| Class | Description |
|---|---|
| ActivationLayer | |
| ActivationLayer.Builder | |
| AutoEncoder |
Autoencoder.
|
| AutoEncoder.Builder | |
| 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, PNORMGlobal 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>> | |
| LayerBuilderTest | |
| LayerConfigTest | |
| LayerConfigValidationTest | |
| 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 | |
| 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 | Description |
|---|---|
| ConvolutionLayer.AlgoMode | |
| PoolingType |
Created by Alex on 17/01/2017.
|
| RBM.HiddenUnit | |
| RBM.VisibleUnit | |
| SubsamplingLayer.PoolingType |