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#ifndef CAFFE_SWISH_LAYER_HPP_
#define CAFFE_SWISH_LAYER_HPP_
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/neuron_layer.hpp"
#include "caffe/layers/sigmoid_layer.hpp"
namespace caffe {
/**
* @brief Swish non-linearity @f$ y = x \sigma (\beta x) @f$.
* A novel activation function that tends to work better than ReLU [1].
*
* [1] Prajit Ramachandran, Barret Zoph, Quoc V. Le. "Searching for
* Activation Functions". arXiv preprint arXiv:1710.05941v2 (2017).
*/
template <typename Dtype>
class SwishLayer : public NeuronLayer<Dtype> {
public:
/**
* @param param provides SwishParameter swish_param,
* with SwishLayer options:
* - beta (\b optional, default 1).
* the value @f$ \beta @f$ in the @f$ y = x \sigma (\beta x) @f$.
*/
explicit SwishLayer(const LayerParameter& param)
: NeuronLayer<Dtype>(param),
sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
sigmoid_input_(new Blob<Dtype>()),
sigmoid_output_(new Blob<Dtype>()) {}
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual inline const char* type() const { return "Swish"; }
protected:
/**
* @param bottom input Blob vector (length 1)
* -# @f$ (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 = x \sigma (\beta x)
* @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}(\beta y +
* \sigma (\beta x)(1 - \beta 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);
/// The internal SigmoidLayer
shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
/// sigmoid_input_ stores the input of the SigmoidLayer.
shared_ptr<Blob<Dtype> > sigmoid_input_;
/// sigmoid_output_ stores the output of the SigmoidLayer.
shared_ptr<Blob<Dtype> > sigmoid_output_;
/// bottom vector holder to call the underlying SigmoidLayer::Forward
vector<Blob<Dtype>*> sigmoid_bottom_vec_;
/// top vector holder to call the underlying SigmoidLayer::Forward
vector<Blob<Dtype>*> sigmoid_top_vec_;
};
} // namespace caffe
#endif // CAFFE_SWISH_LAYER_HPP_
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