n const batch = input.size / innerDimensionSize;\n const input2D = input.as2D(batch, innerDimensionSize);\n\n const ret = ENV.engine.runKernel(backend => backend.ifft(input2D), {input});\n\n return ret.reshape(input.shape);\n}\n\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input: Tensor): Tensor {\n assert(input.dtype === 'float32', `The dtype for rfft() must be real value but\n got ${input.dtype}`);\n\n const innerDimensionSize = input.shape[input.shape.length - 1];\n const batch = input.size / innerDimensionSize;\n\n // Complement the input with zero imaginary numbers.\n const zeros = input.zerosLike();\n const complexInput = complex(input, zeros).as2D(batch, innerDimensionSize);\n\n const ret = fft(complexInput);\n\n // Exclude complex conjugations. These conjugations are put symmetrically.\n const half = Math.floor(innerDimensionSize / 2) + 1;\n const realValues = real(ret);\n const imagValues = imag(ret);\n const realComplexConjugate = realValues.split(\n [half, innerDimensionSize - half], realValues.shape.length - 1);\n const imagComplexConjugate = imagValues.split(\n [half, innerDimensionSize - half], imagValues.shape.length - 1);\n\n const outputShape = input.shape.slice();\n outputShape[input.shape.length - 1] = half;\n\n return complex(realComplexConjugate[0], imagComplexConjugate[0])\n .reshape(outputShape);\n}\n\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input: Tensor): Tensor {\n const innerDimensionSize = input.shape[input.shape.length - 1];\n const batch = input.size / innerDimensionSize;\n\n if (innerDimensionSize <= 2) {\n const complexInput = input.as2D(batch, innerDimensionSize);\n const ret = ifft(complexInput);\n return real(ret);\n } else {\n // The length of unique components of the DFT of a real-valued signal\n // is 2 * (input_len - 1)\n const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n const realInput = real(input).as2D(batch, innerDimensionSize);\n const imagInput = imag(input).as2D(batch, innerDimensionSize);\n\n const realConjugate =\n realInput.slice([0, 1], [batch, innerDimensionSize - 2]).reverse(1);\n const imagConjugate =\n imagInput.slice([0, 1], [batch, innerDimensionSize - 2])\n .reverse(1)\n .mul(scalar(-1)) as Tensor2D;\n\n const r = realInput.concat(realConjugate, 1);\n const i = imagInput.concat(imagConjugate, 1);\n const complexInput = complex(r, i).as2D(outputShape[0], outputShape[1]);\n const ret = ifft(complexInput);\n return real(ret);\n }\n}\n\nexport const fft = op({fft_});\nexport const ifft = op({ifft_});\nexport const rfft = op({rfft_});\nexport const irfft = op({irfft_});\n","../src/ops/sparse_to_dense_util.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\n\n/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(\n sparseIndices: Tensor, sparseValues: Tensor, outputShape: number[],\n defaultValues: Tensor) {\n if (sparseIndices.dtype !== 'int32') {\n throw new Error(\n 'tf.sparseToDense() expects the indices to be int32 type,' +\n ` but the dtype was ${sparseIndices.dtype}.`);\n }\n if (sparseIndices.rank > 2) {\n throw new Error(\n 'sparseIndices should be a scalar, vector, or matrix,' +\n ` but got shape ${sparseIndices.shape}.`);\n }\n\n const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n\n if (outputShape.length !== numDims) {\n throw new Error(\n 'outputShape has incorrect number of elements:,' +\n ` ${outputShape.length}, should be: ${numDims}.`);\n }\n\n const numValues = sparseValues.size;\n if (!(sparseValues.rank === 0 ||\n sparseValues.rank === 1 && numValues === numElems)) {\n throw new Error(\n 'sparseValues has incorrect shape ' +\n `${sparseValues.shape}, should be [] or [${numElems}]`);\n }\n\n if (sparseValues.dtype !== defaultValues.dtype) {\n throw new Error('sparseValues.dtype must match defaultValues.dtype');\n }\n}\n","../src/ops/sparse_to_dense.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport {Scalar, Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction sparseToDense_(\n sparseIndices: Tensor|TensorLike, sparseValues: Tensor|TensorLike,\n outputShape: ShapeMap[R], defaultValue: Scalar|TensorLike): Tensor {\n const $sparseIndices =\n convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n const $sparseValues =\n convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n const $defaultValue = convertToTensor(\n defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n\n sparse_to_dense.validateInput(\n $sparseIndices, $sparseValues, outputShape, $defaultValue);\n\n return ENV.engine.runKernel(\n backend => backend.sparseToDense(\n $sparseIndices, $sparseValues, outputShape, $defaultValue),\n {$sparseIndices, $sparseValues, $defaultValue});\n}\n\nexport const sparseToDense = op({sparseToDense_});\n","../src/ops/gather_nd.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() //[10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction gatherND_(\n x: Tensor|TensorLike, indices: Tensor|TensorLike): Tensor {\n const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n const $x = convertToTensor(x, 'x', 'gatherND');\n return ENV.engine.runKernel(\n backend => backend.gatherND($x, $indices), {$x, $indices}) as\n Tensor;\n}\nexport const gatherND = op({gatherND_});\n","../src/ops/loss_ops.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../globals';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertShapesMatch, sizeFromShape} from '../util';\n\nimport {expandShapeToKeepDim} from './axis_util';\n\nimport {minimum} from './binary_ops';\nimport {op} from './operation';\nimport {ones, scalar} from './tensor_ops';\n\nexport enum Reduction {\n NONE,\n MEAN,\n SUM,\n SUM_BY_NONZERO_WEIGHTS\n}\n\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `losses`, and must be broadcastable to `losses` (i.e., all\n * dimensions must be either `1`, or the same as the corresponding\n * `losses` dimension).\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction computeWeightedLoss_(\n losses: T|TensorLike, weights?: Tensor|TensorLike,\n reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n let $weights: Tensor = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n }\n\n const weightedLoss = ($weights == null) ? $losses : $losses.mul($weights);\n\n if (reduction === Reduction.NONE) {\n return weightedLoss as O;\n }\n if (reduction === Reduction.SUM) {\n return weightedLoss.sum();\n }\n if (reduction === Reduction.MEAN) {\n if ($weights == null) {\n return weightedLoss.mean();\n } else {\n const broadcastFactor =\n sizeFromShape($losses.shape) / sizeFromShape($weights.shape);\n const result = weightedLoss.sum().div($weights.sum());\n return broadcastFactor > 1 ? result.div(scalar(broadcastFactor)) :\n result as O;\n }\n }\n if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n if ($weights == null) {\n return weightedLoss.sum().div(scalar($losses.size));\n } else {\n const broadcastedWeights = $weights.mul(ones($losses.shape));\n\n const numNonZeros =\n broadcastedWeights.notEqual(scalar(0)).sum().toFloat();\n return weightedLoss.sum().div(numNonZeros);\n }\n }\n\n throw Error(`Unknown reduction: ${reduction}`);\n}\n\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction absoluteDifference_(\n labels: T|TensorLike, predictions: T|TensorLike,\n weights?: Tensor|TensorLike,\n reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n const $predictions =\n convertToTensor(predictions, 'predictions', 'absoluteDifference');\n let $weights: Tensor = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n }\n assertShapesMatch(\n $labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n\n const losses = $labels.sub($predictions).abs();\n return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction meanSquaredError_(\n labels: T|TensorLike, predictions: T|TensorLike,\n weights?: Tensor|TensorLike,\n reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n const $predictions =\n convertToTensor(predictions, 'predictions', 'meanSquaredError');\n let $weights: Tensor = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n }\n assertShapesMatch(\n $labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n\n const losses = $labels.squaredDifference($predictions);\n return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction cosineDistance_(\n labels: T|TensorLike, predictions: T|TensorLike, axis: number,\n weights?: Tensor|TensorLike,\n reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n const $predictions =\n convertToTensor(predictions, 'predictions', 'cosineDistance');\n let $weights: Tensor = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n }\n assertShapesMatch(\n $labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n\n const one = scalar(1);\n const losses = one.sub($labels.mul($predictions).sum(axis, true));\n return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction hingeLoss_(\n labels: T|TensorLike, predictions: T|TensorLike,\n weights?: Tensor|TensorLike,\n reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n let $weights: Tensor = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n\n const one = scalar(1);\n // Convert binary labels to (-1, 1)\n $labels = scalar(2).mul($labels).sub(one);\n const losses = one.sub($labels.mul($predictions)).relu();\n return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction logLoss_(\n labels: T|TensorLike, predictions: T|TensorLike,\n weights?: Tensor|TensorLike, epsilon = 1e-7,\n reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n const $labels = convertToTensor(labels, 'labels', 'logLoss');\n const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n let $weights: Tensor = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'logLoss');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n\n const one = scalar(1);\n const epsilonScalar = scalar(epsilon);\n const losses = $labels.mul($predictions.add(epsilonScalar).log())\n .neg()\n .sub(one.sub($labels).mul(\n one.sub($predictions).add(epsilonScalar).log()));\n return computeWeightedLoss(losses, $weights, reduction);\n}\n\nfunction sigmoidCrossEntropyWithLogits_(\n labels: T|TensorLike, logits: T|TensorLike): O {\n const $labels =\n convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n const $logits =\n convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n assertShapesMatch(\n $labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n\n /**\n * Implementation Details:\n *\n * For brevity, let `x = logits`, `z = labels`. The logistic loss is\n * z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n * = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n * = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n * = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n * = (1 - z) * x + log(1 + exp(-x))\n * = x - x * z + log(1 + exp(-x))\n *\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n * x - x * z + log(1 + exp(-x))\n * = log(exp(x)) - x * z + log(1 + exp(-x))\n * = - x * z + log(1 + exp(x))\n *\n * Hence, to ensure stability and avoid overflow, the implementation uses\n * this equivalent formulation:\n * max(x, 0) - x * z + log(1 + exp(-abs(x)))\n */\n const maxOutput = $logits.relu();\n const outputXTarget = $logits.mul($labels);\n const sigmoidOutput = $logits.abs().neg().exp().log1p();\n\n return maxOutput.sub(outputXTarget).add(sigmoidOutput);\n}\n\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n * newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n * + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n */\n/** @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } */\nfunction sigmoidCrossEntropy_(\n multiClassLabels: T|TensorLike, logits: T|TensorLike,\n weights?: Tensor|TensorLike, labelSmoothing = 0,\n reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n let $multiClassLabels = convertToTensor(\n multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n let $weights: Tensor = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n }\n assertShapesMatch(\n $multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n\n if (labelSmoothing > 0) {\n const labelSmoothingScalar = scalar(labelSmoothing);\n const one = scalar(1);\n const half = scalar(0.5);\n\n $multiClassLabels = $multiClassLabels.mul(one.sub(labelSmoothingScalar))\n .add(half.mul(labelSmoothingScalar));\n }\n const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n\n return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n * 'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n * `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n * must be either `1`, or the same as the corresponding `losses`\n * dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`.\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction huberLoss_(\n labels: T|TensorLike, predictions: T|TensorLike,\n weights?: Tensor|TensorLike, delta = 1.0,\n reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n let $weights: Tensor = null;\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'huberLoss');\n }\n assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n\n const deltaScalar = scalar(delta);\n const error = $predictions.sub($labels).abs();\n const quadratic = minimum(error, deltaScalar);\n const linear = error.sub(quadratic);\n\n const losses =\n scalar(0.5).mul(quadratic.square()).add(deltaScalar.mul(linear));\n return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n * which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(\n labels: T, logits: T, dim = -1): O {\n if (dim === -1) {\n dim = logits.rank - 1;\n }\n\n if (dim !== logits.rank - 1) {\n throw Error(\n `Softmax cross entropy along a non-last dimension is not yet ` +\n `supported. Labels / logits was rank ${logits.rank} ` +\n `and dim was ${dim}`);\n }\n // Use a custom gradient for numerical stability.\n const customOp = customGrad((labels, logits) => {\n // Reference:\n // 1. http://cs231n.github.io/linear-classify/#softmax\n // 2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n const keepDims = true;\n const lse = logits.logSumExp([dim], keepDims);\n\n const logResult = logits.toFloat().sub(lse);\n const costVector = logResult.mul(labels).neg();\n\n const value = costVector.sum([dim]) as O;\n\n const gradFunc = (dy: O) => {\n const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n return [\n dy.reshape(dyShape).mul(labels.toFloat().sub(logResult.exp())),\n dy.reshape(dyShape).mul(logResult.exp().sub(labels.toFloat())),\n ];\n };\n return {value, gradFunc};\n });\n\n return customOp(labels, logits);\n}\n\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n * newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n * + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n * broadcastable to `loss` of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n * `Reduction`\n */\n/** @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } */\nfunction softmaxCrossEntropy_(\n onehotLabels: T|TensorLike, logits: T|TensorLike,\n weights?: Tensor|TensorLike, labelSmoothing = 0,\n reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n let $onehotLabels =\n convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n let $weights: Tensor = null;\n\n if (weights != null) {\n $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n }\n\n assertShapesMatch(\n $onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n\n if (labelSmoothing > 0) {\n const labelSmoothingScalar = scalar(labelSmoothing);\n const one = scalar(1);\n const numClasses = scalar($onehotLabels.shape[1]);\n\n $onehotLabels = $onehotLabels.mul(one.sub(labelSmoothingScalar))\n .add(labelSmoothingScalar.div(numClasses));\n }\n\n const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n\n return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const absoluteDifference = op({absoluteDifference_});\nexport const computeWeightedLoss = op({computeWeightedLoss_});\nexport const cosineDistance = op({cosineDistance_});\nexport const hingeLoss = op({hingeLoss_});\nexport const huberLoss = op({huberLoss_});\nexport const logLoss = op({logLoss_});\nexport const meanSquaredError = op({meanSquaredError_});\nexport const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});\nexport const softmaxCrossEntropy = op({softmaxCrossEntropy_});\n","../src/ops/linalg_ops.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Linear algebra ops.\n */\n\nimport {ENV} from '../environment';\nimport {dispose} from '../globals';\nimport {Tensor, Tensor1D, Tensor2D} from '../tensor';\nimport {assert} from '../util';\nimport {eye, squeeze, stack, unstack} from './array_ops';\nimport {split} from './concat_split';\nimport {norm} from './norm';\nimport {op} from './operation';\nimport {sum} from './reduction_ops';\nimport {tensor2d} from './tensor_ops';\n\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print(); // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * console.log(y.get(0, 1) / y.get(0, 0)); // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n * formats:\n * - An Array of `tf.Tensor1D`.\n * - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n * of `xs`.\n * In each case, all the vectors must have the same length and the length\n * must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n * Orthogonalization means that the vectors or the rows of the matrix\n * are orthogonal (zero inner products). Normalization means that each\n * vector or each row of the matrix has an L2 norm that equals `1`.\n */\n/**\n * @doc {heading:'Operations',\n * subheading:'Linear Algebra',\n * namespace:'linalg'}\n */\nfunction gramSchmidt_(xs: Tensor1D[]|Tensor2D): Tensor1D[]|Tensor2D {\n let inputIsTensor2D: boolean;\n if (Array.isArray(xs)) {\n inputIsTensor2D = false;\n assert(\n xs != null && xs.length > 0,\n 'Gram-Schmidt process: input must not be null, undefined, or empty');\n const dim = xs[0].shape[0];\n for (let i = 1; i < xs.length; ++i) {\n assert(\n xs[i].shape[0] === dim,\n 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n `(${xs[i].shape[0]} vs. ${dim})`);\n }\n } else {\n inputIsTensor2D = true;\n xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n }\n\n assert(\n xs.length <= xs[0].shape[0],\n `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n `number of dimensions (${xs[0].shape[0]}).`);\n\n const ys: Tensor1D[] = [];\n const xs1d = xs as Tensor1D[];\n for (let i = 0; i < xs.length; ++i) {\n ys.push(ENV.engine.tidy(() => {\n let x = xs1d[i];\n if (i > 0) {\n for (let j = 0; j < i; ++j) {\n const proj = sum(ys[j].mulStrict(x)).mul(ys[j]);\n x = x.sub(proj);\n }\n }\n return x.div(norm(x, 'euclidean'));\n }));\n }\n\n if (inputIsTensor2D) {\n return stack(ys, 0) as Tensor2D;\n } else {\n return ys;\n }\n}\n\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n * [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print() // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n * it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n * If `true`, compute full-sized `Q`. If `false` (the default),\n * compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n * i.e., its columns all have unit norm and are mutually orthogonal.\n * If `M >= N`,\n * If `fullMatrices` is `false` (default),\n * - `Q` has a shape of `[..., M, N]`,\n * - `R` has a shape of `[..., N, N]`.\n * If `fullMatrices` is `true` (default),\n * - `Q` has a shape of `[..., M, M]`,\n * - `R` has a shape of `[..., M, N]`.\n * If `M < N`,\n * - `Q` has a shape of `[..., M, M]`,\n * - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n */\n/**\n * @doc {heading:'Operations',\n * subheading:'Linear Algebra',\n * namespace:'linalg'}\n */\nfunction qr_(x: Tensor, fullMatrices = false): [Tensor, Tensor] {\n if (x.rank < 2) {\n throw new Error(\n `qr() requires input tensor to have a rank >= 2, but got rank ${\n x.rank}`);\n } else if (x.rank === 2) {\n return qr2d(x as Tensor2D, fullMatrices);\n } else {\n // Rank > 2.\n // TODO(cais): Below we split the input into individual 2D tensors,\n // perform QR decomposition on them and then stack the results back\n // together. We should explore whether this can be parallelized.\n const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n .reduce((value, prev) => value * prev);\n const x2ds = unstack(\n x.reshape([\n outerDimsProd, x.shape[x.shape.length - 2],\n x.shape[x.shape.length - 1]\n ]),\n 0);\n const q2ds: Tensor2D[] = [];\n const r2ds: Tensor2D[] = [];\n x2ds.forEach(x2d => {\n const [q2d, r2d] = qr2d(x2d as Tensor2D, fullMatrices);\n q2ds.push(q2d);\n r2ds.push(r2d);\n });\n const q = stack(q2ds, 0).reshape(x.shape);\n const r = stack(r2ds, 0).reshape(x.shape);\n return [q, r];\n }\n}\n\nfunction qr2d(x: Tensor2D, fullMatrices = false): [Tensor2D, Tensor2D] {\n return ENV.engine.tidy(() => {\n if (x.shape.length !== 2) {\n throw new Error(\n `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n }\n\n const m = x.shape[0];\n const n = x.shape[1];\n\n let q = eye(m) as Tensor2D; // Orthogonal transform so far.\n let r = x.clone(); // Transformed matrix so far.\n\n const one2D = tensor2d([[1]], [1, 1]);\n let w: Tensor2D = one2D.clone();\n\n const iters = m >= n ? n : m;\n for (let j = 0; j < iters; ++j) {\n // This tidy within the for-loop ensures we clean up temporary\n // tensors as soon as they are no longer needed.\n const rTemp = r;\n const wTemp = w;\n const qTemp = q;\n [w, r, q] = ENV.engine.tidy((): [Tensor2D, Tensor2D, Tensor2D] => {\n // Find H = I - tau * w * w', to put zeros below R(j, j).\n const rjEnd1 = r.slice([j, j], [m - j, 1]);\n const normX = rjEnd1.norm();\n const rjj = r.slice([j, j], [1, 1]);\n const s = rjj.sign().neg() as Tensor2D;\n const u1 = rjj.sub(s.mul(normX)) as Tensor2D;\n const wPre = rjEnd1.div(u1);\n if (wPre.shape[0] === 1) {\n w = one2D.clone();\n } else {\n w = one2D.concat(\n wPre.slice([1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) as\n Tensor2D,\n 0);\n }\n const tau = s.matMul(u1).div(normX).neg() as Tensor2D;\n\n // -- R := HR, Q := QH.\n const rjEndAll = r.slice([j, 0], [m - j, n]);\n const tauTimesW = tau.mul(w) as Tensor2D;\n if (j === 0) {\n r = rjEndAll.sub(tauTimesW.matMul(w.transpose().matMul(rjEndAll)));\n } else {\n r = r.slice([0, 0], [j, n])\n .concat(\n rjEndAll.sub(tauTimesW.matMul(\n w.transpose().matMul(rjEndAll))) as Tensor2D,\n 0) as Tensor2D;\n }\n const qAllJEnd = q.slice([0, j], [m, q.shape[1] - j]);\n if (j === 0) {\n q = qAllJEnd.sub(qAllJEnd.matMul(w).matMul(tauTimesW.transpose()));\n } else {\n q = q.slice([0, 0], [m, j])\n .concat(\n qAllJEnd.sub(qAllJEnd.matMul(w).matMul(\n tauTimesW.transpose())) as Tensor2D,\n 1) as Tensor2D;\n }\n return [w, r, q];\n });\n dispose([rTemp, wTemp, qTemp]);\n }\n\n if (!fullMatrices && m > n) {\n q = q.slice([0, 0], [m, n]);\n r = r.slice([0, 0], [n, n]);\n }\n\n return [q, r];\n }) as [Tensor2D, Tensor2D];\n}\n\nexport const gramSchmidt = op({gramSchmidt_});\nexport const qr = op({qr_});\n","../src/ops/image_ops.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ForwardFunc} from '../engine';\nimport {ENV} from '../environment';\nimport {nonMaxSuppressionImpl} from '../kernels/non_max_suppression_impl';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Bilinear resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n * images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n * input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n * corners of images and resized images. If false, rescale by\n * `new_height / height`. Treat similarly the width dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction resizeBilinear_(\n images: T|TensorLike, size: [number, number], alignCorners = false): T {\n const $images = convertToTensor(images, 'images', 'resizeBilinear');\n util.assert(\n $images.rank === 3 || $images.rank === 4,\n `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n `rank ${$images.rank}.`);\n util.assert(\n size.length === 2,\n `Error in resizeBilinear: new shape must 2D, but got shape ` +\n `${size}.`);\n\n let batchImages = $images as Tensor4D;\n let reshapedTo4D = false;\n if ($images.rank === 3) {\n reshapedTo4D = true;\n batchImages =\n $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);\n }\n\n const [newHeight, newWidth] = size;\n const forward: ForwardFunc = (backend, save) =>\n backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n\n const backward = (dy: Tensor4D, saved: Tensor[]) => {\n return {\n batchImages: () => ENV.engine.runKernel(\n backend =>\n backend.resizeBilinearBackprop(dy, batchImages, alignCorners),\n {})\n };\n };\n\n const res = ENV.engine.runKernel(forward, {batchImages}, backward);\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n * images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n * input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n * corners of images and resized images. If false, rescale by\n * `new_height / height`. Treat similarly the width dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction resizeNearestNeighbor_(\n images: T|TensorLike, size: [number, number], alignCorners = false): T {\n const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n util.assert(\n $images.rank === 3 || $images.rank === 4,\n `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n `rank ${$images.rank}.`);\n util.assert(\n size.length === 2,\n `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n `${size}.`);\n util.assert(\n $images.dtype === 'float32' || $images.dtype === 'int32',\n '`images` must have `int32` or `float32` as dtype');\n\n let batchImages = $images as Tensor4D;\n let reshapedTo4D = false;\n if ($images.rank === 3) {\n reshapedTo4D = true;\n batchImages =\n $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);\n }\n const [newHeight, newWidth] = size;\n\n const forward: ForwardFunc = (backend, save) =>\n backend.resizeNearestNeighbor(\n batchImages, newHeight, newWidth, alignCorners);\n\n const backward = (dy: Tensor4D, saved: Tensor[]) => {\n return {\n batchImages: () => ENV.engine.runKernel(\n backend => backend.resizeNearestNeighborBackprop(\n dy, batchImages, alignCorners),\n {})\n };\n };\n\n const res = ENV.engine.runKernel(forward, {batchImages}, backward);\n\n if (reshapedTo4D) {\n return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n }\n return res as T;\n}\n\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union)\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n * the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n * boxes overlap too much with respect to IOU. Must be between [0, 1].\n * Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n * on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction nonMaxSuppression_(\n boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n maxOutputSize: number, iouThreshold = 0.5,\n scoreThreshold = Number.NEGATIVE_INFINITY): Tensor1D {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n\n const inputs = nonMaxSuppSanityCheck(\n $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n maxOutputSize = inputs.maxOutputSize;\n iouThreshold = inputs.iouThreshold;\n scoreThreshold = inputs.scoreThreshold;\n\n return ENV.engine.runKernel(\n b => b.nonMaxSuppression(\n $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold),\n {$boxes});\n}\n\n/** This is the async version of `nonMaxSuppression` */\nasync function nonMaxSuppressionAsync_(\n boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n maxOutputSize: number, iouThreshold = 0.5,\n scoreThreshold = Number.NEGATIVE_INFINITY): Promise {\n const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n\n const inputs = nonMaxSuppSanityCheck(\n $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n maxOutputSize = inputs.maxOutputSize;\n iouThreshold = inputs.iouThreshold;\n scoreThreshold = inputs.scoreThreshold;\n\n const boxesVals = await $boxes.data();\n const scoresVals = await $scores.data();\n const res = nonMaxSuppressionImpl(\n boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n if ($boxes !== boxes) {\n $boxes.dispose();\n }\n if ($scores !== scores) {\n $scores.dispose();\n }\n return res;\n}\n\nfunction nonMaxSuppSanityCheck(\n boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n iouThreshold: number, scoreThreshold: number):\n {maxOutputSize: number, iouThreshold: number, scoreThreshold: number} {\n if (iouThreshold == null) {\n iouThreshold = 0.5;\n }\n if (scoreThreshold == null) {\n scoreThreshold = Number.NEGATIVE_INFINITY;\n }\n const numBoxes = boxes.shape[0];\n maxOutputSize = Math.min(maxOutputSize, numBoxes);\n\n util.assert(\n 0 <= iouThreshold && iouThreshold <= 1,\n `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n util.assert(\n boxes.rank === 2,\n `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n util.assert(\n boxes.shape[1] === 4,\n `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n util.assert(scores.rank === 1, 'scores must be a 1D tensor');\n util.assert(\n scores.shape[0] === numBoxes,\n `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n `but was ${scores.shape[0]}`);\n return {maxOutputSize, iouThreshold, scoreThreshold};\n}\n\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by crop_size.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n * where imageHeight and imageWidth must be positive, specifying the\n * batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n * coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n * `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n * specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n * defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n * on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction cropAndResize_(\n image: Tensor4D|TensorLike,\n boxes: Tensor2D|TensorLike,\n boxInd: Tensor1D|TensorLike,\n cropSize: [number, number],\n method?: 'bilinear'|'nearest',\n extrapolationValue?: number,\n ): Tensor4D {\n const $image = convertToTensor(image, 'image', 'cropAndResize', 'float32');\n const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n method = method || 'bilinear';\n extrapolationValue = extrapolationValue || 0;\n\n const numBoxes = $boxes.shape[0];\n\n util.assert(\n $image.rank === 4,\n 'Error in cropAndResize: image must be rank 4,' +\n `but got rank ${$image.rank}.`);\n util.assert(\n $boxes.rank === 2 && $boxes.shape[1] === 4,\n `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n `but had shape ${$boxes.shape}.`);\n util.assert(\n $boxInd.rank === 1 && $boxInd.shape[0] === numBoxes,\n `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n `but had shape ${$boxes.shape}.`);\n util.assert(\n cropSize.length === 2,\n `Error in cropAndResize: cropSize must be of length 2, but got length ` +\n `${cropSize.length}.`);\n util.assert(\n cropSize[0] >= 1 && cropSize[1] >= 1,\n `cropSize must be atleast [1,1], but was ${cropSize}`);\n util.assert(\n method === 'bilinear' || method === 'nearest',\n `method must be bilinear or nearest, but was ${method}`);\n\n const forward: ForwardFunc = (backend, save) =>\n backend.cropAndResize(\n $image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n\n const res = ENV.engine.runKernel(forward, {$image, $boxes});\n return res as Tensor4D;\n}\n\nexport const resizeBilinear = op({resizeBilinear_});\nexport const resizeNearestNeighbor = op({resizeNearestNeighbor_});\nexport const nonMaxSuppression = op({nonMaxSuppression_});\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\nexport const cropAndResize = op({cropAndResize_});\n","../src/ops/fused_ops.ts":"/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {op} from '../ops/operation';\nimport {Tensor, Tensor3D} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as broadcast_util from './broadcast_util';\nimport {Activation} from './fused_util';\n\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const c = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul(a, b, false, false, 'relu', c);\n * ```\n *\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param bias Matrix to be added to the result.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices', namespace: 'fused'} */\nfunction matMul_(\n a: T|TensorLike, b: T|TensorLike, transposeA = false, transposeB = false,\n bias?: Tensor|TensorLike, activation: Activation = 'linear'): T {\n let $a = convertToTensor(a, 'a', 'fused matMul');\n let $b = convertToTensor(b, 'b', 'fused matMul');\n [$a, $b] = makeTypesMatch($a, $b);\n\n const innerShapeA =\n transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n const innerShapeB =\n transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n const outerShapeA =\n transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n const outerShapeB =\n transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n const outerDimsA = $a.shape.slice(0, -2);\n const outerDimsB = $b.shape.slice(0, -2);\n const batchDimA = util.sizeFromShape(outerDimsA);\n const batchDimB = util.sizeFromShape(outerDimsB);\n\n util.assert(\n $a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank,\n `Error in fused matMul: inputs must have the same rank of at least 2, ` +\n `got ranks ${$a.rank} and ${$b.rank}.`);\n\n util.assert(\n util.arraysEqual(outerDimsA, outerDimsB),\n `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n `${$b.shape} must match.`);\n\n util.assert(\n innerShapeA === innerShapeB,\n `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n `${$b.shape} and transposeA=${transposeA}` +\n ` and transposeB=${transposeB} must match.`);\n\n const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n\n const a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :\n $a.as3D(batchDimA, outerShapeA, innerShapeA);\n const b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :\n $b.as3D(batchDimB, innerShapeB, outerShapeB);\n\n let $bias: Tensor;\n if (bias != null) {\n $bias = convertToTensor(bias, 'bias', 'fused matMul');\n [$bias] = makeTypesMatch($bias, $a);\n\n broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n }\n\n const grad = (dy: Tensor3D, saved: Tensor[]) => {\n const [y] = saved;\n\n let dyActivation: Tensor3D;\n if (activation == null || activation === 'linear') {\n dyActivation = dy;\n } else if (activation === 'relu') {\n dyActivation = dy.mul(y.step()) as Tensor3D;\n } else {\n throw new Error(\n `Gradient for activation ${activation} has not been ` +\n `implemented yet.`);\n }\n\n let biasGradient = {};\n if (bias != null) {\n biasGradient = {\n $bias: () => {\n let res = dyActivation;\n const reduceAxes =\n broadcast_util.getReductionAxes($bias.shape, outShape);\n if (reduceAxes.length > 0) {\n res = res.sum(reduceAxes);\n }\n return res.reshape($bias.shape);\n }\n };\n }\n\n if (!transposeA && !transposeB) {\n return Object.assign(\n {\n $a: () => dyActivation.matMul(b3D, false, true),\n $b: () => a3D.matMul(dyActivation, true, false)\n },\n biasGradient);\n } else if (!transposeA && transposeB) {\n return Object.assign(\n {\n $a: () => dyActivation.matMul(b3D, false, false),\n $b: () => dyActivation.matMul(a3D, true, false)\n },\n biasGradient);\n } else if (transposeA && !transposeB) {\n return Object.assign(\n {\n $a: () => b3D.matMul(dyActivation, false, true),\n $b: () => a3D.matMul(dyActivation, false, false)\n },\n biasGradient);\n } else {\n return Object.assign(\n {\n $a: () => b3D.matMul(dyActivation, true, true),\n $b: () => dyActivation.matMul(a3D, true, true)\n },\n biasGradient);\n }\n };\n\n const inputs: {$a: Tensor, $b: Tensor, $bias?: Tensor} = {$a: a3D, $b: b3D};\n if (bias != null) {\n inputs.$bias = $bias;\n }\n\n const res = ENV.engine.runKernel(\n (backend, save) => save(backend.fusedBatchMatMul(\n a3D, b3D, transposeA, transposeB, $bias, activation)),\n inputs, grad);\n return res.reshape(outShape) as T;\n}\n\nexport const matMul = op({matMul_});\n\nexport {Activation};","../src/kernels/backend_cpu.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\n\nimport {ENV} from '../environment';\nimport {warn} from '../log';\nimport * as array_ops_util from '../ops/array_ops_util';\nimport * as axis_util from '../ops/axis_util';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport * as concat_util from '../ops/concat_util';\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport * as erf_util from '../ops/erf_util';\nimport {Activation} from '../ops/fused_util';\nimport * as gather_nd_util from '../ops/gather_nd_util';\nimport * as ops from '../ops/ops';\nimport {buffer, scalar, tensor, tensor3d, tensor4d} from '../ops/ops';\nimport * as scatter_nd_util from '../ops/scatter_nd_util';\nimport * as selu_util from '../ops/selu_util';\nimport {computeFlatOffset, getStridedSlicedInfo, isSliceContinous} from '../ops/slice_util';\nimport {DataId, Scalar, setTensorTracker, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, TensorBuffer} from '../tensor';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, TypedArray, upcastType} from '../types';\nimport * as util from '../util';\nimport {now} from '../util';\n\nimport {BackendTimingInfo, DataMover, DataStorage, KernelBackend} from './backend';\nimport * as backend_util from './backend_util';\nimport * as complex_util from './complex_util';\nimport {nonMaxSuppressionImpl} from './non_max_suppression_impl';\nimport {split} from './split_shared';\nimport {topkImpl} from './topk_impl';\nimport {whereImpl} from './where_impl';\n\nfunction mapActivation(\n backend: MathBackendCPU, activation: Activation, x: Tensor): Tensor {\n if (activation === 'linear') {\n return backend.linear(x);\n } else if (activation === 'relu') {\n return backend.relu(x);\n }\n throw new Error(\n `Activation ${activation} has not been implemented for the CPU backend.`);\n}\n\ninterface TensorData {\n values?: DataTypeMap[D];\n dtype: D;\n // For complex numbers, the real and imaginary parts are stored as their own\n // individual tensors, with a parent joining the two with the\n // complexTensors field. When this is defined, texture will be null.\n complexTensors?: {real: Tensor, imag: Tensor};\n}\n\nexport class MathBackendCPU implements KernelBackend {\n public blockSize = 48;\n\n private data: DataStorage>;\n private fromPixels2DContext: CanvasRenderingContext2D;\n private firstUse = true;\n\n constructor() {\n if (ENV.get('IS_BROWSER')) {\n this.fromPixels2DContext =\n document.createElement('canvas').getContext('2d');\n }\n }\n\n setDataMover(dataMover: DataMover): void {\n this.data = new DataStorage(dataMover);\n }\n\n register(dataId: DataId, shape: number[], dtype: DataType): void {\n if (this.firstUse) {\n this.firstUse = false;\n if (ENV.get('IS_NODE')) {\n warn(\n '\\n============================\\n' +\n 'Hi there 👋. Looks like you are running TensorFlow.js in ' +\n 'Node.js. To speed things up dramatically, install our node ' +\n 'backend, which binds to TensorFlow C++, by running ' +\n 'npm i @tensorflow/tfjs-node, ' +\n 'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n 'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n 'suffix for CUDA) at the start of your program. ' +\n 'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n '\\n============================\\n');\n }\n }\n if (this.data.has(dataId)) {\n throw new Error(`Data buffer is already registered`);\n }\n this.data.set(dataId, {dtype});\n }\n write(dataId: DataId, values: DataValues): void {\n if (values == null) {\n throw new Error('MathBackendCPU.write(): values can not be null');\n }\n this.data.get(dataId).values = values;\n }\n fromPixels(\n pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n numChannels: number): Tensor3D {\n if (pixels == null) {\n throw new Error(\n 'pixels passed to tf.browser.fromPixels() can not be null');\n }\n let vals: Uint8ClampedArray;\n // tslint:disable-next-line:no-any\n if (ENV.get('IS_NODE') && (pixels as any).getContext == null) {\n throw new Error(\n 'When running in node, pixels must be an HTMLCanvasElement ' +\n 'like the one returned by the `canvas` npm package');\n }\n // tslint:disable-next-line:no-any\n if ((pixels as any).getContext != null) {\n // tslint:disable-next-line:no-any\n vals = (pixels as any)\n .getContext('2d')\n .getImageData(0, 0, pixels.width, pixels.height)\n .data;\n } else if (pixels instanceof ImageData) {\n vals = pixels.data;\n } else if (\n pixels instanceof HTMLImageElement ||\n pixels instanceof HTMLVideoElement) {\n if (this.fromPixels2DContext == null) {\n throw new Error(\n 'Can\\'t read pixels from HTMLImageElement outside ' +\n 'the browser.');\n }\n this.fromPixels2DContext.canvas.width = pixels.width;\n this.fromPixels2DContext.canvas.height = pixels.height;\n this.fromPixels2DContext.drawImage(\n pixels, 0, 0, pixels.width, pixels.height);\n vals = this.fromPixels2DContext\n .getImageData(0, 0, pixels.width, pixels.height)\n .data;\n } else {\n throw new Error(\n 'pixels passed to tf.browser.fromPixels() must be either an ' +\n `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ` +\n `ImageData, but was ${(pixels as {}).constructor.name}`);\n }\n let values: Int32Array;\n if (numChannels === 4) {\n values = new Int32Array(vals);\n } else {\n const numPixels = pixels.width * pixels.height;\n values = new Int32Array(numPixels * numChannels);\n for (let i = 0; i < numPixels; i++) {\n for (let channel = 0; channel < numChannels; ++channel) {\n values[i * numChannels + channel] = vals[i * 4 + channel];\n }\n }\n }\n const outShape: [number, number, number] =\n [pixels.height, pixels.width, numChannels];\n return tensor3d(values, outShape, 'int32');\n }\n async read(dataId: DataId): Promise {\n return this.readSync(dataId);\n }\n readSync(dataId: DataId): DataValues {\n const {dtype, complexTensors} = this.data.get(dataId);\n if (dtype === 'complex64') {\n const realValues = complexTensors.real.dataSync() as Float32Array;\n const imagValues = complexTensors.imag.dataSync() as Float32Array;\n return complex_util.mergeRealAndImagArrays(realValues, imagValues);\n }\n return this.data.get(dataId).values;\n }\n\n disposeData(dataId: DataId): void {\n if (this.data.has(dataId)) {\n const {complexTensors} = this.data.get(dataId);\n if (complexTensors != null) {\n complexTensors.real.dispose();\n complexTensors.imag.dispose();\n }\n this.data.delete(dataId);\n }\n }\n\n async time(f: () => void): Promise {\n const start = now();\n f();\n const kernelMs = now() - start;\n return {kernelMs};\n }\n\n memory() {\n return {\n // Unreliable due to automatic gc. The numbers above are cumulative.\n unreliable: true,\n reasons:\n ['The reported memory is an upper bound. Due to automatic garbage ' +\n 'collection, the true allocated memory may be less.']\n };\n }\n\n complex(real: T, imag: T): T {\n const result = Tensor.make(real.shape, {}, 'complex64') as T;\n\n const resultData = this.data.get(result.dataId);\n // The backend owns the reference to the underlying real and imaginary\n // clones. These will explicitly get disposed when the complex tensor is\n // disposed.\n resultData.complexTensors = {\n real: ENV.engine.keep(real.clone()),\n imag: ENV.engine.keep(imag.clone())\n };\n\n return result;\n }\n real(input: T): T {\n const resultData = this.data.get(input.dataId);\n return resultData.complexTensors.real.clone() as T;\n }\n imag(input: T): T {\n const resultData = this.data.get(input.dataId);\n return resultData.complexTensors.imag.clone() as T;\n }\n\n private assertNotComplex(tensor: Tensor|Tensor[], opName: string) {\n if (!Array.isArray(tensor)) {\n tensor = [tensor];\n }\n tensor.forEach(t => {\n if (t != null) {\n util.assert(\n t.dtype !== 'complex64',\n `${opName} does not support complex64 tensors.`);\n }\n });\n }\n\n slice(x: T, begin: number[], size: number[]): T {\n this.assertNotComplex(x, 'slice');\n\n const isContinous = isSliceContinous(x.shape, begin, size);\n if (isContinous) {\n const flatOffset = computeFlatOffset(begin, x.strides);\n const length = util.sizeFromShape(size);\n const vals = x.dataSync();\n return tensor(\n vals.subarray(flatOffset, flatOffset + length), size,\n x.dtype) as T;\n }\n\n const buffer = ops.buffer(size, x.dtype);\n const xBuf = x.bufferSync();\n for (let i = 0; i < buffer.size; ++i) {\n const loc = buffer.indexToLoc(i);\n const xLoc = loc.map((idx, j) => idx + begin[j]);\n buffer.values[i] = xBuf.get(...xLoc);\n }\n return buffer.toTensor() as T;\n }\n\n stridedSlice(\n x: T, begin: number[], end: number[], strides: number[],\n beginMask: number, endMask: number, ellipsisMask: number,\n newAxisMask: number, shrinkAxisMask: number): T {\n this.assertNotComplex(x, 'stridedSlice');\n\n const [beginIndex, size, shrinkAxis] = getStridedSlicedInfo(\n x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n newAxisMask, shrinkAxisMask);\n\n const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n\n if (shape.some(axis => axis === 0)) {\n return ops.tensor([], shape) as T;\n }\n\n const buffer = ops.buffer(size, x.dtype);\n const xBuf = x.bufferSync();\n for (let i = 0; i < buffer.size; i++) {\n const loc = buffer.indexToLoc(i);\n\n const newLoc: number[] = new Array(loc.length);\n for (let j = 0; j < newLoc.length; j++) {\n newLoc[j] = loc[j] * strides[j] + beginIndex[j];\n }\n buffer.set(xBuf.get(...newLoc), ...loc);\n }\n\n return buffer.toTensor().reshape(shape) as T;\n }\n\n unstack(x: Tensor, axis: number): Tensor[] {\n const num = x.shape[axis];\n const outShape: number[] = new Array(x.rank - 1);\n let outIndex = 0;\n for (let i = 0; i < x.rank; i++) {\n if (i !== axis) {\n outShape[outIndex++] = x.shape[i];\n }\n }\n\n const begin = new Array(x.rank).fill(0);\n const size = x.shape.slice();\n size[axis] = 1;\n const res = new Array(num);\n for (let i = 0; i < res.length; i++) {\n begin[axis] = i;\n res[i] = this.slice(x, begin, size).reshape(outShape);\n }\n return res;\n }\n\n reverse(x: T, axis: number[]): T {\n this.assertNotComplex(x, 'reverse');\n\n const buffer = ops.buffer(x.shape, x.dtype);\n const xBuf = x.bufferSync();\n\n for (let i = 0; i < buffer.size; i++) {\n const outLoc = buffer.indexToLoc(i);\n const inLoc = outLoc.slice();\n axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n buffer.set(xBuf.get(...inLoc), ...outLoc);\n }\n\n return buffer.toTensor() as T;\n }\n\n concat(tensors: Tensor[], axis: number): Tensor {\n this.assertNotComplex(tensors, 'concat');\n const tensors2D = tensors.map(t => {\n const innerSize = util.sizeFromShape(t.shape.slice(axis));\n return t.as2D(-1, innerSize);\n });\n const outShape =\n concat_util.computeOutShape(tensors2D.map(t => t.shape), 1 /* axis */);\n const values =\n ops.buffer(outShape as [number, number], tensors[0].dtype as 'float32')\n .values;\n if (tensors2D[0].shape[0] === 1) {\n // Use built-in TypedArray.set() method for speed.\n let offset = 0;\n tensors2D.forEach(t => {\n values.set(t.dataSync(), offset);\n offset += t.size;\n });\n } else {\n let colOffset = 0;\n tensors2D.forEach(t => {\n const tVals = t.dataSync();\n let tIdx = 0;\n for (let row = 0; row < t.shape[0]; ++row) {\n const resIdx = row * outShape[1] + colOffset;\n for (let col = 0; col < t.shape[1]; ++col) {\n values[resIdx + col] = tVals[tIdx++];\n }\n }\n colOffset += t.shape[1];\n });\n }\n const finalOutShape =\n concat_util.computeOutShape(tensors.map(t => t.shape), axis);\n return tensor(values, finalOutShape, tensors[0].dtype);\n }\n\n neg(x: T): T {\n this.assertNotComplex(x, 'neg');\n\n return this.multiply(ops.scalar(-1), x) as T;\n }\n\n add(a: Tensor, b: Tensor): Tensor {\n if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n return this.broadcastedBinaryComplexOp(\n a.cast('complex64'), b.cast('complex64'),\n (aReal, aImag, bReal, bImag) => {\n return {real: aReal + bReal, imag: aImag + bImag};\n }) as Tensor;\n }\n\n return this.broadcastedBinaryOp(\n a, b, upcastType(a.dtype, b.dtype),\n (aValue, bValue) => aValue + bValue) as Tensor;\n }\n\n addN(tensors: T[]): T {\n this.assertNotComplex(tensors, 'addN');\n\n const vals = tensors.map(t => t.dataSync());\n const result = ops.buffer(tensors[0].shape, tensors[0].dtype as 'float32');\n const resultVals = result.values;\n for (let i = 0; i < tensors.length; i++) {\n const currVals = vals[i];\n for (let j = 0; j < resultVals.length; j++) {\n resultVals[j] += currVals[j];\n }\n }\n return result.toTensor() as T;\n }\n\n subtract(a: Tensor, b: Tensor): Tensor {\n if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n return this.broadcastedBinaryComplexOp(\n a.cast('complex64'), b.cast('complex64'),\n (aReal, aImag, bReal, bImag) => {\n return {real: aReal - bReal, imag: aImag - bImag};\n }) as Tensor;\n }\n\n return this.broadcastedBinaryOp(\n a, b, upcastType(a.dtype, b.dtype),\n (aValue, bValue) => aValue - bValue) as Tensor;\n }\n\n pow(a: T, b: Tensor): T {\n this.assertNotComplex([a, b], 'pow');\n\n return this.broadcastedBinaryOp(\n a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue)) as\n T;\n }\n\n batchMatMul(\n a: Tensor3D, b: Tensor3D, transposeA: boolean,\n transposeB: boolean): Tensor3D {\n this.assertNotComplex([a, b], 'matMul');\n\n const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n const leftDim = transposeA ? a.shape[2] : a.shape[1];\n const rightDim = transposeB ? b.shape[1] : b.shape[2];\n const batchDim = a.shape[0];\n\n const aValues = a.dataSync();\n const bValues = b.dataSync();\n const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n [a.strides[0], 1, a.strides[1]] :\n [a.strides[0], a.strides[1], 1];\n const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n [1, b.strides[1], b.strides[0]] :\n [b.strides[1], 1, b.strides[0]];\n\n const size = leftDim * rightDim;\n const result = buffer([batchDim, leftDim, rightDim], a.dtype);\n const resVals = result.values as TypedArray;\n const blockSize = this.blockSize;\n\n for (let b = 0; b < batchDim; b++) {\n for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n // for when blockSize doesn't evenly divide the input\n const iBlock = Math.min(i0 + blockSize, leftDim);\n const jBlock = Math.min(j0 + blockSize, rightDim);\n const kBlock = Math.min(k0 + blockSize, sharedDim);\n\n for (let i = i0; i < iBlock; i++) {\n for (let j = j0; j < jBlock; j++) {\n let sum = 0.0;\n\n for (let k = k0; k < kBlock; k++) {\n sum += aValues[b * aBatch + i * aOuterStep + k * aInnerStep] *\n bValues[k * bInnerStep + j * bOuterStep + b * bBatch];\n }\n resVals[b * size + (i * rightDim + j)] += sum;\n }\n }\n }\n }\n }\n }\n return result.toTensor() as Tensor3D;\n }\n\n fusedBatchMatMul(\n a: Tensor3D, b: Tensor3D, transposeA: boolean, transposeB: boolean,\n bias?: Tensor, activation?: Activation): Tensor3D {\n let result = this.batchMatMul(a, b, transposeA, transposeB);\n if (bias) {\n result = this.add(result, bias) as Tensor3D;\n }\n if (activation) {\n result = mapActivation(this, activation, result) as Tensor3D;\n }\n return result;\n }\n\n multiply(a: Tensor, b: Tensor): Tensor {\n if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n return this.broadcastedBinaryComplexOp(\n a.cast('complex64'), b.cast('complex64'),\n (aReal, aImag, bReal, bImag) => {\n return {\n real: aReal * bReal - aImag * bImag,\n imag: aReal * bImag + aImag * bReal\n };\n }) as Tensor;\n }\n\n return this.broadcastedBinaryOp(\n a, b, upcastType(a.dtype, b.dtype),\n (aValue, bValue) => aValue * bValue) as Tensor;\n }\n\n realDivide(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'realDivide');\n\n const op = (a: number, b: number) => a / b;\n const outputDtype = 'float32';\n return this.broadcastedBinaryOp(a, b, outputDtype, op) as Tensor;\n }\n\n floorDiv(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'floorDiv');\n\n const op = (a: number, b: number) => Math.floor(a / b);\n const outputDtype = 'int32';\n return this.broadcastedBinaryOp(a, b, outputDtype, op) as Tensor;\n }\n\n sum(x: Tensor, axes: number[]): Tensor {\n this.assertNotComplex(x, 'sum');\n\n axis_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const resultDtype = upcastType(x.dtype, 'int32');\n const result = ops.zeros(outShape, resultDtype);\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = result.dataSync();\n\n const aVals = x.dataSync();\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let sum = 0;\n for (let j = 0; j < reduceSize; ++j) {\n sum += aVals[offset + j];\n }\n vals[i] = sum;\n }\n return result;\n }\n\n prod(x: Tensor, axes: number[]): Tensor {\n this.assertNotComplex(x, 'sum');\n\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const resultDtype = upcastType(x.dtype, 'int32');\n const result = ops.zeros(outShape, resultDtype);\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = result.dataSync();\n\n const aVals = x.dataSync();\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let prod = 1;\n for (let j = 0; j < reduceSize; ++j) {\n prod *= aVals[offset + j];\n }\n vals[i] = prod;\n }\n return result;\n }\n\n unsortedSegmentSum(\n x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n this.assertNotComplex(x, 'unsortedSegmentSum');\n\n const res = [];\n\n // Reshape the segment id's so that they can be broadcast with\n // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n const numIters = x.rank - segmentIds.rank;\n for (let i = 0; i < numIters; ++i) {\n segmentIds = segmentIds.expandDims(i + 1);\n }\n\n for (let i = 0; i < numSegments; ++i) {\n const segmentId = ops.scalar(i, 'int32');\n const mask = ops.equal(segmentId, segmentIds).asType('float32');\n const sum = mask.mul(x).sum(0);\n res.push(sum);\n }\n\n return ops.stack(res);\n }\n\n argMin(x: Tensor, axis: number): Tensor {\n this.assertNotComplex(x, 'argMin');\n\n const axes = [axis];\n axis_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const result = ops.zeros(outShape, 'int32');\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = result.dataSync();\n\n const aVals = x.dataSync();\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let min = aVals[offset];\n let minIndex = 0;\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (value < min) {\n min = value;\n minIndex = j;\n }\n }\n vals[i] = minIndex;\n }\n return result;\n }\n\n argMax(x: Tensor, axis: number): Tensor {\n this.assertNotComplex(x, 'argMax');\n\n const axes = [axis];\n axis_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const result = ops.zeros(outShape, 'int32');\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = result.dataSync();\n\n const aVals = x.dataSync();\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let max = aVals[offset];\n let maxIndex = 0;\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (value > max) {\n max = value;\n maxIndex = j;\n }\n }\n vals[i] = maxIndex;\n }\n return result;\n }\n\n cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n Tensor {\n this.assertNotComplex(x, 'cumsum');\n\n if (axis !== x.rank - 1) {\n throw new Error(\n `backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n `but got axis=${axis}`);\n }\n const resultDtype = upcastType(x.dtype, 'int32');\n const result = ops.zeros(x.shape, resultDtype);\n const vals = result.dataSync();\n\n const aVals = x.dataSync();\n const finalDim = x.shape[x.rank - 1];\n const indexAdjuster = reverse ?\n (i: number, j: number) => i + finalDim - j - 1 :\n (i: number, j: number) => i + j;\n for (let i = 0; i < aVals.length; i += finalDim) {\n for (let j = 0; j < finalDim; j++) {\n const idx = indexAdjuster(i, j);\n if (j === 0) {\n vals[idx] = exclusive ? 0 : aVals[idx];\n } else {\n const prevIdx = indexAdjuster(i, j - 1);\n vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n aVals[idx] + vals[prevIdx];\n }\n }\n }\n return result;\n }\n\n equal(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'equal');\n\n return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n return (aVal === bVal) ? 1 : 0;\n });\n }\n\n notEqual(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'notEqual');\n\n return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n return (aVal !== bVal) ? 1 : 0;\n });\n }\n\n less(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'less');\n\n return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n return (aVal < bVal) ? 1 : 0;\n });\n }\n\n lessEqual(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'lessEqual');\n\n return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n return (aVal <= bVal) ? 1 : 0;\n });\n }\n\n greater(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'greater');\n\n return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n return (aVal > bVal) ? 1 : 0;\n });\n }\n\n greaterEqual(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'greaterEqual');\n\n return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n return (aVal >= bVal) ? 1 : 0;\n });\n }\n\n logicalNot(x: T): T {\n this.assertNotComplex(x, 'logicalNot');\n\n const values = x.dataSync();\n const newValues = new Uint8Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n newValues[i] = values[i] ? 0 : 1;\n }\n return Tensor.make(x.shape, {values: newValues}, 'bool') as T;\n }\n\n logicalAnd(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'logicalAnd');\n\n return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n return aVal && bVal;\n });\n }\n\n logicalOr(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'logicalOr');\n\n return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n return aVal || bVal;\n });\n }\n\n select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([condition, a, b], 'select');\n\n const values = condition.dataSync();\n const aValues = a.dataSync();\n const bValues = b.dataSync();\n const result = ops.zeros(a.shape, upcastType(a.dtype, b.dtype));\n const newValues = result.dataSync();\n let index = 0;\n const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n 1 :\n a.shape[1];\n\n for (let i = 0; i < values.length; i++) {\n for (let j = 0; j < offset; j++) {\n if (values[i] === 1) {\n newValues[index++] = aValues[i];\n } else {\n newValues[index++] = bValues[i];\n }\n }\n }\n return result;\n }\n\n where(condition: Tensor): Tensor2D {\n this.assertNotComplex([condition], 'where');\n\n const condVals = condition.dataSync();\n return whereImpl(condition.shape, condVals);\n }\n\n topk(x: T, k: number, sorted: boolean): [T, T] {\n this.assertNotComplex(x, 'topk');\n\n const xVals = x.dataSync();\n return topkImpl(xVals, x.shape, x.dtype as NumericDataType, k, sorted);\n }\n\n min(x: Tensor, axes: number[]): Tensor {\n this.assertNotComplex(x, 'min');\n\n axis_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const result = ops.zeros(outShape, x.dtype);\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = result.dataSync();\n\n const aVals = x.dataSync();\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let min = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (value < min) {\n min = value;\n }\n }\n vals[i] = min;\n }\n return result;\n }\n\n minimum(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'minimum');\n\n return this.broadcastedBinaryOp(\n a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n }\n\n mod(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'mod');\n\n return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n const rem = aVal % bVal;\n if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n return rem;\n } else {\n return (rem + bVal) % bVal;\n }\n });\n }\n\n max(x: Tensor, axes: number[]): Tensor {\n this.assertNotComplex(x, 'max');\n\n axis_util.assertAxesAreInnerMostDims('max', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const result = ops.zeros(outShape, x.dtype);\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = result.dataSync();\n\n const aVals = x.dataSync();\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let max = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n if (value > max) {\n max = value;\n }\n }\n vals[i] = max;\n }\n return result;\n }\n\n maximum(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'maximum');\n\n return this.broadcastedBinaryOp(\n a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n }\n\n all(x: Tensor, axes: number[]): Tensor {\n this.assertNotComplex(x, 'all');\n\n axis_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const result = ops.zeros(outShape, x.dtype);\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = result.dataSync();\n\n const aVals = x.dataSync();\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let all = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n all = all && value;\n }\n vals[i] = all;\n }\n return result;\n }\n\n any(x: Tensor, axes: number[]): Tensor {\n this.assertNotComplex(x, 'any');\n\n axis_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n const [outShape, reduceShape] =\n axis_util.computeOutAndReduceShapes(x.shape, axes);\n const result = ops.zeros(outShape, x.dtype);\n const reduceSize = util.sizeFromShape(reduceShape);\n const vals = result.dataSync();\n\n const aVals = x.dataSync();\n for (let i = 0; i < vals.length; ++i) {\n const offset = i * reduceSize;\n let anyVal = aVals[offset];\n for (let j = 0; j < reduceSize; ++j) {\n const value = aVals[offset + j];\n anyVal = anyVal || value;\n }\n vals[i] = anyVal;\n }\n return result;\n }\n\n squaredDifference(a: Tensor, b: Tensor): Tensor {\n this.assertNotComplex([a, b], 'squaredDifference');\n\n return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n const diff = aVal - bVal;\n return diff * diff;\n });\n }\n\n ceil(x: T): T {\n this.assertNotComplex(x, 'ceil');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n newValues[i] = Math.ceil(values[i]);\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n floor(x: T): T {\n this.assertNotComplex(x, 'floor');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n newValues[i] = Math.floor(values[i]);\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n sign(x: T): T {\n this.assertNotComplex(x, 'x');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n if (values[i] < 0) {\n newValues[i] = -1;\n } else if (values[i] > 0) {\n newValues[i] = 1;\n } else {\n newValues[i] = 0;\n }\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n round(x: T): T {\n this.assertNotComplex(x, 'round');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n // The algorithm is based on banker's rounding.\n const base = Math.floor(values[i]);\n if (values[i] - base < 0.5) {\n newValues[i] = Math.floor(values[i]);\n } else if (values[i] - base > 0.5) {\n newValues[i] = Math.ceil(values[i]);\n } else {\n if (base % 2.0 === 0.0) {\n newValues[i] = base;\n } else {\n newValues[i] = base + 1.0;\n }\n }\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n exp(x: T): T {\n this.assertNotComplex(x, 'exp');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n newValues[i] = Math.exp(values[i]);\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n expm1(x: T): T {\n this.assertNotComplex(x, 'expm1');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n newValues[i] = Math.expm1(values[i]);\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n log(x: T): T {\n this.assertNotComplex(x, 'log');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n const value = values[i];\n newValues[i] = Math.log(value);\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n log1p(x: T): T {\n this.assertNotComplex(x, 'log1p');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n const value = values[i];\n newValues[i] = Math.log1p(value);\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n sqrt(x: T): T {\n this.assertNotComplex(x, 'sqrt');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n const value = values[i];\n newValues[i] = Math.sqrt(value);\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n rsqrt(x: T): T {\n this.assertNotComplex(x, 'rsqrt');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n const value = values[i];\n newValues[i] = 1 / Math.sqrt(value);\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n square(x: T): T {\n this.assertNotComplex(x, 'square');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n const value = values[i];\n newValues[i] = value * value;\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n reciprocal(x: T): T {\n this.assertNotComplex(x, 'reciprocal');\n\n const values = x.dataSync();\n const newValues = new Float32Array(values.length);\n for (let i = 0; i < values.length; ++i) {\n newValues[i] = 1 / values[i];\n }\n return Tensor.make(x.shape, {values: newValues}) as T;\n }\n\n linear(x: T): T {\n return x;\n }\n\n relu(x: T): T {\n this.assertNotComplex(x, 'relu');\n\n const res = ops.zeros(x.shape, x.dtype);\n const resVals = res.dataSync();\n const inVals = x.dataSync();\n for (let i = 0; i < inVals.length; ++i) {\n resVals[i] = Math.max(0, inVals[i]);\n }\n return res as T;\n }\n\n prelu(x: T, a: T): T {\n this.assertNotComplex([x, a], 'prelu');\n\n return this.broadcastedBinaryOp(\n x, a, x.dtype,\n (xValue, aValue) => xValue < 0 ? aValue * xValue : xValue) as T;\n }\n\n elu(x: T): T {\n this.assertNotComplex(x, 'elu');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n const v = values[i];\n if (v >= 0) {\n resultValues[i] = v;\n } else {\n resultValues[i] = (Math.exp(v) - 1);\n }\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n eluDer(dy: T, y: T): T {\n this.assertNotComplex([dy, y], 'eluDer');\n\n const resultValues = new Float32Array(y.size);\n const values = y.dataSync();\n const dyValues = dy.dataSync();\n for (let i = 0; i < values.length; ++i) {\n const v = values[i];\n if (v >= 1) {\n resultValues[i] = dyValues[i];\n } else {\n resultValues[i] = dyValues[i] * (v + 1);\n }\n }\n return Tensor.make(y.shape, {values: resultValues}) as T;\n }\n\n selu(x: T): T {\n this.assertNotComplex(x, 'selu');\n\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n const scaleAlpha = selu_util.SELU_SCALEALPHA;\n const scale = selu_util.SELU_SCALE;\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n const v = values[i];\n if (v >= 0) {\n resultValues[i] = scale * v;\n } else {\n resultValues[i] = scaleAlpha * (Math.exp(v) - 1);\n }\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n clip(x: T, min: number, max: number): T {\n this.assertNotComplex(x, 'clip');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n const v = values[i];\n resultValues[i] = v > max ? max : (v < min ? min : v);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n abs(x: T): T {\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.abs(values[i]);\n }\n\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n complexAbs(x: T): T {\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n\n for (let i = 0; i < x.size; ++i) {\n const real = values[i * 2];\n const imag = values[i * 2 + 1];\n resultValues[i] = Math.hypot(real, imag);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n int(x: T): T {\n this.assertNotComplex(x, 'int');\n\n const resultValues = new Int32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = values[i];\n }\n return Tensor.make(x.shape, {values: resultValues}, 'int32');\n }\n\n sigmoid(x: T): T {\n this.assertNotComplex(x, 'sigmoid');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = 1 / (1 + Math.exp(-values[i]));\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n softplus(x: T): T {\n this.assertNotComplex(x, 'softplus');\n\n // mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n\n // epsilon is the difference between 1.0 and the next representable float.\n // For a single precision 32 bit float this should be 2^-23, see:\n // https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n const epsilon = 1.1920928955078125e-7;\n const threshold = Math.log(epsilon) + 2.0;\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n\n for (let i = 0; i < values.length; ++i) {\n // Value above which exp(x) may overflow, but softplus(x) == x\n // is within machine epsilon.\n const tooLarge = values[i] > -threshold;\n\n // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n // is within machine epsilon.\n const tooSmall = values[i] < threshold;\n\n const expX = Math.exp(values[i]);\n let result;\n\n if (tooSmall) {\n result = expX;\n } else if (tooLarge) {\n result = values[i];\n } else {\n result = Math.log(1.0 + expX);\n }\n resultValues[i] = result;\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n sin(x: T): T {\n this.assertNotComplex(x, 'sin');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.sin(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n cos(x: T): T {\n this.assertNotComplex(x, 'cos');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.cos(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n tan(x: T): T {\n this.assertNotComplex(x, 'tan');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.tan(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n asin(x: T): T {\n this.assertNotComplex(x, 'asin');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.asin(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n acos(x: T): T {\n this.assertNotComplex(x, 'acos');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.acos(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n atan(x: T): T {\n this.assertNotComplex(x, 'atan');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.atan(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n atan2(a: T, b: T): T {\n this.assertNotComplex([a, b], 'atan2');\n\n return this.broadcastedBinaryOp(\n a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue)) as\n T;\n }\n\n sinh(x: T): T {\n this.assertNotComplex(x, 'sinh');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.sinh(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n cosh(x: T): T {\n this.assertNotComplex(x, 'cosh');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.cosh(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n tanh(x: T): T {\n this.assertNotComplex(x, 'tanh');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = util.tanh(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n asinh(x: T): T {\n this.assertNotComplex(x, 'asinh');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.asinh(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n acosh(x: T): T {\n this.assertNotComplex(x, 'acosh');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.acosh(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n atanh(x: T): T {\n this.assertNotComplex(x, 'atanh');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n resultValues[i] = Math.atanh(values[i]);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n erf(x: T): T {\n this.assertNotComplex(x, 'erf');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n const p = erf_util.ERF_P;\n const a1 = erf_util.ERF_A1;\n const a2 = erf_util.ERF_A2;\n const a3 = erf_util.ERF_A3;\n const a4 = erf_util.ERF_A4;\n const a5 = erf_util.ERF_A5;\n for (let i = 0; i < values.length; ++i) {\n const v = values[i];\n const t = 1.0 / (1.0 + p * v);\n resultValues[i] = 1.0 -\n (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n Math.exp(-v * v);\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n step(x: T, alpha = 0): T {\n this.assertNotComplex(x, 'step');\n\n const resultValues = new Float32Array(x.size);\n const values = x.dataSync();\n for (let i = 0; i < values.length; ++i) {\n const value = values[i];\n if (isNaN(value)) {\n resultValues[i] = NaN;\n } else {\n resultValues[i] = value > 0 ? 1 : alpha;\n }\n }\n return Tensor.make(x.shape, {values: resultValues}) as T;\n }\n\n conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n this.assertNotComplex([x, filter], 'conv2d');\n\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const padLeft = convInfo.padInfo.left;\n const padTop = convInfo.padInfo.top;\n const y = ops.buffer(convInfo.outShape, x.dtype as 'float32');\n\n const xVals = x.dataSync();\n const wVals = filter.dataSync();\n const yVals = y.values;\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * x.strides[0];\n const yOffset1 = b * y.strides[0];\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const yOffset2 = yOffset1 + yR * y.strides[1];\n const xRCorner = yR * convInfo.strideHeight - padLeft;\n for (let wR = 0; wR < filterHeight; wR++) {\n const xR = xRCorner + wR * dilationHeight;\n if (xR < 0 || xR >= convInfo.inHeight) {\n continue;\n }\n const wOffset1 = wR * filter.strides[0];\n const xOffset2 = xOffset1 + xR * x.strides[1];\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const yOffset3 = yOffset2 + yC * convInfo.outChannels;\n const xCCorner = yC * convInfo.strideWidth - padTop;\n for (let wC = 0; wC < filterWidth; wC++) {\n const xC = xCCorner + wC * dilationWidth;\n if (xC < 0 || xC >= convInfo.inWidth) {\n continue;\n }\n const wOffset2 = wOffset1 + wC * filter.strides[1];\n const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n let wOffset3 = wOffset2;\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const xVal = xVals[xOffset3 + d1];\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n yVals[yOffset3 + d2] += xVal * wVals[wOffset3 + d2];\n }\n wOffset3 += convInfo.outChannels;\n }\n }\n }\n }\n }\n }\n return y.toTensor() as Tensor4D;\n }\n\n conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dilationDepth = convInfo.dilationDepth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const padFront = convInfo.padInfo.front;\n const padLeft = convInfo.padInfo.left;\n const padTop = convInfo.padInfo.top;\n const y = ops.buffer(convInfo.outShape, x.dtype as 'float32');\n\n const xVals = x.dataSync();\n const wVals = filter.dataSync();\n const yVals = y.values;\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * x.strides[0];\n const yOffset1 = b * y.strides[0];\n for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n const yOffset2 = yOffset1 + yF * y.strides[1];\n const xFCorner = yF * convInfo.strideDepth - padFront;\n for (let wF = 0; wF < filterDepth; wF++) {\n const xF = xFCorner + wF * dilationDepth;\n if (xF < 0 || xF >= convInfo.inDepth) {\n continue;\n }\n const wOffset1 = wF * filter.strides[0];\n const xOffset2 = xOffset1 + xF * x.strides[1];\n\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const yOffset3 = yOffset2 + yR * y.strides[2];\n const xRCorner = yR * convInfo.strideHeight - padTop;\n for (let wR = 0; wR < filterHeight; wR++) {\n const xR = xRCorner + wR * dilationHeight;\n if (xR < 0 || xR >= convInfo.inHeight) {\n continue;\n }\n const wOffset2 = wOffset1 + wR * filter.strides[1];\n const xOffset3 = xOffset2 + xR * x.strides[2];\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n const xCCorner = yC * convInfo.strideWidth - padLeft;\n for (let wC = 0; wC < filterWidth; wC++) {\n const xC = xCCorner + wC * dilationWidth;\n if (xC < 0 || xC >= convInfo.inWidth) {\n continue;\n }\n const wOffset3 = wOffset2 + wC * filter.strides[2];\n const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n let wOffset4 = wOffset3;\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const xVal = xVals[xOffset4 + d1];\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n }\n wOffset4 += convInfo.outChannels;\n }\n }\n }\n }\n }\n }\n }\n }\n return y.toTensor();\n }\n\n conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n this.assertNotComplex([dy, filter], 'conv2dDerInput');\n\n const dx = ops.buffer(convInfo.inShape, 'float32');\n const dxValues = dx.values;\n const [dxS0, dxS1, dxS2] = dx.strides;\n const dyValues = dy.dataSync();\n const [dyS0, dyS1, dyS2] = dy.strides;\n const fltValues = filter.dataSync();\n const [fltS0, fltS1, fltS2] = filter.strides;\n const {\n batchSize,\n filterHeight,\n filterWidth,\n inChannels,\n inHeight,\n inWidth,\n outChannels,\n outHeight,\n outWidth,\n strideHeight,\n strideWidth\n } = convInfo;\n const topPad = filterHeight - 1 - convInfo.padInfo.top;\n const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n for (let b = 0; b < batchSize; ++b) {\n for (let d1 = 0; d1 < inChannels; ++d1) {\n for (let xR = 0; xR < inHeight; ++xR) {\n const xRCorner = xR - topPad;\n const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n const yRMax =\n Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n for (let xC = 0; xC < inWidth; ++xC) {\n const xCCorner = xC - leftPad;\n const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n const yCMax =\n Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n let dotProd = 0;\n for (let yR = xRMin; yR < yRMax; ++yR) {\n const wR = yR * strideHeight - xRCorner;\n\n for (let yC = xCMin; yC < yCMax; ++yC) {\n const wC = yC * strideWidth - xCCorner;\n const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n for (let d2 = 0; d2 < outChannels; ++d2) {\n const pixel = dyValues[dyOffset + d2];\n const weight = fltValues[fltOffset + d2];\n dotProd += pixel * weight;\n }\n }\n }\n dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n }\n }\n }\n }\n return dx.toTensor();\n }\n\n conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n Tensor5D {\n const dx = ops.buffer(convInfo.inShape, 'float32');\n const dxValues = dx.values;\n const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n const dyValues = dy.dataSync();\n const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n const fltValues = filter.dataSync();\n const [fltS0, fltS1, fltS2, fltS3] = filter.strides;\n const {\n batchSize,\n filterDepth,\n filterHeight,\n filterWidth,\n inChannels,\n inDepth,\n inHeight,\n inWidth,\n outChannels,\n outDepth,\n outHeight,\n outWidth,\n strideDepth,\n strideHeight,\n strideWidth\n } = convInfo;\n const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n const topPad = filterHeight - 1 - convInfo.padInfo.top;\n const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n for (let b = 0; b < batchSize; ++b) {\n for (let d1 = 0; d1 < inChannels; ++d1) {\n // Frames of depth\n for (let xF = 0; xF < inDepth; ++xF) {\n const xFCorner = xF - frontPad;\n const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n const yFMax =\n Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n\n // Rows as per standard 2d matrix notation\n for (let xR = 0; xR < inHeight; ++xR) {\n const xRCorner = xR - topPad;\n const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n const yRMax =\n Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n // Columns as per standard 2d matrix notation\n for (let xC = 0; xC < inWidth; ++xC) {\n const xCCorner = xC - leftPad;\n const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n const yCMax =\n Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n let dotProd = 0;\n for (let yF = xFMin; yF < yFMax; ++yF) {\n const wF = yF * strideDepth - xFCorner;\n\n for (let yR = xRMin; yR < yRMax; ++yR) {\n const wR = yR * strideHeight - xRCorner;\n\n for (let yC = xCMin; yC < yCMax; ++yC) {\n const wC = yC * strideWidth - xCCorner;\n const dyOffset =\n dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n fltS1 * (filterHeight - 1 - wR) +\n fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n\n for (let d2 = 0; d2 < outChannels; ++d2) {\n const pixel = dyValues[dyOffset + d2];\n const weight = fltValues[fltOffset + d2];\n dotProd += pixel * weight;\n }\n }\n }\n }\n dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n dotProd;\n }\n }\n }\n }\n }\n return dx.toTensor();\n }\n\n conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n this.assertNotComplex([x, dy], 'conv2dDerFilter');\n\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dW = ops.buffer(convInfo.filterShape, 'float32');\n\n const leftPad = convInfo.padInfo.left;\n const topPad = convInfo.padInfo.top;\n const xBuf = x.bufferSync();\n const dyBuf = dy.bufferSync();\n for (let wR = 0; wR < filterHeight; ++wR) {\n const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n const yRMax = Math.min(\n convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n\n for (let wC = 0; wC < filterWidth; ++wC) {\n const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n const yCMax = Math.min(\n convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n // Need to convolve.\n let dotProd = 0;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let yR = yRMin; yR < yRMax; ++yR) {\n const xR = wR + yR * strideHeight - topPad;\n for (let yC = yCMin; yC < yCMax; ++yC) {\n const xC = wC + yC * strideWidth - leftPad;\n dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n }\n }\n }\n dW.set(dotProd, wR, wC, d1, d2);\n }\n }\n }\n }\n return dW.toTensor();\n }\n\n conv3dDerFilter(x: Tensor5D, dy: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n const strideDepth = convInfo.strideDepth;\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const filterDepth = convInfo.filterDepth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n\n const dw = ops.buffer(convInfo.filterShape, 'float32');\n const dwValues = dw.values;\n const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n const dyValues = dy.dataSync();\n const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n const xValues = x.dataSync();\n const [xS0, xS1, xS2, xS3] = x.strides;\n\n const frontPad = convInfo.padInfo.front;\n const leftPad = convInfo.padInfo.left;\n const topPad = convInfo.padInfo.top;\n\n for (let wF = 0; wF < filterDepth; ++wF) {\n const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n const yFMax = Math.min(\n convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n const wOffset1 = wF * dwS0;\n\n for (let wR = 0; wR < filterHeight; ++wR) {\n const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n const yRMax = Math.min(\n convInfo.outHeight,\n (convInfo.inHeight + topPad - wR) / strideHeight);\n const wOffset2 = wR * dwS1 + wOffset1;\n\n for (let wC = 0; wC < filterWidth; ++wC) {\n const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n const yCMax = Math.min(\n convInfo.outWidth,\n (convInfo.inWidth + leftPad - wC) / strideWidth);\n const wOffset3 = wC * dwS2 + wOffset2;\n\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const wOffset4 = d1 * dwS3 + wOffset3;\n\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n let dotProd = 0;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * xS0;\n const yOffset1 = b * dyS0;\n\n for (let yF = yFMin; yF < yFMax; ++yF) {\n const xF = wF + yF * strideDepth - frontPad;\n const xOffset2 = xF * xS1 + xOffset1;\n const yOffset2 = yF * dyS1 + yOffset1;\n\n for (let yR = yRMin; yR < yRMax; ++yR) {\n const xR = wR + yR * strideHeight - topPad;\n const xOffset3 = xR * xS2 + xOffset2;\n const yOffset3 = yR * dyS2 + yOffset2;\n\n for (let yC = yCMin; yC < yCMax; ++yC) {\n const xC = wC + yC * strideWidth - leftPad;\n const xOffset4 = xC * xS3 + xOffset3;\n const yOffset4 = yC * dyS3 + yOffset3;\n\n dotProd +=\n xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n }\n }\n }\n }\n dwValues[wOffset4 + d2] = dotProd;\n }\n }\n }\n }\n }\n return dw.toTensor();\n }\n\n depthwiseConv2D(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n this.assertNotComplex([x, filter], 'depthwiseConv2D');\n\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const padLeft = convInfo.padInfo.left;\n const padTop = convInfo.padInfo.top;\n const chMul = convInfo.outChannels / convInfo.inChannels;\n const y = ops.buffer(convInfo.outShape, x.dtype as 'float32');\n const xVals = x.dataSync();\n const wVals = filter.dataSync();\n const yVals = y.values;\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const xOffset1 = b * x.strides[0];\n const yOffset1 = b * y.strides[0];\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const yOffset2 = yOffset1 + yR * y.strides[1];\n const xRCorner = yR * convInfo.strideHeight - padLeft;\n for (let wR = 0; wR < filterHeight; ++wR) {\n const xR = xRCorner + wR * dilationHeight;\n if (xR < 0 || xR >= convInfo.inHeight) {\n continue;\n }\n const wOffset1 = wR * filter.strides[0];\n const xOffset2 = xOffset1 + xR * x.strides[1];\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const yOffset3 = yOffset2 + yC * y.strides[2];\n const xCCorner = yC * convInfo.strideWidth - padTop;\n for (let wC = 0; wC < filterWidth; ++wC) {\n const xC = xCCorner + wC * dilationWidth;\n if (xC < 0 || xC >= convInfo.inWidth) {\n continue;\n }\n const wOffset2 = wOffset1 + wC * filter.strides[1];\n const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n let yOffset4 = yOffset3;\n let wOffset3 = wOffset2;\n for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n const xVal = xVals[xOffset3 + d1];\n for (let q = 0; q < chMul; ++q) {\n yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n }\n yOffset4 += chMul;\n wOffset3 += chMul;\n }\n }\n }\n }\n }\n }\n\n return y.toTensor() as Tensor4D;\n }\n\n depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n this.assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');\n\n const dx = ops.buffer(convInfo.inShape, 'float32');\n const dxValues = dx.values;\n const [dxS0, dxS1, dxS2] = dx.strides;\n const dyValues = dy.dataSync();\n const [dyS0, dyS1, dyS2] = dy.strides;\n const fltValues = filter.dataSync();\n const [fltS0, fltS1, fltS2] = filter.strides;\n const {\n batchSize,\n filterHeight,\n filterWidth,\n inChannels,\n inHeight,\n inWidth,\n outChannels,\n outHeight,\n outWidth,\n strideHeight,\n strideWidth\n } = convInfo;\n const topPad = filterHeight - 1 - convInfo.padInfo.top;\n const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n const chMul = outChannels / inChannels;\n\n for (let b = 0; b < batchSize; ++b) {\n for (let d1 = 0; d1 < inChannels; ++d1) {\n for (let xR = 0; xR < inHeight; ++xR) {\n const xRCorner = xR - topPad;\n const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n const yRMax =\n Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n for (let xC = 0; xC < inWidth; ++xC) {\n const xCCorner = xC - leftPad;\n const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n const yCMax =\n Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n let dotProd = 0;\n for (let yR = xRMin; yR < yRMax; ++yR) {\n const wR = yR * strideHeight - xRCorner;\n\n for (let yC = xCMin; yC < yCMax; ++yC) {\n const wC = yC * strideWidth - xCCorner;\n const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n for (let dm = 0; dm < chMul; ++dm) {\n const d2 = d1 * chMul + dm;\n const pixel = dyValues[dyOffset + d2];\n const weight = fltValues[fltOffset + dm];\n dotProd += pixel * weight;\n }\n }\n }\n dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n }\n }\n }\n }\n return dx.toTensor();\n }\n\n depthwiseConv2DDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n this.assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');\n\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dW = ops.buffer(convInfo.filterShape, 'float32');\n\n const leftPad = convInfo.padInfo.left;\n const topPad = convInfo.padInfo.top;\n const chMul = convInfo.outChannels / convInfo.inChannels;\n\n const xBuf = x.bufferSync();\n const dyBuf = dy.bufferSync();\n for (let wR = 0; wR < filterHeight; ++wR) {\n const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n const yRMax = Math.min(\n convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n\n for (let wC = 0; wC < filterWidth; ++wC) {\n const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n const yCMax = Math.min(\n convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n\n for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n const d1 = Math.trunc(d2 / chMul);\n const dm = d2 % chMul;\n\n let dotProd = 0;\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let yR = yRMin; yR < yRMax; ++yR) {\n const xR = wR + yR * strideHeight - topPad;\n for (let yC = yCMin; yC < yCMax; ++yC) {\n const xC = wC + yC * strideWidth - leftPad;\n dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n }\n }\n }\n dW.set(dotProd, wR, wC, d1, dm);\n }\n }\n }\n return dW.toTensor();\n }\n\n tile(x: T, reps: number[]): T {\n this.assertNotComplex(x, 'tile');\n\n const newShape: number[] = new Array(x.rank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = x.shape[i] * reps[i];\n }\n const result = ops.buffer(newShape, x.dtype);\n const xBuf = x.bufferSync();\n for (let i = 0; i < result.values.length; ++i) {\n const newLoc = result.indexToLoc(i);\n\n const originalLoc: number[] = new Array(x.rank);\n for (let i = 0; i < originalLoc.length; i++) {\n originalLoc[i] = newLoc[i] % x.shape[i];\n }\n\n const originalIndex = xBuf.locToIndex(originalLoc);\n\n result.values[i] = xBuf.values[originalIndex];\n }\n return result.toTensor() as T;\n }\n\n pad(\n x: T, paddings: Array<[number, number]>, constantValue: number): T {\n this.assertNotComplex(x, 'pad');\n\n const outShape = paddings.map(\n (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n const start = paddings.map(p => p[0]);\n const xBuffer = x.bufferSync();\n const buffer = ops.buffer(outShape, x.dtype as 'float32');\n if (constantValue !== 0) {\n buffer.values.fill(constantValue);\n }\n\n for (let i = 0; i < x.size; i++) {\n const coords = xBuffer.indexToLoc(i);\n const outCoords = coords.map((c, i) => c + start[i]);\n buffer.set(xBuffer.get(...coords), ...outCoords);\n }\n return buffer.toTensor() as T;\n }\n\n transpose(x: T, perm: number[]): T {\n this.assertNotComplex(x, 'transpose');\n\n const newShape: number[] = new Array(x.rank);\n for (let i = 0; i < newShape.length; i++) {\n newShape[i] = x.shape[perm[i]];\n }\n const values = x.dataSync();\n const result = buffer(newShape, x.dtype);\n\n const xBuf = x.bufferSync();\n for (let i = 0; i < x.size; ++i) {\n const loc = xBuf.indexToLoc(i);\n\n // Permute location.\n const newLoc: number[] = new Array(loc.length);\n for (let i = 0; i < newLoc.length; i++) {\n newLoc[i] = loc[perm[i]];\n }\n\n const newIndex = result.locToIndex(newLoc);\n result.values[newIndex] = values[i];\n }\n return result.toTensor() as T;\n }\n\n gather(x: T, indices: Tensor1D, axis: number): T {\n this.assertNotComplex([x, indices], 'gather');\n\n const newShape: number[] = x.shape.slice();\n const indicesValues = indices.dataSync();\n newShape[axis] = indicesValues.length;\n const result = buffer(newShape, x.dtype);\n const xBuf = x.bufferSync();\n\n for (let i = 0; i < result.size; ++i) {\n const newLoc = result.indexToLoc(i);\n\n const originalLoc: number[] = newLoc.slice();\n originalLoc[axis] = indicesValues[newLoc[axis]];\n\n const originalIndex = xBuf.locToIndex(originalLoc);\n result.values[i] = xBuf.values[originalIndex];\n }\n return result.toTensor() as T;\n }\n\n batchToSpaceND(\n x: T, blockShape: number[], crops: number[][]): T {\n this.assertNotComplex([x], 'batchToSpaceND');\n\n const prod = blockShape.reduce((a, b) => a * b);\n\n const reshaped = array_ops_util.getReshaped(x.shape, blockShape, prod);\n const permuted =\n array_ops_util.getPermuted(reshaped.length, blockShape.length);\n const reshapedPermuted =\n array_ops_util.getReshapedPermuted(x.shape, blockShape, prod);\n const sliceBeginCoords =\n array_ops_util.getSliceBeginCoords(crops, blockShape.length);\n const sliceSize =\n array_ops_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n\n return x.reshape(reshaped)\n .transpose(permuted)\n .reshape(reshapedPermuted)\n .slice(sliceBeginCoords, sliceSize) as T;\n }\n\n spaceToBatchND(\n x: T, blockShape: number[], paddings: Array<[number, number]>): T {\n this.assertNotComplex([x], 'spaceToBatchND');\n\n const prod = blockShape.reduce((a, b) => a * b);\n\n const completePaddings: Array<[number, number]> = [[0, 0]];\n completePaddings.push(...paddings);\n for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n completePaddings.push([0, 0]);\n }\n\n const paddedX = x.pad(completePaddings);\n\n const reshapedPaddedShape =\n array_ops_util.getReshaped(paddedX.shape, blockShape, prod, false);\n const permutedReshapedPaddedPermutation = array_ops_util.getPermuted(\n reshapedPaddedShape.length, blockShape.length, false);\n const flattenShape = array_ops_util.getReshapedPermuted(\n paddedX.shape, blockShape, prod, false);\n\n return paddedX.reshape(reshapedPaddedShape)\n .transpose(permutedReshapedPaddedPermutation)\n .reshape(flattenShape) as T;\n }\n\n private pool(x: Tensor4D, convInfo: Conv2DInfo, poolType: 'max'|'avg'):\n Tensor4D {\n this.assertNotComplex(x, 'pool');\n\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n\n const initialValue =\n (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n Number.POSITIVE_INFINITY);\n\n const xValues = x.dataSync();\n const output = ops.buffer(convInfo.outShape, x.dtype);\n const outputVals = output.values;\n\n const outputBatchStrides =\n convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n const outputColStrides = convInfo.outShape[3];\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n const outputBatchOffset = b * outputBatchStrides;\n const inputBatchOffset = b * x.strides[0];\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const xRCorner = yR * strideHeight - padTop;\n const xRMin = Math.max(0, xRCorner);\n const xRMax =\n Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const xCCorner = yC * strideWidth - padLeft;\n const xCMin = Math.max(0, xCCorner);\n const xCMax =\n Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n let minMaxValue = initialValue;\n let avgValue = 0;\n let count = 0;\n for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n const xROffset = inputBatchOffset + xR * x.strides[1];\n for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n const xCOffset = xROffset + xC * x.strides[2];\n const pixel = xValues[xCOffset + d];\n if ((poolType === 'max' && pixel > minMaxValue)) {\n minMaxValue = pixel;\n } else if (poolType === 'avg') {\n avgValue += pixel;\n count++;\n }\n }\n if (isNaN(minMaxValue)) {\n break;\n }\n }\n const outputOffset = outputRowOffset + yC * outputColStrides + d;\n outputVals[outputOffset] =\n poolType === 'avg' ? avgValue / count : minMaxValue;\n }\n }\n }\n }\n return output.toTensor() as Tensor4D;\n }\n\n maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n return this.pool(x, convInfo, 'max');\n }\n\n private maxPoolPositions(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n const maxPositions = ops.buffer(convInfo.outShape, 'int32');\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padTop = convInfo.padInfo.top;\n const padLeft = convInfo.padInfo.left;\n\n const xBuf = x.bufferSync();\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n const xRCorner = yR * strideHeight - padTop;\n let xRMin = xRCorner;\n while (xRMin < 0) {\n xRMin += dilationHeight;\n }\n // const xRMin = Math.max(0, xRCorner);\n const xRMax =\n Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n const xCCorner = yC * strideWidth - padLeft;\n let xCMin = xCCorner;\n while (xCMin < 0) {\n xCMin += dilationWidth;\n }\n const xCMax =\n Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n let maxValue = Number.NEGATIVE_INFINITY;\n let maxPosition = -1;\n\n for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n const wR = xR - xRCorner;\n for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n const wC = xC - xCCorner;\n const pixel = xBuf.get(b, xR, xC, d);\n if (pixel > maxValue) {\n maxValue = pixel;\n maxPosition = wR * effectiveFilterWidth + wC;\n }\n }\n }\n maxPositions.set(maxPosition, b, yR, yC, d);\n }\n }\n }\n }\n return maxPositions.toTensor() as Tensor4D;\n }\n\n maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n Tensor4D {\n this.assertNotComplex([x, y], 'maxPoolBackprop');\n\n const maxPositions = this.maxPoolPositions(x, convInfo);\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const dx = ops.buffer(x.shape, 'float32');\n\n const maxPosBuf = maxPositions.bufferSync();\n const dyBuf = dy.bufferSync();\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n // Shader code begins.\n const dyRCorner = dxR - padTop;\n const dyCCorner = dxC - padLeft;\n let dotProd = 0;\n for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n const dyR = (dyRCorner + wR) / strideHeight;\n if (dyR < 0 || dyR >= convInfo.outHeight ||\n Math.floor(dyR) !== dyR) {\n continue;\n }\n for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n const dyC = (dyCCorner + wC) / strideWidth;\n if (dyC < 0 || dyC >= convInfo.outWidth ||\n Math.floor(dyC) !== dyC) {\n continue;\n }\n const maxPos = effectiveFilterHeight * effectiveFilterWidth -\n 1 - maxPosBuf.get(b, dyR, dyC, d);\n const curPos = wR * effectiveFilterWidth + wC;\n\n const mask = maxPos === curPos ? 1 : 0;\n if (mask === 0) {\n continue;\n }\n\n const pixel = dyBuf.get(b, dyR, dyC, d);\n dotProd += pixel * mask;\n }\n }\n dx.set(dotProd, b, dxR, dxC, d);\n }\n }\n }\n }\n return dx.toTensor();\n }\n\n avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n this.assertNotComplex([dy, x], 'avgPoolBackprop');\n\n const strideHeight = convInfo.strideHeight;\n const strideWidth = convInfo.strideWidth;\n const filterHeight = convInfo.filterHeight;\n const filterWidth = convInfo.filterWidth;\n const dilationHeight = convInfo.dilationHeight;\n const dilationWidth = convInfo.dilationWidth;\n const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n const dx = ops.buffer(x.shape, 'float32');\n\n const avgMultiplier = 1 / (filterHeight * filterWidth);\n\n const dyBuf = dy.bufferSync();\n\n for (let b = 0; b < convInfo.batchSize; ++b) {\n for (let d = 0; d < convInfo.inChannels; ++d) {\n for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n // Shader code begins.\n const dyRCorner = dxR - padTop;\n const dyCCorner = dxC - padLeft;\n let dotProd = 0;\n for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n const dyR = (dyRCorner + wR) / strideHeight;\n if (dyR < 0 || dyR >= convInfo.outHeight ||\n Math.floor(dyR) !== dyR) {\n continue;\n }\n for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n const dyC = (dyCCorner + wC) / strideWidth;\n if (dyC < 0 || dyC >= convInfo.outWidth ||\n Math.floor(dyC) !== dyC) {\n continue;\n }\n\n const pixel = dyBuf.get(b, dyR, dyC, d);\n dotProd += pixel;\n }\n }\n dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n }\n }\n }\n }\n return dx.toTensor();\n }\n\n cast(x: T, dtype: DataType): T {\n return backend_util.castTensor(x, dtype, this);\n }\n\n reshape(x: Tensor, shape: ShapeMap[R]): Tensor {\n return backend_util.reshapeTensor(x, shape);\n }\n\n avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n this.assertNotComplex(x, 'avgPool');\n\n return this.pool(x, convInfo, 'avg').toFloat();\n }\n\n resizeBilinear(\n x: Tensor4D, newHeight: number, newWidth: number,\n alignCorners: boolean): Tensor4D {\n this.assertNotComplex(x, 'resizeBilinear');\n\n const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n const xValues = x.dataSync();\n const result = new Float32Array(\n util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n\n const effectiveInputSize: [number, number] = [\n (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n ];\n\n const effectiveOutputSize: [number, number] = [\n (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n ];\n let outputIdx = 0;\n const effectiveRowSizeRatio =\n effectiveInputSize[0] / effectiveOutputSize[0];\n const effectiveColSizeRatio =\n effectiveInputSize[1] / effectiveOutputSize[1];\n for (let b = 0; b < batch; b++) {\n for (let r = 0; r < newHeight; r++) {\n const sourceFracRow = effectiveRowSizeRatio * r;\n const sourceRowFloor = Math.floor(sourceFracRow);\n const rowFrac = sourceFracRow - sourceRowFloor;\n const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n for (let c = 0; c < newWidth; c++) {\n const sourceFracCol = effectiveColSizeRatio * c;\n const sourceColFloor = Math.floor(sourceFracCol);\n const colFrac = sourceFracCol - sourceColFloor;\n const sourceColCeil =\n Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n const topRightOffset = topRowOffset + +sourceColCeil * x.strides[2];\n const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n for (let d = 0; d < numChannels; d++) {\n // Begin shader.\n\n // Compute the fractional index of the source.\n const topLeft = xValues[topLeftOffest + d];\n const bottomLeft = xValues[botLeftOffset + d];\n const topRight = xValues[topRightOffset + d];\n const bottomRight = xValues[botRightOffest + d];\n\n const top = topLeft + (topRight - topLeft) * colFrac;\n const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n const newValue = top + (bottom - top) * rowFrac;\n\n result[outputIdx++] = newValue;\n }\n }\n }\n }\n return ops.tensor(result, [batch, newHeight, newWidth, numChannels]);\n }\n\n resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n this.assertNotComplex([dy, x], 'resizeBilinearBackprop');\n\n const [batch, xHeight, xWidth, depth] = x.shape;\n const [, yHeight, yWidth] = dy.shape;\n\n const output = new Float32Array(batch * xHeight * xWidth * depth);\n\n // In the backwards pass, we want to find the pixels that were generated\n // for each pixel in the input image the forward pass and add the\n // corresponding coefficient from dy to the gradient (with some\n // interpolation).\n\n const effectiveXSize: [number, number] = [\n (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n ];\n\n const effectiveYSize: [number, number] = [\n (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n ];\n\n const heightScale = effectiveXSize[0] / effectiveYSize[0];\n const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n // Reference implementation\n // tslint:disable-next-line:max-line-length\n // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n\n const dyValues = dy.dataSync();\n let offset = 0;\n for (let b = 0; b < batch; b++) {\n const bOffset = b * x.strides[0];\n for (let r = 0; r < yHeight; r++) {\n const dxR = r * heightScale;\n const topDxRIndex = Math.floor(dxR);\n const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n\n const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n\n const dxRLerp = dxR - topDxRIndex;\n const inverseDxRLerp = 1.0 - dxRLerp;\n for (let c = 0; c < yWidth; c++) {\n const dxC = c * widthScale;\n const leftDxCIndex = Math.floor(dxC);\n const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n const dxCLerp = dxC - leftDxCIndex;\n const inverseDxCLerp = 1.0 - dxCLerp;\n\n const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n const bottomLeftRCOffset =\n bottomDxROffset + leftDxCIndex * x.strides[2];\n const bottomRightRCOffset =\n bottomDxROffset + rightDxCIndex * x.strides[2];\n\n const inverseDxRLerpTimesInverseDxCLerp =\n inverseDxRLerp * inverseDxCLerp;\n const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n for (let d = 0; d < depth; d++) {\n const dyVal = dyValues[offset++];\n output[topLeftRCOffset + d] +=\n dyVal * inverseDxRLerpTimesInverseDxCLerp;\n output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n output[bottomLeftRCOffset + d] +=\n dyVal * dxRLerpTimesInverseDxCLerp;\n output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n }\n }\n }\n }\n return ops.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n }\n\n resizeNearestNeighbor(\n x: Tensor4D, newHeight: number, newWidth: number,\n alignCorners: boolean): Tensor4D {\n this.assertNotComplex(x, 'resizeNearestNeighbor');\n\n const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n const xValues = x.dataSync();\n const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n\n const effectiveInputSize: [number, number] = [\n (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n ];\n\n const effectiveOutputSize: [number, number] = [\n (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n ];\n\n const effectiveRowSizeRatio =\n effectiveInputSize[0] / effectiveOutputSize[0];\n const effectiveColSizeRatio =\n effectiveInputSize[1] / effectiveOutputSize[1];\n\n let outputOffset = 0;\n for (let b = 0; b < batch; b++) {\n const batchOffset = b * x.strides[0];\n for (let r = 0; r < newHeight; r++) {\n const sourceFracRow = effectiveRowSizeRatio * r;\n const sourceNearestRow = Math.min(\n oldHeight - 1,\n alignCorners ? Math.round(sourceFracRow) :\n Math.floor(sourceFracRow));\n const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n for (let c = 0; c < newWidth; c++) {\n const sourceFracCol = effectiveColSizeRatio * c;\n const sourceNearestCol = Math.min(\n oldWidth - 1,\n alignCorners ? Math.round(sourceFracCol) :\n Math.floor(sourceFracCol));\n const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n for (let d = 0; d < numChannels; d++) {\n // Begin shader.\n // Compute the fractional index of the source.\n const newVal = xValues[colOffset + d];\n output[outputOffset++] = newVal;\n }\n }\n }\n }\n return ops.tensor(\n output, [batch, newHeight, newWidth, numChannels], x.dtype);\n }\n\n resizeNearestNeighborBackprop(\n dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n this.assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n\n const [batch, xHeight, xWidth, depth] = x.shape;\n const [, yHeight, yWidth] = dy.shape;\n\n const output = new Float32Array(batch * xHeight * xWidth * depth);\n const dyValues = dy.dataSync();\n\n // In the backwards pass, we want to find the pixels that were generated\n // for each pixel in the input image the forward pass\n\n const effectiveXSize: [number, number] = [\n (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n ];\n\n const effectiveYSize: [number, number] = [\n (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n ];\n\n const heightScale = effectiveXSize[0] / effectiveYSize[0];\n const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n const invHeightScale = 1 / heightScale;\n const invWidthScale = 1 / widthScale;\n\n // This defines the size of the window of values around a particular\n // index in dy that we want to search for contributions to dx.\n const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n // Loop over the output space.\n for (let b = 0; b < batch; b++) {\n const batchOffset = b * x.strides[0];\n for (let r = 0; r < xHeight; r++) {\n const rowOffset = batchOffset + r * x.strides[1];\n\n // Compute bounds for where in dy we will look\n const startRLerp = Math.floor(r * invHeightScale);\n const startDyR = Math.floor(startRLerp - (winHeight / 2));\n for (let c = 0; c < xWidth; c++) {\n const colOffset = rowOffset + c * x.strides[2];\n\n // Compute bounds for where in dy we will look\n const startCLerp = Math.floor(c * invWidthScale);\n const startDyC = Math.floor(startCLerp - (winWidth / 2));\n\n for (let d = 0; d < depth; d++) {\n let accum = 0;\n // loop over dy\n\n for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n const dyR = dyRIndex + startDyR;\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= yHeight) {\n continue;\n }\n\n const dyROffset = batchOffset + dyR * dy.strides[1];\n const sourceFracRow = dyR * heightScale;\n const sourceNearestRow = Math.min(\n xHeight - 1,\n alignCorners ? Math.round(sourceFracRow) :\n Math.floor(sourceFracRow));\n if (r !== sourceNearestRow) {\n continue;\n }\n for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n const dyC = dyCIndex + startDyC;\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= yWidth) {\n continue;\n }\n\n const dyCOffset = dyROffset + dyC * dy.strides[2];\n const sourceFracCol = dyC * widthScale;\n const sourceNearestCol = Math.min(\n xWidth - 1,\n alignCorners ? Math.round(sourceFracCol) :\n Math.floor(sourceFracCol));\n\n if (c === sourceNearestCol) {\n accum += dyValues[dyCOffset + d];\n }\n }\n }\n output[colOffset + d] = accum;\n }\n }\n }\n }\n return ops.tensor4d(output, x.shape, x.dtype);\n }\n\n batchNormalization(\n x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n offset?: Tensor4D|Tensor1D): Tensor4D {\n this.assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n\n const xVals = x.dataSync();\n const mVals = mean.dataSync();\n const varVals = variance.dataSync();\n const sVals = scale ? scale.dataSync() : new Float32Array([1]);\n const offVals = offset ? offset.dataSync() : new Float32Array([0]);\n const outVals = new Float32Array(xVals.length);\n\n const offValsLength = offVals.length;\n const sValsLength = sVals.length;\n const varValsLength = varVals.length;\n const mValsLength = mVals.length;\n\n let offi = 0;\n let mi = 0;\n let si = 0;\n let vi = 0;\n for (let i = 0; i < xVals.length; ++i) {\n outVals[i] = offVals[offi++] +\n (xVals[i] - mVals[mi++]) * sVals[si++] /\n Math.sqrt(varVals[vi++] + varianceEpsilon);\n if (offi >= offValsLength) {\n offi = 0;\n }\n if (mi >= mValsLength) {\n mi = 0;\n }\n if (si >= sValsLength) {\n si = 0;\n }\n if (vi >= varValsLength) {\n vi = 0;\n }\n }\n return tensor4d(outVals, x.shape);\n }\n\n localResponseNormalization4D(\n x: Tensor4D, depthRadius: number, bias: number, alpha: number,\n beta: number): Tensor4D {\n this.assertNotComplex(x, 'localResponseNormalization4D');\n\n const channels = x.shape[3];\n const maxD = channels - 1;\n const xValues = x.dataSync();\n const size = util.sizeFromShape(x.shape);\n const result = new Float32Array(size);\n\n function sumAcrossChannels(offset: number) {\n const currentChannel = offset % channels;\n let beginSumOffset =\n offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n const endSumOffset = offset - currentChannel +\n Math.min(currentChannel + depthRadius, maxD);\n\n let sum = 0.0;\n for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n const z = xValues[beginSumOffset];\n sum += z * z;\n }\n return sum;\n }\n\n for (let offset = 0; offset < size; offset++) {\n const sum = sumAcrossChannels(offset);\n const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n result[offset] = val;\n }\n\n return ops.tensor4d(result, x.shape);\n }\n\n LRNGrad(\n dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D,\n depthRadius: number, bias: number, alpha: number,\n beta: number): Tensor4D {\n this.assertNotComplex(dy, 'LRNGrad');\n const channels = dy.shape[3];\n const dyValues = dy.dataSync();\n const inputImageValues = inputImage.dataSync();\n const outputImageValues = outputImage.dataSync();\n const result = new Float32Array(util.sizeFromShape(dy.shape));\n const size = util.sizeFromShape(dy.shape);\n\n for (let offset = 0; offset < size; offset++) {\n const currentChannel = offset % channels;\n const depthBegin =\n (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n const depthEnd = (offset - currentChannel) +\n Math.min(channels, currentChannel + depthRadius + 1);\n\n let norm = 0;\n for (let k = depthBegin; k < depthEnd; k++) {\n norm += Math.pow(inputImageValues[k], 2);\n }\n norm = alpha * norm + bias;\n\n for (let k = depthBegin; k < depthEnd; k++) {\n let dyi = -2 * alpha * beta * inputImageValues[k] *\n outputImageValues[offset] / norm;\n if (offset === k) {\n dyi += Math.pow(norm, -beta);\n }\n dyi *= dyValues[offset];\n result[k] += dyi;\n }\n }\n return ops.tensor4d(result, dy.shape);\n }\n\n multinomial(\n logits: Tensor2D, normalized: boolean, numSamples: number,\n seed: number): Tensor2D {\n this.assertNotComplex(logits, 'multinomial');\n\n const probabilities = normalized ? logits : ops.softmax(logits);\n const batchSize = probabilities.shape[0];\n const numEvents = probabilities.shape[1];\n const res = ops.zeros([batchSize, numSamples], 'int32');\n const resVals = res.dataSync();\n const probVals = probabilities.dataSync();\n\n for (let b = 0; b < batchSize; ++b) {\n const offset = b * numEvents;\n // The cdf won't include the last event. It will be implicit if no other\n // event happened.\n const cdf = new Float32Array(numEvents - 1);\n cdf[0] = probVals[offset];\n for (let event = 1; event < cdf.length; ++event) {\n cdf[event] = cdf[event - 1] + probVals[offset + event];\n }\n\n const random = seedrandom.alea(seed.toString());\n const outOffset = b * numSamples;\n for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n const r = random();\n\n // Assume last event happened by default.\n resVals[outOffset + sampleId] = cdf.length;\n\n for (let event = 0; event < cdf.length; event++) {\n if (r < cdf[event]) {\n resVals[outOffset + sampleId] = event;\n break;\n }\n }\n }\n }\n return res;\n }\n\n oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n Tensor2D {\n this.assertNotComplex(indices, 'oneHot');\n\n const res = new Float32Array(indices.size * depth);\n res.fill(offValue);\n const indicesVal = indices.dataSync();\n\n for (let event = 0; event < indices.size; ++event) {\n if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n res[event * depth + indicesVal[event]] = onValue;\n }\n }\n return ops.tensor2d(res, [indices.size, depth], 'int32');\n }\n\n nonMaxSuppression(\n boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n iouThreshold: number, scoreThreshold: number): Tensor1D {\n this.assertNotComplex(boxes, 'nonMaxSuppression');\n\n const boxesVals = boxes.dataSync();\n const scoresVals = scores.dataSync();\n return nonMaxSuppressionImpl(\n boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n }\n\n fft(x: Tensor2D): Tensor2D {\n return this.fftBatch(x, false);\n }\n\n ifft(x: Tensor2D): Tensor2D {\n return this.fftBatch(x, true);\n }\n\n /**\n * Calculate FFT of inner most elements of batch tensor.\n */\n private fftBatch(x: Tensor2D, inverse: boolean): Tensor2D {\n const batch = x.shape[0];\n const innerDim = x.shape[1];\n // Collects real and imaginary values separately.\n const realResult = ops.buffer(x.shape, 'float32');\n const imagResult = ops.buffer(x.shape, 'float32');\n\n const real = ops.real(x).as2D(batch, innerDim);\n const imag = ops.imag(x).as2D(batch, innerDim);\n\n for (let b = 0; b < batch; b++) {\n // TODO: Support slice ops for complex type.\n const r = real.slice([b, 0], [1, innerDim]);\n const i = imag.slice([b, 0], [1, innerDim]);\n const input = ops.complex(r, i);\n // Run FFT by batch element.\n const res = this.fftImpl(input, inverse).dataSync() as Float32Array;\n for (let d = 0; d < innerDim; d++) {\n const c = complex_util.getComplexWithIndex(res, d);\n realResult.values[b * innerDim + d] = c.real;\n imagResult.values[b * innerDim + d] = c.imag;\n }\n }\n\n const t = ops.complex(realResult.toTensor(), imagResult.toTensor());\n return t.as2D(batch, innerDim);\n }\n\n private fftImpl(x: Tensor2D, inverse: boolean): Tensor2D {\n const x1D = x.as1D();\n\n const n = x1D.size;\n\n if (this.isExponentOf2(n)) {\n let result = this.fftRadix2(x1D, n, inverse).as2D(x.shape[0], x.shape[1]);\n if (inverse) {\n result = ops.complex(\n ops.real(result).div(scalar(n)),\n ops.imag(result).div(scalar(n))) as Tensor2D;\n }\n return result;\n } else {\n const data = x.dataSync();\n const rawOutput =\n this.fourierTransformByMatmul(data, n, inverse) as Float32Array;\n const output = complex_util.splitRealAndImagArrays(rawOutput);\n return ops.complex(output.real, output.imag).as2D(x.shape[0], x.shape[1]);\n }\n }\n\n private isExponentOf2(size: number): boolean {\n return (size & size - 1) === 0;\n }\n\n // FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\n private fftRadix2(input: Tensor1D, size: number, inverse: boolean): Tensor1D {\n if (size === 1) {\n return input;\n }\n const data = input.dataSync() as Float32Array;\n const half = size / 2;\n const evenComplex = complex_util.complexWithEvenIndex(data);\n let evenTensor = ops.complex(evenComplex.real, evenComplex.imag).as1D();\n const oddComplex = complex_util.complexWithOddIndex(data);\n let oddTensor = ops.complex(oddComplex.real, oddComplex.imag).as1D();\n\n // Recursive call for half part of original input.\n evenTensor = this.fftRadix2(evenTensor, half, inverse);\n oddTensor = this.fftRadix2(oddTensor, half, inverse);\n\n const e = complex_util.exponents(size, inverse);\n const exponent = ops.complex(e.real, e.imag).mul(oddTensor);\n\n const addPart = evenTensor.add(exponent);\n const subPart = evenTensor.sub(exponent);\n\n const realTensor = ops.real(addPart).concat(ops.real(subPart));\n const imagTensor = ops.imag(addPart).concat(ops.imag(subPart));\n\n return ops.complex(realTensor, imagTensor).as1D();\n }\n\n // Calculate fourier transform by multplying sinusoid matrix.\n private fourierTransformByMatmul(\n data: TypedArray, size: number, inverse: boolean): TypedArray {\n const ret = new Float32Array(size * 2);\n // TODO: Use matmul instead once it supports complex64 type.\n for (let r = 0; r < size; r++) {\n let real = 0.0;\n let imag = 0.0;\n for (let c = 0; c < size; c++) {\n const e = complex_util.exponent(r * c, size, inverse);\n const term = complex_util.getComplexWithIndex(data as Float32Array, c);\n real += term.real * e.real - term.imag * e.imag;\n imag += term.real * e.imag + term.imag * e.real;\n }\n if (inverse) {\n real /= size;\n imag /= size;\n }\n complex_util.assignToTypedArray(ret, real, imag, r);\n }\n return ret;\n }\n\n depthToSpace(x: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n Tensor4D {\n util.assert(\n dataFormat === 'NHWC',\n `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${\n dataFormat}`);\n util.assert(\n blockSize > 1,\n `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n\n const batchSize = x.shape[0];\n const inputHeight = x.shape[1];\n const inputWidth = x.shape[2];\n const inputDepth = x.shape[3];\n\n const outputHeight = inputHeight * blockSize;\n const outputWidth = inputWidth * blockSize;\n const outputDepth = inputDepth / (blockSize * blockSize);\n\n const xValues = x.dataSync();\n const result =\n new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n\n let outputIdx = 0;\n for (let b = 0; b < batchSize; ++b) {\n for (let h = 0; h < outputHeight; ++h) {\n const inH = Math.floor(h / blockSize);\n const offsetH = (h % blockSize);\n for (let w = 0; w < outputWidth; ++w) {\n const inW = Math.floor(w / blockSize);\n const offsetW = (w % blockSize);\n const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n for (let d = 0; d < outputDepth; ++d) {\n const inD = d + offsetD;\n const inputIdx =\n inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n result[outputIdx++] = xValues[inputIdx];\n }\n }\n }\n }\n return ops.tensor4d(\n result, [batchSize, outputHeight, outputWidth, outputDepth]);\n }\n\n private broadcastedBinaryOp(\n a: Tensor, b: Tensor, dtype: DataType,\n op: (a: number, b: number) => number): Tensor {\n const newShape =\n broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n const result = ops.buffer(newShape, dtype);\n const aVals = a.dataSync();\n const bVals = b.dataSync();\n const aBroadcastDims = broadcast_util.getBroadcastDims(a.shape, newShape);\n const bBroadcastDims = broadcast_util.getBroadcastDims(b.shape, newShape);\n\n const resVals = result.values;\n if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n for (let i = 0; i < resVals.length; ++i) {\n resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n }\n } else {\n const aBuf = a.bufferSync();\n const bBuf = b.bufferSync();\n for (let i = 0; i < resVals.length; ++i) {\n const loc = result.indexToLoc(i);\n\n const aLoc = loc.slice(-a.rank);\n aBroadcastDims.forEach(d => aLoc[d] = 0);\n const aIndex = aBuf.locToIndex(aLoc);\n\n const bLoc = loc.slice(-b.rank);\n bBroadcastDims.forEach(d => bLoc[d] = 0);\n const bIndex = bBuf.locToIndex(bLoc);\n\n resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n }\n }\n return result.toTensor();\n }\n\n private broadcastedBinaryComplexOp(\n a: Tensor, b: Tensor,\n op:\n (aReal: number, aImag: number, bReal: number,\n bImag: number) => {real: number, imag: number}): Tensor {\n const newShape =\n broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n const realResult = ops.buffer(newShape, 'float32');\n const imagResult = ops.buffer(newShape, 'float32');\n\n const aVals = a.dataSync();\n const bVals = b.dataSync();\n const aBroadcastDims = broadcast_util.getBroadcastDims(a.shape, newShape);\n const bBroadcastDims = broadcast_util.getBroadcastDims(b.shape, newShape);\n\n const realVals = realResult.values;\n const imagVals = imagResult.values;\n\n if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n for (let i = 0; i < realVals.length; i++) {\n const aIdx = i % aVals.length;\n const bIdx = i % bVals.length;\n\n const result =\n op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2],\n bVals[bIdx * 2 + 1]);\n\n realVals[i] = result.real;\n imagVals[i] = result.imag;\n }\n } else {\n const aRealBuf = this.data.get(a.dataId).complexTensors.real.bufferSync();\n const bRealBuf = this.data.get(b.dataId).complexTensors.real.bufferSync();\n for (let i = 0; i < realVals.length; i++) {\n const loc = realResult.indexToLoc(i);\n\n const aLoc = loc.slice(-a.rank);\n aBroadcastDims.forEach(d => aLoc[d] = 0);\n const aIndex = aRealBuf.locToIndex(aLoc);\n\n const bLoc = loc.slice(-b.rank);\n bBroadcastDims.forEach(d => bLoc[d] = 0);\n const bIndex = bRealBuf.locToIndex(bLoc);\n\n const opResult =\n op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2],\n bVals[bIndex * 2 + 1]);\n\n realVals[i] = opResult.real;\n imagVals[i] = opResult.imag;\n }\n }\n return this.complex(realResult.toTensor(), imagResult.toTensor());\n }\n\n split(x: T, sizeSplits: number[], axis: number): T[] {\n return split(x, sizeSplits, axis);\n }\n\n dispose() {}\n\n floatPrecision() {\n return 32;\n }\n\n cropAndResize(\n images: Tensor4D,\n boxes: Tensor2D,\n boxIndex: Tensor1D,\n cropSize: [number, number],\n method: string,\n extrapolationValue: number,\n ) {\n const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n const numBoxes = boxes.shape[0];\n\n const [cropHeight, cropWidth] = cropSize;\n const output = ops.buffer(\n [numBoxes, cropHeight, cropWidth, numChannels], images.dtype);\n\n const boxVals = boxes.dataSync();\n const boxIndVals = boxIndex.dataSync();\n const imageVals = images.dataSync();\n\n const inStride = images.strides; // to calculate flat indexes into image\n const outStride = output.strides; // to calculate flat indexes into output\n\n // Reference implementation\n // tslint:disable-next-line:max-line-length\n // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n for (let b = 0; b < numBoxes; b++) {\n const startInd = b * 4;\n const y1 = boxVals[startInd];\n const x1 = boxVals[startInd + 1];\n const y2 = boxVals[startInd + 2];\n const x2 = boxVals[startInd + 3];\n\n const bInd: number = boxIndVals[b];\n if (bInd >= batch) {\n continue;\n }\n\n const heightScale = (cropHeight > 1) ?\n (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n 0;\n const widthScale =\n (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n\n for (let y = 0; y < cropHeight; y++) {\n const yInd: number = (cropHeight > 1) ?\n y1 * (imageHeight - 1) + y * (heightScale) :\n 0.5 * (y1 + y2) * (imageHeight - 1);\n\n if (yInd < 0 || yInd > imageHeight - 1) {\n for (let x = 0; x < cropWidth; x++) {\n for (let c = 0; c < numChannels; c++) {\n const ind =\n c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = extrapolationValue;\n }\n }\n continue;\n }\n\n if (method === 'bilinear') {\n const topInd = Math.floor(yInd);\n const bottomInd = Math.ceil(yInd);\n const yLerp = yInd - topInd;\n\n for (let x = 0; x < cropWidth; x++) {\n const xInd = (cropWidth > 1) ?\n x1 * (imageWidth - 1) + x * widthScale :\n 0.5 * (x1 + x2) * (imageWidth - 1);\n\n if (xInd < 0 || xInd > imageWidth - 1) {\n for (let c = 0; c < numChannels; c++) {\n const ind =\n c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = extrapolationValue;\n }\n continue;\n }\n\n const leftInd = Math.floor(xInd);\n const rightInd = Math.ceil(xInd);\n const xLerp = xInd - leftInd;\n\n for (let c = 0; c < numChannels; c++) {\n let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n bInd * inStride[0];\n const topLeft = imageVals[ind];\n\n ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n bInd * inStride[0];\n const topRight = imageVals[ind];\n\n ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n bInd * inStride[0];\n const bottomLeft = imageVals[ind];\n\n ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n bInd * inStride[0];\n const bottomRight = imageVals[ind];\n\n const top = topLeft + (topRight - topLeft) * xLerp;\n const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n\n ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = top + ((bottom - top) * yLerp);\n }\n }\n } else { // method == \"nearest\"\n for (let x = 0; x < cropWidth; ++x) {\n const xInd = (cropWidth > 1) ?\n x1 * (imageWidth - 1) + x * widthScale :\n 0.5 * (x1 + x2) * (imageWidth - 1);\n\n if (xInd < 0 || xInd > imageWidth - 1) {\n for (let c = 0; c < numChannels; c++) {\n const ind =\n c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[ind] = extrapolationValue;\n }\n continue;\n }\n\n const closestX = Math.round(xInd);\n const closestY = Math.round(yInd);\n for (let c = 0; c < numChannels; c++) {\n const inInd = c + closestX * inStride[2] +\n closestY * inStride[1] + bInd * inStride[0];\n const outInd =\n c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n output.values[outInd] = imageVals[inInd];\n }\n }\n }\n }\n }\n return output.toTensor() as Tensor4D;\n }\n\n sparseToDense(\n sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n defaultValue: Scalar): Tensor {\n const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n scatter_nd_util.calculateShapes(\n sparseValues, sparseIndices, outputShape);\n const sumDupeIndices = false;\n return this.scatter(\n sparseIndices, sparseValues, outputShape, outputSize, sliceSize,\n numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n }\n\n gatherND(x: Tensor, indices: Tensor): Tensor {\n const indicesShape = indices.shape;\n const sliceRank = indicesShape[indicesShape.length - 1];\n\n const [resultShape, numSlices, sliceSize, strides] =\n gather_nd_util.prepareAndValidate(x, indices);\n if (numSlices === 0) {\n return tensor([], resultShape, x.dtype);\n }\n\n const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n const indicesData = indices.dataSync();\n const xData = x.dataSync();\n\n for (let i = 0; i < numSlices; i++) {\n const index = [];\n let flattenIndex = 0;\n for (let j = 0; j < sliceRank; j++) {\n const dim = indicesData[i * sliceRank + j];\n flattenIndex += dim * strides[j];\n index.push(dim);\n }\n if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n throw new Error(\n `Invalid indices: ${index} does not index into ${x.shape}`);\n }\n\n for (let k = 0; k < sliceSize; k++) {\n buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n }\n }\n return buffer.toTensor().reshape(resultShape);\n }\n\n scatterND(\n indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor {\n const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n scatter_nd_util.calculateShapes(updates, indices, shape);\n const defaultValue = scalar(0);\n const sumDupeIndices = true;\n return this.scatter(\n indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank,\n strides, defaultValue, sumDupeIndices);\n }\n\n private scatter(\n indices: Tensor, updates: Tensor, shape: ShapeMap[R], outputSize: number,\n sliceSize: number, numUpdates: number, sliceRank: number,\n strides: number[], defaultValue: Scalar,\n sumDupeIndices: boolean): Tensor {\n const flattenShape = [outputSize / sliceSize, sliceSize];\n const indicesData = indices.dataSync();\n const updatesData = updates.dataSync();\n\n if (outputSize === 0) {\n return tensor([], shape, updates.dtype);\n }\n\n const buffer = new TensorBuffer(flattenShape, updates.dtype as 'float32');\n buffer.values.fill(defaultValue.dataSync()[0]);\n\n for (let i = 0; i < numUpdates; i++) {\n const index = [];\n let flattenIndex = 0;\n for (let j = 0; j < sliceRank; j++) {\n const dim = indicesData[i * sliceRank + j];\n index.push(dim);\n flattenIndex += dim * strides[j];\n }\n\n if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n throw new Error(\n `Invalid indices: ${index} does not index into ${shape}`);\n }\n\n for (let k = 0; k < sliceSize; k++) {\n if (sumDupeIndices) {\n buffer.values[flattenIndex * sliceSize + k] +=\n updatesData[i * sliceSize + k];\n } else {\n buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n updatesData[0] :\n updatesData[i * sliceSize + k];\n }\n }\n }\n return buffer.toTensor().reshape(shape);\n }\n}\n\nENV.registerBackend(\n 'cpu', () => new MathBackendCPU(), 1 /* priority */, setTensorTracker);\n","../src/browser_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nconst delayCallback: Function = typeof requestAnimationFrame !== 'undefined' ?\n requestAnimationFrame : // Browsers\n setImmediate; // Node.js\n\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n */\n/** @doc {heading: 'Performance', subheading: 'Timing'} */\nfunction nextFrame(): Promise {\n return new Promise(resolve => delayCallback(() => resolve()));\n}\n\nexport {nextFrame};\n","../src/io/types.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/* Type definitions for exporting and importing of models. */\n\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP: {[dtype: string]: number} = {\n 'float32': 4,\n 'int32': 4,\n 'uint16': 2,\n 'uint8': 1,\n 'bool': 1,\n};\n\n/**\n * A weight manifest.\n *\n * The weight manifest consists of an ordered list of weight-manifest groups.\n * Each weight-manifest group (\"group\" for short hereafter) consists of a\n * number of weight values stored in a number of paths.\n * See the documentation of `WeightManifestGroupConfig` below for more details.\n */\nexport declare type WeightsManifestConfig = WeightsManifestGroupConfig[];\n\n/**\n * A weight-manifest group.\n *\n * Consists of an ordered list of weight values encoded in binary format,\n * stored in an ordered list of paths.\n */\nexport declare interface WeightsManifestGroupConfig {\n /**\n * An ordered list of paths.\n *\n * Paths are intentionally abstract in order to be general. For example, they\n * can be relative URL paths or relative paths on the file system.\n */\n paths: string[];\n\n /**\n * Specifications of the weights stored in the paths.\n */\n weights: WeightsManifestEntry[];\n}\n\n/**\n * An entry in the weight manifest.\n *\n * The entry contains specification of a weight.\n */\nexport declare interface WeightsManifestEntry {\n /**\n * Name of the weight, e.g., 'Dense_1/bias'\n */\n name: string;\n\n /**\n * Shape of the weight.\n */\n shape: number[];\n\n /**\n * Data type of the weight.\n */\n dtype: 'float32'|'int32'|'bool';\n\n /**\n * Information for dequantization of the weight.\n */\n quantization?: {\n scale: number, // The scaling constant to multiply by.\n min: number, // The (possibly nudged) minimum weight to add.\n dtype: 'uint16'|'uint8' // The dtype of the quantized weights.\n };\n}\n\n/**\n * Options for saving a model.\n */\nexport interface SaveConfig {\n /**\n * Whether to save only the trainable weights of the model, ignoring the\n * untrainable ones.\n */\n trainableOnly?: boolean;\n}\n\n/**\n * Result of a saving operation.\n */\nexport interface SaveResult {\n /**\n * Information about the model artifacts saved.\n */\n modelArtifactsInfo: ModelArtifactsInfo;\n\n /**\n * HTTP responses from the server that handled the model-saving request (if\n * any). This is applicable only to server-based saving routes.\n */\n responses?: Response[];\n\n /**\n * Error messages and related data (if any).\n */\n errors?: Array<{}|string>;\n}\n\nexport declare interface ModelArtifactsInfo {\n /**\n * Timestamp for when the model is saved.\n */\n dateSaved: Date;\n\n /**\n * Type of the model topology\n *\n * Possible values:\n * - JSON: JSON config (human-readable, e.g., Keras JSON).\n * - GraphDef: TensorFlow\n * [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)\n * protocol buffer (binary).\n */\n modelTopologyType: 'JSON'|'GraphDef';\n\n /**\n * Size of model topology (Keras JSON or GraphDef), in bytes.\n */\n modelTopologyBytes?: number;\n\n /**\n * Size of weight specification or manifest, in bytes.\n */\n weightSpecsBytes?: number;\n\n /**\n * Size of weight value data, in bytes.\n */\n weightDataBytes?: number;\n}\n\n/**\n * The serialized artifacts of a model, including topology and weights.\n *\n * The `modelTopology`, `weightSpecs` and `weightData` fields of this interface\n * are optional, in order to support topology- or weights-only saving and\n * loading.\n */\nexport declare interface ModelArtifacts {\n /**\n * Model topology.\n *\n * For Keras-style `tf.Model`s, this is a JSON object.\n * For TensorFlow-style models (e.g., `FrozenModel`), this is a binary buffer\n * carrying the `GraphDef` protocol buffer.\n */\n modelTopology?: {}|ArrayBuffer;\n\n /**\n * Weight specifications.\n *\n * This corresponds to the weightsData below.\n */\n weightSpecs?: WeightsManifestEntry[];\n\n /**\n * Binary buffer for all weight values concatenated in the order specified\n * by `weightSpecs`.\n */\n weightData?: ArrayBuffer;\n}\n\n/**\n * Type definition for handlers of loading operations.\n */\nexport type LoadHandler = () => Promise;\n\n/**\n * Type definition for handlers of saving operations.\n */\nexport type SaveHandler = (modelArtifact: ModelArtifacts) =>\n Promise;\n\n/**\n * Interface for a model import/export handler.\n *\n * The `save` and `load` handlers are both optional, in order to allow handlers\n * that support only saving or loading.\n */\n// tslint:disable-next-line:interface-name\nexport interface IOHandler {\n save?: SaveHandler;\n load?: LoadHandler;\n}\n\n/**\n * An interface for the manager of a model store.\n *\n * A model store is defined as a storage medium on which multiple models can\n * be stored. Each stored model has a unique `path` as its identifier.\n * A `ModelStoreManager` for the store allows actions including\n *\n * - Listing the models stored in the store.\n * - Deleting a model from the store.\n */\nexport interface ModelStoreManager {\n /**\n * List all models in the model store.\n *\n * @returns A dictionary mapping paths of existing models to their\n * model artifacts info. Model artifacts info include type of the model's\n * topology, byte sizes of the topology, weights, etc.\n */\n listModels(): Promise<{[path: string]: ModelArtifactsInfo}>;\n\n /**\n * Remove a model specified by `path`.\n *\n * @param path\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n * is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n */\n removeModel(path: string): Promise;\n}\n\n/**\n * Callback for the progress of a long-running action such as an HTTP\n * request for a large binary object.\n *\n * `fraction` should be a number in the [0, 1] interval, indicating how\n * much of the action has completed.\n */\nexport type OnProgressCallback = (fraction: number) => void;\n\nexport interface LoadOptions {\n /**\n * RequestInit (options) for HTTP requests.\n *\n * For detailed information on the supported fields, see\n * https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n */\n requestInit?: RequestInit;\n\n /**\n * Progress callback.\n */\n onProgress?: OnProgressCallback;\n\n /**\n * A function used to override the `window.fetch` function.\n */\n fetchFunc?: Function;\n\n /**\n * Strict loading model: whether extraneous weights or missing\n * weights should trigger an `Error`.\n *\n * If `true`, require that the provided weights exactly match those\n * required by the layers. `false` means that both extra weights\n * and missing weights will be silently ignored.\n *\n * Default: `true`.\n */\n strict?: boolean;\n\n /**\n * Path prefix for weight files, by default this is calculated from the\n * path of the model JSON file.\n *\n * For instance, if the path to the model JSON file is\n * 'http://localhost/foo/model.json', then the default path prefix will be\n * 'http://localhost/foo/'. If a weight file has the path value\n * 'group1-shard1of2' in the weight manifest, then the weight file will be\n * loaded from 'http://localhost/foo/group1-shard1of2' by default. However,\n * if you provide a `weightPathPrefix` value of\n * 'http://localhost/foo/alt-weights', then the weight file will be loaded\n * from the path 'http://localhost/foo/alt-weights/group1-shard1of2' instead.\n */\n weightPathPrefix?: string;\n\n /**\n * Whether the module or model is to be loaded from TF Hub.\n * \n * Setting this to `true` allows passing a TF-Hub module URL, omitting the\n * standard model file name and the query parameters.\n *\n * Default: `false`.\n */\n fromTFHub?: boolean;\n}\n","../src/io/io_utils.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tensor} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {TypedArray} from '../types';\nimport {sizeFromShape} from '../util';\nimport {DTYPE_VALUE_SIZE_MAP, ModelArtifacts, ModelArtifactsInfo, WeightsManifestEntry} from './types';\n\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @returns A `Promise` of\n * - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n * concatenated.\n * - An `Array` of `WeightManifestEntry`s, carrying information including\n * tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors: NamedTensorMap):\n Promise<{data: ArrayBuffer, specs: WeightsManifestEntry[]}> {\n // TODO(adarob, cais): Support quantization.\n const specs: WeightsManifestEntry[] = [];\n const dataPromises: Array> = [];\n for (const name in tensors) {\n const t = tensors[name];\n\n if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool') {\n throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n }\n specs.push({name, shape: t.shape, dtype: t.dtype});\n dataPromises.push(t.data());\n }\n const tensorValues = await Promise.all(dataPromises);\n return {data: concatenateTypedArrays(tensorValues), specs};\n}\n\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n * concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n * whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n * to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(\n buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap {\n // TODO(adarob, cais): Support quantization.\n const out: NamedTensorMap = {};\n let offset = 0;\n for (const spec of specs) {\n const name = spec.name;\n const dtype = spec.dtype;\n const shape = spec.shape;\n const size = sizeFromShape(shape);\n let typedArray: TypedArray;\n\n if ('quantization' in spec) {\n const quantization = spec.quantization;\n if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n throw new Error(\n `Weight ${spec.name} has unknown ` +\n `quantization dtype ${quantization.dtype}. ` +\n `Supported quantization dtypes are: 'uint8' and 'uint16'.`);\n }\n const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n const byteBuffer =\n buffer.slice(offset, offset + size * quantizationSizeFactor);\n const quantizedArray = (quantization.dtype === 'uint8') ?\n new Uint8Array(byteBuffer) :\n new Uint16Array(byteBuffer);\n if (dtype === 'float32') {\n typedArray = Float32Array.from(\n quantizedArray, v => v * quantization.scale + quantization.min);\n } else if (dtype === 'int32') {\n typedArray = Int32Array.from(\n quantizedArray,\n v => Math.round(v * quantization.scale + quantization.min));\n } else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n offset += size * quantizationSizeFactor;\n } else {\n const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n\n if (dtype === 'float32') {\n typedArray = new Float32Array(byteBuffer);\n } else if (dtype === 'int32') {\n typedArray = new Int32Array(byteBuffer);\n } else if (dtype === 'bool') {\n typedArray = new Uint8Array(byteBuffer);\n } else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n offset += size * dtypeFactor;\n }\n\n let value: Tensor;\n if (dtype === 'float32') {\n value = tensor(typedArray, shape, 'float32');\n } else if (dtype === 'int32') {\n value = tensor(typedArray, shape, 'int32');\n } else if (dtype === 'bool') {\n value = tensor(typedArray, shape, 'bool');\n } else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n out[name] = value;\n }\n return out;\n}\n\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs: TypedArray[]): ArrayBuffer {\n // TODO(adarob, cais): Support quantization.\n if (xs === null) {\n throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n }\n\n let totalByteLength = 0;\n\n // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n // can have a different byte length from that of the `TypedArray` itself,\n // for example, when the `TypedArray` is created from an offset in an\n // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n // the `TypedArray` in byte length. If an element of `xs` does not show\n // this property, a new `TypedArray` that satisfy this property will be\n // constructed and pushed into `normalizedXs`.\n const normalizedXs: TypedArray[] = [];\n xs.forEach((x: TypedArray) => {\n totalByteLength += x.byteLength;\n // tslint:disable:no-any\n normalizedXs.push(\n x.byteLength === x.buffer.byteLength ? x :\n new (x.constructor as any)(x));\n if (!(x as any instanceof Float32Array || x as any instanceof Int32Array ||\n x as any instanceof Uint8Array)) {\n throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n }\n // tslint:enable:no-any\n });\n\n const y = new Uint8Array(totalByteLength);\n let offset = 0;\n normalizedXs.forEach((x: TypedArray) => {\n y.set(new Uint8Array(x.buffer), offset);\n offset += x.byteLength;\n });\n\n return y.buffer;\n}\n\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n typeof btoa === 'undefined');\n\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str: string): number {\n if (useNodeBuffer) {\n return Buffer.byteLength(str);\n }\n return new Blob([str]).size;\n}\n\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer: ArrayBuffer): string {\n if (useNodeBuffer) {\n return Buffer.from(buffer).toString('base64');\n }\n return btoa(String.fromCharCode.apply(null, new Uint8Array(buffer)));\n}\n\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str: string): ArrayBuffer {\n if (useNodeBuffer) {\n const buf = Buffer.from(str, 'base64');\n return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n }\n const s = atob(str);\n const buffer = new Uint8Array(s.length);\n for (let i = 0; i < s.length; ++i) {\n buffer.set([s.charCodeAt(i)], i);\n }\n return buffer.buffer;\n}\n\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer {\n let totalByteLength = 0;\n buffers.forEach((buffer: ArrayBuffer) => {\n totalByteLength += buffer.byteLength;\n });\n\n const temp = new Uint8Array(totalByteLength);\n let offset = 0;\n buffers.forEach((buffer: ArrayBuffer) => {\n temp.set(new Uint8Array(buffer), offset);\n offset += buffer.byteLength;\n });\n return temp.buffer;\n}\n\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path: string): string {\n const SEPARATOR = '/';\n path = path.trim();\n while (path.endsWith(SEPARATOR)) {\n path = path.slice(0, path.length - 1);\n }\n const items = path.split(SEPARATOR);\n return items[items.length - 1];\n}\n\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts):\n ModelArtifactsInfo {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error('Expected JSON model topology, received ArrayBuffer.');\n }\n\n return {\n dateSaved: new Date(),\n modelTopologyType: 'JSON',\n modelTopologyBytes: modelArtifacts.modelTopology == null ?\n 0 :\n stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n 0 :\n stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n weightDataBytes: modelArtifacts.weightData == null ?\n 0 :\n modelArtifacts.weightData.byteLength,\n };\n}\n","../src/io/router_registry.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IOHandler} from './types';\n\nexport type IORouter = (url: string|string[], onProgress?: Function) =>\n IOHandler;\n\nexport class IORouterRegistry {\n // Singleton instance.\n private static instance: IORouterRegistry;\n\n private saveRouters: IORouter[];\n private loadRouters: IORouter[];\n\n private constructor() {\n this.saveRouters = [];\n this.loadRouters = [];\n }\n\n private static getInstance(): IORouterRegistry {\n if (IORouterRegistry.instance == null) {\n IORouterRegistry.instance = new IORouterRegistry();\n }\n return IORouterRegistry.instance;\n }\n\n /**\n * Register a save-handler router.\n *\n * @param saveRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `save` method defined or `null`.\n */\n static registerSaveRouter(saveRouter: IORouter) {\n IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n }\n\n /**\n * Register a load-handler router.\n *\n * @param loadRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `load` method defined or `null`.\n */\n static registerLoadRouter(loadRouter: IORouter) {\n IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n }\n\n /**\n * Look up IOHandler for saving, given a URL-like string.\n *\n * @param url\n * @returns If only one match is found, an instance of IOHandler with the\n * `save` method defined. If no match is found, `null`.\n * @throws Error, if more than one match is found.\n */\n static getSaveHandlers(url: string|string[]): IOHandler[] {\n return IORouterRegistry.getHandlers(url, 'save');\n }\n\n /**\n * Look up IOHandler for loading, given a URL-like string.\n *\n * @param url\n * @param onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n * @returns All valid handlers for `url`, given the currently registered\n * handler routers.\n */\n static getLoadHandlers(url: string|string[], onProgress?: Function):\n IOHandler[] {\n return IORouterRegistry.getHandlers(url, 'load', onProgress);\n }\n\n private static getHandlers(\n url: string|string[], handlerType: 'save'|'load',\n onProgress?: Function): IOHandler[] {\n const validHandlers: IOHandler[] = [];\n const routers = handlerType === 'load' ? this.getInstance().loadRouters :\n this.getInstance().saveRouters;\n routers.forEach(router => {\n const handler = router(url, onProgress);\n if (handler !== null) {\n validHandlers.push(handler);\n }\n });\n return validHandlers;\n }\n}\n\nexport const registerSaveRouter = (loudRouter: IORouter) =>\n IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter: IORouter) =>\n IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url: string|string[]) =>\n IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url: string|string[], onProgress?: Function) =>\n IORouterRegistry.getLoadHandlers(url);\n","../src/io/model_management.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n * string.\n * - Moving or copying model from one path to another in the same medium or from\n * one medium to another, by using URL strings.\n */\n\nimport {assert} from '../util';\n\nimport {IORouterRegistry} from './router_registry';\nimport {ModelArtifactsInfo, ModelStoreManager} from './types';\n\nconst URL_SCHEME_SUFFIX = '://';\n\nexport class ModelStoreManagerRegistry {\n // Singleton instance.\n private static instance: ModelStoreManagerRegistry;\n\n private managers: {[scheme: string]: ModelStoreManager};\n\n private constructor() {\n this.managers = {};\n }\n\n private static getInstance(): ModelStoreManagerRegistry {\n if (ModelStoreManagerRegistry.instance == null) {\n ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n }\n return ModelStoreManagerRegistry.instance;\n }\n\n /**\n * Register a save-handler router.\n *\n * @param saveRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `save` method defined or `null`.\n */\n static registerManager(scheme: string, manager: ModelStoreManager) {\n assert(scheme != null, 'scheme must not be undefined or null.');\n if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n }\n assert(scheme.length > 0, 'scheme must not be an empty string.');\n const registry = ModelStoreManagerRegistry.getInstance();\n assert(\n registry.managers[scheme] == null,\n `A model store manager is already registered for scheme '${scheme}'.`);\n registry.managers[scheme] = manager;\n }\n\n static getManager(scheme: string): ModelStoreManager {\n const manager = this.getInstance().managers[scheme];\n if (manager == null) {\n throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n }\n return manager;\n }\n\n static getSchemes(): string[] {\n return Object.keys(this.getInstance().managers);\n }\n}\n\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n * Scheme: e.g., 'localstorage' in the example above.\n * Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url: string): {scheme: string, path: string} {\n if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n throw new Error(\n `The url string provided does not contain a scheme. ` +\n `Supported schemes are: ` +\n `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n }\n return {\n scheme: url.split(URL_SCHEME_SUFFIX)[0],\n path: url.split(URL_SCHEME_SUFFIX)[1],\n };\n}\n\nasync function cloneModelInternal(\n sourceURL: string, destURL: string,\n deleteSource = false): Promise {\n assert(\n sourceURL !== destURL,\n `Old path and new path are the same: '${sourceURL}'`);\n\n const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n assert(\n loadHandlers.length > 0,\n `Copying failed because no load handler is found for source URL ${\n sourceURL}.`);\n assert(\n loadHandlers.length < 2,\n `Copying failed because more than one (${loadHandlers.length}) ` +\n `load handlers for source URL ${sourceURL}.`);\n const loadHandler = loadHandlers[0];\n\n const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n assert(\n saveHandlers.length > 0,\n `Copying failed because no save handler is found for destination URL ` +\n `${destURL}.`);\n assert(\n saveHandlers.length < 2,\n `Copying failed because more than one (${loadHandlers.length}) ` +\n `save handlers for destination URL ${destURL}.`);\n const saveHandler = saveHandlers[0];\n\n const sourceScheme = parseURL(sourceURL).scheme;\n const sourcePath = parseURL(sourceURL).path;\n const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n\n const modelArtifacts = await loadHandler.load();\n\n // If moving within the same storage medium, remove the old model as soon as\n // the loading is done. Without doing this, it is possible that the combined\n // size of the two models will cause the cloning to fail.\n if (deleteSource && sameMedium) {\n await ModelStoreManagerRegistry.getManager(sourceScheme)\n .removeModel(sourcePath);\n }\n\n const saveResult = await saveHandler.save(modelArtifacts);\n\n // If moving between mediums, the deletion is done after the save succeeds.\n // This guards against the case in which saving to the destination medium\n // fails.\n if (deleteSource && !sameMedium) {\n await ModelStoreManagerRegistry.getManager(sourceScheme)\n .removeModel(sourcePath);\n }\n\n return saveResult.modelArtifactsInfo;\n}\n\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n * 'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function listModels(): Promise<{[url: string]: ModelArtifactsInfo}> {\n const schemes = ModelStoreManagerRegistry.getSchemes();\n const out: {[url: string]: ModelArtifactsInfo} = {};\n for (const scheme of schemes) {\n const schemeOut =\n await ModelStoreManagerRegistry.getManager(scheme).listModels();\n for (const path in schemeOut) {\n const url = scheme + URL_SCHEME_SUFFIX + path;\n out[url] = schemeOut[path];\n }\n }\n return out;\n}\n\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n * 'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n * is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function removeModel(url: string): Promise {\n const schemeAndPath = parseURL(url);\n const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n return await manager.removeModel(schemeAndPath.path);\n}\n\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n * `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n * `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n * 'localstorage://demo/management/model1',\n * 'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n * is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n * if `oldPath` and `newPath` are identical.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function copyModel(\n sourceURL: string, destURL: string): Promise {\n const deleteSource = false;\n return await cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n * `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n * `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n * 'localstorage://demo/management/model1',\n * 'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n * is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n * if `oldPath` and `newPath` are identical.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function moveModel(\n sourceURL: string, destURL: string): Promise {\n const deleteSource = true;\n return await cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\nexport {moveModel, copyModel, removeModel, listModels};\n","../src/io/indexed_db.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {getModelArtifactsInfoForJSON} from './io_utils';\nimport {ModelStoreManagerRegistry} from './model_management';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n// as the type of topology (JSON vs binary), byte size of the topology, byte\n// size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase(): Promise {\n const idbFactory = getIndexedDBFactory();\n\n return new Promise((resolve, reject) => {\n const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n deleteRequest.onsuccess = () => resolve();\n deleteRequest.onerror = error => reject(error);\n });\n}\n\nfunction getIndexedDBFactory(): IDBFactory {\n if (!ENV.get('IS_BROWSER')) {\n // TODO(cais): Add more info about what IOHandler subtypes are available.\n // Maybe point to a doc page on the web and/or automatically determine\n // the available IOHandlers and print them in the error message.\n throw new Error(\n 'Failed to obtain IndexedDB factory because the current environment' +\n 'is not a web browser.');\n }\n // tslint:disable-next-line:no-any\n const theWindow: any = window;\n const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n theWindow.shimIndexedDB;\n if (factory == null) {\n throw new Error(\n 'The current browser does not appear to support IndexedDB.');\n }\n return factory;\n}\n\nfunction setUpDatabase(openRequest: IDBRequest) {\n const db = openRequest.result as IDBDatabase;\n db.createObjectStore(MODEL_STORE_NAME, {keyPath: 'modelPath'});\n db.createObjectStore(INFO_STORE_NAME, {keyPath: 'modelPath'});\n}\n\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB implements IOHandler {\n protected readonly indexedDB: IDBFactory;\n protected readonly modelPath: string;\n\n static readonly URL_SCHEME = 'indexeddb://';\n\n constructor(modelPath: string) {\n this.indexedDB = getIndexedDBFactory();\n\n if (modelPath == null || !modelPath) {\n throw new Error(\n 'For IndexedDB, modelPath must not be null, undefined or empty.');\n }\n this.modelPath = modelPath;\n }\n\n async save(modelArtifacts: ModelArtifacts): Promise {\n // TODO(cais): Support saving GraphDef models.\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserLocalStorage.save() does not support saving model topology ' +\n 'in binary formats yet.');\n }\n\n return this.databaseAction(this.modelPath, modelArtifacts) as\n Promise;\n }\n\n async load(): Promise {\n return this.databaseAction(this.modelPath) as Promise;\n }\n\n /**\n * Perform database action to put model artifacts into or read model artifacts\n * from IndexedDB object store.\n *\n * Whether the action is put or get depends on whether `modelArtifacts` is\n * specified. If it is specified, the action will be put; otherwise the action\n * will be get.\n *\n * @param modelPath A unique string path for the model.\n * @param modelArtifacts If specified, it will be the model artifacts to be\n * stored in IndexedDB.\n * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n * of `ModelArtifacts`, if the action is get.\n */\n private databaseAction(modelPath: string, modelArtifacts?: ModelArtifacts):\n Promise {\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n openRequest.onsuccess = () => {\n const db = openRequest.result as IDBDatabase;\n\n if (modelArtifacts == null) {\n // Read model out from object store.\n const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const getRequest = modelStore.get(this.modelPath);\n getRequest.onsuccess = () => {\n if (getRequest.result == null) {\n db.close();\n return reject(new Error(\n `Cannot find model with path '${this.modelPath}' ` +\n `in IndexedDB.`));\n } else {\n resolve(getRequest.result.modelArtifacts);\n }\n };\n getRequest.onerror = error => {\n db.close();\n return reject(getRequest.error);\n };\n modelTx.oncomplete = () => db.close();\n } else {\n // Put model into object store.\n const modelArtifactsInfo: ModelArtifactsInfo =\n getModelArtifactsInfoForJSON(modelArtifacts);\n // First, put ModelArtifactsInfo into info store.\n const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const putInfoRequest =\n infoStore.put({modelPath: this.modelPath, modelArtifactsInfo});\n let modelTx: IDBTransaction;\n putInfoRequest.onsuccess = () => {\n // Second, put model data into model store.\n modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const putModelRequest = modelStore.put({\n modelPath: this.modelPath,\n modelArtifacts,\n modelArtifactsInfo\n });\n putModelRequest.onsuccess = () => resolve({modelArtifactsInfo});\n putModelRequest.onerror = error => {\n // If the put-model request fails, roll back the info entry as\n // well.\n infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const deleteInfoRequest = infoStore.delete(this.modelPath);\n deleteInfoRequest.onsuccess = () => {\n db.close();\n return reject(putModelRequest.error);\n };\n deleteInfoRequest.onerror = error => {\n db.close();\n return reject(putModelRequest.error);\n };\n };\n };\n putInfoRequest.onerror = error => {\n db.close();\n return reject(putInfoRequest.error);\n };\n infoTx.oncomplete = () => {\n if (modelTx == null) {\n db.close();\n } else {\n modelTx.oncomplete = () => db.close();\n }\n };\n }\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n}\n\nexport const indexedDBRouter: IORouter = (url: string|string[]) => {\n if (!ENV.get('IS_BROWSER')) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n * tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n * non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n * which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath: string): IOHandler {\n return new BrowserIndexedDB(modelPath);\n}\n\nfunction maybeStripScheme(key: string) {\n return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n key;\n}\n\nexport class BrowserIndexedDBManager implements ModelStoreManager {\n private indexedDB: IDBFactory;\n\n constructor() {\n this.indexedDB = getIndexedDBFactory();\n }\n\n async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n return new Promise<{[path: string]: ModelArtifactsInfo}>(\n (resolve, reject) => {\n const openRequest =\n this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n openRequest.onsuccess = () => {\n const db = openRequest.result as IDBDatabase;\n const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n const store = tx.objectStore(INFO_STORE_NAME);\n // tslint:disable:max-line-length\n // Need to cast `store` as `any` here because TypeScript's DOM\n // library does not have the `getAll()` method even though the\n // method is supported in the latest version of most mainstream\n // browsers:\n // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n // tslint:enable:max-line-length\n // tslint:disable-next-line:no-any\n const getAllInfoRequest = (store as any).getAll() as IDBRequest;\n getAllInfoRequest.onsuccess = () => {\n const out: {[path: string]: ModelArtifactsInfo} = {};\n for (const item of getAllInfoRequest.result) {\n out[item.modelPath] = item.modelArtifactsInfo;\n }\n resolve(out);\n };\n getAllInfoRequest.onerror = error => {\n db.close();\n return reject(getAllInfoRequest.error);\n };\n tx.oncomplete = () => db.close();\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n\n async removeModel(path: string): Promise {\n path = maybeStripScheme(path);\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n openRequest.onsuccess = () => {\n const db = openRequest.result as IDBDatabase;\n const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n\n const getInfoRequest = infoStore.get(path);\n let modelTx: IDBTransaction;\n getInfoRequest.onsuccess = () => {\n if (getInfoRequest.result == null) {\n db.close();\n return reject(new Error(\n `Cannot find model with path '${path}' ` +\n `in IndexedDB.`));\n } else {\n // First, delete the entry in the info store.\n const deleteInfoRequest = infoStore.delete(path);\n const deleteModelData = () => {\n // Second, delete the entry in the model store.\n modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const deleteModelRequest = modelStore.delete(path);\n deleteModelRequest.onsuccess = () =>\n resolve(getInfoRequest.result.modelArtifactsInfo);\n deleteModelRequest.onerror = error =>\n reject(getInfoRequest.error);\n };\n // Proceed with deleting model data regardless of whether deletion\n // of info data succeeds or not.\n deleteInfoRequest.onsuccess = deleteModelData;\n deleteInfoRequest.onerror = error => {\n deleteModelData();\n db.close();\n return reject(getInfoRequest.error);\n };\n }\n };\n getInfoRequest.onerror = error => {\n db.close();\n return reject(getInfoRequest.error);\n };\n\n infoTx.oncomplete = () => {\n if (modelTx == null) {\n db.close();\n } else {\n modelTx.oncomplete = () => db.close();\n }\n };\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n}\n\nif (ENV.get('IS_BROWSER')) {\n // Wrap the construction and registration, to guard against browsers that\n // don't support Local Storage.\n try {\n ModelStoreManagerRegistry.registerManager(\n BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n } catch (err) {\n }\n}\n","../src/io/local_storage.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {assert} from '../util';\nimport {arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON} from './io_utils';\nimport {ModelStoreManagerRegistry} from './model_management';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\n\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts(): string[] {\n if (!ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {\n throw new Error(\n 'purgeLocalStorageModels() cannot proceed because local storage is ' +\n 'unavailable in the current environment.');\n }\n const LS = window.localStorage;\n const purgedModelPaths: string[] = [];\n for (let i = 0; i < LS.length; ++i) {\n const key = LS.key(i);\n const prefix = PATH_PREFIX + PATH_SEPARATOR;\n if (key.startsWith(prefix) && key.length > prefix.length) {\n LS.removeItem(key);\n const modelName = getModelPathFromKey(key);\n if (purgedModelPaths.indexOf(modelName) === -1) {\n purgedModelPaths.push(modelName);\n }\n }\n }\n return purgedModelPaths;\n}\n\nfunction getModelKeys(path: string):\n {info: string, topology: string, weightSpecs: string, weightData: string} {\n return {\n info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)\n };\n}\n\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key: string) {\n const items = key.split(PATH_SEPARATOR);\n if (items.length < 3) {\n throw new Error(`Invalid key format: ${key}`);\n }\n return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\n\nfunction maybeStripScheme(key: string) {\n return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n key;\n}\n\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage implements IOHandler {\n protected readonly LS: Storage;\n protected readonly modelPath: string;\n protected readonly keys: {[key: string]: string};\n\n static readonly URL_SCHEME = 'localstorage://';\n\n constructor(modelPath: string) {\n if (!ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {\n // TODO(cais): Add more info about what IOHandler subtypes are\n // available.\n // Maybe point to a doc page on the web and/or automatically determine\n // the available IOHandlers and print them in the error message.\n throw new Error(\n 'The current environment does not support local storage.');\n }\n this.LS = window.localStorage;\n\n if (modelPath == null || !modelPath) {\n throw new Error(\n 'For local storage, modelPath must not be null, undefined or empty.');\n }\n this.modelPath = modelPath;\n this.keys = getModelKeys(this.modelPath);\n }\n\n /**\n * Save model artifacts to browser local storage.\n *\n * See the documentation to `browserLocalStorage` for details on the saved\n * artifacts.\n *\n * @param modelArtifacts The model artifacts to be stored.\n * @returns An instance of SaveResult.\n */\n async save(modelArtifacts: ModelArtifacts): Promise {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserLocalStorage.save() does not support saving model topology ' +\n 'in binary formats yet.');\n } else {\n const topology = JSON.stringify(modelArtifacts.modelTopology);\n const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n\n const modelArtifactsInfo: ModelArtifactsInfo =\n getModelArtifactsInfoForJSON(modelArtifacts);\n\n try {\n this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n this.LS.setItem(this.keys.topology, topology);\n this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n this.LS.setItem(\n this.keys.weightData,\n arrayBufferToBase64String(modelArtifacts.weightData));\n\n return {modelArtifactsInfo};\n } catch (err) {\n // If saving failed, clean up all items saved so far.\n for (const key in this.keys) {\n this.LS.removeItem(this.keys[key]);\n }\n\n throw new Error(\n `Failed to save model '${this.modelPath}' to local storage: ` +\n `size quota being exceeded is a possible cause of this failure: ` +\n `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n }\n }\n }\n\n /**\n * Load a model from local storage.\n *\n * See the documentation to `browserLocalStorage` for details on the saved\n * artifacts.\n *\n * @returns The loaded model (if loading succeeds).\n */\n async load(): Promise {\n const info =\n JSON.parse(this.LS.getItem(this.keys.info)) as ModelArtifactsInfo;\n if (info == null) {\n throw new Error(\n `In local storage, there is no model with name '${this.modelPath}'`);\n }\n\n if (info.modelTopologyType !== 'JSON') {\n throw new Error(\n 'BrowserLocalStorage does not support loading non-JSON model ' +\n 'topology yet.');\n }\n\n const out: ModelArtifacts = {};\n\n // Load topology.\n const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n if (topology == null) {\n throw new Error(\n `In local storage, the topology of model '${this.modelPath}' ` +\n `is missing.`);\n }\n out.modelTopology = topology;\n\n // Load weight specs.\n const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n if (weightSpecs == null) {\n throw new Error(\n `In local storage, the weight specs of model '${this.modelPath}' ` +\n `are missing.`);\n }\n out.weightSpecs = weightSpecs;\n\n // Load weight data.\n const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n if (weightDataBase64 == null) {\n throw new Error(\n `In local storage, the binary weight values of model ` +\n `'${this.modelPath}' are missing.`);\n }\n out.weightData = base64StringToArrayBuffer(weightDataBase64);\n\n return out;\n }\n}\n\nexport const localStorageRouter: IORouter = (url: string|string[]) => {\n if (!ENV.get('IS_BROWSER')) {\n return null;\n } else {\n if (!Array.isArray(url) &&\n url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n return browserLocalStorage(\n url.slice(BrowserLocalStorage.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n * - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n * model, such as date saved, type of the topology, size in bytes, etc.\n * - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n * style models, this is a stringized JSON.\n * - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n * model, can be used to decode the saved binary weight values (see\n * item below).\n * - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n * weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n * non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n * `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath: string): IOHandler {\n return new BrowserLocalStorage(modelPath);\n}\n\nexport class BrowserLocalStorageManager implements ModelStoreManager {\n private readonly LS: Storage;\n\n constructor() {\n assert(ENV.get('IS_BROWSER'), 'Current environment is not a web browser');\n assert(\n typeof window.localStorage !== 'undefined',\n 'Current browser does not appear to support localStorage');\n this.LS = window.localStorage;\n }\n\n async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n const out: {[path: string]: ModelArtifactsInfo} = {};\n const prefix = PATH_PREFIX + PATH_SEPARATOR;\n const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n for (let i = 0; i < this.LS.length; ++i) {\n const key = this.LS.key(i);\n if (key.startsWith(prefix) && key.endsWith(suffix)) {\n const modelPath = getModelPathFromKey(key);\n out[modelPath] = JSON.parse(this.LS.getItem(key)) as ModelArtifactsInfo;\n }\n }\n return out;\n }\n\n async removeModel(path: string): Promise {\n path = maybeStripScheme(path);\n const keys = getModelKeys(path);\n if (this.LS.getItem(keys.info) == null) {\n throw new Error(`Cannot find model at path '${path}'`);\n }\n const info = JSON.parse(this.LS.getItem(keys.info)) as ModelArtifactsInfo;\n\n this.LS.removeItem(keys.info);\n this.LS.removeItem(keys.topology);\n this.LS.removeItem(keys.weightSpecs);\n this.LS.removeItem(keys.weightData);\n return info;\n }\n}\n\nif (ENV.get('IS_BROWSER')) {\n // Wrap the construction and registration, to guard against browsers that\n // don't support Local Storage.\n try {\n ModelStoreManagerRegistry.registerManager(\n BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n } catch (err) {\n }\n}\n","../src/io/browser_files.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\n\nimport {ENV} from '../environment';\nimport {basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\n\nexport class BrowserDownloads implements IOHandler {\n private readonly modelTopologyFileName: string;\n private readonly weightDataFileName: string;\n private readonly jsonAnchor: HTMLAnchorElement;\n private readonly weightDataAnchor: HTMLAnchorElement;\n\n static readonly URL_SCHEME = 'downloads://';\n\n constructor(fileNamePrefix?: string) {\n if (!ENV.get('IS_BROWSER')) {\n // TODO(cais): Provide info on what IOHandlers are available under the\n // current environment.\n throw new Error(\n 'browserDownloads() cannot proceed because the current environment ' +\n 'is not a browser.');\n }\n\n if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n }\n if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n }\n\n this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n this.weightDataFileName =\n fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n }\n\n async save(modelArtifacts: ModelArtifacts): Promise {\n const weightsURL = window.URL.createObjectURL(new Blob(\n [modelArtifacts.weightData], {type: 'application/octet-stream'}));\n\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserDownloads.save() does not support saving model topology ' +\n 'in binary formats yet.');\n } else {\n const weightsManifest: WeightsManifestConfig = [{\n paths: ['./' + this.weightDataFileName],\n weights: modelArtifacts.weightSpecs\n }];\n const modelTopologyAndWeightManifest = {\n modelTopology: modelArtifacts.modelTopology,\n weightsManifest\n };\n const modelTopologyAndWeightManifestURL =\n window.URL.createObjectURL(new Blob(\n [JSON.stringify(modelTopologyAndWeightManifest)],\n {type: 'application/json'}));\n\n // If anchor elements are not provided, create them without attaching them\n // to parents, so that the downloaded file names can be controlled.\n const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n this.jsonAnchor;\n jsonAnchor.download = this.modelTopologyFileName;\n jsonAnchor.href = modelTopologyAndWeightManifestURL;\n // Trigger downloads by calling the `click` methods on the download\n // anchors.\n jsonAnchor.click();\n\n if (modelArtifacts.weightData != null) {\n const weightDataAnchor = this.weightDataAnchor == null ?\n document.createElement('a') :\n this.weightDataAnchor;\n weightDataAnchor.download = this.weightDataFileName;\n weightDataAnchor.href = weightsURL;\n weightDataAnchor.click();\n }\n\n return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};\n }\n }\n}\n\nclass BrowserFiles implements IOHandler {\n private readonly files: File[];\n\n constructor(files: File[]) {\n if (files == null || files.length < 1) {\n throw new Error(\n `When calling browserFiles, at least 1 file is required, ` +\n `but received ${files}`);\n }\n this.files = files;\n }\n\n async load(): Promise {\n const jsonFile = this.files[0];\n const weightFiles = this.files.slice(1);\n\n return new Promise((resolve, reject) => {\n const jsonReader = new FileReader();\n jsonReader.onload = (event: Event) => {\n // tslint:disable-next-line:no-any\n const modelJSON = JSON.parse((event.target as any).result);\n const modelTopology = modelJSON.modelTopology as {};\n if (modelTopology == null) {\n reject(new Error(\n `modelTopology field is missing from file ${jsonFile.name}`));\n return;\n }\n\n if (weightFiles.length === 0) {\n resolve({modelTopology});\n }\n\n const weightsManifest =\n modelJSON.weightsManifest as WeightsManifestConfig;\n if (weightsManifest == null) {\n reject(new Error(\n `weightManifest field is missing from file ${jsonFile.name}`));\n return;\n }\n\n let pathToFile: {[path: string]: File};\n try {\n pathToFile =\n this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n } catch (err) {\n reject(err);\n return;\n }\n\n const weightSpecs: WeightsManifestEntry[] = [];\n const paths: string[] = [];\n const perFileBuffers: ArrayBuffer[] = [];\n weightsManifest.forEach(weightsGroup => {\n weightsGroup.paths.forEach(path => {\n paths.push(path);\n perFileBuffers.push(null);\n });\n weightSpecs.push(...weightsGroup.weights);\n });\n\n weightsManifest.forEach(weightsGroup => {\n weightsGroup.paths.forEach(path => {\n const weightFileReader = new FileReader();\n weightFileReader.onload = (event: Event) => {\n // tslint:disable-next-line:no-any\n const weightData = (event.target as any).result as ArrayBuffer;\n const index = paths.indexOf(path);\n perFileBuffers[index] = weightData;\n if (perFileBuffers.indexOf(null) === -1) {\n resolve({\n modelTopology,\n weightSpecs,\n weightData: concatenateArrayBuffers(perFileBuffers),\n });\n }\n };\n weightFileReader.onerror = error =>\n reject(`Failed to weights data from file of path '${path}'.`);\n weightFileReader.readAsArrayBuffer(pathToFile[path]);\n });\n });\n };\n jsonReader.onerror = error => reject(\n `Failed to read model topology and weights manifest JSON ` +\n `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n `Keras-style tf.Model artifacts only.`);\n jsonReader.readAsText(jsonFile);\n });\n }\n\n /**\n * Check the compatibility between weights manifest and weight files.\n */\n private checkManifestAndWeightFiles(\n manifest: WeightsManifestConfig, files: File[]): {[path: string]: File} {\n const basenames: string[] = [];\n const fileNames = files.map(file => basename(file.name));\n const pathToFile: {[path: string]: File} = {};\n for (const group of manifest) {\n group.paths.forEach(path => {\n const pathBasename = basename(path);\n if (basenames.indexOf(pathBasename) !== -1) {\n throw new Error(\n `Duplicate file basename found in weights manifest: ` +\n `'${pathBasename}'`);\n }\n basenames.push(pathBasename);\n if (fileNames.indexOf(pathBasename) === -1) {\n throw new Error(\n `Weight file with basename '${pathBasename}' is not provided.`);\n } else {\n pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n }\n });\n }\n\n if (basenames.length !== files.length) {\n throw new Error(\n `Mismatch in the number of files in weights manifest ` +\n `(${basenames.length}) and the number of weight files provided ` +\n `(${files.length}).`);\n }\n return pathToFile;\n }\n}\n\nexport const browserDownloadsRouter: IORouter = (url: string|string[]) => {\n if (!ENV.get('IS_BROWSER')) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel'));\n * // This will trigger downloading of two files:\n * // 'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n * `tf.Model`, `fileNamePrefix` should follow either of the following two\n * formats:\n * 1. `null` or `undefined`, in which case the default file\n * names will be used:\n * - 'model.json' for the JSON file containing the model topology and\n * weights manifest.\n * - 'model.weights.bin' for the binary file containing the binary weight\n * values.\n * 2. A single string or an Array of a single string, as the file name prefix.\n * For example, if `'foo'` is provided, the downloaded JSON\n * file and binary weights file will be named 'foo.json' and\n * 'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n */\n/** @doc {heading: 'Models', subheading: 'Loading', namespace: 'io'} */\nexport function browserDownloads(fileNamePrefix = 'model'): IOHandler {\n return new BrowserDownloads(fileNamePrefix);\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadModel`, an instance of `tf.Model`\n * (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * // elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (``)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tfl.loadModel(tf.io.browserFiles(\n * [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n * loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n * which an `Array` of `File`s is expected (in that order):\n * - A JSON file containing the model topology and weight manifest.\n * - Optionally, One or more binary files containing the binary weights.\n * These files must have names that match the paths in the `weightsManifest`\n * contained by the aforementioned JSON file, or errors will be thrown\n * during loading. These weights files have the same format as the ones\n * generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n * Python PIP package. If no weights files are provided, only the model\n * topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n */\n/** @doc {heading: 'Models', subheading: 'Loading', namespace: 'io'} */\nexport function browserFiles(files: File[]): IOHandler {\n return new BrowserFiles(files);\n}\n","../src/io/weights_loader.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport {decodeWeights} from './io_utils';\nimport {DTYPE_VALUE_SIZE_MAP, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n * length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(\n fetchURLs: string[], requestOptions?: RequestInit, fetchFunc?: Function,\n onProgress?: Function): Promise {\n if (fetchFunc == null) {\n fetchFunc = fetch;\n }\n\n // Create the requests for all of the weights in parallel.\n const requests =\n fetchURLs.map(fetchURL => fetchFunc(fetchURL, requestOptions));\n\n const fetchStartFraction = 0;\n const fetchEndFraction = 0.5;\n\n if (onProgress != null) {\n util.monitorPromisesProgress(\n requests, onProgress, fetchStartFraction, fetchEndFraction);\n }\n\n const responses = await Promise.all(requests);\n const bufferPromises = responses.map(response => response.arrayBuffer());\n\n const bufferStartFraction = 0.5;\n const bufferEndFraction = 1;\n\n if (onProgress != null) {\n util.monitorPromisesProgress(\n bufferPromises, onProgress, bufferStartFraction, bufferEndFraction);\n }\n\n const buffers = await Promise.all(bufferPromises);\n return buffers;\n}\n\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n * Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(\n manifest: WeightsManifestConfig, filePathPrefix = '',\n weightNames?: string[],\n requestOptions?: RequestInit): Promise {\n // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n // single weight from a group, the whole group will be fetched. At a future\n // date, we should support fetching only the individual shards within a\n // group that are needed to reconstruct the requested weight.\n // TODO(cais): Use `decodeWeights` for implementation.\n\n const fetchWeights = (fetchUrls: string[]) =>\n loadWeightsAsArrayBuffer(fetchUrls, requestOptions);\n const loadWeights = weightsLoaderFactory(fetchWeights);\n\n return loadWeights(manifest, filePathPrefix, weightNames);\n}\n\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n * filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n * fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(\n fetchWeightsFunction: (fetchUrls: string[]) => Promise):\n (manifest: WeightsManifestConfig, filePathPrefix?: string,\n weightNames?: string[]) => Promise {\n return async(\n manifest: WeightsManifestConfig, filePathPrefix = '',\n weightNames?: string[]): Promise => {\n // Collect all the groups, weights, and their relative offsets to be\n // fetched.\n const groupIndicesToFetchMap = manifest.map(() => false);\n const groupWeightsToFetch: {\n [group: number]: Array<{\n manifestEntry: WeightsManifestEntry; groupOffset: number;\n sizeBytes: number;\n }>\n } = {};\n const weightsFound =\n weightNames != null ? weightNames.map(() => false) : [];\n const allManifestWeightNames: string[] = [];\n manifest.forEach((manifestGroupConfig, groupIndex) => {\n let groupOffset = 0;\n manifestGroupConfig.weights.forEach(weightsEntry => {\n const rawDtype = ('quantization' in weightsEntry) ?\n weightsEntry.quantization.dtype :\n weightsEntry.dtype;\n\n const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n util.sizeFromShape(weightsEntry.shape);\n\n const enqueueWeightsForFetchingFn = () => {\n groupIndicesToFetchMap[groupIndex] = true;\n if (groupWeightsToFetch[groupIndex] == null) {\n groupWeightsToFetch[groupIndex] = [];\n }\n\n groupWeightsToFetch[groupIndex].push({\n manifestEntry: weightsEntry,\n groupOffset,\n sizeBytes: weightsBytes\n });\n };\n\n if (weightNames != null) {\n weightNames.forEach((weightName, weightIndex) => {\n if (weightName === weightsEntry.name) {\n enqueueWeightsForFetchingFn();\n weightsFound[weightIndex] = true;\n }\n });\n } else {\n enqueueWeightsForFetchingFn();\n }\n\n allManifestWeightNames.push(weightsEntry.name);\n groupOffset += weightsBytes;\n });\n });\n\n if (!weightsFound.every(found => found)) {\n const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n throw new Error(\n `Could not find weights in manifest with names: ` +\n `${weightsNotFound.join(', ')}. \\n` +\n `Manifest JSON has weights with names: ` +\n `${allManifestWeightNames.join(', ')}.`);\n }\n\n // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n // IDs.\n const groupIndicesToFetch =\n groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n if (shouldFetch) {\n accumulator.push(i);\n }\n return accumulator;\n }, []);\n\n const fetchUrls: string[] = [];\n groupIndicesToFetch.forEach(i => {\n manifest[i].paths.forEach(filepath => {\n const fetchUrl = filePathPrefix +\n (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n fetchUrls.push(fetchUrl);\n });\n });\n const buffers = await fetchWeightsFunction(fetchUrls);\n\n const weightsTensorMap: NamedTensorMap = {};\n let bufferIndexOffset = 0;\n groupIndicesToFetch.forEach(i => {\n const numBuffers = manifest[i].paths.length;\n\n let groupBytes = 0;\n for (let i = 0; i < numBuffers; i++) {\n groupBytes += buffers[bufferIndexOffset + i].byteLength;\n }\n\n // Create a buffer for the whole group.\n const groupBuffer = new ArrayBuffer(groupBytes);\n const groupByteBuffer = new Uint8Array(groupBuffer);\n let groupBufferOffset = 0;\n for (let i = 0; i < numBuffers; i++) {\n const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n groupByteBuffer.set(buffer, groupBufferOffset);\n groupBufferOffset += buffer.byteLength;\n }\n\n const weightsEntries = groupWeightsToFetch[i];\n weightsEntries.forEach(weightsEntry => {\n const byteBuffer = groupBuffer.slice(\n weightsEntry.groupOffset,\n weightsEntry.groupOffset + weightsEntry.sizeBytes);\n const nameToTensorMap =\n decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n for (const name in nameToTensorMap) {\n weightsTensorMap[name] = nameToTensorMap[name];\n }\n });\n\n bufferIndexOffset += numBuffers;\n });\n\n return weightsTensorMap;\n };\n}\n","../src/io/browser_http.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\n\nimport {assert} from '../util';\nimport {concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeightsAsArrayBuffer} from './weights_loader';\n\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\n\nexport class BrowserHTTPRequest implements IOHandler {\n protected readonly path: string|string[];\n protected readonly requestInit: RequestInit;\n\n private readonly fetchFunc: Function;\n\n readonly DEFAULT_METHOD = 'POST';\n\n static readonly URL_SCHEME_REGEX = /^https?:\\/\\//;\n\n constructor(\n path: string|string[], requestInit?: RequestInit,\n private readonly weightPathPrefix?: string, fetchFunc?: Function,\n private readonly onProgress?: Function) {\n if (fetchFunc == null) {\n if (typeof fetch === 'undefined') {\n throw new Error(\n 'browserHTTPRequest is not supported outside the web browser ' +\n 'without a fetch polyfill.');\n }\n // Make sure fetch is always bound to window (the\n // original object) when available.\n fetchFunc = fetch.bind(typeof window === 'undefined' ? null : window);\n } else {\n assert(\n typeof fetchFunc === 'function',\n 'Must pass a function that matches the signature of ' +\n '`fetch` (see ' +\n 'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n }\n\n this.fetchFunc = (path: string, requestInits: RequestInit) => {\n // tslint:disable-next-line:no-any\n return fetchFunc(path, requestInits).catch((error: any) => {\n throw new Error(`Request for ${path} failed due to error: ${error}`);\n });\n };\n\n assert(\n path != null && path.length > 0,\n 'URL path for browserHTTPRequest must not be null, undefined or ' +\n 'empty.');\n\n if (Array.isArray(path)) {\n assert(\n path.length === 2,\n 'URL paths for browserHTTPRequest must have a length of 2, ' +\n `(actual length is ${path.length}).`);\n }\n this.path = path;\n\n if (requestInit != null && requestInit.body != null) {\n throw new Error(\n 'requestInit is expected to have no pre-existing body, but has one.');\n }\n this.requestInit = requestInit || {};\n }\n\n async save(modelArtifacts: ModelArtifacts): Promise {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserHTTPRequest.save() does not support saving model topology ' +\n 'in binary formats yet.');\n }\n\n const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);\n init.body = new FormData();\n\n const weightsManifest: WeightsManifestConfig = [{\n paths: ['./model.weights.bin'],\n weights: modelArtifacts.weightSpecs,\n }];\n const modelTopologyAndWeightManifest = {\n modelTopology: modelArtifacts.modelTopology,\n weightsManifest\n };\n\n init.body.append(\n 'model.json',\n new Blob(\n [JSON.stringify(modelTopologyAndWeightManifest)],\n {type: JSON_TYPE}),\n 'model.json');\n\n if (modelArtifacts.weightData != null) {\n init.body.append(\n 'model.weights.bin',\n new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}),\n 'model.weights.bin');\n }\n\n const response = await this.getFetchFunc()(this.path as string, init);\n\n if (response.ok) {\n return {\n modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n responses: [response],\n };\n } else {\n throw new Error(\n `BrowserHTTPRequest.save() failed due to HTTP response status ` +\n `${response.status}.`);\n }\n }\n\n /**\n * Load model artifacts via HTTP request(s).\n *\n * See the documentation to `browserHTTPRequest` for details on the saved\n * artifacts.\n *\n * @returns The loaded model artifacts (if loading succeeds).\n */\n async load(): Promise {\n return Array.isArray(this.path) ? this.loadBinaryModel() :\n this.loadJSONModel();\n }\n\n /**\n * Loads the model topology file and build the in memory graph of the model.\n */\n private async loadBinaryTopology(): Promise {\n const response = await this.getFetchFunc()(this.path[0], this.requestInit);\n\n if (!response.ok) {\n throw new Error(`Request to ${this.path[0]} failed with error: ${\n response.statusText}`);\n }\n return await response.arrayBuffer();\n }\n\n protected async loadBinaryModel(): Promise {\n const graphPromise = this.loadBinaryTopology();\n const manifestPromise =\n await this.getFetchFunc()(this.path[1], this.requestInit);\n if (!manifestPromise.ok) {\n throw new Error(`Request to ${this.path[1]} failed with error: ${\n manifestPromise.statusText}`);\n }\n\n const results = await Promise.all([graphPromise, manifestPromise]);\n const [modelTopology, weightsManifestResponse] = results;\n\n const weightsManifest =\n await weightsManifestResponse.json() as WeightsManifestConfig;\n\n let weightSpecs: WeightsManifestEntry[];\n let weightData: ArrayBuffer;\n if (weightsManifest != null) {\n const results = await this.loadWeights(weightsManifest);\n [weightSpecs, weightData] = results;\n }\n\n return {modelTopology, weightSpecs, weightData};\n }\n\n protected async loadJSONModel(): Promise {\n const modelConfigRequest =\n await this.getFetchFunc()(this.path as string, this.requestInit);\n\n if (!modelConfigRequest.ok) {\n throw new Error(`Request to ${this.path} failed with error: ${\n modelConfigRequest.statusText}`);\n }\n const modelConfig = await modelConfigRequest.json();\n const modelTopology = modelConfig['modelTopology'];\n const weightsManifest = modelConfig['weightsManifest'];\n\n // We do not allow both modelTopology and weightsManifest to be missing.\n if (modelTopology == null && weightsManifest == null) {\n throw new Error(\n `The JSON from HTTP path ${this.path} contains neither model ` +\n `topology or manifest for weights.`);\n }\n\n let weightSpecs: WeightsManifestEntry[];\n let weightData: ArrayBuffer;\n if (weightsManifest != null) {\n const weightsManifest =\n modelConfig['weightsManifest'] as WeightsManifestConfig;\n const results = await this.loadWeights(weightsManifest);\n [weightSpecs, weightData] = results;\n }\n\n return {modelTopology, weightSpecs, weightData};\n }\n\n private async loadWeights(weightsManifest: WeightsManifestConfig):\n Promise<[WeightsManifestEntry[], ArrayBuffer]> {\n const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n const [prefix, suffix] = parseUrl(weightPath);\n const pathPrefix = this.weightPathPrefix || prefix;\n\n const weightSpecs = [];\n for (const entry of weightsManifest) {\n weightSpecs.push(...entry.weights);\n }\n\n const fetchURLs: string[] = [];\n weightsManifest.forEach(weightsGroup => {\n weightsGroup.paths.forEach(path => {\n fetchURLs.push(pathPrefix + path + suffix);\n });\n });\n return [\n weightSpecs,\n concatenateArrayBuffers(await loadWeightsAsArrayBuffer(\n fetchURLs, this.requestInit, this.getFetchFunc(), this.onProgress))\n ];\n }\n\n /**\n * Helper method to get the `fetch`-like function set for this instance.\n *\n * This is mainly for avoiding confusion with regard to what context\n * the `fetch`-like function is bound to. In the default (browser) case,\n * the function will be bound to `window`, instead of `this`.\n */\n private getFetchFunc() {\n return this.fetchFunc;\n }\n}\n\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url: string): [string, string] {\n const lastSlash = url.lastIndexOf('/');\n const lastSearchParam = url.lastIndexOf('?');\n const prefix = url.substring(0, lastSlash);\n const suffix =\n lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n return [prefix + '/', suffix];\n}\n\nexport function isHTTPScheme(url: string): boolean {\n return url.match(BrowserHTTPRequest.URL_SCHEME_REGEX) != null;\n}\n\nexport const httpRequestRouter: IORouter =\n (url: string|string[], onProgress?: Function) => {\n if (typeof fetch === 'undefined') {\n // browserHTTPRequest uses `fetch`, if one wants to use it in node.js\n // they have to setup a global fetch polyfill.\n return null;\n } else {\n let isHTTP = true;\n if (Array.isArray(url)) {\n isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n } else {\n isHTTP = isHTTPScheme(url);\n }\n if (isHTTP) {\n return browserHTTPRequest(url, null, null, null, onProgress);\n }\n }\n return null;\n };\nIORouterRegistry.registerSaveRouter(httpRequestRouter);\nIORouterRegistry.registerLoadRouter(httpRequestRouter);\n\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n * - A JSON file consisting of `modelTopology` and `weightsManifest`.\n * - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n * tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.browserHTTPRequest(\n * 'http://model-server:5000/upload', {method: 'PUT'}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following Python code snippet based on the\n * [flask](https://github.com/pallets/flask) server framework implements a\n * server that can receive the request. Upon receiving the model artifacts\n * via the requst, this particular server reconsistutes instances of\n * [Keras Models](https://keras.io/models/model/) in memory.\n *\n * ```python\n * # pip install -U flask flask-cors tensorflow tensorflowjs\n *\n * from __future__ import absolute_import\n * from __future__ import division\n * from __future__ import print_function\n *\n * import io\n *\n * from flask import Flask, Response, request\n * from flask_cors import CORS, cross_origin\n * import tensorflow as tf\n * import tensorflowjs as tfjs\n * import werkzeug.formparser\n *\n *\n * class ModelReceiver(object):\n *\n * def __init__(self):\n * self._model = None\n * self._model_json_bytes = None\n * self._model_json_writer = None\n * self._weight_bytes = None\n * self._weight_writer = None\n *\n * @property\n * def model(self):\n * self._model_json_writer.flush()\n * self._weight_writer.flush()\n * self._model_json_writer.seek(0)\n * self._weight_writer.seek(0)\n *\n * json_content = self._model_json_bytes.read()\n * weights_content = self._weight_bytes.read()\n * return tfjs.converters.deserialize_keras_model(\n * json_content,\n * weight_data=[weights_content],\n * use_unique_name_scope=True)\n *\n * def stream_factory(self,\n * total_content_length,\n * content_type,\n * filename,\n * content_length=None):\n * # Note: this example code is *not* thread-safe.\n * if filename == 'model.json':\n * self._model_json_bytes = io.BytesIO()\n * self._model_json_writer = io.BufferedWriter(self._model_json_bytes)\n * return self._model_json_writer\n * elif filename == 'model.weights.bin':\n * self._weight_bytes = io.BytesIO()\n * self._weight_writer = io.BufferedWriter(self._weight_bytes)\n * return self._weight_writer\n *\n *\n * def main():\n * app = Flask('model-server')\n * CORS(app)\n * app.config['CORS_HEADER'] = 'Content-Type'\n *\n * model_receiver = ModelReceiver()\n *\n * @app.route('/upload', methods=['POST'])\n * @cross_origin()\n * def upload():\n * print('Handling request...')\n * werkzeug.formparser.parse_form_data(\n * request.environ, stream_factory=model_receiver.stream_factory)\n * print('Received model:')\n * with tf.Graph().as_default(), tf.Session():\n * model = model_receiver.model\n * model.summary()\n * # You can perform `model.predict()`, `model.fit()`,\n * # `model.evaluate()` etc. here.\n * return Response(status=200)\n *\n * app.run('localhost', 5000)\n *\n *\n * if __name__ == '__main__':\n * main()\n * ```\n *\n * @param path A single URL path or an Array of URL paths.\n * Currently, only a single URL path is supported. Array input is reserved\n * for future development.\n * Can be an absolute HTTP path (e.g.,\n * 'http://localhost:8000/model-upload)') or a relative path (e.g.,\n * './model-upload').\n * @param requestInit Request configurations to be used when sending\n * HTTP request to server using `fetch`. It can contain fields such as\n * `method`, `credentials`, `headers`, `mode`, etc. See\n * https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n * for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param weightPathPrefix Optional, this specifies the path prefix for weight\n * files, by default this is calculated from the path param.\n * @param fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n * the `fetch` from node-fetch can be used here.\n * @param onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n * @returns An instance of `IOHandler`.\n */\nexport function browserHTTPRequest(\n path: string|string[], requestInit?: RequestInit, weightPathPrefix?: string,\n fetchFunc?: Function, onProgress?: Function): IOHandler {\n return new BrowserHTTPRequest(\n path, requestInit, weightPathPrefix, fetchFunc, onProgress);\n}\n","../src/io/passthrough.ts":"/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers that pass through the in-memory ModelArtifacts format.\n */\n\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestEntry} from './types';\n\nclass PassthroughLoader implements IOHandler {\n constructor(\n private readonly modelTopology?: {}|ArrayBuffer,\n private readonly weightSpecs?: WeightsManifestEntry[],\n private readonly weightData?: ArrayBuffer) {}\n\n async load(): Promise {\n let result = {};\n if (this.modelTopology != null) {\n result = {modelTopology: this.modelTopology, ...result};\n }\n if (this.weightSpecs != null && this.weightSpecs.length > 0) {\n result = {weightSpecs: this.weightSpecs, ...result};\n }\n if (this.weightData != null && this.weightData.byteLength > 0) {\n result = {weightData: this.weightData, ...result};\n }\n return result;\n }\n}\n\nclass PassthroughSaver implements IOHandler {\n constructor(\n private readonly saveHandler:\n (artifacts: ModelArtifacts) => Promise) {}\n\n async save(modelArtifacts: ModelArtifacts) {\n return this.saveHandler(modelArtifacts);\n }\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadModel`, an instance of `tf.Model`\n * (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadModel(tf.io.fromMemory(\n * modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelTopology a object containing model topology (i.e., parsed from\n * the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n * names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n * concatenated in the order described by the weightSpecs.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(\n modelTopology: {}, weightSpecs?: WeightsManifestEntry[],\n weightData?: ArrayBuffer): IOHandler {\n return new PassthroughLoader(modelTopology, weightSpecs, weightData);\n}\n\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n * // ... do something with the artifacts ...\n * return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n * `SaveResult`.\n */\nexport function withSaveHandler(\n saveHandler: (artifacts: ModelArtifacts) =>\n Promise): IOHandler {\n return new PassthroughSaver(saveHandler);\n}\n","../src/ops/confusion_matrix.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {oneHot} from './array_ops';\nimport {op} from './operation';\n\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * // [0, 1, 1],\n * // [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n * for the classes. The shape is `[numExamples]`, where\n * `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n * 0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n * Its value must be larger than the largest element in `labels` and\n * `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n * row `r` and column `c` is the number of times examples of actual class\n * `r` were predicted as class `c`.\n */\n/** @doc {heading: 'Operations', subheading: 'Evaluation'} */\nexport function confusionMatrix_(\n labels: Tensor1D|TensorLike, predictions: Tensor1D|TensorLike,\n numClasses: number): Tensor2D {\n const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n const $predictions =\n convertToTensor(predictions, 'predictions', 'confusionMatrix');\n\n util.assert(\n numClasses == null || numClasses > 0 && Number.isInteger(numClasses),\n `If provided, numClasses must be a positive integer, ` +\n `but got ${numClasses}`);\n util.assert(\n $labels.rank === 1,\n `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n util.assert(\n $predictions.rank === 1,\n `Expected the rank of predictions to be 1, ` +\n `but got ${$predictions.rank}`);\n util.assert(\n $labels.shape[0] === $predictions.shape[0],\n `Mismatch in the number of examples: ` +\n `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n `Labels and predictions should have the same number of elements.`);\n util.assert(\n numClasses > 0 && Number.isInteger(numClasses),\n `numClasses is required to be a positive integer, but got ${numClasses}`);\n // TODO(cais): In the future, if oneHot supports tensors inputs for\n // `numClasses`, `confusionMatrix` can make `numClasses` optional.\n\n const oneHotLabels = oneHot($labels.asType('int32'), numClasses) as Tensor2D;\n const oneHotPredictions =\n oneHot($predictions.asType('int32'), numClasses) as Tensor2D;\n return oneHotLabels.transpose().matMul(oneHotPredictions).asType('int32');\n}\n\nexport const confusionMatrix = op({confusionMatrix_});\n","../src/serialization.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {assert} from './util';\n\n/**\n * Types to support JSON-esque data structures internally.\n *\n * Internally ConfigDict's use camelCase keys and values where the\n * values are class names to be instantiated. On the python side, these\n * will be snake_case. Internally we allow Enums into the values for better\n * type safety, but these need to be converted to raw primitives (usually\n * strings) for round-tripping with python.\n *\n * toConfig returns the TS-friendly representation. model.toJSON() returns\n * the pythonic version as that's the portable format. If you need to\n * python-ify a non-model level toConfig output, you'll need to use a\n * convertTsToPythonic from serialization_utils in -Layers.\n *\n */\nexport type ConfigDictValue =\n boolean|number|string|null|ConfigDictArray|ConfigDict;\nexport interface ConfigDict {\n [key: string]: ConfigDictValue;\n}\nexport interface ConfigDictArray extends Array {}\n\n/**\n * Type to represent the class-type of Serializable objects.\n *\n * Ie the class prototype with access to the constructor and any\n * static members/methods. Instance methods are not listed here.\n *\n * Source for this idea: https://stackoverflow.com/a/43607255\n */\nexport type SerializableConstructor = {\n // tslint:disable-next-line:no-any\n new (...args: any[]): T; className: string; fromConfig: FromConfigMethod;\n};\nexport type FromConfigMethod =\n (cls: SerializableConstructor, config: ConfigDict) => T;\n\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport abstract class Serializable {\n /**\n * Return the class name for this class to use in serialization contexts.\n *\n * Generally speaking this will be the same thing that constructor.name\n * would have returned. However, the class name needs to be robust\n * against minification for serialization/deserialization to work properly.\n *\n * There's also places such as initializers.VarianceScaling, where\n * implementation details between different languages led to different\n * class hierarchies and a non-leaf node is used for serialization purposes.\n */\n getClassName(): string {\n return (this.constructor as SerializableConstructor)\n .className;\n }\n\n /**\n * Return all the non-weight state needed to serialize this object.\n */\n abstract getConfig(): ConfigDict;\n\n /**\n * Creates an instance of T from a ConfigDict.\n *\n * This works for most descendants of serializable. A few need to\n * provide special handling.\n * @param cls A Constructor for the class to instantiate.\n * @param config The Configuration for the object.\n */\n static fromConfig(\n cls: SerializableConstructor, config: ConfigDict): T {\n return new cls(config);\n }\n}\n\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n private static instance: SerializationMap;\n classNameMap: {\n [className: string]:\n [SerializableConstructor, FromConfigMethod]\n };\n\n private constructor() {\n this.classNameMap = {};\n }\n\n /**\n * Returns the singleton instance of the map.\n */\n static getMap(): SerializationMap {\n if (SerializationMap.instance == null) {\n SerializationMap.instance = new SerializationMap();\n }\n return SerializationMap.instance;\n }\n\n /**\n * Registers the class as serializable.\n */\n static register(cls: SerializableConstructor) {\n SerializationMap.getMap().classNameMap[cls.className] =\n [cls, cls.fromConfig];\n }\n}\n\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n * static className = 'MyCustomLayer';\n *\n * constructor(config) {\n * super(config);\n * }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n * called `className` defined and the value must be a non-empty string.\n */\n/** @doc {heading: 'Models', subheading: 'Serialization'} */\nexport function registerClass(\n cls: SerializableConstructor) {\n assert(\n cls.className != null,\n `Class being registered does not have the static className property ` +\n `defined.`);\n assert(\n typeof cls.className === 'string',\n `className is required to be a string, but got type ` +\n typeof cls.className);\n assert(\n cls.className.length > 0,\n `Class being registered has an empty-string as its className, which ` +\n `is disallowed.`);\n\n SerializationMap.register(cls);\n}\n","../src/test_util.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\nimport {Features} from './environment_util';\nimport {Tensor} from './tensor';\nimport {TypedArray} from './types';\nimport * as util from './util';\nimport {isString} from './util';\n\n// TODO(smilkov): Move these constants to jasmine_util.\nexport const WEBGL_ENVS: Features = {\n 'HAS_WEBGL': true\n};\nexport const PACKED_ENVS: Features = {\n 'WEBGL_PACK': true\n};\nexport const NODE_ENVS: Features = {\n 'IS_NODE': true\n};\nexport const CHROME_ENVS: Features = {\n 'IS_CHROME': true\n};\nexport const BROWSER_ENVS: Features = {\n 'IS_BROWSER': true\n};\nexport const CPU_ENVS: Features = {\n 'HAS_WEBGL': false\n};\n\nexport const ALL_ENVS: Features = {};\n\nexport function expectArraysClose(\n actual: Tensor|TypedArray|number[],\n expected: Tensor|TypedArray|number[]|boolean[]|number|boolean,\n epsilon?: number) {\n if (epsilon == null) {\n epsilon = ENV.get('TEST_EPSILON');\n }\n const exp = typeof expected === 'number' || typeof expected === 'boolean' ?\n [expected] as number[] :\n expected as number[];\n return expectArraysPredicate(\n actual, exp, (a, b) => areClose(a as number, Number(b), epsilon));\n}\n\nfunction expectArraysPredicate(\n actual: Tensor|TypedArray|number[]|string[],\n expected: Tensor|TypedArray|number[]|boolean[]|string[],\n predicate: (a: {}, b: {}) => boolean) {\n if (!(actual instanceof Tensor) && !(expected instanceof Tensor)) {\n const aType = actual.constructor.name;\n const bType = expected.constructor.name;\n\n if (aType !== bType) {\n throw new Error(\n `Arrays are of different type actual: ${aType} ` +\n `vs expected: ${bType}`);\n }\n } else if (actual instanceof Tensor && expected instanceof Tensor) {\n if (actual.dtype !== expected.dtype) {\n throw new Error(\n `Arrays are of different type actual: ${actual.dtype} ` +\n `vs expected: ${expected.dtype}.`);\n }\n if (!util.arraysEqual(actual.shape, expected.shape)) {\n throw new Error(\n `Arrays are of different shape actual: ${actual.shape} ` +\n `vs expected: ${expected.shape}.`);\n }\n }\n\n let actualValues: TypedArray|number[]|string[];\n let expectedValues: TypedArray|number[]|boolean[]|string[];\n if (actual instanceof Tensor) {\n actualValues = actual.dataSync();\n } else {\n actualValues = actual;\n }\n if (expected instanceof Tensor) {\n expectedValues = expected.dataSync();\n } else {\n expectedValues = expected;\n }\n\n if (actualValues.length !== expectedValues.length) {\n throw new Error(\n `Arrays have different lengths actual: ${actualValues.length} vs ` +\n `expected: ${expectedValues.length}.\\n` +\n `Actual: ${actualValues}.\\n` +\n `Expected: ${expectedValues}.`);\n }\n for (let i = 0; i < expectedValues.length; ++i) {\n const a = actualValues[i];\n const e = expectedValues[i];\n\n if (!predicate(a, e)) {\n throw new Error(\n `Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n `Actual: ${actualValues}.\\n` +\n `Expected: ${expectedValues}.`);\n }\n }\n}\n\nexport interface DoneFn {\n (): void;\n fail: (message?: Error|string) => void;\n}\n\nexport function expectPromiseToFail(fn: () => Promise<{}>, done: DoneFn): void {\n fn().then(() => done.fail(), () => done());\n}\n\nexport function expectArraysEqual(\n actual: Tensor|TypedArray|number[]|string[],\n expected: Tensor|TypedArray|number[]|boolean[]|string[]|number|boolean|\n string) {\n const exp = typeof expected === 'string' || typeof expected === 'number' ||\n typeof expected === 'boolean' ?\n [expected] as number[] :\n expected as number[];\n if (actual instanceof Tensor && actual.dtype === 'string' ||\n expected instanceof Tensor && expected.dtype === 'string' ||\n Array.isArray(actual) && isString(actual[0]) ||\n Array.isArray(expected) && isString(expected[0])) {\n // tslint:disable-next-line:triple-equals\n return expectArraysPredicate(actual, exp, (a, b) => a == b);\n }\n return expectArraysClose(actual as Tensor, expected as Tensor, 0);\n}\n\nexport function expectNumbersClose(a: number, e: number, epsilon?: number) {\n if (epsilon == null) {\n epsilon = ENV.get('TEST_EPSILON');\n }\n if (!areClose(a, e, epsilon)) {\n throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n }\n}\n\nfunction areClose(a: number, e: number, epsilon: number): boolean {\n if (isNaN(a) && isNaN(e)) {\n return true;\n }\n if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n return false;\n }\n return true;\n}\n\nexport function expectValuesInRange(\n actual: Tensor|TypedArray|number[], low: number, high: number) {\n let actualVals: TypedArray|number[];\n if (actual instanceof Tensor) {\n actualVals = actual.dataSync();\n } else {\n actualVals = actual;\n }\n for (let i = 0; i < actualVals.length; i++) {\n if (actualVals[i] < low || actualVals[i] > high) {\n throw new Error(\n `Value out of range:${actualVals[i]} low: ${low}, high: ${high}`);\n }\n }\n}\n\nexport function expectArrayBuffersEqual(\n actual: ArrayBuffer, expected: ArrayBuffer) {\n // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n // a Float32Array solves this issue.\n expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n","../src/version.ts":"/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '0.15.4';\nexport {version};\n","../src/optimizers/optimizer.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {variableGrads} from '../globals';\nimport {Serializable} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport abstract class Optimizer extends Serializable {\n /**\n * Executes `f()` and minimizes the scalar output of `f()` by computing\n * gradients of y with respect to the list of trainable variables provided by\n * `varList`. If no list is provided, it defaults to all trainable variables.\n *\n * @param f The function to execute and whose output to minimize.\n * @param returnCost Whether to return the scalar cost value produced by\n * executing `f()`.\n * @param varList An optional list of variables to update. If specified, only\n * the trainable variables in varList will be updated by minimize. Defaults to\n * all trainable variables.\n */\n /** @doc {heading: 'Training', subheading: 'Optimizers'} */\n minimize(f: () => Scalar, returnCost = false, varList?: Variable[]): Scalar\n |null {\n const {value, grads} = this.computeGradients(f, varList);\n\n this.applyGradients(grads);\n\n // Dispose gradients.\n const varNames = Object.keys(grads);\n varNames.forEach(varName => grads[varName].dispose());\n\n if (returnCost) {\n return value as Scalar;\n } else {\n value.dispose();\n return null;\n }\n }\n\n /**\n * Executes f() and computes the gradient of the scalar output of f() with\n * respect to the list of trainable variables provided by `varList`. If no\n * list is provided, it defaults to all trainable variables.\n *\n * @param f The function to execute and whose output to use for computing\n * gradients with respect to variables.\n * @param varList An optional list of variables to compute gradients with\n * respect to. If specified, only the trainable variables in varList will have\n * gradients computed with respect to. Defaults to all trainable variables.\n */\n computeGradients(f: () => Scalar, varList?: Variable[]):\n {value: Scalar, grads: NamedTensorMap} {\n return variableGrads(f, varList);\n }\n\n /**\n * Updates variables by using the computed gradients.\n *\n * @param variableGradients A mapping of variable name to its gradient value.\n */\n abstract applyGradients(variableGradients: NamedTensorMap): void;\n}\n\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n value: (instance: Optimizer) => {\n return instance.minimize != null && instance.computeGradients != null &&\n instance.applyGradients != null;\n }\n});\n","../src/optimizers/adadelta_optimizer.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n /** @nocollapse */\n static className = 'AdadeltaOptimizer';\n private c: Scalar;\n private epsilonScalar: Scalar;\n private rhoScalar: Scalar;\n private oneMinusRho: Scalar;\n\n private accumulatedGrads: NamedVariableMap = {};\n private accumulatedUpdates: NamedVariableMap = {};\n\n constructor(\n protected learningRate: number, protected rho: number,\n protected epsilon: number = null) {\n super();\n\n this.c = keep(scalar(-learningRate));\n this.rhoScalar = keep(scalar(rho));\n this.oneMinusRho = keep(scalar(1 - rho));\n\n if (epsilon === null) {\n epsilon = ENV.get('EPSILON');\n }\n\n this.epsilonScalar = keep(scalar(epsilon));\n }\n\n applyGradients(variableGradients: NamedVariableMap) {\n for (const variableName in variableGradients) {\n const value = ENV.engine.registeredVariables[variableName];\n if (this.accumulatedGrads[variableName] == null) {\n const trainable = false;\n tidy(() => {\n this.accumulatedGrads[variableName] =\n zerosLike(value).variable(trainable);\n });\n }\n if (this.accumulatedUpdates[variableName] == null) {\n const trainable = false;\n tidy(() => {\n this.accumulatedUpdates[variableName] =\n zerosLike(value).variable(trainable);\n });\n }\n\n const gradient = variableGradients[variableName];\n const accumulatedGrad = this.accumulatedGrads[variableName];\n const accumulatedUpdate = this.accumulatedUpdates[variableName];\n\n tidy(() => {\n const newAccumulatedGrad =\n this.rhoScalar.mul(accumulatedGrad)\n .add(this.oneMinusRho.mul(gradient.square()));\n\n const updates = accumulatedUpdate.add(this.epsilonScalar)\n .sqrt()\n .div(accumulatedGrad.add(this.epsilonScalar).sqrt())\n .mul(gradient);\n\n const newAccumulatedUpdate =\n this.rhoScalar.mul(accumulatedUpdate)\n .add(this.oneMinusRho.mul(updates.square()));\n\n this.accumulatedGrads[variableName].assign(newAccumulatedGrad);\n this.accumulatedUpdates[variableName].assign(newAccumulatedUpdate);\n\n const newValue = this.c.mul(updates).add(value);\n value.assign(newValue);\n });\n }\n }\n\n dispose() {\n this.c.dispose();\n this.epsilonScalar.dispose();\n this.rhoScalar.dispose();\n this.oneMinusRho.dispose();\n if (this.accumulatedUpdates != null) {\n Object.keys(this.accumulatedUpdates)\n .forEach(name => this.accumulatedUpdates[name].dispose());\n Object.keys(this.accumulatedGrads)\n .forEach(name => this.accumulatedGrads[name].dispose());\n }\n }\n getConfig(): ConfigDict {\n return {\n learningRate: this.learningRate,\n rho: this.rho,\n epsilon: this.epsilon\n };\n }\n static fromConfig(\n cls: SerializableConstructor, config: ConfigDict): T {\n return new cls(config.learningRate, config.rho, config.epsilon);\n }\n}\nregisterClass(AdadeltaOptimizer);\n","../src/optimizers/adagrad_optimizer.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {fill, scalar} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n /** @nocollapse */\n static className = 'AdagradOptimizer';\n private c: Scalar;\n private epsilon: Scalar;\n\n private accumulatedGrads: NamedVariableMap = {};\n\n constructor(\n protected learningRate: number, private initialAccumulatorValue = 0.1) {\n super();\n this.c = keep(scalar(-learningRate));\n\n this.epsilon = keep(scalar(ENV.get('EPSILON')));\n }\n\n applyGradients(variableGradients: NamedVariableMap) {\n for (const variableName in variableGradients) {\n const value = ENV.engine.registeredVariables[variableName];\n if (this.accumulatedGrads[variableName] == null) {\n const trainable = false;\n tidy(() => {\n this.accumulatedGrads[variableName] =\n fill(value.shape, this.initialAccumulatorValue)\n .variable(trainable);\n });\n }\n\n const gradient = variableGradients[variableName];\n const accumulatedGrad = this.accumulatedGrads[variableName];\n\n tidy(() => {\n const newAccumulatedGrad = accumulatedGrad.add(gradient.square());\n this.accumulatedGrads[variableName].assign(newAccumulatedGrad);\n\n const newValue =\n this.c\n .mul(gradient.div(newAccumulatedGrad.add(this.epsilon).sqrt()))\n .add(value);\n value.assign(newValue);\n });\n }\n }\n\n dispose() {\n this.epsilon.dispose();\n this.c.dispose();\n if (this.accumulatedGrads != null) {\n Object.keys(this.accumulatedGrads)\n .forEach(name => this.accumulatedGrads[name].dispose());\n }\n }\n getConfig(): ConfigDict {\n return {\n learningRate: this.learningRate,\n initialAccumulatorValue: this.initialAccumulatorValue,\n };\n }\n static fromConfig(\n cls: SerializableConstructor, config: ConfigDict): T {\n return new cls(config.learningRate, config.initialAccumulatorValue);\n }\n}\nregisterClass(AdagradOptimizer);\n","../src/optimizers/adam_optimizer.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\nexport class AdamOptimizer extends Optimizer {\n /** @nocollapse */\n static className = 'AdamOptimizer';\n private c: Scalar;\n private epsScalar: Scalar;\n private beta1Scalar: Scalar;\n private beta2Scalar: Scalar;\n private accBeta1: Variable;\n private accBeta2: Variable;\n private oneMinusBeta1: Scalar;\n private oneMinusBeta2: Scalar;\n private one: Scalar;\n\n private accumulatedFirstMoment: NamedVariableMap = {};\n private accumulatedSecondMoment: NamedVariableMap = {};\n\n constructor(\n protected learningRate: number, protected beta1: number,\n protected beta2: number, protected epsilon: number = null) {\n super();\n this.c = keep(scalar(-learningRate));\n // b1, b2 keep initial value of beta* hyperparameters.\n this.beta1Scalar = keep(scalar(beta1));\n this.beta2Scalar = keep(scalar(beta2));\n tidy(() => {\n // accB* will be updated by batch.\n this.accBeta1 = scalar(beta1).variable();\n this.accBeta2 = scalar(beta2).variable();\n });\n this.oneMinusBeta1 = keep(scalar(1 - beta1));\n this.oneMinusBeta2 = keep(scalar(1 - beta2));\n this.one = keep(scalar(1));\n\n if (epsilon === null) {\n epsilon = ENV.get('EPSILON');\n }\n\n this.epsScalar = keep(scalar(epsilon));\n }\n\n applyGradients(variableGradients: NamedVariableMap) {\n tidy(() => {\n const oneMinusAccBeta1 = this.one.sub(this.accBeta1);\n const oneMinusAccBeta2 = this.one.sub(this.accBeta2);\n\n for (const variableName in variableGradients) {\n const value = ENV.engine.registeredVariables[variableName];\n if (this.accumulatedFirstMoment[variableName] == null) {\n const trainable = false;\n this.accumulatedFirstMoment[variableName] =\n zerosLike(value).variable(trainable);\n }\n if (this.accumulatedSecondMoment[variableName] == null) {\n const trainable = false;\n this.accumulatedSecondMoment[variableName] =\n zerosLike(value).variable(trainable);\n }\n\n const gradient = variableGradients[variableName];\n const firstMoment = this.accumulatedFirstMoment[variableName];\n const secondMoment = this.accumulatedSecondMoment[variableName];\n\n const newFirstMoment = this.beta1Scalar.mul(firstMoment)\n .add(this.oneMinusBeta1.mul(gradient));\n const newSecondMoment =\n this.beta2Scalar.mul(secondMoment)\n .add(this.oneMinusBeta2.mul(gradient.square()));\n\n const biasCorrectedFirstMoment = newFirstMoment.div(oneMinusAccBeta1);\n const biasCorrectedSecondMoment = newSecondMoment.div(oneMinusAccBeta2);\n\n this.accumulatedFirstMoment[variableName].assign(newFirstMoment);\n this.accumulatedSecondMoment[variableName].assign(newSecondMoment);\n\n const newValue =\n this.c\n .mul(biasCorrectedFirstMoment.div(\n this.epsScalar.add(biasCorrectedSecondMoment.sqrt())))\n .add(value);\n value.assign(newValue);\n }\n\n this.accBeta1.assign(this.accBeta1.mul(this.beta1Scalar));\n this.accBeta2.assign(this.accBeta2.mul(this.beta2Scalar));\n });\n }\n\n dispose() {\n this.c.dispose();\n this.epsScalar.dispose();\n this.beta1Scalar.dispose();\n this.beta2Scalar.dispose();\n this.accBeta1.dispose();\n this.accBeta2.dispose();\n this.oneMinusBeta1.dispose();\n this.oneMinusBeta2.dispose();\n this.one.dispose();\n\n if (this.accumulatedFirstMoment != null) {\n Object.keys(this.accumulatedFirstMoment)\n .forEach(name => this.accumulatedFirstMoment[name].dispose());\n }\n\n if (this.accumulatedSecondMoment != null) {\n Object.keys(this.accumulatedSecondMoment)\n .forEach(name => this.accumulatedSecondMoment[name].dispose());\n }\n }\n getConfig(): ConfigDict {\n return {\n learningRate: this.learningRate,\n beta1: this.beta1,\n beta2: this.beta2,\n epsilon: this.epsilon,\n };\n }\n static fromConfig(\n cls: SerializableConstructor, config: ConfigDict): T {\n return new cls(\n config.learningRate, config.beta1, config.beta2, config.epsilon);\n }\n}\nregisterClass(AdamOptimizer);\n","../src/optimizers/adamax_optimizer.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\nexport class AdamaxOptimizer extends Optimizer {\n /** @nocollapse */\n static className = 'AdamaxOptimizer';\n private c: Scalar;\n private epsScalar: Scalar;\n private accBeta1: Variable;\n private beta1Scalar: Scalar;\n private beta2Scalar: Scalar;\n private decayScalar: Scalar;\n private oneMinusBeta1: Scalar;\n private one: Scalar;\n private iteration: Variable;\n\n private accumulatedFirstMoment: NamedVariableMap = {};\n private accumulatedWeightedInfNorm: NamedVariableMap = {};\n\n constructor(\n protected learningRate: number, protected beta1: number,\n protected beta2: number, protected epsilon: number = null,\n protected decay = 0.0) {\n super();\n this.c = keep(scalar(-learningRate));\n\n // b1, b2 keep initial value of beta* hyperparameters.\n this.beta1Scalar = keep(scalar(beta1));\n this.beta2Scalar = keep(scalar(beta2));\n\n this.decayScalar = keep(scalar(decay));\n\n tidy(() => {\n this.iteration = scalar(0).variable();\n this.accBeta1 = scalar(beta1).variable();\n });\n\n this.oneMinusBeta1 = keep(scalar(1 - beta1));\n this.one = keep(scalar(1));\n\n if (epsilon === null) {\n epsilon = ENV.get('EPSILON');\n }\n\n this.epsScalar = keep(scalar(epsilon));\n }\n\n applyGradients(variableGradients: NamedVariableMap) {\n tidy(() => {\n const oneMinusAccBeta1 = this.one.sub(this.accBeta1);\n const lr = this.c.div(this.one.add(this.decayScalar.mul(this.iteration)));\n\n for (const variableName in variableGradients) {\n const value = ENV.engine.registeredVariables[variableName];\n if (this.accumulatedFirstMoment[variableName] == null) {\n const trainable = false;\n this.accumulatedFirstMoment[variableName] =\n zerosLike(value).variable(trainable);\n }\n if (this.accumulatedWeightedInfNorm[variableName] == null) {\n const trainable = false;\n this.accumulatedWeightedInfNorm[variableName] =\n zerosLike(value).variable(trainable);\n }\n\n const gradient = variableGradients[variableName];\n const firstMoment = this.accumulatedFirstMoment[variableName];\n const weightedInfNorm = this.accumulatedWeightedInfNorm[variableName];\n\n const newFirstMoment = this.beta1Scalar.mul(firstMoment)\n .add(this.oneMinusBeta1.mul(gradient));\n\n const ut0 = this.beta2Scalar.mul(weightedInfNorm);\n const ut1 = gradient.abs();\n\n const newWeightedInfNorm = ut0.maximum(ut1);\n\n this.accumulatedFirstMoment[variableName].assign(newFirstMoment);\n this.accumulatedWeightedInfNorm[variableName].assign(\n newWeightedInfNorm);\n\n const newValue =\n lr.div(oneMinusAccBeta1)\n .mul(newFirstMoment.div(this.epsScalar.add(newWeightedInfNorm)))\n .add(value);\n\n value.assign(newValue);\n }\n\n this.iteration.assign(this.iteration.add(this.one));\n this.accBeta1.assign(this.accBeta1.mul(this.beta1Scalar));\n });\n }\n\n dispose() {\n this.c.dispose();\n this.epsScalar.dispose();\n this.accBeta1.dispose();\n this.beta1Scalar.dispose();\n this.beta2Scalar.dispose();\n this.oneMinusBeta1.dispose();\n\n this.decayScalar.dispose();\n this.iteration.dispose();\n\n this.one.dispose();\n\n if (this.accumulatedFirstMoment != null) {\n Object.keys(this.accumulatedFirstMoment)\n .forEach(name => this.accumulatedFirstMoment[name].dispose());\n }\n\n if (this.accumulatedWeightedInfNorm != null) {\n Object.keys(this.accumulatedWeightedInfNorm)\n .forEach(name => this.accumulatedWeightedInfNorm[name].dispose());\n }\n }\n getConfig(): ConfigDict {\n return {\n learningRate: this.learningRate,\n beta1: this.beta1,\n beta2: this.beta2,\n epsilon: this.epsilon,\n decay: this.decay\n };\n }\n static fromConfig(\n cls: SerializableConstructor, config: ConfigDict): T {\n return new cls(\n config.learningRate, config.beta1, config.beta2, config.epsilon,\n config.decay);\n }\n}\nregisterClass(AdamaxOptimizer);\n","../src/optimizers/sgd_optimizer.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n /** @nocollapse */\n static className = 'SGDOptimizer';\n protected c: Scalar;\n\n constructor(protected learningRate: number) {\n super();\n this.setLearningRate(learningRate);\n }\n\n applyGradients(variableGradients: NamedTensorMap) {\n const varNames = Object.keys(variableGradients);\n varNames.forEach(varName => {\n const gradient = variableGradients[varName];\n const value = ENV.engine.registeredVariables[varName];\n\n tidy(() => {\n const newValue = this.c.mul(gradient).add(value);\n value.assign(newValue);\n });\n });\n }\n\n /**\n * Sets the learning rate of the optimizer.\n */\n setLearningRate(learningRate: number) {\n this.learningRate = learningRate;\n if (this.c != null) {\n this.c.dispose();\n }\n this.c = keep(scalar(-learningRate));\n }\n\n dispose() {\n this.c.dispose();\n }\n\n getConfig(): ConfigDict {\n return {learningRate: this.learningRate};\n }\n static fromConfig(\n cls: SerializableConstructor, config: ConfigDict): T {\n return new cls(config.learningRate);\n }\n}\nregisterClass(SGDOptimizer);\n","../src/optimizers/momentum_optimizer.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Tensor} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {SGDOptimizer} from './sgd_optimizer';\n\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n /** @nocollapse */\n static className = 'MomentumOptimizer';\n private m: Scalar;\n private accumulations: NamedVariableMap;\n\n constructor(\n protected learningRate: number, private momentum: number,\n private useNesterov = false) {\n super(learningRate);\n this.m = scalar(this.momentum);\n this.accumulations = {};\n }\n\n applyGradients(variableGradients: NamedVariableMap) {\n for (const variableName in variableGradients) {\n const value = ENV.engine.registeredVariables[variableName];\n if (this.accumulations[variableName] == null) {\n const trainable = false;\n tidy(() => {\n this.accumulations[variableName] =\n zerosLike(value).variable(trainable);\n });\n }\n\n const accumulation = this.accumulations[variableName];\n const gradient = variableGradients[variableName];\n\n tidy(() => {\n let newValue: Tensor;\n const newAccumulation = this.m.mul(accumulation).add(gradient);\n if (this.useNesterov) {\n newValue =\n this.c.mul(gradient.add(newAccumulation.mul(this.m))).add(value);\n } else {\n newValue = this.c.mul(newAccumulation).add(value);\n }\n this.accumulations[variableName].assign(newAccumulation);\n value.assign(newValue);\n });\n }\n }\n\n dispose() {\n super.dispose();\n this.m.dispose();\n if (this.accumulations != null) {\n for (const variableName in this.accumulations) {\n this.accumulations[variableName].dispose();\n }\n }\n }\n\n /**\n * Sets the momentum of the optimizer.\n *\n * @param momentum\n */\n setMomentum(momentum: number) {\n this.momentum = momentum;\n }\n\n getConfig(): ConfigDict {\n return {\n learningRate: this.learningRate,\n momentum: this.momentum,\n useNesterov: this.useNesterov\n };\n }\n static fromConfig(\n cls: SerializableConstructor, config: ConfigDict): T {\n return new cls(config.learningRate, config.momentum, config.useNesterov);\n }\n}\nregisterClass(MomentumOptimizer);\n","../src/optimizers/rmsprop_optimizer.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n /** @nocollapse */\n static className = 'RMSPropOptimizer';\n private c: Scalar;\n private epsilonScalar: Scalar;\n private decayScalar: Scalar;\n private momentumScalar: Scalar;\n private oneMinusDecay: Scalar;\n private centered: boolean;\n\n private accumulatedMeanSquares: NamedVariableMap = {};\n private accumulatedMeanGrads: NamedVariableMap = {};\n private accumulatedMoments: NamedVariableMap = {};\n\n constructor(\n protected learningRate: number, protected decay = 0.9,\n protected momentum = 0.0, protected epsilon: number = null,\n centered = false) {\n super();\n\n this.c = keep(scalar(learningRate));\n this.decayScalar = keep(scalar(decay));\n this.momentumScalar = keep(scalar(momentum));\n this.oneMinusDecay = keep(scalar(1 - decay));\n this.centered = centered;\n\n if (epsilon === null) {\n epsilon = ENV.get('EPSILON');\n }\n\n this.epsilonScalar = keep(scalar(epsilon));\n }\n\n applyGradients(variableGradients: NamedVariableMap) {\n for (const variableName in variableGradients) {\n const value = ENV.engine.registeredVariables[variableName];\n if (this.accumulatedMeanSquares[variableName] == null) {\n const trainable = false;\n tidy(() => {\n this.accumulatedMeanSquares[variableName] =\n zerosLike(value).variable(trainable);\n });\n }\n if (this.accumulatedMeanGrads[variableName] == null && this.centered) {\n const trainable = false;\n tidy(() => {\n this.accumulatedMeanGrads[variableName] =\n zerosLike(value).variable(trainable);\n });\n }\n if (this.accumulatedMoments[variableName] == null) {\n const trainable = false;\n tidy(() => {\n this.accumulatedMoments[variableName] =\n zerosLike(value).variable(trainable);\n });\n }\n\n const accumulatedMeanSquare = this.accumulatedMeanSquares[variableName];\n const accumulatedMeanGrad = this.accumulatedMeanGrads[variableName];\n const accumulatedMoments = this.accumulatedMoments[variableName];\n const gradient = variableGradients[variableName];\n\n tidy(() => {\n const newAccumulatedMeanSquare =\n this.decayScalar.mul(accumulatedMeanSquare)\n .add(this.oneMinusDecay.mul(gradient.square()));\n\n if (this.centered) {\n // Centered gradient\n const newAccumulatedMeanGrad =\n this.decayScalar.mul(accumulatedMeanGrad)\n .add(this.oneMinusDecay.mul(gradient));\n\n const newAccumulatedMoments =\n this.momentumScalar.mul(accumulatedMoments)\n .add(this.c.mul(gradient).div(\n newAccumulatedMeanSquare\n .sub(newAccumulatedMeanGrad.square().add(\n this.epsilonScalar))\n .sqrt()));\n\n this.accumulatedMeanSquares[variableName].assign(\n newAccumulatedMeanSquare);\n this.accumulatedMeanGrads[variableName].assign(\n newAccumulatedMeanGrad);\n this.accumulatedMoments[variableName].assign(newAccumulatedMoments);\n\n const newValue = value.sub(newAccumulatedMoments);\n value.assign(newValue);\n } else {\n // Plain gradient\n const newAccumulatedMeanSquare =\n this.decayScalar.mul(accumulatedMeanSquare)\n .add(this.oneMinusDecay.mul(gradient.square()));\n\n const newAccumulatedMoments =\n this.momentumScalar.mul(accumulatedMoments)\n .add(this.c.mul(gradient).div(\n newAccumulatedMeanSquare.add(this.epsilonScalar).sqrt()));\n\n this.accumulatedMeanSquares[variableName].assign(\n newAccumulatedMeanSquare);\n this.accumulatedMoments[variableName].assign(newAccumulatedMoments);\n\n const newValue = value.sub(newAccumulatedMoments);\n value.assign(newValue);\n }\n });\n }\n }\n\n dispose() {\n this.c.dispose();\n this.epsilonScalar.dispose();\n this.decayScalar.dispose();\n this.momentumScalar.dispose();\n this.oneMinusDecay.dispose();\n if (this.accumulatedMeanSquares != null) {\n Object.keys(this.accumulatedMeanSquares)\n .forEach(name => this.accumulatedMeanSquares[name].dispose());\n }\n if (this.accumulatedMeanGrads != null && this.centered) {\n Object.keys(this.accumulatedMeanGrads)\n .forEach(name => this.accumulatedMeanGrads[name].dispose());\n }\n if (this.accumulatedMoments != null) {\n Object.keys(this.accumulatedMoments)\n .forEach(name => this.accumulatedMoments[name].dispose());\n }\n }\n\n getConfig(): ConfigDict {\n return {\n learningRate: this.learningRate,\n decay: this.decay,\n momentum: this.momentum,\n epsilon: this.epsilon,\n centered: this.centered\n };\n }\n static fromConfig(\n cls: SerializableConstructor, config: ConfigDict): T {\n return new cls(\n config.learningRate, config.decay, config.momentum, config.epsilon,\n config.centered);\n }\n}\nregisterClass(RMSPropOptimizer);\n","../src/optimizers/optimizer_constructors.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AdadeltaOptimizer} from './adadelta_optimizer';\nimport {AdagradOptimizer} from './adagrad_optimizer';\nimport {AdamOptimizer} from './adam_optimizer';\nimport {AdamaxOptimizer} from './adamax_optimizer';\nimport {MomentumOptimizer} from './momentum_optimizer';\nimport {RMSPropOptimizer} from './rmsprop_optimizer';\nimport {SGDOptimizer} from './sgd_optimizer';\n\nexport class OptimizerConstructors {\n /**\n * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n *\n * ```js\n * // Fit a quadratic function by learning the coefficients a, b, c.\n * const xs = tf.tensor1d([0, 1, 2, 3]);\n * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n *\n * const a = tf.scalar(Math.random()).variable();\n * const b = tf.scalar(Math.random()).variable();\n * const c = tf.scalar(Math.random()).variable();\n *\n * // y = a * x^2 + b * x + c.\n * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n * const loss = (pred, label) => pred.sub(label).square().mean();\n *\n * const learningRate = 0.01;\n * const optimizer = tf.train.sgd(learningRate);\n *\n * // Train the model.\n * for (let i = 0; i < 10; i++) {\n * optimizer.minimize(() => loss(f(xs), ys));\n * }\n *\n * // Make predictions.\n * console.log(\n * `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n * const preds = f(xs).dataSync();\n * preds.forEach((pred, i) => {\n * console.log(`x: ${i}, pred: ${pred}`);\n * });\n * ```\n *\n * @param learningRate The learning rate to use for the SGD algorithm.\n */\n /**\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static sgd(learningRate: number): SGDOptimizer {\n return new SGDOptimizer(learningRate);\n }\n\n /**\n * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n * descent.\n *\n * See\n * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n * http://proceedings.mlr.press/v28/sutskever13.pdf)\n *\n * @param learningRate The learning rate to use for the Momentum gradient\n * descent algorithm.\n * @param momentum The momentum to use for the momentum gradient descent\n * algorithm.\n */\n /**\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static momentum(learningRate: number, momentum: number, useNesterov = false):\n MomentumOptimizer {\n return new MomentumOptimizer(learningRate, momentum, useNesterov);\n }\n\n /**\n * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n * descent. This implementation uses plain momentum and is not centered\n * version of RMSProp.\n *\n * See\n * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n *\n * @param learningRate The learning rate to use for the RMSProp gradient\n * descent algorithm.\n * @param decay The discounting factor for the history/coming gradient.\n * @param momentum The momentum to use for the RMSProp gradient descent\n * algorithm.\n * @param epsilon Small value to avoid zero denominator.\n * @param centered If true, gradients are normalized by the estimated\n * variance of the gradient.\n */\n /**\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static rmsprop(\n learningRate: number, decay = .9, momentum = 0.0, epsilon: number = null,\n centered = false): RMSPropOptimizer {\n return new RMSPropOptimizer(\n learningRate, decay, momentum, epsilon, centered);\n }\n\n /**\n * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n *\n * @param learningRate The learning rate to use for the Adam gradient\n * descent algorithm.\n * @param beta1 The exponential decay rate for the 1st moment estimates.\n * @param beta2 The exponential decay rate for the 2nd moment estimates.\n * @param epsilon A small constant for numerical stability.\n */\n /**\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static adam(\n learningRate = 0.001, beta1 = 0.9, beta2 = 0.999,\n epsilon: number = null): AdamOptimizer {\n return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n }\n\n /**\n * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n *\n * @param learningRate The learning rate to use for the Adadelta gradient\n * descent algorithm.\n * @param rho The learning rate decay over each update.\n * @param epsilon A constant epsilon used to better condition the grad\n * update.\n */\n /**\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static adadelta(learningRate = .001, rho = .95, epsilon: number = null):\n AdadeltaOptimizer {\n return new AdadeltaOptimizer(learningRate, rho, epsilon);\n }\n\n /**\n * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n *\n * @param learningRate The learning rate to use for the Adamax gradient\n * descent algorithm.\n * @param beta1 The exponential decay rate for the 1st moment estimates.\n * @param beta2 The exponential decay rate for the 2nd moment estimates.\n * @param epsilon A small constant for numerical stability.\n * @param decay The learning rate decay over each update.\n */\n /**\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static adamax(\n learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon: number = null,\n decay = 0.0): AdamaxOptimizer {\n return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n }\n\n /**\n * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n * See\n * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n * or\n * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n *\n * @param learningRate The learning rate to use for the Adagrad gradient\n * descent algorithm.\n * @param initialAccumulatorValue Starting value for the accumulators, must be\n * positive.\n */\n /**\n * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n */\n static adagrad(learningRate: number, initialAccumulatorValue = 0.1):\n AdagradOptimizer {\n return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n }\n}\n","../src/train.ts":"/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// So typings can propagate.\nimport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nimport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nimport {AdamOptimizer} from './optimizers/adam_optimizer';\nimport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nimport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nimport {OptimizerConstructors} from './optimizers/optimizer_constructors';\nimport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nimport {SGDOptimizer} from './optimizers/sgd_optimizer';\n\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\n\nexport const train = {\n sgd: OptimizerConstructors.sgd,\n momentum: OptimizerConstructors.momentum,\n adadelta: OptimizerConstructors.adadelta,\n adagrad: OptimizerConstructors.adagrad,\n rmsprop: OptimizerConstructors.rmsprop,\n adamax: OptimizerConstructors.adamax,\n adam: OptimizerConstructors.adam\n};\n","../src/index.ts":"/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// backend_cpu.ts and backend_webgl.ts are standalone files and should be\n// explicitly included here. Below, there is an export from backend_webgl, but\n// that doesn't count since it's exporting a Typescript interface.\nimport './kernels/backend_webgl';\nimport './kernels/backend_cpu';\n\nimport {nextFrame} from './browser_util';\nimport * as environment from './environment';\nimport {deprecationWarn, disableDeprecationWarnings, enableProdMode, Environment} from './environment';\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as serialization from './serialization';\nimport {setOpHandler} from './tensor';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport {version} from './version';\nimport * as webgl from './webgl';\n\nexport {InferenceModel, ModelPredictConfig} from './model_types';\n\n// Optimizers.\nexport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nexport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nexport {AdamOptimizer} from './optimizers/adam_optimizer';\nexport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nexport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nexport {Optimizer} from './optimizers/optimizer';\nexport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nexport {SGDOptimizer} from './optimizers/sgd_optimizer';\nexport {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer, variable, Variable} from './tensor';\nexport {NamedTensorMap} from './tensor_types';\nexport {DataType, DataTypeMap, DataValues, Rank, ShapeMap} from './types';\n\nexport * from './ops/ops';\nexport {LSTMCellFunc} from './ops/lstm';\nexport {Reduction} from './ops/loss_ops';\n\nexport * from './train';\nexport * from './globals';\n\nexport {Features} from './environment_util';\nexport {TimingInfo} from './engine';\nexport {ENV, Environment} from './environment';\n\nexport const setBackend = Environment.setBackend;\nexport const getBackend = Environment.getBackend;\nexport const disposeVariables = Environment.disposeVariables;\nexport const memory = Environment.memory;\nexport {version as version_core};\n\n// Top-level method exports.\nexport {nextFrame, enableProdMode, disableDeprecationWarnings, deprecationWarn};\n\n// Second level exports.\nexport {browser, environment, io, math, serialization, test_util, util, webgl};\n\n// Backend specific.\nexport {KernelBackend, BackendTimingInfo, DataMover, DataStorage} from './kernels/backend';\n\nimport * as ops from './ops/ops';\nsetOpHandler(ops);\n"},"lineCount":null}},"hash":"58ffbce423a8962a9e62266b05f472ac","cacheData":{"env":{}}}