Uses of Class
org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

Packages that use LayerWorkspaceMgr

Package	Description
org.deeplearning4j.nn.api
org.deeplearning4j.nn.api.layers
org.deeplearning4j.nn.conf
org.deeplearning4j.nn.conf.dropout
org.deeplearning4j.nn.conf.preprocessor
org.deeplearning4j.nn.conf.weightnoise
org.deeplearning4j.nn.graph
org.deeplearning4j.nn.graph.vertex
org.deeplearning4j.nn.graph.vertex.impl
org.deeplearning4j.nn.graph.vertex.impl.rnn
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.convolution
org.deeplearning4j.nn.layers.convolution.subsampling
org.deeplearning4j.nn.layers.convolution.upsampling
org.deeplearning4j.nn.layers.feedforward
org.deeplearning4j.nn.layers.feedforward.autoencoder
org.deeplearning4j.nn.layers.feedforward.dense
org.deeplearning4j.nn.layers.feedforward.elementwise
org.deeplearning4j.nn.layers.feedforward.embedding
org.deeplearning4j.nn.layers.mkldnn
org.deeplearning4j.nn.layers.normalization
org.deeplearning4j.nn.layers.objdetect
org.deeplearning4j.nn.layers.ocnn
org.deeplearning4j.nn.layers.pooling
org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.layers.samediff
org.deeplearning4j.nn.layers.training
org.deeplearning4j.nn.layers.util
org.deeplearning4j.nn.layers.variational
org.deeplearning4j.nn.layers.wrapper
org.deeplearning4j.nn.multilayer
org.deeplearning4j.nn.updater
org.deeplearning4j.nn.workspace
org.deeplearning4j.optimize
org.deeplearning4j.optimize.api
org.deeplearning4j.optimize.solvers
org.deeplearning4j.preprocessors
org.deeplearning4j.util

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.api

Methods in org.deeplearning4j.nn.api with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	Layer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)	Perform forward pass and return the activations array with the last set input
INDArray	Layer.activate(INDArray input, boolean training, LayerWorkspaceMgr mgr)	Perform forward pass and return the activations array with the specified input
Pair<Gradient,INDArray>	Layer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)	Calculate the gradient relative to the error in the next layer
void	Model.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)	Update the score
void	Model.fit(INDArray data, LayerWorkspaceMgr workspaceMgr)	Fit the model to the given data
void	Layer.setInput(INDArray input, LayerWorkspaceMgr workspaceMgr)	Set the layer input.
void	Updater.update(Trainable layer, Gradient gradient, int iteration, int epoch, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)	Updater: updates the model

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.api.layers

Methods in org.deeplearning4j.nn.api.layers with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
double	IOutputLayer.computeScore(double fullNetworkRegScore, boolean training, LayerWorkspaceMgr workspaceMgr)	Compute score after labels and input have been set.
INDArray	IOutputLayer.computeScoreForExamples(double fullNetworkRegScore, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
INDArray	RecurrentLayer.rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT, LayerWorkspaceMgr workspaceMg)	Similar to rnnTimeStep, this method is used for activations using the state stored in the stateMap as the initialization.
INDArray	RecurrentLayer.rnnTimeStep(INDArray input, LayerWorkspaceMgr workspaceMgr)	Do one or more time steps using the previous time step state stored in stateMap. Can be used to efficiently do forward pass one or n-steps at a time (instead of doing forward pass always from t=0) If stateMap is empty, default initialization (usually zeros) is used Implementations also update stateMap at the end of this method
Pair<Gradient,INDArray>	RecurrentLayer.tbpttBackpropGradient(INDArray epsilon, int tbpttBackLength, LayerWorkspaceMgr workspaceMgr)	Truncated BPTT equivalent of Layer.backpropGradient().

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.conf

Methods in org.deeplearning4j.nn.conf with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	InputPreProcessor.backprop(INDArray output, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)	Reverse the preProcess during backprop.
INDArray	InputPreProcessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)	Pre preProcess input/activations for a multi layer network

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.conf.dropout

Methods in org.deeplearning4j.nn.conf.dropout with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	AlphaDropout.applyDropout(INDArray inputActivations, INDArray output, int iteration, int epoch, LayerWorkspaceMgr workspaceMgr)
INDArray	Dropout.applyDropout(INDArray inputActivations, INDArray output, int iteration, int epoch, LayerWorkspaceMgr workspaceMgr)
INDArray	GaussianDropout.applyDropout(INDArray inputActivations, INDArray output, int iteration, int epoch, LayerWorkspaceMgr workspaceMgr)
INDArray	GaussianNoise.applyDropout(INDArray inputActivations, INDArray output, int iteration, int epoch, LayerWorkspaceMgr workspaceMgr)
INDArray	IDropout.applyDropout(INDArray inputActivations, INDArray resultArray, int iteration, int epoch, LayerWorkspaceMgr workspaceMgr)
INDArray	SpatialDropout.applyDropout(INDArray inputActivations, INDArray output, int iteration, int epoch, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.conf.preprocessor

Methods in org.deeplearning4j.nn.conf.preprocessor with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	Cnn3DToFeedForwardPreProcessor.backprop(INDArray epsilons, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	CnnToFeedForwardPreProcessor.backprop(INDArray epsilons, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	CnnToRnnPreProcessor.backprop(INDArray output, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	ComposableInputPreProcessor.backprop(INDArray output, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	FeedForwardToCnn3DPreProcessor.backprop(INDArray epsilons, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	FeedForwardToCnnPreProcessor.backprop(INDArray epsilons, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	FeedForwardToRnnPreProcessor.backprop(INDArray output, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	RnnToCnnPreProcessor.backprop(INDArray output, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	RnnToFeedForwardPreProcessor.backprop(INDArray output, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	Cnn3DToFeedForwardPreProcessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	CnnToFeedForwardPreProcessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	CnnToRnnPreProcessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	ComposableInputPreProcessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	FeedForwardToCnn3DPreProcessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	FeedForwardToCnnPreProcessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	FeedForwardToRnnPreProcessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	RnnToCnnPreProcessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	RnnToFeedForwardPreProcessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.conf.weightnoise

Methods in org.deeplearning4j.nn.conf.weightnoise with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	DropConnect.getParameter(Layer layer, String paramKey, int iteration, int epoch, boolean train, LayerWorkspaceMgr workspaceMgr)
INDArray	IWeightNoise.getParameter(Layer layer, String paramKey, int iteration, int epoch, boolean train, LayerWorkspaceMgr workspaceMgr)	Get the parameter, after applying weight noise
INDArray	WeightNoise.getParameter(Layer layer, String paramKey, int iteration, int epoch, boolean train, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.graph

Methods in org.deeplearning4j.nn.graph with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
void	ComputationGraph.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
protected void	ComputationGraph.doTruncatedBPTT(INDArray[] inputs, INDArray[] labels, INDArray[] featureMasks, INDArray[] labelMasks, LayerWorkspaceMgr workspaceMgr)	Fit the network using truncated BPTT
void	ComputationGraph.fit(INDArray data, LayerWorkspaceMgr workspaceMgr)
protected void	ComputationGraph.validateArrayWorkspaces(LayerWorkspaceMgr mgr, INDArray array, ArrayType arrayType, String vertexName, boolean isInputVertex, String op)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.graph.vertex

Methods in org.deeplearning4j.nn.graph.vertex with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
Pair<Gradient,INDArray[]>	BaseWrapperVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	GraphVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)	Do backward pass
INDArray	BaseWrapperVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	GraphVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)	Do forward pass using the stored inputs
void	BaseGraphVertex.setInput(int inputNumber, INDArray input, LayerWorkspaceMgr workspaceMgr)
void	BaseWrapperVertex.setInput(int inputNumber, INDArray input, LayerWorkspaceMgr workspaceMgr)
void	GraphVertex.setInput(int inputNumber, INDArray input, LayerWorkspaceMgr workspaceMgr)	Set the input activations.

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.graph.vertex.impl

Methods in org.deeplearning4j.nn.graph.vertex.impl with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
void	LayerVertex.applyPreprocessorAndSetInput(LayerWorkspaceMgr workspaceMgr)
double	LayerVertex.computeScore(double r, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	LayerVertex.computeScoreForExamples(double r, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	ElementWiseVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	InputVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	L2NormalizeVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	L2Vertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	LayerVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	MergeVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	PoolHelperVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	PreprocessorVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	ReshapeVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	ScaleVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	ShiftVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	StackVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	SubsetVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	UnstackVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
INDArray	ElementWiseVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	InputVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	L2NormalizeVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	L2Vertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	LayerVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	MergeVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	PoolHelperVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	PreprocessorVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	ReshapeVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	ScaleVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	ShiftVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	StackVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	SubsetVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	UnstackVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
void	LayerVertex.setInput(int inputNumber, INDArray input, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.graph.vertex.impl.rnn

Methods in org.deeplearning4j.nn.graph.vertex.impl.rnn with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
Pair<Gradient,INDArray[]>	DuplicateToTimeSeriesVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	LastTimeStepVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	ReverseTimeSeriesVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
INDArray	DuplicateToTimeSeriesVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	LastTimeStepVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	ReverseTimeSeriesVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers

Methods in org.deeplearning4j.nn.layers with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	AbstractLayer.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	ActivationLayer.activate(boolean training, LayerWorkspaceMgr mgr)
INDArray	BaseLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	BaseOutputLayer.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	DropoutLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	FrozenLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	FrozenLayer.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	FrozenLayerWithBackprop.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	FrozenLayerWithBackprop.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	LossLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	LossLayer.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	RepeatVector.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
protected void	AbstractLayer.applyDropOutIfNecessary(boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	ActivationLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	BaseLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	BaseOutputLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	BasePretrainNetwork.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	DropoutLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	FrozenLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	FrozenLayerWithBackprop.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	LossLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	RepeatVector.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
void	AbstractLayer.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
void	BaseLayer.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
void	BaseOutputLayer.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
void	FrozenLayer.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
void	FrozenLayerWithBackprop.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
void	LossLayer.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
double	BaseOutputLayer.computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)	Compute score after labels and input have been set.
double	LossLayer.computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)	Compute score after labels and input have been set.
INDArray	BaseOutputLayer.computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
INDArray	LossLayer.computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
void	AbstractLayer.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
void	BaseLayer.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
void	BaseOutputLayer.fit(INDArray data, LayerWorkspaceMgr workspaceMgr)
void	DropoutLayer.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
void	FrozenLayer.fit(INDArray data, LayerWorkspaceMgr workspaceMgr)
void	FrozenLayerWithBackprop.fit(INDArray data, LayerWorkspaceMgr workspaceMgr)
void	LossLayer.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
void	RepeatVector.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
protected abstract INDArray	BaseOutputLayer.getLabels2d(LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)
protected INDArray	OutputLayer.getLabels2d(LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)
protected INDArray	BaseLayer.getParamWithNoise(String param, boolean training, LayerWorkspaceMgr workspaceMgr)	Get the parameter, after applying any weight noise (such as DropConnect) if necessary.
protected INDArray	BaseLayer.preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)
protected INDArray	RepeatVector.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
protected INDArray	BaseOutputLayer.preOutput2d(boolean training, LayerWorkspaceMgr workspaceMgr)
protected Pair<INDArray,INDArray>	BaseLayer.preOutputWithPreNorm(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
void	AbstractLayer.setInput(INDArray input, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.convolution

Methods in org.deeplearning4j.nn.layers.convolution with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	Cnn3DLossLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	CnnLossLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Convolution1DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	ConvolutionLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Cropping1DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Cropping2DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Cropping3DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Deconvolution2DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Deconvolution3DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	DepthwiseConvolution2DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	SeparableConvolution2DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	SpaceToBatch.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	SpaceToDepth.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	ZeroPadding1DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	ZeroPadding3DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	ZeroPaddingLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Cnn3DLossLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	CnnLossLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Convolution1DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Convolution3DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	ConvolutionHelper.backpropGradient(INDArray input, INDArray weights, INDArray bias, INDArray delta, int[] kernel, int[] strides, int[] pad, INDArray biasGradView, INDArray weightGradView, IActivation afn, ConvolutionLayer.AlgoMode mode, ConvolutionLayer.BwdFilterAlgo bwdFilterAlgo, ConvolutionLayer.BwdDataAlgo bwdDataAlgo, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	ConvolutionLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Cropping1DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Cropping2DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Cropping3DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Deconvolution2DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Deconvolution3DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	DepthwiseConvolution2DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	SeparableConvolution2DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	SpaceToBatch.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	SpaceToDepth.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	ZeroPadding1DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	ZeroPadding3DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	ZeroPaddingLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
double	Cnn3DLossLayer.computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)
double	CnnLossLayer.computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Cnn3DLossLayer.computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
INDArray	CnnLossLayer.computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
void	ConvolutionLayer.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
protected Pair<INDArray,INDArray>	Convolution1DLayer.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
protected Pair<INDArray,INDArray>	Convolution3DLayer.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
INDArray	Convolution3DLayer.preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	ConvolutionHelper.preOutput(INDArray input, INDArray weights, INDArray bias, int[] kernel, int[] strides, int[] pad, ConvolutionLayer.AlgoMode mode, ConvolutionLayer.FwdAlgo fwdAlgo, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)
protected Pair<INDArray,INDArray>	ConvolutionLayer.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)	PreOutput method that also returns the im2col2d array (if being called for backprop), as this can be re-used instead of being calculated again.
protected Pair<INDArray,INDArray>	Deconvolution2DLayer.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
protected INDArray	Deconvolution3DLayer.preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)
protected Pair<INDArray,INDArray>	DepthwiseConvolution2DLayer.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
protected Pair<INDArray,INDArray>	SeparableConvolution2DLayer.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
protected INDArray	SpaceToBatch.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
protected INDArray	SpaceToDepth.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
protected Pair<INDArray,INDArray>	Convolution1DLayer.preOutput4d(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
protected Pair<INDArray,INDArray>	ConvolutionLayer.preOutput4d(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)	preOutput4d: Used so that ConvolutionLayer subclasses (such as Convolution1DLayer) can maintain their standard non-4d preOutput method, while overriding this to return 4d activations (for use in backprop) without modifying the public API

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.convolution.subsampling

Methods in org.deeplearning4j.nn.layers.convolution.subsampling with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	Subsampling1DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Subsampling3DLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	SubsamplingHelper.activate(INDArray input, boolean training, int[] kernel, int[] strides, int[] pad, PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)
INDArray	SubsamplingLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Subsampling1DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Subsampling3DLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	SubsamplingHelper.backpropGradient(INDArray input, INDArray epsilon, int[] kernel, int[] strides, int[] pad, PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	SubsamplingLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
void	Subsampling3DLayer.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
void	SubsamplingLayer.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.convolution.upsampling

Methods in org.deeplearning4j.nn.layers.convolution.upsampling with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	Upsampling1D.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Upsampling2D.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Upsampling3D.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Upsampling1D.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Upsampling2D.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	Upsampling3D.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
void	Upsampling2D.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
void	Upsampling3D.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
protected INDArray	Upsampling1D.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
protected INDArray	Upsampling2D.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
protected INDArray	Upsampling3D.preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.feedforward

Methods in org.deeplearning4j.nn.layers.feedforward with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	PReLU.activate(boolean training, LayerWorkspaceMgr mgr)
Pair<Gradient,INDArray>	PReLU.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.feedforward.autoencoder

Methods in org.deeplearning4j.nn.layers.feedforward.autoencoder with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	AutoEncoder.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	AutoEncoder.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
void	AutoEncoder.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
INDArray	AutoEncoder.decode(INDArray y, LayerWorkspaceMgr workspaceMgr)
INDArray	AutoEncoder.encode(INDArray v, boolean training, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.feedforward.dense

Methods in org.deeplearning4j.nn.layers.feedforward.dense with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
void	DenseLayer.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.feedforward.elementwise

Methods in org.deeplearning4j.nn.layers.feedforward.elementwise with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
Pair<Gradient,INDArray>	ElementWiseMultiplicationLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
INDArray	ElementWiseMultiplicationLayer.preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.feedforward.embedding

Methods in org.deeplearning4j.nn.layers.feedforward.embedding with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	EmbeddingLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	EmbeddingSequenceLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
protected void	EmbeddingLayer.applyDropOutIfNecessary(boolean training, LayerWorkspaceMgr workspaceMgr)
protected void	EmbeddingSequenceLayer.applyDropOutIfNecessary(boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	EmbeddingLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	EmbeddingSequenceLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
protected INDArray	EmbeddingLayer.preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)
protected INDArray	EmbeddingSequenceLayer.preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.mkldnn

Methods in org.deeplearning4j.nn.layers.mkldnn with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	MKLDNNLocalResponseNormalizationHelper.activate(INDArray x, boolean training, double k, double n, double alpha, double beta, LayerWorkspaceMgr workspaceMgr)
INDArray	MKLDNNSubsamplingHelper.activate(INDArray input, boolean training, int[] kernel, int[] strides, int[] pad, PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	MKLDNNBatchNormHelper.backpropGradient(INDArray input, INDArray epsilon, long[] shape, INDArray gamma, INDArray beta, INDArray dGammaView, INDArray dBetaView, double eps, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	MKLDNNConvHelper.backpropGradient(INDArray input, INDArray weights, INDArray bias, INDArray delta, int[] kernel, int[] strides, int[] pad, INDArray biasGradView, INDArray weightGradView, IActivation afn, ConvolutionLayer.AlgoMode mode, ConvolutionLayer.BwdFilterAlgo bwdFilterAlgo, ConvolutionLayer.BwdDataAlgo bwdDataAlgo, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	MKLDNNLocalResponseNormalizationHelper.backpropGradient(INDArray input, INDArray epsilon, double k, double n, double alpha, double beta, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	MKLDNNSubsamplingHelper.backpropGradient(INDArray input, INDArray epsilon, int[] kernel, int[] strides, int[] pad, PoolingType poolingType, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)
INDArray	MKLDNNBatchNormHelper.preOutput(INDArray x, boolean training, long[] shape, INDArray gamma, INDArray beta, INDArray mean, INDArray var, double decay, double eps, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)
INDArray	MKLDNNConvHelper.preOutput(INDArray input, INDArray weights, INDArray bias, int[] kernel, int[] strides, int[] pad, ConvolutionLayer.AlgoMode mode, ConvolutionLayer.FwdAlgo fwdAlgo, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.normalization

Methods in org.deeplearning4j.nn.layers.normalization with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	BatchNormalization.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	LocalResponseNormalization.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	LocalResponseNormalizationHelper.activate(INDArray x, boolean training, double k, double n, double alpha, double beta, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	BatchNormalization.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	BatchNormalizationHelper.backpropGradient(INDArray input, INDArray epsilon, long[] shape, INDArray gamma, INDArray beta, INDArray dGammaView, INDArray dBetaView, double eps, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	LocalResponseNormalization.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	LocalResponseNormalizationHelper.backpropGradient(INDArray input, INDArray epsilon, double k, double n, double alpha, double beta, LayerWorkspaceMgr workspaceMgr)
void	BatchNormalization.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
void	LocalResponseNormalization.fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
INDArray	BatchNormalization.preOutput(INDArray x, Layer.TrainingMode training, LayerWorkspaceMgr workspaceMgr)
INDArray	BatchNormalizationHelper.preOutput(INDArray x, boolean training, long[] shape, INDArray gamma, INDArray beta, INDArray mean, INDArray var, double decay, double eps, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.objdetect

Methods in org.deeplearning4j.nn.layers.objdetect with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	Yolo2OutputLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
static INDArray	YoloUtils.activate(@NonNull INDArray boundingBoxPriors, @NonNull INDArray input, boolean nchw, LayerWorkspaceMgr layerWorkspaceMgr)
static INDArray	YoloUtils.activate(@NonNull INDArray boundingBoxPriors, @NonNull INDArray input, LayerWorkspaceMgr layerWorkspaceMgr)
Pair<Gradient,INDArray>	Yolo2OutputLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
void	Yolo2OutputLayer.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
double	Yolo2OutputLayer.computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	Yolo2OutputLayer.computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.ocnn

Methods in org.deeplearning4j.nn.layers.ocnn with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	OCNNOutputLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	OCNNOutputLayer.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	OCNNOutputLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
double	OCNNOutputLayer.computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)	Compute score after labels and input have been set.
INDArray	OCNNOutputLayer.computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
protected INDArray	OCNNOutputLayer.getLabels2d(LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)
protected INDArray	OCNNOutputLayer.preOutput2d(boolean training, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.pooling

Methods in org.deeplearning4j.nn.layers.pooling with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	GlobalPoolingLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	GlobalPoolingLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.recurrent

Methods in org.deeplearning4j.nn.layers.recurrent with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	BidirectionalLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	BidirectionalLayer.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	GravesBidirectionalLSTM.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	GravesBidirectionalLSTM.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	GravesLSTM.activate(boolean training, LayerWorkspaceMgr workspaceMgr)	Deprecated.
INDArray	GravesLSTM.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)	Deprecated.
INDArray	LastTimeStepLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	LastTimeStepLayer.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	LSTM.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	LSTM.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
FwdPassReturn	LSTMHelper.activate(Layer layer, NeuralNetConfiguration conf, IActivation gateActivationFn, INDArray input, INDArray recurrentWeights, INDArray inputWeights, INDArray biases, boolean training, INDArray prevOutputActivations, INDArray prevMemCellState, boolean forBackprop, boolean forwards, String inputWeightKey, INDArray maskArray, boolean hasPeepholeConnections, LayerWorkspaceMgr workspaceMgr)
INDArray	MaskZeroLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	MaskZeroLayer.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	RnnLossLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	RnnOutputLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	SimpleRnn.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	TimeDistributedLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	TimeDistributedLayer.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
static FwdPassReturn	LSTMHelpers.activateHelper(BaseRecurrentLayer layer, NeuralNetConfiguration conf, IActivation gateActivationFn, INDArray input, INDArray recurrentWeights, INDArray originalInputWeights, INDArray biases, boolean training, INDArray originalPrevOutputActivations, INDArray originalPrevMemCellState, boolean forBackprop, boolean forwards, String inputWeightKey, INDArray maskArray, boolean hasPeepholeConnections, LSTMHelper helper, CacheMode cacheMode, LayerWorkspaceMgr workspaceMgr, boolean isHelperAllowFallback)	Returns FwdPassReturn object with activations/INDArrays.
Pair<Gradient,INDArray>	BidirectionalLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	GravesBidirectionalLSTM.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	GravesLSTM.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)	Deprecated.
Pair<Gradient,INDArray>	LastTimeStepLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	LSTM.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	LSTMHelper.backpropGradient(NeuralNetConfiguration conf, IActivation gateActivationFn, INDArray input, INDArray recurrentWeights, INDArray inputWeights, INDArray epsilon, boolean truncatedBPTT, int tbpttBackwardLength, FwdPassReturn fwdPass, boolean forwards, String inputWeightKey, String recurrentWeightKey, String biasWeightKey, Map<String,INDArray> gradientViews, INDArray maskArray, boolean hasPeepholeConnections, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	MaskZeroLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	RnnLossLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	RnnOutputLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	SimpleRnn.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	TimeDistributedLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
static Pair<Gradient,INDArray>	LSTMHelpers.backpropGradientHelper(BaseRecurrentLayer layer, NeuralNetConfiguration conf, IActivation gateActivationFn, INDArray input, INDArray recurrentWeights, INDArray inputWeights, INDArray epsilon, boolean truncatedBPTT, int tbpttBackwardLength, FwdPassReturn fwdPass, boolean forwards, String inputWeightKey, String recurrentWeightKey, String biasWeightKey, Map<String,INDArray> gradientViews, INDArray maskArray, boolean hasPeepholeConnections, LSTMHelper helper, LayerWorkspaceMgr workspaceMgr, boolean isHelperAllowFallback)
void	BidirectionalLayer.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
double	RnnLossLayer.computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	RnnLossLayer.computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
INDArray	RnnOutputLayer.computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
void	BidirectionalLayer.fit(INDArray data, LayerWorkspaceMgr workspaceMgr)
protected INDArray	RnnOutputLayer.getLabels2d(LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)
protected INDArray	RnnOutputLayer.preOutput2d(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	BidirectionalLayer.rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT, LayerWorkspaceMgr workspaceMgr)
INDArray	GravesBidirectionalLSTM.rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT, LayerWorkspaceMgr workspaceMgr)
INDArray	GravesLSTM.rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT, LayerWorkspaceMgr workspaceMgr)	Deprecated.
INDArray	LSTM.rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT, LayerWorkspaceMgr workspaceMgr)
INDArray	SimpleRnn.rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT, LayerWorkspaceMgr workspaceMgr)
INDArray	BidirectionalLayer.rnnTimeStep(INDArray input, LayerWorkspaceMgr workspaceMgr)
INDArray	GravesBidirectionalLSTM.rnnTimeStep(INDArray input, LayerWorkspaceMgr workspaceMgr)
INDArray	GravesLSTM.rnnTimeStep(INDArray input, LayerWorkspaceMgr workspaceMgr)	Deprecated.
INDArray	LSTM.rnnTimeStep(INDArray input, LayerWorkspaceMgr workspaceMgr)
INDArray	SimpleRnn.rnnTimeStep(INDArray input, LayerWorkspaceMgr workspaceMgr)
void	BidirectionalLayer.setInput(INDArray input, LayerWorkspaceMgr layerWorkspaceMgr)
Pair<Gradient,INDArray>	BidirectionalLayer.tbpttBackpropGradient(INDArray epsilon, int tbpttBackLength, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	GravesBidirectionalLSTM.tbpttBackpropGradient(INDArray epsilon, int tbpttBackwardLength, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	GravesLSTM.tbpttBackpropGradient(INDArray epsilon, int tbpttBackwardLength, LayerWorkspaceMgr workspaceMgr)	Deprecated.
Pair<Gradient,INDArray>	LSTM.tbpttBackpropGradient(INDArray epsilon, int tbpttBackwardLength, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	SimpleRnn.tbpttBackpropGradient(INDArray epsilon, int tbpttBackLength, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.samediff

Methods in org.deeplearning4j.nn.layers.samediff with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	SameDiffLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	SameDiffOutputLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	SameDiffLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	SameDiffOutputLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
double	SameDiffOutputLayer.computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	SameDiffOutputLayer.computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray[]>	SameDiffGraphVertex.doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr)
INDArray	SameDiffGraphVertex.doForward(boolean training, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.training

Methods in org.deeplearning4j.nn.layers.training with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
Pair<Gradient,INDArray>	CenterLossOutputLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
void	CenterLossOutputLayer.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
double	CenterLossOutputLayer.computeScore(double fullNetRegTerm, boolean training, LayerWorkspaceMgr workspaceMgr)	Compute score after labels and input have been set.
INDArray	CenterLossOutputLayer.computeScoreForExamples(double fullNetRegTerm, LayerWorkspaceMgr workspaceMgr)	Compute the score for each example individually, after labels and input have been set.
protected INDArray	CenterLossOutputLayer.getLabels2d(LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.util

Methods in org.deeplearning4j.nn.layers.util with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	MaskLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	MaskLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.variational

Methods in org.deeplearning4j.nn.layers.variational with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	VariationalAutoencoder.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	VariationalAutoencoder.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	VariationalAutoencoder.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
void	VariationalAutoencoder.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
void	VariationalAutoencoder.fit(INDArray data, LayerWorkspaceMgr workspaceMgr)
INDArray	VariationalAutoencoder.generateRandomGivenZ(INDArray latentSpaceValues, LayerWorkspaceMgr workspaceMgr)	Given a specified values for the latent space as input (latent space being z in p(z|data)), randomly generate output x, where x ~ P(x|z)
protected INDArray	VariationalAutoencoder.getParamWithNoise(String param, boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	VariationalAutoencoder.preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)
void	VariationalAutoencoder.setInput(INDArray input, LayerWorkspaceMgr layerWorkspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.layers.wrapper

Methods in org.deeplearning4j.nn.layers.wrapper with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	BaseWrapperLayer.activate(boolean training, LayerWorkspaceMgr workspaceMgr)
INDArray	BaseWrapperLayer.activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)
Pair<Gradient,INDArray>	BaseWrapperLayer.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
void	BaseWrapperLayer.computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
void	BaseWrapperLayer.fit(INDArray data, LayerWorkspaceMgr workspaceMgr)
void	BaseWrapperLayer.setInput(INDArray input, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.multilayer

Methods in org.deeplearning4j.nn.multilayer with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	MultiLayerNetwork.activate(boolean training, LayerWorkspaceMgr mgr)
INDArray	MultiLayerNetwork.activate(INDArray input, boolean training, LayerWorkspaceMgr mgr)
protected INDArray	MultiLayerNetwork.activationFromPrevLayer(int curr, INDArray input, boolean training, LayerWorkspaceMgr mgr)
Pair<Gradient,INDArray>	MultiLayerNetwork.backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
void	MultiLayerNetwork.computeGradientAndScore(LayerWorkspaceMgr layerWorkspaceMgr)
protected void	MultiLayerNetwork.doTruncatedBPTT(INDArray input, INDArray labels, INDArray featuresMaskArray, INDArray labelsMaskArray, LayerWorkspaceMgr workspaceMgr)
void	MultiLayerNetwork.fit(INDArray data, LayerWorkspaceMgr workspaceMgr)
void	MultiLayerNetwork.setInput(INDArray input, LayerWorkspaceMgr mgr)
protected void	MultiLayerNetwork.validateArrayWorkspaces(LayerWorkspaceMgr mgr, INDArray array, ArrayType arrayType, int layerIdx, boolean isPreprocessor, String op)

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.updater

Methods in org.deeplearning4j.nn.updater with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
void	BaseMultiLayerUpdater.update(Trainable layer, Gradient gradient, int iteration, int epoch, int batchSize, LayerWorkspaceMgr workspaceMgr)
void	BaseMultiLayerUpdater.update(Gradient gradient, int iteration, int epoch, int batchSize, LayerWorkspaceMgr workspaceMgr)	Update the gradient for the model.

Uses of LayerWorkspaceMgr in org.deeplearning4j.nn.workspace

Methods in org.deeplearning4j.nn.workspace that return LayerWorkspaceMgr

Modifier and Type	Method	Description
LayerWorkspaceMgr	LayerWorkspaceMgr.Builder.build()
static LayerWorkspaceMgr	LayerWorkspaceMgr.noWorkspaces()
static LayerWorkspaceMgr	LayerWorkspaceMgr.noWorkspaces(Map<String,org.bytedeco.javacpp.Pointer> helperWorkspacePointers)
static LayerWorkspaceMgr	LayerWorkspaceMgr.noWorkspacesImmutable()

Uses of LayerWorkspaceMgr in org.deeplearning4j.optimize

Methods in org.deeplearning4j.optimize with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
void	Solver.optimize(LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.optimize.api

Methods in org.deeplearning4j.optimize.api with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
Pair<Gradient,Double>	ConvexOptimizer.gradientAndScore(LayerWorkspaceMgr workspaceMgr)	The gradient and score for this optimizer
boolean	ConvexOptimizer.optimize(LayerWorkspaceMgr workspaceMgr)	Calls optimize
double	LineOptimizer.optimize(INDArray parameters, INDArray gradient, INDArray searchDirection, LayerWorkspaceMgr workspaceMgr)	Line optimizer
void	ConvexOptimizer.updateGradientAccordingToParams(Gradient gradient, Model model, int batchSize, LayerWorkspaceMgr workspaceMgr)	Update the gradient according to the configuration such as adagrad, momentum, and sparsity

Uses of LayerWorkspaceMgr in org.deeplearning4j.optimize.solvers

Methods in org.deeplearning4j.optimize.solvers with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
Pair<Gradient,Double>	BaseOptimizer.gradientAndScore(LayerWorkspaceMgr workspaceMgr)
double	BackTrackLineSearch.optimize(INDArray parameters, INDArray gradients, INDArray searchDirection, LayerWorkspaceMgr workspaceMgr)
boolean	BaseOptimizer.optimize(LayerWorkspaceMgr workspaceMgr)	Optimize call.
boolean	StochasticGradientDescent.optimize(LayerWorkspaceMgr workspaceMgr)
double	BackTrackLineSearch.setScoreFor(INDArray parameters, LayerWorkspaceMgr workspaceMgr)
void	BaseOptimizer.updateGradientAccordingToParams(Gradient gradient, Model model, int batchSize, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.preprocessors

Methods in org.deeplearning4j.preprocessors with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
INDArray	KerasFlattenRnnPreprocessor.backprop(INDArray epsilons, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	PermutePreprocessor.backprop(INDArray output, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	ReshapePreprocessor.backprop(INDArray output, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	KerasFlattenRnnPreprocessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	PermutePreprocessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)
INDArray	ReshapePreprocessor.preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr)

Uses of LayerWorkspaceMgr in org.deeplearning4j.util

Methods in org.deeplearning4j.util with parameters of type LayerWorkspaceMgr

Modifier and Type	Method	Description
static INDArray	ConvolutionUtils.adapt2dMask(INDArray mask, INDArray output, @NonNull CNN2DFormat format, LayerWorkspaceMgr workspaceMgr, ArrayType type)
static Pair<INDArray,int[]>	TimeSeriesUtils.pullLastTimeSteps(INDArray pullFrom, INDArray mask, LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)	Extract out the last time steps (2d array from 3d array input) accounting for the mask layer, if present.
static INDArray	TimeSeriesUtils.reshape2dTo3d(INDArray in, long miniBatchSize, LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)
static INDArray	ConvolutionUtils.reshape2dTo4d(INDArray in2d, long[] toShape, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr, ArrayType type)
static INDArray	ConvolutionUtils.reshape2dTo5d(Convolution3D.DataFormat format, INDArray in2d, long n, long d, long h, long w, long ch, LayerWorkspaceMgr workspaceMgr, ArrayType type)
static INDArray	ConvolutionUtils.reshape3dMask(INDArray mask, LayerWorkspaceMgr workspaceMgr, ArrayType type)
static INDArray	TimeSeriesUtils.reshape3dTo2d(INDArray in, LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)
static INDArray	ConvolutionUtils.reshape4dMask(INDArray mask, LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)
static INDArray	ConvolutionUtils.reshape4dTo2d(INDArray in, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr, ArrayType type)
static INDArray	ConvolutionUtils.reshape4dTo2d(INDArray in, LayerWorkspaceMgr workspaceMgr, ArrayType type)
static INDArray	ConvolutionUtils.reshape5dTo2d(Convolution3D.DataFormat format, INDArray in, LayerWorkspaceMgr workspaceMgr, ArrayType type)
static INDArray	ConvolutionUtils.reshapeCnn3dMask(Convolution3D.DataFormat format, INDArray mask, INDArray label, LayerWorkspaceMgr workspaceMgr, ArrayType type)
static INDArray	ConvolutionUtils.reshapeMaskIfRequired(INDArray mask, INDArray output, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr, ArrayType type)
static INDArray	ConvolutionUtils.reshapeMaskIfRequired(INDArray mask, INDArray output, LayerWorkspaceMgr workspaceMgr, ArrayType type)	Deprecated. Use ConvolutionUtils.reshapeMaskIfRequired(INDArray, INDArray, CNN2DFormat, LayerWorkspaceMgr, ArrayType)
static INDArray	TimeSeriesUtils.reshapePerOutputTimeSeriesMaskTo2d(INDArray perOutputTimeSeriesMask, LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)
static INDArray	TimeSeriesUtils.reshapeTimeSeriesMaskToCnn4dMask(INDArray timeSeriesMask, LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)	Reshape time series mask arrays.
static INDArray	TimeSeriesUtils.reshapeTimeSeriesMaskToVector(INDArray timeSeriesMask, LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)	Reshape time series mask arrays.
static INDArray	TimeSeriesUtils.reverseTimeSeries(INDArray in, LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)	Reverse an input time series along the time dimension
static INDArray	TimeSeriesUtils.reverseTimeSeries(INDArray in, LayerWorkspaceMgr workspaceMgr, ArrayType arrayType, RNNFormat dataFormat)
static INDArray	TimeSeriesUtils.reverseTimeSeriesMask(INDArray mask, LayerWorkspaceMgr workspaceMgr, ArrayType arrayType)	Reverse a (per time step) time series mask, with shape [minibatch, timeSeriesLength]