LSTM recurrent layer. More...

#include "all_layers.hpp"

Inheritance diagram for cv::dnn::LSTMLayer:

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

virtual Blob	getC () const =0
	Returns current \( c_{t-1} \) value (deep copy). More...

virtual Blob	getH () const =0
	Returns current \( h_{t-1} \) value (deep copy). More...

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

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

virtual void	setC (const Blob &C)=0
	Set \( c_{t-1} \) value that will be used in next forward() calls. More...

virtual void	setH (const Blob &H)=0
	Set \( h_{t-1} \) value that will be used in next forward() calls. More...

virtual void	setOutShape (const BlobShape &outTailShape=BlobShape::empty())=0
	Specifies shape of output blob which will be [[`T`], `N`] + `outTailShape`. More...

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

virtual void	setUseTimstampsDim (bool use=true)=0
	Specifies either interpet first dimension of input blob as timestamp dimenion either as sample. More...

virtual void	setWeights (const Blob &Wh, const Blob &Wx, const Blob &b)=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)

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< LSTMLayer >	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

LSTM recurrent layer.

Member Function Documentation

§ create()

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

static

Creates instance of LSTM layer

§ forward()

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

virtual

In common case it use single input with \(x_t\) values to compute output(s) \(h_t\) (and \(c_t\)).

Parameters

input	should contain packed values \(x_t\)
output	contains computed outputs: \(h_t\) (and \(c_t\) if setProduceCellOutput() flag was set to true).

If setUseTimstampsDim() is set to true then input[0] should has at least two dimensions with the following shape: [T, N, [data dims]], where T specifies number of timpestamps, N is number of independent streams (i.e. \( x_{t_0 + t}^{stream} \) is stored inside input[0][t, stream, ...]).

If setUseTimstampsDim() is set to fase then input[0] should contain single timestamp, its shape should has form [N, [data dims]] with at least one dimension. (i.e. \( x_{t}^{stream} \) is stored inside input[0][stream, ...]).

Implements cv::dnn::Layer.

§ getC()

virtual Blob cv::dnn::LSTMLayer::getC ( ) const

pure virtual

Returns current \( c_{t-1} \) value (deep copy).

§ getH()

virtual Blob cv::dnn::LSTMLayer::getH ( ) const

pure virtual

Returns current \( h_{t-1} \) value (deep copy).

§ inputNameToIndex()

int cv::dnn::LSTMLayer::inputNameToIndex ( String inputName )

virtual

Returns index of input blob into the input array.

Parameters

inputName label of input blob

Each layer input and output can be labeled to easily identify them using "%<layer_name%>[.output_name]" notation. This method maps label of input blob to its index into input vector.

Reimplemented from cv::dnn::Layer.

§ outputNameToIndex()

int cv::dnn::LSTMLayer::outputNameToIndex ( String outputName )

virtual

Returns index of output blob in output array.

See also: inputNameToIndex()

Reimplemented from cv::dnn::Layer.

§ setC()

virtual void cv::dnn::LSTMLayer::setC ( const Blob & C )

pure virtual

Set \( c_{t-1} \) value that will be used in next forward() calls.

By-default \( c_{t-1} \) is inited by zeros and updated after each forward() call.

§ setH()

virtual void cv::dnn::LSTMLayer::setH ( const Blob & H )

pure virtual

Set \( h_{t-1} \) value that will be used in next forward() calls.

By-default \( h_{t-1} \) is inited by zeros and updated after each forward() call.

§ setOutShape()

virtual void cv::dnn::LSTMLayer::setOutShape ( const BlobShape & outTailShape = BlobShape::empty() )

pure virtual

Specifies shape of output blob which will be [[T], N] + outTailShape.

If this parameter is empty or unset then outTailShape = [Wh.size(0)] will be used, where Wh is parameter from setWeights().

§ setProduceCellOutput()

virtual void cv::dnn::LSTMLayer::setProduceCellOutput ( bool produce = false )

pure virtual

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

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

§ setUseTimstampsDim()

virtual void cv::dnn::LSTMLayer::setUseTimstampsDim ( bool use = true )

pure virtual

Specifies either interpet first dimension of input blob as timestamp dimenion either as sample.

If flag is set to true then shape of input blob will be interpeted as [T, N, [data dims]] where T specifies number of timpestamps, N is number of independent streams. In this case each forward() call will iterate through T timestamps and update layer's state T times.

If flag is set to false then shape of input blob will be interpeted as [N, [data dims]]. In this case each forward() call will make one iteration and produce one timestamp with shape [N, [out dims]].

§ setWeights()

virtual void cv::dnn::LSTMLayer::setWeights	(	const Blob &	Wh,
		const Blob &	Wx,
		const Blob &	b
	)

pure virtual

Set trained weights for LSTM layer. LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights.

Let \(x_t\) be current input, \(h_t\) be current output, \(c_t\) be current state. Than current output and current cell state is computed as follows:

\begin{eqnarray*} h_t &= o_t \odot tanh(c_t), \\ c_t &= f_t \odot c_{t-1} + i_t \odot g_t, \\ \end{eqnarray*}

where \(\odot\) is per-element multiply operation and \(i_t, f_t, o_t, g_t\) is internal gates that are computed using learned wights.

Gates are computed as follows:

\begin{eqnarray*} i_t &= sigmoid&(W_{xi} x_t + W_{hi} h_{t-1} + b_i), \\ f_t &= sigmoid&(W_{xf} x_t + W_{hf} h_{t-1} + b_f), \\ o_t &= sigmoid&(W_{xo} x_t + W_{ho} h_{t-1} + b_o), \\ g_t &= tanh &(W_{xg} x_t + W_{hg} h_{t-1} + b_g), \\ \end{eqnarray*}

where \(W_{x?}\), \(W_{h?}\) and \(b_{?}\) are learned weights represented as matrices: \(W_{x?} \in R^{N_h \times N_x}\), \(W_{h?} \in R^{N_h \times N_h}\), \(b_? \in R^{N_h}\).

For simplicity and performance purposes we use \( W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] \) (i.e. \(W_x\) is vertical contacentaion of \( W_{x?} \)), \( W_x \in R^{4N_h \times N_x} \). The same for \( W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} \) and for \( b = [b_i; b_f, b_o, b_g]\), \(b \in R^{4N_h} \).

Parameters

Wh	is matrix defining how previous output is transformed to internal gates (i.e. according to abovemtioned notation is \( W_h \))
Wx	is matrix defining how current input is transformed to internal gates (i.e. according to abovemtioned notation is \( W_x \))
b	is bias vector (i.e. according to abovemtioned notation is \( b \))

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

Static Public Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

§ create()

§ forward()

§ getC()

§ getH()

§ inputNameToIndex()

§ outputNameToIndex()

§ setC()

§ setH()

§ setOutShape()

§ setProduceCellOutput()

§ setUseTimstampsDim()

§ setWeights()