/usr/include/caffe/layers/bias_layer.hpp is in libcaffe-cpu-dev 1.0.0~rc4-1.
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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 | #ifndef CAFFE_BIAS_LAYER_HPP_
#define CAFFE_BIAS_LAYER_HPP_
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
namespace caffe {
/**
* @brief Computes a sum of two input Blobs, with the shape of the latter Blob
* "broadcast" to match the shape of the former. Equivalent to tiling
* the latter Blob, then computing the elementwise sum.
*
* The second input may be omitted, in which case it's learned as a parameter
* of the layer. Note: in case bias and scaling are desired, both operations can
* be handled by `ScaleLayer` configured with `bias_term: true`.
*/
template <typename Dtype>
class BiasLayer : public Layer<Dtype> {
public:
explicit BiasLayer(const LayerParameter& param)
: Layer<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 "Bias"; }
virtual inline int MinBottomBlobs() const { return 1; }
virtual inline int MaxBottomBlobs() const { return 2; }
virtual inline int ExactNumTopBlobs() const { return 1; }
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);
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);
private:
Blob<Dtype> bias_multiplier_;
int outer_dim_, bias_dim_, inner_dim_, dim_;
};
} // namespace caffe
#endif // CAFFE_BIAS_LAYER_HPP_
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