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

Packages that use Layer

Package	Description
org.deeplearning4j.nn.conf
org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.setup
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.recurrent

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	ActivationLayer
class	AutoEncoder Autoencoder.
class	BaseOutputLayer
class	BasePretrainNetwork
class	BaseRecurrentLayer
class	BatchNormalization Batch normalization configuration
class	ConvolutionLayer
class	DenseLayer Dense layer: fully connected feed forward layer trainable by backprop.
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	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	OutputLayer Output layer with different objective co-occurrences for different objectives.
class	RBM Restricted Boltzmann Machine.
class	RnnOutputLayer
class	SubsamplingLayer Subsampling layer also referred to as pooling in convolution neural nets Supports the following pooling types: MAX AVG NON

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()

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

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

Modifier and Type	Method and Description
Layer	ConvolutionLayerSetup.getLayer(int i, MultiLayerConfiguration.Builder builder) Deprecated.

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	BaseLayer<LayerConfT extends Layer> A layer with a bias and activation function

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

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

Modifier and Type	Class and Description
class	BaseRecurrentLayer<LayerConfT extends Layer>