Caffe
|
An abstract class for implementing recurrent behavior inside of an unrolled network. This Layer type cannot be instantiated – instead, you should use one of its implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer. More...
#include <recurrent_layer.hpp>
Public Member Functions | |
RecurrentLayer (const LayerParameter ¶m) | |
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 const char * | type () const |
Returns the layer type. | |
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 ¶m) | |
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 =0 |
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 =0 |
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 | RecurrentInputShapes (vector< BlobShape > *shapes) const =0 |
Fills shapes with the shapes of the recurrent input Blob&s. Subclasses should define this – see RNNLayer and LSTMLayer for examples. | |
virtual void | RecurrentOutputBlobNames (vector< string > *names) const =0 |
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 | OutputBlobNames (vector< string > *names) const =0 |
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. | |
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) |
Protected Attributes | |
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_ |
An abstract class for implementing recurrent behavior inside of an unrolled network. This Layer type cannot be instantiated – instead, you should use one of its implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer.
|
inlinevirtual |
Return whether to allow force_backward for a given bottom blob index.
If AllowForceBackward(i) == false, we will ignore the force_backward setting and backpropagate to blob i only if it needs gradient information (as is done when force_backward == false).
Reimplemented from caffe::Layer< Dtype >.
|
inlinevirtual |
Returns the exact number of top blobs required by the layer, or -1 if no exact number is required.
This method should be overridden to return a non-negative value if your layer expects some exact number of top blobs.
Reimplemented from caffe::Layer< Dtype >.
|
protectedvirtual |
bottom | input Blob vector (length 2-3) |
top | output Blob vector (length 1)
|
Implements caffe::Layer< Dtype >.
|
virtual |
Does layer-specific setup: your layer should implement this function as well as Reshape.
bottom | the preshaped input blobs, whose data fields store the input data for this layer |
top | the allocated but unshaped output blobs |
This method should do one-time layer specific setup. This includes reading and processing relevent parameters from the layer_param_
. Setting up the shapes of top blobs and internal buffers should be done in Reshape
, which will be called before the forward pass to adjust the top blob sizes.
Reimplemented from caffe::Layer< Dtype >.
|
inlinevirtual |
Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required.
This method should be overridden to return a non-negative value if your layer expects some maximum number of bottom blobs.
Reimplemented from caffe::Layer< Dtype >.
|
inlinevirtual |
Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required.
This method should be overridden to return a non-negative value if your layer expects some minimum number of bottom blobs.
Reimplemented from caffe::Layer< Dtype >.
|
virtual |
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs.
bottom | the input blobs, with the requested input shapes |
top | the top blobs, which should be reshaped as needed |
This method should reshape top blobs as needed according to the shapes of the bottom (input) blobs, as well as reshaping any internal buffers and making any other necessary adjustments so that the layer can accommodate the bottom blobs.
Implements caffe::Layer< Dtype >.