Processes time-varying inputs using a simple recurrent neural network (RNN). Implemented as a network unrolling the RNN computation in time. More...

#include <rnn_layer.hpp>

Inheritance diagram for caffe::RNNLayer< Dtype >:

Public Member Functions
	RNNLayer (const LayerParameter &param)

virtual const char *	type () const

Public Member Functions inherited from caffe::RecurrentLayer< Dtype >
	RecurrentLayer (const LayerParameter &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 void	Reset ()

virtual int	MinBottomBlobs () const

virtual int	MaxBottomBlobs () const

virtual int	ExactNumTopBlobs () const

virtual bool	AllowForceBackward (const int bottom_index) const

Protected Member Functions
virtual void	FillUnrolledNet (NetParameter *net_param) const
	Fills net_param with the recurrent network architecture. Subclasses should define this – see RNNLayer and LSTMLayer for examples.

virtual void	RecurrentInputBlobNames (vector< string > *names) const
	Fills names with the names of the 0th timestep recurrent input Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.

virtual void	RecurrentOutputBlobNames (vector< string > *names) const
	Fills names with the names of the Tth timestep recurrent output Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.

virtual void	RecurrentInputShapes (vector< BlobShape > *shapes) const
	Fills shapes with the shapes of the recurrent input Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples.

virtual void	OutputBlobNames (vector< string > *names) const
	Fills names with the names of the output blobs, concatenated across all timesteps. Should return a name for each top Blob. Subclasses should define this – see RNNLayer and LSTMLayer for examples.

Protected Member Functions inherited from caffe::RecurrentLayer< Dtype >
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)

Additional Inherited Members
Protected Attributes inherited from caffe::RecurrentLayer< Dtype >
shared_ptr< Net< Dtype > >	unrolled_net_
	A Net to implement the Recurrent functionality.

int	N_
	The number of independent streams to process simultaneously.

int	T_
	The number of timesteps in the layer's input, and the number of timesteps over which to backpropagate through time.

bool	static_input_
	Whether the layer has a "static" input copied across all timesteps.

int	last_layer_index_
	The last layer to run in the network. (Any later layers are losses added to force the recurrent net to do backprop.)

bool	expose_hidden_
	Whether the layer's hidden state at the first and last timesteps are layer inputs and outputs, respectively.

vector< Blob< Dtype > * >	recur_input_blobs_

vector< Blob< Dtype > * >	recur_output_blobs_

vector< Blob< Dtype > * >	output_blobs_

Blob< Dtype > *	x_input_blob_

Blob< Dtype > *	x_static_input_blob_

Blob< Dtype > *	cont_input_blob_

Detailed Description

template<typename Dtype>
class caffe::RNNLayer< Dtype >

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 , computes hidden state $h_t := \tanh[ W_{hh} h_{t_1} + W_{xh} x_t + b_h ]$ , and outputs $o_t := \tanh[ W_{ho} h_t + b_o ]$ .

The documentation for this class was generated from the following files:

include/caffe/layers/rnn_layer.hpp
src/caffe/layers/rnn_layer.cpp

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

template<typename Dtype> class caffe::RNNLayer< Dtype >

template<typename Dtype>
class caffe::RNNLayer< Dtype >