Caffe
Public Member Functions | Protected Member Functions | List of all members
caffe::RNNLayer< Dtype > Class Template Reference

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 >:
caffe::RecurrentLayer< Dtype > caffe::Layer< Dtype >

Public Member Functions

 RNNLayer (const LayerParameter &param)
 
virtual const char * type () const
 Returns the layer type.
 
- 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)
 Does layer-specific setup: your layer should implement this function as well as Reshape. More...
 
virtual void Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More...
 
virtual void Reset ()
 
virtual int MinBottomBlobs () const
 Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxBottomBlobs () const
 Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual int ExactNumTopBlobs () const
 Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More...
 
virtual bool AllowForceBackward (const int bottom_index) const
 Return whether to allow force_backward for a given bottom blob index. More...
 
- Public Member Functions inherited from caffe::Layer< Dtype >
 Layer (const LayerParameter &param)
 
void SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Implements common layer setup functionality. More...
 
Dtype Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 Given the bottom blobs, compute the top blobs and the loss. More...
 
void Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Given the top blob error gradients, compute the bottom blob error gradients. More...
 
vector< shared_ptr< Blob< Dtype > > > & blobs ()
 Returns the vector of learnable parameter blobs.
 
const LayerParameter & layer_param () const
 Returns the layer parameter.
 
virtual void ToProto (LayerParameter *param, bool write_diff=false)
 Writes the layer parameter to a protocol buffer.
 
Dtype loss (const int top_index) const
 Returns the scalar loss associated with a top blob at a given index.
 
void set_loss (const int top_index, const Dtype value)
 Sets the loss associated with a top blob at a given index.
 
virtual int ExactNumBottomBlobs () const
 Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More...
 
virtual int MinTopBlobs () const
 Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More...
 
virtual int MaxTopBlobs () const
 Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More...
 
virtual bool EqualNumBottomTopBlobs () const
 Returns true if the layer requires an equal number of bottom and top blobs. More...
 
virtual bool AutoTopBlobs () const
 Return whether "anonymous" top blobs are created automatically by the layer. More...
 
bool param_propagate_down (const int param_id)
 Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More...
 
void set_param_propagate_down (const int param_id, const bool value)
 Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id.
 

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)
 Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable.
 
virtual void Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true.
 
- Protected Member Functions inherited from caffe::Layer< Dtype >
virtual void Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
 Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable.
 
virtual void CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
 
void SetLossWeights (const vector< Blob< Dtype > *> &top)
 

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_
 
- Protected Attributes inherited from caffe::Layer< Dtype >
LayerParameter layer_param_
 
Phase phase_
 
vector< shared_ptr< Blob< Dtype > > > blobs_
 
vector< bool > param_propagate_down_
 
vector< Dtype > loss_
 

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 $ x_t $, 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: