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

Packages that use BaseLayer

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

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

Subclasses of BaseLayer 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	AutoEncoder Autoencoder.
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	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	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

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

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

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

Modifier and Type	Method and Description
static void	LayerValidation.updaterValidation(String layerName, BaseLayer 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 BaseLayer in org.deeplearning4j.nn.conf.layers.variational

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

Modifier and Type	Class and Description
class	VariationalAutoencoder Variational Autoencoder layer

Uses of BaseLayer in org.deeplearning4j.nn.layers

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

Modifier and Type	Class and Description
class	BaseLayer<LayerConfT extends BaseLayer> A layer with parameters

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

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

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