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

Packages that use AbstractLayer

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.pooling
org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.layers.training

Uses of AbstractLayer in org.deeplearning4j.nn.layers

Subclasses of AbstractLayer 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	BaseLayer<LayerConfT extends BaseLayer> A layer with parameters
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	DropoutLayer Created by davekale on 12/7/16.
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 AbstractLayer in org.deeplearning4j.nn.layers.convolution

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

Modifier and Type	Class and Description
class	Convolution1DLayer 1D (temporal) convolutional layer.
class	ConvolutionLayer Convolution layer
class	ZeroPaddingLayer Zero padding layer for convolutional neural networks.

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

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

Modifier and Type	Class and Description
class	Subsampling1DLayer 1D (temporal) subsampling layer.
class	SubsamplingLayer Subsampling layer.

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

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

Modifier and Type	Class and Description
class	AutoEncoder Autoencoder.

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

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

Modifier and Type	Class and Description
class	DenseLayer

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

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

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

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

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

Subclasses of AbstractLayer 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 AbstractLayer in org.deeplearning4j.nn.layers.pooling

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.pooling

Modifier and Type	Class and Description
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.

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

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

Modifier and Type	Class and Description
class	BaseRecurrentLayer<LayerConfT extends BaseLayer>
class	GravesBidirectionalLSTM RNN tutorial: http://deeplearning4j.org/usingrnns.html READ THIS FIRST Bdirectional LSTM layer implementation.
class	GravesLSTM LSTM layer implementation.
class	LSTM 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.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.training

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.training

Modifier and Type	Class and Description
class	CenterLossOutputLayer Center loss is similar to triplet loss except that it enforces intraclass consistency and doesn't require feed forward of multiple examples.