Uses of Interface
org.deeplearning4j.nn.api.Layer

Packages that use Layer

Package	Description
org.deeplearning4j.gradientcheck
org.deeplearning4j.nn.api
org.deeplearning4j.nn.api.layers
org.deeplearning4j.nn.conf.constraint
org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.convolutional
org.deeplearning4j.nn.conf.layers.misc
org.deeplearning4j.nn.conf.layers.objdetect
org.deeplearning4j.nn.conf.layers.recurrent
org.deeplearning4j.nn.conf.layers.samediff
org.deeplearning4j.nn.conf.layers.util
org.deeplearning4j.nn.conf.layers.variational
org.deeplearning4j.nn.conf.ocnn
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.autoencoder
org.deeplearning4j.nn.layers.feedforward.dense
org.deeplearning4j.nn.layers.feedforward.elementwise
org.deeplearning4j.nn.layers.feedforward.embedding
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.updater.graph

Uses of Layer in org.deeplearning4j.gradientcheck

Methods in org.deeplearning4j.gradientcheck with parameters of type Layer

Modifier and Type	Method and Description
static boolean	GradientCheckUtil.checkGradientsPretrainLayer(Layer layer, double epsilon, double maxRelError, double minAbsoluteError, boolean print, boolean exitOnFirstError, org.nd4j.linalg.api.ndarray.INDArray input, int rngSeed) Check backprop gradients for a pretrain layer NOTE: gradient checking pretrain layers can be difficult...

Uses of Layer in org.deeplearning4j.nn.api

Methods in org.deeplearning4j.nn.api that return Layer

Modifier and Type	Method and Description
Layer	Layer.clone() Deprecated.
Layer	Layer.transpose() Deprecated.

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

Modifier and Type	Method and Description
void	Updater.setStateViewArray(Layer layer, org.nd4j.linalg.api.ndarray.INDArray viewArray, boolean initialize) Set the internal (historical) state view array for this updater
void	Updater.update(Layer layer, Gradient gradient, int iteration, int epoch, int miniBatchSize, LayerWorkspaceMgr workspaceMgr) Updater: updates the model

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

Subinterfaces of Layer in org.deeplearning4j.nn.api.layers

Modifier and Type	Interface and Description
interface	IOutputLayer Interface for output layers (those that calculate gradients with respect to a labels array)
interface	RecurrentLayer Created by Alex on 28/08/2016.

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

Modifier and Type	Method and Description
void	LayerConstraint.applyConstraint(Layer layer, int iteration, int epoch) Apply a given constraint to a layer at each iteration in the provided epoch, after parameters have been updated.

Uses of Layer in org.deeplearning4j.nn.conf.constraint

Methods in org.deeplearning4j.nn.conf.constraint with parameters of type Layer

Modifier and Type	Method and Description
void	BaseConstraint.applyConstraint(Layer layer, int iteration, int epoch)

Uses of Layer in org.deeplearning4j.nn.conf.layers

Methods in org.deeplearning4j.nn.conf.layers that return Layer

Modifier and Type	Method and Description
Layer	SeparableConvolution2D.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	AutoEncoder.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	ZeroPadding3DLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Upsampling1D.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	LSTM.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	CnnLossLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	OutputLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Upsampling3D.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	ZeroPaddingLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Subsampling1DLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Subsampling3DLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	DenseLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	DropoutLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	SpaceToBatchLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Convolution3D.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	CenterLossOutputLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	GravesLSTM.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	ConvolutionLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	GravesBidirectionalLSTM.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams) Deprecated.
Layer	ZeroPadding1DLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	RnnOutputLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	EmbeddingLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	ActivationLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	BatchNormalization.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	GlobalPoolingLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Convolution1DLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	LossLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Deconvolution2D.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	LocalResponseNormalization.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	SubsamplingLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Upsampling2D.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
abstract Layer	Layer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	RnnLossLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	DepthwiseConvolution2D.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	EmbeddingSequenceLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	SpaceToDepthLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)

Uses of Layer in org.deeplearning4j.nn.conf.layers.convolutional

Methods in org.deeplearning4j.nn.conf.layers.convolutional that return Layer

Modifier and Type	Method and Description
Layer	Cropping3D.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Cropping1D.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Cropping2D.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)

Uses of Layer in org.deeplearning4j.nn.conf.layers.misc

Methods in org.deeplearning4j.nn.conf.layers.misc that return Layer

Modifier and Type	Method and Description
Layer	FrozenLayerWithBackprop.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	FrozenLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	ElementWiseMultiplicationLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)

Uses of Layer in org.deeplearning4j.nn.conf.layers.objdetect

Methods in org.deeplearning4j.nn.conf.layers.objdetect that return Layer

Modifier and Type	Method and Description
Layer	Yolo2OutputLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)

Uses of Layer in org.deeplearning4j.nn.conf.layers.recurrent

Methods in org.deeplearning4j.nn.conf.layers.recurrent that return Layer

Modifier and Type	Method and Description
Layer	LastTimeStep.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	Bidirectional.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	SimpleRnn.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)

Uses of Layer in org.deeplearning4j.nn.conf.layers.samediff

Methods in org.deeplearning4j.nn.conf.layers.samediff that return Layer

Modifier and Type	Method and Description
abstract Layer	AbstractSameDiffLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	BaseSameDiffLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)

Uses of Layer in org.deeplearning4j.nn.conf.layers.util

Methods in org.deeplearning4j.nn.conf.layers.util that return Layer

Modifier and Type	Method and Description
Layer	MaskLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	MaskZeroLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)

Uses of Layer in org.deeplearning4j.nn.conf.layers.variational

Methods in org.deeplearning4j.nn.conf.layers.variational that return Layer

Modifier and Type	Method and Description
Layer	VariationalAutoencoder.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)

Uses of Layer in org.deeplearning4j.nn.conf.ocnn

Methods in org.deeplearning4j.nn.conf.ocnn that return Layer

Modifier and Type	Method and Description
Layer	OCNNOutputLayer.instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)

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

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

Modifier and Type	Method and Description
org.nd4j.linalg.api.ndarray.INDArray	IWeightNoise.getParameter(Layer layer, String paramKey, int iteration, int epoch, boolean train, LayerWorkspaceMgr workspaceMgr) Get the parameter, after applying weight noise
org.nd4j.linalg.api.ndarray.INDArray	WeightNoise.getParameter(Layer layer, String paramKey, int iteration, int epoch, boolean train, LayerWorkspaceMgr workspaceMgr)
org.nd4j.linalg.api.ndarray.INDArray	DropConnect.getParameter(Layer layer, String paramKey, int iteration, int epoch, boolean train, LayerWorkspaceMgr workspaceMgr)

Uses of Layer in org.deeplearning4j.nn.graph

Fields in org.deeplearning4j.nn.graph declared as Layer

Modifier and Type	Field and Description
protected Layer[]	ComputationGraph.layers A list of layers.

Methods in org.deeplearning4j.nn.graph that return Layer

Modifier and Type	Method and Description
Layer	ComputationGraph.getLayer(int idx) Get the layer by the number of that layer, in range 0 to getNumLayers()-1 NOTE: This is different from the internal GraphVertex index for the layer
Layer	ComputationGraph.getLayer(String name) Get a given layer by name.
Layer[]	ComputationGraph.getLayers() Get all layers in the ComputationGraph
Layer	ComputationGraph.getOutputLayer(int outputLayerIdx) Get the specified output layer, by index.

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

Methods in org.deeplearning4j.nn.graph.vertex that return Layer

Modifier and Type	Method and Description
Layer	BaseWrapperVertex.getLayer()
Layer	GraphVertex.getLayer() Get the Layer (if any).

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

Methods in org.deeplearning4j.nn.graph.vertex.impl that return Layer

Modifier and Type	Method and Description
Layer	SubsetVertex.getLayer()
Layer	ElementWiseVertex.getLayer()
Layer	ReshapeVertex.getLayer()
Layer	L2Vertex.getLayer()
Layer	ShiftVertex.getLayer()
Layer	LayerVertex.getLayer()
Layer	MergeVertex.getLayer()
Layer	PreprocessorVertex.getLayer()
Layer	InputVertex.getLayer()
Layer	L2NormalizeVertex.getLayer()
Layer	ScaleVertex.getLayer()
Layer	PoolHelperVertex.getLayer()
Layer	UnstackVertex.getLayer()
Layer	StackVertex.getLayer()

Constructors in org.deeplearning4j.nn.graph.vertex.impl with parameters of type Layer

Constructor and Description
LayerVertex(ComputationGraph graph, String name, int vertexIndex, Layer layer, InputPreProcessor layerPreProcessor, boolean outputVertex) Create a network input vertex:
LayerVertex(ComputationGraph graph, String name, int vertexIndex, VertexIndices[] inputVertices, VertexIndices[] outputVertices, Layer layer, InputPreProcessor layerPreProcessor, boolean outputVertex)

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

Methods in org.deeplearning4j.nn.graph.vertex.impl.rnn that return Layer

Modifier and Type	Method and Description
Layer	ReverseTimeSeriesVertex.getLayer()
Layer	DuplicateToTimeSeriesVertex.getLayer()
Layer	LastTimeStepVertex.getLayer()

Uses of Layer in org.deeplearning4j.nn.layers

Classes in org.deeplearning4j.nn.layers that implement Layer

Modifier and Type	Class and Description
class	AbstractLayer<LayerConfT extends Layer> A layer with input and output, no parameters or gradients
class	ActivationLayer Activation Layer Used to apply activation on input and corresponding derivative on epsilon.
class	BaseLayer<LayerConfT extends BaseLayer> A layer with parameters
class	BaseOutputLayer<LayerConfT extends BaseOutputLayer> Output layer with different objective in co-occurrences for different objectives.
class	BasePretrainNetwork<LayerConfT extends BasePretrainNetwork> Baseline class for any Neural Network used as a layer in a deep network *
class	DropoutLayer Created by davekale on 12/7/16.
class	FrozenLayer For purposes of transfer learning A frozen layers wraps another dl4j layer within it.
class	FrozenLayerWithBackprop Frozen layer freezes parameters of the layer it wraps, but allows the backpropagation to continue.
class	LossLayer LossLayer is a flexible output "layer" that performs a loss function on an input without MLP logic.
class	OutputLayer Output layer with different objective incooccurrences for different objectives.

Methods in org.deeplearning4j.nn.layers that return Layer

Modifier and Type	Method and Description
abstract Layer	AbstractLayer.clone()
Layer	BaseLayer.clone()
Layer	FrozenLayerWithBackprop.clone()
Layer	FrozenLayer.clone()
Layer	ActivationLayer.clone()
Layer	FrozenLayerWithBackprop.getInsideLayer()
Layer	FrozenLayer.getInsideLayer()
Layer	AbstractLayer.transpose()
Layer	BaseLayer.transpose()
Layer	DropoutLayer.transpose()
Layer	ActivationLayer.transpose()
Layer	LossLayer.transpose()

Constructors in org.deeplearning4j.nn.layers with parameters of type Layer

Constructor and Description
FrozenLayer(Layer insideLayer)
FrozenLayerWithBackprop(Layer insideLayer)

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

Classes in org.deeplearning4j.nn.layers.convolution that implement Layer

Modifier and Type	Class and Description
class	CnnLossLayer Convolutional Neural Network Loss Layer. Handles calculation of gradients etc for various objective functions. NOTE: CnnLossLayer does not have any parameters.
class	Convolution1DLayer 1D (temporal) convolutional layer.
class	Convolution3DLayer 3D convolution layer implementation.
class	ConvolutionLayer Convolution layer
class	Cropping1DLayer Zero cropping layer for 1D convolutional neural networks.
class	Cropping2DLayer Zero cropping layer for convolutional neural networks.
class	Cropping3DLayer Cropping layer for 3D convolutional neural networks.
class	Deconvolution2DLayer 2D deconvolution layer implementation.
class	DepthwiseConvolution2DLayer 2D depth-wise convolution layer configuration.
class	SeparableConvolution2DLayer 2D Separable convolution layer implementation Separable convolutions split a regular convolution operation into two simpler operations, which are usually computationally more efficient.
class	SpaceToBatch Space to batch utility layer for convolutional input types.
class	SpaceToDepth Space to channels utility layer for convolutional input types.
class	ZeroPadding1DLayer Zero padding 1D layer for convolutional neural networks.
class	ZeroPadding3DLayer Zero padding 3D layer for convolutional neural networks.
class	ZeroPaddingLayer Zero padding layer for convolutional neural networks.

Methods in org.deeplearning4j.nn.layers.convolution that return Layer

Modifier and Type	Method and Description
Layer	ZeroPadding3DLayer.clone()
Layer	SpaceToDepth.clone()
Layer	ZeroPaddingLayer.clone()
Layer	Cropping1DLayer.clone()
Layer	SpaceToBatch.clone()
Layer	Cropping2DLayer.clone()
Layer	ZeroPadding1DLayer.clone()
Layer	Cropping3DLayer.clone()
Layer	SpaceToDepth.transpose()
Layer	SpaceToBatch.transpose()
Layer	ConvolutionLayer.transpose()

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

Classes in org.deeplearning4j.nn.layers.convolution.subsampling that implement Layer

Modifier and Type	Class and Description
class	Subsampling1DLayer 1D (temporal) subsampling layer.
class	Subsampling3DLayer Subsampling 3D layer, used for downsampling a 3D convolution
class	SubsamplingLayer Subsampling layer.

Methods in org.deeplearning4j.nn.layers.convolution.subsampling that return Layer

Modifier and Type	Method and Description
Layer	Subsampling3DLayer.clone()
Layer	SubsamplingLayer.clone()
Layer	Subsampling3DLayer.transpose()
Layer	SubsamplingLayer.transpose()

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

Classes in org.deeplearning4j.nn.layers.convolution.upsampling that implement Layer

Modifier and Type	Class and Description
class	Upsampling1D 1D Upsampling layer.
class	Upsampling2D 2D Upsampling layer.
class	Upsampling3D 3D Upsampling layer.

Methods in org.deeplearning4j.nn.layers.convolution.upsampling that return Layer

Modifier and Type	Method and Description
Layer	Upsampling3D.clone()
Layer	Upsampling2D.clone()
Layer	Upsampling3D.transpose()
Layer	Upsampling2D.transpose()

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

Classes in org.deeplearning4j.nn.layers.feedforward.autoencoder that implement Layer

Modifier and Type	Class and Description
class	AutoEncoder Autoencoder.

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

Classes in org.deeplearning4j.nn.layers.feedforward.dense that implement Layer

Modifier and Type	Class and Description
class	DenseLayer

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

Classes in org.deeplearning4j.nn.layers.feedforward.elementwise that implement Layer

Modifier and Type	Class and Description
class	ElementWiseMultiplicationLayer Elementwise multiplication layer with weights: implements out = activationFn(input .* w + b) where: - w is a learnable weight vector of length nOut - ".*" is element-wise multiplication - b is a bias vector Note that the input and output sizes of the element-wise layer are the same for this layer

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

Classes in org.deeplearning4j.nn.layers.feedforward.embedding that implement Layer

Modifier and Type	Class and Description
class	EmbeddingLayer Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to numClass-1) as input.
class	EmbeddingSequenceLayer Embedding layer for sequences: feed-forward layer that expects fixed-length number (inputLength) of integers/indices per example as input, ranged from 0 to numClasses - 1.

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

Classes in org.deeplearning4j.nn.layers.normalization that implement Layer

Modifier and Type	Class and Description
class	BatchNormalization Batch normalization layer.
class	LocalResponseNormalization Deep neural net normalization approach normalizes activations between layers "brightness normalization" Used for nets like AlexNet

Methods in org.deeplearning4j.nn.layers.normalization that return Layer

Modifier and Type	Method and Description
Layer	BatchNormalization.clone()
Layer	LocalResponseNormalization.clone()
Layer	BatchNormalization.transpose()
Layer	LocalResponseNormalization.transpose()

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

Classes in org.deeplearning4j.nn.layers.objdetect that implement Layer

Modifier and Type	Class and Description
class	Yolo2OutputLayer Output (loss) layer for YOLOv2 object detection model, based on the papers: YOLO9000: Better, Faster, Stronger - Redmon & Farhadi (2016) - https://arxiv.org/abs/1612.08242 and You Only Look Once: Unified, Real-Time Object Detection - Redmon et al.

Methods in org.deeplearning4j.nn.layers.objdetect that return Layer

Modifier and Type	Method and Description
Layer	Yolo2OutputLayer.clone()

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

Classes in org.deeplearning4j.nn.layers.ocnn that implement Layer

Modifier and Type	Class and Description
class	OCNNOutputLayer Layer implementation for OCNNOutputLayer See OCNNOutputLayer for details.

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

Classes in org.deeplearning4j.nn.layers.pooling that implement Layer

Modifier and Type	Class and Description
class	GlobalPoolingLayer Global pooling layer - used to do pooling over time for RNNs, and 2d pooling for CNNs. Supports the following PoolingTypes: SUM, AVG, MAX, PNORM Global pooling layer can also handle mask arrays when dealing with variable length inputs.

Methods in org.deeplearning4j.nn.layers.pooling that return Layer

Modifier and Type	Method and Description
Layer	GlobalPoolingLayer.clone()

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

Classes in org.deeplearning4j.nn.layers.recurrent that implement Layer

Modifier and Type	Class and Description
class	BaseRecurrentLayer<LayerConfT extends BaseLayer>
class	BidirectionalLayer Bidirectional is a "wrapper" layer: it wraps any uni-directional RNN layer to make it bidirectional. Note that multiple different modes are supported - these specify how the activations should be combined from the forward and backward RNN networks.
class	GravesBidirectionalLSTM RNN tutorial: http://deeplearning4j.org/usingrnns.html READ THIS FIRST Bdirectional LSTM layer implementation.
class	GravesLSTM LSTM layer implementation.
class	LastTimeStepLayer LastTimeStep is a "wrapper" layer: it wraps any RNN layer, and extracts out the last time step during forward pass, and returns it as a row vector (per example).
class	LSTM LSTM layer implementation.
class	MaskZeroLayer Masks timesteps with 0 activation.
class	RnnLossLayer Recurrent Neural Network Loss Layer. Handles calculation of gradients etc for various objective functions. NOTE: Unlike RnnOutputLayer this RnnLossLayer does not have any parameters - i.e., there is no time distributed dense component here.
class	RnnOutputLayer Recurrent Neural Network Output Layer. Handles calculation of gradients etc for various objective functions. Functionally the same as OutputLayer, but handles output and label reshaping automatically. Input and output activations are same as other RNN layers: 3 dimensions with shape [miniBatchSize,nIn,timeSeriesLength] and [miniBatchSize,nOut,timeSeriesLength] respectively.
class	SimpleRnn Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer.

Methods in org.deeplearning4j.nn.layers.recurrent that return Layer

Modifier and Type	Method and Description
Layer	BidirectionalLayer.clone()
Layer	LSTM.transpose()
Layer	BidirectionalLayer.transpose()
Layer	GravesLSTM.transpose()
Layer	GravesBidirectionalLSTM.transpose()

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

Modifier and Type

Method and Description

FwdPassReturn

LSTMHelper.activate(Layer layer, NeuralNetConfiguration conf, org.nd4j.linalg.activations.IActivation gateActivationFn, org.nd4j.linalg.api.ndarray.INDArray input, org.nd4j.linalg.api.ndarray.INDArray recurrentWeights, org.nd4j.linalg.api.ndarray.INDArray inputWeights, org.nd4j.linalg.api.ndarray.INDArray biases, boolean training, org.nd4j.linalg.api.ndarray.INDArray prevOutputActivations, org.nd4j.linalg.api.ndarray.INDArray prevMemCellState, boolean forBackprop, boolean forwards, String inputWeightKey, org.nd4j.linalg.api.ndarray.INDArray maskArray, boolean hasPeepholeConnections, LayerWorkspaceMgr workspaceMgr)

Constructors in org.deeplearning4j.nn.layers.recurrent with parameters of type Layer

Constructor and Description
LastTimeStepLayer(Layer underlying)
MaskZeroLayer(Layer underlying)

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

Classes in org.deeplearning4j.nn.layers.samediff that implement Layer

Modifier and Type	Class and Description
class	SameDiffLayer

Methods in org.deeplearning4j.nn.layers.samediff that return Layer

Modifier and Type	Method and Description
Layer	SameDiffLayer.clone()

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

Classes in org.deeplearning4j.nn.layers.training that implement Layer

Modifier and Type	Class and Description
class	CenterLossOutputLayer Center loss is similar to triplet loss except that it enforces intraclass consistency and doesn't require feed forward of multiple examples.

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

Classes in org.deeplearning4j.nn.layers.util that implement Layer

Modifier and Type	Class and Description
class	MaskLayer MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through this layer.

Methods in org.deeplearning4j.nn.layers.util that return Layer

Modifier and Type	Method and Description
Layer	MaskLayer.clone()

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

Classes in org.deeplearning4j.nn.layers.variational that implement Layer

Modifier and Type	Class and Description
class	VariationalAutoencoder Variational Autoencoder layer

Methods in org.deeplearning4j.nn.layers.variational that return Layer

Modifier and Type	Method and Description
Layer	VariationalAutoencoder.clone()
Layer	VariationalAutoencoder.transpose()

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

Classes in org.deeplearning4j.nn.layers.wrapper that implement Layer

Modifier and Type	Class and Description
class	BaseWrapperLayer Abstract wrapper layer.

Fields in org.deeplearning4j.nn.layers.wrapper declared as Layer

Modifier and Type	Field and Description
protected Layer	BaseWrapperLayer.underlying

Methods in org.deeplearning4j.nn.layers.wrapper that return Layer

Modifier and Type	Method and Description
Layer	BaseWrapperLayer.clone()
Layer	BaseWrapperLayer.transpose()

Constructors in org.deeplearning4j.nn.layers.wrapper with parameters of type Layer

Constructor and Description
BaseWrapperLayer(Layer underlying)

Uses of Layer in org.deeplearning4j.nn.multilayer

Classes in org.deeplearning4j.nn.multilayer that implement Layer

Modifier and Type	Class and Description
class	MultiLayerNetwork MultiLayerNetwork is a neural network with multiple layers in a stack, and usually an output layer.

Fields in org.deeplearning4j.nn.multilayer declared as Layer

Modifier and Type	Field and Description
protected Layer[]	MultiLayerNetwork.layers

Fields in org.deeplearning4j.nn.multilayer with type parameters of type Layer

Modifier and Type	Field and Description
protected LinkedHashMap<String,Layer>	MultiLayerNetwork.layerMap

Methods in org.deeplearning4j.nn.multilayer that return Layer

Modifier and Type	Method and Description
Layer	MultiLayerNetwork.getLayer(int i)
Layer	MultiLayerNetwork.getLayer(String name)
Layer[]	MultiLayerNetwork.getLayers()
Layer	MultiLayerNetwork.getOutputLayer() Get the output layer
Layer	MultiLayerNetwork.transpose()

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

Modifier and Type	Method and Description
void	MultiLayerNetwork.setLayers(Layer[] layers)

Uses of Layer in org.deeplearning4j.nn.updater

Fields in org.deeplearning4j.nn.updater with type parameters of type Layer

Modifier and Type	Field and Description
protected Map<String,Layer>	BaseMultiLayerUpdater.layersByName

Methods in org.deeplearning4j.nn.updater that return Layer

Modifier and Type	Method and Description
protected abstract Layer[]	BaseMultiLayerUpdater.getOrderedLayers()
protected Layer[]	MultiLayerUpdater.getOrderedLayers()
protected Layer[]	LayerUpdater.getOrderedLayers()

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

Modifier and Type	Method and Description
void	UpdaterBlock.postApply(Layer layer, String paramName, org.nd4j.linalg.api.ndarray.INDArray gradientView, org.nd4j.linalg.api.ndarray.INDArray paramsView) Apply L1 and L2 regularization, if necessary.
void	BaseMultiLayerUpdater.preApply(Layer layer, Gradient gradient, int iteration) Pre-apply: Apply gradient normalization/clipping
void	BaseMultiLayerUpdater.setStateViewArray(Layer layer, org.nd4j.linalg.api.ndarray.INDArray viewArray, boolean initialize)
void	BaseMultiLayerUpdater.update(Layer layer, Gradient gradient, int iteration, int epoch, int batchSize, LayerWorkspaceMgr workspaceMgr)
static boolean	UpdaterUtils.updaterConfigurationsEquals(Layer layer1, String param1, Layer layer2, String param2)

Constructors in org.deeplearning4j.nn.updater with parameters of type Layer

Constructor and Description
LayerUpdater(Layer layer)
LayerUpdater(Layer layer, org.nd4j.linalg.api.ndarray.INDArray updaterState)

Uses of Layer in org.deeplearning4j.nn.updater.graph

Fields in org.deeplearning4j.nn.updater.graph declared as Layer

Modifier and Type	Field and Description
protected Layer[]	ComputationGraphUpdater.orderedLayers

Methods in org.deeplearning4j.nn.updater.graph that return Layer

Modifier and Type	Method and Description
protected Layer[]	ComputationGraphUpdater.getOrderedLayers()