CaffeOnACL/include/caffe/layers/sigmoid_layer.hpp at master · hexujun/CaffeOnACL · GitHub

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#ifndef CAFFE_SIGMOID_LAYER_HPP_

#define CAFFE_SIGMOID_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"

#include "caffe/layer.hpp"

#include "caffe/proto/caffe.pb.h"

#include "caffe/layers/neuron_layer.hpp"

namespace caffe {

/**

* @brief Sigmoid function non-linearity @f$

* y = (1 + \exp(-x))^{-1}

* @f$, a classic choice in neural networks.

* Note that the gradient vanishes as the values move away from 0.

* The ReLULayer is often a better choice for this reason.

template <typename Dtype>

class SigmoidLayer : public NeuronLayer<Dtype> {

public:

explicit SigmoidLayer(const LayerParameter& param)

: NeuronLayer<Dtype>(param) {}

virtual inline const char* type() const { return "Sigmoid"; }

protected:

/**

* @param bottom input Blob vector (length 1)

* -# @f$ (N \times C \times H \times W) @f$

* the inputs @f$ x @f$

* @param top output Blob vector (length 1)

* -# @f$ (N \times C \times H \times W) @f$

* the computed outputs @f$

* y = (1 + \exp(-x))^{-1}

* @f$

virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,

const vector<Blob<Dtype>*>& top);

virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,

const vector<Blob<Dtype>*>& top);

/**

* @brief Computes the error gradient w.r.t. the sigmoid inputs.

* @param top output Blob vector (length 1), providing the error gradient with

* respect to the outputs

* -# @f$ (N \times C \times H \times W) @f$

* containing error gradients @f$ \frac{\partial E}{\partial y} @f$

* with respect to computed outputs @f$ y @f$

* @param propagate_down see Layer::Backward.

* @param bottom input Blob vector (length 1)

* -# @f$ (N \times C \times H \times W) @f$

* the inputs @f$ x @f$; Backward fills their diff with

* gradients @f$

* \frac{\partial E}{\partial x}

* = \frac{\partial E}{\partial y} y (1 - y)

* @f$ if propagate_down[0]

virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,

const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,

const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

};

} // namespace caffe

#endif // CAFFE_SIGMOID_LAYER_HPP_

CaffeOnACL/include/caffe/layers/sigmoid_layer.hpp at master · hexujun/CaffeOnACL · GitHub

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