Modifier and Type  Class and Description 

class 
BaseOutputLayer<LayerConfT extends BaseOutputLayer>
Output layer with different objective
in cooccurrences for different objectives.

class 
BasePretrainNetwork<LayerConfT extends BasePretrainNetwork>
Baseline class for any Neural Network used
as a layer in a deep network *

class 
DropoutLayer
Created by davekale on 12/7/16.

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

class 
OutputLayer
Output layer with different objective
incooccurrences for different objectives.

Modifier and Type  Class and Description 

class 
Cnn3DLossLayer
3D Convolutional Neural Network Loss Layer.

class 
CnnLossLayer
Convolutional Neural Network Loss Layer.

class 
Convolution1DLayer
1D (temporal) convolutional layer.

class 
Convolution3DLayer
3D convolution layer implementation.

class 
ConvolutionLayer
Convolution layer

class 
Deconvolution2DLayer
2D deconvolution layer implementation.

class 
Deconvolution3DLayer
3D deconvolution layer implementation.

class 
DepthwiseConvolution2DLayer
2D depthwise convolution layer configuration.

class 
SeparableConvolution2DLayer
2D Separable convolution layer implementation
Separable convolutions split a regular convolution operation into two
simpler operations, which are usually computationally more efficient.

Modifier and Type  Class and Description 

class 
PReLU
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.

Modifier and Type  Class and Description 

class 
AutoEncoder
Autoencoder.

Modifier and Type  Class and Description 

class 
DenseLayer 
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 
Modifier and Type  Class and Description 

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.

Modifier and Type  Class and Description 

class 
BatchNormalization
Batch normalization layer.

Modifier and Type  Class and Description 

class 
OCNNOutputLayer
Layer implementation for
OCNNOutputLayer
See OCNNOutputLayer
for details. 
Modifier and Type  Class and Description 

class 
BaseRecurrentLayer<LayerConfT extends BaseRecurrentLayer> 
class 
GravesBidirectionalLSTM
RNN tutorial: https://deeplearning4j.konduit.ai/models/recurrent
READ THIS FIRST
Bdirectional LSTM layer implementation.

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 
LSTM
LSTM layer implementation.

class 
RnnLossLayer
Recurrent Neural Network Loss Layer.

class 
RnnOutputLayer
Recurrent Neural Network Output Layer.

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

Modifier and Type  Class and Description 

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

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