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

Packages that use Layer

Package	Description
org.deeplearning4j.nn.api
org.deeplearning4j.nn.api.layers
org.deeplearning4j.nn.conf.layers
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.feedforward.autoencoder
org.deeplearning4j.nn.layers.feedforward.dense
org.deeplearning4j.nn.layers.feedforward.embedding
org.deeplearning4j.nn.layers.feedforward.rbm
org.deeplearning4j.nn.layers.normalization
org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.multilayer
org.deeplearning4j.nn.updater
org.deeplearning4j.util

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() Clone the layer
Layer	Layer.transpose() Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)

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

Modifier and Type	Method and Description
void	Layer.merge(Layer layer, int batchSize) Parameter averaging
void	Updater.setStateViewArray(Layer layer, org.nd4j.linalg.api.ndarray.INDArray viewArray, boolean initialize) Set the internal (historical) state view array for this updater
int	Updater.stateSizeForLayer(Layer layer) Calculate and return the state size for this updater (for the given layer).
void	Updater.update(Layer layer, Gradient gradient, int iteration, int miniBatchSize) 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.

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	SubsamplingLayer.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	OutputLayer.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	BatchNormalization.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	GravesLSTM.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	EmbeddingLayer.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	ConvolutionLayer.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	AutoEncoder.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
abstract Layer	Layer.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	GravesBidirectionalLSTM.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	RBM.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	LocalResponseNormalization.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	RnnOutputLayer.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	DenseLayer.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
Layer	ActivationLayer.instantiate(NeuralNetConfiguration conf, Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)

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	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	LayerVertex.getLayer()
Layer	SubsetVertex.getLayer()
Layer	PreprocessorVertex.getLayer()
Layer	MergeVertex.getLayer()
Layer	ElementWiseVertex.getLayer()
Layer	InputVertex.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	LastTimeStepVertex.getLayer()
Layer	DuplicateToTimeSeriesVertex.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	ActivationLayer Activation Layer Used to apply activation on input and corresponding derivative on epsilon.
class	BaseLayer<LayerConfT extends Layer> A layer with a bias and activation function
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	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
Layer	BaseLayer.clone()
Layer	BaseLayer.transpose()
Layer	ActivationLayer.transpose()

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

Modifier and Type	Method and Description
void	BaseLayer.merge(Layer l, int batchSize) Averages the given logistic regression from a mini batch into this layer
void	ActivationLayer.merge(Layer layer, int batchSize)

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	ConvolutionLayer Convolution layer

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

Modifier and Type	Method and Description
Layer	ConvolutionLayer.transpose()

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

Modifier and Type	Method and Description
void	ConvolutionLayer.merge(Layer layer, int batchSize)

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	SubsamplingLayer Subsampling layer.

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

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

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

Modifier and Type	Method and Description
void	SubsamplingLayer.merge(Layer layer, int batchSize)

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.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.

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

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

Modifier and Type	Class and Description
class	RBM Restricted Boltzmann Machine.

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

Modifier and Type	Method and Description
Layer	RBM.transpose()

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	BatchNormalization.transpose()
Layer	LocalResponseNormalization.transpose()

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

Modifier and Type	Method and Description
void	BatchNormalization.merge(Layer layer, int batchSize)
void	LocalResponseNormalization.merge(Layer layer, int batchSize)

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 Layer>
class	GravesBidirectionalLSTM RNN tutorial: http://deeplearning4j.org/usingrnns.html READ THIS FIRST Bdirectional LSTM layer implementation.
class	GravesLSTM LSTM layer implementation.
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.

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

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

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

Modifier and Type

Method and Description

static FwdPassReturn

LSTMHelpers.activateHelper(Layer layer, NeuralNetConfiguration conf, org.nd4j.linalg.api.ndarray.INDArray input, org.nd4j.linalg.api.ndarray.INDArray recurrentWeights, org.nd4j.linalg.api.ndarray.INDArray originalInputWeights, org.nd4j.linalg.api.ndarray.INDArray biases, boolean training, org.nd4j.linalg.api.ndarray.INDArray originalPrevOutputActivations, org.nd4j.linalg.api.ndarray.INDArray originalPrevMemCellState, boolean forBackprop, boolean forwards, String inputWeightKey) Returns FwdPassReturn object with activations/INDArrays.

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.merge(Layer layer, int batchSize) Averages the given logistic regression from a mini batch in to this one
void	MultiLayerNetwork.setLayers(Layer[] layers)

Uses of Layer in org.deeplearning4j.nn.updater

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

Modifier and Type	Method and Description
void	LayerUpdater.applyLrDecayPolicy(LearningRatePolicy decay, Layer layer, int iteration, String variable) Update learning rate based on policy
void	BaseUpdater.applyLrDecayPolicy(LearningRatePolicy decay, Layer layer, int iteration, String variable) Deprecated. Update learning rate based on policy
void	LayerUpdater.applyMomentumDecayPolicy(Layer layer, int iteration, String variable) Update momentum if schedule exist
void	BaseUpdater.applyMomentumDecayPolicy(Layer layer, int iteration, String variable) Deprecated. Update momentum if schedule exist
org.nd4j.linalg.learning.GradientUpdater	AdaGradUpdater.init(String variable, Layer layer) Deprecated.
org.nd4j.linalg.learning.GradientUpdater	SgdUpdater.init(String variable, Layer layer) Deprecated.
org.nd4j.linalg.learning.GradientUpdater	NoOpUpdater.init(String variable, Layer layer) Deprecated.
org.nd4j.linalg.learning.GradientUpdater	NesterovsUpdater.init(String variable, Layer layer) Deprecated.
org.nd4j.linalg.learning.GradientUpdater	RmsPropUpdater.init(String variable, Layer layer) Deprecated.
org.nd4j.linalg.learning.GradientUpdater	LayerUpdater.init(String variable, Layer layer)
org.nd4j.linalg.learning.GradientUpdater	AdamUpdater.init(String variable, Layer layer) Deprecated.
abstract org.nd4j.linalg.learning.GradientUpdater	BaseUpdater.init(String variable, Layer layer) Deprecated.
org.nd4j.linalg.learning.GradientUpdater	AdaDeltaUpdater.init(String variable, Layer layer) Deprecated.
void	LayerUpdater.postApply(Layer layer, org.nd4j.linalg.api.ndarray.INDArray gradient, String param, int miniBatchSize) Apply the regularization
void	BaseUpdater.postApply(Layer layer, org.nd4j.linalg.api.ndarray.INDArray gradient, String param, int miniBatchSize) Deprecated. Apply the regularization
void	LayerUpdater.preApply(Layer layer, Gradient gradient, int iteration) Apply gradient normalization: scale based on L2, clipping etc.
void	BaseUpdater.preApply(Layer layer, Gradient gradient, int iteration) Deprecated. Apply gradient normalization: scale based on L2, clipping etc.
void	LayerUpdater.setStateViewArray(Layer layer, org.nd4j.linalg.api.ndarray.INDArray viewArray, boolean initialize)
void	MultiLayerUpdater.setStateViewArray(Layer layer, org.nd4j.linalg.api.ndarray.INDArray viewArray, boolean initialize)
void	BaseUpdater.setStateViewArray(Layer layer, org.nd4j.linalg.api.ndarray.INDArray viewArray, boolean initialize) Deprecated.
int	LayerUpdater.stateSizeForLayer(Layer layer)
int	MultiLayerUpdater.stateSizeForLayer(Layer layer)
int	BaseUpdater.stateSizeForLayer(Layer layer) Deprecated.
void	LayerUpdater.update(Layer layer, Gradient gradient, int iteration, int miniBatchSize)
void	MultiLayerUpdater.update(Layer layer, Gradient gradient, int iteration, int batchSize)
void	BaseUpdater.update(Layer layer, Gradient gradient, int iteration, int miniBatchSize) Deprecated.

Uses of Layer in org.deeplearning4j.util

Methods in org.deeplearning4j.util that return Layer

Modifier and Type	Method and Description
static Layer	NetSaverLoaderUtils.loadLayerParameters(Layer layer, String paramPath) Load existing parameters to the layer

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

Modifier and Type	Method and Description
static org.nd4j.linalg.api.ndarray.INDArray	Dropout.applyDropConnect(Layer layer, String variable) Apply drop connect to the given variable
static Layer	NetSaverLoaderUtils.loadLayerParameters(Layer layer, String paramPath) Load existing parameters to the layer