Classical recurrent layer. More...

#include "all_layers.hpp"

Inheritance diagram for cv::dnn::RNNLayer:

Public Member Functions
void	forward (std::vector< Blob *> &input, std::vector< Blob > &output)

virtual void	setProduceHiddenOutput (bool produce=false)=0
	If this flag is set to true then layer will produce \( h_t \) as second output. More...

virtual void	setWeights (const Blob &Wxh, const Blob &bh, const Blob &Whh, const Blob &Who, const Blob &bo)=0

Public Member Functions inherited from cv::dnn::Layer
	Layer ()

	Layer (const LayerParams &params)
	Initializes only name, type and blobs fields. More...

virtual	~Layer ()

virtual void	allocate (const std::vector< Blob *> &input, std::vector< Blob > &output)=0
	Allocates internal buffers and output blobs with respect to the shape of inputs. More...

void	allocate (const std::vector< Blob > &inputs, std::vector< Blob > &outputs)

std::vector< Blob >	allocate (const std::vector< Blob > &inputs)

void	forward (const std::vector< Blob > &inputs, std::vector< Blob > &outputs)

virtual int	inputNameToIndex (String inputName)
	Returns index of input blob into the input array. More...

virtual int	outputNameToIndex (String outputName)
	Returns index of output blob in output array. More...

void	run (const std::vector< Blob > &inputs, std::vector< Blob > &outputs)
	Allocates layer and computes output. More...

void	setParamsFrom (const LayerParams &params)
	Initializes only name, type and blobs fields. More...

Static Public Member Functions
static Ptr< RNNLayer >	create ()

Additional Inherited Members
Public Attributes inherited from cv::dnn::Layer
std::vector< Blob >	blobs
	List of learned parameters must be stored here to allow read them by using Net::getParam(). More...

String	name
	Name of the layer instance, can be used for logging or other internal purposes. More...

String	type
	Type name which was used for creating layer by layer factory. More...

Detailed Description

Classical recurrent layer.

Member Function Documentation

§ create()

static Ptr<RNNLayer> cv::dnn::RNNLayer::create ( )

static

Creates instance of RNNLayer

§ forward()

void cv::dnn::RNNLayer::forward	(	std::vector< Blob *> &	input,
		std::vector< Blob > &	output
	)

virtual

Accepts two inputs \(x_t\) and \(h_{t-1}\) and compute two outputs \(o_t\) and \(h_t\).

Parameters

input	should contain packed input \(x_t\).
output	should contain output \(o_t\) (and \(h_t\) if setProduceHiddenOutput() is set to true).

input[0] should have shape [T, N, data_dims] where T and N is number of timestamps and number of independent samples of \(x_t\) respectively.

output[0] will have shape [T, N, \(N_o\)], where \(N_o\) is number of rows in \( W_{xo} \) matrix.

If setProduceHiddenOutput() is set to true then output[1] will contain a Blob with shape [T, N, \(N_h\)], where \(N_h\) is number of rows in \( W_{hh} \) matrix.

Implements cv::dnn::Layer.

§ setProduceHiddenOutput()

virtual void cv::dnn::RNNLayer::setProduceHiddenOutput ( bool produce = false )

pure virtual

If this flag is set to true then layer will produce \( h_t \) as second output.

Shape of the second output is the same as first output.

§ setWeights()

virtual void cv::dnn::RNNLayer::setWeights	(	const Blob &	Wxh,
		const Blob &	bh,
		const Blob &	Whh,
		const Blob &	Who,
		const Blob &	bo
	)

pure virtual

Setups learned weights.

Recurrent-layer behavior on each step is defined by current input \( x_t \), previous state \( h_t \) and learned weights as follows:

\begin{eqnarray*} h_t &= tanh&(W_{hh} h_{t-1} + W_{xh} x_t + b_h), \\ o_t &= tanh&(W_{ho} h_t + b_o), \end{eqnarray*}

Parameters

Wxh	is \( W_{xh} \) matrix
bh	is \( b_{h} \) vector
Whh	is \( W_{hh} \) matrix
Who	is \( W_{xo} \) matrix
bo	is \( b_{o} \) vector

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

/build/master-contrib_docs-lin64/opencv_contrib/modules/dnn/include/opencv2/dnn/all_layers.hpp

Public Member Functions