Package org.deeplearning4j.nn.conf.layers

Class Summary

Class	Description
AbstractLSTM
AbstractLSTM.Builder<T extends AbstractLSTM.Builder<T>>
ActivationLayer
ActivationLayer.Builder
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>>
BaseUpsamplingLayer	Upsampling base layer
BaseUpsamplingLayer.UpsamplingBuilder<T extends BaseUpsamplingLayer.UpsamplingBuilder<T>>
BatchNormalization
BatchNormalization.Builder
CapsuleLayer
CapsuleLayer.Builder
CapsuleStrengthLayer
CapsuleStrengthLayer.Builder
CenterLossOutputLayer
CenterLossOutputLayer.Builder
Cnn3DLossLayer
Cnn3DLossLayer.Builder
CnnLossLayer
CnnLossLayer.Builder
Convolution1D
Convolution1DLayer
Convolution1DLayer.Builder
Convolution2D
Convolution3D
Convolution3D.Builder
ConvolutionLayer
ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>>
ConvolutionLayer.Builder
Deconvolution2D
Deconvolution2D.Builder
Deconvolution3D
Deconvolution3D.Builder
DenseLayer
DenseLayer.Builder
DepthwiseConvolution2D
DepthwiseConvolution2D.Builder
DropoutLayer
DropoutLayer.Builder
EmbeddingLayer
EmbeddingLayer.Builder
EmbeddingSequenceLayer
EmbeddingSequenceLayer.Builder
FeedForwardLayer
FeedForwardLayer.Builder<T extends FeedForwardLayer.Builder<T>>
GlobalPoolingLayer
GlobalPoolingLayer.Builder
GravesBidirectionalLSTM	Deprecated
GravesBidirectionalLSTM.Builder
GravesLSTM	Deprecated
GravesLSTM.Builder
InputTypeUtil
Layer	A neural network layer.
Layer.Builder<T extends Layer.Builder<T>>
LayerValidation
LearnedSelfAttentionLayer
LearnedSelfAttentionLayer.Builder
LocallyConnected1D
LocallyConnected1D.Builder
LocallyConnected2D
LocallyConnected2D.Builder
LocalResponseNormalization
LocalResponseNormalization.Builder
LossLayer
LossLayer.Builder
LSTM
LSTM.Builder
NoParamLayer
OutputLayer
OutputLayer.Builder
Pooling1D	1D Pooling (subsampling) layer.
Pooling2D	2D Pooling (subsampling) layer.
PReLULayer
PReLULayer.Builder
PrimaryCapsules
PrimaryCapsules.Builder
RecurrentAttentionLayer
RecurrentAttentionLayer.Builder
RnnLossLayer
RnnLossLayer.Builder
RnnOutputLayer
RnnOutputLayer.Builder
SelfAttentionLayer
SelfAttentionLayer.Builder
SeparableConvolution2D
SeparableConvolution2D.Builder
SpaceToBatchLayer
SpaceToBatchLayer.Builder<T extends SpaceToBatchLayer.Builder<T>>
SpaceToDepthLayer
SpaceToDepthLayer.Builder<T extends SpaceToDepthLayer.Builder<T>>
Subsampling1DLayer
Subsampling1DLayer.Builder
Subsampling3DLayer
Subsampling3DLayer.BaseSubsamplingBuilder<T extends Subsampling3DLayer.BaseSubsamplingBuilder<T>>
Subsampling3DLayer.Builder
SubsamplingLayer
SubsamplingLayer.BaseSubsamplingBuilder<T extends SubsamplingLayer.BaseSubsamplingBuilder<T>>
SubsamplingLayer.Builder
Upsampling1D
Upsampling1D.Builder
Upsampling2D
Upsampling2D.Builder
Upsampling3D
Upsampling3D.Builder
ZeroPadding1DLayer
ZeroPadding1DLayer.Builder
ZeroPadding3DLayer
ZeroPadding3DLayer.Builder
ZeroPaddingLayer
ZeroPaddingLayer.Builder

Enum Summary

Enum	Description
Convolution3D.DataFormat	An optional dataFormat: "NDHWC" or "NCDHW".
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	Pooling type: MAX: Max pooling - output is the maximum value of the input values AVG: Average pooling - output is the average value of the input values SUM: Sum pooling - output is the sum of the input values PNORM: P-norm pooling
SpaceToDepthLayer.DataFormat	Deprecated Use CNN2DFormat instead
Subsampling3DLayer.PoolingType
SubsamplingLayer.PoolingType