/usr/include/caffe/layers/power_layer.hpp is in libcaffe-cpu-dev 1.0.0~rc4-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | #ifndef CAFFE_POWER_LAYER_HPP_
#define CAFFE_POWER_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 Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
* as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
* and power @f$ \gamma @f$.
*/
template <typename Dtype>
class PowerLayer : public NeuronLayer<Dtype> {
public:
/**
* @param param provides PowerParameter power_param,
* with PowerLayer options:
* - scale (\b optional, default 1) the scale @f$ \alpha @f$
* - shift (\b optional, default 0) the shift @f$ \beta @f$
* - power (\b optional, default 1) the power @f$ \gamma @f$
*/
explicit PowerLayer(const LayerParameter& param)
: NeuronLayer<Dtype>(param) {}
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual inline const char* type() const { return "Power"; }
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 = (\alpha x + \beta) ^ \gamma
* @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 power 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}
* \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} =
* \frac{\partial E}{\partial y}
* \frac{\alpha \gamma y}{\alpha x + \beta}
* @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);
/// @brief @f$ \gamma @f$ from layer_param_.power_param()
Dtype power_;
/// @brief @f$ \alpha @f$ from layer_param_.power_param()
Dtype scale_;
/// @brief @f$ \beta @f$ from layer_param_.power_param()
Dtype shift_;
/// @brief Result of @f$ \alpha \gamma @f$
Dtype diff_scale_;
};
} // namespace caffe
#endif // CAFFE_POWER_LAYER_HPP_
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