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

Packages that use BaseLayer

Package	Description
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.convolution
org.deeplearning4j.nn.layers.feedforward
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.ocnn
org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.layers.training

Uses of BaseLayer in org.deeplearning4j.nn.layers

Subclasses of BaseLayer in org.deeplearning4j.nn.layers

Modifier and Type	Class and Description
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 BaseLayer in org.deeplearning4j.nn.layers.convolution

Subclasses of BaseLayer 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	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.

Uses of BaseLayer in org.deeplearning4j.nn.layers.feedforward

Subclasses of BaseLayer in org.deeplearning4j.nn.layers.feedforward

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.

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

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

Modifier and Type	Class and Description
class	AutoEncoder Autoencoder.

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

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

Modifier and Type	Class and Description
class	DenseLayer

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

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

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

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

Modifier and Type	Class and Description
class	BatchNormalization Batch normalization layer.

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

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

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

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

Subclasses of BaseLayer 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 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. 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.

Methods in org.deeplearning4j.nn.layers.recurrent with parameters of type BaseLayer

Modifier and Type

Method and Description

static FwdPassReturn

LSTMHelpers.activateHelper(BaseLayer layer, NeuralNetConfiguration conf, IActivation gateActivationFn, INDArray input, INDArray recurrentWeights, INDArray originalInputWeights, INDArray biases, boolean training, INDArray originalPrevOutputActivations, INDArray originalPrevMemCellState, boolean forBackprop, boolean forwards, String inputWeightKey, INDArray maskArray, boolean hasPeepholeConnections, LSTMHelper helper, CacheMode cacheMode, LayerWorkspaceMgr workspaceMgr) Returns FwdPassReturn object with activations/INDArrays.

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

Subclasses of BaseLayer 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.