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.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.layers.wrapper
org.deeplearning4j.nn.conf.ocnn
org.deeplearning4j.nn.conf.serde
org.deeplearning4j.nn.conf.serde.legacyformat
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.ocnn
org.deeplearning4j.nn.layers.util
org.deeplearning4j.nn.params
org.deeplearning4j.nn.transferlearning
org.deeplearning4j.util

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
List<String>	ParamInitializer.biasKeys(Layer layer) Bias parameter keys given the layer configuration
boolean	ParamInitializer.isBiasParam(Layer layer, String key) Is the specified parameter a bias?
boolean	ParamInitializer.isWeightParam(Layer layer, String key) Is the specified parameter a weight?
long	ParamInitializer.numParams(Layer layer)
List<String>	ParamInitializer.paramKeys(Layer layer) Get a list of all parameter keys given the layer configuration
List<String>	ParamInitializer.weightKeys(Layer layer) Weight parameter keys given the layer configuration

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)
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.layer(int layerName, Layer layer, String... layerInputs) Add a layer, with no InputPreProcessor, with the specified name and specified inputs.
NeuralNetConfiguration.ListBuilder	NeuralNetConfiguration.ListBuilder.layer(Layer layer)
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.layer(Layer layer) Layer class.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.layer(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.layer(String layerName, Layer layer, String... layerInputs) Add a layer, with no InputPreProcessor, with the specified name and specified inputs.
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 Activation layer is a simple layer that applies the specified activation function to the input activations
class	AutoEncoder Autoencoder layer.
class	BaseLayer A neural network layer.
class	BaseOutputLayer
class	BasePretrainNetwork
class	BaseRecurrentLayer
class	BaseUpsamplingLayer Upsampling base layer
class	BatchNormalization Batch normalization layer See: Ioffe and Szegedy, 2014, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift https://arxiv.org/abs/1502.03167
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	Cnn3DLossLayer 3D Convolutional Neural Network Loss Layer. Handles calculation of gradients etc for various loss (objective) functions. NOTE: Cnn3DLossLayer does not have any parameters.
class	CnnLossLayer Convolutional Neural Network Loss Layer. Handles calculation of gradients etc for various loss (objective) functions. NOTE: CnnLossLayer does not have any parameters.
class	Convolution1D 1D convolution layer.
class	Convolution1DLayer 1D (temporal) convolutional layer.
class	Convolution2D 2D convolution layer
class	Convolution3D 3D convolution layer configuration
class	ConvolutionLayer 2D Convolution layer (for example, spatial convolution over images).
class	Deconvolution2D 2D deconvolution layer configuration Deconvolutions are also known as transpose convolutions or fractionally strided convolutions.
class	DenseLayer Dense layer: a standard fully connected feed forward layer
class	DepthwiseConvolution2D 2D depth-wise convolution layer configuration.
class	DropoutLayer Dropout layer.
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.
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 Deprecated. use Bidirectional instead. With the Bidirectional layer wrapper you can make any recurrent layer bidirectional, in particular GravesLSTM. Note that this layer adds the output of both directions, which translates into "ADD" mode in Bidirectional. Usage: .layer(new Bidirectional(Bidirectional.Mode.ADD, new GravesLSTM.Builder()....build()))
class	GravesLSTM Deprecated. Will be eventually removed. Use LSTM instead, which has similar prediction accuracy, but supports CuDNN for faster network training on CUDA (Nvidia) GPUs
class	LocallyConnected1D SameDiff version of a 1D locally connected layer.
class	LocallyConnected2D SameDiff version of a 2D locally connected layer.
class	LocalResponseNormalization Local response normalization layer See section 3.3 of http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
class	LossLayer LossLayer is a flexible output layer that performs a loss function on an input without MLP logic. LossLayer is similar to OutputLayer in that both perform loss calculations for network outputs vs.
class	LSTM LSTM recurrent neural network layer without peephole connections.
class	NoParamLayer
class	OutputLayer Output layer used for training via backpropagation based on labels and a specified loss function.
class	Pooling1D 1D Pooling (subsampling) layer.
class	Pooling2D 2D Pooling (subsampling) layer.
class	PReLULayer Parametrized Rectified Linear Unit (PReLU)
class	RnnLossLayer Recurrent Neural Network Loss Layer. Handles calculation of gradients etc for various objective (loss) functions. Note: Unlike RnnOutputLayer this RnnLossLayer does not have any parameters - i.e., there is no time distributed dense component here.
class	RnnOutputLayer A version of OutputLayer for recurrent neural networks.
class	SeparableConvolution2D 2D Separable convolution layer configuration.
class	SpaceToBatchLayer Space to batch utility layer configuration for convolutional input types.
class	SpaceToDepthLayer Space to channels utility layer configuration for convolutional input types.
class	Subsampling1DLayer 1D (temporal) subsampling layer - also known as pooling layer. Expects input of shape [minibatch, nIn, sequenceLength].
class	Subsampling3DLayer 3D subsampling / pooling layer for convolutional neural networks
class	SubsamplingLayer Subsampling layer also referred to as pooling in convolution neural nets Supports the following pooling types: MAX, AVG, SUM, PNORM
class	Upsampling1D Upsampling 1D layer Repeats each step size times along the temporal/sequence axis (dimension 2) For input shape [minibatch, channels, sequenceLength] output has shape [minibatch, channels, size * sequenceLength] Example:
class	Upsampling2D Upsampling 2D layer Repeats each value (or rather, set of depth values) in the height and width dimensions by size[0] and size[1] times respectively. If input has shape [minibatch, channels, height, width] then output has shape [minibatch, channels, height*size[0], width*size[1]] Example:
class	Upsampling3D Upsampling 3D layer Repeats each value (all channel values for each x/y/z location) by size[0], size[1] and size[2] If input has shape [minibatch, channels, depth, height, width] then output has shape [minibatch, channels, size[0] * depth, size[1] * height, size[2] * width]
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 (2D CNNs).

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, IDropout iDropout, double l2, double l2Bias, double l1, double l1Bias, Distribution dist, List<LayerConstraint> allParamConstraints, List<LayerConstraint> weightConstraints, List<LayerConstraint> biasConstraints)
static void	LayerValidation.generalValidation(String layerName, Layer layer, IDropout iDropOut, Double l2, Double l2Bias, Double l1, Double l1Bias, Distribution dist, List<LayerConstraint> allParamConstraints, List<LayerConstraint> weightConstraints, List<LayerConstraint> biasConstraints)

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

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

Modifier and Type	Class and Description
class	Cropping1D Cropping layer for convolutional (1d) neural networks.
class	Cropping2D Cropping layer for convolutional (2d) neural networks.
class	Cropping3D Cropping layer for convolutional (3d) neural networks.

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	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
class	FrozenLayer FrozenLayer is used for the purposes of transfer learning. A frozen layer wraps another DL4J Layer within it.
class	FrozenLayerWithBackprop Frozen layer freezes parameters of the layer it wraps, but allows the backpropagation to continue.
class	RepeatVector RepeatVector layer configuration.

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()
Layer	FrozenLayerWithBackprop.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)
FrozenLayerWithBackprop(Layer layer)

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

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

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.

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

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

Modifier and Type	Class and Description
class	Bidirectional 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	LastTimeStep LastTimeStep is a "wrapper" layer: it wraps any RNN (or CNN1D) layer, and extracts out the last time step during forward pass, and returns it as a row vector (per example).
class	SimpleRnn Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer.

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

Modifier and Type	Method and Description
Layer	LastTimeStep.getUnderlying()

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

Modifier and Type	Method and Description
Bidirectional.Builder	Bidirectional.Builder.rnnLayer(Layer layer)

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

Constructor and Description
Bidirectional(Bidirectional.Mode mode, Layer layer) Create a Bidirectional wrapper for the specified layer
Bidirectional(Layer layer) Create a Bidirectional wrapper, with the default Mode (CONCAT) for the specified layer
LastTimeStep(Layer underlying)

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

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

Modifier and Type	Class and Description
class	AbstractSameDiffLayer
class	SameDiffLambdaLayer SameDiffLambdaLayer is defined to be used as the base class for implementing lambda layers using SameDiff Lambda layers are layers without parameters - and as a result, have a much simpler API - users need only extend SameDiffLambdaLayer and implement a single method
class	SameDiffLayer A base layer used for implementing Deeplearning4j layers using SameDiff.
class	SameDiffOutputLayer A base layer used for implementing Deeplearning4j Output layers using SameDiff.

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

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

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.
class	MaskZeroLayer Wrapper which masks timesteps with activation equal to the specified masking value (0.0 default).

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

Modifier and Type	Method and Description
MaskZeroLayer.Builder	MaskZeroLayer.Builder.setUnderlying(Layer underlying)

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

Constructor and Description
MaskZeroLayer(Layer underlying, double maskingValue)

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.layers.wrapper

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

Modifier and Type	Class and Description
class	BaseWrapperLayer Base wrapper layer: the idea is to pass through all methods to the underlying layer, and selectively override them as required.

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

Modifier and Type	Field and Description
protected Layer	BaseWrapperLayer.underlying

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

Constructor and Description
BaseWrapperLayer(Layer underlying)

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

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

Modifier and Type	Class and Description
class	OCNNOutputLayer An implementation of one class neural networks from: https://arxiv.org/pdf/1802.06360.pdf The one class neural network approach is an extension of the standard output layer with a single set of weights, an activation function, and a bias to: 2 sets of weights, a learnable "r" parameter that is held static 1 traditional set of weights.

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

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

Modifier and Type	Method and Description
Layer	FrozenLayerDeserializer.deserialize(org.nd4j.shade.jackson.core.JsonParser jp, org.nd4j.shade.jackson.databind.DeserializationContext deserializationContext)

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

Modifier and Type	Method and Description
protected boolean	BaseNetConfigDeserializer.requiresDropoutFromLegacy(Layer[] layers)
protected boolean	BaseNetConfigDeserializer.requiresIUpdaterFromLegacy(Layer[] layers)

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

Method parameters in org.deeplearning4j.nn.conf.serde.legacyformat with type arguments of type Layer

Modifier and Type	Method and Description
static void	LegacyLayerDeserializer.registerLegacyClassDefaultName(Class<? extends Layer> clazz)
static void	LegacyLayerDeserializer.registerLegacyClassSpecifiedName(String name, Class<? extends Layer> clazz)

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.layers.ocnn

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

Modifier and Type	Method and Description
List<String>	OCNNParamInitializer.biasKeys(Layer layer)
boolean	OCNNParamInitializer.isBiasParam(Layer layer, String key)
boolean	OCNNParamInitializer.isWeightParam(Layer layer, String key)
long	OCNNParamInitializer.numParams(Layer layer)
List<String>	OCNNParamInitializer.paramKeys(Layer layer)
List<String>	OCNNParamInitializer.weightKeys(Layer layer)

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

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

Modifier and Type	Class and Description
class	IdentityLayer Identity layer, passes data through unaltered.

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
List<String>	WrapperLayerParamInitializer.biasKeys(Layer layer)
List<String>	GravesLSTMParamInitializer.biasKeys(Layer layer)
List<String>	SimpleRnnParamInitializer.biasKeys(Layer layer)
List<String>	SeparableConvolutionParamInitializer.biasKeys(Layer layer)
List<String>	DepthwiseConvolutionParamInitializer.biasKeys(Layer layer)
List<String>	PReLUParamInitializer.biasKeys(Layer layer)
List<String>	EmptyParamInitializer.biasKeys(Layer layer)
List<String>	FrozenLayerWithBackpropParamInitializer.biasKeys(Layer layer)
List<String>	DefaultParamInitializer.biasKeys(Layer layer)
List<String>	SameDiffParamInitializer.biasKeys(Layer layer)
List<String>	VariationalAutoencoderParamInitializer.biasKeys(Layer layer)
List<String>	BidirectionalParamInitializer.biasKeys(Layer layer)
List<String>	GravesBidirectionalLSTMParamInitializer.biasKeys(Layer layer)
List<String>	LSTMParamInitializer.biasKeys(Layer layer)
List<String>	ConvolutionParamInitializer.biasKeys(Layer layer)
List<String>	FrozenLayerParamInitializer.biasKeys(Layer layer)
List<String>	BatchNormalizationParamInitializer.biasKeys(Layer layer)
protected boolean	DefaultParamInitializer.hasBias(Layer layer)
boolean	WrapperLayerParamInitializer.isBiasParam(Layer layer, String key)
boolean	GravesLSTMParamInitializer.isBiasParam(Layer layer, String key)
boolean	SimpleRnnParamInitializer.isBiasParam(Layer layer, String key)
boolean	SeparableConvolutionParamInitializer.isBiasParam(Layer layer, String key)
boolean	DepthwiseConvolutionParamInitializer.isBiasParam(Layer layer, String key)
boolean	PReLUParamInitializer.isBiasParam(Layer layer, String key)
boolean	EmptyParamInitializer.isBiasParam(Layer layer, String key)
boolean	FrozenLayerWithBackpropParamInitializer.isBiasParam(Layer layer, String key)
boolean	DefaultParamInitializer.isBiasParam(Layer layer, String key)
boolean	SameDiffParamInitializer.isBiasParam(Layer layer, String key)
boolean	VariationalAutoencoderParamInitializer.isBiasParam(Layer layer, String key)
boolean	BidirectionalParamInitializer.isBiasParam(Layer layer, String key)
boolean	GravesBidirectionalLSTMParamInitializer.isBiasParam(Layer layer, String key)
boolean	LSTMParamInitializer.isBiasParam(Layer layer, String key)
boolean	ConvolutionParamInitializer.isBiasParam(Layer layer, String key)
boolean	FrozenLayerParamInitializer.isBiasParam(Layer layer, String key)
boolean	BatchNormalizationParamInitializer.isBiasParam(Layer layer, String key)
boolean	WrapperLayerParamInitializer.isWeightParam(Layer layer, String key)
boolean	GravesLSTMParamInitializer.isWeightParam(Layer layer, String key)
boolean	SimpleRnnParamInitializer.isWeightParam(Layer layer, String key)
boolean	SeparableConvolutionParamInitializer.isWeightParam(Layer layer, String key)
boolean	DepthwiseConvolutionParamInitializer.isWeightParam(Layer layer, String key)
boolean	PReLUParamInitializer.isWeightParam(Layer layer, String key)
boolean	EmptyParamInitializer.isWeightParam(Layer layer, String key)
boolean	FrozenLayerWithBackpropParamInitializer.isWeightParam(Layer layer, String key)
boolean	DefaultParamInitializer.isWeightParam(Layer layer, String key)
boolean	SameDiffParamInitializer.isWeightParam(Layer layer, String key)
boolean	VariationalAutoencoderParamInitializer.isWeightParam(Layer layer, String key)
boolean	BidirectionalParamInitializer.isWeightParam(Layer layer, String key)
boolean	GravesBidirectionalLSTMParamInitializer.isWeightParam(Layer layer, String key)
boolean	LSTMParamInitializer.isWeightParam(Layer layer, String key)
boolean	ConvolutionParamInitializer.isWeightParam(Layer layer, String key)
boolean	FrozenLayerParamInitializer.isWeightParam(Layer layer, String key)
boolean	BatchNormalizationParamInitializer.isWeightParam(Layer layer, String key)
long	WrapperLayerParamInitializer.numParams(Layer layer)
long	GravesLSTMParamInitializer.numParams(Layer l)
long	SimpleRnnParamInitializer.numParams(Layer layer)
long	ElementWiseParamInitializer.numParams(Layer layer)
long	SeparableConvolutionParamInitializer.numParams(Layer l)
long	DepthwiseConvolutionParamInitializer.numParams(Layer l)
long	PReLUParamInitializer.numParams(Layer l)
long	EmptyParamInitializer.numParams(Layer layer)
long	FrozenLayerWithBackpropParamInitializer.numParams(Layer layer)
long	DefaultParamInitializer.numParams(Layer l)
long	Convolution3DParamInitializer.numParams(Layer l)
long	SameDiffParamInitializer.numParams(Layer layer)
long	BidirectionalParamInitializer.numParams(Layer layer)
long	GravesBidirectionalLSTMParamInitializer.numParams(Layer l)
long	LSTMParamInitializer.numParams(Layer l)
long	ConvolutionParamInitializer.numParams(Layer l)
long	FrozenLayerParamInitializer.numParams(Layer layer)
long	BatchNormalizationParamInitializer.numParams(Layer l)
List<String>	WrapperLayerParamInitializer.paramKeys(Layer layer)
List<String>	GravesLSTMParamInitializer.paramKeys(Layer layer)
List<String>	SimpleRnnParamInitializer.paramKeys(Layer layer)
List<String>	SeparableConvolutionParamInitializer.paramKeys(Layer layer)
List<String>	DepthwiseConvolutionParamInitializer.paramKeys(Layer layer)
List<String>	PReLUParamInitializer.paramKeys(Layer layer)
List<String>	EmptyParamInitializer.paramKeys(Layer layer)
List<String>	FrozenLayerWithBackpropParamInitializer.paramKeys(Layer layer)
List<String>	DefaultParamInitializer.paramKeys(Layer layer)
List<String>	SameDiffParamInitializer.paramKeys(Layer layer)
List<String>	VariationalAutoencoderParamInitializer.paramKeys(Layer l)
List<String>	BidirectionalParamInitializer.paramKeys(Layer layer)
List<String>	GravesBidirectionalLSTMParamInitializer.paramKeys(Layer layer)
List<String>	LSTMParamInitializer.paramKeys(Layer layer)
List<String>	ConvolutionParamInitializer.paramKeys(Layer layer)
List<String>	FrozenLayerParamInitializer.paramKeys(Layer layer)
List<String>	BatchNormalizationParamInitializer.paramKeys(Layer layer)
List<String>	WrapperLayerParamInitializer.weightKeys(Layer layer)
List<String>	GravesLSTMParamInitializer.weightKeys(Layer layer)
List<String>	SimpleRnnParamInitializer.weightKeys(Layer layer)
List<String>	SeparableConvolutionParamInitializer.weightKeys(Layer layer)
List<String>	DepthwiseConvolutionParamInitializer.weightKeys(Layer layer)
List<String>	PReLUParamInitializer.weightKeys(Layer layer)
List<String>	EmptyParamInitializer.weightKeys(Layer layer)
List<String>	FrozenLayerWithBackpropParamInitializer.weightKeys(Layer layer)
List<String>	DefaultParamInitializer.weightKeys(Layer layer)
List<String>	SameDiffParamInitializer.weightKeys(Layer layer)
List<String>	VariationalAutoencoderParamInitializer.weightKeys(Layer layer)
List<String>	BidirectionalParamInitializer.weightKeys(Layer layer)
List<String>	GravesBidirectionalLSTMParamInitializer.weightKeys(Layer layer)
List<String>	LSTMParamInitializer.weightKeys(Layer layer)
List<String>	ConvolutionParamInitializer.weightKeys(Layer layer)
List<String>	FrozenLayerParamInitializer.weightKeys(Layer layer)
List<String>	BatchNormalizationParamInitializer.weightKeys(Layer layer)

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

Uses of Layer in org.deeplearning4j.util

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

Modifier and Type	Method and Description
static void	OutputLayerUtil.validateOutputLayer(String layerName, Layer layer) Validate the output layer (or loss layer) configuration, to detect invalid consfiugrations.