/usr/include/caffe/layers/rnn_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 | #ifndef CAFFE_RNN_LAYER_HPP_
#define CAFFE_RNN_LAYER_HPP_
#include <string>
#include <utility>
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/layers/recurrent_layer.hpp"
#include "caffe/net.hpp"
#include "caffe/proto/caffe.pb.h"
namespace caffe {
template <typename Dtype> class RecurrentLayer;
/**
* @brief Processes time-varying inputs using a simple recurrent neural network
* (RNN). Implemented as a network unrolling the RNN computation in time.
*
* Given time-varying inputs @f$ x_t @f$, computes hidden state @f$
* h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ]
* @f$, and outputs @f$
* o_t := \tanh[ W_{ho} h_t + b_o ]
* @f$.
*/
template <typename Dtype>
class RNNLayer : public RecurrentLayer<Dtype> {
public:
explicit RNNLayer(const LayerParameter& param)
: RecurrentLayer<Dtype>(param) {}
virtual inline const char* type() const { return "RNN"; }
protected:
virtual void FillUnrolledNet(NetParameter* net_param) const;
virtual void RecurrentInputBlobNames(vector<string>* names) const;
virtual void RecurrentOutputBlobNames(vector<string>* names) const;
virtual void RecurrentInputShapes(vector<BlobShape>* shapes) const;
virtual void OutputBlobNames(vector<string>* names) const;
};
} // namespace caffe
#endif // CAFFE_RNN_LAYER_HPP_
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