// TanH neuron activation function layer.
// Adapted from ReLU layer code written by Yangqing Jia

#include <algorithm>
#include <vector>

#include "caffe/layer.hpp"
#include "caffe/vision_layers.hpp"

#ifdef __SSE2__
#include "caffe/fmath.hpp"
#define EXP(x) fmath::exp(x)
#else
#define cast_uint32_t static_cast<uint32_t>
static inline float
fastpow2 (float p)
{
	float offset = (p < 0) ? 1.0f : 0.0f;
	float clipp = (p < -126) ? -126.0f : p;
	int w = clipp;
	float z = clipp - w + offset;
	union { uint32_t i; float f; } v = { cast_uint32_t ( (1 << 23) * (clipp + 121.2740575f + 27.7280233f / (4.84252568f - z) - 1.49012907f * z) ) };

	return v.f;
}

	static inline float
fastexp (float p)
{
	return fastpow2 (1.442695040f * p);
}
#define EXP(x) fastexp(x)
#endif


namespace caffe {

template <typename Dtype>
void TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  const Dtype* bottom_data = bottom[0]->cpu_data();
  Dtype* top_data = top[0]->mutable_cpu_data();
  Dtype exp2x;
  const int count = bottom[0]->count();
  for (int i = 0; i < count; ++i) {
    //top_data[i] = tanh(bottom_data[i]);

    exp2x = EXP(2*bottom_data[i]);
    top_data[i] = (exp2x - Dtype(1))/(exp2x + Dtype(1));
  }
}

template <typename Dtype>
void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down,
    const vector<Blob<Dtype>*>& bottom) {
  if (propagate_down[0]) {
    const Dtype* top_data = top[0]->cpu_data();
    const Dtype* top_diff = top[0]->cpu_diff();
    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
    const int count = bottom[0]->count();
    Dtype tanhx;
    for (int i = 0; i < count; ++i) {
      tanhx = top_data[i];
      bottom_diff[i] = top_diff[i] * (1 - tanhx * tanhx);
    }
  }
}

#ifdef CPU_ONLY
STUB_GPU(TanHLayer);
#endif

INSTANTIATE_CLASS(TanHLayer);

}  // namespace caffe