Package org.deeplearning4j.nn.conf.layers

Class Summary

Class	Description
AbstractLSTM	LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
AbstractLSTM.Builder<T extends AbstractLSTM.Builder<T>>
ActivationLayer
ActivationLayer.Builder
AutoEncoder	Autoencoder.
AutoEncoder.Builder
BaseLayer	A neural network layer.
BaseLayer.Builder<T extends BaseLayer.Builder<T>>
BaseOutputLayer
BaseOutputLayer.Builder<T extends BaseOutputLayer.Builder<T>>
BasePretrainNetwork
BasePretrainNetwork.Builder<T extends BasePretrainNetwork.Builder<T>>
BaseRecurrentLayer
BaseRecurrentLayer.Builder<T extends BaseRecurrentLayer.Builder<T>>
BatchNormalization	Batch normalization configuration
BatchNormalization.Builder
CenterLossOutputLayer	Center loss is similar to triplet loss except that it enforces intraclass consistency and doesn't require feed forward of multiple examples.
CenterLossOutputLayer.Builder
Convolution1DLayer	1D (temporal) convolutional layer.
Convolution1DLayer.Builder
ConvolutionLayer
ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>>
ConvolutionLayer.Builder
DenseLayer	Dense layer: fully connected feed forward layer trainable by backprop.
DenseLayer.Builder
DropoutLayer
DropoutLayer.Builder
EmbeddingLayer	Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to numClass-1) as input.
EmbeddingLayer.Builder
FeedForwardLayer	Created by jeffreytang on 7/21/15.
FeedForwardLayer.Builder<T extends FeedForwardLayer.Builder<T>>
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.
GlobalPoolingLayer.Builder
GravesBidirectionalLSTM	LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
GravesBidirectionalLSTM.Builder
GravesLSTM	LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
GravesLSTM.Builder
InputTypeUtil	Utilities for calculating input types
Layer	A neural network layer.
Layer.Builder<T extends Layer.Builder<T>>
LayerValidation	Created by Alex on 22/02/2017.
LocalResponseNormalization	Created by nyghtowl on 10/29/15.
LocalResponseNormalization.Builder
LossLayer	LossLayer is a flexible output "layer" that performs a loss function on an input without MLP logic.
LossLayer.Builder
LSTM	LSTM recurrent net without peephole connections.
LSTM.Builder
OutputLayer	Output layer with different objective co-occurrences for different objectives.
OutputLayer.Builder
RBM	Restricted Boltzmann Machine.
RBM.Builder
RnnOutputLayer
RnnOutputLayer.Builder
Subsampling1DLayer	1D (temporal) subsampling layer.
Subsampling1DLayer.Builder
SubsamplingLayer	Subsampling layer also referred to as pooling in convolution neural nets Supports the following pooling types: MAX AVG NON
SubsamplingLayer.BaseSubsamplingBuilder<T extends SubsamplingLayer.BaseSubsamplingBuilder<T>>
SubsamplingLayer.Builder
ZeroPaddingLayer	Zero padding layer for convolutional neural networks.
ZeroPaddingLayer.Builder

Enum Summary

Enum	Description
ConvolutionLayer.AlgoMode	The "PREFER_FASTEST" mode will pick the fastest algorithm for the specified parameters from the ConvolutionLayer.FwdAlgo, ConvolutionLayer.BwdFilterAlgo, and ConvolutionLayer.BwdDataAlgo lists, but they may be very memory intensive, so if weird errors occur when using cuDNN, please try the "NO_WORKSPACE" mode.
ConvolutionLayer.BwdDataAlgo	The backward data algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".
ConvolutionLayer.BwdFilterAlgo	The backward filter algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".
ConvolutionLayer.FwdAlgo	The forward algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".
PoolingType	Created by Alex on 17/01/2017.
RBM.HiddenUnit
RBM.VisibleUnit
SubsamplingLayer.PoolingType