Uses of Class
org.deeplearning4j.nn.layers.AbstractLayer

Packages that use AbstractLayer

Package	Description
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.convolution
org.deeplearning4j.nn.layers.convolution.subsampling
org.deeplearning4j.nn.layers.convolution.upsampling
org.deeplearning4j.nn.layers.feedforward.autoencoder
org.deeplearning4j.nn.layers.feedforward.dense
org.deeplearning4j.nn.layers.feedforward.elementwise
org.deeplearning4j.nn.layers.feedforward.embedding
org.deeplearning4j.nn.layers.normalization
org.deeplearning4j.nn.layers.objdetect
org.deeplearning4j.nn.layers.ocnn
org.deeplearning4j.nn.layers.pooling
org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.layers.samediff
org.deeplearning4j.nn.layers.training
org.deeplearning4j.nn.layers.util

Uses of AbstractLayer in org.deeplearning4j.nn.layers

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers

Modifier and Type	Class and Description
class	ActivationLayer Activation Layer Used to apply activation on input and corresponding derivative on epsilon.
class	BaseLayer<LayerConfT extends BaseLayer> A layer with parameters
class	BaseOutputLayer<LayerConfT extends BaseOutputLayer> Output layer with different objective in co-occurrences 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.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.convolution

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.convolution

Modifier and Type	Class and Description
class	CnnLossLayer Convolutional Neural Network Loss Layer. Handles calculation of gradients etc for various objective functions. NOTE: CnnLossLayer does not have any parameters.
class	Convolution1DLayer 1D (temporal) convolutional layer.
class	Convolution3DLayer 3D convolution layer implementation.
class	ConvolutionLayer Convolution layer
class	Cropping1DLayer Zero cropping layer for 1D convolutional neural networks.
class	Cropping2DLayer Zero cropping layer for convolutional neural networks.
class	Cropping3DLayer Cropping layer for 3D convolutional neural networks.
class	Deconvolution2DLayer 2D deconvolution layer implementation.
class	DepthwiseConvolution2DLayer 2D depth-wise 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.
class	SpaceToBatch Space to batch utility layer for convolutional input types.
class	SpaceToDepth Space to channels utility layer for convolutional input types.
class	ZeroPadding1DLayer Zero padding 1D layer for convolutional neural networks.
class	ZeroPadding3DLayer Zero padding 3D layer for convolutional neural networks.
class	ZeroPaddingLayer Zero padding layer for convolutional neural networks.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.convolution.subsampling

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.convolution.subsampling

Modifier and Type	Class and Description
class	Subsampling1DLayer 1D (temporal) subsampling layer.
class	Subsampling3DLayer Subsampling 3D layer, used for downsampling a 3D convolution
class	SubsamplingLayer Subsampling layer.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.convolution.upsampling

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.convolution.upsampling

Modifier and Type	Class and Description
class	Upsampling1D 1D Upsampling layer.
class	Upsampling2D 2D Upsampling layer.
class	Upsampling3D 3D Upsampling layer.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.feedforward.autoencoder

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.feedforward.autoencoder

Modifier and Type	Class and Description
class	AutoEncoder Autoencoder.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.feedforward.dense

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.feedforward.dense

Modifier and Type	Class and Description
class	DenseLayer

Uses of AbstractLayer in org.deeplearning4j.nn.layers.feedforward.elementwise

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.feedforward.elementwise

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 element-wise multiplication - b is a bias vector Note that the input and output sizes of the element-wise layer are the same for this layer

Uses of AbstractLayer in org.deeplearning4j.nn.layers.feedforward.embedding

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.feedforward.embedding

Modifier and Type	Class and Description
class	EmbeddingLayer Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to numClass-1) as input.
class	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.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.normalization

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.normalization

Modifier and Type	Class and Description
class	BatchNormalization Batch normalization layer.
class	LocalResponseNormalization Deep neural net normalization approach normalizes activations between layers "brightness normalization" Used for nets like AlexNet

Uses of AbstractLayer in org.deeplearning4j.nn.layers.objdetect

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.objdetect

Modifier and Type	Class and Description
class	Yolo2OutputLayer Output (loss) layer for YOLOv2 object detection model, based on the papers: YOLO9000: Better, Faster, Stronger - Redmon & Farhadi (2016) - https://arxiv.org/abs/1612.08242 and You Only Look Once: Unified, Real-Time Object Detection - Redmon et al.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.ocnn

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.ocnn

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

Uses of AbstractLayer in org.deeplearning4j.nn.layers.pooling

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.pooling

Modifier and Type	Class and Description
class	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.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.recurrent

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.recurrent

Modifier and Type	Class and Description
class	BaseRecurrentLayer<LayerConfT extends BaseLayer>
class	GravesBidirectionalLSTM RNN tutorial: http://deeplearning4j.org/usingrnns.html READ THIS FIRST Bdirectional LSTM layer implementation.
class	GravesLSTM LSTM layer implementation.
class	LSTM LSTM layer implementation.
class	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.
class	RnnOutputLayer Recurrent Neural Network Output Layer. Handles calculation of gradients etc for various objective functions. Functionally the same as OutputLayer, but handles output and label reshaping automatically. Input and output activations are same as other RNN layers: 3 dimensions with shape [miniBatchSize,nIn,timeSeriesLength] and [miniBatchSize,nOut,timeSeriesLength] respectively.
class	SimpleRnn Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.samediff

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.samediff

Modifier and Type	Class and Description
class	SameDiffLayer

Uses of AbstractLayer in org.deeplearning4j.nn.layers.training

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.training

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.

Uses of AbstractLayer in org.deeplearning4j.nn.layers.util

Subclasses of AbstractLayer in org.deeplearning4j.nn.layers.util

Modifier and Type	Class and Description
class	MaskLayer MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through this layer.