Uses of Package
org.deeplearning4j.nn.conf.layers

Packages that use org.deeplearning4j.nn.conf.layers

Package	Description
org.deeplearning4j.nn.api
org.deeplearning4j.nn.conf
org.deeplearning4j.nn.conf.inputs
org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.convolutional
org.deeplearning4j.nn.conf.layers.misc
org.deeplearning4j.nn.conf.layers.objdetect
org.deeplearning4j.nn.conf.layers.recurrent
org.deeplearning4j.nn.conf.layers.samediff
org.deeplearning4j.nn.conf.layers.util
org.deeplearning4j.nn.conf.layers.variational
org.deeplearning4j.nn.conf.layers.wrapper
org.deeplearning4j.nn.conf.ocnn
org.deeplearning4j.nn.conf.preprocessor
org.deeplearning4j.nn.conf.serde
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.convolution
org.deeplearning4j.nn.layers.convolution.subsampling
org.deeplearning4j.nn.layers.mkldnn
org.deeplearning4j.nn.layers.ocnn
org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.layers.util
org.deeplearning4j.nn.params
org.deeplearning4j.nn.transferlearning
org.deeplearning4j.util

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.api

Class and Description
Layer A neural network layer.

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf

Class and 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.
Layer A neural network layer.

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.inputs

Class and Description
Convolution3D.DataFormat An optional dataFormat: "NDHWC" or "NCDHW".

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.layers

Class and 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
ActivationLayer Activation layer is a simple layer that applies the specified activation function to the input activations
ActivationLayer.Builder
AutoEncoder Autoencoder layer.
AutoEncoder.Builder
BaseLayer A neural network layer.
BaseLayer.Builder
BaseOutputLayer
BaseOutputLayer.Builder
BasePretrainNetwork
BasePretrainNetwork.Builder
BaseRecurrentLayer
BaseRecurrentLayer.Builder
BaseUpsamplingLayer Upsampling base layer
BaseUpsamplingLayer.UpsamplingBuilder
BatchNormalization Batch normalization layer See: Ioffe and Szegedy, 2014, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift https://arxiv.org/abs/1502.03167
BatchNormalization.Builder
CapsuleLayer.Builder
CapsuleStrengthLayer.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
Cnn3DLossLayer 3D Convolutional Neural Network Loss Layer. Handles calculation of gradients etc for various loss (objective) functions. NOTE: Cnn3DLossLayer does not have any parameters.
Cnn3DLossLayer.Builder
CnnLossLayer Convolutional Neural Network Loss Layer. Handles calculation of gradients etc for various loss (objective) functions. NOTE: CnnLossLayer does not have any parameters.
CnnLossLayer.Builder
Convolution1DLayer 1D (temporal) convolutional layer.
Convolution1DLayer.Builder
Convolution3D 3D convolution layer configuration
Convolution3D.Builder
Convolution3D.DataFormat An optional dataFormat: "NDHWC" or "NCDHW".
ConvolutionLayer 2D Convolution layer (for example, spatial convolution over images).
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.BaseConvBuilder
ConvolutionLayer.Builder
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".
Deconvolution2D 2D deconvolution layer configuration Deconvolutions are also known as transpose convolutions or fractionally strided convolutions.
Deconvolution2D.Builder
DenseLayer Dense layer: a standard fully connected feed forward layer
DenseLayer.Builder
DepthwiseConvolution2D 2D depth-wise convolution layer configuration.
DepthwiseConvolution2D.Builder
DropoutLayer Dropout layer.
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
EmbeddingSequenceLayer Embedding layer for sequences: feed-forward layer that expects fixed-length number (inputLength) of integers/indices per example as input, ranged from 0 to numClasses - 1.
EmbeddingSequenceLayer.Builder
FeedForwardLayer Created by jeffreytang on 7/21/15.
FeedForwardLayer.Builder
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 Deprecated. use Bidirectional instead. With the Bidirectional layer wrapper you can make any recurrent layer bidirectional, in particular GravesLSTM. Note that this layer adds the output of both directions, which translates into "ADD" mode in Bidirectional. Usage: .layer(new Bidirectional(Bidirectional.Mode.ADD, new GravesLSTM.Builder()....build()))
GravesBidirectionalLSTM.Builder Deprecated.
GravesLSTM Deprecated. Will be eventually removed. Use LSTM instead, which has similar prediction accuracy, but supports CuDNN for faster network training on CUDA (Nvidia) GPUs
Layer A neural network layer.
Layer.Builder
LearnedSelfAttentionLayer Implements Dot Product Self Attention with learned queries Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using learned queries.
LearnedSelfAttentionLayer.Builder
LocallyConnected1D SameDiff version of a 1D locally connected layer.
LocallyConnected1D.Builder
LocallyConnected2D SameDiff version of a 2D locally connected layer.
LocallyConnected2D.Builder
LocalResponseNormalization Local response normalization layer See section 3.3 of http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
LocalResponseNormalization.Builder
LossLayer LossLayer is a flexible output layer that performs a loss function on an input without MLP logic. LossLayer is similar to OutputLayer in that both perform loss calculations for network outputs vs.
LossLayer.Builder
LSTM LSTM recurrent neural network layer without peephole connections.
NoParamLayer
OutputLayer Output layer used for training via backpropagation based on labels and a specified loss function.
OutputLayer.Builder
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
PReLULayer Parametrized Rectified Linear Unit (PReLU)
PReLULayer.Builder
PrimaryCapsules.Builder
RecurrentAttentionLayer Implements Recurrent Dot Product Attention Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using the hidden state as the query and all time steps as keys/values.
RecurrentAttentionLayer.Builder
RnnLossLayer Recurrent Neural Network Loss Layer. Handles calculation of gradients etc for various objective (loss) functions. Note: Unlike RnnOutputLayer this RnnLossLayer does not have any parameters - i.e., there is no time distributed dense component here.
RnnLossLayer.Builder
RnnOutputLayer A version of OutputLayer for recurrent neural networks.
SelfAttentionLayer Implements Dot Product Self Attention Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using each timestep as the query.
SelfAttentionLayer.Builder
SeparableConvolution2D 2D Separable convolution layer configuration.
SeparableConvolution2D.Builder
SpaceToBatchLayer Space to batch utility layer configuration for convolutional input types.
SpaceToBatchLayer.Builder
SpaceToDepthLayer Space to channels utility layer configuration for convolutional input types.
SpaceToDepthLayer.Builder
SpaceToDepthLayer.DataFormat
Subsampling1DLayer 1D (temporal) subsampling layer - also known as pooling layer. Expects input of shape [minibatch, nIn, sequenceLength].
Subsampling1DLayer.Builder
Subsampling3DLayer 3D subsampling / pooling layer for convolutional neural networks
Subsampling3DLayer.BaseSubsamplingBuilder
Subsampling3DLayer.Builder
Subsampling3DLayer.PoolingType
SubsamplingLayer Subsampling layer also referred to as pooling in convolution neural nets Supports the following pooling types: MAX, AVG, SUM, PNORM
SubsamplingLayer.BaseSubsamplingBuilder
SubsamplingLayer.Builder
SubsamplingLayer.PoolingType
Upsampling1D Upsampling 1D layer Repeats each step size times along the temporal/sequence axis (dimension 2) For input shape [minibatch, channels, sequenceLength] output has shape [minibatch, channels, size * sequenceLength] Example:
Upsampling1D.Builder
Upsampling2D Upsampling 2D layer Repeats each value (or rather, set of depth values) in the height and width dimensions by size[0] and size[1] times respectively. If input has shape [minibatch, channels, height, width] then output has shape [minibatch, channels, height*size[0], width*size[1]] Example:
Upsampling2D.Builder
Upsampling3D Upsampling 3D layer Repeats each value (all channel values for each x/y/z location) by size[0], size[1] and size[2] If input has shape [minibatch, channels, depth, height, width] then output has shape [minibatch, channels, size[0] * depth, size[1] * height, size[2] * width]
Upsampling3D.Builder
ZeroPadding1DLayer Zero padding 1D layer for convolutional neural networks.
ZeroPadding3DLayer Zero padding 3D layer for convolutional neural networks.
ZeroPaddingLayer Zero padding layer for convolutional neural networks (2D CNNs).

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.layers.convolutional

Class and Description
Layer A neural network layer.
Layer.Builder
NoParamLayer

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.layers.misc

Class and Description
BaseLayer A neural network layer.
BaseLayer.Builder
FeedForwardLayer Created by jeffreytang on 7/21/15.
FeedForwardLayer.Builder
Layer A neural network layer.
Layer.Builder

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.layers.objdetect

Class and Description
Layer A neural network layer.
Layer.Builder

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.layers.recurrent

Class and Description
BaseLayer A neural network layer.
BaseLayer.Builder
BaseRecurrentLayer
BaseRecurrentLayer.Builder
FeedForwardLayer Created by jeffreytang on 7/21/15.
FeedForwardLayer.Builder
Layer A neural network layer.
Layer.Builder

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.layers.samediff

Class and Description
Layer A neural network layer.
Layer.Builder

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.layers.util

Class and Description
Layer A neural network layer.
Layer.Builder
NoParamLayer

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.layers.variational

Class and Description
BaseLayer A neural network layer.
BaseLayer.Builder
BasePretrainNetwork
BasePretrainNetwork.Builder
FeedForwardLayer Created by jeffreytang on 7/21/15.
FeedForwardLayer.Builder
Layer A neural network layer.
Layer.Builder

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.layers.wrapper

Class and Description
Layer A neural network layer.
Layer.Builder

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.ocnn

Class and Description
BaseLayer A neural network layer.
BaseLayer.Builder
BaseOutputLayer
BaseOutputLayer.Builder
FeedForwardLayer Created by jeffreytang on 7/21/15.
FeedForwardLayer.Builder
Layer A neural network layer.
Layer.Builder

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.preprocessor

Class and Description
Convolution3D.DataFormat An optional dataFormat: "NDHWC" or "NCDHW".

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.conf.serde

Class and Description
BaseLayer A neural network layer.
BaseOutputLayer
Layer A neural network layer.

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.layers

Class and Description
BaseLayer A neural network layer.
BaseOutputLayer
BasePretrainNetwork
Layer A neural network layer.

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.layers.convolution

Class and 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".

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.layers.convolution.subsampling

Class and Description
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

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.layers.mkldnn

Class and 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 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

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.layers.ocnn

Class and Description
Layer A neural network layer.

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.layers.recurrent

Class and Description
AbstractLSTM LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
FeedForwardLayer Created by jeffreytang on 7/21/15.
GravesBidirectionalLSTM Deprecated. use Bidirectional instead. With the Bidirectional layer wrapper you can make any recurrent layer bidirectional, in particular GravesLSTM. Note that this layer adds the output of both directions, which translates into "ADD" mode in Bidirectional. Usage: .layer(new Bidirectional(Bidirectional.Mode.ADD, new GravesLSTM.Builder()....build()))

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.layers.util

Class and Description
Layer A neural network layer.

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.params

Class and Description
Layer A neural network layer.

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.nn.transferlearning

Class and 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.
Layer A neural network layer.

Classes in org.deeplearning4j.nn.conf.layers used by org.deeplearning4j.util

Class and Description
Convolution3D.DataFormat An optional dataFormat: "NDHWC" or "NCDHW".
Layer A neural network layer.
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