Classical 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 void	setWeights (const Mat &Wxh, const Mat &bh, const Mat &Whh, const Mat &Who, const Mat &bo)=0

virtual void	setProduceHiddenOutput (bool produce=false)=0
	If this flag is set to true then layer will produce as second output. [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 int	inputNameToIndex (String inputName)
	Returns index of input blob into the input array. [詳解]

virtual CV_WRAP int	outputNameToIndex (const String &outputName)
	Returns index of output blob in output array. [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< RNNLayer >	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

Classical recurrent layer

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

input: should contain packed input .
output: should contain output (and 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 Mat with shape [T, N, $N_h$ ], where $N_h$ is number of rows in $W_{hh}$ matrix.

関数詳解

◆ create()

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

static

Creates instance of RNNLayer

◆ 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 Mat &	Wxh,
		const Mat &	bh,
		const Mat &	Whh,
		const Mat &	Who,
		const Mat &	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:

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

引数

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

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()

◆ setProduceHiddenOutput()

◆ setWeights()