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.layers
org.deeplearning4j.nn.conf.layers.misc
org.deeplearning4j.nn.conf.layers.variational
org.deeplearning4j.nn.conf.serde
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.convolution
org.deeplearning4j.nn.layers.convolution.subsampling
org.deeplearning4j.nn.layers.recurrent
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
Layer A neural network layer.

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
ActivationLayer.Builder
AutoEncoder Autoencoder.
AutoEncoder.Builder
BaseLayer A neural network layer.
BaseLayer.Builder
BaseOutputLayer
BaseOutputLayer.Builder
BasePretrainNetwork
BasePretrainNetwork.Builder
BaseRecurrentLayer
BaseRecurrentLayer.Builder
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.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".
DenseLayer Dense layer: fully connected feed forward layer trainable by backprop.
DropoutLayer
EmbeddingLayer Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to numClass-1) as input.
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 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
Layer A neural network layer.
Layer.Builder
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.
OutputLayer Output layer with different objective co-occurrences for different objectives.
OutputLayer.Builder
PoolingType Created by Alex on 17/01/2017.
RBM Restricted Boltzmann Machine.
RBM.Builder
RBM.HiddenUnit
RBM.VisibleUnit
RnnOutputLayer
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
SubsamplingLayer.Builder
SubsamplingLayer.PoolingType
ZeroPaddingLayer Zero padding layer for convolutional neural networks.

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

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

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.serde

Class and Description
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 Created by Alex on 17/01/2017.

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 LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf

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
Layer A neural network layer.

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

Class and Description
PoolingType Created by Alex on 17/01/2017.
RBM.HiddenUnit
RBM.VisibleUnit