Modifier and Type  Class and Description 

class 
BaseLayerSpace<L extends BaseLayer>
BaseLayerSpace contains the common Layer hyperparameters; should match
BaseLayer in terms of features 
Modifier and Type  Class and Description 

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

class 
AutoEncoder
Autoencoder layer.

class 
BaseOutputLayer 
class 
BasePretrainNetwork 
class 
BaseRecurrentLayer 
class 
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 
class 
CenterLossOutputLayer
Center loss is similar to triplet loss except that it enforces intraclass consistency and doesn't require feed
forward of multiple examples.

class 
Cnn3DLossLayer
3D Convolutional Neural Network Loss Layer.

class 
CnnLossLayer
Convolutional Neural Network Loss Layer.

class 
Convolution1D
1D convolution layer.

class 
Convolution1DLayer
1D (temporal) convolutional layer.

class 
Convolution2D
2D convolution layer

class 
Convolution3D
3D convolution layer configuration

class 
ConvolutionLayer
2D Convolution layer (for example, spatial convolution over images).

class 
Deconvolution2D
2D deconvolution layer configuration
Deconvolutions are also known as transpose convolutions or fractionally strided convolutions. 
class 
DenseLayer
Dense layer: a standard fully connected feed forward layer

class 
DepthwiseConvolution2D
2D depthwise convolution layer configuration.

class 
DropoutLayer
Dropout layer.

class 
EmbeddingLayer
Embedding layer: feedforward layer that expects single integers per example as input (class numbers, in range 0 to
numClass1) as input.

class 
EmbeddingSequenceLayer
Embedding layer for sequences: feedforward layer that expects fixedlength number (inputLength) of integers/indices
per example as input, ranged from 0 to numClasses  1.

class 
FeedForwardLayer
Created by jeffreytang on 7/21/15.

class 
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())) 
class 
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 
class 
LossLayer
LossLayer is a flexible output layer that performs a loss function on an input without MLP logic.

class 
LSTM
LSTM recurrent neural network layer without peephole connections.

class 
OutputLayer
Output layer used for training via backpropagation based on labels and a specified loss function.

class 
PReLULayer
Parametrized Rectified Linear Unit (PReLU)
f(x) = alpha * x for x < 0, f(x) = x for x >= 0
alpha has the same shape as x and is a learned parameter. 
class 
RnnLossLayer
Recurrent Neural Network Loss Layer.

class 
RnnOutputLayer
A version of
OutputLayer for recurrent neural networks. 
class 
SeparableConvolution2D
2D Separable convolution layer configuration.

Modifier and Type  Method and Description 

BaseLayer 
BaseLayer.clone() 
Modifier and Type  Class and Description 

class 
ElementWiseMultiplicationLayer
Elementwise multiplication layer with weights: implements
out = activationFn(input .* w + b) where: w is a learnable weight vector of length nOut  ".*" is elementwise multiplication  b is a bias vector Note that the input and output sizes of the elementwise layer are the same for this layer created by jingshu 
class 
RepeatVector
RepeatVector layer configuration.

Modifier and Type  Class and Description 

class 
SimpleRnn
Simple RNN  aka "vanilla" RNN is the simplest type of recurrent neural network layer.

Modifier and Type  Class and Description 

class 
VariationalAutoencoder
Variational Autoencoder layer
See: Kingma & Welling, 2013: AutoEncoding Variational Bayes  https://arxiv.org/abs/1312.6114
This implementation allows multiple encoder and decoder layers, the number and sizes of which can be set
independently.

Modifier and Type  Class and Description 

class 
OCNNOutputLayer
An implementation of one class neural networks from:
https://arxiv.org/pdf/1802.06360.pdf
The one class neural network approach is an extension of the standard output layer with a single set of weights, an
activation function, and a bias to: 2 sets of weights, a learnable "r" parameter that is held static 1 traditional
set of weights. 1 additional weight matrix

Modifier and Type  Method and Description 

protected void 
BaseNetConfigDeserializer.handleActivationBackwardCompatibility(BaseLayer baseLayer,
org.nd4j.shade.jackson.databind.node.ObjectNode on) 
protected void 
BaseNetConfigDeserializer.handleL1L2BackwardCompatibility(BaseLayer baseLayer,
org.nd4j.shade.jackson.databind.node.ObjectNode on) 
protected void 
BaseNetConfigDeserializer.handleUpdaterBackwardCompatibility(BaseLayer layer,
org.nd4j.shade.jackson.databind.node.ObjectNode on) 
protected void 
BaseNetConfigDeserializer.handleWeightInitBackwardCompatibility(BaseLayer baseLayer,
org.nd4j.shade.jackson.databind.node.ObjectNode on) 
Modifier and Type  Class and Description 

class 
BaseLayer<LayerConfT extends BaseLayer>
A layer with parameters

Modifier and Type  Class and Description 

class 
BaseRecurrentLayer<LayerConfT extends BaseLayer> 
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