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#ifndef CAFFE_PRELU_LAYER_HPP_
#define CAFFE_PRELU_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 Parameterized Rectified Linear Unit non-linearity @f$
* y_i = \max(0, x_i) + a_i \min(0, x_i)
* @f$. The differences from ReLULayer are 1) negative slopes are
* learnable though backprop and 2) negative slopes can vary across
* channels. The number of axes of input blob should be greater than or
* equal to 2. The 1st axis (0-based) is seen as channels.
*/
template <typename Dtype>
class PReLULayer : public NeuronLayer<Dtype> {
public:
/**
* @param param provides PReLUParameter prelu_param,
* with PReLULayer options:
* - filler (\b optional, FillerParameter,
* default {'type': constant 'value':0.25}).
* - channel_shared (\b optional, default false).
* negative slopes are shared across channels.
*/
explicit PReLULayer(const LayerParameter& param)
: NeuronLayer<Dtype>(param) {}
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual inline const char* type() const { return "PReLU"; }
protected:
/**
* @param bottom input Blob vector (length 1)
* -# @f$ (N \times C \times ...) @f$
* the inputs @f$ x @f$
* @param top output Blob vector (length 1)
* -# @f$ (N \times C \times ...) @f$
* the computed outputs for each channel @f$i@f$ @f$
* y_i = \max(0, x_i) + a_i \min(0, x_i)
* @f$.
*/
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
/**
* @brief Computes the error gradient w.r.t. the PReLU inputs.
*
* @param top output Blob vector (length 1), providing the error gradient with
* respect to the outputs
* -# @f$ (N \times C \times ...) @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 ...) @f$
* the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
* diff with gradients @f$
* \frac{\partial E}{\partial x_i} = \left\{
* \begin{array}{lr}
* a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
* \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0
* \end{array} \right.
* @f$.
* If param_propagate_down_[0] is true, it fills the diff with gradients
* @f$
* \frac{\partial E}{\partial a_i} = \left\{
* \begin{array}{lr}
* \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
* 0 & \mathrm{if} \; x_i > 0
* \end{array} \right.
* @f$.
*/
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);
bool channel_shared_;
Blob<Dtype> multiplier_; // dot multiplier for backward computation of params
Blob<Dtype> backward_buff_; // temporary buffer for backward computation
Blob<Dtype> bottom_memory_; // memory for in-place computation
};
} // namespace caffe
#endif // CAFFE_PRELU_LAYER_HPP_
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