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

Packages that use Layer

Package	Description
org.deeplearning4j.nn.api
org.deeplearning4j.nn.conf
org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.misc
org.deeplearning4j.nn.conf.layers.variational
org.deeplearning4j.nn.conf.serde
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.params
org.deeplearning4j.nn.transferlearning

Uses of Layer in org.deeplearning4j.nn.api

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

Modifier and Type	Method and Description
int	ParamInitializer.numParams(Layer layer)

Uses of Layer in org.deeplearning4j.nn.conf

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

Modifier and Type	Field and Description
protected Layer	NeuralNetConfiguration.layer
protected Layer	NeuralNetConfiguration.Builder.layer

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

Modifier and Type	Method and Description
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.addLayer(String layerName, Layer layer, InputPreProcessor preProcessor, String... layerInputs) Add a layer and an InputPreProcessor, with the specified name and specified inputs.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.addLayer(String layerName, Layer layer, String... layerInputs) Add a layer, with no InputPreProcessor, with the specified name and specified inputs.
NeuralNetConfiguration.ListBuilder	NeuralNetConfiguration.ListBuilder.layer(int ind, Layer layer)
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.layer(Layer layer) Layer class.
NeuralNetConfiguration.ListBuilder	NeuralNetConfiguration.Builder.list(Layer... layers) Create a ListBuilder (for creating a MultiLayerConfiguration) with the specified layers Usage:

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

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

Modifier and Type	Class and Description
class	AbstractLSTM LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
class	ActivationLayer
class	AutoEncoder Autoencoder.
class	BaseLayer A neural network layer.
class	BaseOutputLayer
class	BasePretrainNetwork
class	BaseRecurrentLayer
class	BatchNormalization Batch normalization configuration
class	CenterLossOutputLayer Center loss is similar to triplet loss except that it enforces intraclass consistency and doesn't require feed forward of multiple examples.
class	Convolution1DLayer 1D (temporal) convolutional layer.
class	ConvolutionLayer
class	DenseLayer Dense layer: fully connected feed forward layer trainable by backprop.
class	DropoutLayer
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	FeedForwardLayer Created by jeffreytang on 7/21/15.
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.
class	GravesBidirectionalLSTM LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
class	GravesLSTM LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
class	LocalResponseNormalization Created by nyghtowl on 10/29/15.
class	LossLayer LossLayer is a flexible output "layer" that performs a loss function on an input without MLP logic.
class	LSTM LSTM recurrent net without peephole connections.
class	OutputLayer Output layer with different objective co-occurrences for different objectives.
class	RBM Restricted Boltzmann Machine.
class	RnnOutputLayer
class	Subsampling1DLayer 1D (temporal) subsampling layer.
class	SubsamplingLayer Subsampling layer also referred to as pooling in convolution neural nets Supports the following pooling types: MAX AVG NON
class	ZeroPaddingLayer Zero padding layer for convolutional neural networks.

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

Modifier and Type	Method and Description
abstract <E extends Layer> E	Layer.Builder.build()

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

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

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

Modifier and Type	Method and Description
static void	LayerValidation.generalValidation(String layerName, Layer layer, boolean useRegularization, boolean useDropConnect, double dropOut, double l2, double l2Bias, double l1, double l1Bias, Distribution dist)
static void	LayerValidation.generalValidation(String layerName, Layer layer, boolean useRegularization, boolean useDropConnect, Double dropOut, Double l2, Double l2Bias, Double l1, Double l1Bias, Distribution dist)
static void	LayerValidation.updaterValidation(String layerName, Layer layer, Double learningRate, Double momentum, Map<Integer,Double> momentumSchedule, Double adamMeanDecay, Double adamVarDecay, Double rho, Double rmsDecay, Double epsilon) Validate the updater configuration - setting the default updater values, if necessary

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

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

Modifier and Type	Class and Description
class	FrozenLayer Created by Alex on 10/07/2017.

Fields in org.deeplearning4j.nn.conf.layers.misc declared as Layer

Modifier and Type	Field and Description
protected Layer	FrozenLayer.layer

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

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

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

Modifier and Type	Method and Description
FrozenLayer.Builder	FrozenLayer.Builder.layer(Layer layer)

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

Constructor and Description
FrozenLayer(Layer layer)

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

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

Modifier and Type	Class and Description
class	VariationalAutoencoder Variational Autoencoder layer

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

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

Modifier and Type	Method and Description
protected void	BaseNetConfigDeserializer.handleUpdaterBackwardCompatibility(Layer[] layers)

Uses of Layer in org.deeplearning4j.nn.layers

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

Modifier and Type	Class and Description
class	AbstractLayer<LayerConfT extends Layer> A layer with input and output, no parameters or gradients

Uses of Layer in org.deeplearning4j.nn.params

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

Modifier and Type	Method and Description
int	FrozenLayerParamInitializer.numParams(Layer layer)
int	ConvolutionParamInitializer.numParams(Layer l)
int	EmptyParamInitializer.numParams(Layer layer)
int	BatchNormalizationParamInitializer.numParams(Layer l)
int	LSTMParamInitializer.numParams(Layer l)
int	GravesBidirectionalLSTMParamInitializer.numParams(Layer l)
int	GravesLSTMParamInitializer.numParams(Layer l)
int	DefaultParamInitializer.numParams(Layer l)

Uses of Layer in org.deeplearning4j.nn.transferlearning

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

Modifier and Type	Method and Description
TransferLearning.Builder	TransferLearning.Builder.addLayer(Layer layer) Add layers to the net Required if layers are removed.
TransferLearning.GraphBuilder	TransferLearning.GraphBuilder.addLayer(String layerName, Layer layer, InputPreProcessor preProcessor, String... layerInputs) Add a layer with a specified preprocessor
TransferLearning.GraphBuilder	TransferLearning.GraphBuilder.addLayer(String layerName, Layer layer, String... layerInputs) Add a layer of the specified configuration to the computation graph