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

Packages that use BaseLayer

Package	Description
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

Uses of BaseLayer in org.deeplearning4j.nn.layers

Subclasses of BaseLayer in org.deeplearning4j.nn.layers

Modifier and Type	Class and Description
class	ActivationLayer Activation Layer Used to apply activation on input and corresponding derivative on epsilon.
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	LossLayer LossLayer is a flexible output "layer" that performs a loss function on an input without MLP logic.
class	OutputLayer Output layer with different objective incooccurrences for different objectives.

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

Subclasses of BaseLayer in org.deeplearning4j.nn.layers.convolution

Modifier and Type	Class and Description
class	ConvolutionLayer Convolution layer

Uses of BaseLayer in org.deeplearning4j.nn.layers.convolution.subsampling

Subclasses of BaseLayer in org.deeplearning4j.nn.layers.convolution.subsampling

Modifier and Type	Class and Description
class	SubsamplingLayer Subsampling layer.

Uses of BaseLayer in org.deeplearning4j.nn.layers.feedforward.autoencoder

Subclasses of BaseLayer in org.deeplearning4j.nn.layers.feedforward.autoencoder

Modifier and Type	Class and Description
class	AutoEncoder Autoencoder.

Uses of BaseLayer in org.deeplearning4j.nn.layers.feedforward.dense

Subclasses of BaseLayer in org.deeplearning4j.nn.layers.feedforward.dense

Modifier and Type	Class and Description
class	DenseLayer

Uses of BaseLayer in org.deeplearning4j.nn.layers.feedforward.embedding

Subclasses of BaseLayer in org.deeplearning4j.nn.layers.feedforward.embedding

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 BaseLayer in org.deeplearning4j.nn.layers.feedforward.rbm

Subclasses of BaseLayer in org.deeplearning4j.nn.layers.feedforward.rbm

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

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

Subclasses of BaseLayer in org.deeplearning4j.nn.layers.normalization

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

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

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

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.