| 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>> | |
| BaseUpsamplingLayer |
Upsampling base layer
|
| BaseUpsamplingLayer.UpsamplingBuilder<T extends BaseUpsamplingLayer.UpsamplingBuilder<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 | |
| CnnLossLayer |
Convolutional Neural Network Loss Layer.
Handles calculation of gradients etc for various objective functions. NOTE: CnnLossLayer does not have any parameters. |
| CnnLossLayer.Builder | |
| Convolution1D |
1D convolution layer
|
| Convolution1DLayer |
1D (temporal) convolutional layer.
|
| Convolution1DLayer.Builder | |
| Convolution2D |
2D convolution layer
|
| Convolution3D |
3D convolution layer configuration
|
| Convolution3D.Builder | |
| ConvolutionLayer | |
| ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>> | |
| ConvolutionLayer.Builder | |
| Deconvolution2D |
2D deconvolution layer configuration
Deconvolutions are also known as transpose convolutions or fractionally strided convolutions.
|
| Deconvolution2D.Builder | |
| DenseLayer |
Dense layer: fully connected feed forward layer trainable by backprop.
|
| DenseLayer.Builder | |
| DepthwiseConvolution2D |
2D depth-wise convolution layer configuration.
|
| DepthwiseConvolution2D.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 | |
| 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<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, PNORMGlobal pooling layer can also handle mask arrays when dealing with variable length inputs. |
| GlobalPoolingLayer.Builder | |
| GravesBidirectionalLSTM | Deprecated
use
Bidirectional instead. |
| 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 | |
| NoParamLayer | |
| OutputLayer |
Output layer with different objective co-occurrences for different objectives.
|
| OutputLayer.Builder | |
| Pooling1D |
1D Pooling layer.
|
| Pooling2D |
2D Pooling layer.
|
| RnnLossLayer |
Recurrent Neural Network Loss Layer.
Handles calculation of gradients etc for various objective functions. NOTE: Unlike RnnOutputLayer this RnnLossLayer does not have any parameters - i.e., there is no time
distributed dense component here. |
| RnnLossLayer.Builder | |
| RnnOutputLayer | |
| RnnOutputLayer.Builder | |
| SeparableConvolution2D |
2D Separable convolution layer configuration.
|
| SeparableConvolution2D.Builder | |
| SpaceToBatchLayer |
Space to batch utility layer configuration for convolutional input types.
|
| SpaceToBatchLayer.Builder<T extends SpaceToBatchLayer.Builder<T>> | |
| SpaceToDepthLayer |
Space to channels utility layer configuration for convolutional input types.
|
| SpaceToDepthLayer.Builder<T extends SpaceToDepthLayer.Builder<T>> | |
| Subsampling1DLayer |
1D (temporal) subsampling layer.
|
| Subsampling1DLayer.Builder | |
| Subsampling3DLayer |
3D subsampling / pooling layer for convolutional neural networks
|
| Subsampling3DLayer.BaseSubsamplingBuilder<T extends Subsampling3DLayer.BaseSubsamplingBuilder<T>> | |
| Subsampling3DLayer.Builder | |
| SubsamplingLayer |
Subsampling layer also referred to as pooling in convolution neural nets
Supports the following pooling types: MAX, AVG, SUM, PNORM, NONE
|
| SubsamplingLayer.BaseSubsamplingBuilder<T extends SubsamplingLayer.BaseSubsamplingBuilder<T>> | |
| SubsamplingLayer.Builder | |
| Upsampling1D |
Upsampling 1D layer
|
| Upsampling1D.Builder | |
| Upsampling2D |
Upsampling 2D layer
|
| Upsampling2D.Builder | |
| Upsampling3D |
Upsampling 3D layer
|
| Upsampling3D.Builder | |
| ZeroPadding1DLayer |
Zero padding 1D layer for convolutional neural networks.
|
| ZeroPadding1DLayer.Builder | |
| ZeroPadding3DLayer |
Zero padding 3D layer for convolutional neural networks.
|
| ZeroPadding3DLayer.Builder | |
| ZeroPaddingLayer |
Zero padding layer for convolutional neural networks.
|
| ZeroPaddingLayer.Builder |
| Enum | Description |
|---|---|
| Convolution3D.DataFormat | |
| 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.
|
| SpaceToDepthLayer.DataFormat | |
| Subsampling3DLayer.PoolingType | |
| SubsamplingLayer.PoolingType |
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