LSTM recurrent layer [詳解]

#include <all_layers.hpp>

cv::dnn::Layerを継承しています。

公開メンバ関数
virtual CV_DEPRECATED void	setWeights (const Mat &Wh, const Mat &Wx, const Mat &b)=0
	Set trained weights for LSTM layer. [詳解]

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

virtual CV_DEPRECATED void	setUseTimstampsDim (bool use=true)=0
	Specifies either interpret first dimension of input blob as timestamp dimension either as sample. [詳解]

virtual CV_DEPRECATED void	setProduceCellOutput (bool produce=false)=0
	If this flag is set to true then layer will produce as second output. [詳解]

int	inputNameToIndex (String inputName) CV_OVERRIDE
	Returns index of input blob into the input array. [詳解]

int	outputNameToIndex (const String &outputName) CV_OVERRIDE
	Returns index of output blob in output array. [詳解]

基底クラス cv::dnn::Layer に属する継承公開メンバ関数
virtual CV_DEPRECATED_EXTERNAL void	finalize (const std::vector< Mat * > &input, std::vector< Mat > &output)
	Computes and sets internal parameters according to inputs, outputs and blobs. [詳解]

virtual CV_WRAP void	finalize (InputArrayOfArrays inputs, OutputArrayOfArrays outputs)
	Computes and sets internal parameters according to inputs, outputs and blobs. [詳解]

virtual CV_DEPRECATED_EXTERNAL void	forward (std::vector< Mat * > &input, std::vector< Mat > &output, std::vector< Mat > &internals)
	Given the `input` blobs, computes the output `blobs`. [詳解]

virtual void	forward (InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals)
	Given the `input` blobs, computes the output `blobs`. [詳解]

void	forward_fallback (InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals)
	Given the `input` blobs, computes the output `blobs`. [詳解]

CV_DEPRECATED_EXTERNAL void	finalize (const std::vector< Mat > &inputs, CV_OUT std::vector< Mat > &outputs)
	これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。 [詳解]

CV_DEPRECATED std::vector< Mat >	finalize (const std::vector< Mat > &inputs)
	これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。 [詳解]

CV_DEPRECATED CV_WRAP void	run (const std::vector< Mat > &inputs, CV_OUT std::vector< Mat > &outputs, CV_IN_OUT std::vector< Mat > &internals)
	Allocates layer and computes output. [詳解]

virtual bool	supportBackend (int backendId)
	Ask layer if it support specific backend for doing computations. [詳解]

virtual Ptr< BackendNode >	initHalide (const std::vector< Ptr< BackendWrapper > > &inputs)
	Returns Halide backend node. [詳解]

virtual Ptr< BackendNode >	initInfEngine (const std::vector< Ptr< BackendWrapper > > &inputs)

virtual Ptr< BackendNode >	initNgraph (const std::vector< Ptr< BackendWrapper > > &inputs, const std::vector< Ptr< BackendNode > > &nodes)

virtual Ptr< BackendNode >	initVkCom (const std::vector< Ptr< BackendWrapper > > &inputs)

virtual Ptr< BackendNode >	initCUDA (void *context, const std::vector< Ptr< BackendWrapper > > &inputs, const std::vector< Ptr< BackendWrapper > > &outputs)
	Returns a CUDA backend node [詳解]

virtual void	applyHalideScheduler (Ptr< BackendNode > &node, const std::vector< Mat * > &inputs, const std::vector< Mat > &outputs, int targetId) const
	Automatic Halide scheduling based on layer hyper-parameters. [詳解]

virtual Ptr< BackendNode >	tryAttach (const Ptr< BackendNode > &node)
	Implement layers fusing. [詳解]

virtual bool	setActivation (const Ptr< ActivationLayer > &layer)
	Tries to attach to the layer the subsequent activation layer, i.e. do the layer fusion in a partial case. [詳解]

virtual bool	tryFuse (Ptr< Layer > &top)
	Try to fuse current layer with a next one [詳解]

virtual void	getScaleShift (Mat &scale, Mat &shift) const
	Returns parameters of layers with channel-wise multiplication and addition. [詳解]

virtual void	unsetAttached ()
	"Deattaches" all the layers, attached to particular layer.

virtual bool	getMemoryShapes (const std::vector< MatShape > &inputs, const int requiredOutputs, std::vector< MatShape > &outputs, std::vector< MatShape > &internals) const

virtual int64	getFLOPS (const std::vector< MatShape > &inputs, const std::vector< MatShape > &outputs) const

virtual bool	updateMemoryShapes (const std::vector< MatShape > &inputs)

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

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

基底クラス cv::Algorithm に属する継承公開メンバ関数
virtual CV_WRAP void	clear ()
	Clears the algorithm state [詳解]

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage [詳解]

CV_WRAP void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。

virtual CV_WRAP void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage [詳解]

virtual CV_WRAP bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read [詳解]

virtual CV_WRAP void	save (const String &filename) const

virtual CV_WRAP String	getDefaultName () const

静的公開メンバ関数
static Ptr< LSTMLayer >	create (const LayerParams &params)

基底クラス cv::Algorithm に属する継承静的公開メンバ関数
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node [詳解]

template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file [詳解]

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String [詳解]

その他の継承メンバ
基底クラス cv::dnn::Layer に属する継承公開変数類
CV_PROP_RW std::vector< Mat >	blobs
	List of learned parameters must be stored here to allow read them by using Net::getParam().

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

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

CV_PROP int	preferableTarget
	prefer target for layer forwarding

基底クラス cv::Algorithm に属する継承限定公開メンバ関数
void	writeFormat (FileStorage &fs) const

詳解

LSTM recurrent layer

関数詳解

◆ create()

static Ptr< LSTMLayer > cv::dnn::LSTMLayer::create ( const LayerParams & params )

static

Creates instance of LSTM layer

◆ inputNameToIndex()

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

virtual

Returns index of input blob into the input array.

引数

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.

cv::dnn::Layerを再実装しています。

◆ outputNameToIndex()

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

virtual

Returns index of output blob in output array.

参照: inputNameToIndex()

cv::dnn::Layerを再実装しています。

◆ setOutShape()

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

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 CV_DEPRECATED 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.

非推奨:: Use flag use_timestamp_dim in LayerParams.

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

◆ setUseTimstampsDim()

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

pure virtual

Specifies either interpret first dimension of input blob as timestamp dimension either as sample.

非推奨:: Use flag produce_cell_output in LayerParams.

If flag is set to true then shape of input blob will be interpreted as [T, N, [data dims]] where T specifies number of timestamps, 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 interpreted 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 CV_DEPRECATED void cv::dnn::LSTMLayer::setWeights	(	const Mat &	Wh,
		const Mat &	Wx,
		const Mat &	b
	)

pure virtual

Set trained weights for LSTM layer.

非推奨:: Use LayerParams::blobs instead.

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 weights.

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 concatenation 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]$ , $b \in R^{4N_h}$ .

引数

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

このクラス詳解は次のファイルから抽出されました:

all_layers.hpp

公開メンバ関数

静的公開メンバ関数

その他の継承メンバ

詳解

関数詳解

◆ create()

◆ inputNameToIndex()

◆ outputNameToIndex()

◆ setOutShape()

◆ setProduceCellOutput()

◆ setUseTimstampsDim()

◆ setWeights()