A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A

a(double) - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout

AbstractLayer<LayerConfT extends Layer> - Class in org.deeplearning4j.nn.layers

A layer with input and output, no parameters or gradients

AbstractLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.AbstractLayer

AbstractLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.AbstractLayer

AbstractLSTM - Class in org.deeplearning4j.nn.conf.layers

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

AbstractLSTM(AbstractLSTM.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.AbstractLSTM

AbstractLSTM.Builder<T extends AbstractLSTM.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers

AbstractSameDiffLayer - Class in org.deeplearning4j.nn.conf.layers.samediff

AbstractSameDiffLayer(AbstractSameDiffLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

AbstractSameDiffLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

AbstractSameDiffLayer.Builder<T extends AbstractSameDiffLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers.samediff

accumulator - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

accumulator - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

accumulator - Variable in class org.deeplearning4j.optimize.solvers.accumulation.LocalHandler

accumulator - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

activate(boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Layer

Perform forward pass and return the activations array with the last set input

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Layer

Perform forward pass and return the activations array with the specified input

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ActivationLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer

activate(INDArray, IActivation) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

activate(INDArray, boolean, int[], int[], int[], PoolingType, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.DropoutLayer

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.PReLU

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

activate(INDArray, boolean, double, double, double, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

activate(INDArray, INDArray) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils

Essentially: just apply activation functions...

activate(INDArray, INDArray, LayerWorkspaceMgr) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM

activate(Layer, NeuralNetConfiguration, IActivation, INDArray, INDArray, INDArray, INDArray, boolean, INDArray, INDArray, boolean, boolean, String, INDArray, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.recurrent.LSTMHelper

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.RepeatVector

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.util.MaskLayer

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

activate(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Deprecated.

Use MultiLayerNetwork.output(INDArray)

activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

activateHelper(BaseLayer, NeuralNetConfiguration, IActivation, INDArray, INDArray, INDArray, INDArray, boolean, INDArray, INDArray, boolean, boolean, String, INDArray, boolean, LSTMHelper, CacheMode, LayerWorkspaceMgr) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers

Returns FwdPassReturn object with activations/INDArrays.

activateSelectedLayers(int, int, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Calculate activation for few layers at once.

activation(String) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder

Deprecated.

Use ActivationLayer.Builder.activation(Activation) or @activation(IActivation)

activation(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder

activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder

activation(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Set the activation function for the layer.

activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Set the activation function for the layer, from an Activation enumeration value.

activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

activation(IActivation) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Activation function / neuron non-linearity
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

activation(Activation) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Activation function / neuron non-linearity
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

activation - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

activation(IActivation) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

The activation function to use with ocnn

activation(IActivation) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Activation function / neuron non-linearity

activation(Activation) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Activation function / neuron non-linearity

activationExceedsZeroOneRange(IActivation, boolean) - Static method in class org.deeplearning4j.util.OutputLayerUtil

activationFn - Variable in class org.deeplearning4j.nn.conf.layers.ActivationLayer

activationFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

activationFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

activationFn - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

activationFn - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

activationFromPrevLayer(int, INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

ActivationLayer - Class in org.deeplearning4j.nn.conf.layers

Activation layer is a simple layer that applies the specified activation function to the input activations

ActivationLayer(ActivationLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.ActivationLayer

ActivationLayer(Activation) - Constructor for class org.deeplearning4j.nn.conf.layers.ActivationLayer

ActivationLayer(IActivation) - Constructor for class org.deeplearning4j.nn.conf.layers.ActivationLayer

ActivationLayer - Class in org.deeplearning4j.nn.layers

Activation Layer Used to apply activation on input and corresponding derivative on epsilon.

ActivationLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.ActivationLayer

ActivationLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.ActivationLayer

ActivationLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

activationsVertexName() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer

Output layers should terminate in a single scalar value (i.e., a score) - however, sometimes the output activations (such as softmax probabilities) need to be returned.

adapt2dMask(INDArray, INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils

AdaptiveThresholdAlgorithm - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold

An adaptive threshold algorithm used to determine the encoding threshold for distributed training.
The idea: the threshold can be too high or too low for optimal training - both cases are bad.
So instead, we'll define a range of "acceptable" sparsity ratio values (default: 1e-4 to 1e-2).
The sparsity ratio is defined as numValues(encodedUpdate)/numParameters

If the sparsity ratio falls outside of this acceptable range, we'll either increase or decrease the threshold.
The threshold changed multiplicatively using the decay rate:
To increase threshold: newThreshold = decayRate * threshold
To decrease threshold: newThreshold = (1.0/decayRate) * threshold
The default decay rate used is AdaptiveThresholdAlgorithm.DEFAULT_DECAY_RATE=0.965936 which corresponds to an a maximum increase or decrease of the threshold by a factor of:
* 2.0 in 20 iterations
* 100 in 132 iterations
* 1000 in 200 iterations


A high threshold leads to few values being encoded and communicated - a small "sparsity ratio".
Too high threshold (too low sparsity ratio): fast network communication but slow training (few parameter updates being communicated).

A low threshold leads to many values being encoded and communicated - a large "sparsity ratio".
Too low threshold (too high sparsity ratio): slower network communication and maybe slow training (lots of parameter updates being communicated - but they are all very small, changing network's predictions only a tiny amount).

A sparsity ratio of 1.0 means all values are present in the encoded update vector.
A sparsity ratio of 0.0 means all values were excluded from the encoded update vector.

AdaptiveThresholdAlgorithm() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

Create the adaptive threshold algorithm with the default initial threshold AdaptiveThresholdAlgorithm.DEFAULT_INITIAL_THRESHOLD, default minimum sparsity target AdaptiveThresholdAlgorithm.DEFAULT_MIN_SPARSITY_TARGET, default maximum sparsity target AdaptiveThresholdAlgorithm.DEFAULT_MAX_SPARSITY_TARGET, and default decay rate AdaptiveThresholdAlgorithm.DEFAULT_DECAY_RATE

AdaptiveThresholdAlgorithm(double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

Create the adaptive threshold algorithm with the specified initial threshold, but defaults for the other values: default minimum sparsity target AdaptiveThresholdAlgorithm.DEFAULT_MIN_SPARSITY_TARGET, default maximum sparsity target AdaptiveThresholdAlgorithm.DEFAULT_MAX_SPARSITY_TARGET, and default decay rate AdaptiveThresholdAlgorithm.DEFAULT_DECAY_RATE

AdaptiveThresholdAlgorithm(double, double, double, double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

add(ThresholdAlgorithm) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer

add(ThresholdAlgorithm) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithmReducer

Add a ThresholdAlgorithm instance to the reducer

add(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

addAll(Collection<? extends E>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

addBiasParam(String, long...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

Add a bias parameter to the layer, with the specified shape.

addDistribution(int, ReconstructionDistribution) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution.Builder

Add another distribution to the composite distribution.

addInputs(String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Specify the inputs to the network, and their associated labels.

addInputs(Collection<String>) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Specify the inputs to the network, and their associated labels.

addInputs(String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

addLayer(String, Layer, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Add a layer, with no InputPreProcessor, with the specified name and specified inputs.

addLayer(String, Layer, InputPreProcessor, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Add a layer and an InputPreProcessor, with the specified name and specified inputs.

addLayer(Layer) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Add layers to the net Required if layers are removed.

addLayer(String, Layer, String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Add a layer of the specified configuration to the computation graph

addLayer(String, Layer, InputPreProcessor, String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Add a layer with a specified preprocessor

addListeners(TrainingListener...) - Method in interface org.deeplearning4j.nn.api.Model

This method ADDS additional TrainingListener to existing listeners

addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

This method ADDS additional TrainingListener to existing listeners

addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

This method ADDS additional TrainingListener to existing listeners

addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

This method ADDS additional TrainingListener to existing listeners

addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

This method ADDS additional TrainingListener to existing listeners

addNormalizerToModel(File, Normalizer<?>) - Static method in class org.deeplearning4j.util.ModelSerializer

This method appends normalizer to a given persisted model.

addObjectToFile(File, String, Object) - Static method in class org.deeplearning4j.util.ModelSerializer

Add an object to the (already existing) model file using Java Object Serialization.

addPreProcessors(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

Add preprocessors automatically, given the specified types of inputs for the network.

addVariable(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

addVertex(String, GraphVertex, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Add a GraphVertex to the network configuration.

addVertex(String, GraphVertex, String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Add a vertex of the given configuration to the computation graph

addWeightParam(String, long...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

Add a weight parameter to the layer, with the specified shape.

ALF - Variable in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

allowCollapse - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

allowDisconnected - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

allowDisconnected(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Used only during validation after building.
If true: don't throw an exception on configurations containing vertices that are 'disconnected'.

allowInputModification(boolean) - Method in interface org.deeplearning4j.nn.api.Layer

A performance optimization: mark whether the layer is allowed to modify its input array in-place.

allowInputModification(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

allowInputModification(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

allowInputModification(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

allowInputModification(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

allowInputModification(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

allowNoOutput - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

allowNoOutput(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Used only during validation after building.
If true: don't throw an exception on configurations without any outputs.

allParamConstraints - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder

allParamConstraints - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

allThreadThresholdAlgorithms - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

alpha - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

alpha - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder

alpha(double) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder

alpha - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

alpha(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder

LRN scaling constant alpha.

AlphaDropout - Class in org.deeplearning4j.nn.conf.dropout

AlphaDropout is a dropout technique proposed by Klaumbauer et al.

AlphaDropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.AlphaDropout

AlphaDropout(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.AlphaDropout

AlphaDropout(double, ISchedule, double, double) - Constructor for class org.deeplearning4j.nn.conf.dropout.AlphaDropout

ancestor(int, Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Returns the ancestor of the given tree

And(FailureTestingListener.FailureTrigger...) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.And

appliedConfiguration - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

appliedNeuralNetConfiguration(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

appliedNeuralNetConfigurationBuilder() - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

apply(INDArray...) - Method in class org.deeplearning4j.nn.adapters.ArgmaxAdapter

This method does conversion from INDArrays to int[], where each element will represents position of the highest element in output INDArray I.e.

apply(INDArray...) - Method in class org.deeplearning4j.nn.adapters.Regression2dAdapter

apply(INDArray...) - Method in interface org.deeplearning4j.nn.api.OutputAdapter

This method provides conversion from multiple INDArrays to T

apply(INDArray) - Method in class org.deeplearning4j.nn.conf.constraint.BaseConstraint

apply(INDArray) - Method in class org.deeplearning4j.nn.conf.constraint.MaxNormConstraint

apply(INDArray) - Method in class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint

apply(INDArray) - Method in class org.deeplearning4j.nn.conf.constraint.NonNegativeConstraint

apply(INDArray) - Method in class org.deeplearning4j.nn.conf.constraint.UnitNormConstraint

applyConstraint(Layer, int, int) - Method in interface org.deeplearning4j.nn.api.layers.LayerConstraint

Apply a given constraint to a layer at each iteration in the provided epoch, after parameters have been updated.

applyConstraint(Layer, int, int) - Method in class org.deeplearning4j.nn.conf.constraint.BaseConstraint

applyConstraints(int, int) - Method in interface org.deeplearning4j.nn.api.Model

Apply any constraints to the model

applyConstraints(int, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

applyConstraints(int, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

applyConstraints(Model) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout

applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.Dropout

applyDropout(INDArray, INDArray, double) - Method in interface org.deeplearning4j.nn.conf.dropout.DropoutHelper

Apply the dropout during forward pass

applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.GaussianDropout

applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.GaussianNoise

applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.conf.dropout.IDropout

applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.SpatialDropout

applyDropOutIfNecessary(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

applyDropOutIfNecessary(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer

applyDropOutIfNecessary(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer

applyGlobalConfig(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

applyGlobalConfig(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

applyGlobalConfigToLayer(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D

applyGlobalConfigToLayer(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D

applyGlobalConfigToLayer(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

applyGlobalConfigToLayer(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

applyMask(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

applyMask(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

applyPostProcessor(int, int, Double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

applyPreprocessorAndSetInput(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

applyToComputationGraphConfiguration(ComputationGraphConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

applyToMultiLayerConfiguration(MultiLayerConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

applyToNeuralNetConfiguration(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

applyUpdate(StepFunction, INDArray, INDArray, boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

This method applies accumulated updates via given StepFunction

applyUpdate(StepFunction, INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

This method applies accumulated updates via given StepFunction

applyUpdate(StepFunction, INDArray, INDArray, boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

This method applies accumulated updates via given StepFunction

applyUpdate(StepFunction, INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

This method applies accumulated updates via given StepFunction

applyUpdate(StepFunction, INDArray, INDArray, boolean) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator

This method applies accumulated updates via given StepFunction

applyUpdate(StepFunction, INDArray, INDArray, double) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator

This method applies accumulated updates via given StepFunction

ArgmaxAdapter - Class in org.deeplearning4j.nn.adapters

This OutputAdapter implementation is suited for silent conversion of 2D SoftMax output

ArgmaxAdapter() - Constructor for class org.deeplearning4j.nn.adapters.ArgmaxAdapter

arr(INDArray) - Static method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType

arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional

arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional3D

arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat

arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward

arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent

ArrayType - Enum in org.deeplearning4j.nn.workspace

Array type enumeration for use with LayerWorkspaceMgr

Array types:
INPUT: The array set to the input field of a layer (i.e., input activations)
ACTIVATIONS: The output activations for a layer's feed-forward method
ACTIVATION_GRAD: Activation gradient arrays - aka "epsilons" - output from a layer's backprop method
FF_WORKING_MEM: Working memory during feed-forward.

assertInputSet(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

assertInputSet(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

assertNInNOutSet(String, String, long, long, long) - Static method in class org.deeplearning4j.nn.conf.layers.LayerValidation

Asserts that the layer nIn and nOut values are set for the layer

assertNOutSet(String, String, long, long) - Static method in class org.deeplearning4j.nn.conf.layers.LayerValidation

Asserts that the layer nOut value is set for the layer

asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter

atomicBoundary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

AutoEncoder - Class in org.deeplearning4j.nn.conf.layers

Autoencoder layer.

AutoEncoder - Class in org.deeplearning4j.nn.layers.feedforward.autoencoder

Autoencoder.

AutoEncoder(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder

AutoEncoder(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder

AutoEncoder.Builder - Class in org.deeplearning4j.nn.conf.layers

AutoencoderScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc

Score function for a MultiLayerNetwork or ComputationGraph with a single AutoEncoder layer.

AutoencoderScoreCalculator(RegressionEvaluation.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator

availableCheckpoints() - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener

List all available checkpoints.

average - Variable in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

B

b(double) - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout

backingQueue - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

backprop - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

backprop - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Deprecated.

backprop(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Deprecated.

doesn't affect training any more. Use ComputationGraph.fit(MultiDataSet) when training for backprop.

backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout

backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.Dropout

backprop(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.conf.dropout.DropoutHelper

Perform backpropagation.

backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.GaussianDropout

backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.GaussianNoise

backprop(INDArray, INDArray, int, int) - Method in interface org.deeplearning4j.nn.conf.dropout.IDropout

Perform backprop.

backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.SpatialDropout

backprop(INDArray, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor

Reverse the preProcess during backprop.

backprop - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

backprop - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Deprecated.

backprop(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Deprecated.

doesn't affect training any more. Use MultiLayerNetwork.fit(DataSetIterator) when training for backprop.

backprop(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

Deprecated.

backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor

backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor

backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor

backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor

backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor

backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor

backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor

backprop - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

backprop(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

backpropDropOutIfPresent(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Layer

Calculate the gradient relative to the error in the next layer

backpropGradient(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Calculate the gradient of the network with respect to some external errors.

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ActivationLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer

backpropGradient(INDArray, INDArray, INDArray, int[], int[], int[], INDArray, INDArray, IActivation, ConvolutionLayer.AlgoMode, ConvolutionLayer.BwdFilterAlgo, ConvolutionLayer.BwdDataAlgo, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

backpropGradient(INDArray, INDArray, int[], int[], int[], PoolingType, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.DropoutLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.PReLU

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

backpropGradient(INDArray, INDArray, int[], INDArray, INDArray, INDArray, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

backpropGradient(INDArray, INDArray, double, double, double, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM

backpropGradient(NeuralNetConfiguration, IActivation, INDArray, INDArray, INDArray, INDArray, boolean, int, FwdPassReturn, boolean, String, String, String, Map<String, INDArray>, INDArray, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.recurrent.LSTMHelper

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.RepeatVector

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.util.MaskLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

backpropGradientHelper(NeuralNetConfiguration, IActivation, INDArray, INDArray, INDArray, INDArray, boolean, int, FwdPassReturn, boolean, String, String, String, Map<String, INDArray>, INDArray, boolean, LSTMHelper, LayerWorkspaceMgr) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers

BackpropType - Enum in org.deeplearning4j.nn.conf

Defines the type of backpropagation.

backpropType - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

backpropType - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

backpropType(BackpropType) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

The type of backprop.

backpropType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

backpropType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

backpropType(BackpropType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

The type of backprop.

backpropType - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

backpropType(BackpropType) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

The type of backprop.

BackTrackLineSearch - Class in org.deeplearning4j.optimize.solvers

BackTrackLineSearch(Model, StepFunction, ConvexOptimizer) - Constructor for class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

BackTrackLineSearch(Model, ConvexOptimizer) - Constructor for class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

BACKWARD_PREFIX - Static variable in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

barrier - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

barrier - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

barrier - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

BaseConstraint - Class in org.deeplearning4j.nn.conf.constraint

BaseConstraint() - Constructor for class org.deeplearning4j.nn.conf.constraint.BaseConstraint

BaseConstraint(Set<String>, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.BaseConstraint

BaseConvBuilder(int[], int[], int[], int[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

BaseConvBuilder(int[], int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

BaseConvBuilder(int[], int[], int[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

BaseConvBuilder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

BaseConvBuilder(int[], int[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

BaseConvBuilder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

BaseConvBuilder(int, int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

BaseConvBuilder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

BaseConvBuilder() - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

BaseEarlyStoppingTrainer<T extends Model> - Class in org.deeplearning4j.earlystopping.trainer

Base/abstract class for conducting early stopping training locally (single machine).
Can be used to train a MultiLayerNetwork or a ComputationGraph via early stopping

BaseEarlyStoppingTrainer(EarlyStoppingConfiguration<T>, T, DataSetIterator, MultiDataSetIterator, EarlyStoppingListener<T>) - Constructor for class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer

BaseEvaluation<T extends BaseEvaluation> - Class in org.deeplearning4j.eval

Deprecated.

Use BaseEvaluation

BaseEvaluation() - Constructor for class org.deeplearning4j.eval.BaseEvaluation

Deprecated.

BaseGraphVertex - Class in org.deeplearning4j.nn.graph.vertex

BaseGraphVertex defines a set of common functionality for GraphVertex instances.

BaseGraphVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

BaseIEvaluationScoreCalculator<T extends Model,U extends IEvaluation> - Class in org.deeplearning4j.earlystopping.scorecalc.base

Base score function based on an IEvaluation instance.

BaseIEvaluationScoreCalculator(MultiDataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator

BaseIEvaluationScoreCalculator(DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator

BaseInputPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor

BaseInputPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor

BaseLayer - Class in org.deeplearning4j.nn.conf.layers

A neural network layer.

BaseLayer(BaseLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseLayer

BaseLayer<LayerConfT extends BaseLayer> - Class in org.deeplearning4j.nn.layers

A layer with parameters

BaseLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.BaseLayer

BaseLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.BaseLayer

BaseLayer.Builder<T extends BaseLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers

BaseMLNScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc.base

Abstract score calculator for MultiLayerNetwonk

BaseMLNScoreCalculator(DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseMLNScoreCalculator

BaseMultiLayerUpdater<T extends Model> - Class in org.deeplearning4j.nn.updater

BaseMultiLayerUpdater - core functionality for applying updaters to MultiLayerNetwork and ComputationGraph.

BaseMultiLayerUpdater(T, boolean) - Constructor for class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

BaseMultiLayerUpdater(T, INDArray, boolean) - Constructor for class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

BaseNetConfigDeserializer<T> - Class in org.deeplearning4j.nn.conf.serde

A custom (abstract) deserializer that handles backward compatibility (currently only for updater refactoring that happened after 0.8.0).

BaseNetConfigDeserializer(JsonDeserializer<?>, Class<T>) - Constructor for class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer

BaseOptimizer - Class in org.deeplearning4j.optimize.solvers

Base optimizer

BaseOptimizer(NeuralNetConfiguration, StepFunction, Collection<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.BaseOptimizer

BaseOutputLayer - Class in org.deeplearning4j.nn.conf.layers

BaseOutputLayer(BaseOutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer

BaseOutputLayer<LayerConfT extends BaseOutputLayer> - Class in org.deeplearning4j.nn.layers

Output layer with different objective in co-occurrences for different objectives.

BaseOutputLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.BaseOutputLayer

BaseOutputLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.BaseOutputLayer

BaseOutputLayer.Builder<T extends BaseOutputLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers

BasePretrainNetwork - Class in org.deeplearning4j.nn.conf.layers

BasePretrainNetwork(BasePretrainNetwork.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork

BasePretrainNetwork<LayerConfT extends BasePretrainNetwork> - Class in org.deeplearning4j.nn.layers

Baseline class for any Neural Network used as a layer in a deep network *

BasePretrainNetwork(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.BasePretrainNetwork

BasePretrainNetwork(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.BasePretrainNetwork

BasePretrainNetwork.Builder<T extends BasePretrainNetwork.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers

BaseRecurrentLayer - Class in org.deeplearning4j.nn.conf.layers

BaseRecurrentLayer(BaseRecurrentLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer

BaseRecurrentLayer<LayerConfT extends BaseLayer> - Class in org.deeplearning4j.nn.layers.recurrent

BaseRecurrentLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

BaseRecurrentLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

BaseRecurrentLayer.Builder<T extends BaseRecurrentLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers

BaseScoreCalculator<T extends Model> - Class in org.deeplearning4j.earlystopping.scorecalc.base

BaseScoreCalculator(DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

BaseScoreCalculator(MultiDataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

BaseSubsamplingBuilder(Subsampling3DLayer.PoolingType, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(Subsampling3DLayer.PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(Subsampling3DLayer.PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(Subsampling3DLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(SubsamplingLayer.PoolingType, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(SubsamplingLayer.PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(SubsamplingLayer.PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(SubsamplingLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

BaseSubsamplingBuilder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

BaseTrainingListener - Class in org.deeplearning4j.optimize.api

A no-op implementation of a TrainingListener to be used as a starting point for custom training callbacks.

BaseTrainingListener() - Constructor for class org.deeplearning4j.optimize.api.BaseTrainingListener

BaseUpsamplingLayer - Class in org.deeplearning4j.nn.conf.layers

Upsampling base layer

BaseUpsamplingLayer(BaseUpsamplingLayer.UpsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer

BaseUpsamplingLayer.UpsamplingBuilder<T extends BaseUpsamplingLayer.UpsamplingBuilder<T>> - Class in org.deeplearning4j.nn.conf.layers

BaseWrapperLayer - Class in org.deeplearning4j.nn.conf.layers.wrapper

Base wrapper layer: the idea is to pass through all methods to the underlying layer, and selectively override them as required.

BaseWrapperLayer(Layer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

BaseWrapperLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

BaseWrapperLayer(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

BaseWrapperLayer - Class in org.deeplearning4j.nn.layers.wrapper

Abstract wrapper layer.

BaseWrapperLayer(Layer) - Constructor for class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

BaseWrapperVertex - Class in org.deeplearning4j.nn.graph.vertex

A base class for wrapper vertices: i.e., those vertices that have another vertex inside.

BaseWrapperVertex(GraphVertex) - Constructor for class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

BasicGradientsAccumulator - Class in org.deeplearning4j.optimize.solvers.accumulation

This class provides accumulation for gradients for both input (i.e.

BasicGradientsAccumulator(int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

Creates new GradientsAccumulator with starting threshold of 1e-3

BasicGradientsAccumulator(int, MessageHandler) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

Creates new GradientsAccumulator with custom starting threshold

BatchNormalization - Class in org.deeplearning4j.nn.conf.layers

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

BatchNormalization() - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization

BatchNormalization - Class in org.deeplearning4j.nn.layers.normalization

Batch normalization layer.
Rerences:
http://arxiv.org/pdf/1502.03167v3.pdf
http://arxiv.org/pdf/1410.7455v8.pdf
https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html Batch normalization should be applied between the output of a layer (with identity activation) and the activation function.

BatchNormalization(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.normalization.BatchNormalization

BatchNormalization.Builder - Class in org.deeplearning4j.nn.conf.layers

BatchNormalizationHelper - Interface in org.deeplearning4j.nn.layers.normalization

Helper for the batch normalization layer.

BatchNormalizationParamInitializer - Class in org.deeplearning4j.nn.params

Batch normalization variable init

BatchNormalizationParamInitializer() - Constructor for class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

batchSize() - Method in interface org.deeplearning4j.nn.api.Model

The current inputs batch size

batchSize() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

batchSize() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

batchSize() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

batchSize() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

batchSize() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

batchSize() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

batchSize() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

The batch size for the optimizer

batchSize() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

BernoulliReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational

Bernoulli reconstruction distribution for variational autoencoder.
Outputs are modelled by a Bernoulli distribution - i.e., the Bernoulli distribution should be used for binary data (all values 0 or 1); the VAE models the probability of the output being 0 or 1.
Consequently, the sigmoid activation function should be used to bound activations to the range of 0 to 1.

BernoulliReconstructionDistribution() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

Create a BernoulliReconstructionDistribution with the default Sigmoid activation function

BernoulliReconstructionDistribution(Activation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

BernoulliReconstructionDistribution(IActivation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

BestScoreEpochTerminationCondition - Class in org.deeplearning4j.earlystopping.termination

Created by Sadat Anwar on 3/26/16.

BestScoreEpochTerminationCondition(double) - Constructor for class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition

BestScoreEpochTerminationCondition(double, boolean) - Constructor for class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition

Deprecated.

"lessBetter" argument no longer used

beta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization

beta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

beta(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Used only when 'true' is passed to BatchNormalization.Builder.lockGammaBeta(boolean).

beta - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

beta(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder

Scaling constant beta.

BETA - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

betaConstraints - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.CenterLossParamInitializer

BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.Convolution3DParamInitializer

BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.DefaultParamInitializer

BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.LSTMParamInitializer

BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

BIAS_KEY_BACKWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

BIAS_KEY_FORWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

BIAS_KEY_SUFFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

biasConstraints - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder

biasConstraints - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

biasInit - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

biasInit - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

biasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Bias initialization value, for layers with biases.

biasInit - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

biasInit(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Constant for bias initialization.

biasInit - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

biasInit(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Constant for bias initialization.

biasKeys(Layer) - Method in interface org.deeplearning4j.nn.api.ParamInitializer

Bias parameter keys given the layer configuration

biasKeys(Layer) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer

biasUpdater - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

biasUpdater - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

biasUpdater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Gradient updater configuration, for the biases only.

biasUpdater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

biasUpdater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

biasUpdater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

Gradient updater configuration, for the biases only.

biasUpdater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

biasUpdater - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

biasUpdater(IUpdater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Gradient updater configuration, for the biases only.

biasUpdater - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

biasUpdater(IUpdater) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Gradient updater configuration, for the biases only.

Bidirectional - Class in org.deeplearning4j.nn.conf.layers.recurrent

Bidirectional is a "wrapper" layer: it wraps any uni-directional RNN layer to make it bidirectional.
Note that multiple different modes are supported - these specify how the activations should be combined from the forward and backward RNN networks.

Bidirectional(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

Create a Bidirectional wrapper, with the default Mode (CONCAT) for the specified layer

Bidirectional(Bidirectional.Mode, Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

Create a Bidirectional wrapper for the specified layer

Bidirectional.Builder - Class in org.deeplearning4j.nn.conf.layers.recurrent

Bidirectional.Mode - Enum in org.deeplearning4j.nn.conf.layers.recurrent

This Mode enumeration defines how the activations for the forward and backward networks should be combined.
ADD: out = forward + backward (elementwise addition)
MUL: out = forward * backward (elementwise multiplication)
AVERAGE: out = 0.5 * (forward + backward)
CONCAT: Concatenate the activations.
Where 'forward' is the activations for the forward RNN, and 'backward' is the activations for the backward RNN.

BidirectionalLayer - Class in org.deeplearning4j.nn.layers.recurrent

Bidirectional is a "wrapper" layer: it wraps any uni-directional RNN layer to make it bidirectional.
Note that multiple different modes are supported - these specify how the activations should be combined from the forward and backward RNN networks.

BidirectionalLayer(NeuralNetConfiguration, Layer, Layer, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

BidirectionalParamInitializer - Class in org.deeplearning4j.nn.params

Parameter initializer for bidirectional wrapper layer

BidirectionalParamInitializer(Bidirectional) - Constructor for class org.deeplearning4j.nn.params.BidirectionalParamInitializer

BinomialDistribution - Class in org.deeplearning4j.nn.conf.distribution

A binomial distribution, with 2 parameters: number of trials, and probability of success

BinomialDistribution(int, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.BinomialDistribution

Create a distribution

bitmapMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

blocks - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

blocks - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder

blocks(int[]) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder

blocks(int) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder

blockSize - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

blockSize - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder

boundary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

boundary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

boundary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

boundingBoxPriors(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder

Bounding box priors dimensions [width, height].

broadcastUpdates(INDArray, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

broadcastUpdates(INDArray, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.LocalHandler

broadcastUpdates(INDArray, int, int) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.MessageHandler

This method does broadcast of given update message across network

build() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder

Create the early stopping configuration

build() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Create the ComputationGraphConfiguration from the Builder pattern

build() - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder

Deprecated.

build() - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder

Deprecated.

build() - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.LossLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.LSTM.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder

build() - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder

build() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

build() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Return a configuration based on this builder

build() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

Build the multi layer network based on this neural network and overr ridden parameters

build() - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

build() - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

build() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Returns a model with the fine tune configuration and specified architecture changes.

build() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Returns a computation graph build to specifications.

build() - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder

build() - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

build() - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder

This method returns configured PerformanceListener instance

build() - Method in class org.deeplearning4j.optimize.Solver.Builder

build() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

Builder() - Constructor for class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder

Builder(double) - Constructor for class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder

Builder - sets the level of corruption - 0.0 (none) to 1.0 (all values corrupted)

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder

Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder

Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder

Builder(double, boolean) - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Builder(double, double) - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Builder(double, double, boolean) - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Builder(boolean) - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder

Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder

Builder(Convolution3D.DataFormat) - Constructor for class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder

Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder

Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder

Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder

Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder

Constructor with specified kernel size, stride of 1, padding of 0

Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

Builder(int[], int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder

Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder

Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder

Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder

Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder

Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder

Builder(int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder

Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder

Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder

Builder(int, int, int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder

Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder

Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder

Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder

Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder

Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder

Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.DenseLayer.Builder

Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder

Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder

Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder

Builder(double) - Constructor for class org.deeplearning4j.nn.conf.layers.DropoutLayer.Builder

Create a dropout layer with standard Dropout, with the specified probability of retaining the input activation.

Builder(IDropout) - Constructor for class org.deeplearning4j.nn.conf.layers.DropoutLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder

Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder

Deprecated.

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder

Deprecated.

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Layer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

Builder(double, double, double) - Constructor for class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer.Builder

Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer.Builder

Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LSTM.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer.Builder

Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder

Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder

Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.PReLULayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder

Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder

Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder

Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder

Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer.Builder

Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder

Builder(int[], int[][]) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder

Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder

Builder(int, SpaceToDepthLayer.DataFormat) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder

Builder(SubsamplingLayer.PoolingType, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder(SubsamplingLayer.PoolingType, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder(PoolingType, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder(SubsamplingLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder(PoolingType, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder(SubsamplingLayer.PoolingType, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Builder(Subsampling3DLayer.PoolingType, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Builder(Subsampling3DLayer.PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Builder(Subsampling3DLayer.PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Builder(PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Builder(PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Builder(Subsampling3DLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Builder(SubsamplingLayer.PoolingType, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Builder(SubsamplingLayer.PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Builder(SubsamplingLayer.PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Builder(PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Builder(PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Builder(SubsamplingLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling1D.Builder

Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder

Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder

Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder

Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder

Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder

Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder

Use same padding for left and right boundaries in depth, height and width.

Builder(int, int, int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder

Explicit padding of left and right boundaries in depth, height and width dimensions

Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder

Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder

Builder(int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder

Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder

Builder(String, Class<?>, InputType, InputType) - Constructor for class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Builder(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Builder() - Constructor for class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

builder() - Static method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

Builder() - Constructor for class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Builder(MultiLayerNetwork) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Multilayer Network to tweak for transfer learning

builder() - Static method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

Builder() - Constructor for class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder

Builder(String) - Constructor for class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Builder(File) - Constructor for class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Builder() - Constructor for class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder

Builder() - Constructor for class org.deeplearning4j.optimize.Solver.Builder

Builder(int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

This

bypassMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

bypassMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

C

CACHE_MODE_ALL_ZEROS - Static variable in class org.deeplearning4j.nn.conf.memory.MemoryReport

A simple Map containing all zeros for each CacheMode key

cachedFwdPass - Variable in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

cachedFwdPass - Variable in class org.deeplearning4j.nn.layers.recurrent.LSTM

cachedPassBackward - Variable in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

cachedPassForward - Variable in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

cacheMemory(long, long) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder

Reports the cached/cacheable memory requirements.

cacheMemory(Map<CacheMode, Long>, Map<CacheMode, Long>) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder

Reports the cached/cacheable memory requirements.

CacheMode - Enum in org.deeplearning4j.nn.conf

cacheMode - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

cacheMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

cacheMode(CacheMode) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

This method defines how/if preOutput cache is handled: NONE: cache disabled (default value) HOST: Host memory will be used DEVICE: GPU memory will be used (on CPU backends effect will be the same as for HOST)

cacheMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

cacheMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

cacheMode(CacheMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

This method defines how/if preOutput cache is handled: NONE: cache disabled (default value) HOST: Host memory will be used DEVICE: GPU memory will be used (on CPU backends effect will be the same as for HOST)

cacheMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

cacheMode - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

cacheMode - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

cacheModeMapFor(long) - Static method in class org.deeplearning4j.nn.conf.memory.MemoryReport

Get a map of CacheMode with all keys associated with the specified value

calcBackpropGradients(boolean, boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Do backprop (gradient calculation)

calcBackpropGradients(INDArray, boolean, boolean, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Calculate gradients and errors.

calcL1(boolean) - Method in interface org.deeplearning4j.nn.api.Layer

Calculate the l1 regularization term
0.0 if regularization is not used.

calcL1() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Calculate the L1 regularization term for all layers in the entire network.

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.ActivationLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.DropoutLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.RepeatVector

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

calcL1(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

calcL2(boolean) - Method in interface org.deeplearning4j.nn.api.Layer

Calculate the l2 regularization term
0.0 if regularization is not used.

calcL2() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Calculate the L2 regularization term for all layers in the entire network.

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.ActivationLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.DropoutLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.RepeatVector

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

calcL2(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

calculateGradients(INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Calculate parameter gradients and input activation gradients given the input and labels

calculateIndices() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Calculate the indices needed for the network:
(a) topological sort order
(b) Map: vertex index -> vertex name
(c) Map: vertex name -> vertex index

calculateScore(T) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator

calculateScore(T) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

calculateScore(ComputationGraph) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG

Deprecated.

calculateScore(T) - Method in interface org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator

Calculate the score for the given MultiLayerNetwork

calculateThreshold(int, int, Double, Boolean, Double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

calculateThreshold(int, int, Double, Boolean, Double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm

calculateThreshold(int, int, Double, Boolean, Double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm

calculateThreshold(int, int, Double, Boolean, Double, INDArray) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithm

call(EvaluativeListener, Model, long, IEvaluation[]) - Method in interface org.deeplearning4j.optimize.listeners.callbacks.EvaluationCallback

call(EvaluativeListener, Model, long, IEvaluation[]) - Method in class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback

call(FailureTestingListener.CallType, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener

callback - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener

This callback will be invoked after evaluation finished

candidates - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

canDoBackward() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

canDoBackward() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

canDoBackward() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Whether the GraphVertex can do backward pass.

canDoBackward() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

canDoForward() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

canDoForward() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

canDoForward() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Whether the GraphVertex can do forward pass.

CENTER_KEY - Static variable in class org.deeplearning4j.nn.params.CenterLossParamInitializer

CenterLossOutputLayer - Class in org.deeplearning4j.nn.conf.layers

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

CenterLossOutputLayer(CenterLossOutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

CenterLossOutputLayer - Class in org.deeplearning4j.nn.layers.training

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

CenterLossOutputLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer

CenterLossOutputLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer

CenterLossOutputLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

CenterLossParamInitializer - Class in org.deeplearning4j.nn.params

Initialize Center Loss params.

CenterLossParamInitializer() - Constructor for class org.deeplearning4j.nn.params.CenterLossParamInitializer

checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

Check backprop gradients for a MultiLayerNetwork.

checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, Set<String>) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray, boolean, int) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray, boolean, int, Set<String>) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray, boolean, int, Set<String>, Integer) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray, boolean, int, Set<String>, Consumer<MultiLayerNetwork>) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[]) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

Check backprop gradients for a ComputationGraph

checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[], INDArray[], INDArray[]) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[], INDArray[], INDArray[], Set<String>) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[], INDArray[], INDArray[], Set<String>, Integer) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[], INDArray[], INDArray[], Set<String>, Consumer<ComputationGraph>) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

checkGradientsPretrainLayer(Layer, double, double, double, boolean, boolean, INDArray, int) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil

Check backprop gradients for a pretrain layer NOTE: gradient checking pretrain layers can be difficult...

Checkpoint - Class in org.deeplearning4j.optimize.listeners

A model checkpoint, used with CheckpointListener

Checkpoint() - Constructor for class org.deeplearning4j.optimize.listeners.Checkpoint

CheckpointListener - Class in org.deeplearning4j.optimize.listeners

CheckpointListener: The goal of this listener is to periodically save a copy of the model during training..
Model saving may be done:
1.

CheckpointListener.Builder - Class in org.deeplearning4j.optimize.listeners

checkSupported() - Method in interface org.deeplearning4j.nn.conf.dropout.DropoutHelper

checkSupported() - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper

checkSupported() - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper

checkSupported(double) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper

checkSupported(double, double, double, double) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper

checkSupported(IActivation, IActivation, boolean) - Method in interface org.deeplearning4j.nn.layers.recurrent.LSTMHelper

children() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

ClassificationScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc

Score function for evaluating a MultiLayerNetwork according to an evaluation metric (Evaluation.Metric such as accuracy, F1 score, etc.

ClassificationScoreCalculator(Evaluation.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator

ClassificationScoreCalculator(Evaluation.Metric, MultiDataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator

Classifier - Interface in org.deeplearning4j.nn.api

A classifier (this is for supervised learning)

clear() - Method in interface org.deeplearning4j.nn.api.Model

Clear input

clear() - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout

clear() - Method in class org.deeplearning4j.nn.conf.dropout.Dropout

clear() - Method in class org.deeplearning4j.nn.conf.dropout.GaussianDropout

clear() - Method in class org.deeplearning4j.nn.conf.dropout.GaussianNoise

clear() - Method in interface org.deeplearning4j.nn.conf.dropout.IDropout

Clear the internal state (for example, dropout mask) if any is present

clear() - Method in class org.deeplearning4j.nn.conf.dropout.SpatialDropout

clear() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

Clear any previously set weight/bias parameters (including their shapes)

clear() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient

clear() - Method in interface org.deeplearning4j.nn.gradient.Gradient

Clear residual parameters (useful for returning a gradient and then clearing old objects)

clear() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

clear() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

clear() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

clear() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Clear the internal state (if any) of the GraphVertex.

clear() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

clear() - Method in class org.deeplearning4j.nn.layers.BaseLayer

clear() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

clear() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer

clear() - Method in class org.deeplearning4j.nn.layers.LossLayer

clear() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

clear() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

clear() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

clear() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Clear the inputs.

clear() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

clearLayerMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Remove the mask arrays from all layers.
See ComputationGraph.setLayerMaskArrays(INDArray[], INDArray[]) for details on mask arrays.

clearLayerMaskArrays() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Remove the mask arrays from all layers.
See MultiLayerNetwork.setLayerMaskArrays(INDArray, INDArray) for details on mask arrays.

clearLayersStates() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

This method just makes sure there's no state preserved within layers

clearLayersStates() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

This method just makes sure there's no state preserved within layers

clearNoiseWeightParams() - Method in interface org.deeplearning4j.nn.api.Layer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.ActivationLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.BaseLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.RepeatVector

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.util.MaskLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

clearTbpttState - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

clearTbpttState - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

clearVariables() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

clearVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

clearVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

clearVertex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

This method clears inpjut for this vertex

clearVertex() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

clone() - Method in class org.deeplearning4j.eval.ROC.CountsForThreshold

Deprecated.

clone() - Method in interface org.deeplearning4j.nn.api.layers.LayerConstraint

clone() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

clone() - Method in class org.deeplearning4j.nn.conf.constraint.BaseConstraint

clone() - Method in class org.deeplearning4j.nn.conf.constraint.MaxNormConstraint

clone() - Method in class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint

clone() - Method in class org.deeplearning4j.nn.conf.constraint.NonNegativeConstraint

clone() - Method in class org.deeplearning4j.nn.conf.constraint.UnitNormConstraint

clone() - Method in class org.deeplearning4j.nn.conf.distribution.Distribution

clone() - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout

clone() - Method in class org.deeplearning4j.nn.conf.dropout.Dropout

clone() - Method in class org.deeplearning4j.nn.conf.dropout.GaussianDropout

clone() - Method in class org.deeplearning4j.nn.conf.dropout.GaussianNoise

clone() - Method in interface org.deeplearning4j.nn.conf.dropout.IDropout

clone() - Method in class org.deeplearning4j.nn.conf.dropout.SpatialDropout

clone() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.StackVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex

clone() - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex

clone() - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

clone() - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D

clone() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D

clone() - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D

clone() - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.Layer

clone() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

clone() - Method in class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

clone() - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector

clone() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

clone() - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D

clone() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

clone() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D

clone() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D

clone() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D

clone() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

clone() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

clone() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

Creates and returns a deep copy of the configuration.

clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor

clone() - Method in class org.deeplearning4j.nn.conf.stepfunctions.StepFunction

clone() - Method in class org.deeplearning4j.nn.conf.weightnoise.DropConnect

clone() - Method in interface org.deeplearning4j.nn.conf.weightnoise.IWeightNoise

clone() - Method in class org.deeplearning4j.nn.conf.weightnoise.WeightNoise

clone() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

clone() - Method in class org.deeplearning4j.nn.layers.BaseLayer

clone() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer

clone() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer

clone() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer

clone() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer

clone() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer

clone() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer

clone() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

clone() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

clone() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

clone() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer

clone() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

clone() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

clone() - Method in class org.deeplearning4j.nn.layers.util.MaskLayer

clone() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Clones the multilayernetwork

clone() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater

clone() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.NoOpResidualPostProcessor

clone() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.ResidualClippingPostProcessor

clone() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ResidualPostProcessor

clone() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

clone() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm

clone() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm

clone() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithm

cnn1dMaskReduction(INDArray, int, int, int, int, ConvolutionMode) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Given a mask array for a 1D CNN layer of shape [minibatch, sequenceLength], reduce the mask according to the 1D CNN layer configuration.

cnn2dMaskReduction(INDArray, int[], int[], int[], int[], ConvolutionMode) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Reduce a 2d CNN layer mask array (of 0s and 1s) according to the layer configuration.

Cnn3DLossLayer - Class in org.deeplearning4j.nn.conf.layers

3D Convolutional Neural Network Loss Layer.
Handles calculation of gradients etc for various loss (objective) functions.
NOTE: Cnn3DLossLayer does not have any parameters.

Cnn3DLossLayer - Class in org.deeplearning4j.nn.layers.convolution

3D Convolutional Neural Network Loss Layer.
Handles calculation of gradients etc for various objective functions.
NOTE: Cnn3DLossLayer does not have any parameters.

Cnn3DLossLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

Cnn3DLossLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

Cnn3DToFeedForwardPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor

A preprocessor to allow CNN and standard feed-forward network layers to be used together.
For example, CNN3D -> Denselayer
This does two things:
(b) Reshapes 5d activations out of CNN layer, with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) into 2d activations (with shape [numExamples, inputDepth*inputHeight*inputWidth*numChannels]) for use in feed forward layer (a) Reshapes epsilons (weights*deltas) out of FeedFoward layer (which is 2D or 3D with shape [numExamples, inputDepth* inputHeight*inputWidth*numChannels]) into 5d epsilons (with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) suitable to feed into CNN layers.
Note: numChannels is equivalent to featureMaps referenced in different literature

Cnn3DToFeedForwardPreProcessor(long, long, long, long, boolean) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

Cnn3DToFeedForwardPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

Cnn3DToFeedForwardPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

CnnLossLayer - Class in org.deeplearning4j.nn.conf.layers

Convolutional Neural Network Loss Layer.
Handles calculation of gradients etc for various loss (objective) functions.
NOTE: CnnLossLayer does not have any parameters.

CnnLossLayer - Class in org.deeplearning4j.nn.layers.convolution

Convolutional Neural Network Loss Layer.
Handles calculation of gradients etc for various objective functions.
NOTE: CnnLossLayer does not have any parameters.

CnnLossLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

CnnLossLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

CnnToFeedForwardPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor

A preprocessor to allow CNN and standard feed-forward network layers to be used together.
For example, CNN -> Denselayer
This does two things:
(b) Reshapes 4d activations out of CNN layer, with shape [numExamples, numChannels, inputHeight, inputWidth]) into 2d activations (with shape [numExamples, inputHeight*inputWidth*numChannels]) for use in feed forward layer (a) Reshapes epsilons (weights*deltas) out of FeedFoward layer (which is 2D or 3D with shape [numExamples, inputHeight*inputWidth*numChannels]) into 4d epsilons (with shape [numExamples, numChannels, inputHeight, inputWidth]) suitable to feed into CNN layers.
Note: numChannels is equivalent to channels or featureMaps referenced in different literature

CnnToFeedForwardPreProcessor(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

CnnToFeedForwardPreProcessor(long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

CnnToFeedForwardPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

CnnToRnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor

A preprocessor to allow CNN and RNN layers to be used together.
For example, ConvolutionLayer -> GravesLSTM Functionally equivalent to combining CnnToFeedForwardPreProcessor + FeedForwardToRnnPreProcessor
Specifically, this does two things:
(a) Reshape 4d activations out of CNN layer, with shape [timeSeriesLength*miniBatchSize, numChannels, inputHeight, inputWidth]) into 3d (time series) activations (with shape [numExamples, inputHeight*inputWidth*numChannels, timeSeriesLength]) for use in RNN layers
(b) Reshapes 3d epsilons (weights.*deltas) out of RNN layer (with shape [miniBatchSize,inputHeight*inputWidth*numChannels,timeSeriesLength]) into 4d epsilons with shape [miniBatchSize*timeSeriesLength, numChannels, inputHeight, inputWidth] suitable to feed into CNN layers.

CnnToRnnPreProcessor(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor

COEFFICIENTS_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer

collapseDimensions(boolean) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder

Whether to collapse dimensions when pooling or not.

collapsedIndex - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

collapsedMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

collapsedMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

collapseThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

CollectScoresIterationListener - Class in org.deeplearning4j.optimize.listeners

CollectScoresIterationListener simply stores the model scores internally (along with the iteration) every 1 or N iterations (this is configurable).

CollectScoresIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener

Constructor for collecting scores with default saving frequency of 1

CollectScoresIterationListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener

Constructor for collecting scores with the specified frequency.

CollectScoresListener - Class in org.deeplearning4j.optimize.listeners

A simple listener that collects scores to a list every N iterations.

CollectScoresListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresListener

CollectScoresListener(int, boolean) - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresListener

ComposableInputPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor

Composable input pre processor

ComposableInputPreProcessor(InputPreProcessor...) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor

ComposableIterationListener - Class in org.deeplearning4j.optimize.listeners

Deprecated.

Not required - DL4J networks can use multiple listeners simultaneously

ComposableIterationListener(TrainingListener...) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener

Deprecated.

ComposableIterationListener(Collection<TrainingListener>) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener

Deprecated.

CompositeReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational

CompositeReconstructionDistribution is a reconstruction distribution built from multiple other ReconstructionDistribution instances.
The typical use is to combine for example continuous and binary data in the same model, or to combine different distributions for continuous variables.

CompositeReconstructionDistribution(int[], ReconstructionDistribution[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution

CompositeReconstructionDistribution.Builder - Class in org.deeplearning4j.nn.conf.layers.variational

compressor - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

ComputationGraph - Class in org.deeplearning4j.nn.graph

A ComputationGraph network is a neural network with arbitrary (directed acyclic graph) connection structure.

ComputationGraph(ComputationGraphConfiguration) - Constructor for class org.deeplearning4j.nn.graph.ComputationGraph

ComputationGraphConfiguration - Class in org.deeplearning4j.nn.conf

ComputationGraphConfiguration is a configuration object for neural networks with arbitrary connection structure.

ComputationGraphConfiguration() - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

ComputationGraphConfiguration.GraphBuilder - Class in org.deeplearning4j.nn.conf

ComputationGraphConfigurationDeserializer - Class in org.deeplearning4j.nn.conf.serde

ComputationGraphConfigurationDeserializer(JsonDeserializer<?>) - Constructor for class org.deeplearning4j.nn.conf.serde.ComputationGraphConfigurationDeserializer

ComputationGraphUpdater - Class in org.deeplearning4j.nn.updater.graph

Gradient updater for ComputationGraph.

ComputationGraphUpdater(ComputationGraph) - Constructor for class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater

ComputationGraphUpdater(ComputationGraph, INDArray, boolean) - Constructor for class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater

computationGraphUpdater - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

ComputationGraphUtil - Class in org.deeplearning4j.nn.graph.util

computeGradient(INDArray, INDArray, IActivation, INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction

computeGradientAndScore(LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Model

Update the score

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

computeGradientAndScore() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

computeGradientAndScore(INDArray, INDArray, IActivation, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

computeGradientAndScore() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

computeLossFunctionScoreArray(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution

computeOutputSize() - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D

computeOutputSize() - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer

Compute score after labels and input have been set.

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Compute score after labels and input have been set.

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer

Compute score after labels and input have been set.

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

Compute score after labels and input have been set.

computeScore(INDArray, INDArray, IActivation, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

computeScore(double, double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer

Compute score after labels and input have been set.

computeScoreArray(INDArray, INDArray, IActivation, INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer

Compute the score for each example individually, after labels and input have been set.

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Compute the score for each example individually, after labels and input have been set.

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

Compute the score for each example individually, after labels and input have been set.

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

Compute the score for each example individually, after labels and input have been set.

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer

Compute the score for each example individually, after labels and input have been set.

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

Compute the score for each example individually, after labels and input have been set.

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

Compute the score for each example individually, after labels and input have been set.

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

Compute the score for each example individually, after labels and input have been set.

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

computeScoreForExamples(double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer

Compute the score for each example individually, after labels and input have been set.

conf() - Method in interface org.deeplearning4j.nn.api.Model

The configuration for the neural network

conf() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

conf - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

conf() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

conf() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

conf - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

conf() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

conf() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

conf() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

conf - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

config - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

configuration - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

CONFIGURATION_JSON - Static variable in class org.deeplearning4j.util.ModelSerializer

configure(NeuralNetConfiguration) - Method in class org.deeplearning4j.optimize.Solver.Builder

configureR - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

configureR(boolean) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

Whether to use the specified OCNNOutputLayer.Builder.initialRValue or use the weight initialization with the neural network for the r value

confs - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

confs(List<NeuralNetConfiguration>) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

confs - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

Confusion(PrecisionRecallCurve.Point, int, int, int, int) - Constructor for class org.deeplearning4j.eval.curves.PrecisionRecallCurve.Confusion

Deprecated.

ConfusionMatrix<T extends Comparable<? super T>> - Class in org.deeplearning4j.eval

Deprecated.

Use ConfusionMatrix

ConfusionMatrix(List<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix

Deprecated.

Use ConfusionMatrix

ConfusionMatrix() - Constructor for class org.deeplearning4j.eval.ConfusionMatrix

Deprecated.

Use ConfusionMatrix

ConfusionMatrix(ConfusionMatrix<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix

Deprecated.

Use ConfusionMatrix

ConjugateGradient - Class in org.deeplearning4j.optimize.solvers

Originally based on cc.mallet.optimize.ConjugateGradient Rewritten based on Conjugate Gradient algorithm in Bengio et al., Deep Learning (in preparation) Ch8.

ConjugateGradient(NeuralNetConfiguration, StepFunction, Collection<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.ConjugateGradient

connect(List<Tree>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Connects the given trees and sets the parents of the children

ConstantDistribution - Class in org.deeplearning4j.nn.conf.distribution

Constant distribution: a "distribution" where all values are set to the specified constant

ConstantDistribution(double) - Constructor for class org.deeplearning4j.nn.conf.distribution.ConstantDistribution

Create a Constant distribution with given value

constrainAllParameters(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder

Set constraints to be applied to this layer.

constrainAllParameters(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Set constraints to be applied to all layers.

constrainBeta(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Set constraints to be applied to the beta parameter of this batch normalisation layer.

constrainBias(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder

Set constraints to be applied to bias parameters of this layer.

constrainBias(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Set constraints to be applied to all layers.

constrainGamma(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Set constraints to be applied to the gamma parameter of this batch normalisation layer.

constrainInputWeights(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder

Set constraints to be applied to the RNN input weight parameters of this layer.

constrainPointWise(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

Set constraints to be applied to the point-wise convolution weight parameters of this layer.

constrainRecurrent(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder

Set constraints to be applied to the RNN recurrent weight parameters of this layer.

constraints - Variable in class org.deeplearning4j.nn.conf.layers.Layer

constraints(List<LayerConstraint>) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Set constraints to be applied to all layers.

constraints - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

constrainWeights(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder

Set constraints to be applied to the weight parameters of this layer.

constrainWeights(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Set constraints to be applied to all layers.

consumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

contains(Object) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

containsAll(Collection<?>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

ConvexOptimizer - Interface in org.deeplearning4j.optimize.api

Convex optimizer.

Convolution1D - Class in org.deeplearning4j.nn.conf.layers

1D convolution layer.

Convolution1D() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1D

Convolution1DLayer - Class in org.deeplearning4j.nn.conf.layers

1D (temporal) convolutional layer.

Convolution1DLayer - Class in org.deeplearning4j.nn.layers.convolution

1D (temporal) convolutional layer.

Convolution1DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer

Convolution1DLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer

Convolution1DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

Convolution1DUtils - Class in org.deeplearning4j.util

Shape utilities for 1D convolution layers

Convolution2D - Class in org.deeplearning4j.nn.conf.layers

2D convolution layer

Convolution2D() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution2D

Convolution3D - Class in org.deeplearning4j.nn.conf.layers

3D convolution layer configuration

Convolution3D(Convolution3D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D

3-dimensional convolutional layer configuration nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the depth The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size

Convolution3D.Builder - Class in org.deeplearning4j.nn.conf.layers

Convolution3D.DataFormat - Enum in org.deeplearning4j.nn.conf.layers

An optional dataFormat: "NDHWC" or "NCDHW".

Convolution3DLayer - Class in org.deeplearning4j.nn.layers.convolution

3D convolution layer implementation.

Convolution3DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer

Convolution3DLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer

Convolution3DParamInitializer - Class in org.deeplearning4j.nn.params

Initialize 3D convolution parameters.

Convolution3DParamInitializer() - Constructor for class org.deeplearning4j.nn.params.Convolution3DParamInitializer

Convolution3DUtils - Class in org.deeplearning4j.util

Shape utilities for 3D convolution layers

convolutional(long, long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType

Input type for convolutional (CNN) data, that is 4d with shape [miniBatchSize, channels, height, width].

convolutional(int, int, int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder

See InputType.convolutional(long, long, long)

convolutional3D(long, long, long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType

Deprecated.

Use InputType.convolutional3D(Convolution3D.DataFormat, long, long, long, long)

convolutional3D(Convolution3D.DataFormat, long, long, long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType

Input type for 3D convolutional (CNN3D) 5d data:
If NDHWC format [miniBatchSize, depth, height, width, channels]
If NDCWH

convolutionalFlat(long, long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType

Input type for convolutional (CNN) data, where the data is in flattened (row vector) format.

convolutionalFlat(int, int, int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder

* See InputType.convolutionalFlat(long, long, long)

convolutionDim - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

ConvolutionHelper - Interface in org.deeplearning4j.nn.layers.convolution

Helper for the convolution layer.

ConvolutionLayer - Class in org.deeplearning4j.nn.conf.layers

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

ConvolutionLayer(ConvolutionLayer.BaseConvBuilder<?>) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

ConvolutionLayer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the channels The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size

ConvolutionLayer - Class in org.deeplearning4j.nn.layers.convolution

Convolution layer

ConvolutionLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

ConvolutionLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

ConvolutionLayer.AlgoMode - Enum in org.deeplearning4j.nn.conf.layers

The "PREFER_FASTEST" mode will pick the fastest algorithm for the specified parameters from the ConvolutionLayer.FwdAlgo, ConvolutionLayer.BwdFilterAlgo, and ConvolutionLayer.BwdDataAlgo lists, but they may be very memory intensive, so if weird errors occur when using cuDNN, please try the "NO_WORKSPACE" mode.

ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>> - Class in org.deeplearning4j.nn.conf.layers

ConvolutionLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

ConvolutionLayer.BwdDataAlgo - Enum in org.deeplearning4j.nn.conf.layers

The backward data algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".

ConvolutionLayer.BwdFilterAlgo - Enum in org.deeplearning4j.nn.conf.layers

The backward filter algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".

ConvolutionLayer.FwdAlgo - Enum in org.deeplearning4j.nn.conf.layers

The forward algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".

ConvolutionMode - Enum in org.deeplearning4j.nn.conf

ConvolutionMode defines how convolution operations should be executed for Convolutional and Subsampling layers, for a given input size and network configuration (specifically stride/padding/kernel sizes).
Currently, 3 modes are provided:

Strict: Output size for Convolutional and Subsampling layers are calculated as follows, in each dimension: outputSize = (inputSize - kernelSize + 2*padding) / stride + 1.

convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

Set the convolution mode for the Convolution layer.

convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder

Set the convolution mode for the Convolution layer.

convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

Set the convolution mode for the Convolution layer.

convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

Set the convolution mode for the Convolution layer.

convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

convolutionMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Sets the convolution mode for convolutional layers, which impacts padding and output sizes.

convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Sets the convolution mode for convolutional layers, which impacts padding and output sizes.

convolutionMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

ConvolutionParamInitializer - Class in org.deeplearning4j.nn.params

Initialize convolution params.

ConvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.ConvolutionParamInitializer

ConvolutionUtils - Class in org.deeplearning4j.util

Convolutional shape utilities

copyToLegacy(IEvaluation<?>, Class<T>) - Static method in class org.deeplearning4j.eval.EvaluationUtils

Deprecated.

corruptionLevel(double) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder

Level of corruption - 0.0 (none) to 1.0 (all values corrupted)

corruptionLevel - Variable in class org.deeplearning4j.nn.conf.layers.AutoEncoder

CountsForThreshold(double) - Constructor for class org.deeplearning4j.eval.ROC.CountsForThreshold

Deprecated.

CountsForThreshold(double, long, long) - Constructor for class org.deeplearning4j.eval.ROC.CountsForThreshold

Deprecated.

crashDumpOutputDirectory(File) - Static method in class org.deeplearning4j.util.CrashReportingUtil

Method that can be use to customize the output directory for memory crash reporting.

crashDumpsEnabled(boolean) - Static method in class org.deeplearning4j.util.CrashReportingUtil

Method that can be used to enable or disable memory crash reporting.

CrashReportingUtil - Class in org.deeplearning4j.util

A utility for generating crash reports when an out of memory error occurs.

createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

createBias(long, double, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

createCenterLossMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer

createDepthWiseWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

createDepthWiseWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

createDistribution(Distribution) - Static method in class org.deeplearning4j.nn.conf.distribution.Distributions

createGradient(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

createPointWiseWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

createStepFunction(StepFunction) - Static method in class org.deeplearning4j.optimize.stepfunctions.StepFunctions

createVisibleBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer

createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer

createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DeconvolutionParamInitializer

createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

createWeightMatrix(long, long, WeightInit, Distribution, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

createWeightMatrix(long, long, WeightInit, Distribution, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer

createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer

Cropping1D - Class in org.deeplearning4j.nn.conf.layers.convolutional

Cropping layer for convolutional (1d) neural networks.

Cropping1D(int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D

Cropping1D(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D

Cropping1D(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D

Cropping1D(Cropping1D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D

Cropping1D.Builder - Class in org.deeplearning4j.nn.conf.layers.convolutional

Cropping1DLayer - Class in org.deeplearning4j.nn.layers.convolution

Zero cropping layer for 1D convolutional neural networks.

Cropping1DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer

Cropping2D - Class in org.deeplearning4j.nn.conf.layers.convolutional

Cropping layer for convolutional (2d) neural networks.

Cropping2D(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D

Cropping2D(int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D

Cropping2D(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D

Cropping2D(Cropping2D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D

Cropping2D.Builder - Class in org.deeplearning4j.nn.conf.layers.convolutional

Cropping2DLayer - Class in org.deeplearning4j.nn.layers.convolution

Zero cropping layer for convolutional neural networks.

Cropping2DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer

Cropping3D - Class in org.deeplearning4j.nn.conf.layers.convolutional

Cropping layer for convolutional (3d) neural networks.

Cropping3D(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D

Cropping3D(int, int, int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D

Cropping3D(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D

Cropping3D(Cropping3D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D

Cropping3D.Builder - Class in org.deeplearning4j.nn.conf.layers.convolutional

Cropping3DLayer - Class in org.deeplearning4j.nn.layers.convolution

Cropping layer for 3D convolutional neural networks.

Cropping3DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer

CUDNN_WORKSPACE_KEY - Static variable in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

cudnnAlgoMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.

cudnnAlgoMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.

cudnnAlgoMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage of cuDNN.

cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage of cuDNN.

cudnnAlgoMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.

cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization

cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.

cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.

cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.

cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

cudnnBwdDataAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

cudnnBwdDataAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

cudnnBwdDataMode(ConvolutionLayer.BwdDataAlgo) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

cudnnBwdFilterAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

cudnnBwdFilterAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

cudnnBwdFilterMode(ConvolutionLayer.BwdFilterAlgo) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

cudnnFwdAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

cudnnFwdAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

cudnnFwdMode(ConvolutionLayer.FwdAlgo) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

currentConsumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

currentConsumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

currentStep - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

currentThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

CUSTOM_REGISTRATION_PROPERTY - Static variable in class org.deeplearning4j.nn.conf.serde.JsonMappers

Deprecated.

Use DL4JSystemProperties.CUSTOM_REGISTRATION_PROPERTY

D

dampingFactor - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder

dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer

dataFormat(Convolution3D.DataFormat) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder

dataFormat(SpaceToDepthLayer.DataFormat) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder

dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

DataSetLossCalculator - Class in org.deeplearning4j.earlystopping.scorecalc

Given a DataSetIterator: calculate the total loss for the model on that data set.

DataSetLossCalculator(DataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator

Calculate the score (loss function value) on a given data set (usually a test set)

DataSetLossCalculator(MultiDataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator

Calculate the score (loss function value) on a given data set (usually a test set)

DataSetLossCalculatorCG - Class in org.deeplearning4j.earlystopping.scorecalc

Deprecated.

Use DataSetLossCalculator instead for both MultiLayerNetwork and ComputationGraph

DataSetLossCalculatorCG(DataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG

Deprecated.

Calculate the score (loss function value) on a given data set (usually a test set)

DataSetLossCalculatorCG(MultiDataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG

Deprecated.

Calculate the score (loss function value) on a given data set (usually a test set)

dead - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

decay - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

decay(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

At test time: we can use a global estimate of the mean and variance, calculated using a moving average of the batch means/variances.

decay - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization

decode(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder

DECODER_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

decoderLayerSizes(int...) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

Size of the decoder layers, in units.

decoderLayerSizes - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

decodeUpdates(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

Deprecated.

decompressionThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

Deconvolution2D - Class in org.deeplearning4j.nn.conf.layers

2D deconvolution layer configuration
Deconvolutions are also known as transpose convolutions or fractionally strided convolutions.

Deconvolution2D(ConvolutionLayer.BaseConvBuilder<?>) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D

Deconvolution2D layer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the channels The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size

Deconvolution2D.Builder - Class in org.deeplearning4j.nn.conf.layers

Deconvolution2DLayer - Class in org.deeplearning4j.nn.layers.convolution

2D deconvolution layer implementation.

Deconvolution2DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer

Deconvolution2DLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer

DeconvolutionParamInitializer - Class in org.deeplearning4j.nn.params

DeconvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.DeconvolutionParamInitializer

DeepLearningException - Exception in org.deeplearning4j.exception

DeepLearningException() - Constructor for exception org.deeplearning4j.exception.DeepLearningException

DeepLearningException(String, Throwable, boolean, boolean) - Constructor for exception org.deeplearning4j.exception.DeepLearningException

DeepLearningException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DeepLearningException

DeepLearningException(String) - Constructor for exception org.deeplearning4j.exception.DeepLearningException

DeepLearningException(Throwable) - Constructor for exception org.deeplearning4j.exception.DeepLearningException

DEFAULT_ALPHA - Static variable in class org.deeplearning4j.nn.conf.dropout.AlphaDropout

DEFAULT_DECAY_RATE - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

DEFAULT_DECAY_RATE - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm

DEFAULT_EDGE_VALUE - Static variable in class org.deeplearning4j.eval.EvaluationBinary

Deprecated.

DEFAULT_EPS - Static variable in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

DEFAULT_EPSILON - Static variable in class org.deeplearning4j.nn.conf.constraint.BaseConstraint

DEFAULT_FLATTENING_ORDER - Static variable in class org.deeplearning4j.nn.gradient.DefaultGradient

DEFAULT_INITIAL_MEMORY - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

DEFAULT_INITIAL_THRESHOLD - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

DEFAULT_INITIAL_THRESHOLD - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm

DEFAULT_LAMBDA - Static variable in class org.deeplearning4j.nn.conf.dropout.AlphaDropout

DEFAULT_MAX_SPARSITY_TARGET - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

DEFAULT_MIN_SPARSITY_TARGET - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

DEFAULT_PRECISION - Static variable in class org.deeplearning4j.eval.EvaluationBinary

Deprecated.

DEFAULT_RATE - Static variable in class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint

DEFAULT_RESHAPE_ORDER - Static variable in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

DEFAULT_SPARSITY_TARGET - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm

DEFAULT_STATS_PRECISION - Static variable in class org.deeplearning4j.eval.ROCBinary

Deprecated.

Use ROCBinary

DEFAULT_STATS_PRECISION - Static variable in class org.deeplearning4j.eval.ROCMultiClass

Deprecated.

Use ROCMultiClass

DEFAULT_WEIGHT_INIT_ORDER - Static variable in class org.deeplearning4j.nn.weights.WeightInitUtil

Default order for the arrays created by WeightInitUtil.

defaultConfiguration - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

defaultConfiguration - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

defaultDeserializer - Variable in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer

DefaultGradient - Class in org.deeplearning4j.nn.gradient

Default gradient implementation.

DefaultGradient() - Constructor for class org.deeplearning4j.nn.gradient.DefaultGradient

DefaultGradient(INDArray) - Constructor for class org.deeplearning4j.nn.gradient.DefaultGradient

defaultNoWorkspace() - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder

Set the default to be scoped out for all array types.

DefaultParamInitializer - Class in org.deeplearning4j.nn.params

Static weight initializer with just a weight matrix and a bias

DefaultParamInitializer() - Constructor for class org.deeplearning4j.nn.params.DefaultParamInitializer

DefaultStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions

Default step function

DefaultStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction

DefaultStepFunction - Class in org.deeplearning4j.optimize.stepfunctions

Default step function

DefaultStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction

defaultWorkspace(String, WorkspaceConfiguration) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder

Set the default workspace for all array types to the specified workspace name/configuration NOTE: This will NOT override any settings previously set.

defineInputs(String...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams

Define the inputs to the DL4J SameDiff Vertex with specific names

defineInputs(int) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams

Define the inputs to the DL4J SameDiff vertex with generated names.

defineLayer(SameDiff, SDVariable, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D

defineLayer(SameDiff, SDVariable, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D

defineLayer(SameDiff, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer

The defineLayer method is used to define the foward pass for the layer

defineLayer(SameDiff, SDVariable, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer

defineLayer(SameDiff, SDVariable, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer

Define the layer

defineLayer(SameDiff, SDVariable, SDVariable, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer

Define the output layer

defineLayer(SameDiff, SDVariable) - Method in class org.deeplearning4j.nn.layers.util.IdentityLayer

defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D

defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D

defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

Define the parameters for the network.

defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer

defineParametersAndInputs(SDVertexParams) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex

defineParametersAndInputs(SDVertexParams) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

Define the parameters - and inputs - for the network.

defineVertex(SameDiff, SameDiffLambdaVertex.VertexInputs) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex

The defineVertex method is used to define the foward pass for the vertex

defineVertex(SameDiff, Map<String, SDVariable>, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex

defineVertex(SameDiff, Map<String, SDVariable>, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

Define the vertex

deleteExisting(boolean) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

If the checkpoint listener is set to save to a non-empty directory, should the CheckpointListener-related content be deleted?
This is disabled by default (and instead, an exception will be thrown if existing data is found)
WARNING: Be careful when enabling this, as it deletes all saved checkpoint models in the specified directory!

DenseLayer - Class in org.deeplearning4j.nn.conf.layers

Dense layer: a standard fully connected feed forward layer

DenseLayer - Class in org.deeplearning4j.nn.layers.feedforward.dense

DenseLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer

DenseLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer

DenseLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

depth() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Finds the channels of the tree.

depth(Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Returns the distance between this node and the specified subnode

DEPTH_WISE_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

depthMultiplier - Variable in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder

depthMultiplier(int) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder

Set channels multiplier for depth-wise convolution

depthMultiplier - Variable in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

depthMultiplier(int) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

Set channels multiplier of channels-wise step in separable convolution

DepthwiseConvolution2D - Class in org.deeplearning4j.nn.conf.layers

2D depth-wise convolution layer configuration.

DepthwiseConvolution2D(DepthwiseConvolution2D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D

DepthwiseConvolution2D.Builder - Class in org.deeplearning4j.nn.conf.layers

DepthwiseConvolution2DLayer - Class in org.deeplearning4j.nn.layers.convolution

2D depth-wise convolution layer configuration.

DepthwiseConvolution2DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer

DepthwiseConvolution2DLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer

DepthwiseConvolutionParamInitializer - Class in org.deeplearning4j.nn.params

Initialize depth-wise convolution parameters.

DepthwiseConvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.distribution.serde.LegacyDistributionDeserializer

deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer

deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.ComputationGraphConfigurationDeserializer

deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.FrozenLayerDeserializer

deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyIntArrayDeserializer

deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.MultiLayerConfigurationDeserializer

DetectedObject - Class in org.deeplearning4j.nn.layers.objdetect

A detected object, by an object detection algorithm.

DetectedObject(int, double, double, double, double, INDArray, double) - Constructor for class org.deeplearning4j.nn.layers.objdetect.DetectedObject

dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

Set dilation size for 3D convolutions in (depth, height, width) order

dilation - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

Kernel dilation.

dilation - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

dilation(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

dilation - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

dilation(int, int, int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

dilation - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Kernel dilation.

dilation - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

dimension - Variable in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

dimensions - Variable in class org.deeplearning4j.nn.conf.constraint.BaseConstraint

dist - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

dist(Distribution) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Distribution to sample initial weights from.

dist - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

dist - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

dist(Distribution) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Distribution to sample initial weights from.

dist(Distribution) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Distribution to sample initial weights from.

dist - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

distRecurrent - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder

distRecurrent - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer

Distribution - Class in org.deeplearning4j.nn.conf.distribution

An abstract distribution.

Distribution() - Constructor for class org.deeplearning4j.nn.conf.distribution.Distribution

distributionInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

distributionInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution

distributionInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

distributionInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

distributionInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper

distributionInputSize(int) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution

Get the number of distribution parameters for the given input data size.

Distributions - Class in org.deeplearning4j.nn.conf.distribution

Static methods for instantiating an nd4j distribution from a DL4J distribution configuration object.

divideByMinibatch(boolean, Gradient, int) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

DL4JException - Exception in org.deeplearning4j.exception

Base exception for DL4J

DL4JException() - Constructor for exception org.deeplearning4j.exception.DL4JException

DL4JException(String) - Constructor for exception org.deeplearning4j.exception.DL4JException

DL4JException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JException

DL4JException(Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JException

DL4JInvalidConfigException - Exception in org.deeplearning4j.exception

Exception signifying that the specified configuration is invalid

DL4JInvalidConfigException() - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException

DL4JInvalidConfigException(String) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException

DL4JInvalidConfigException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException

DL4JInvalidConfigException(Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException

DL4JInvalidInputException - Exception in org.deeplearning4j.exception

DL4J Exception thrown when invalid input is provided (wrong rank, wrong size, etc)

DL4JInvalidInputException() - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException

DL4JInvalidInputException(String) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException

DL4JInvalidInputException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException

DL4JInvalidInputException(Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Do backward pass

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex

doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

doEvaluation(DataSetIterator, T...) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork

This method executes evaluation of the model against given iterator and evaluation implementations

doEvaluation(MultiDataSetIterator, T...) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork

This method executes evaluation of the model against given iterator and evaluation implementations

doEvaluation(DataSetIterator, T...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Perform evaluation on the given data (DataSetIterator) with the given IEvaluation instance

doEvaluation(MultiDataSetIterator, T...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Perform evaluation on the given data (MultiDataSetIterator) with the given IEvaluation instance

doEvaluation(DataSetIterator, T...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Perform evaluation using an arbitrary IEvaluation instance.

doEvaluation(MultiDataSetIterator, T[]) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

doEvaluationHelper(DataSetIterator, T...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

doForward(boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Do forward pass using the stored inputs

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex

doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

doInit() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

doInit() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

doInit() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

doTruncatedBPTT(INDArray[], INDArray[], INDArray[], INDArray[], LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Fit the network using truncated BPTT

doTruncatedBPTT(INDArray, INDArray, INDArray, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

drainTo(Collection<? super E>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

drainTo(Collection<? super E>, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

drainTo(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

drainTo(long, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

DropConnect - Class in org.deeplearning4j.nn.conf.weightnoise

DropConnect, based on Wan et.

DropConnect(double) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.DropConnect

DropConnect(double, boolean) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.DropConnect

DropConnect(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.DropConnect

DropConnect(ISchedule, boolean) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.DropConnect

Dropout - Class in org.deeplearning4j.nn.conf.dropout

Implements standard (inverted) dropout.

Regarding dropout probability.

Dropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.Dropout

Dropout(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.Dropout

Dropout(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.Dropout

dropOut(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder

Dropout probability.

dropOut(IDropout) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder

Set the dropout for all layers in this network

dropOut(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Dropout probability.

dropOut(IDropout) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Set the dropout for all layers in this network
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

dropout(IDropout) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Set the dropout

dropOut(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Dropout probability.

dropout - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

dropoutApplied - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

DropoutHelper - Interface in org.deeplearning4j.nn.conf.dropout

A helper interface for native dropout implementations

DropoutLayer - Class in org.deeplearning4j.nn.conf.layers

Dropout layer.

DropoutLayer - Class in org.deeplearning4j.nn.layers

Created by davekale on 12/7/16.

DropoutLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.DropoutLayer

DropoutLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.DropoutLayer

DropoutLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

ds - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener

dsIterator - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener

dummyBias - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

dummyBiasGrad - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

DummyConfig - Class in org.deeplearning4j.nn.conf.misc

A 'dummy' training configuration for use in frozen layers

DummyConfig() - Constructor for class org.deeplearning4j.nn.conf.misc.DummyConfig

DuplicateToTimeSeriesVertex - Class in org.deeplearning4j.nn.conf.graph.rnn

DuplicateToTimeSeriesVertex is a vertex that goes from 2d activations to a 3d time series activations, by means of duplication.

DuplicateToTimeSeriesVertex(String) - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex

DuplicateToTimeSeriesVertex - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn

DuplicateToTimeSeriesVertex is a vertex that goes from 2d activations to a 3d time series activations, by means of duplication.

DuplicateToTimeSeriesVertex(ComputationGraph, String, int, String) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

DuplicateToTimeSeriesVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], String) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

E

EarlyStoppingConfiguration<T extends Model> - Class in org.deeplearning4j.earlystopping

Early stopping configuration: Specifies the various configuration options for running training with early stopping.
Users need to specify the following:
(a) EarlyStoppingModelSaver: How models will be saved (to disk, to memory, etc) (Default: in memory)
(b) Termination conditions: at least one termination condition must be specified
(i) Iteration termination conditions: calculated once for each minibatch.

EarlyStoppingConfiguration.Builder<T extends Model> - Class in org.deeplearning4j.earlystopping

EarlyStoppingGraphTrainer - Class in org.deeplearning4j.earlystopping.trainer

Class for conducting early stopping training locally (single machine).
Can be used to train a ComputationGraph

EarlyStoppingGraphTrainer(EarlyStoppingConfiguration<ComputationGraph>, ComputationGraph, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer

EarlyStoppingGraphTrainer(EarlyStoppingConfiguration<ComputationGraph>, ComputationGraph, DataSetIterator, EarlyStoppingListener<ComputationGraph>) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer

Constructor for training using a DataSetIterator

EarlyStoppingGraphTrainer(EarlyStoppingConfiguration<ComputationGraph>, ComputationGraph, MultiDataSetIterator, EarlyStoppingListener<ComputationGraph>) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer

Constructor for training using a MultiDataSetIterator

EarlyStoppingListener<T extends Model> - Interface in org.deeplearning4j.earlystopping.listener

EarlyStoppingListener is a listener interface for conducting early stopping training.

EarlyStoppingModelSaver<T extends Model> - Interface in org.deeplearning4j.earlystopping

Interface for saving MultiLayerNetworks learned during early stopping, and retrieving them again later

EarlyStoppingResult<T extends Model> - Class in org.deeplearning4j.earlystopping

EarlyStoppingResult: contains the results of the early stopping training, such as: - Why the training was terminated - Score vs.

EarlyStoppingResult(EarlyStoppingResult.TerminationReason, String, Map<Integer, Double>, int, double, int, T) - Constructor for class org.deeplearning4j.earlystopping.EarlyStoppingResult

EarlyStoppingResult.TerminationReason - Enum in org.deeplearning4j.earlystopping

EarlyStoppingTrainer - Class in org.deeplearning4j.earlystopping.trainer

Class for conducting early stopping training locally (single machine), for training a MultiLayerNetwork.

EarlyStoppingTrainer(EarlyStoppingConfiguration<MultiLayerNetwork>, MultiLayerConfiguration, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer

EarlyStoppingTrainer(EarlyStoppingConfiguration<MultiLayerNetwork>, MultiLayerNetwork, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer

EarlyStoppingTrainer(EarlyStoppingConfiguration<MultiLayerNetwork>, MultiLayerNetwork, DataSetIterator, EarlyStoppingListener<MultiLayerNetwork>) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer

effectiveKernelSize(int, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils

effectiveKernelSize(int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils

element() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

ElementWiseMultiplicationLayer - Class in org.deeplearning4j.nn.conf.layers.misc

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

ElementWiseMultiplicationLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer

ElementWiseMultiplicationLayer(ElementWiseMultiplicationLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer

ElementWiseMultiplicationLayer - Class in org.deeplearning4j.nn.layers.feedforward.elementwise

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

ElementWiseMultiplicationLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer

ElementWiseMultiplicationLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer

ElementWiseMultiplicationLayer.Builder - Class in org.deeplearning4j.nn.conf.layers.misc

ElementWiseParamInitializer - Class in org.deeplearning4j.nn.params

created by jingshu

ElementWiseParamInitializer() - Constructor for class org.deeplearning4j.nn.params.ElementWiseParamInitializer

ElementWiseVertex - Class in org.deeplearning4j.nn.conf.graph

An ElementWiseVertex is used to combine the activations of two or more layer in an element-wise manner
For example, the activations may be combined by addition, subtraction, multiplication (product), average or by selecting the maximum.
Addition, Average, Max and Product may use an arbitrary number of input arrays.

ElementWiseVertex(ElementWiseVertex.Op) - Constructor for class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

ElementWiseVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

An ElementWiseVertex is used to combine the activations of two or more layer in an element-wise manner
For example, the activations may be combined by addition, subtraction or multiplication or by selecting the maximum.

ElementWiseVertex(ComputationGraph, String, int, ElementWiseVertex.Op) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex

ElementWiseVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], ElementWiseVertex.Op) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex

ElementWiseVertex.Op - Enum in org.deeplearning4j.nn.conf.graph

ElementWiseVertex.Op - Enum in org.deeplearning4j.nn.graph.vertex.impl

EmbeddingLayer - Class in org.deeplearning4j.nn.conf.layers

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

EmbeddingLayer - Class in org.deeplearning4j.nn.layers.feedforward.embedding

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

EmbeddingLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer

EmbeddingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

EmbeddingSequenceLayer - Class in org.deeplearning4j.nn.conf.layers

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.

EmbeddingSequenceLayer - Class in org.deeplearning4j.nn.layers.feedforward.embedding

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.

EmbeddingSequenceLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer

EmbeddingSequenceLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

EmptyParamInitializer - Class in org.deeplearning4j.nn.params

EmptyParamInitializer() - Constructor for class org.deeplearning4j.nn.params.EmptyParamInitializer

encode(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder

EncodedGradientsAccumulator - Class in org.deeplearning4j.optimize.solvers.accumulation

This GradientsAccumulator is suited for CUDA backend.

EncodedGradientsAccumulator(int, double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

EncodedGradientsAccumulator(int, ThresholdAlgorithm, ResidualPostProcessor, boolean) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

EncodedGradientsAccumulator(int, MessageHandler, long, int, Double, boolean) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

EncodedGradientsAccumulator.Builder - Class in org.deeplearning4j.optimize.solvers.accumulation

ENCODER_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

encoderLayerSizes(int...) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

Size of the encoder layers, in units.

encoderLayerSizes - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

encodeUpdates(int, int, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

encodingDebugMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

encodingDebugMode(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

encodingDebugMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

encodingDebugMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

EncodingHandler - Class in org.deeplearning4j.optimize.solvers.accumulation

This MessageHandler implementation is suited for debugging mostly, but still can be used in production environment if you really want that.

EncodingHandler(ThresholdAlgorithm, ResidualPostProcessor, Double, boolean) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

epochCount - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

epochCount - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

epochCount - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

epochCount - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

epochCount - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

EpochTerminationCondition - Interface in org.deeplearning4j.earlystopping.termination

Interface for termination conditions to be evaluated once per epoch (i.e., once per pass of the full data set), based on a score and epoch number

epochTerminationConditions(EpochTerminationCondition...) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder

Termination conditions to be evaluated every N epochs, with N set by evaluateEveryNEpochs option

epochTerminationConditions(List<EpochTerminationCondition>) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder

Termination conditions to be evaluated every N epochs, with N set by evaluateEveryNEpochs option

eps - Variable in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

eps - Variable in class org.deeplearning4j.nn.conf.graph.L2Vertex

eps - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

eps(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Epsilon value for batch normalization; small floating point value added to variance (algorithm 1 in http://arxiv.org/pdf/1502.03167v3.pdf) to reduce/avoid underflow issues.
Default: 1e-5

eps - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization

eps - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

eps(double) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

eps - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

epsilon - Variable in class org.deeplearning4j.nn.conf.constraint.BaseConstraint

epsilon - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution

equals(Object) - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex

equals(Object) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction

equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction

equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction

equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction

equals(Object) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Indicates whether some other object is "equal to" this one.

equals(Object) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

equals(Object) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Indicates whether some other object is "equal to" this one.

equals(Object) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

error() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Returns the prediction error for this node

errorIfGraphIfMLN() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

errorSum() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Returns the total prediction error for this tree and its children

esConfig - Variable in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer

evalAtIndex(IEvaluation, INDArray[], INDArray[], int) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener

evaluate(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network (classification performance - single output ComputationGraphs only)

evaluate(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network (classification performance - single output ComputationGraphs only)

evaluate(DataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network on the provided data set (single output ComputationGraphs only).

evaluate(MultiDataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network on the provided data set (single output ComputationGraphs only).

evaluate(DataSetIterator, List<String>, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network (for classification) on the provided data set, with top N accuracy in addition to standard accuracy.

evaluate(MultiDataSetIterator, List<String>, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network (for classification) on the provided data set, with top N accuracy in addition to standard accuracy.

evaluate(DataSetIterator, Map<Integer, T[]>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Perform evaluation for networks with multiple outputs.

evaluate(MultiDataSetIterator, Map<Integer, T[]>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Perform evaluation for networks with multiple outputs.

evaluate(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Evaluate the network (classification performance)

evaluate(DataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Evaluate the network on the provided data set.

evaluate(DataSetIterator, List<String>, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Evaluate the network (for classification) on the provided data set, with top N accuracy in addition to standard accuracy.

evaluateEveryNEpochs(int) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder

How frequently should evaluations be conducted (in terms of epochs)? Defaults to every (1) epochs.

evaluateRegression(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the (single output layer only) network for regression performance

evaluateRegression(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the (single output layer only) network for regression performance

evaluateRegression(DataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the (single output layer only) network for regression performance

evaluateRegression(MultiDataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the (single output layer only) network for regression performance

evaluateRegression(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Evaluate the network for regression performance

evaluateROC(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network (must be a binary classifier) on the specified data, using the ROC class.

evaluateROC(DataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network (must be a binary classifier) on the specified data, using the ROC class

evaluateROC(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network (must be a binary classifier) on the specified data, using the ROC class.

evaluateROC(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network (must be a binary classifier) on the specified data, using the ROC class

evaluateROC(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Evaluate the network (must be a binary classifier) on the specified data, using the ROC class.

evaluateROC(DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Evaluate the network (must be a binary classifier) on the specified data, using the ROC class

evaluateROCMultiClass(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network on the specified data, using the ROCMultiClass class.

evaluateROCMultiClass(DataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network on the specified data, using the ROCMultiClass class

evaluateROCMultiClass(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Evaluate the network on the specified data, using the ROCMultiClass class

evaluateROCMultiClass(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Evaluate the network on the specified data, using the ROCMultiClass class.

evaluateROCMultiClass(DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Evaluate the network on the specified data, using the ROCMultiClass class

evaluation - Variable in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator

evaluation - Variable in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator

Evaluation - Class in org.deeplearning4j.eval

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation() - Constructor for class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation(int) - Constructor for class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation(int, Integer) - Constructor for class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation(List<String>) - Constructor for class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation(Map<Integer, String>) - Constructor for class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation(List<String>, int) - Constructor for class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation(double) - Constructor for class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation(double, Integer) - Constructor for class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation(INDArray) - Constructor for class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation(List<String>, INDArray) - Constructor for class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

Evaluation.Metric - Enum in org.deeplearning4j.eval

Deprecated.

EvaluationAveraging - Enum in org.deeplearning4j.eval

Deprecated.

Use EvaluationAveraging

EvaluationBinary - Class in org.deeplearning4j.eval

Deprecated.

EvaluationBinary(INDArray) - Constructor for class org.deeplearning4j.eval.EvaluationBinary

Deprecated.

EvaluationBinary(int, Integer) - Constructor for class org.deeplearning4j.eval.EvaluationBinary

Deprecated.

EvaluationCalibration - Class in org.deeplearning4j.eval

Deprecated.

Use EvaluationCalibration

EvaluationCalibration() - Constructor for class org.deeplearning4j.eval.EvaluationCalibration

Deprecated.

Use EvaluationCalibration

EvaluationCalibration(int, int) - Constructor for class org.deeplearning4j.eval.EvaluationCalibration

Deprecated.

Use EvaluationCalibration

EvaluationCalibration(int, int, boolean) - Constructor for class org.deeplearning4j.eval.EvaluationCalibration

Deprecated.

Use EvaluationCalibration

EvaluationCallback - Interface in org.deeplearning4j.optimize.listeners.callbacks

This interface describes callback, which can be used with EvaluativeListener, to extend its functionality.

evaluations - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener

EvaluationUtils - Class in org.deeplearning4j.eval

Deprecated.

Use EvaluationUtils

EvaluationUtils() - Constructor for class org.deeplearning4j.eval.EvaluationUtils

Deprecated.

EvaluativeListener - Class in org.deeplearning4j.optimize.listeners

This TrainingListener implementation provides simple way for model evaluation during training.

EvaluativeListener(DataSetIterator, int) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

Evaluation will be launched after each *frequency* iterations, with Evaluation datatype

EvaluativeListener(DataSetIterator, int, InvocationType) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

EvaluativeListener(MultiDataSetIterator, int) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

Evaluation will be launched after each *frequency* iterations, with Evaluation datatype

EvaluativeListener(MultiDataSetIterator, int, InvocationType) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

EvaluativeListener(DataSetIterator, int, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

Evaluation will be launched after each *frequency* iteration

EvaluativeListener(DataSetIterator, int, InvocationType, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

Evaluation will be launched after each *frequency* iteration

EvaluativeListener(MultiDataSetIterator, int, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

Evaluation will be launched after each *frequency* iteration

EvaluativeListener(MultiDataSetIterator, int, InvocationType, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

Evaluation will be launched after each *frequency* iteration

EvaluativeListener(DataSet, int, InvocationType) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

EvaluativeListener(MultiDataSet, int, InvocationType) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

EvaluativeListener(DataSet, int, InvocationType, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

EvaluativeListener(MultiDataSet, int, InvocationType, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener

exampleCount - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

exampleNegLogProbability(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

exampleNegLogProbability(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution

exampleNegLogProbability(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

exampleNegLogProbability(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

exampleNegLogProbability(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper

exampleNegLogProbability(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution

Calculate the negative log probability for each example individually

expectedConsumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

ExponentialReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational

Exponential reconstruction distribution.
Supports data in range [0,infinity)

ExponentialReconstructionDistribution() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

ExponentialReconstructionDistribution(String) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

Deprecated.

Use ExponentialReconstructionDistribution.ExponentialReconstructionDistribution(Activation)

ExponentialReconstructionDistribution(Activation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

ExponentialReconstructionDistribution(IActivation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

exportScores(OutputStream) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener

Export the scores in tab-delimited (one per line) UTF-8 format.

exportScores(OutputStream, String) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener

Export the scores in delimited (one per line) UTF-8 format with the specified delimiter

exportScores(File) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener

Export the scores to the specified file in delimited (one per line) UTF-8 format, tab delimited

exportScores(File, String) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener

Export the scores to the specified file in delimited (one per line) UTF-8 format, using the specified delimiter

extCounter - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

externalSource - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

externalUpdatesAvailable - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

F

f1(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation

Deprecated.

f1Score(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier

Sets the input and labels and returns a score for the prediction wrt true labels

f1Score(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier

Returns the f1 score for the given examples.

f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Sets the input and labels and returns a score for the prediction wrt true labels

f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Returns the f1 score for the given examples.

f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

Returns the f1 score for the given examples.

f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

Returns the f1 score for the given examples.

f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer

Sets the input and labels and returns a score for the prediction wrt true labels

f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer

Returns the f1 score for the given examples.

f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

Returns the f1 score for the given examples.

f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

Returns the f1 score for the given examples.

f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

Returns the f1 score for the given examples.

f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

f1Score(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Sets the input and labels and returns a score for the prediction wrt true labels

f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Sets the input and labels and returns a score for the prediction wrt true labels

fa - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

FailureTestingListener - Class in org.deeplearning4j.optimize.listeners

WARNING: THIS LISTENER SHOULD ONLY BE USED FOR MANUAL TESTING PURPOSES
It intentionally causes various types of failures according to some criteria, in order to test the response to it.
This is useful for example in: (a) Testing Spark fault tolerance
(b) Testing OOM exception crash dump information
Generally it should not be used in unit tests either, depending on how it is configured.

Two aspects need to be configured to use this listener: 1.

FailureTestingListener(FailureTestingListener.FailureMode, FailureTestingListener.FailureTrigger) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener

FailureTestingListener.And - Class in org.deeplearning4j.optimize.listeners

FailureTestingListener.CallType - Enum in org.deeplearning4j.optimize.listeners

FailureTestingListener.FailureMode - Enum in org.deeplearning4j.optimize.listeners

FailureTestingListener.FailureTrigger - Class in org.deeplearning4j.optimize.listeners

FailureTestingListener.HostNameTrigger - Class in org.deeplearning4j.optimize.listeners

FailureTestingListener.IterationEpochTrigger - Class in org.deeplearning4j.optimize.listeners

FailureTestingListener.Or - Class in org.deeplearning4j.optimize.listeners

FailureTestingListener.RandomProb - Class in org.deeplearning4j.optimize.listeners

FailureTestingListener.TimeSinceInitializedTrigger - Class in org.deeplearning4j.optimize.listeners

FailureTestingListener.UserNameTrigger - Class in org.deeplearning4j.optimize.listeners

FailureTrigger() - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureTrigger

fallbackToSingleConsumerMode(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

fallbackToSingleConsumerMode(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

fallbackToSingleConsumerMode(boolean) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.Registerable

This method enables/disables bypass mode

falseNegativeRate(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation

Deprecated.

falsePositiveRate(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation

Deprecated.

FancyBlockingQueue<E> - Class in org.deeplearning4j.optimize.solvers.accumulation

This BlockingQueue implementation is suited only for symmetric gradients updates, and should NOT be used anywhere else.

FancyBlockingQueue(BlockingQueue<E>) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

FancyBlockingQueue(BlockingQueue<E>, int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

fBeta(double, EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation

Deprecated.

featurize(MultiDataSet) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

During training frozen vertices/layers can be treated as "featurizing" the input The forward pass through these frozen layer/vertices can be done in advance and the dataset saved to disk to iterate quickly on the smaller unfrozen part of the model Currently does not support datasets with feature masks

featurize(DataSet) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

During training frozen vertices/layers can be treated as "featurizing" the input The forward pass through these frozen layer/vertices can be done in advance and the dataset saved to disk to iterate quickly on the smaller unfrozen part of the model Currently does not support datasets with feature masks

feedForward(long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType

InputType for feed forward network data

feedForward(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder

See InputType.feedForward(long)

feedForward(INDArray, int, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Conduct forward pass using a single input array.

feedForward(INDArray[], int, boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Conduct forward pass using an array of inputs.

feedForward(INDArray[], int, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Conduct forward pass using an array of inputs

feedForward(boolean, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Conduct forward pass using the stored inputs

feedForward(INDArray, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Conduct forward pass using a single input array.

feedForward(INDArray[], boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Conduct forward pass using an array of inputs

feedForward(INDArray[], boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Conduct forward pass using an array of inputs.

feedForward() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Conduct forward pass using the stored inputs, at test time

feedForward(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Conduct forward pass using the stored inputs

feedForward(boolean, boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

feedForward(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Compute activations from input to output of the output layer

feedForward(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Compute activations from input to output of the output layer

feedForward(boolean, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Perform feed-forward, optionally (not) clearing the layer input arrays.
Note: when using clearInputs=false, there can be some performance and memory overhead: this is because the arrays are defined outside of workspaces (which are enabled by default) - otherwise, old/invalidated arrays could still be accessed after calling this method.

feedForward() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Compute activations from input to output of the output layer

feedForward(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Compute activations from input to output of the output layer

feedForward(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Compute the activations from the input to the output layer, given mask arrays (that may be null) The masking arrays are used in situations such an one-to-many and many-to-one rucerrent neural network (RNN) designs, as well as for supporting time series of varying lengths within the same minibatch for RNNs.

FeedForwardLayer - Class in org.deeplearning4j.nn.conf.layers

Created by jeffreytang on 7/21/15.

FeedForwardLayer(FeedForwardLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

FeedForwardLayer.Builder<T extends FeedForwardLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers

feedForwardMaskArray(INDArray, MaskState, int) - Method in interface org.deeplearning4j.nn.api.Layer

Feed forward the input mask array, setting in in the layer as appropriate.

feedForwardMaskArray(INDArray, MaskState, int) - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex

feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

FeedForwardToCnn3DPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor

A preprocessor to allow 3D CNN and standard feed-forward network layers to be used together.
For example, DenseLayer -> Convolution3D
This does two things:
(a) Reshapes activations out of FeedFoward layer (which is 2D with shape [numExamples, inputDepth*inputHeight*inputWidth*numChannels]) into 5d activations (with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) suitable to feed into CNN layers.
(b) Reshapes 5d epsilons from 3D CNN layer, with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) into 2d epsilons (with shape [numExamples, inputDepth*inputHeight*inputWidth*numChannels]) for use in feed forward layer

FeedForwardToCnn3DPreProcessor(int, int, int, int, boolean) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor

FeedForwardToCnn3DPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor

FeedForwardToCnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor

A preprocessor to allow CNN and standard feed-forward network layers to be used together.
For example, DenseLayer -> CNN
This does two things:
(a) Reshapes activations out of FeedFoward layer (which is 2D or 3D with shape [numExamples, inputHeight*inputWidth*numChannels]) into 4d activations (with shape [numExamples, numChannels, inputHeight, inputWidth]) suitable to feed into CNN layers.
(b) Reshapes 4d epsilons (weights*deltas) from CNN layer, with shape [numExamples, numChannels, inputHeight, inputWidth]) into 2d epsilons (with shape [numExamples, inputHeight*inputWidth*numChannels]) for use in feed forward layer Note: numChannels is equivalent to channels or featureMaps referenced in different literature

FeedForwardToCnnPreProcessor(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor

Reshape to a channels x rows x columns tensor

FeedForwardToCnnPreProcessor(long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor

feedForwardToLayer(int, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Compute the activations from the input to the specified layer.
To compute activations for all layers, use feedForward(...) methods
Note: output list includes the original input.

feedForwardToLayer(int, INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Compute the activations from the input to the specified layer.
To compute activations for all layers, use feedForward(...) methods
Note: output list includes the original input.

feedForwardToLayer(int, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Compute the activations from the input to the specified layer, using the currently set input for the network.
To compute activations for all layers, use feedForward(...) methods
Note: output list includes the original input.

FeedForwardToRnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor

A preprocessor to allow RNN and feed-forward network layers to be used together.
For example, DenseLayer -> GravesLSTM
This does two things:
(a) Reshapes activations out of FeedFoward layer (which is 2D with shape [miniBatchSize*timeSeriesLength,layerSize]) into 3d activations (with shape [miniBatchSize,layerSize,timeSeriesLength]) suitable to feed into RNN layers.
(b) Reshapes 3d epsilons (weights*deltas from RNN layer, with shape [miniBatchSize,layerSize,timeSeriesLength]) into 2d epsilons (with shape [miniBatchSize*timeSeriesLength,layerSize]) for use in feed forward layer

FeedForwardToRnnPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor

ffToLayerActivationsDetached(boolean, FwdPassType, boolean, int, int[], INDArray[], INDArray[], INDArray[], boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Feed-forward through the network - returning all array activations detached from any workspace.

ffToLayerActivationsDetached(boolean, FwdPassType, boolean, int, INDArray, INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Feed-forward through the network - returning all array activations in a list, detached from any workspace.

ffToLayerActivationsInWS(boolean, int, int[], FwdPassType, boolean, INDArray[], INDArray[], INDArray[], boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Feed-forward through the network - if workspaces are used, all returned activations will be present in workspace WS_ALL_LAYERS_ACT.
Note: if using workspaces for training, requires that WS_ALL_LAYERS_ACT is open externally.

ffToLayerActivationsInWs(int, FwdPassType, boolean, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Feed-forward through the network at training time - returning a list of all activations in a workspace (WS_ALL_LAYERS_ACT) if workspaces are enabled for training; or detached if no workspaces are used.
Note: if using workspaces for training, this method requires that WS_ALL_LAYERS_ACT is open externally.
If using NO workspaces, requires that no external workspace is open
Note that this method does NOT clear the inputs to each layer - instead, they are in the WS_ALL_LAYERS_ACT workspace for use in later backprop.

finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator

finalScore(U) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator

finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

finalScore(Evaluation) - Method in class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator

finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator

finalScore(RegressionEvaluation) - Method in class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator

finalScore(IEvaluation) - Method in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator

finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator

finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

FineTuneConfiguration - Class in org.deeplearning4j.nn.transferlearning

Configuration for fine tuning.

FineTuneConfiguration() - Constructor for class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

fineTuneConfiguration(FineTuneConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Fine tune configurations specified will overwrite the existing configuration if any Usage example: specify a learning rate will set specified learning rate on all layers Refer to the fineTuneConfiguration class for more details

fineTuneConfiguration(FineTuneConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Set parameters to selectively override existing learning parameters Usage eg.

FineTuneConfiguration.Builder - Class in org.deeplearning4j.nn.transferlearning

firstChild() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

firstNotAppliedIndexEverywhere() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

firstOne - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer

fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer

fit() - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer

fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer

fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer

fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer

fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer

fit() - Method in interface org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer

Conduct early stopping training

fit(DataSetIterator) - Method in interface org.deeplearning4j.nn.api.Classifier

Train the model based on the datasetiterator

fit(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier

Fit the model

fit(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier

Fit the model

fit(INDArray, int[]) - Method in interface org.deeplearning4j.nn.api.Classifier

Fit the model

fit() - Method in interface org.deeplearning4j.nn.api.Model

Deprecated.

fit(INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Model

Fit the model to the given data

fit(DataSet) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork

This method fits model with a given DataSet

fit(MultiDataSet) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork

This method fits model with a given MultiDataSet

fit(DataSetIterator) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork

This method fits model with a given DataSetIterator

fit(MultiDataSetIterator) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork

This method fits model with a given MultiDataSetIterator

fit(DataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Fit the ComputationGraph using a DataSet.

fit(DataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Perform minibatch training on all minibatches in the DataSetIterator, for the specified number of epochs.

fit(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Fit the ComputationGraph using a DataSetIterator.
Note that this method can only be used with ComputationGraphs with 1 input and 1 output
Method doesn't do layerwise pretraining.
For pretraining use method pretrain..

fit(MultiDataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Fit the ComputationGraph using a MultiDataSet

fit(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Perform minibatch training on all minibatches in the MultiDataSetIterator, for the specified number of epochs.

fit(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Fit the ComputationGraph using a MultiDataSetIterator Method doesn't do layerwise pretraining.
For pretraining use method pretrain..

fit(INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Fit the ComputationGraph given arrays of inputs and labels.

fit(INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Fit the ComputationGraph using the specified inputs and labels (and mask arrays)

fit() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

fit() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

fit() - Method in class org.deeplearning4j.nn.layers.BaseLayer

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer

fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Fit the model

fit(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Fit the model

fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Fit the model

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

fit(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

fit(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

fit() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

fit() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

fit() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

fit() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.DropoutLayer

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer

fit() - Method in class org.deeplearning4j.nn.layers.FrozenLayer

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

fit() - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer

fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.LossLayer

fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer

Fit the model

fit(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer

Fit the model

fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.LossLayer

Fit the model

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

fit(DataSet) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

fit() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

fit(DataSet) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

fit() - Method in class org.deeplearning4j.nn.layers.RepeatVector

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.RepeatVector

fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

fit(DataSet) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

fit() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

fit() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

fit(DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Perform minibatch training on all minibatches in the DataSetIterator, for the specified number of epochs.

fit(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Perform minibatch training on all minibatches in the DataSetIterator.
Note that this method does not do layerwise pretraining.
For pretraining use method pretrain..

fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Fit the model

fit(INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Fit the model

fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

fit(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Fit the model

fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Fit the model

fit() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

fit(MultiDataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

fit(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Perform minibatch training on all minibatches in the MultiDataSetIterator, for the specified number of epochs.

fit(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Perform minibatch training on all minibatches in the MultiDataSetIterator.
Note: The MultiDataSets in the MultiDataSetIterator must have exactly 1 input and output array (as MultiLayerNetwork only supports 1 input and 1 output)

fitFeaturized(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

Fit from a featurized dataset.

fitFeaturized(MultiDataSet) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

fitFeaturized(DataSet) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

fitFeaturized(DataSetIterator) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

FixedAlgorithmThresholdReducer() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer

FixedThresholdAlgorithm - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold

A simple fixed threshold algorithm, not adaptive in any way.

FixedThresholdAlgorithm() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm

FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold

flattenedGradients - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

flattenedGradients - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

flattenedParams - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

flattenedParams - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

flatteningOrderForVariable(String) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient

flatteningOrderForVariable(String) - Method in interface org.deeplearning4j.nn.gradient.Gradient

Return the gradient flattening order for the specified variable, or null if it is not explicitly set

fn - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

fn - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

forgetGateBiasInit - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder

forgetGateBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder

Set forget gate bias initalizations.

forgetGateBiasInit - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM

forgetGateBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder

Deprecated.

Set forget gate bias initalizations.

format(double) - Static method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

formatter - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

formatter2 - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

FORWARD_PREFIX - Static variable in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

frequency - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener

from - Variable in class org.deeplearning4j.nn.conf.graph.UnstackVertex

fromFileString(String) - Static method in class org.deeplearning4j.optimize.listeners.Checkpoint

fromIActivation(IActivation) - Static method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayerUtils

fromJson(String) - Static method in class org.deeplearning4j.eval.curves.Histogram

Deprecated.

Use Histogram

fromJson(String) - Static method in class org.deeplearning4j.eval.curves.RocCurve

Deprecated.

Use RocCurve

fromJson(String) - Static method in class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

fromJson(String) - Static method in class org.deeplearning4j.eval.EvaluationBinary

Deprecated.

fromJson(String) - Static method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

Create a computation graph configuration from json

fromJson(String) - Static method in class org.deeplearning4j.nn.conf.memory.MemoryReport

fromJson(String) - Static method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

Create a neural net configuration from json

fromJson(String) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

Create a neural net configuration from json

fromJson(String) - Static method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

fromYaml(String) - Static method in class org.deeplearning4j.eval.curves.Histogram

Deprecated.

Use Histogram

fromYaml(String) - Static method in class org.deeplearning4j.eval.curves.RocCurve

Deprecated.

Use RocCurve

fromYaml(String) - Static method in class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

fromYaml(String) - Static method in class org.deeplearning4j.eval.EvaluationBinary

Deprecated.

fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

Create a neural net configuration from YAML

fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.memory.MemoryReport

fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

Create a neural net configuration from json

fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

Create a neural net configuration from json

fromYaml(String) - Static method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

FrozenLayer - Class in org.deeplearning4j.nn.conf.layers.misc

FrozenLayer is used for the purposes of transfer learning.
A frozen layer wraps another DL4J Layer within it.

FrozenLayer(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

FrozenLayer - Class in org.deeplearning4j.nn.layers

For purposes of transfer learning A frozen layers wraps another dl4j layer within it.

FrozenLayer(Layer) - Constructor for class org.deeplearning4j.nn.layers.FrozenLayer

FrozenLayer.Builder - Class in org.deeplearning4j.nn.conf.layers.misc

FrozenLayerDeserializer - Class in org.deeplearning4j.nn.conf.serde

A custom deserializer for handling Frozen layers This is used to handle the 2 different Layer JSON formats - old/legacy, and current

FrozenLayerDeserializer() - Constructor for class org.deeplearning4j.nn.conf.serde.FrozenLayerDeserializer

FrozenLayerParamInitializer - Class in org.deeplearning4j.nn.params

Parameter initializer for FrozenLayer instances.

FrozenLayerParamInitializer() - Constructor for class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

FrozenLayerWithBackprop - Class in org.deeplearning4j.nn.conf.layers.misc

Frozen layer freezes parameters of the layer it wraps, but allows the backpropagation to continue.

FrozenLayerWithBackprop(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

FrozenLayerWithBackprop - Class in org.deeplearning4j.nn.layers

Frozen layer freezes parameters of the layer it wraps, but allows the backpropagation to continue.

FrozenLayerWithBackprop(Layer) - Constructor for class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

FrozenLayerWithBackpropParamInitializer - Class in org.deeplearning4j.nn.params

Parameter initializer for FrozenLayer instances.

FrozenLayerWithBackpropParamInitializer() - Constructor for class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

FrozenVertex - Class in org.deeplearning4j.nn.conf.graph

FrozenVertex is used for the purposes of transfer learning.
A frozen vertex wraps another DL4J GraphVertex within it.

FrozenVertex(GraphVertex) - Constructor for class org.deeplearning4j.nn.conf.graph.FrozenVertex

FrozenVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

FrozenVertex is used for the purposes of transfer learning A frozen layers wraps another DL4J GraphVertex within it.

FrozenVertex(GraphVertex) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.FrozenVertex

fwdPassOutput - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

fwdPassOutputAsArrays - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

FwdPassReturn - Class in org.deeplearning4j.nn.layers.recurrent

Created by benny on 12/31/15.

FwdPassReturn() - Constructor for class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

FwdPassType - Enum in org.deeplearning4j.nn.api

Type of forward pass to do.

fz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

G

ga - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

gamma - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

gamma(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

Used only when 'true' is passed to BatchNormalization.Builder.lockGammaBeta(boolean).

gamma - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization

GAMMA - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

gammaConstraints - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

gateActivationFn - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder

gateActivationFn - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM

gateActivationFunction(String) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder

Activation function for the LSTM gates.

gateActivationFunction(Activation) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder

Activation function for the LSTM gates.

gateActivationFunction(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder

Activation function for the LSTM gates.

gateActivationFunction(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder

Deprecated.

Activation function for the LSTM gates.

gateActivationFunction(Activation) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder

Deprecated.

Activation function for the LSTM gates.

gateActivationFunction(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder

Deprecated.

Activation function for the LSTM gates.

GaussianDistribution - Class in org.deeplearning4j.nn.conf.distribution

Deprecated.

Use NormalDistribution which is identical to this implementation

GaussianDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.GaussianDistribution

Deprecated.

Create a gaussian distribution (equivalent to normal) with the given mean and std

GaussianDropout - Class in org.deeplearning4j.nn.conf.dropout

Gaussian dropout.

GaussianDropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianDropout

GaussianDropout(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianDropout

GaussianDropout(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianDropout

GaussianNoise - Class in org.deeplearning4j.nn.conf.dropout

Applies additive, mean-zero Gaussian noise to the input - i.e., x = x + N(0,stddev).
Note that this differs from GaussianDropout, which applies multiplicative mean-1 N(1,s) noise.
Note also that schedules for the standard deviation value can also be used.

GaussianNoise(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianNoise

GaussianNoise(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianNoise

GaussianNoise(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianNoise

GaussianReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational

Gaussian reconstruction distribution for variational autoencoder.
Outputs are modelled by a Gaussian distribution, with the mean and variances (diagonal covariance matrix) for each output determined by the network forward pass.

GaussianReconstructionDistribution() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

Create a GaussianReconstructionDistribution with the default identity activation function.

GaussianReconstructionDistribution(Activation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

GaussianReconstructionDistribution(IActivation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

generalValidation(String, Layer, IDropout, Double, Double, Double, Double, Distribution, List<LayerConstraint>, List<LayerConstraint>, List<LayerConstraint>) - Static method in class org.deeplearning4j.nn.conf.layers.LayerValidation

generalValidation(String, Layer, IDropout, double, double, double, double, Distribution, List<LayerConstraint>, List<LayerConstraint>, List<LayerConstraint>) - Static method in class org.deeplearning4j.nn.conf.layers.LayerValidation

generateAtMean(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

generateAtMean(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution

generateAtMean(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

generateAtMean(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

generateAtMean(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper

generateAtMean(INDArray) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution

Generate a sample from P(x|z), where x = E[P(x|z)] i.e., return the mean value for the distribution

generateAtMeanGivenZ(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

Given a specified values for the latent space as input (latent space being z in p(z|data)), generate output from P(x|z), where x = E[P(x|z)]
i.e., return the mean value for the distribution P(x|z)

generateMemoryStatus(Model, int, InputType...) - Static method in class org.deeplearning4j.util.CrashReportingUtil

Generate memory/system report as a String, for the specified network.

generateRandom(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

generateRandom(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution

generateRandom(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

generateRandom(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

generateRandom(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper

generateRandom(INDArray) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution

Randomly sample from P(x|z) using the specified distribution parameters

generateRandomGivenZ(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

Given a specified values for the latent space as input (latent space being z in p(z|data)), randomly generate output x, where x ~ P(x|z)

get0(INDArray[]) - Static method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

get3DOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[], boolean) - Static method in class org.deeplearning4j.util.Convolution3DUtils

Get the output size (depth/height/width) for the given input data and CNN3D configuration

get3DSameModeTopLeftPadding(int[], int[], int[], int[], int[]) - Static method in class org.deeplearning4j.util.Convolution3DUtils

Get top and left padding for same mode only for 3d convolutions

getAlpha() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

getAverageThresholdAlgorithm() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

This should ONLY be called once all training threads have completed

getBegin() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

getBestModel() - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver

Retrieve the best model that was previously saved

getBestModel() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingResult

getBestModel() - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver

getBestModel() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver

getBestModel() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver

getBiasParameterKeys() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

getBottomRightXY() - Method in class org.deeplearning4j.nn.layers.objdetect.DetectedObject

Get the bottom right X/Y coordinates of the detected object

getBroadcastDims(int[], int) - Static method in class org.deeplearning4j.nn.conf.constraint.BaseConstraint

getBytesPerElement(DataBuffer.Type) - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport

getChildren() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

getComputationGraphUpdater() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

getComputationGraphUpdater() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

getConf(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

getConf() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

getConf() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

getConfidenceMatrix(INDArray, int, int) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

Get the confidence matrix (confidence for all x/y positions) for the specified bounding box, from the network output activations array

getConfig() - Method in interface org.deeplearning4j.nn.api.Trainable

getConfig() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

getConfig() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getConfig() - Method in class org.deeplearning4j.nn.graph.vertex.impl.FrozenVertex

getConfig() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

getConfig() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getConfig() - Method in class org.deeplearning4j.nn.layers.FrozenLayer

getConfig() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getConfig() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

getConfig() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

getConfig() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getConfig() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getConfiguration() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

This method returns configuration of this ComputationGraph

getCorruptedInput(INDArray, double) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

Corrupts the given input by doing a binomial sampling given the corruption level

getDeconvolutionOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Get the output size of a deconvolution operation for given input data.

getDefaultConfiguration() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getDefaultStepFunctionForOptimizer(Class<? extends ConvexOptimizer>) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

getDelta() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

getDelta(long) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

getDepth() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional

Deprecated.

getDeserializedType() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyGraphVertexDeserializer

getDeserializedType() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyLayerDeserializer

getDeserializedType() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyPreprocessorDeserializer

getDeserializedType() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyReconstructionDistributionDeserializer

getEnd() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

getEpoch(Model) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener

getEpochCount() - Method in interface org.deeplearning4j.nn.api.Layer

getEpochCount() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

getEpochCount() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Returns the number of epochs that the ComputationGraph has done.

getEpochCount() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getEpochCount() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getEpochCount() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getEpochCount() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getEpochCount(Model) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

getEps() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

getEpsilon() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

getEpsilon() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getEpsilon() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Get the epsilon/error (i.e., dL/dOutput) array previously set for this GraphVertex

getExternalSource() - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

getExternalSource() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

getExternalSource() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator

getFileForCheckpoint(Checkpoint) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener

Get the model file for the given checkpoint.

getFileForCheckpoint(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener

Get the model file for the given checkpoint number.

getFileHeader() - Static method in class org.deeplearning4j.optimize.listeners.Checkpoint

getFinalResult() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer

getFinalResult() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithmReducer

getFlattenedGradientsView() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

getFlattenedGradientsView() - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater

getFlattenedGradientsView() - Method in class org.deeplearning4j.nn.updater.LayerUpdater

getFlattenedGradientsView() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater

getFlattenedSize() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat

getGlobalPosition() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

getGradientCheck() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

getGradientFor(String) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient

getGradientFor(String) - Method in interface org.deeplearning4j.nn.gradient.Gradient

The gradient for the given variable

getGradientNormalization() - Method in interface org.deeplearning4j.nn.api.TrainingConfig

getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer

getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer

getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig

getGradientNormalizationThreshold() - Method in interface org.deeplearning4j.nn.api.TrainingConfig

getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer

getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig

getGradientsAccumulator() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

This method returns GradientsAccumulator instance used in this optimizer.

getGradientsAccumulator() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in interface org.deeplearning4j.nn.api.ParamInitializer

Return a map of gradients (in their standard non-flattened representation), taken from the flattened (row vector) gradientView array.

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.DeconvolutionParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer

Return a map of gradients (in their standard non-flattened representation), taken from the flattened (row vector) gradientView array.

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer

getGradientsViewArray() - Method in interface org.deeplearning4j.nn.api.Model

getGradientsViewArray() - Method in interface org.deeplearning4j.nn.api.Trainable

getGradientsViewArray() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

getGradientsViewArray() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

getGradientsViewArray() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getGradientsViewArray() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.BaseLayer

getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getGradientsViewArray() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getGradientUpdater() - Method in class org.deeplearning4j.nn.updater.UpdaterBlock

getHeadWord() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

getHeightAndWidth(NeuralNetConfiguration) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Get the height and width from the configuration

getHeightAndWidth(int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Get the height and width for an image

getHelper() - Method in interface org.deeplearning4j.nn.api.Layer

getHelper() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getHelper() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

getHelper() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

getHelper() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

getHelper() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

getHelper() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getHelper() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

getHelper() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getHelper() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getHelperWorkspace(String) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

Get the pointer to the helper memory.

getHWDFromInputType(InputType) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Get heigh/width/channels as length 3 int[] from the InputType

getIndex() - Method in interface org.deeplearning4j.nn.api.Layer

Get the layer index.

getIndex() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getIndex() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

getIndex() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getIndex() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

getIndex() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getIndex() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getInnerConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

getInnerConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

getInput(int) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex.VertexInputs

getInput(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the previously set input for the ComputationGraph

getInput() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getInput() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

getInput() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getInputMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the previously set feature/input mask arrays for the ComputationGraph

getInputMiniBatchSize() - Method in interface org.deeplearning4j.nn.api.Layer

Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)

getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getInputPreProcess(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

getInputs(SameDiff) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex

getInputs() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the previously set inputs for the ComputationGraph

getInputs() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getInputs() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Get the array of inputs previously set for this GraphVertex

getInputVertices() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

A representation of the vertices that are inputs to this vertex (inputs duing forward pass)
Specifically, if inputVertices[X].getVertexIndex() = Y, and inputVertices[X].getVertexEdgeNumber() = Z then the Zth output of vertex Y is the Xth input to this vertex

getInputVertices() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getInputVertices() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

A representation of the vertices that are inputs to this vertex (inputs duing forward pass)
Specifically, if inputVertices[X].getVertexIndex() = Y, and inputVertices[X].getVertexEdgeNumber() = Z then the Zth output connection (see GraphVertex.getNumOutputConnections() of vertex Y is the Xth input to this vertex

getInsideLayer() - Method in class org.deeplearning4j.nn.layers.FrozenLayer

getInsideLayer() - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

getInstance() - Static method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.CenterLossParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.DeconvolutionParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.DefaultParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.EmptyParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.LSTMParamInitializer

getInstance(long[], long[]) - Static method in class org.deeplearning4j.nn.params.PReLUParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.PretrainParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

getInstance() - Static method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer

getIter(Model) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener

getIterationCount() - Method in interface org.deeplearning4j.nn.api.Layer

getIterationCount() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Returns the number of iterations (parameter updates) that the ComputationGraph has done

getIterationCount() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getIterationCount() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getIterationCount() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getIterationCount(Model) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

getIUpdaterWithDefaultConfig() - Method in enum org.deeplearning4j.nn.conf.Updater

getL1ByParam(String) - Method in interface org.deeplearning4j.nn.api.TrainingConfig

Get the L1 coefficient for the given parameter.

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM

Deprecated.

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer

Get the L1 coefficient for the given parameter.

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig

getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer

getL2ByParam(String) - Method in interface org.deeplearning4j.nn.api.TrainingConfig

Get the L2 coefficient for the given parameter.

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM

Deprecated.

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer

Get the L2 coefficient for the given parameter.

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig

getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer

getLabelMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the previously set label/output mask arrays for the ComputationGraph

getLabels() - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer

Get the labels array previously set with IOutputLayer.setLabels(INDArray)

getLabels() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

getLabels() - Method in class org.deeplearning4j.nn.layers.LossLayer

getLabels() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

getLabels2d() - Method in class org.deeplearning4j.nn.layers.LossLayer

getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.OutputLayer

getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer

getLambda() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

getLastEtlTime() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

This method returns ETL time field value

getLastEtlTime() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getLatestModel() - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver

Retrieve the most recent model that was previously saved

getLatestModel() - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver

getLatestModel() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver

getLatestModel() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver

getLayer(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the layer by the number of that layer, in range 0 to getNumLayers()-1 NOTE: This is different from the internal GraphVertex index for the layer

getLayer(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get a given layer by name.

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getLayer() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Get the Layer (if any).

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex

getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex

getLayer() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

getLayer(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getLayer(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getLayerActivationTypes(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

For the given input shape/type for the network, return a map of activation sizes for each layer and vertex in the graph.

getLayerActivationTypes(boolean, InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

For the given input shape/type for the network, return a map of activation sizes for each layer and vertex in the graph.

getLayerActivationTypes() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

For the (perhaps partially constructed) network configuration, return a map of activation sizes for each layer and vertex in the graph.
Note 1: The network configuration may be incomplete, but the inputs have been added to the layer already.
Note 2: To use this method, the network input types must have been set using ComputationGraphConfiguration.GraphBuilder.setInputTypes(InputType...) first

getLayerActivationTypes(InputType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

For the given input shape/type for the network, return a list of activation sizes for each layer in the network.
i.e., list.get(i) is the output activation sizes for layer i

getLayerActivationTypes() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

For the (perhaps partially constructed) network configuration, return a list of activation sizes for each layer in the network.
Note: To use this method, the network input type must have been set using NeuralNetConfiguration.ListBuilder.setInputType(InputType) first

getLayerActivationWSConfig(int) - Static method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getLayerName() - Method in interface org.deeplearning4j.nn.api.TrainingConfig

getLayerName() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

getLayerName() - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig

getLayerNames() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getLayerParams() - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

getLayers() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get all layers in the ComputationGraph

getLayers() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getLayerwise() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

getLayerWiseConfigurations() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getLayerWorkingMemWSConfig(int) - Static method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getLearningRate(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the current learning rate, for the specified layer, from the network.

getLearningRate(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Get the current learning rate, for the specified layer, from the network.

getLearningRate(MultiLayerNetwork, int) - Static method in class org.deeplearning4j.util.NetworkUtils

Get the current learning rate, for the specified layer, fromthe network.

getLearningRate(ComputationGraph, String) - Static method in class org.deeplearning4j.util.NetworkUtils

Get the current learning rate, for the specified layer, from the network.

getLeaves() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Gets the leaves of the tree.

getLeaves(List<T>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Gets the leaves of the tree.

getLegacyJsonMapper() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyGraphVertexDeserializer

getLegacyJsonMapper() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyLayerDeserializer

getLegacyJsonMapper() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyPreprocessorDeserializer

getLegacyJsonMapper() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyReconstructionDistributionDeserializer

getLegacyNamesMap() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyGraphVertexDeserializer

getLegacyNamesMap() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyLayerDeserializer

getLegacyNamesMap() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyPreprocessorDeserializer

getLegacyNamesMap() - Method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyReconstructionDistributionDeserializer

getListeners() - Method in interface org.deeplearning4j.nn.api.Layer

Get the iteration listeners for this layer.

getListeners() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the trainingListeners for the ComputationGraph

getListeners() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getListeners() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

getListeners() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getListeners() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

getListeners() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getListeners() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getLocalPosition() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

getLocalPosition(long) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

getLossFn() - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer

getMapper() - Static method in class org.deeplearning4j.nn.conf.serde.JsonMappers

getMapperYaml() - Static method in class org.deeplearning4j.nn.conf.serde.JsonMappers

getMask() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getMaskArray() - Method in interface org.deeplearning4j.nn.api.Layer

getMaskArray() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getMaskArray() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getMaskArray() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

getMaskArray() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getMaskArray() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getMaxIterations() - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

getMean() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution

getMeanCache() - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper

getMemoryBytes(MemoryType, int, MemoryUseMode, CacheMode, DataBuffer.Type) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport

getMemoryBytes(MemoryType, int, MemoryUseMode, CacheMode) - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport

Get the memory estimate (in bytes) for the specified type of memory, using the current ND4J data type

getMemoryBytes(MemoryType, int, MemoryUseMode, CacheMode, DataBuffer.Type) - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport

Get the memory estimate (in bytes) for the specified type of memory

getMemoryBytes(MemoryType, int, MemoryUseMode, CacheMode, DataBuffer.Type) - Method in class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

Get a MemoryReport for the given computation graph configuration.

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex

This is a report of the estimated memory consumption for the given vertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM

Deprecated.

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM

Deprecated.

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Layer

This is a report of the estimated memory consumption for the given layer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LSTM

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer

This is a report of the estimated memory consumption for the given layer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer

getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

Get a MemoryReport for the given MultiLayerConfiguration.

getMemoryReport(AbstractLSTM, InputType) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers

getMemoryReport(GravesBidirectionalLSTM, InputType) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers

getMemoryReport(boolean, FeedForwardLayer, InputType) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers

getMinibatchDivisionSubsets(INDArray) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

getModelType(Model) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener

getModuleName() - Method in interface org.deeplearning4j.nn.conf.module.GraphBuilderModule

A module should return its name.

getN() - Method in class org.deeplearning4j.nn.layers.RepeatVector

getName() - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport

getName() - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport

Name of the object that the memory report was generated for

getName() - Method in class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport

getNIn() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

getnLayers() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Get the number of layers in the network

getNOut() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

getNOut() - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer

getNumberOfTrials() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution

getNumInputArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

The number of inputs to this network

getNumInputArrays() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

getNumInputArrays() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getNumInputArrays() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Get the number of input arrays.

getNumLayers() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Returns the number of layers in the ComputationGraph

getNumOutputArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

The number of output (arrays) for this network

getNumOutputConnections() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

getNumOutputConnections() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getNumOutputConnections() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Get the number of outgoing connections from this GraphVertex.

getObjectFromFile(File, String) - Static method in class org.deeplearning4j.util.ModelSerializer

Get an object with the specified key from the model file, that was previously added to the file using ModelSerializer.addObjectToFile(File, String, Object)

getOptimalBufferSize(long, int, int) - Static method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

This method returns optimal bufferSize for a given model We know, that updates are guaranteed to have MAX size of params / 16.

getOptimalBufferSize(Model, int, int) - Static method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

getOptimizer() - Method in interface org.deeplearning4j.nn.api.Model

Returns this models optimizer

getOptimizer() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork

This method returns Optimizer used for training

getOptimizer() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

getOptimizer() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getOptimizer() - Method in class org.deeplearning4j.nn.layers.BaseLayer

getOptimizer() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getOptimizer() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

getOptimizer() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getOptimizer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getOptimizer() - Method in class org.deeplearning4j.optimize.Solver

getOrderedLayers() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

getOrderedLayers() - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater

getOrderedLayers() - Method in class org.deeplearning4j.nn.updater.LayerUpdater

getOrderedLayers() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater

getOutputLayer(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the specified output layer, by index.

getOutputLayer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Get the output layer

getOutputLayerIndices() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

getOutputSize(INDArray, int, int, int, ConvolutionMode) - Static method in class org.deeplearning4j.util.Convolution1DUtils

getOutputSize(int, int, int, int, ConvolutionMode, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils

Get the output size (height) for the given input data and CNN1D configuration

getOutputSize(INDArray, int, int, int, ConvolutionMode, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils

Get the output size (height) for the given input data and CNN1D configuration

getOutputSize(INDArray, int[], int[], int[], ConvolutionMode) - Static method in class org.deeplearning4j.util.ConvolutionUtils

getOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Get the output size (height/width) for the given input data and CNN configuration

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex

Determine the type of output for this GraphVertex, given the specified inputs.

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex

getOutputType(InputType) - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor

For a given type of input to this preprocessor, what is the type of the output?

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Layer

For a given type of input to this layer, what is the type of the output?

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer

getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer

getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor

getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor

getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor

getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor

getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor

getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor

getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor

getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.layers.util.IdentityLayer

getOutputTypeCnn1DLayers(InputType, int, int, int, int, ConvolutionMode, long, long, String, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil

getOutputTypeCnn3DLayers(InputType, int[], int[], int[], int[], ConvolutionMode, long, long, String, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil

getOutputTypeCnnLayers(InputType, int[], int[], int[], int[], ConvolutionMode, long, long, String, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil

getOutputTypeDeconvLayer(InputType, int[], int[], int[], int[], ConvolutionMode, long, long, String, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil

getOutputVertices() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

A representation of the vertices that this vertex is connected to (outputs duing forward pass) Specifically, if outputVertices[X].getVertexIndex() = Y, and outputVertices[X].getVertexEdgeNumber() = Z then the Xth output of this vertex is connected to the Zth input of vertex Y

getOutputVertices() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getOutputVertices() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

A representation of the vertices that this vertex is connected to (outputs duing forward pass) Specifically, if outputVertices[X].getVertexIndex() = Y, and outputVertices[X].getVertexEdgeNumber() = Z then the Xth output of this vertex is connected to the Zth input of vertex Y

getParam(String) - Method in interface org.deeplearning4j.nn.api.Model

Get the parameter

getParam(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

getParam(String) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

getParam(String) - Method in class org.deeplearning4j.nn.layers.BaseLayer

getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

getParam(String) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

getParam(String) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

getParam(String) - Method in class org.deeplearning4j.nn.layers.RepeatVector

getParam(String) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

getParam(String) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

getParam(String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

getParam(String) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

getParam(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getParameter(Layer, String, int, int, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.weightnoise.DropConnect

getParameter(Layer, String, int, int, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.conf.weightnoise.IWeightNoise

Get the parameter, after applying weight noise

getParameter(Layer, String, int, int, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.weightnoise.WeightNoise

getParameterKeys() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

getParams() - Method in interface org.deeplearning4j.nn.api.layers.LayerConstraint

getParams() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

getParams() - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater

getParams() - Method in class org.deeplearning4j.nn.updater.LayerUpdater

getParams() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater

getParamShapes() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

Get the parameter shapes for all parameters

getParamWithNoise(String, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer

Get the parameter, after applying any weight noise (such as DropConnect) if necessary.

getParamWithNoise(String, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

getPnorm() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

getPredictedClass() - Method in class org.deeplearning4j.nn.layers.objdetect.DetectedObject

Get the index of the predicted class (based on maximum predicted probability)

getPredictedObjects(INDArray, double) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

getPredictedObjects(INDArray, INDArray, double, double) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils

Given the network output and a detection threshold (in range 0 to 1) determine the objects detected by the network.
Supports minibatches - the returned DetectedObject instances have an example number index.
Note that the dimensions are grid cell units - for example, with 416x416 input, 32x downsampling by the network (before getting to the Yolo2OutputLayer) we have 13x13 grid cells (each corresponding to 32 pixels in the input image).

getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter

getPreProcessor() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Layer

For the given type of input to this layer, what preprocessor (if any) is required?
Returns null if no preprocessor is required, otherwise returns an appropriate InputPreProcessor for this layer, such as a CnnToFeedForwardPreProcessor

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer

getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer

getPreProcessorForInputTypeCnn3DLayers(InputType, String) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil

Utility method for determining the appropriate preprocessor for CNN layers, such as ConvolutionLayer and SubsamplingLayer

getPreProcessorForInputTypeCnnLayers(InputType, String) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil

Utility method for determining the appropriate preprocessor for CNN layers, such as ConvolutionLayer and SubsamplingLayer

getPreprocessorForInputTypeRnnLayers(InputType, String) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil

getProbabilityMatrix(INDArray, int, int) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

Get the probability matrix (probability of the specified class, assuming an object is present, for all x/y positions), from the network output activations array

getProbabilityOfSuccess() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution

getReportClass() - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport

getReportClass() - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport

getReportClass() - Method in class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport

getSameModeBottomRightPadding(int[], int[], int[], int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Get bottom and right padding for same mode only.

getSameModeTopLeftPadding(int, int, int, int, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils

Get top padding for same mode only.

getSameModeTopLeftPadding(int[], int[], int[], int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Get top and left padding for same mode only.

getScoreCalculator() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration

getScoreVsIter() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener

getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType

Returns the shape of this InputType

getShape() - Method in class org.deeplearning4j.nn.conf.inputs.InputType

Returns the shape of this InputType without minibatch dimension in the returned array

getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional

getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional3D

getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat

getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward

getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent

getShape(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

getSize() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D

getSize() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

getSize() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

getStateViewArray() - Method in interface org.deeplearning4j.nn.api.Updater

getStateViewArray() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

getStateViewArrayCopy() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

A synchronized version of BaseMultiLayerUpdater.getStateViewArray() that duplicates the view array internally.

getStd() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution

getStepFunction() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

This method returns StepFunction defined within this Optimizer instance

getStepMax() - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

getTokens() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

getTopLeftXY() - Method in class org.deeplearning4j.nn.layers.objdetect.DetectedObject

Get the top left X/Y coordinates of the detected object

getTotalMemoryBytes(int, MemoryUseMode, CacheMode, DataBuffer.Type) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport

getTotalMemoryBytes(int, MemoryUseMode, CacheMode) - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport

Get the total memory use in bytes for the given configuration (using the current ND4J data type)

getTotalMemoryBytes(int, MemoryUseMode, CacheMode, DataBuffer.Type) - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport

Get the total memory use in bytes for the given configuration

getTotalMemoryBytes(int, MemoryUseMode, CacheMode, DataBuffer.Type) - Method in class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport

getTrainingListeners() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType

getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional

getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional3D

getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat

getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward

getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent

getType() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

The type of node; mainly extra meta data

getUnderlying() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep

getUnflattenedType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat

getUpdater() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the ComputationGraphUpdater for the network.

getUpdater(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the ComputationGraphUpdater for this network

getUpdater() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Get the updater for this MultiLayerNetwork

getUpdater(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

getUpdater(Model) - Static method in class org.deeplearning4j.nn.updater.UpdaterCreator

getUpdater() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

getUpdater() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

getUpdaterByParam(String) - Method in interface org.deeplearning4j.nn.api.TrainingConfig

Get the updater for the given parameter.

getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer

Get the updater for the given parameter.

getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer

Get the updater for the given parameter.

getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

Get the updater for the given parameter.

getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig

getVarCache() - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper

getVertex(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return a given GraphVertex by name, or null if no vertex with that name exists

getVertexEdgeNumber() - Method in class org.deeplearning4j.nn.graph.vertex.VertexIndices

The edge number.

getVertexIndex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

getVertexIndex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getVertexIndex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Get the index of the GraphVertex

getVertexIndex() - Method in class org.deeplearning4j.nn.graph.vertex.VertexIndices

Index of the vertex

getVertexName() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

getVertexName() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

getVertexName() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Get the name/label of the GraphVertex

getVertexParams() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

getVertices() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Returns an array of all GraphVertex objects.

getWeightParameterKeys() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

GLOBAL_MEAN - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

GLOBAL_VAR - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

globalConfiguration - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

GlobalPoolingLayer - Class in org.deeplearning4j.nn.conf.layers

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.

GlobalPoolingLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

GlobalPoolingLayer(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

GlobalPoolingLayer - Class in org.deeplearning4j.nn.layers.pooling

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.
mask arrays are assumed to be 2d, and are fed forward through the network during training or post-training forward pass:
- Time series (RNNs, 1d CNNs): mask arrays are shape [miniBatchSize, maxTimeSeriesLength] and contain values 0 or 1 only
- CNNs (2d): mask have shape [miniBatchSize, 1, height, 1] or [miniBatchSize, 1, 1, width] or [minibatch, 1, height, width].

GlobalPoolingLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer

GlobalPoolingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

gMeasure(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation

Deprecated.

goldLabel() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

gradient() - Method in interface org.deeplearning4j.nn.api.Model

Get the gradient.

gradient(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

gradient(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution

gradient(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

gradient(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

gradient(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper

gradient(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution

Calculate the gradient of the negative log probability with respect to the preOutDistributionParams

gradient(List<String>) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient

gradient() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient

Gradient - Interface in org.deeplearning4j.nn.gradient

Generic gradient

gradient(List<String>) - Method in interface org.deeplearning4j.nn.gradient.Gradient

The full gradient as one flat vector

gradient() - Method in interface org.deeplearning4j.nn.gradient.Gradient

The full gradient as one flat vector

gradient - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

gradient() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

gradient() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

gradient - Variable in class org.deeplearning4j.nn.layers.BaseLayer

gradient() - Method in class org.deeplearning4j.nn.layers.BaseLayer

gradient() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Gets the gradient from one training iteration

gradient() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

gradient() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

gradient() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

gradient() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

gradient() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

gradient() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

gradient() - Method in class org.deeplearning4j.nn.layers.FrozenLayer

gradient() - Method in class org.deeplearning4j.nn.layers.LossLayer

Gets the gradient from one training iteration

gradient() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM

gradient() - Method in class org.deeplearning4j.nn.layers.RepeatVector

gradient() - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer

Gets the gradient from one training iteration

gradient - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

gradient() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

gradient() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

gradient - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

gradient() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

gradient - Variable in class org.deeplearning4j.optimize.listeners.SharedGradient

GRADIENT_KEY - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

gradientAndScore() - Method in interface org.deeplearning4j.nn.api.Model

Get the gradient and score

gradientAndScore() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

gradientAndScore() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

gradientAndScore() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

gradientAndScore() - Method in class org.deeplearning4j.nn.layers.FrozenLayer

gradientAndScore() - Method in class org.deeplearning4j.nn.layers.LossLayer

gradientAndScore() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

gradientAndScore() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

gradientAndScore() - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer

gradientAndScore() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

gradientAndScore() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

gradientAndScore() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

gradientAndScore(LayerWorkspaceMgr) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

The gradient and score for this optimizer

gradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

gradientCheck - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder

gradientCheck(boolean) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder

gradientCheck - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

GradientCheckUtil - Class in org.deeplearning4j.gradientcheck

A utility for numerically checking gradients.

gradientForVariable() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient

gradientForVariable() - Method in interface org.deeplearning4j.nn.gradient.Gradient

Gradient look up table

GradientNormalization - Enum in org.deeplearning4j.nn.conf

Gradient normalization strategies.

gradientNormalization - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

gradientNormalization(GradientNormalization) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Gradient normalization strategy.

gradientNormalization - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

gradientNormalization - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

gradientNormalization - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

gradientNormalization - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

gradientNormalization(GradientNormalization) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Gradient normalization strategy.

gradientNormalization(GradientNormalization) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Gradient normalization strategy.

gradientNormalization - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

gradientNormalizationThreshold(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer, GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue
Not used otherwise.
L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping.

gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

gradientNormalizationThreshold(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer, GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue
Not used otherwise.
L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

gradientNormalizationThreshold(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer, GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue
Not used otherwise.
L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping

gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

gradients - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

gradients - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

gradients - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

gradients - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

GradientsAccumulator - Interface in org.deeplearning4j.optimize.solvers.accumulation

gradientsFlattened - Variable in class org.deeplearning4j.nn.layers.BaseLayer

gradientsFlattened - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

gradientsForMinibatchDivision - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

GradientStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions

Normal gradient step function

GradientStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction

GradientStepFunction - Class in org.deeplearning4j.optimize.stepfunctions

Normal gradient step function

GradientStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction

gradientViews - Variable in class org.deeplearning4j.nn.layers.BaseLayer

gradientViews - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

gradTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

gradTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

gradTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

graph - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

GraphBuilder(NeuralNetConfiguration.Builder) - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

GraphBuilder(ComputationGraphConfiguration, NeuralNetConfiguration.Builder) - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

graphBuilder() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Create a GraphBuilder (for creating a ComputationGraphConfiguration).

GraphBuilder(ComputationGraph) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Computation Graph to tweak for transfer learning

GraphBuilderModule - Interface in org.deeplearning4j.nn.conf.module

GraphBuilderModule for nn layers.

graphIndices - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

Topological sort and vertex index/name + name/index mapping

GraphIndices - Class in org.deeplearning4j.nn.graph.util

Simple helper class for ComputationGraph topological sort and vertex index/name + name/index mapping

GraphIndices() - Constructor for class org.deeplearning4j.nn.graph.util.GraphIndices

GraphVertex - Class in org.deeplearning4j.nn.conf.graph

A GraphVertex is a vertex in the computation graph type of neural network.

GraphVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.GraphVertex

GraphVertex - Interface in org.deeplearning4j.nn.graph.vertex

A GraphVertex is a vertex in the computation graph.

GravesBidirectionalLSTM - Class in org.deeplearning4j.nn.conf.layers

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()))

GravesBidirectionalLSTM - Class in org.deeplearning4j.nn.layers.recurrent

RNN tutorial: http://deeplearning4j.org/usingrnns.html READ THIS FIRST Bdirectional LSTM layer implementation.

GravesBidirectionalLSTM(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

GravesBidirectionalLSTM(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

GravesBidirectionalLSTM.Builder - Class in org.deeplearning4j.nn.conf.layers

Deprecated.

GravesBidirectionalLSTMParamInitializer - Class in org.deeplearning4j.nn.params

LSTM Parameter initializer, for LSTM based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf

GravesBidirectionalLSTMParamInitializer() - Constructor for class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

GravesLSTM - Class in org.deeplearning4j.nn.conf.layers

Deprecated.

Will be eventually removed. Use LSTM instead, which has similar prediction accuracy, but supports CuDNN for faster network training on CUDA (Nvidia) GPUs

GravesLSTM - Class in org.deeplearning4j.nn.layers.recurrent

Deprecated.

Will be eventually removed. Use LSTM instead, which has similar prediction accuracy, but supports CuDNN for faster network training on CUDA (Nvidia) GPUs

GravesLSTM(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

GravesLSTM(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

GravesLSTM.Builder - Class in org.deeplearning4j.nn.conf.layers

Deprecated.

GravesLSTMParamInitializer - Class in org.deeplearning4j.nn.params

LSTM Parameter initializer, for LSTM based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf

GravesLSTMParamInitializer() - Constructor for class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

gz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

H

handler - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

handler - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

handler - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

handleUpdaterBackwardCompatibility(BaseLayer, ObjectNode) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer

hasAFrozenLayer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

hasAnything() - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

hasAnything() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

hasAnything() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator

This method checks if there are any (probably external) updates available

hasAnything() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

hasAnything(long) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder

If true (default): include bias parameters in the model.

hasBias - Variable in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer

hasBias() - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer

hasBias() - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D

hasBias - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

If true (default): include bias parameters in the model.

hasBias - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

hasBias() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

hasBias() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D

hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer.Builder

If true (default): include bias parameters in the model.

hasBias() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer

hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder

If true: include bias parameters in the layer.

hasBias() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer

hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder

If true: include bias parameters in the layer.

hasBias() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer

hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

hasBias() - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D

hasBias() - Method in class org.deeplearning4j.nn.layers.BaseLayer

Does this layer have no bias term? Many layers (dense, convolutional, output, embedding) have biases by default, but no-bias versions are possible via configuration

hasBias() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

hasBias() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

hasBias() - Method in class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer

hasBias() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer

hasBias() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer

hasBias(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

hashCode() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution

hashCode() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.StackVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex

hashCode() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

hashCode() - Method in class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction

hashCode() - Method in class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction

hashCode() - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction

hashCode() - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction

hashCode() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

hashCode() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

hasLayer() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Whether the GraphVertex contains a Layer object or not

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex

hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex

hasLayer() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

hasLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

hasLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution

hasLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

hasLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

hasLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper

hasLossFunction() - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution

Does this reconstruction distribution has a standard neural network loss function (such as mean squared error, which is deterministic) or is it a standard VAE with a probabilistic reconstruction distribution?

hasLossFunction() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

Does the reconstruction distribution have a loss function (such as mean squared error) or is it a standard probabilistic reconstruction distribution?

hasNext() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter

hasSomething - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

helper - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

helper - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

helper - Variable in class org.deeplearning4j.nn.layers.recurrent.LSTM

helperCountFail - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

helperCountFail - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

helperCountFail - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

helperMemoryUse() - Method in interface org.deeplearning4j.nn.layers.LayerHelper

Return the currently allocated memory for the helper.
(a) Excludes: any shared memory used by multiple helpers/layers
(b) Excludes any temporary memory (c) Includes all memory that persists for longer than the helper method
This is mainly used for debugging and reporting purposes.

helperWorkspacePointers - Variable in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

helperWorkspaces - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

helperWorkspaces - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

hiddenLayerSize - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

hiddenLayerSize(int) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

The hidden layer size for the one class neural network.

Histogram - Class in org.deeplearning4j.eval.curves

Deprecated.

Use Histogram

Histogram(String, double, double, int[]) - Constructor for class org.deeplearning4j.eval.curves.Histogram

Deprecated.

Use Histogram

HostNameTrigger(String) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.HostNameTrigger

I

i2d - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

ia - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

id - Variable in class org.deeplearning4j.optimize.listeners.SharedGradient

IdentityLayer - Class in org.deeplearning4j.nn.layers.util

Identity layer, passes data through unaltered.

IdentityLayer(String) - Constructor for class org.deeplearning4j.nn.layers.util.IdentityLayer

IDropout - Interface in org.deeplearning4j.nn.conf.dropout

IDropout instances operate on an activations array, modifying or dropping values at training time only.

iDropout - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder

iDropout - Variable in class org.deeplearning4j.nn.conf.layers.Layer

idropOut - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

IEarlyStoppingTrainer<T extends Model> - Interface in org.deeplearning4j.earlystopping.trainer

Interface for early stopping trainers

IEvaluation<T extends IEvaluation> - Interface in org.deeplearning4j.eval

Deprecated.

Use IEvaluation

incrementEpochCount() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Increment the epoch count (in the underlying ComputationGraphConfiguration by 1).

incrementEpochCount() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Increment the epoch count (in the underlying MultiLayerConfiguration by 1).

incrementIterationCount(Model, int) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

index - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

index - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

index - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

index - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

IndexedTail - Class in org.deeplearning4j.optimize.solvers.accumulation

This class provides queue-like functionality for multiple readers/multiple writers, with transparent duplication and collapsing ability

IndexedTail(int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

IndexedTail(int, boolean, long[]) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

inferenceWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Deprecated.

Use NeuralNetConfiguration.Builder.inferenceWorkspaceMode(WorkspaceMode)

inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

inferenceWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

This method defines Workspace mode being used during inference:
NONE: workspace won't be used
ENABLED: workspaces will be used for inference (reduced memory and better performance)

inferenceWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

This method defines Workspace mode being used during inference:
NONE: workspace won't be used
ENABLED: workspaces will be used for inference (reduced memory and better performance)

inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

inferInputLength(boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder

Set input sequence inference mode for embedding layer.

inferInputType(INDArray) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType

inferInputTypes(INDArray...) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType

inferVersion() - Static method in class org.deeplearning4j.util.CrashReportingUtil

init() - Method in interface org.deeplearning4j.nn.api.Model

Init the model

init() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork

This method does initialization of model PLEASE NOTE: All implementations should track own state, to avoid double spending

init(NeuralNetConfiguration, INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.ParamInitializer

Initialize the parameters

init() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Initialize the ComputationGraph network

init(INDArray, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Initialize the ComputationGraph, optionally with an existing parameters array.

init() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

Init the model

init() - Method in class org.deeplearning4j.nn.layers.FrozenLayer

Init the model

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

init() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

init() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

Init the model

init() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

init() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Initialize the MultiLayerNetwork.

init(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Initialize the MultiLayerNetwork, optionally with an existing parameters array.

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer

Initialize the parameters

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer

init() - Method in class org.deeplearning4j.nn.updater.UpdaterBlock

initCalled - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

initCalled - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

initDone - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

initGradientsView() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

This method: initializes the flattened gradients array (used in backprop) and sets the appropriate subset in all layers.

initGradientsView() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

This method: initializes the flattened gradients array (used in backprop) and sets the appropriate subset in all layers.

initialize() - Method in class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition

initialize() - Method in interface org.deeplearning4j.earlystopping.termination.EpochTerminationCondition

Initialize the epoch termination condition (often a no-op)

initialize() - Method in class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition

initialize() - Method in interface org.deeplearning4j.earlystopping.termination.IterationTerminationCondition

Initialize the iteration termination condition (sometimes a no-op)

initialize() - Method in class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition

initialize() - Method in class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition

initialize() - Method in class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition

initialize() - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition

initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.And

initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureTrigger

initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.HostNameTrigger

initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.RandomProb

initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.TimeSinceInitializedTrigger

initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.UserNameTrigger

initialize(GradientsAccumulator) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

initialize(GradientsAccumulator) - Method in class org.deeplearning4j.optimize.solvers.accumulation.LocalHandler

initialize(GradientsAccumulator) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.MessageHandler

This method does initial configuration of given MessageHandler instance

initializeConstraints(Layer.Builder<?>) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM

Deprecated.

initializeConstraints(Layer.Builder<?>) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM

Deprecated.

initializeConstraints(Layer.Builder<?>) - Method in class org.deeplearning4j.nn.conf.layers.Layer

Initialize the weight constraints.

initializeConstraints(Layer.Builder<?>) - Method in class org.deeplearning4j.nn.conf.layers.LSTM

initializeConstraints(Layer.Builder<?>) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D

initialized() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureTrigger

initializedMinibatchDivision - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

initializeHelper() - Method in class org.deeplearning4j.nn.conf.dropout.Dropout

Initialize the CuDNN dropout helper, if possible

initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D

initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D

initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

Set the initial parameter values for this layer, if required

initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer

initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex

initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

Set the initial parameter values for this layer, if required

initializer() - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder

initializer() - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

initializer() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D

initializer() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D

initializer() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D

initializer() - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM

Deprecated.

initializer() - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM

Deprecated.

initializer() - Method in class org.deeplearning4j.nn.conf.layers.Layer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

initializer() - Method in class org.deeplearning4j.nn.conf.layers.LossLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.LSTM

initializer() - Method in class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

initializer() - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector

initializer() - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

initializer() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn

initializer() - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D

initializer() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder

initializer() - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

initializer() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer

initializer() - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer

initialMemory - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

initialMemory - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

initialResidualPostProcessor - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

initialRValue - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

initialRValue(double) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

The initial r value to use for ocnn for definition, see the paper, note this is only active when OCNNOutputLayer.Builder.configureR is specified as true

initialThresholdAlgorithm - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

initOptimizer() - Method in class org.deeplearning4j.optimize.Solver

initWeights(int, int, WeightInit, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

initWeights(int[], float, float) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil

initWeights(double, double, int[], WeightInit, Distribution, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil

Deprecated.

initWeights(double, double, long[], WeightInit, Distribution, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil

Initializes a matrix with the given weight initialization scheme.

initWeights(double, double, int[], WeightInit, Distribution, char, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil

Deprecated.

initWeights(double, double, long[], WeightInit, Distribution, char, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil

InMemoryModelSaver<T extends Model> - Class in org.deeplearning4j.earlystopping.saver

Save the best (and latest) models for early stopping training to memory for later retrieval Note: Assumes that network is cloneable via .clone() method

InMemoryModelSaver() - Constructor for class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver

input() - Method in interface org.deeplearning4j.nn.api.Model

The input/feature matrix for the model

input() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

input - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

input() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

input() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

input - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

input() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

input() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

input - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

input() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

INPUT_KEY - Static variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

INPUT_KEY - Static variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

INPUT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

INPUT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.LSTMParamInitializer

INPUT_WEIGHT_KEY_BACKWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

INPUT_WEIGHT_KEY_FORWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

inputDepth - Variable in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

inputHeight - Variable in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

inputHeight - Variable in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

inputLength(int) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder

Set input sequence length for this embedding layer.

inputMaskArray - Variable in class org.deeplearning4j.nn.layers.BaseOutputLayer

inputMaskArrayState - Variable in class org.deeplearning4j.nn.layers.BaseOutputLayer

inputModificationAllowed - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

inputPreProcessor(String, InputPreProcessor) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Specify the processors for a given layer These are used at each layer for doing things like normalization and shaping of input.
Note: preprocessors can also be defined using the ComputationGraphConfiguration.GraphBuilder.addLayer(String, Layer, InputPreProcessor, String...) method.

InputPreProcessor - Interface in org.deeplearning4j.nn.conf

Input pre processor used for pre processing input before passing it to the neural network.

inputPreProcessor(Integer, InputPreProcessor) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Specify the processors.

inputPreProcessors - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

inputPreProcessors - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

inputPreProcessors(Map<Integer, InputPreProcessor>) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

inputPreProcessors - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

inputs - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex

inputs - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams

inputs - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

inputShape(long...) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer.Builder

Explicitly set input shape of incoming activations so that parameters can be initialized properly.

InputType - Class in org.deeplearning4j.nn.conf.inputs

The InputType class is used to track and define the types of activations etc used in a ComputationGraph.

InputType() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType

inputType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

inputType() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

A convenience method for setting input types: note that for example .inputType().convolutional(h,w,d) is equivalent to .setInputType(InputType.convolutional(h,w,d))

InputType.InputTypeConvolutional - Class in org.deeplearning4j.nn.conf.inputs

InputType.InputTypeConvolutional3D - Class in org.deeplearning4j.nn.conf.inputs

InputType.InputTypeConvolutionalFlat - Class in org.deeplearning4j.nn.conf.inputs

InputType.InputTypeFeedForward - Class in org.deeplearning4j.nn.conf.inputs

InputType.InputTypeRecurrent - Class in org.deeplearning4j.nn.conf.inputs

InputType.Type - Enum in org.deeplearning4j.nn.conf.inputs

The type of activations in/out of a given GraphVertex
FF: Standard feed-foward (2d minibatch, 1d per example) data
RNN: Recurrent neural network (3d minibatch) time series data
CNN: 2D Convolutional neural network (4d minibatch, [miniBatchSize, channels, height, width]) CNNFlat: Flattened 2D conv net data (2d minibatch, [miniBatchSize, height * width * channels]) CNN3D: 3D convolutional neural network (5d minibatch, [miniBatchSize, channels, height, width, channels])

InputTypeBuilder() - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder

InputTypeConvolutional() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional

InputTypeConvolutional3D() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional3D

InputTypeConvolutionalFlat() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat

InputTypeFeedForward() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward

InputTypeRecurrent(long) - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent

InputTypeUtil - Class in org.deeplearning4j.nn.conf.layers

Utilities for calculating input types

InputTypeUtil() - Constructor for class org.deeplearning4j.nn.conf.layers.InputTypeUtil

inputVars - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

InputVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

An InputVertex simply defines the location (and connection structure) of inputs to the ComputationGraph.

InputVertex(ComputationGraph, String, int, VertexIndices[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.InputVertex

inputVertices - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

A representation of the vertices that are inputs to this vertex (inputs during forward pass) Specifically, if inputVertices[X].getVertexIndex() = Y, and inputVertices[X].getVertexEdgeNumber() = Z then the Zth output of vertex Y is the Xth input to this vertex

inputWeightConstraints - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder

inputWidth - Variable in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

inputWidth - Variable in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex

Create a GraphVertex instance, for the given computation graph, given the configuration instance.

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM

Deprecated.

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM

Deprecated.

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Layer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LSTM

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer

instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer

instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer

intializeConfigurations() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

InvalidInputTypeException - Exception in org.deeplearning4j.nn.conf.inputs

InvalidInputTypeException: Thrown if the GraphVertex cannot handle the type of input provided.

InvalidInputTypeException(String) - Constructor for exception org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException

InvalidInputTypeException(String, Throwable) - Constructor for exception org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException

InvalidInputTypeException(Throwable) - Constructor for exception org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException

InvalidScoreIterationTerminationCondition - Class in org.deeplearning4j.earlystopping.termination

Terminate training at this iteration if score is NaN or Infinite for the last minibatch

InvalidScoreIterationTerminationCondition() - Constructor for class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition

InvalidStepException - Exception in org.deeplearning4j.exception

Created by agibsonccc on 8/20/14.

InvalidStepException(String) - Constructor for exception org.deeplearning4j.exception.InvalidStepException

Constructs a new exception with the specified detail message.

InvalidStepException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.InvalidStepException

Constructs a new exception with the specified detail message and cause.

InvalidStepException(Throwable) - Constructor for exception org.deeplearning4j.exception.InvalidStepException

Constructs a new exception with the specified cause and a detail message of (cause==null ? null : cause.toString()) (which typically contains the class and detail message of cause).

InvalidStepException(String, Throwable, boolean, boolean) - Constructor for exception org.deeplearning4j.exception.InvalidStepException

Constructs a new exception with the specified detail message, cause, suppression enabled or disabled, and writable stack trace enabled or disabled.

invocationCount - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener

InvocationType - Enum in org.deeplearning4j.optimize.api

This enum holds options for TrainingListener invocation scheme

invocationType - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener

invokeListener(Model) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener

iou(DetectedObject, DetectedObject) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils

Returns intersection over union (IOU) between o1 and o2.

IOutputLayer - Interface in org.deeplearning4j.nn.api.layers

Interface for output layers (those that calculate gradients with respect to a labels array)

isBiasParam(Layer, String) - Method in interface org.deeplearning4j.nn.api.ParamInitializer

Is the specified parameter a bias?

isBiasParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer

isDead() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

isDebug - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

isDebug - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

isDone - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

isDone - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

isEmpty() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

isEmpty() - Method in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

isFirst - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

isFirst - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

isInference() - Method in enum org.deeplearning4j.nn.conf.memory.MemoryType

isInitCalled() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

isInputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

isInputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

isInputVertex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Whether the GraphVertex is an input vertex

isInputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex

isLeaf() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Returns whether the node has any children or not

isMinibatch - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

isMinibatch - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization

isMiniBatch() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

isMiniBatch() - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater

isMiniBatch() - Method in class org.deeplearning4j.nn.updater.LayerUpdater

isMiniBatch() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater

isNCDHW - Variable in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

isOutputVertex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Whether the GraphVertex is an output vertex

isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex

isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex

isPreTerminal() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Node has one child that is a leaf

isPretrain() - Method in interface org.deeplearning4j.nn.api.TrainingConfig

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

isPretrain() - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

isPretrain() - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig

isPretrainLayer() - Method in interface org.deeplearning4j.nn.api.Layer

Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.ActivationLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.DropoutLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer

Returns true if the layer can be trained in an unsupervised/pretrain manner (VAE, RBMs etc)

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.PReLU

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.LossLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.RepeatVector

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.util.MaskLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

isPretrainLayer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

isPretrainParam(String) - Method in interface org.deeplearning4j.nn.api.TrainingConfig

Is the specified parameter a layerwise pretraining only parameter?
For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop.
Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer

Is the specified parameter a layerwise pretraining only parameter?
For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop.
Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer

isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig

isPretrainParam(String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

isPretrainUpdaterBlock() - Method in class org.deeplearning4j.nn.updater.UpdaterBlock

isSingleLayerUpdater() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

isSingleLayerUpdater() - Method in class org.deeplearning4j.nn.updater.LayerUpdater

isWeightParam(Layer, String) - Method in interface org.deeplearning4j.nn.api.ParamInitializer

Is the specified parameter a weight?

isWeightParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer

iter - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator

iterationCount - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

iterationCount - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

iterationCount - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

iterationCount - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

iterationCount - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

iterationCount - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.api.IterationListener

Deprecated.

Event listener for each iteration

iterationDone(Model, int, int) - Method in interface org.deeplearning4j.optimize.api.TrainingListener

Event listener for each iteration.

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresListener

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.ComposableIterationListener

Deprecated.

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener

Event listener for each iteration

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener

Deprecated.

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.ScoreIterationListener

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.TimeIterationListener

IterationEpochTrigger(boolean, int) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.IterationEpochTrigger

IterationListener - Class in org.deeplearning4j.optimize.api

Deprecated.

Use TrainingListener instead

IterationListener() - Constructor for class org.deeplearning4j.optimize.api.IterationListener

Deprecated.

iterations - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

IterationTerminationCondition - Interface in org.deeplearning4j.earlystopping.termination

Interface for termination conditions to be evaluated once per iteration (i.e., once per minibatch).

iterationTerminationConditions(IterationTerminationCondition...) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder

Termination conditions to be evaluated every iteration (minibatch)

iterator - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator

iterator - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

iterator() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

iupdater - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

iUpdater - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

iUpdater - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

IWeightNoise - Interface in org.deeplearning4j.nn.conf.weightnoise

IWeightNoise instances operate on an weight array(s), modifying values at training time or test time, before they are used.

iz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

J

JsonMappers - Class in org.deeplearning4j.nn.conf.serde

JSON mappers for deserializing neural net configurations, etc.

JsonMappers() - Constructor for class org.deeplearning4j.nn.conf.serde.JsonMappers

K

k(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder

LRN scaling constant k.

k - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

K_KEY - Static variable in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

keepAll() - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Keep all model checkpoints - i.e., don't delete any.

keepLast(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Keep only the last N most recent model checkpoint files.

keepLastAndEvery(int, int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Keep the last N most recent model checkpoint files, and every M checkpoint files.
For example: suppose you save every 100 iterations, for 2050 iteration, and use keepLastAndEvery(3,5).

kernelSize(int) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder

Size of the convolution

kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

Set kernel size for 3D convolutions in (depth, height, width) order

kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder

Size of the convolution rows/columns

kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder

Size of the convolution rows/columns

kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder

Size of the convolution rows/columns

kernelSize(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

Size of the convolution rows/columns (height/width)

kernelSize(int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Kernel size

kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Kernel size

kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Kernel size

kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

keys() - Static method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

L

l1 - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

l1(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

L1 regularization coefficient (weights only).

l1 - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

l1 - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

l1(double) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

L1 regularization coefficient (weights only).

l1 - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

l1 - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

l1 - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

l1(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

L1 regularization coefficient for the weights.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

l1(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

L1 regularization coefficient for the weights

l1 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

l1Bias - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

l1Bias(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

L1 regularization coefficient for the bias.

l1Bias - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

l1Bias - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

l1Bias(double) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

L1 regularization coefficient for the bias.

l1Bias - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

l1Bias - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

l1Bias - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

l1Bias(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

L1 regularization coefficient for the bias.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

l1Bias(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

L1 regularization coefficient for the bias parameters

l1Bias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

l2 - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

l2(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

L2 regularization coefficient (weights only).

l2 - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

l2 - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

l2(double) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

L2 regularization coefficient (weights only).

l2 - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

l2 - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

l2 - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

l2(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

L2 regularization coefficient for the weights.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

l2(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

L2 regularization coefficient for the weights

l2 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

l2Bias - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

l2Bias(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

L2 regularization coefficient for the bias.

l2Bias - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

l2Bias - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

l2Bias(double) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

L2 regularization coefficient for the bias.

l2Bias - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

l2Bias - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

l2Bias - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

l2Bias(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

L2 regularization coefficient for the bias.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

l2Bias(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

L2 regularization coefficient for the bias parameters

l2Bias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

L2NormalizeVertex - Class in org.deeplearning4j.nn.conf.graph

L2NormalizeVertex performs L2 normalization on a single input, along the specified dimensions.

L2NormalizeVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

L2NormalizeVertex(int[], double) - Constructor for class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

L2NormalizeVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

L2NormalizeVertex performs L2 normalization on a single input.

L2NormalizeVertex(ComputationGraph, String, int, int[], double) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex

L2NormalizeVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], int[], double) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex

L2Vertex - Class in org.deeplearning4j.nn.conf.graph

L2Vertex calculates the L2 (Euclidean) least squares error of two inputs, on a per-example basis.

L2Vertex() - Constructor for class org.deeplearning4j.nn.conf.graph.L2Vertex

Constructor with default epsilon value of 1e-8

L2Vertex(double) - Constructor for class org.deeplearning4j.nn.conf.graph.L2Vertex

L2Vertex - Class in org.deeplearning4j.nn.graph.vertex.impl

L2Vertex calculates the L2 least squares error of two inputs.

L2Vertex(ComputationGraph, String, int, double) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex

L2Vertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], double) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex

label() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

labelProbabilities(INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier

Returns the probabilities for each label for each example row wise

labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Returns the probabilities for each label for each example row wise

labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer

Returns the probabilities for each label for each example row wise

labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Returns the probabilities for each label for each example row wise

labels - Variable in class org.deeplearning4j.nn.layers.BaseOutputLayer

labels - Variable in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

labels - Variable in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

labels - Variable in class org.deeplearning4j.nn.layers.LossLayer

labels - Variable in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

labels - Variable in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

labels - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

labels - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

LABELS_KEY - Static variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

labelsRequired() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer

Whether labels are required for calculating the score.

lambbaNoObj(double) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder

Loss function coefficient for the "no object confidence" components of the loss function.

lambda - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder

lambda(double) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder

lambda - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer

lambdaCoord(double) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder

Loss function coefficient for position and size/scale components of the loss function.

lastAct - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

lastBP - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

lastCheckpoint() - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener

Return the most recent checkpoint, if one exists - otherwise returns null

lastChild() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

lastDeletedIndex - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

lastEE - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

lastES - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

lastEtlTime - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

lastEtlTime - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

lastFF - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

lastIteration - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

lastIterWasDense - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

lastMemCell - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

lastSparsityRatio - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

lastStep - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

lastThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

lastThresholdLogTime - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

LastTimeStep - Class in org.deeplearning4j.nn.conf.layers.recurrent

LastTimeStep is a "wrapper" layer: it wraps any RNN (or CNN1D) layer, and extracts out the last time step during forward pass, and returns it as a row vector (per example).

LastTimeStep(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep

LastTimeStepLayer - Class in org.deeplearning4j.nn.layers.recurrent

LastTimeStep is a "wrapper" layer: it wraps any RNN layer, and extracts out the last time step during forward pass, and returns it as a row vector (per example).

LastTimeStepLayer(Layer) - Constructor for class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer

LastTimeStepVertex - Class in org.deeplearning4j.nn.conf.graph.rnn

LastTimeStepVertex is used in the context of recurrent neural network activations, to go from 3d (time series) activations to 2d activations, by extracting out the last time step of activations for each example.
This can be used for example in sequence to sequence architectures, and potentially for sequence classification.

LastTimeStepVertex(String) - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

LastTimeStepVertex - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn

LastTimeStepVertex is used in the context of recurrent neural network activations, to go from 3d (time series) activations to 2d activations, by extracting out the last time step of activations for each example.
This can be used for example in sequence to sequence architectures, and potentially for sequence classification.

LastTimeStepVertex(ComputationGraph, String, int, String) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex

LastTimeStepVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], String) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex

Layer - Interface in org.deeplearning4j.nn.api

Interface for a layer of a neural network.

layer(int, Layer, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Add a layer, with no InputPreProcessor, with the specified name and specified inputs.

layer(String, Layer, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Add a layer, with no InputPreProcessor, with the specified name and specified inputs.

layer(String, Layer, InputPreProcessor, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Add a layer and an InputPreProcessor, with the specified name and specified inputs.

Layer - Class in org.deeplearning4j.nn.conf.layers

A neural network layer.

Layer(Layer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Layer

layer(Layer) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer.Builder

layer - Variable in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

layer - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

layer(Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Layer class.

layer - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

layer(int, Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

layer(Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

Layer.Builder<T extends Layer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers

Layer.TrainingMode - Enum in org.deeplearning4j.nn.api

Layer.Type - Enum in org.deeplearning4j.nn.api

layerConf() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

layerConf() - Method in class org.deeplearning4j.nn.layers.BaseLayer

layerConf() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

LayerConstraint - Interface in org.deeplearning4j.nn.api.layers

LayerHelper - Interface in org.deeplearning4j.nn.layers

layerId() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

layerId() - Method in class org.deeplearning4j.nn.layers.FrozenLayer

layerId() - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

layerId() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

layerIndex - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

layerInputSize(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return the input size (number of inputs) for the specified layer.
Note that the meaning of the "input size" can depend on the type of layer.

layerInputSize(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return the input size (number of inputs) for the specified layer.
Note that the meaning of the "input size" can depend on the type of layer.

layerInputSize(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Return the input size (number of inputs) for the specified layer.
Note that the meaning of the "input size" can depend on the type of layer.

layerMap - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

LayerMemoryReport - Class in org.deeplearning4j.nn.conf.memory

A MemoryReport Designed to report estimated memory use for a single layer or graph vertex.

LayerMemoryReport(LayerMemoryReport.Builder) - Constructor for class org.deeplearning4j.nn.conf.memory.LayerMemoryReport

LayerMemoryReport.Builder - Class in org.deeplearning4j.nn.conf.memory

layerName - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder

layerName - Variable in class org.deeplearning4j.nn.conf.layers.Layer

layers - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

A list of layers.

layers - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

layersByName - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

layerSize(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return the layer size (number of units) for the specified layer.

layerSize(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return the layer size (number of units) for the specified layer.
Note that the meaning of the "layer size" can depend on the type of layer.

layerSize(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Return the layer size (number of units) for the specified layer.
Note that the meaning of the "layer size" can depend on the type of layer.

LayerUpdater - Class in org.deeplearning4j.nn.updater

Updater for a single layer, excluding MultiLayerNetwork (which also implements the Layer interface)

LayerUpdater(Layer) - Constructor for class org.deeplearning4j.nn.updater.LayerUpdater

LayerUpdater(Layer, INDArray, boolean) - Constructor for class org.deeplearning4j.nn.updater.LayerUpdater

LayerValidation - Class in org.deeplearning4j.nn.conf.layers

Utility methods for validating layer configurations

LayerVertex - Class in org.deeplearning4j.nn.conf.graph

LayerVertex is a GraphVertex with a neural network Layer (and, optionally an InputPreProcessor) in it

LayerVertex(NeuralNetConfiguration, InputPreProcessor) - Constructor for class org.deeplearning4j.nn.conf.graph.LayerVertex

LayerVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

LayerVertex is a GraphVertex with a neural network Layer (and, optionally an InputPreProcessor) in it

LayerVertex(ComputationGraph, String, int, Layer, InputPreProcessor, boolean) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

Create a network input vertex:

LayerVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], Layer, InputPreProcessor, boolean) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

layerWiseConfigurations - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

LayerWorkspaceMgr - Class in org.deeplearning4j.nn.workspace

WorkspaceMgr for DL4J layers.

LayerWorkspaceMgr(Set<ArrayType>, Map<ArrayType, WorkspaceConfiguration>, Map<ArrayType, String>) - Constructor for class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

LayerWorkspaceMgr.Builder - Class in org.deeplearning4j.nn.workspace

LBFGS - Class in org.deeplearning4j.optimize.solvers

LBFGS

LBFGS(NeuralNetConfiguration, StepFunction, Collection<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LBFGS

legacyBatchScaledL2 - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

legacyBatchScaledL2 - Variable in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer

legacyBatchScaledL2 - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

legacyBatchScaledL2(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

legacyBatchScaledL2 - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

legacyBatchScaledL2 - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

legacyBatchScaledL2(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Not recommended for use.

legacyBatchScaledL2 - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

LegacyDistributionDeserializer - Class in org.deeplearning4j.nn.conf.distribution.serde

Jackson Json deserializer to handle legacy format for distributions.
Now, we use 'type' field which contains class information.
Previously, we used wrapper objects for type information instead (see TestDistributionDeserializer for examples)

LegacyDistributionDeserializer() - Constructor for class org.deeplearning4j.nn.conf.distribution.serde.LegacyDistributionDeserializer

LegacyDistributionHelper - Class in org.deeplearning4j.nn.conf.distribution.serde

A dummy helper "distribution" for deserializing distributions in legacy/different JSON format.

LegacyGraphVertexDeserializer - Class in org.deeplearning4j.nn.conf.serde.legacyformat

Deserializer for GraphVertex JSON in legacy format - see BaseLegacyDeserializer

LegacyGraphVertexDeserializer() - Constructor for class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyGraphVertexDeserializer

LegacyGraphVertexDeserializerHelper - Class in org.deeplearning4j.nn.conf.serde.legacyformat

Simple helper class to redirect legacy JSON format to LegacyGraphVertexDeserializer via annotation on GraphVertex

LegacyIntArrayDeserializer - Class in org.deeplearning4j.nn.conf.serde.legacyformat

Deserialize either an int[] to an int[], or a single int x to int[]{x,x} Used when supporting a configuration format change from single int value to int[], as for Upsampling2D between 1.0.0-alpha and 1.0.0-beta

LegacyIntArrayDeserializer() - Constructor for class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyIntArrayDeserializer

LegacyLayerDeserializer - Class in org.deeplearning4j.nn.conf.serde.legacyformat

Deserializer for Layer JSON in legacy format - see BaseLegacyDeserializer

LegacyLayerDeserializer() - Constructor for class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyLayerDeserializer

LegacyLayerDeserializerHelper - Class in org.deeplearning4j.nn.conf.serde.legacyformat

Simple helper class to redirect legacy JSON format to LegacyLayerDeserializer via annotation on Layer

LegacyPreprocessorDeserializer - Class in org.deeplearning4j.nn.conf.serde.legacyformat

Deserializer for InputPreProcessor JSON in legacy format - see BaseLegacyDeserializer

LegacyPreprocessorDeserializer() - Constructor for class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyPreprocessorDeserializer

LegacyPreprocessorDeserializerHelper - Class in org.deeplearning4j.nn.conf.serde.legacyformat

Simple helper class to redirect legacy JSON format to LegacyPreprocessorDeserializer via annotation on InputPreProcessor

LegacyReconstructionDistributionDeserializer - Class in org.deeplearning4j.nn.conf.serde.legacyformat

Deserializer for ReconstructionDistribution JSON in legacy format - see BaseLegacyDeserializer

LegacyReconstructionDistributionDeserializer() - Constructor for class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyReconstructionDistributionDeserializer

LegacyReconstructionDistributionDeserializerHelper - Class in org.deeplearning4j.nn.conf.serde.legacyformat

Simple helper class to redirect legacy JSON format to LegacyReconstructionDistributionDeserializer via annotation on ReconstructionDistribution

leverageTo(String) - Method in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

This method is OPTIONAL, and written mostly for future use

leverageTo(ArrayType, INDArray) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

LineGradientDescent - Class in org.deeplearning4j.optimize.solvers

Stochastic Gradient Descent with Line Search

LineGradientDescent(NeuralNetConfiguration, StepFunction, Collection<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LineGradientDescent

lineMaximizer - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

LineOptimizer - Interface in org.deeplearning4j.optimize.api

Line optimizer interface adapted from mallet

list() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Create a ListBuilder (for creating a MultiLayerConfiguration)
Usage:

list(Layer...) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Create a ListBuilder (for creating a MultiLayerConfiguration) with the specified layers
Usage:

ListBuilder(NeuralNetConfiguration.Builder, Map<Integer, NeuralNetConfiguration.Builder>) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

ListBuilder(NeuralNetConfiguration.Builder) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

listener(TrainingListener...) - Method in class org.deeplearning4j.optimize.Solver.Builder

listeners - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

listeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.optimize.Solver.Builder

listObjectsInFile(File) - Static method in class org.deeplearning4j.util.ModelSerializer

List the keys of all objects added using the method ModelSerializer.addObjectToFile(File, String, Object)

load(File, boolean) - Static method in class org.deeplearning4j.nn.graph.ComputationGraph

Restore a ComputationGraph to a file, saved using ComputationGraph.save(File) or ModelSerializer

load(File, boolean) - Static method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Restore a MultiLayerNetwork to a file, saved using MultiLayerNetwork.save(File) or ModelSerializer

loadCheckpointCG(Checkpoint) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener

Load a ComputationGraph for the given checkpoint

loadCheckpointCG(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener

Load a ComputationGraph for the given checkpoint

loadCheckpointMLN(Checkpoint) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener

Load a MultiLayerNetwork for the given checkpoint

loadCheckpointMLN(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener

Load a MultiLayerNetwork for the given checkpoint number

LocalFileGraphSaver - Class in org.deeplearning4j.earlystopping.saver

Save the best (and latest/most recent) ComputationGraphs learned during early stopping training to the local file system.
Instances of this class will save 3 files for best (and optionally, latest) models:
(a) The network configuration: bestGraphConf.json
(b) The network parameters: bestGraphParams.bin
(c) The network updater: bestGraphUpdater.bin

NOTE: The model updater is an object that contains the internal state for training features such as AdaGrad, Momentum and RMSProp.
The updater is not required to use the network at test time; it is saved in case further training is required.

LocalFileGraphSaver(String) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver

Constructor that uses default character set for configuration (json) encoding

LocalFileGraphSaver(String, Charset) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver

LocalFileModelSaver - Class in org.deeplearning4j.earlystopping.saver

Save the best (and latest/most recent) models learned during early stopping training to the local file system.
Instances of this class will save 3 files for best (and optionally, latest) models:
(a) The network configuration: bestModelConf.json
(b) The network parameters: bestModelParams.bin
(c) The network updater: bestModelUpdater.bin

NOTE: The model updater is an object that contains the internal state for training features such as AdaGrad, Momentum and RMSProp.
The updater is not required to use the network at test time; it is saved in case further training is required.

LocalFileModelSaver(File) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver

LocalFileModelSaver(String) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver

Constructor that uses default character set for configuration (json) encoding

LocalFileModelSaver(String, Charset) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver

LocalHandler - Class in org.deeplearning4j.optimize.solvers.accumulation

MessageHandler implementation suited for ParallelWrapper running on single box PLEASE NOTE: This handler does NOT provide any network connectivity.

LocalHandler() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.LocalHandler

LocallyConnected1D - Class in org.deeplearning4j.nn.conf.layers

SameDiff version of a 1D locally connected layer.

LocallyConnected1D(LocallyConnected1D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.LocallyConnected1D

LocallyConnected1D.Builder - Class in org.deeplearning4j.nn.conf.layers

LocallyConnected2D - Class in org.deeplearning4j.nn.conf.layers

SameDiff version of a 2D locally connected layer.

LocallyConnected2D(LocallyConnected2D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.LocallyConnected2D

LocallyConnected2D.Builder - Class in org.deeplearning4j.nn.conf.layers

LocalResponseNormalization - Class in org.deeplearning4j.nn.conf.layers

Local response normalization layer
See section 3.3 of http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf

LocalResponseNormalization - Class in org.deeplearning4j.nn.layers.normalization

Deep neural net normalization approach normalizes activations between layers "brightness normalization" Used for nets like AlexNet

LocalResponseNormalization(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

LocalResponseNormalization(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

LocalResponseNormalization.Builder - Class in org.deeplearning4j.nn.conf.layers

LocalResponseNormalizationHelper - Interface in org.deeplearning4j.nn.layers.normalization

Helper for the local response normalization layer.

lock - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

lock - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

lockGammaBeta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

lockGammaBeta(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

If set to true: lock the gamma and beta parameters to the values for each activation, specified by BatchNormalization.Builder.gamma(double) and BatchNormalization.Builder.beta(double).

lockGammaBeta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization

locks - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

log - Static variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

log - Static variable in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

log - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

LogNormalDistribution - Class in org.deeplearning4j.nn.conf.distribution

A log-normal distribution, with two parameters: mean and standard deviation.

LogNormalDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.LogNormalDistribution

Create a log-normal distribution with the given mean and std

logProb - Variable in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

logSaving(boolean) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

If true (the default) log a message every time a model is saved

logTestMode(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

logTestMode(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

logTestMode(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

logTestMode(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

logThresholdIfReq(boolean, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

lossClassPredictions(ILossFunction) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder

Loss function for the class predictions - defaults to L2 loss (i.e., sum of squared errors, as per the paper), however Loss MCXENT could also be used (which is more common for classification).

lossFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder

lossFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer

lossFn - Variable in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer

lossFn - Variable in class org.deeplearning4j.nn.conf.layers.CnnLossLayer

lossFn - Variable in class org.deeplearning4j.nn.conf.layers.LossLayer

lossFn - Variable in class org.deeplearning4j.nn.conf.layers.RnnLossLayer

lossFunction(LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder

lossFunction(ILossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder

lossFunction - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder

lossFunction(LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder

lossFunction - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork

lossFunction(IActivation, LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

Configure the VAE to use the specified loss function for the reconstruction, instead of a ReconstructionDistribution.

lossFunction(Activation, LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

Configure the VAE to use the specified loss function for the reconstruction, instead of a ReconstructionDistribution.

lossFunction(IActivation, ILossFunction) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

Configure the VAE to use the specified loss function for the reconstruction, instead of a ReconstructionDistribution.

lossFunctionExpectsProbability(ILossFunction) - Static method in class org.deeplearning4j.util.OutputLayerUtil

LossFunctionWrapper - Class in org.deeplearning4j.nn.conf.layers.variational

LossFunctionWrapper allows training of a VAE model with a standard (possibly deterministic) neural network loss function for the reconstruction, instead of using a ReconstructionDistribution as would normally be done with a VAE model.

LossFunctionWrapper(IActivation, ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper

LossFunctionWrapper(Activation, ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper

LossLayer - Class in org.deeplearning4j.nn.conf.layers

LossLayer is a flexible output layer that performs a loss function on an input without MLP logic.
LossLayer is similar to OutputLayer in that both perform loss calculations for network outputs vs.

LossLayer(LossLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer

LossLayer - Class in org.deeplearning4j.nn.layers

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

LossLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.LossLayer

LossLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.LossLayer

LossLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

lossPositionScale(ILossFunction) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder

Loss function for position/scale component of the loss function

LSTM - Class in org.deeplearning4j.nn.conf.layers

LSTM recurrent neural network layer without peephole connections.

LSTM - Class in org.deeplearning4j.nn.layers.recurrent

LSTM layer implementation.

LSTM(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.LSTM

LSTM(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.LSTM

LSTM.Builder - Class in org.deeplearning4j.nn.conf.layers

LSTMHelper - Interface in org.deeplearning4j.nn.layers.recurrent

Helper for the recurrent LSTM layer (no peephole connections).

LSTMHelpers - Class in org.deeplearning4j.nn.layers.recurrent

RNN tutorial: http://deeplearning4j.org/usingrnns.html READ THIS FIRST if you want to understand what the heck is happening here.

LSTMParamInitializer - Class in org.deeplearning4j.nn.params

LSTM Parameter initializer, for LSTM based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf

LSTMParamInitializer() - Constructor for class org.deeplearning4j.nn.params.LSTMParamInitializer

M

maintenance() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

This method does maintenance of updates within

mapper() - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

Object mapper for serialization of configurations

mapperYaml() - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

Object mapper for serialization of configurations

markExternalUpdates(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

markExternalUpdates(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

markExternalUpdates(boolean) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator

This method allows to highlight early availability of updates

mask - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

maskArray - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

maskArray - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

maskedPoolingConvolution(PoolingType, INDArray, INDArray, int) - Static method in class org.deeplearning4j.util.MaskedReductionUtil

maskedPoolingEpsilonCnn(PoolingType, INDArray, INDArray, INDArray, int) - Static method in class org.deeplearning4j.util.MaskedReductionUtil

maskedPoolingEpsilonTimeSeries(PoolingType, INDArray, INDArray, INDArray, int) - Static method in class org.deeplearning4j.util.MaskedReductionUtil

maskedPoolingTimeSeries(PoolingType, INDArray, INDArray, int) - Static method in class org.deeplearning4j.util.MaskedReductionUtil

MaskedReductionUtil - Class in org.deeplearning4j.util

This is a TEMPORARY class for implementing global pooling with masking.

MaskLayer - Class in org.deeplearning4j.nn.conf.layers.util

MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through this layer.

MaskLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskLayer

MaskLayer - Class in org.deeplearning4j.nn.layers.util

MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through this layer.

MaskLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.util.MaskLayer

maskShape - Variable in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

MaskState - Enum in org.deeplearning4j.nn.api

MaskState: specifies whether a mask should be applied or not.

maskState - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

MaskZeroLayer - Class in org.deeplearning4j.nn.conf.layers.util

Wrapper which masks timesteps with activation equal to the specified masking value (0.0 default).

MaskZeroLayer(MaskZeroLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

MaskZeroLayer(Layer, double) - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

MaskZeroLayer - Class in org.deeplearning4j.nn.layers.recurrent

Masks timesteps with activation equal to the specified masking value, defaulting to 0.0.

MaskZeroLayer(Layer, double) - Constructor for class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer

MaskZeroLayer.Builder - Class in org.deeplearning4j.nn.conf.layers.util

matthewsCorrelation(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation

Deprecated.

maxAppliedIndexEverywhere() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

MaxEpochsTerminationCondition - Class in org.deeplearning4j.earlystopping.termination

Terminate training if the number of epochs exceeds the maximum number of epochs

MaxEpochsTerminationCondition(int) - Constructor for class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition

MaxNormConstraint - Class in org.deeplearning4j.nn.conf.constraint

Constrain the maximum L2 norm of the incoming weights for each unit to be less than or equal to the specified value.

MaxNormConstraint(double, Set<String>, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MaxNormConstraint

MaxNormConstraint(double, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MaxNormConstraint

Apply to weights but not biases by default

maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

maxNumLineSearchIterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Maximum number of line search iterations.

maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

maxNumLineSearchIterations(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

MaxScoreIterationTerminationCondition - Class in org.deeplearning4j.earlystopping.termination

Iteration termination condition for terminating training if the minibatch score exceeds a certain value.

MaxScoreIterationTerminationCondition(double) - Constructor for class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition

MaxTimeIterationTerminationCondition - Class in org.deeplearning4j.earlystopping.termination

Terminate training based on max time.

MaxTimeIterationTerminationCondition(long, TimeUnit) - Constructor for class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.StackVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex

maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

mds - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener

mdsIterator - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

mdsIterator - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener

memCellActivations - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

memCellState - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

memoryInfo(int, InputType...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Generate information regarding memory use for the network, for the given input types and minibatch size.

memoryInfo(int, InputType) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Generate information regarding memory use for the network, for the given input type and minibatch size.

memoryParameters(long, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

This method allows to define buffer memory parameters for this GradientsAccumulator Default values: 100MB initialMemory, 5 queueSize

MemoryReport - Class in org.deeplearning4j.nn.conf.memory

A MemoryReport is designed to represent the estimated memory usage of a model, as a function of:
- Training vs.

MemoryReport() - Constructor for class org.deeplearning4j.nn.conf.memory.MemoryReport

MemoryType - Enum in org.deeplearning4j.nn.conf.memory

Type of memory

MemoryUseMode - Enum in org.deeplearning4j.nn.conf.memory

This simple enumeration defines the memory is used during inference or training.

merge(ThresholdAlgorithmReducer) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer

merge(ThresholdAlgorithmReducer) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithmReducer

Combine two reducers and return the result

MergeVertex - Class in org.deeplearning4j.nn.conf.graph

A MergeVertex is used to combine the activations of two or more layers/GraphVertex by means of concatenation/merging.
Exactly how this is done depends on the type of input.
For 2d (feed forward layer) inputs: MergeVertex([numExamples,layerSize1],[numExamples,layerSize2]) -> [numExamples,layerSize1 + layerSize2]
For 3d (time series) inputs: MergeVertex([numExamples,layerSize1,timeSeriesLength],[numExamples,layerSize2,timeSeriesLength]) -> [numExamples,layerSize1 + layerSize2,timeSeriesLength]
For 4d (convolutional) inputs: MergeVertex([numExamples,depth1,width,height],[numExamples,depth2,width,height]) -> [numExamples,depth1 + depth2,width,height]

MergeVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.MergeVertex

MergeVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

A MergeVertex is used to combine the activations of two or more layers/GraphVertex by means of concatenation/merging.
Exactly how this is done depends on the type of input.
For 2d (feed forward layer) inputs: MergeVertex([numExamples,layerSize1],[numExamples,layerSize2]) -> [numExamples,layerSize1 + layerSize2]
For 3d (time series) inputs: MergeVertex([numExamples,layerSize1,timeSeriesLength],[numExamples,layerSize2,timeSeriesLength]) -> [numExamples,layerSize1 + layerSize2,timeSeriesLength]
For 4d (convolutional) inputs: MergeVertex([numExamples,depth1,width,height],[numExamples,depth2,width,height]) -> [numExamples,depth1 + depth2,width,height]

MergeVertex(ComputationGraph, String, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex

MergeVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex

messageHandler(MessageHandler) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

This method allows to specify MessageHandler instance Default value: EncodingHandler

MessageHandler - Interface in org.deeplearning4j.optimize.solvers.accumulation

This interface describes communication primitive for GradientsAccumulator PLEASE NOTE: All implementations of this interface must be thread-safe.

messages - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

metric - Variable in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator

metric - Variable in class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator

metric - Variable in class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator

metric - Variable in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator

metric - Variable in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator

minibatch(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder

If doing minibatch training or not.

miniBatch - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

miniBatch(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Process input as minibatch vs full dataset.

miniBatch - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

miniBatch(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Whether scores and gradients should be divided by the minibatch size.
Most users should leave this ast he default value of true.

miniBatch - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

minibatchCount - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

minimize() - Method in enum org.deeplearning4j.eval.RegressionEvaluation.Metric

Deprecated.

minimize - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

minimize(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Objective function to minimize or maximize cost function Default set to minimize true.

minimize - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

minimize(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

minimize - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator

minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator

minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator

minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG

Deprecated.

minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator

minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator

minimizeScore() - Method in interface org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator

minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator

minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

MinMaxNormConstraint - Class in org.deeplearning4j.nn.conf.constraint

Constrain the minimum AND maximum L2 norm of the incoming weights for each unit to be between the specified values.

MinMaxNormConstraint(double, double, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint

Apply to weights but not biases by default

MinMaxNormConstraint(double, double, double, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint

Apply to weights but not biases by default

MinMaxNormConstraint(double, double, double, Set<String>, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.StackVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex

minVertexInputs() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

mode(Bidirectional.Mode) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder

model - Variable in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer

Model - Interface in org.deeplearning4j.nn.api

A Model is meant for predicting something from data.

model(Model) - Method in class org.deeplearning4j.optimize.Solver.Builder

model - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

modelSaver(EarlyStoppingModelSaver<T>) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder

How should models be saved? (Default: in memory)

ModelSavingCallback - Class in org.deeplearning4j.optimize.listeners.callbacks

This callback will save model after each EvaluativeListener invocation.

ModelSavingCallback(String) - Constructor for class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback

This constructor will create ModelSavingCallback instance that will save models in current folder PLEASE NOTE: Make sure you have write access to the current folder

ModelSavingCallback(File, String) - Constructor for class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback

This constructor will create ModelSavingCallback instance that will save models in specified folder PLEASE NOTE: Make sure you have write access to the target folder

ModelSerializer - Class in org.deeplearning4j.util

Utility class suited to save/restore neural net models

movingAverage(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Calculate a moving average given the length

MultiDataSetIteratorAdapter - Class in org.deeplearning4j.datasets.iterator.impl

Iterator that adapts a DataSetIterator to a MultiDataSetIterator

MultiDataSetIteratorAdapter(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter

MultiLayerConfiguration - Class in org.deeplearning4j.nn.conf

Configuration for a multi layer network

MultiLayerConfiguration() - Constructor for class org.deeplearning4j.nn.conf.MultiLayerConfiguration

MultiLayerConfiguration.Builder - Class in org.deeplearning4j.nn.conf

MultiLayerConfigurationDeserializer - Class in org.deeplearning4j.nn.conf.serde

MultiLayerConfigurationDeserializer(JsonDeserializer<?>) - Constructor for class org.deeplearning4j.nn.conf.serde.MultiLayerConfigurationDeserializer

MultiLayerNetwork - Class in org.deeplearning4j.nn.multilayer

MultiLayerNetwork is a neural network with multiple layers in a stack, and usually an output layer.
For neural networks with a more complex connection architecture, use ComputationGraph which allows for an arbitrary directed acyclic graph connection structure between layers.

MultiLayerNetwork(MultiLayerConfiguration) - Constructor for class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

MultiLayerNetwork(String, INDArray) - Constructor for class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Initialize the network based on the configuration

MultiLayerNetwork(MultiLayerConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Initialize the network based on the configuraiton

MultiLayerUpdater - Class in org.deeplearning4j.nn.updater

MultiLayerUpdater: Gradient updater for MultiLayerNetworks.

MultiLayerUpdater(MultiLayerNetwork) - Constructor for class org.deeplearning4j.nn.updater.MultiLayerUpdater

MultiLayerUpdater(MultiLayerNetwork, INDArray, boolean) - Constructor for class org.deeplearning4j.nn.updater.MultiLayerUpdater

N

n(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder

Number of adjacent kernel maps to use when doing LRN.

n - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

name(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder

Layer name assigns layer string name.

name(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder

name(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder

name() - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction

needsLabels() - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer

Returns true if labels are required for this output layer

needsLabels() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

needsLabels() - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

needsLabels() - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

needsLabels() - Method in class org.deeplearning4j.nn.layers.LossLayer

needsLabels() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

needsLabels() - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

needsLabels() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

needsLabels() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

NegativeDefaultStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions

Inverse step function

NegativeDefaultStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction

NegativeDefaultStepFunction - Class in org.deeplearning4j.optimize.stepfunctions

Inverse step function

NegativeDefaultStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction

NegativeGradientStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions

Subtract the line

NegativeGradientStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction

NegativeGradientStepFunction - Class in org.deeplearning4j.optimize.stepfunctions

Subtract the line

NegativeGradientStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction

negLogProbability(INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

negLogProbability(INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution

negLogProbability(INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

negLogProbability(INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

negLogProbability(INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper

negLogProbability(INDArray, INDArray, boolean) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution

Calculate the negative log probability (summed or averaged over each example in the minibatch)

network - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

networkInputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

networkInputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

List of inputs to the network, by name

networkInputTypes - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

NetworkMemoryReport - Class in org.deeplearning4j.nn.conf.memory

Network memory reports is a class that is used to store/represent the memory requirements of a MultiLayerNetwork or ComputationGraph, composed of multiple layers and/or vertices.

NetworkMemoryReport(Map<String, MemoryReport>, Class<?>, String, InputType...) - Constructor for class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport

networkOutputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

networkOutputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

List of network outputs, by name

NetworkUtils - Class in org.deeplearning4j.util

NeuralNetConfiguration - Class in org.deeplearning4j.nn.conf

A Serializable configuration for neural nets that covers per layer parameters

NeuralNetConfiguration() - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration

NeuralNetConfiguration.Builder - Class in org.deeplearning4j.nn.conf

NeuralNetConfiguration builder, used as a starting point for creating a MultiLayerConfiguration or ComputationGraphConfiguration.
Note that values set here on the layer will be applied to all relevant layers - unless the value is overridden on a layer's configuration

NeuralNetConfiguration.ListBuilder - Class in org.deeplearning4j.nn.conf

Fluent interface for building a list of configurations

NeuralNetConfiguration.ListBuilder.InputTypeBuilder - Class in org.deeplearning4j.nn.conf

Helper class for setting input types

NeuralNetwork - Interface in org.deeplearning4j.nn.api

newEval() - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator

newEval() - Method in class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator

newEval() - Method in class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator

newEval() - Method in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator

newReducer() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

newReducer() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm

newReducer() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm

newReducer() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithm

Create a new ThresholdAlgorithmReducer.

newShape - Variable in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

next(int) - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter

next() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter

nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder

nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder

nIn - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

Number of inputs for the layer (usually the size of the last layer).

nIn(long) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

Number of inputs for the layer (usually the size of the last layer).

nIn - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer.Builder

nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder

nInReplace(int, int, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call.

nInReplace(int, int, WeightInit, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call.

nInReplace(String, int, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call.

nInReplace(String, int, WeightInit, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call.

nms(List<DetectedObject>, double) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils

Performs non-maximum suppression (NMS) on objects, using their IOU with threshold to match pairs.

NO_PARAMS_MARKER - Static variable in class org.deeplearning4j.util.ModelSerializer

noLeverageOverride - Variable in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

NonNegativeConstraint - Class in org.deeplearning4j.nn.conf.constraint

Constrain the parameters to be non-negative.

NonNegativeConstraint() - Constructor for class org.deeplearning4j.nn.conf.constraint.NonNegativeConstraint

NoOpResidualPostProcessor - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.residual

This residual post process is a "no op" post processor.

NoOpResidualPostProcessor() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.NoOpResidualPostProcessor

NoParamLayer - Class in org.deeplearning4j.nn.conf.layers

NoParamLayer(Layer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.NoParamLayer

NormalDistribution - Class in org.deeplearning4j.nn.conf.distribution

A normal (Gaussian) distribution, with two parameters: mean and standard deviation

NormalDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.NormalDistribution

Create a normal distribution with the given mean and std

NORMALIZER_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer

notifyDead() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder

nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder

nOut - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

Number of outputs - used to set the layer size (number of units/nodes for the current layer).

nOut(long) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

Number of outputs - used to set the layer size (number of units/nodes for the current layer).

nOut - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer.Builder

nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder

nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

Set the size of the VAE state Z.

nOut(int) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

nOutReplace(int, int, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer

nOutReplace(int, int, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer

nOutReplace(int, int, WeightInit, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer Can specify different weight init schemes for the specified layer and the layer that follows it.

nOutReplace(int, int, Distribution, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer Can specify different weight init schemes for the specified layer and the layer that follows it.

nOutReplace(int, int, WeightInit, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer Can specify different weight init schemes for the specified layer and the layer that follows it.

nOutReplace(int, int, Distribution, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer Can specify different weight init schemes for the specified layer and the layer that follows it.

nOutReplace(String, int, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Modify the architecture of a vertex layer by changing nOut Note this will also affect the vertex layer that follows the layer specified, unless it is the output layer Currently does not support modifying nOut of layers that feed into non-layer vertices like merge, subset etc To modify nOut for such vertices use remove vertex, followed by add vertex Can specify different weight init schemes for the specified layer and the layer that follows it.

nOutReplace(String, int, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Modify the architecture of a vertex layer by changing nOut Note this will also affect the vertex layer that follows the layer specified, unless it is the output layer Currently does not support modifying nOut of layers that feed into non-layer vertices like merge, subset etc To modify nOut for such vertices use remove vertex, followed by add vertex Can specify different weight init schemes for the specified layer and the layer that follows it.

nOutReplace(String, int, Distribution, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Modified nOut of specified layer.

nOutReplace(String, int, WeightInit, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

nOutReplace(String, int, Distribution, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

nOutReplace(String, int, WeightInit, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

noWorkspaceFor(ArrayType) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder

Specify that no workspace should be used for array of the specified type - i.e., these arrays should all be scoped out.

noWorkspaces(Map<String, Pointer>) - Static method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

noWorkspaces() - Static method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

noWorkspacesImmutable() - Static method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

nu - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

nu(double) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

For nu definition see the paper

NU_KEY - Static variable in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

numChannels - Variable in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

numChannels - Variable in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

numChannels(int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Returns the number of feature maps for a given shape (must be at least 3 dimensions

numElementsDrained - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

numElementsReady - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

numFeatureMap(NeuralNetConfiguration) - Static method in class org.deeplearning4j.util.ConvolutionUtils

numLabels() - Method in interface org.deeplearning4j.nn.api.Classifier

Returns the number of possible labels

numLabels() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Returns the number of possible labels

numLabels() - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

numLabels() - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

numLabels() - Method in class org.deeplearning4j.nn.layers.LossLayer

Returns the number of possible labels

numLabels() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

numLabels() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

numLabels() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

numLabels() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Returns the number of possible labels

numParams() - Method in interface org.deeplearning4j.nn.api.Model

the number of parameters for the model

numParams(boolean) - Method in interface org.deeplearning4j.nn.api.Model

the number of parameters for the model

numParams(NeuralNetConfiguration) - Method in interface org.deeplearning4j.nn.api.ParamInitializer

numParams(Layer) - Method in interface org.deeplearning4j.nn.api.ParamInitializer

numParams() - Method in interface org.deeplearning4j.nn.api.Trainable

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex

numParams(boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

numParams() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

numParams(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

numParams() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

numParams() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

numParams() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

The number of parameters for the model

numParams(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

numParams() - Method in class org.deeplearning4j.nn.layers.BaseLayer

The number of parameters for the model

numParams(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

numParams() - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

The number of parameters for the model, for backprop (i.e., excluding visible bias)

numParams() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

numParams() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

numParams() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

numParams() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

numParams() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

numParams() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

numParams() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

numParams(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

numParams() - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer

numParams() - Method in class org.deeplearning4j.nn.layers.RepeatVector

numParams() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

numParams() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

numParams() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

numParams(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

numParams() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

numParams(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

numParams() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Returns a 1 x m vector where the vector is composed of a flattened vector of all of the weights for the various neuralNets and output layer

numParams(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer

numParams(Layer) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer

numSamples(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

Set the number of samples per data point (from VAE state Z) used when doing pretraining.

numSamples - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

O

oa - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

OCNNLossFunction() - Constructor for class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction

OCNNOutputLayer - Class in org.deeplearning4j.nn.conf.ocnn

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.

OCNNOutputLayer(OCNNOutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer

OCNNOutputLayer(int, double, IActivation, int, double, boolean) - Constructor for class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer

OCNNOutputLayer - Class in org.deeplearning4j.nn.layers.ocnn

Layer implementation for OCNNOutputLayer See OCNNOutputLayer for details.

OCNNOutputLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

OCNNOutputLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

OCNNOutputLayer.Builder - Class in org.deeplearning4j.nn.conf.ocnn

OCNNOutputLayer.OCNNLossFunction - Class in org.deeplearning4j.nn.layers.ocnn

OCNNParamInitializer - Class in org.deeplearning4j.nn.layers.ocnn

Param initializer for OCNNOutputLayer

OCNNParamInitializer() - Constructor for class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

offer(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

offer(E, long, TimeUnit) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

oldScore - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

onBackwardPass(Model) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener

onBackwardPass(Model) - Method in interface org.deeplearning4j.optimize.api.TrainingListener

Called once per iteration (backward pass) after gradients have been calculated, and updated Gradients are available via Model.gradient().

onBackwardPass(Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener

onBackwardPass(Model) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

onCompletion(EarlyStoppingResult<T>) - Method in interface org.deeplearning4j.earlystopping.listener.EarlyStoppingListener

Method that is called at the end of early stopping training

onEpoch(int, double, EarlyStoppingConfiguration<T>, T) - Method in interface org.deeplearning4j.earlystopping.listener.EarlyStoppingListener

Method that is called at the end of each epoch completed during early stopping training

onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener

onEpochEnd(Model) - Method in interface org.deeplearning4j.optimize.api.TrainingListener

Called once at the end of each epoch, when using methods such as MultiLayerNetwork.fit(DataSetIterator), ComputationGraph.fit(DataSetIterator) or ComputationGraph.fit(MultiDataSetIterator)

onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener

onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener

onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener

onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

onEpochStart(Model) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener

onEpochStart(Model) - Method in interface org.deeplearning4j.optimize.api.TrainingListener

Called once at the start of each epoch, when using methods such as MultiLayerNetwork.fit(DataSetIterator), ComputationGraph.fit(DataSetIterator) or ComputationGraph.fit(MultiDataSetIterator)

onEpochStart(Model) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener

onEpochStart(Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener

onEpochStart(Model) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

onForwardPass(Model, List<INDArray>) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener

onForwardPass(Model, Map<String, INDArray>) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener

onForwardPass(Model, List<INDArray>) - Method in interface org.deeplearning4j.optimize.api.TrainingListener

Called once per iteration (forward pass) for activations (usually for a MultiLayerNetwork), only at training time

onForwardPass(Model, Map<String, INDArray>) - Method in interface org.deeplearning4j.optimize.api.TrainingListener

Called once per iteration (forward pass) for activations (usually for a ComputationGraph), only at training time

onForwardPass(Model, List<INDArray>) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener

onForwardPass(Model, Map<String, INDArray>) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener

onForwardPass(Model, List<INDArray>) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

onForwardPass(Model, Map<String, INDArray>) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

onGradientCalculation(Model) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener

onGradientCalculation(Model) - Method in interface org.deeplearning4j.optimize.api.TrainingListener

Called once per iteration (backward pass) before the gradients are updated Gradients are available via Model.gradient().

onGradientCalculation(Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener

onGradientCalculation(Model) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

onStart(EarlyStoppingConfiguration<T>, T) - Method in interface org.deeplearning4j.earlystopping.listener.EarlyStoppingListener

Method to be called when early stopping training is first started

op - Variable in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex

optimizationAlgo - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

optimizationAlgo(OptimizationAlgorithm) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Optimization algorithm to use.

optimizationAlgo - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

optimizationAlgo(OptimizationAlgorithm) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

optimizationAlgo - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

OptimizationAlgorithm - Enum in org.deeplearning4j.nn.api

Optimization algorithm to use

optimize(LayerWorkspaceMgr) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

Calls optimize

optimize(INDArray, INDArray, INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.optimize.api.LineOptimizer

Line optimizer

optimize(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.Solver

optimize(INDArray, INDArray, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

optimize(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

Optimize call.

optimize(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.StochasticGradientDescent

optimizer - Variable in class org.deeplearning4j.nn.layers.BaseLayer

optimizer - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

Or(FailureTestingListener.FailureTrigger...) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.Or

orderedLayers - Variable in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater

ordering - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

org.deeplearning4j.datasets.iterator.impl - package org.deeplearning4j.datasets.iterator.impl

org.deeplearning4j.earlystopping - package org.deeplearning4j.earlystopping

org.deeplearning4j.earlystopping.listener - package org.deeplearning4j.earlystopping.listener

org.deeplearning4j.earlystopping.saver - package org.deeplearning4j.earlystopping.saver

org.deeplearning4j.earlystopping.scorecalc - package org.deeplearning4j.earlystopping.scorecalc

org.deeplearning4j.earlystopping.scorecalc.base - package org.deeplearning4j.earlystopping.scorecalc.base

org.deeplearning4j.earlystopping.termination - package org.deeplearning4j.earlystopping.termination

org.deeplearning4j.earlystopping.trainer - package org.deeplearning4j.earlystopping.trainer

org.deeplearning4j.eval - package org.deeplearning4j.eval

org.deeplearning4j.eval.curves - package org.deeplearning4j.eval.curves

org.deeplearning4j.eval.meta - package org.deeplearning4j.eval.meta

org.deeplearning4j.exception - package org.deeplearning4j.exception

org.deeplearning4j.gradientcheck - package org.deeplearning4j.gradientcheck

org.deeplearning4j.nn.adapters - package org.deeplearning4j.nn.adapters

org.deeplearning4j.nn.api - package org.deeplearning4j.nn.api

org.deeplearning4j.nn.api.layers - package org.deeplearning4j.nn.api.layers

org.deeplearning4j.nn.conf - package org.deeplearning4j.nn.conf

org.deeplearning4j.nn.conf.constraint - package org.deeplearning4j.nn.conf.constraint

org.deeplearning4j.nn.conf.distribution - package org.deeplearning4j.nn.conf.distribution

org.deeplearning4j.nn.conf.distribution.serde - package org.deeplearning4j.nn.conf.distribution.serde

org.deeplearning4j.nn.conf.dropout - package org.deeplearning4j.nn.conf.dropout

org.deeplearning4j.nn.conf.graph - package org.deeplearning4j.nn.conf.graph

org.deeplearning4j.nn.conf.graph.rnn - package org.deeplearning4j.nn.conf.graph.rnn

org.deeplearning4j.nn.conf.inputs - package org.deeplearning4j.nn.conf.inputs

org.deeplearning4j.nn.conf.layers - package org.deeplearning4j.nn.conf.layers

org.deeplearning4j.nn.conf.layers.convolutional - package org.deeplearning4j.nn.conf.layers.convolutional

org.deeplearning4j.nn.conf.layers.misc - package org.deeplearning4j.nn.conf.layers.misc

org.deeplearning4j.nn.conf.layers.objdetect - package org.deeplearning4j.nn.conf.layers.objdetect

org.deeplearning4j.nn.conf.layers.recurrent - package org.deeplearning4j.nn.conf.layers.recurrent

org.deeplearning4j.nn.conf.layers.samediff - package org.deeplearning4j.nn.conf.layers.samediff

org.deeplearning4j.nn.conf.layers.util - package org.deeplearning4j.nn.conf.layers.util

org.deeplearning4j.nn.conf.layers.variational - package org.deeplearning4j.nn.conf.layers.variational

org.deeplearning4j.nn.conf.layers.wrapper - package org.deeplearning4j.nn.conf.layers.wrapper

org.deeplearning4j.nn.conf.memory - package org.deeplearning4j.nn.conf.memory

org.deeplearning4j.nn.conf.misc - package org.deeplearning4j.nn.conf.misc

org.deeplearning4j.nn.conf.module - package org.deeplearning4j.nn.conf.module

org.deeplearning4j.nn.conf.ocnn - package org.deeplearning4j.nn.conf.ocnn

org.deeplearning4j.nn.conf.preprocessor - package org.deeplearning4j.nn.conf.preprocessor

org.deeplearning4j.nn.conf.serde - package org.deeplearning4j.nn.conf.serde

org.deeplearning4j.nn.conf.serde.legacyformat - package org.deeplearning4j.nn.conf.serde.legacyformat

org.deeplearning4j.nn.conf.stepfunctions - package org.deeplearning4j.nn.conf.stepfunctions

org.deeplearning4j.nn.conf.weightnoise - package org.deeplearning4j.nn.conf.weightnoise

org.deeplearning4j.nn.gradient - package org.deeplearning4j.nn.gradient

org.deeplearning4j.nn.graph - package org.deeplearning4j.nn.graph

org.deeplearning4j.nn.graph.util - package org.deeplearning4j.nn.graph.util

org.deeplearning4j.nn.graph.vertex - package org.deeplearning4j.nn.graph.vertex

org.deeplearning4j.nn.graph.vertex.impl - package org.deeplearning4j.nn.graph.vertex.impl

org.deeplearning4j.nn.graph.vertex.impl.rnn - package org.deeplearning4j.nn.graph.vertex.impl.rnn

org.deeplearning4j.nn.layers - package org.deeplearning4j.nn.layers

org.deeplearning4j.nn.layers.convolution - package org.deeplearning4j.nn.layers.convolution

org.deeplearning4j.nn.layers.convolution.subsampling - package org.deeplearning4j.nn.layers.convolution.subsampling

org.deeplearning4j.nn.layers.convolution.upsampling - package org.deeplearning4j.nn.layers.convolution.upsampling

org.deeplearning4j.nn.layers.feedforward - package org.deeplearning4j.nn.layers.feedforward

org.deeplearning4j.nn.layers.feedforward.autoencoder - package org.deeplearning4j.nn.layers.feedforward.autoencoder

org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive - package org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive

org.deeplearning4j.nn.layers.feedforward.dense - package org.deeplearning4j.nn.layers.feedforward.dense

org.deeplearning4j.nn.layers.feedforward.elementwise - package org.deeplearning4j.nn.layers.feedforward.elementwise

org.deeplearning4j.nn.layers.feedforward.embedding - package org.deeplearning4j.nn.layers.feedforward.embedding

org.deeplearning4j.nn.layers.normalization - package org.deeplearning4j.nn.layers.normalization

org.deeplearning4j.nn.layers.objdetect - package org.deeplearning4j.nn.layers.objdetect

org.deeplearning4j.nn.layers.ocnn - package org.deeplearning4j.nn.layers.ocnn

org.deeplearning4j.nn.layers.pooling - package org.deeplearning4j.nn.layers.pooling

org.deeplearning4j.nn.layers.recurrent - package org.deeplearning4j.nn.layers.recurrent

org.deeplearning4j.nn.layers.samediff - package org.deeplearning4j.nn.layers.samediff

org.deeplearning4j.nn.layers.training - package org.deeplearning4j.nn.layers.training

org.deeplearning4j.nn.layers.util - package org.deeplearning4j.nn.layers.util

org.deeplearning4j.nn.layers.variational - package org.deeplearning4j.nn.layers.variational

org.deeplearning4j.nn.layers.wrapper - package org.deeplearning4j.nn.layers.wrapper

org.deeplearning4j.nn.multilayer - package org.deeplearning4j.nn.multilayer

org.deeplearning4j.nn.params - package org.deeplearning4j.nn.params

org.deeplearning4j.nn.transferlearning - package org.deeplearning4j.nn.transferlearning

org.deeplearning4j.nn.updater - package org.deeplearning4j.nn.updater

org.deeplearning4j.nn.updater.graph - package org.deeplearning4j.nn.updater.graph

org.deeplearning4j.nn.weights - package org.deeplearning4j.nn.weights

org.deeplearning4j.nn.workspace - package org.deeplearning4j.nn.workspace

org.deeplearning4j.optimize - package org.deeplearning4j.optimize

org.deeplearning4j.optimize.api - package org.deeplearning4j.optimize.api

org.deeplearning4j.optimize.listeners - package org.deeplearning4j.optimize.listeners

org.deeplearning4j.optimize.listeners.callbacks - package org.deeplearning4j.optimize.listeners.callbacks

org.deeplearning4j.optimize.solvers - package org.deeplearning4j.optimize.solvers

org.deeplearning4j.optimize.solvers.accumulation - package org.deeplearning4j.optimize.solvers.accumulation

org.deeplearning4j.optimize.solvers.accumulation.encoding - package org.deeplearning4j.optimize.solvers.accumulation.encoding

org.deeplearning4j.optimize.solvers.accumulation.encoding.residual - package org.deeplearning4j.optimize.solvers.accumulation.encoding.residual

org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold - package org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold

org.deeplearning4j.optimize.stepfunctions - package org.deeplearning4j.optimize.stepfunctions

org.deeplearning4j.util - package org.deeplearning4j.util

OrthogonalDistribution - Class in org.deeplearning4j.nn.conf.distribution

Orthogonal distribution, with gain parameter.
See http://arxiv.org/abs/1312.6120 for details

OrthogonalDistribution(double) - Constructor for class org.deeplearning4j.nn.conf.distribution.OrthogonalDistribution

Create a log-normal distribution with the given mean and std

output(Model, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator

output(Model, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator

output(MultiLayerNetwork, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseMLNScoreCalculator

output(MultiLayerNetwork, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseMLNScoreCalculator

output(T, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

output(T, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

output(Model, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator

output(Model, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator

output(Model, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator

output(Model, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator

output(Model, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

output(Model, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

output(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return an array of network outputs (predictions) at test time, given the specified network inputs Network outputs are for output layers only.

output(boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return an array of network outputs (predictions), given the specified network inputs Network outputs are for output layers only.

output(boolean, MemoryWorkspace, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return an array of network outputs (predictions), given the specified network inputs Network outputs are for output layers only.
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.

output(boolean, INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return an array of network outputs (predictions), given the specified network inputs Network outputs are for output layers only.

output(boolean, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return an array of network outputs (predictions), given the specified network inputs Network outputs are for output layers only.

output(INDArray[], INDArray[], INDArray[], OutputAdapter<T>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

This method uses provided OutputAdapter to return custom object built from INDArray PLEASE NOTE: This method uses dedicated Workspace for output generation to avoid redundant allocations

output(boolean, INDArray[], INDArray[], INDArray[], MemoryWorkspace) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Return an array of network outputs (predictions), given the specified network inputs Network outputs are for output layers only.
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.

output(boolean, boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

An output method for the network, with optional clearing of the layer inputs.
Note: most users should use ComputationGraph.output(boolean, INDArray...) or similar methods, unless they are doing non-standard operations (like providing the input arrays externally)

output(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Generate the output for all examples/batches in the input iterator, and concatenate them into a single array per network output

output(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Generate the output for all examples/batches in the input iterator, and concatenate them into a single array per network output

output(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Label the probabilities of the input

output(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Label the probabilities of the input

output(INDArray, boolean, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Calculate the output of the network, with masking arrays.

output(INDArray, boolean, MemoryWorkspace) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Get the network output, which is optionally placed in the specified memory workspace.
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.

output(INDArray, boolean, INDArray, INDArray, MemoryWorkspace) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Get the network output, which is optionally placed in the specified memory workspace.
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.

output(INDArray, INDArray, INDArray, OutputAdapter<T>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

This method uses provided OutputAdapter to return custom object built from INDArray PLEASE NOTE: This method uses dedicated Workspace for output generation to avoid redundant allocations

output(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Label the probabilities of the input

output(DataSetIterator, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Generate the output for all examples/batches in the input iterator, and concatenate them into a single array

output(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Generate the output for all examples/batches in the input iterator, and concatenate them into a single array

output(Model, INDArray) - Static method in class org.deeplearning4j.util.NetworkUtils

Currently supports MultiLayerNetwork and ComputationGraph models.

OutputAdapter<T> - Interface in org.deeplearning4j.nn.api

This interface describes entity used to conver neural network output to specified class.

outputFromFeaturized(INDArray[]) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

Use to get the output from a featurized input

outputFromFeaturized(INDArray) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

Use to get the output from a featurized input

outputKey - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

outputKey - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

outputKey - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

OutputLayer - Class in org.deeplearning4j.nn.conf.layers

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

OutputLayer(OutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer

OutputLayer - Class in org.deeplearning4j.nn.layers

Output layer with different objective incooccurrences for different objectives.

OutputLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.OutputLayer

OutputLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.OutputLayer

OutputLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

OutputLayerUtil - Class in org.deeplearning4j.util

Utility methods for output layer configuration/validation

outputOfLayerDetached(boolean, FwdPassType, int, INDArray, INDArray, INDArray, MemoryWorkspace) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Provide the output of the specified layer, detached from any workspace.

outputOfLayersDetached(boolean, FwdPassType, int[], INDArray[], INDArray[], INDArray[], boolean, boolean, MemoryWorkspace) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Provide the output of the specified layers, detached from any workspace.

outputSingle(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

A convenience method that returns a single INDArray, instead of an INDArray[].

outputSingle(boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

A convenience method that returns a single INDArray, instead of an INDArray[].

outputSingle(boolean, boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Identical to ComputationGraph.outputSingle(boolean, boolean, INDArray...) but has the option of not clearing the input arrays (useful when later backpropagating external errors).

outputSingle(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Generate the output for all examples/batches in the input iterator, and concatenate them into a single array.

outputSingle(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Generate the output for all examples/batches in the input iterator, and concatenate them into a single array.

outputVar - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

outputVar - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

outputVar - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

outputVertex - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

outputVertices - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

A representation of the vertices that this vertex is connected to (outputs duing forward pass) Specifically, if outputVertices[X].getVertexIndex() = Y, and outputVertices[X].getVertexEdgeNumber() = Z then the output of this vertex (there is only one output) is connected to the Zth input of vertex Y

overlap(double, double, double, double) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils

Returns overlap between lines [x1, x2] and [x3.

ownCounter - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

oz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

P

padding(int) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder

Padding value for the convolution.

padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

Set padding size for 3D convolutions in (depth, height, width) order

padding - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder

padding - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder

padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder

Padding of the convolution in rows/columns (height/width) dimensions

padding(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

Padding - rows/columns (height/width)

padding - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder

padding(int[][]) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder

padding - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

padding(int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Padding

padding - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Padding

padding - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

padding - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Padding

padding - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

ParamAndGradientIterationListener - Class in org.deeplearning4j.optimize.listeners

Deprecated.

StatsListener can be used instead, storing data using FileStatsStorage - UI is not required

ParamAndGradientIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener

Deprecated.

Default constructor for output to console only every iteration, tab delimited

ParamAndGradientIterationListener(int, boolean, boolean, boolean, boolean, boolean, boolean, boolean, File, String) - Constructor for class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener

Deprecated.

Full constructor with all options.

ParamInitializer - Interface in org.deeplearning4j.nn.api

Param initializer for a layer

paramKeys(Layer) - Method in interface org.deeplearning4j.nn.api.ParamInitializer

Get a list of all parameter keys given the layer configuration

paramKeys(Layer) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer

paramReshapeOrder(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

Returns the memory layout ('c' or 'f' order - i.e., row/column major) of the parameters.

paramReshapeOrder(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

params() - Method in interface org.deeplearning4j.nn.api.Model

Parameters of the model (if any)

params() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork

This method returns model parameters as single INDArray

params() - Method in interface org.deeplearning4j.nn.api.Trainable

params - Variable in class org.deeplearning4j.nn.conf.constraint.BaseConstraint

params(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the parameters for the ComputationGraph

params() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

params() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

params() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

params() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

params() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

Returns the parameters of the neural network as a flattened row vector

params() - Method in class org.deeplearning4j.nn.layers.ActivationLayer

params - Variable in class org.deeplearning4j.nn.layers.BaseLayer

params() - Method in class org.deeplearning4j.nn.layers.BaseLayer

Returns the parameters of the neural network as a flattened row vector

params() - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

params() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

params() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

params() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

params() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

params() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

params() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

params() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

params() - Method in class org.deeplearning4j.nn.layers.DropoutLayer

params() - Method in class org.deeplearning4j.nn.layers.LossLayer

params() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

params() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

params() - Method in class org.deeplearning4j.nn.layers.RepeatVector

params - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

params() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

params - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

params() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

Returns the parameters of the neural network as a flattened row vector

params - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

params() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

Returns the parameters of the neural network as a flattened row vector

params - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

params() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

params() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

params(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Returns a 1 x m vector where the vector is composed of a flattened vector of all of the weights for the various neuralNets(w,hbias NOT VBIAS) and output layer

params() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Returns a 1 x m vector where the vector is composed of a flattened vector of all of the weights for the various neuralNets(w,hbias NOT VBIAS) and output layer

PARAMS_KEY - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

paramsFlattened - Variable in class org.deeplearning4j.nn.layers.BaseLayer

paramsFlattened - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

paramsOrder - Variable in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

paramsShape - Variable in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

ParamState() - Constructor for class org.deeplearning4j.nn.updater.UpdaterBlock.ParamState

paramTable() - Method in interface org.deeplearning4j.nn.api.Model

The param table

paramTable(boolean) - Method in interface org.deeplearning4j.nn.api.Model

Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters

paramTable(boolean) - Method in interface org.deeplearning4j.nn.api.Trainable

paramTable() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

paramTable(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

paramTable(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

paramTable(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

paramTable(boolean) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Get the parameter table for the vertex

paramTable(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

paramTable() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

paramTable() - Method in class org.deeplearning4j.nn.layers.BaseLayer

paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer

paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

paramTable() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

paramTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

paramTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

paramTable() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

paramTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

paramTable() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

paramTable() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

paramTable() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

paramTable() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

paramTable(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

parent(Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Returns the parent of the passed in tree via traversal

parent() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

parties - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

parties - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

parties - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

peek() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

PerformanceListener - Class in org.deeplearning4j.optimize.listeners

Simple IterationListener that tracks time spend on training per iteration.

PerformanceListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.PerformanceListener

PerformanceListener(int, boolean) - Constructor for class org.deeplearning4j.optimize.listeners.PerformanceListener

PerformanceListener.Builder - Class in org.deeplearning4j.optimize.listeners

pnorm(int) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder

P-norm constant.

pnorm - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

pnorm(int) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

pnorm - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

Point(int, double, double, double) - Constructor for class org.deeplearning4j.eval.curves.PrecisionRecallCurve.Point

Deprecated.

POINT_WISE_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

pointWiseConstraints - Variable in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

poll() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

poll(long, TimeUnit) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

poll() - Method in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

PoolHelperVertex - Class in org.deeplearning4j.nn.conf.graph

Removes the first column and row from an input.

PoolHelperVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.PoolHelperVertex

PoolHelperVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

A custom layer for removing the first column and row from an input.

PoolHelperVertex(ComputationGraph, String, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex

PoolHelperVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex

Pooling1D - Class in org.deeplearning4j.nn.conf.layers

1D Pooling (subsampling) layer.

Pooling1D() - Constructor for class org.deeplearning4j.nn.conf.layers.Pooling1D

Pooling2D - Class in org.deeplearning4j.nn.conf.layers

2D Pooling (subsampling) layer.

Pooling2D() - Constructor for class org.deeplearning4j.nn.conf.layers.Pooling2D

poolingDimensions(int...) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder

Pooling dimensions.

poolingType(PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder

PoolingType - Enum in org.deeplearning4j.nn.conf.layers

Pooling type:
MAX: Max pooling - output is the maximum value of the input values
AVG: Average pooling - output is the average value of the input values
SUM: Sum pooling - output is the sum of the input values
PNORM: P-norm pooling

poolingType - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

poolingType(Subsampling3DLayer.PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

poolingType - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

poolingType - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

poolingType(SubsamplingLayer.PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

poolingType - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

positions - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

postApply(Trainable, String, INDArray, INDArray) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock

Apply L1 and L2 regularization, if necessary.

postFirstStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

postStep(INDArray) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

After the step has been made, do an action

postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

Post step to update searchDirection with new gradient and parameter information

postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.ConjugateGradient

postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.LBFGS

postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.LineGradientDescent

postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.StochasticGradientDescent

preApply(Trainable, Gradient, int) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

Pre-apply: Apply gradient normalization/clipping

precision(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation

Deprecated.

PrecisionRecallCurve - Class in org.deeplearning4j.eval.curves

Deprecated.

Use ReliabilityDiagram

PrecisionRecallCurve(double[], double[], double[], int[], int[], int[], int) - Constructor for class org.deeplearning4j.eval.curves.PrecisionRecallCurve

Deprecated.

Use ReliabilityDiagram

PrecisionRecallCurve.Confusion - Class in org.deeplearning4j.eval.curves

Deprecated.

PrecisionRecallCurve.Point - Class in org.deeplearning4j.eval.curves

Deprecated.

predict(INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier

Takes in a list of examples For each row, returns a label

predict(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier

Takes in a DataSet of examples For each row, returns a label

predict(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Returns the predictions for each example in the dataset

predict(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

Return predicted label names

predict(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

predict(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

predict(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

predict(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

predict(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer

Returns the predictions for each example in the dataset

predict(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer

Return predicted label names

predict(INDArray) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

predict(DataSet) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

predict(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

predict(DataSet) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

predict(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

predict(DataSet) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

predict(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Returns the predictions for each example in the dataset

predict(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Return predicted label names

Prediction - Class in org.deeplearning4j.eval.meta

Deprecated.

Use Prediction

Prediction(int, int, Object) - Constructor for class org.deeplearning4j.eval.meta.Prediction

Deprecated.

prediction() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

PReLU - Class in org.deeplearning4j.nn.layers.feedforward

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.

PReLU(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.PReLU

PReLU(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.feedforward.PReLU

PReLULayer - Class in org.deeplearning4j.nn.conf.layers

Parametrized Rectified Linear Unit (PReLU)

PReLULayer.Builder - Class in org.deeplearning4j.nn.conf.layers

PReLUParamInitializer - Class in org.deeplearning4j.nn.params

PReLU weight initializer.

PReLUParamInitializer(long[], long[]) - Constructor for class org.deeplearning4j.nn.params.PReLUParamInitializer

preOutput - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer

preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer

preOutput(INDArray, INDArray, INDArray, int[], int[], int[], ConvolutionLayer.AlgoMode, ConvolutionLayer.FwdAlgo, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

PreOutput method that also returns the im2col2d array (if being called for backprop), as this can be re-used instead of being calculated again.

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer

preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer

preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer

preOutput(INDArray, Layer.TrainingMode, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

preOutput(INDArray, boolean, int[], INDArray, INDArray, INDArray, INDArray, double, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper

preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.RepeatVector

preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

preOutput2d(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

preOutput2d(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

preOutput2d(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

preOutput4d(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer

preOutput4d(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

preOutput4d: Used so that ConvolutionLayer subclasses (such as Convolution1DLayer) can maintain their standard non-4d preOutput method, while overriding this to return 4d activations (for use in backprop) without modifying the public API

preProcess(INDArray, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor

Pre preProcess input/activations for a multi layer network

preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor

preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor

preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor

preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor

preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor

preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor

preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor

preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor

preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor

preProcessLine() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

Pre preProcess a line before an iteration

preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

Pre preProcess to setup initial searchDirection approximation

preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.ConjugateGradient

preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.LBFGS

preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.LineGradientDescent

preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.StochasticGradientDescent

PREPROCESSOR_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer

PreprocessorVertex - Class in org.deeplearning4j.nn.conf.graph

PreprocessorVertex is a simple adaptor class that allows a InputPreProcessor to be used in a ComputationGraph GraphVertex, without it being associated with a layer.

PreprocessorVertex(InputPreProcessor) - Constructor for class org.deeplearning4j.nn.conf.graph.PreprocessorVertex

PreprocessorVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

PreprocessorVertex is a simple adaptor class that allows a InputPreProcessor to be used in a ComputationGraph GraphVertex, without it being associated with a layer.

PreprocessorVertex(ComputationGraph, String, int, InputPreProcessor) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex

PreprocessorVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], InputPreProcessor) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex

pretrain - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Deprecated.

pretrain(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Deprecated.

doesn't affect training any more. Use ComputationGraph.pretrain(MultiDataSetIterator) when training for layerwise pretraining.

pretrain - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

pretrain - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork

pretrain - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Deprecated.

pretrain(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Deprecated.

doesn't affect training any more. Use MultiLayerNetwork.pretrain(DataSetIterator) when training for layerwise pretraining.

pretrain - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

pretrain - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

pretrain(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

Deprecated.

pretrain - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

pretrain(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Perform layerwise pretraining for one epoch - see ComputationGraph.pretrain(DataSetIterator, int)

pretrain(DataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Pretrain network with a single input and single output.

pretrain(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Pretrain network with multiple inputs and/or outputs

pretrain(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Pretrain network with multiple inputs and/or outputs
This method performs layerwise pretraining on all pre-trainable layers in the network (VAEs, Autoencoders, etc), for the specified number of epochs each.

pretrain(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Perform layerwise pretraining for one epoch - see MultiLayerNetwork.pretrain(DataSetIterator, int)

pretrain(DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Perform layerwise pretraining on all pre-trainable layers in the network (VAEs, Autoencoders, etc), for the specified number of epochs each.

pretrain(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

pretrain - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

pretrainLayer(String, DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Pretrain a specified layer with the given DataSetIterator

pretrainLayer(String, MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Pretrain a specified layer with the given MultiDataSetIterator

pretrainLayer(int, DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Fit for one epoch - see MultiLayerNetwork.pretrainLayer(int, DataSetIterator, int)

pretrainLayer(int, DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Perform layerwise unsupervised training on a single pre-trainable layer in the network (VAEs, Autoencoders, etc) for the specified number of epochs
If the specified layer index (0 to numLayers - 1) is not a pretrainable layer, this is a no-op.

pretrainLayer(int, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Perform layerwise unsupervised training on a single pre-trainable layer in the network (VAEs, Autoencoders, etc)
If the specified layer index (0 to numLayers - 1) is not a pretrainable layer, this is a no-op.

PretrainParamInitializer - Class in org.deeplearning4j.nn.params

Pretrain weight initializer.

PretrainParamInitializer() - Constructor for class org.deeplearning4j.nn.params.PretrainParamInitializer

prevAct - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

prevMemCell - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

printConfiguration() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Prints the configuration

processResidual(int, int, double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.NoOpResidualPostProcessor

processResidual(int, int, double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.ResidualClippingPostProcessor

processResidual(int, int, double, INDArray) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ResidualPostProcessor

pullLastTimeSteps(INDArray, INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Extract out the last time steps (2d array from 3d array input) accounting for the mask layer, if present.

pullLastTimeSteps(INDArray, INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Extract out the last time steps (2d array from 3d array input) accounting for the mask layer, if present.

purge() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

put(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

put(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

This mehtod adds update, with optional collapse

put(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

PXZ_B - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

Key for bias parameters connecting the last decoder layer and p(data|z) (according to whatever ReconstructionDistribution is set for the VAE)

PXZ_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

PXZ_W - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

Key for weight parameters connecting the last decoder layer and p(data|z) (according to whatever ReconstructionDistribution is set for the VAE)

PZX_LOGSTD2_B - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

Key for bias parameters for log(sigma^2) in p(z|data)

PZX_LOGSTD2_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

PZX_LOGSTD2_W - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

Key for weight parameters connecting the last encoder layer and the log(sigma^2) values for p(z|data)

PZX_MEAN_B - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

Key for bias parameters for the mean values for p(z|data)

PZX_MEAN_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

PZX_MEAN_W - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

Key for weight parameters connecting the last encoder layer and the mean values for p(z|data)

PZX_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

pzxActivationFn(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

Activation function for the input to P(z|data).
Care should be taken with this, as some activation functions (relu, etc) are not suitable due to being bounded in range [0,infinity).

pzxActivationFn - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

pzxActivationFunction(Activation) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

Activation function for the input to P(z|data).
Care should be taken with this, as some activation functions (relu, etc) are not suitable due to being bounded in range [0,infinity).

Q

queueSize - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

queueSize - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

R

R_KEY - Static variable in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

RandomProb(FailureTestingListener.CallType, double) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.RandomProb

recall(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation

Deprecated.

receiveUpdate(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

This method accepts updates suitable for StepFunction and puts them to the queue, which is used in backpropagation loop PLEASE NOTE: array is expected to be ready for use and match params dimensionality

receiveUpdate(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

This method accepts updates suitable for StepFunction and puts them to the queue, which is used in backpropagation loop

receiveUpdate(INDArray) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator

This method accepts updates suitable for StepFunction and puts them to the queue, which is used in backpropagation loop PLEASE NOTE: array is expected to be ready for use and match params dimensionality

reconstruct(INDArray, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Reconstructs the input.

ReconstructionDistribution - Interface in org.deeplearning4j.nn.conf.layers.variational

The ReconstructionDistribution is used with variational autoencoders VariationalAutoencoder to specify the form of the distribution p(data|x).

reconstructionDistribution(ReconstructionDistribution) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder

The reconstruction distribution for the data given the hidden state - i.e., P(data|Z).
This should be selected carefully based on the type of data being modelled.

reconstructionDistribution - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

reconstructionError(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

Return the reconstruction error for this variational autoencoder.
NOTE (important): This method is used ONLY for VAEs that have a standard neural network loss function (i.e., an ILossFunction instance such as mean squared error) instead of using a probabilistic reconstruction distribution P(x|z) for the reconstructions (as presented in the VAE architecture by Kingma and Welling).
You can check if the VAE has a loss function using VariationalAutoencoder.hasLossFunction()
Consequently, the reconstruction error is a simple deterministic function (no Monte-Carlo sampling is required, unlike VariationalAutoencoder.reconstructionProbability(INDArray, int) and VariationalAutoencoder.reconstructionLogProbability(INDArray, int))

reconstructionLogProbability(INDArray, int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

Return the log reconstruction probability given the specified number of samples.
See VariationalAutoencoder.reconstructionLogProbability(INDArray, int) for more details

reconstructionProbability(INDArray, int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

Calculate the reconstruction probability, as described in An & Cho, 2015 - "Variational Autoencoder based Anomaly Detection using Reconstruction Probability" (Algorithm 4)
The authors describe it as follows: "This is essentially the probability of the data being generated from a given latent variable drawn from the approximate posterior distribution."

Specifically, for each example x in the input, calculate p(x).

reconstructionProbNumSamples - Variable in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

recurrent(long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType

InputType for recurrent neural network (time series) data

recurrent(long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType

InputType for recurrent neural network (time series) data

recurrent(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder

See InputType.recurrent(long)}

RECURRENT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

Weights for previous time step -> current time step connections

RECURRENT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.LSTMParamInitializer

Weights for previous time step -> current time step connections

RECURRENT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

RECURRENT_WEIGHT_KEY_BACKWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

RECURRENT_WEIGHT_KEY_FORWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

Weights for previous time step -> current time step connections

recurrentConstraints - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder

RecurrentLayer - Interface in org.deeplearning4j.nn.api.layers

Created by Alex on 28/08/2016.

Registerable - Interface in org.deeplearning4j.optimize.solvers.accumulation

registerConsumers(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

registerConsumers(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

registerConsumers(int) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.Registerable

This method notifies producer about number of consumers for the current consumption cycle

registered - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

registerLegacyClassDefaultName(Class<? extends GraphVertex>) - Static method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyGraphVertexDeserializer

registerLegacyClassDefaultName(Class<? extends Layer>) - Static method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyLayerDeserializer

registerLegacyClassDefaultName(Class<? extends InputPreProcessor>) - Static method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyPreprocessorDeserializer

registerLegacyClassDefaultName(Class<? extends ReconstructionDistribution>) - Static method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyReconstructionDistributionDeserializer

registerLegacyClassSpecifiedName(String, Class<? extends GraphVertex>) - Static method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyGraphVertexDeserializer

registerLegacyClassSpecifiedName(String, Class<? extends Layer>) - Static method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyLayerDeserializer

registerLegacyClassSpecifiedName(String, Class<? extends InputPreProcessor>) - Static method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyPreprocessorDeserializer

registerLegacyClassSpecifiedName(String, Class<? extends ReconstructionDistribution>) - Static method in class org.deeplearning4j.nn.conf.serde.legacyformat.LegacyReconstructionDistributionDeserializer

registerLegacyCustomClassesForJSON(Class<?>...) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

Register a set of classes (Layer, GraphVertex, InputPreProcessor, IActivation, ILossFunction, ReconstructionDistribution ONLY) for JSON deserialization.

This is required ONLY when BOTH of the following conditions are met:
1.

registerLegacyCustomClassesForJSON(List<Pair<String, Class>>) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

Register a set of classes (Layer, GraphVertex, InputPreProcessor, IActivation, ILossFunction, ReconstructionDistribution ONLY) for JSON deserialization, with custom names.
Using this method directly should never be required (instead: use NeuralNetConfiguration.registerLegacyCustomClassesForJSON(Class[]) but is added in case it is required in non-standard circumstances.

registerLegacyCustomClassesForJSONList(List<Class<?>>) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

Regression2dAdapter - Class in org.deeplearning4j.nn.adapters

This OutputAdapter implementation takes single 2D nn output in, and returns JVM double[][] array

Regression2dAdapter() - Constructor for class org.deeplearning4j.nn.adapters.Regression2dAdapter

RegressionEvaluation - Class in org.deeplearning4j.eval

Deprecated.

Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation

RegressionEvaluation() - Constructor for class org.deeplearning4j.eval.RegressionEvaluation

Deprecated.

Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation

RegressionEvaluation(long) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation

Deprecated.

Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation

RegressionEvaluation(long, long) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation

Deprecated.

Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation

RegressionEvaluation(String...) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation

Deprecated.

Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation

RegressionEvaluation(List<String>) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation

Deprecated.

Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation

RegressionEvaluation(List<String>, long) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation

Deprecated.

Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation

RegressionEvaluation.Metric - Enum in org.deeplearning4j.eval

Deprecated.

Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation.Metric

RegressionScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc

Calculate the regression score of the network (MultiLayerNetwork or ComputationGraph) on a test set, using the specified regression metric - RegressionEvaluation.Metric

RegressionScoreCalculator(RegressionEvaluation.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator

ReliabilityDiagram - Class in org.deeplearning4j.eval.curves

Deprecated.

Use ReliabilityDiagram

ReliabilityDiagram(String, double[], double[]) - Constructor for class org.deeplearning4j.eval.curves.ReliabilityDiagram

Deprecated.

Use ReliabilityDiagram

relocatable - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

remainingCapacity() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

remove() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter

remove() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

remove(Object) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

removeAll(Collection<?>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

removeLayersFromOutput(int) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Remove last "n" layers of the net At least an output layer must be added back in

removeOutputLayer() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Helper method to remove the outputLayer of the net.

removeVertex(String) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Intended for use with the transfer learning API.

removeVertex(String, boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Intended for use with the transfer learning API.

removeVertexAndConnections(String) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Remove specified vertex and it's connections from the computation graph

removeVertexKeepConnections(String) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Remove the specified vertex from the computation graph but keep it's connections.

RepeatVector - Class in org.deeplearning4j.nn.conf.layers.misc

RepeatVector layer configuration.

RepeatVector(RepeatVector.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.RepeatVector

RepeatVector - Class in org.deeplearning4j.nn.layers

RepeatVector layer.

RepeatVector(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.RepeatVector

RepeatVector(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.RepeatVector

RepeatVector.Builder<T extends RepeatVector.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers.misc

repetitionFactor(int) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder

Set repetition factor for RepeatVector layer

reportBatch(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder

This method defines, if batches/sec should be reported together with other data

reportETL(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder

This method defines, if ETL time per iteration should be reported together with other data

reportIteration(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder

This method defines, if iteration number should be reported together with other data

reportSample(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder

This method defines, if samples/sec should be reported together with other data

reportScore(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder

This method defines, if score should be reported together with other data

reportTime(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder

This method defines, if time per iteration should be reported together with other data

requiresDropoutFromLegacy(Layer[]) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer

requiresIUpdaterFromLegacy(Layer[]) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer

reset() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter

reset() - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator

reset() - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

reset() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator

reset() - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator

reset() - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

reset() - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer

reset() - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

This method resets all accumulated updates (if any)

reset() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

This method resets all accumulated updates (if any)

reset() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator

This method resets all accumulated updates (if any)

resetLayerDefaultConfig() - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer

Reset the learning related configs of the layer to default.

resetLayerDefaultConfig() - Method in class org.deeplearning4j.nn.conf.layers.Layer

Reset the learning related configs of the layer to default.

resetSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter

reshape2dTo3d(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

reshape2dTo3d(INDArray, int, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

reshape2dTo4d(INDArray, int[], LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils

reshape2dTo5d(Convolution3D.DataFormat, INDArray, int, int, int, int, int, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils

reshape3dMask(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils

reshape3dTo2d(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

reshape3dTo2d(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

reshape4dMask(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils

reshape4dTo2d(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils

reshape5dTo2d(Convolution3D.DataFormat, INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils

reshapeCnn3dMask(Convolution3D.DataFormat, INDArray, INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils

reshapeCnnMaskToTimeSeriesMask(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Reshape CNN-style 4d mask array of shape [seqLength*minibatch,1,1,1] to time series mask [mb,seqLength] This should match the assumptions (f order, etc) in RnnOutputLayer

reshapeMaskIfRequired(INDArray, INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils

reshapeOrder - Variable in class org.deeplearning4j.nn.conf.graph.ReshapeVertex

reshapePerOutputTimeSeriesMaskTo2d(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

reshapePerOutputTimeSeriesMaskTo2d(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

reshapeTimeSeriesMaskToCnn4dMask(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Reshape time series mask arrays.

reshapeTimeSeriesMaskToVector(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Reshape time series mask arrays.

reshapeTimeSeriesMaskToVector(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Reshape time series mask arrays.

reshapeVectorToTimeSeriesMask(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Reshape time series mask arrays.

ReshapeVertex - Class in org.deeplearning4j.nn.conf.graph

Adds the ability to reshape and flatten the tensor in the computation graph.
NOTE: This class should only be used if you know exactly what you are doing with reshaping activations.

ReshapeVertex(int...) - Constructor for class org.deeplearning4j.nn.conf.graph.ReshapeVertex

Reshape with the default reshape order of 'c'

ReshapeVertex(char, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.graph.ReshapeVertex

ReshapeVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

Adds the ability to reshape and flatten the tensor in the computation graph.

ReshapeVertex(ComputationGraph, String, int, char, int[], int[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex

ReshapeVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], char, int[], int[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex

reshapeWeights(int[], INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil

Reshape the parameters view, without modifying the paramsView array values.

reshapeWeights(long[], INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil

Reshape the parameters view, without modifying the paramsView array values.

reshapeWeights(int[], INDArray, char) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil

Reshape the parameters view, without modifying the paramsView array values.

reshapeWeights(long[], INDArray, char) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil

Reshape the parameters view, without modifying the paramsView array values.

ResidualClippingPostProcessor - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.residual

Residual clipping post processor clips the values of a residual every N iterations as follows:
For residual vector R, and C = thresholdMultipleClipValue, T is the current encoding threshold
R[i] = C*T if R[i] > C*T
R[i] = -C*T if R[i] < -C*T
R[i] is unmodified otherwise

Note: Regarding the frequency, a value around 5 is suggested as a good balance between applying frequently enough, and minimizing the computational overhead.

ResidualClippingPostProcessor(double, int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.ResidualClippingPostProcessor

residualDebugOutputIfRequired(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

residualPostProcessor - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

residualPostProcessor(ResidualPostProcessor) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

Set the residual post processor

ResidualPostProcessor - Interface in org.deeplearning4j.optimize.solvers.accumulation.encoding

ResidualPostProcessor: is (as the name suggests) is used to post process the residual vector for DL4J's gradient sharing implementation.

residualPostProcessor - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

resolve(DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer

restoreComputationGraph(String) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a computation graph from a file

restoreComputationGraph(String, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a computation graph from a file

restoreComputationGraph(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a computation graph from a InputStream

restoreComputationGraph(InputStream) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a computation graph from a InputStream

restoreComputationGraph(File) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a computation graph from a file

restoreComputationGraph(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a computation graph from a file

restoreComputationGraphAndNormalizer(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Restore a ComputationGraph and Normalizer (if present - null if not) from the InputStream.

restoreComputationGraphAndNormalizer(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Restore a ComputationGraph and Normalizer (if present - null if not) from a File

restoreMultiLayerNetwork(File) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a multi layer network from a file

restoreMultiLayerNetwork(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a multi layer network from a file

restoreMultiLayerNetwork(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a MultiLayerNetwork from InputStream from an input stream
Note: the input stream is read fully and closed by this method.

restoreMultiLayerNetwork(InputStream) - Static method in class org.deeplearning4j.util.ModelSerializer

Restore a multi layer network from an input stream
* Note: the input stream is read fully and closed by this method.

restoreMultiLayerNetwork(String) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a MultilayerNetwork model from a file

restoreMultiLayerNetwork(String, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Load a MultilayerNetwork model from a file

restoreMultiLayerNetworkAndNormalizer(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Restore a MultiLayerNetwork and Normalizer (if present - null if not) from the InputStream.

restoreMultiLayerNetworkAndNormalizer(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Restore a MultiLayerNetwork and Normalizer (if present - null if not) from a File

restoreNormalizerFromFile(File) - Static method in class org.deeplearning4j.util.ModelSerializer

This method restores normalizer from a given persisted model file PLEASE NOTE: File should be model file saved earlier with ModelSerializer with addNormalizerToModel being called

restoreNormalizerFromInputStream(InputStream) - Static method in class org.deeplearning4j.util.ModelSerializer

This method restores the normalizer form a persisted model file.

retainAll(Collection<?>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

reverseTimeSeries(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Reverse an input time series along the time dimension

reverseTimeSeries(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Reverse an input time series along the time dimension

reverseTimeSeriesMask(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Reverse a (per time step) time series mask, with shape [minibatch, timeSeriesLength]

reverseTimeSeriesMask(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils

Reverse a (per time step) time series mask, with shape [minibatch, timeSeriesLength]

ReverseTimeSeriesVertex - Class in org.deeplearning4j.nn.conf.graph.rnn

ReverseTimeSeriesVertex is used in recurrent neural networks to revert the order of time series.

ReverseTimeSeriesVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

Creates a new ReverseTimeSeriesVertex that doesn't pay attention to masks

ReverseTimeSeriesVertex(String) - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

Creates a new ReverseTimeSeriesVertex that uses the mask array of a given input

ReverseTimeSeriesVertex - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn

ReverseTimeSeriesVertex is used in recurrent neural networks to revert the order of time series.

ReverseTimeSeriesVertex(ComputationGraph, String, int, String) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex

rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer

Similar to rnnTimeStep, this method is used for activations using the state stored in the stateMap as the initialization.

rnnActivateUsingStoredState(INDArray[], boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Similar to rnnTimeStep and feedForward() methods.

rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM

rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn

rnnActivateUsingStoredState(INDArray, boolean, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Similar to rnnTimeStep and feedForward() methods.

rnnClearPreviousState() - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer

Reset/clear the stateMap for rnnTimeStep() and tBpttStateMap for rnnActivateUsingStoredState()

rnnClearPreviousState() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Clear the previous state of the RNN layers (if any), used in ComputationGraph.rnnTimeStep(INDArray...)

rnnClearPreviousState() - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

Reset/clear the stateMap for rnnTimeStep() and tBpttStateMap for rnnActivateUsingStoredState()

rnnClearPreviousState() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

rnnClearPreviousState() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Clear the previous state of the RNN layers (if any).

rnnGetPreviousState() - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer

Returns a shallow copy of the RNN stateMap (that contains the stored history for use in methods such as rnnTimeStep

rnnGetPreviousState(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the state of the RNN layer, as used in ComputationGraph.rnnTimeStep(INDArray...).

rnnGetPreviousState(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get the state of the RNN layer, as used in ComputationGraph.rnnTimeStep(INDArray...).

rnnGetPreviousState() - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

Returns a shallow copy of the stateMap

rnnGetPreviousState() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

rnnGetPreviousState(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Get the state of the RNN layer, as used in rnnTimeStep().

rnnGetPreviousStates() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Get a map of states for ALL RNN layers, as used in ComputationGraph.rnnTimeStep(INDArray...).

rnnGetTBPTTState() - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer

Get the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer.

rnnGetTBPTTState() - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

rnnGetTBPTTState() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

rnnLayer(Layer) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder

RnnLossLayer - Class in org.deeplearning4j.nn.conf.layers

Recurrent Neural Network Loss Layer.
Handles calculation of gradients etc for various objective (loss) functions.
Note: Unlike RnnOutputLayer this RnnLossLayer does not have any parameters - i.e., there is no time distributed dense component here.

RnnLossLayer - Class in org.deeplearning4j.nn.layers.recurrent

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.

RnnLossLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

RnnLossLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

RnnOutputLayer - Class in org.deeplearning4j.nn.conf.layers

A version of OutputLayer for recurrent neural networks.

RnnOutputLayer - Class in org.deeplearning4j.nn.layers.recurrent

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.

RnnOutputLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

RnnOutputLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

RnnOutputLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

rnnSetPreviousState(Map<String, INDArray>) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer

Set the stateMap (stored history).

rnnSetPreviousState(int, Map<String, INDArray>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the state of the RNN layer, for use in ComputationGraph.rnnTimeStep(INDArray...)

rnnSetPreviousState(String, Map<String, INDArray>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the state of the RNN layer, for use in ComputationGraph.rnnTimeStep(INDArray...)

rnnSetPreviousState(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

Set the state map.

rnnSetPreviousState(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

rnnSetPreviousState(int, Map<String, INDArray>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Set the state of the RNN layer.

rnnSetPreviousStates(Map<String, Map<String, INDArray>>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the states for all RNN layers, for use in ComputationGraph.rnnTimeStep(INDArray...)

rnnSetTBPTTState(Map<String, INDArray>) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer

Set the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer.

rnnSetTBPTTState(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

rnnSetTBPTTState(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer

Do one or more time steps using the previous time step state stored in stateMap.
Can be used to efficiently do forward pass one or n-steps at a time (instead of doing forward pass always from t=0)
If stateMap is empty, default initialization (usually zeros) is used
Implementations also update stateMap at the end of this method

rnnTimeStep(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

If this ComputationGraph contains one or more RNN layers: conduct forward pass (prediction) but using previous stored state for any RNN layers.

rnnTimeStep(MemoryWorkspace, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

See ComputationGraph.rnnTimeStep(INDArray...) for details.
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.

rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM

rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn

rnnTimeStep(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

If this MultiLayerNetwork contains one or more RNN layers: conduct forward pass (prediction) but using previous stored state for any RNN layers.

rnnTimeStep(INDArray, MemoryWorkspace) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

See MultiLayerNetwork.rnnTimeStep(INDArray) for details
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.

RnnToCnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor

A preprocessor to allow RNN and CNN layers to be used together
For example, time series (video) input -> ConvolutionLayer, or conceivable GravesLSTM -> ConvolutionLayer
Functionally equivalent to combining RnnToFeedForwardPreProcessor + FeedForwardToCnnPreProcessor
Specifically, this does two things:
(a) Reshape 3d activations out of RNN layer, with shape [miniBatchSize, numChannels*inputHeight*inputWidth, timeSeriesLength]) into 4d (CNN) activations (with shape [numExamples*timeSeriesLength, numChannels, inputWidth, inputHeight])
(b) Reshapes 4d epsilons (weights.*deltas) out of CNN layer (with shape [numExamples*timeSeriesLength, numChannels, inputHeight, inputWidth]) into 3d epsilons with shape [miniBatchSize, numChannels*inputHeight*inputWidth, timeSeriesLength] suitable to feed into CNN layers.

RnnToCnnPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor

RnnToFeedForwardPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor

A preprocessor to allow RNN and feed-forward network layers to be used together.
For example, GravesLSTM -> OutputLayer or GravesLSTM -> DenseLayer
This does two things:
(a) Reshapes activations out of RNN layer (which is 3D with shape [miniBatchSize,layerSize,timeSeriesLength]) into 2d activations (with shape [miniBatchSize*timeSeriesLength,layerSize]) suitable for use in feed-forward layers.
(b) Reshapes 2d epsilons (weights*deltas from feed forward layer, with shape [miniBatchSize*timeSeriesLength,layerSize]) into 3d epsilons (with shape [miniBatchSize,layerSize,timeSeriesLength]) for use in RNN layer

RnnToFeedForwardPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor

rnnUpdateStateWithTBPTTState() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Update the internal state of RNN layers after a truncated BPTT fit call

ROC - Class in org.deeplearning4j.eval

Deprecated.

Use ROC

ROC() - Constructor for class org.deeplearning4j.eval.ROC

Deprecated.

Use ROC

ROC(int) - Constructor for class org.deeplearning4j.eval.ROC

Deprecated.

Use ROC

ROC(int, boolean) - Constructor for class org.deeplearning4j.eval.ROC

Deprecated.

Use ROC

ROC(int, boolean, int) - Constructor for class org.deeplearning4j.eval.ROC

Deprecated.

Use ROC

ROC.CountsForThreshold - Class in org.deeplearning4j.eval

Deprecated.

Use ROC.CountsForThreshold

ROCBinary - Class in org.deeplearning4j.eval

Deprecated.

Use ROCBinary

ROCBinary() - Constructor for class org.deeplearning4j.eval.ROCBinary

Deprecated.

Use ROCBinary

ROCBinary(int) - Constructor for class org.deeplearning4j.eval.ROCBinary

Deprecated.

Use ROCBinary

ROCBinary(int, boolean) - Constructor for class org.deeplearning4j.eval.ROCBinary

Deprecated.

Use ROCBinary

RocCurve - Class in org.deeplearning4j.eval.curves

Deprecated.

Use RocCurve

RocCurve(double[], double[], double[]) - Constructor for class org.deeplearning4j.eval.curves.RocCurve

Deprecated.

Use RocCurve

ROCMultiClass - Class in org.deeplearning4j.eval

Deprecated.

Use ROCMultiClass

ROCMultiClass() - Constructor for class org.deeplearning4j.eval.ROCMultiClass

Deprecated.

Use ROCMultiClass

ROCMultiClass(int) - Constructor for class org.deeplearning4j.eval.ROCMultiClass

Deprecated.

Use ROCMultiClass

ROCMultiClass(int, boolean) - Constructor for class org.deeplearning4j.eval.ROCMultiClass

Deprecated.

Use ROCMultiClass

ROCScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc

Calculate ROC AUC (area under ROC curve) or AUCPR (area under precision recall curve) for a MultiLayerNetwork or ComputationGraph

ROCScoreCalculator(ROCScoreCalculator.ROCType, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator

ROCScoreCalculator(ROCScoreCalculator.ROCType, MultiDataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator

ROCScoreCalculator(ROCScoreCalculator.ROCType, ROCScoreCalculator.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator

ROCScoreCalculator(ROCScoreCalculator.ROCType, ROCScoreCalculator.Metric, MultiDataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator

ROCScoreCalculator.Metric - Enum in org.deeplearning4j.earlystopping.scorecalc

ROCScoreCalculator.ROCType - Enum in org.deeplearning4j.earlystopping.scorecalc

rootFolder - Variable in class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback

S

sameDiff - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

sameDiff - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

sameDiff - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

SameDiffGraphVertex - Class in org.deeplearning4j.nn.layers.samediff

Implementation of a SameDiff graph vertex.

SameDiffGraphVertex(SameDiffVertex, ComputationGraph, String, int, INDArray, boolean) - Constructor for class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

SameDiffLambdaLayer - Class in org.deeplearning4j.nn.conf.layers.samediff

SameDiffLambdaLayer is defined to be used as the base class for implementing lambda layers using SameDiff
Lambda layers are layers without parameters - and as a result, have a much simpler API - users need only extend SameDiffLambdaLayer and implement a single method

SameDiffLambdaLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer

SameDiffLambdaVertex - Class in org.deeplearning4j.nn.conf.layers.samediff

SameDiffLambdaVertex is defined to be used as the base class for implementing lambda vertices using SameDiff
Lambda vertices are vertices without parameters - and as a result, have a much simpler API - users need only extend SameDiffLambdaVertex and implement a single method to define their vertex

SameDiffLambdaVertex() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex

SameDiffLambdaVertex.VertexInputs - Class in org.deeplearning4j.nn.conf.layers.samediff

SameDiffLayer - Class in org.deeplearning4j.nn.conf.layers.samediff

A base layer used for implementing Deeplearning4j layers using SameDiff.

SameDiffLayer(SameDiffLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer

SameDiffLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer

SameDiffLayer - Class in org.deeplearning4j.nn.layers.samediff

SameDiffLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

SameDiffLayer.Builder<T extends SameDiffLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers.samediff

SameDiffLayerUtils - Class in org.deeplearning4j.nn.conf.layers.samediff

Utility methods for DL4J SameDiff layers

SameDiffOutputLayer - Class in org.deeplearning4j.nn.conf.layers.samediff

A base layer used for implementing Deeplearning4j Output layers using SameDiff.

SameDiffOutputLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer

SameDiffOutputLayer - Class in org.deeplearning4j.nn.layers.samediff

SameDiffOutputLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

SameDiffParamInitializer - Class in org.deeplearning4j.nn.params

SameDiffParamInitializer() - Constructor for class org.deeplearning4j.nn.params.SameDiffParamInitializer

SameDiffVertex - Class in org.deeplearning4j.nn.conf.layers.samediff

A SameDiff-based GraphVertex.

SameDiffVertex() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

Sample the hidden distribution given the visible

sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder

sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

Sample the visible distribution given the hidden

sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder

save(File) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Save the ComputationGraph to a file.

save(File, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Save the ComputationGraph to a file.

save(File) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Save the MultiLayerNetwork to a file.

save(File, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Save the MultiLayerNetwork to a file.

save(Model, String) - Method in class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback

This method saves model

saveBestModel(T, double) - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver

Save the best model (so far) learned during early stopping training

saveBestModel(T, double) - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver

saveBestModel(ComputationGraph, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver

saveBestModel(MultiLayerNetwork, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver

saveEvery(long, TimeUnit) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Save a model periodically

saveEvery(long, TimeUnit, boolean) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Save a model periodically (if sinceLast == false), or if the specified amount of time has elapsed since the last model was saved (if sinceLast == true)

saveEveryEpoch() - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Save a model at the end of every epoch

saveEveryNEpochs(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Save a model at the end of every N epochs

saveEveryNIterations(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Save a model every N iterations

saveEveryNIterations(int, boolean) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder

Save a model every N iterations (if sinceLast == false), or if N iterations have passed since the last model vas saved (if sinceLast == true)

saveLastModel(boolean) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder

Save the last model? If true: save the most recent model at each epoch, in addition to the best model (whenever the best model improves).

saveLatestModel(T, double) - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver

Save the latest (most recent) model learned during early stopping

saveLatestModel(T, double) - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver

saveLatestModel(ComputationGraph, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver

saveLatestModel(MultiLayerNetwork, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver

scale(int) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport

Multiply all memory usage by the specified scaling factor

scaleFactor - Variable in class org.deeplearning4j.nn.conf.graph.ScaleVertex

ScaleVertex - Class in org.deeplearning4j.nn.conf.graph

A ScaleVertex is used to scale the size of activations of a single layer: this is simply multiplication by a fixed scalar value
For example, ResNet activations can be scaled in repeating blocks to keep variance under control.

ScaleVertex(double) - Constructor for class org.deeplearning4j.nn.conf.graph.ScaleVertex

ScaleVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

A ScaleVertex is used to scale the size of activations of a single layer
For example, ResNet activations can be scaled in repeating blocks to keep variance under control.

ScaleVertex(ComputationGraph, String, int, double) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex

ScaleVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], double) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex

score() - Method in interface org.deeplearning4j.nn.api.Model

The score for the model

score - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

score(DataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Sets the input and labels and returns a score for the prediction with respect to the true labels
This is equivalent to ComputationGraph.score(DataSet, boolean) with training==true.
NOTE: this version of the score function can only be used with ComputationGraph networks that have a single input and a single output.

score(DataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Sets the input and labels and returns a score for the prediction with respect to the true labels
NOTE: this version of the score function can only be used with ComputationGraph networks that have a single input and a single output.

score(MultiDataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Score the network given the MultiDataSet, at test time

score(MultiDataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Sets the input and labels and returns a score for the prediction with respect to the true labels

score() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

score() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

score - Variable in class org.deeplearning4j.nn.layers.BaseLayer

score() - Method in class org.deeplearning4j.nn.layers.BaseLayer

Objective function: the specified objective

score() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

score() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

score() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

score() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

score() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

score() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

score() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

score() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

score() - Method in class org.deeplearning4j.nn.layers.RepeatVector

score - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

score() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

score() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

score - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

score(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Sets the input and labels and returns a score for the prediction with respect to the true labels
This is equivalent to MultiLayerNetwork.score(DataSet, boolean) with training==false.

score(DataSet, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Calculate the score (loss function) of the prediction with respect to the true labels

score() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Score of the model (relative to the objective function)

score() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

The score for the optimizer so far

score - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

score() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

SCORE_KEY - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

scoreCalculator(ScoreCalculator) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder

Score calculator.

scoreCalculator(Supplier<ScoreCalculator>) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder

Score calculator.

ScoreCalculator<T extends Model> - Interface in org.deeplearning4j.earlystopping.scorecalc

ScoreCalculator interface is used to calculate a score for a neural network.

scoreExamples(DataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Calculate the score for each example in a DataSet individually.

scoreExamples(MultiDataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Calculate the score for each example in a DataSet individually.

scoreExamples(DataSetIterator, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

scoreExamples(DataSet, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Calculate the score for each example in a DataSet individually.

scoreForMetric(Evaluation.Metric) - Method in class org.deeplearning4j.eval.Evaluation

Deprecated.

Use ND4J Evaluation class, which has the same interface: Evaluation.Metric

scoreForMetric(RegressionEvaluation.Metric) - Method in class org.deeplearning4j.eval.RegressionEvaluation

Deprecated.

Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation

ScoreImprovementEpochTerminationCondition - Class in org.deeplearning4j.earlystopping.termination

Terminate training if best model score does not improve for N epochs

ScoreImprovementEpochTerminationCondition(int) - Constructor for class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition

ScoreImprovementEpochTerminationCondition(int, double) - Constructor for class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition

ScoreIterationListener - Class in org.deeplearning4j.optimize.listeners

Score iteration listener.

ScoreIterationListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.ScoreIterationListener

ScoreIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.ScoreIterationListener

Default constructor printing every 10 iterations

scoreMinibatch(Model, INDArray, INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator

scoreMinibatch(Model, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator

scoreMinibatch(MultiLayerNetwork, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseMLNScoreCalculator

scoreMinibatch(T, INDArray, INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

scoreMinibatch(T, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

scoreMinibatch(Model, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator

scoreMinibatch(Model, INDArray, INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator

scoreMinibatch(Model, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator

scoreMinibatch(Model, INDArray, INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

scoreMinibatch(Model, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

scoreSum - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator

SDLayerParams - Class in org.deeplearning4j.nn.conf.layers.samediff

SDLayerParams is used to define the parameters for a Deeplearning4j SameDiff layer

SDLayerParams(Map<String, long[]>, Map<String, long[]>) - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams

SDVertexParams - Class in org.deeplearning4j.nn.conf.layers.samediff

SDVertexParams is used to define the inputs - and the parameters - for a SameDiff vertex

SDVertexParams() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams

SEARCH_DIR - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

searchState - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

secondary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

secondary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

seed - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

seed(long) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Random number generator seed.

seed - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

seed(long) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

RNG seed for reproducibility

seed(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

RNG seed for reproducibility

seed - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

sendMessage(INDArray, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

This method does loops encoded data back to updates queue

SeparableConvolution2D - Class in org.deeplearning4j.nn.conf.layers

2D Separable convolution layer configuration.

SeparableConvolution2D(SeparableConvolution2D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D

SeparableConvolution2D layer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the channels The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size

SeparableConvolution2D.Builder - Class in org.deeplearning4j.nn.conf.layers

SeparableConvolution2DLayer - Class in org.deeplearning4j.nn.layers.convolution

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

SeparableConvolution2DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer

SeparableConvolution2DLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer

SeparableConvolutionParamInitializer - Class in org.deeplearning4j.nn.params

Initialize separable convolution params.

SeparableConvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

setAbsTolx(double) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

Sets the tolerance of absolute diff in function value.

setBackpropGradientsViewArray(INDArray) - Method in interface org.deeplearning4j.nn.api.Model

Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

setBackpropGradientsViewArray(INDArray) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

See Model.setBackpropGradientsViewArray(INDArray)

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setBatchSize(int) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

Set the batch size for the optimizer

setBatchSize(int) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

setBegin(int) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setCacheMode(CacheMode) - Method in interface org.deeplearning4j.nn.api.Layer

This method sets given CacheMode for current layer

setCacheMode(CacheMode) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

This method sets specified CacheMode for all layers within network

setCacheMode(CacheMode) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setCacheMode(CacheMode) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

setCacheMode(CacheMode) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setCacheMode(CacheMode) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setCacheMode(CacheMode) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setCacheMode(CacheMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

This method sets specified CacheMode for all layers within network

setConf(NeuralNetConfiguration) - Method in interface org.deeplearning4j.nn.api.Model

Setter for the configuration

setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setConstraints(List<LayerConstraint>) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

setConstraints(List<LayerConstraint>) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

setDepth(long) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional

Deprecated.

setEnd(int) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setEpochCount(int) - Method in interface org.deeplearning4j.nn.api.Layer

Set the current epoch count (number of epochs passed ) for the layer/network

setEpochCount(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

setEpochCount(int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setEpochCount(int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setEpochCount(int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setEpochCount(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setEpsilon(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

setEpsilon(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

setEpsilon(INDArray) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Set the errors (epsilon - aka dL/dActivation) for this GraphVertex

setError(double) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setExternalSource(IndexedTail) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

setExternalSource(IndexedTail) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

This method allows to pass external updates to accumulator, they will be populated across all workers using this GradientsAccumulator instance

setExternalSource(IndexedTail) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator

This method allows to pass external updates to accumulator, they will be populated across all workers using this GradientsAccumulator instance

setFeatureExtractor(int) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Specify a layer to set as a "feature extractor" The specified layer and the layers preceding it will be "frozen" with parameters staying constant

setFeatureExtractor(String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Specify a layer vertex to set as a "feature extractor" The specified layer vertex and the layers on the path from an input vertex to it it will be "frozen" with parameters staying constant

setFrequency(int) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder

Desired TrainingListener activation frequency

setGoldLabel(int) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setGradientFor(String, INDArray) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient

setGradientFor(String, INDArray, Character) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient

setGradientFor(String, INDArray) - Method in interface org.deeplearning4j.nn.gradient.Gradient

Update gradient for the given variable

setGradientFor(String, INDArray, Character) - Method in interface org.deeplearning4j.nn.gradient.Gradient

Update gradient for the given variable; also (optionally) specify the order in which the array should be flattened to a row vector

setGradientsAccumulator(GradientsAccumulator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

This method allows you to specificy GradientsAccumulator instance to be used with this model

setGradientsAccumulator(GradientsAccumulator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

This method allows you to specificy GradientsAccumulator instance to be used with this model PLEASE NOTE: Do not use this method unless you understand how to use GradientsAccumulator & updates sharing.

setGradientsAccumulator(GradientsAccumulator) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

This method specifies GradientsAccumulator instance to be used for updates sharing across multiple models

setGradientsAccumulator(GradientsAccumulator) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

setHeadWord(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setHelperWorkspace(String, Pointer) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

Set the pointer to the helper memory.

setIndex(int) - Method in interface org.deeplearning4j.nn.api.Layer

Set the layer index.

setIndex(int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setIndex(int) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

setIndex(int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setIndex(int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setIndex(int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setIndex(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setInput(INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Layer

Set the layer input.

setInput(int, INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the specified input for the ComputationGraph

setInput(int, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

setInput(int, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

setInput(int, INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Set the input activations.

setInput(int, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

setInput(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setInput(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setInput(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setInput(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setInput(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Note that if input isn't null and the neuralNets are null, this is a way of initializing the neural network

setInput(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setInputMiniBatchSize(int) - Method in interface org.deeplearning4j.nn.api.Layer

Set current/last input mini-batch size.
Used for score and gradient calculations.

setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setInputPreProcessor(int, InputPreProcessor) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

Specify the preprocessor for the added layers for cases where they cannot be inferred automatically.

setInputs(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set all inputs for the ComputationGraph network

setInputs(INDArray...) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

setInputs(INDArray...) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Set all inputs for this GraphVertex

setInputs(String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Sets new inputs for the computation graph.

setInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

Set input filter size for this locally connected 1D layer

setInputSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

Set input filter size (h,w) for this locally connected 2D layer

setInputType(InputType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

setInputType(InputType) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder

setInputTypes(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Specify the types of inputs to the network, so that:
(a) preprocessors can be automatically added, and
(b) the nIns (input size) for each layer can be automatically calculated and set
The order here is the same order as .addInputs().

setInputTypes(InputType...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Sets the input type of corresponding inputs.

setInputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

setInputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

setInputVertices(VertexIndices[]) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Sets the input vertices.

setIterationCount(int) - Method in interface org.deeplearning4j.nn.api.Layer

Set the current iteration count (number of parameter updates) for the layer/network

setIterationCount(int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setIterationCount(int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setIterationCount(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setIWM - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

setLabel(int, INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the specified label for the ComputationGraph

setLabel(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setLabels(INDArray) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer

Set the labels array for this output layer

setLabels(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set all labels for the ComputationGraph network

setLabels(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

setLabels(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer

setLabels(INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

setLabels(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setLastEtlTime(long) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

This method allows to set ETL field time, useful for performance tracking

setLastEtlTime(long) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setLayerAsFrozen() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

setLayerAsFrozen() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

setLayerAsFrozen() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Only applies to layer vertices.

setLayerAsFrozen() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

setLayerMaskArrays(INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the mask arrays for features and labels.

setLayerMaskArrays(INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Set the mask arrays for features and labels.

setLayerName(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

setLayerName(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop

setLayerName(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

setLayerName(String) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

setLayers(Layer[]) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setLayerWiseConfigurations(MultiLayerConfiguration) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setLearningRate(double) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the learning rate for all layers in the network to the specified value.

setLearningRate(ISchedule) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the learning rate schedule for all layers in the network to the specified schedule.

setLearningRate(String, double) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the learning rate for a single layer in the network to the specified value.

setLearningRate(String, ISchedule) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the learning rate schedule for a single layer in the network to the specified value.
Note also that ComputationGraph.setLearningRate(ISchedule) should also be used in preference, when all layers need to be set to a new LR schedule.
This schedule will replace any/all existing schedules, and also any fixed learning rate values.
Note also that the iteration/epoch counts will not be reset.

setLearningRate(double) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Set the learning rate for all layers in the network to the specified value.

setLearningRate(ISchedule) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Set the learning rate schedule for all layers in the network to the specified schedule.

setLearningRate(int, double) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Set the learning rate for a single layer in the network to the specified value.

setLearningRate(int, ISchedule) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Set the learning rate schedule for a single layer in the network to the specified value.
Note also that MultiLayerNetwork.setLearningRate(ISchedule) should also be used in preference, when all layers need to be set to a new LR schedule.
This schedule will replace any/all existing schedules, and also any fixed learning rate values.
Note also that the iteration/epoch counts will not be reset.

setLearningRate(MultiLayerNetwork, double) - Static method in class org.deeplearning4j.util.NetworkUtils

Set the learning rate for all layers in the network to the specified value.

setLearningRate(MultiLayerNetwork, ISchedule) - Static method in class org.deeplearning4j.util.NetworkUtils

Set the learning rate schedule for all layers in the network to the specified schedule.

setLearningRate(MultiLayerNetwork, int, double) - Static method in class org.deeplearning4j.util.NetworkUtils

Set the learning rate for a single layer in the network to the specified value.

setLearningRate(MultiLayerNetwork, int, ISchedule) - Static method in class org.deeplearning4j.util.NetworkUtils

Set the learning rate schedule for a single layer in the network to the specified value.
Note also that NetworkUtils.setLearningRate(MultiLayerNetwork, ISchedule) should also be used in preference, when all layers need to be set to a new LR schedule.
This schedule will replace any/all existing schedules, and also any fixed learning rate values.
Note also that the iteration/epoch counts will not be reset.

setLearningRate(ComputationGraph, double) - Static method in class org.deeplearning4j.util.NetworkUtils

Set the learning rate for all layers in the network to the specified value.

setLearningRate(ComputationGraph, ISchedule) - Static method in class org.deeplearning4j.util.NetworkUtils

Set the learning rate schedule for all layers in the network to the specified schedule.

setLearningRate(ComputationGraph, String, double) - Static method in class org.deeplearning4j.util.NetworkUtils

Set the learning rate for a single layer in the network to the specified value.

setLearningRate(ComputationGraph, String, ISchedule) - Static method in class org.deeplearning4j.util.NetworkUtils

Set the learning rate schedule for a single layer in the network to the specified value.
Note also that NetworkUtils.setLearningRate(ComputationGraph, ISchedule) should also be used in preference, when all layers need to be set to a new LR schedule.
This schedule will replace any/all existing schedules, and also any fixed learning rate values.
Note also that the iteration/epoch counts will not be reset.

setListener(EarlyStoppingListener<T>) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer

setListener(EarlyStoppingListener<T>) - Method in interface org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer

Set the early stopping listener

setListeners(TrainingListener...) - Method in interface org.deeplearning4j.nn.api.Layer

Set the iteration listeners for this layer.

setListeners(Collection<TrainingListener>) - Method in interface org.deeplearning4j.nn.api.Layer

Set the iteration listeners for this layer.

setListeners(Collection<TrainingListener>) - Method in interface org.deeplearning4j.nn.api.Model

Set the trainingListeners for the ComputationGraph (and all layers in the network)

setListeners(TrainingListener...) - Method in interface org.deeplearning4j.nn.api.Model

Set the trainingListeners for the ComputationGraph (and all layers in the network)

setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the trainingListeners for the ComputationGraph (and all layers in the network)

setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the trainingListeners for the ComputationGraph (and all layers in the network)

setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setListeners(Collection<TrainingListener>) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.optimize.Solver

setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

setMask(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setMaskArray(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer

Set the mask array.

setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setMaskValue(double) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder

setMaxIterations(int) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

setMean(double) - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Layer

Set the nIn value (number of inputs, or input channels for CNNs) based on the given input type

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer

setNoLeverageOverride(String) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

setOutputs(String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Set the network output labels.

setOutputs(String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

Set outputs to the computation graph, will add to ones that are existing Also determines the order, like in ComputationGraphConfiguration

setOutputVertex(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

setOutputVertex(boolean) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

Set the GraphVertex to be an output vertex

setOutputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

setOutputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

setOutputVertices(VertexIndices[]) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex

set the output vertices.

setParam(String, INDArray) - Method in interface org.deeplearning4j.nn.api.Model

Set the parameter with a new ndarray

setParam(String, INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer

setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setParam(String, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setParameters(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Sets parameters for the model.

setParams(Set<String>) - Method in interface org.deeplearning4j.nn.api.layers.LayerConstraint

Set the parameters that this layer constraint should be applied to

setParams(INDArray) - Method in interface org.deeplearning4j.nn.api.Model

Set the parameters for this model.

setParams(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer

setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.BaseLayer

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.RepeatVector

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setParams(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Set the parameters for this model.

setParamsViewArray(INDArray) - Method in interface org.deeplearning4j.nn.api.Model

Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.

setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer

setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setParamTable(Map<String, INDArray>) - Method in interface org.deeplearning4j.nn.api.Model

Setter for the param table

setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.BaseLayer

setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer

setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer

setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setParent(Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setParse(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setPrediction(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter

setPretrain(boolean) - Method in interface org.deeplearning4j.nn.api.TrainingConfig

setPretrain(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork

setPretrain(boolean) - Method in class org.deeplearning4j.nn.conf.layers.Layer

setPretrain(boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

setPretrain(boolean) - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig

setPretrain(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

setProbabilityOfSuccess(double) - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution

setRelTolx(double) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

Sets the tolerance of relative diff in function value.

setScore(double) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

setScore(double) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

setScoreFor(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer

setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer

setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork

setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer

setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer

setStateViewArray(Trainable, INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.Updater

Set the internal (historical) state view array for this updater

setStateViewArray(INDArray) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

Set the view array.

setStateViewArray(Trainable, INDArray, boolean) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

setStd(double) - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution

setStepMax(double) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch

setTags(List<String>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setTokens(List<String>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setTWM - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

setType(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setUnderlying(Layer) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder

setUpdater(ComputationGraphUpdater) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Set the computationGraphUpdater for the network

setUpdater(Updater) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Set the updater for the MultiLayerNetwork

setUpdater(Updater) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

setUpdater(Updater) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

setUpdaterComputationGraph(ComputationGraphUpdater) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

setUpdaterComputationGraph(ComputationGraphUpdater) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

setupSearchState(Pair<Gradient, Double>) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

Based on the gradient and score setup a search state

setupSearchState(Pair<Gradient, Double>) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

Setup the initial search state

setupSearchState(Pair<Gradient, Double>) - Method in class org.deeplearning4j.optimize.solvers.LBFGS

setValue(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setVector(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

setWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

shape - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

shape - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

sharedAxes(long...) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer.Builder

Set the broadcasting axes of PReLU's alpha parameter.

SharedGradient - Class in org.deeplearning4j.optimize.listeners

SharedGradient() - Constructor for class org.deeplearning4j.optimize.listeners.SharedGradient

shiftFactor - Variable in class org.deeplearning4j.nn.conf.graph.ShiftVertex

ShiftVertex - Class in org.deeplearning4j.nn.conf.graph

A ShiftVertex is used to shift the activations of a single layer.

ShiftVertex(double) - Constructor for class org.deeplearning4j.nn.conf.graph.ShiftVertex

ShiftVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

A ShiftVertex is used to shift the activations of a single layer
One could use it to add a bias or as part of some other calculation.

ShiftVertex(ComputationGraph, String, int, double) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex

ShiftVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], double) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex

SimpleRnn - Class in org.deeplearning4j.nn.conf.layers.recurrent

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

SimpleRnn(SimpleRnn.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn

SimpleRnn - Class in org.deeplearning4j.nn.layers.recurrent

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

SimpleRnn(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.SimpleRnn

SimpleRnn.Builder - Class in org.deeplearning4j.nn.conf.layers.recurrent

SimpleRnnParamInitializer - Class in org.deeplearning4j.nn.params

SimpleRnnParamInitializer() - Constructor for class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

size - Variable in class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer

size - Variable in class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer.UpsamplingBuilder

size(int) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D.Builder

Upsampling size

size(int[]) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D.Builder

Upsampling size int array with a single element.

size - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling1D

size(int) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder

Upsampling size int, used for both height and width

size(int[]) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder

Upsampling size array

size - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling2D

size(int) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder

Upsampling size as int, so same upsampling size is used for depth, width and height

size(int[]) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder

Upsampling size as int, so same upsampling size is used for depth, width and height

size - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling3D

size() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

skipDueToPretrainConfig() - Method in class org.deeplearning4j.nn.updater.UpdaterBlock

sleep(long) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

sleep(AtomicLong, long) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

sleepMode - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

SleepyTrainingListener - Class in org.deeplearning4j.optimize.listeners

This TrainingListener implementation provides a way to "sleep" during specific Neural Network training phases.
Suitable for debugging/testing purposes only.

SleepyTrainingListener() - Constructor for class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

SleepyTrainingListener.SleepMode - Enum in org.deeplearning4j.optimize.listeners

SleepyTrainingListener.TimeMode - Enum in org.deeplearning4j.optimize.listeners

smartDecompress(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

smartDecompress(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

SmartFancyBlockingQueue - Class in org.deeplearning4j.optimize.solvers.accumulation

This class provides additional functionality to FancyBlockingQueue: it tracks memory use of stored compressed INDArrays, and if their size becomes too big, it: a) decompresses them into single INDArray b) removes original updates messages c) keeps updating single INDArray until it gets consumed d) once that happened - it automatically switches back to original behavior

SmartFancyBlockingQueue(int, INDArray) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

SmartFancyBlockingQueue(int, BlockingQueue<INDArray>, INDArray) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

smartLock - Variable in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue

solver - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

solver - Variable in class org.deeplearning4j.nn.layers.BaseLayer

solver - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

solver - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Solver - Class in org.deeplearning4j.optimize

Generic purpose solver

Solver() - Constructor for class org.deeplearning4j.optimize.Solver

Solver.Builder - Class in org.deeplearning4j.optimize

SpaceToBatch - Class in org.deeplearning4j.nn.layers.convolution

Space to batch utility layer for convolutional input types.

SpaceToBatch(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

SpaceToBatch(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

SpaceToBatchLayer - Class in org.deeplearning4j.nn.conf.layers

Space to batch utility layer configuration for convolutional input types.

SpaceToBatchLayer(SpaceToBatchLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer

SpaceToBatchLayer.Builder<T extends SpaceToBatchLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers

SpaceToDepth - Class in org.deeplearning4j.nn.layers.convolution

Space to channels utility layer for convolutional input types.

SpaceToDepth(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

SpaceToDepth(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

SpaceToDepthLayer - Class in org.deeplearning4j.nn.conf.layers

Space to channels utility layer configuration for convolutional input types.

SpaceToDepthLayer(SpaceToDepthLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer

SpaceToDepthLayer.Builder<T extends SpaceToDepthLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers

SpaceToDepthLayer.DataFormat - Enum in org.deeplearning4j.nn.conf.layers

sparsity(double) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder

Autoencoder sparity parameter

sparsity - Variable in class org.deeplearning4j.nn.conf.layers.AutoEncoder

SpatialDropout - Class in org.deeplearning4j.nn.conf.dropout

Spatial dropout: can only be applied to 3D (time series), 4D (convolutional 2D) or 5D (convolutional 3D) activations.

SpatialDropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.SpatialDropout

SpatialDropout(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.SpatialDropout

SpatialDropout(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.SpatialDropout

stackSize - Variable in class org.deeplearning4j.nn.conf.graph.UnstackVertex

StackVertex - Class in org.deeplearning4j.nn.conf.graph

StackVertex allows for stacking of inputs so that they may be forwarded through a network.

StackVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.StackVertex

StackVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

StackVertex allows for stacking of inputs so that they may be forwarded through a network.

StackVertex(ComputationGraph, String, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.StackVertex

StackVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.StackVertex

standardMemory(long, long) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder

Report the standard memory

state - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

STATE_KEY_PREV_ACTIVATION - Static variable in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

STATE_KEY_PREV_ACTIVATION - Static variable in class org.deeplearning4j.nn.layers.recurrent.LSTM

STATE_KEY_PREV_ACTIVATION - Static variable in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn

STATE_KEY_PREV_MEMCELL - Static variable in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

STATE_KEY_PREV_MEMCELL - Static variable in class org.deeplearning4j.nn.layers.recurrent.LSTM

stateMap - Variable in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

stateMap stores the INDArrays needed to do rnnTimeStep() forward pass.

std - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

step(INDArray, INDArray, double) - Method in interface org.deeplearning4j.optimize.api.StepFunction

Step with the given parameters

step(INDArray, INDArray) - Method in interface org.deeplearning4j.optimize.api.StepFunction

Step with no parameters

step() - Method in interface org.deeplearning4j.optimize.api.StepFunction

step - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

step - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction

Does x = x + stepSize * line

step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction

step() - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction

step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction

step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction

step() - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction

step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction

step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction

step() - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction

step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction

step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction

step() - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction

stepFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

stepFunction(StepFunction) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Deprecated.

stepFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

StepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions

Custom step function for line search.

StepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.StepFunction

stepFunction(StepFunction) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

stepFunction - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

StepFunction - Interface in org.deeplearning4j.optimize.api

Custom step function for line search

stepFunction - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

StepFunctions - Class in org.deeplearning4j.optimize.stepfunctions

stepMax - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

StochasticGradientDescent - Class in org.deeplearning4j.optimize.solvers

Stochastic Gradient Descent Standard fix step size No line search

StochasticGradientDescent(NeuralNetConfiguration, StepFunction, Collection<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.StochasticGradientDescent

storage - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

storeUpdate(INDArray, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

This method accepts updates suitable for StepFunction, and accumulates/propagates it across all workers

storeUpdate(INDArray, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

This method accepts updates suitable for StepFunction, and accumulates/propagates it across all workers

storeUpdate(INDArray, int, int) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator

This method accepts updates suitable for StepFunction, and accumulates/propagates it across all workers

stride(int) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder

Stride for the convolution.

stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder

Set stride size for 3D convolutions in (depth, height, width) order

stride - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder

stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder

stride - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder

stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder

Stride of the convolution in rows/columns (height/width) dimensions

stride(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder

stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder

stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder

Stride of the convolution rows/columns (height/width)

stride(int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder

Stride

stride - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder

stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder

Stride

stride - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

stride - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder

stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder

Stride

stride - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

Subsampling1DLayer - Class in org.deeplearning4j.nn.conf.layers

1D (temporal) subsampling layer - also known as pooling layer.
Expects input of shape [minibatch, nIn, sequenceLength].

Subsampling1DLayer - Class in org.deeplearning4j.nn.layers.convolution.subsampling

1D (temporal) subsampling layer.

Subsampling1DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer

Subsampling1DLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer

Subsampling1DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

Subsampling3DLayer - Class in org.deeplearning4j.nn.conf.layers

3D subsampling / pooling layer for convolutional neural networks

Subsampling3DLayer(Subsampling3DLayer.BaseSubsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer

Subsampling3DLayer - Class in org.deeplearning4j.nn.layers.convolution.subsampling

Subsampling 3D layer, used for downsampling a 3D convolution

Subsampling3DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

Subsampling3DLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

Subsampling3DLayer.BaseSubsamplingBuilder<T extends Subsampling3DLayer.BaseSubsamplingBuilder<T>> - Class in org.deeplearning4j.nn.conf.layers

Subsampling3DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

Subsampling3DLayer.PoolingType - Enum in org.deeplearning4j.nn.conf.layers

SubsamplingHelper - Interface in org.deeplearning4j.nn.layers.convolution.subsampling

Helper for the subsampling layer.

SubsamplingLayer - Class in org.deeplearning4j.nn.conf.layers

Subsampling layer also referred to as pooling in convolution neural nets Supports the following pooling types: MAX, AVG, SUM, PNORM

SubsamplingLayer(SubsamplingLayer.BaseSubsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

SubsamplingLayer - Class in org.deeplearning4j.nn.layers.convolution.subsampling

Subsampling layer.

SubsamplingLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

SubsamplingLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

SubsamplingLayer.BaseSubsamplingBuilder<T extends SubsamplingLayer.BaseSubsamplingBuilder<T>> - Class in org.deeplearning4j.nn.conf.layers

SubsamplingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

SubsamplingLayer.PoolingType - Enum in org.deeplearning4j.nn.conf.layers

subsetAndReshape(List<String>, Map<String, long[]>, INDArray, AbstractSameDiffLayer, SameDiffVertex) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

SubsetVertex - Class in org.deeplearning4j.nn.conf.graph

SubsetVertex is used to select a subset of the activations out of another GraphVertex.
For example, a subset of the activations out of a layer.
Note that this subset is specifying by means of an interval of the original activations.

SubsetVertex(int, int) - Constructor for class org.deeplearning4j.nn.conf.graph.SubsetVertex

SubsetVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

SubsetVertex is used to select a subset of the activations out of another GraphVertex.
For example, a subset of the activations out of a layer.
Note that this subset is specifying by means of an interval of the original activations.

SubsetVertex(ComputationGraph, String, int, int, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex

SubsetVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], int, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex

summary() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

String detailing the architecture of the computation graph.

summary(InputType...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

String detailing the architecture of the computation graph.

summary() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

String detailing the architecture of the multilayernetwork.

summary(InputType) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

String detailing the architecture of the multilayernetwork.

synchronize(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

synchronize(int, boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

synchronize(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

synchronizeIterEpochCounts() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

synchronizeIterEpochCounts() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

T

tags() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

take() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

TargetSparsityThresholdAlgorithm - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold

Targets a specific sparisty throughout training

TargetSparsityThresholdAlgorithm() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm

Create the adaptive threshold algorithm with the default initial threshold TargetSparsityThresholdAlgorithm.DEFAULT_INITIAL_THRESHOLD, default sparsity target TargetSparsityThresholdAlgorithm.DEFAULT_SPARSITY_TARGET and default decay rate TargetSparsityThresholdAlgorithm.DEFAULT_DECAY_RATE

TargetSparsityThresholdAlgorithm(double, double, double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm

taskByModel(Model) - Static method in class org.deeplearning4j.util.ModelSerializer

tbpttBackLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

tbpttBackLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

tbpttBackLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

tbpttBackLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

tbpttBackLength(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

When doing truncated BPTT: how many steps of backward should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
This is the k2 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf

tbpttBackLength - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer

Truncated BPTT equivalent of Layer.backpropGradient().

tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM

tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn

tBPTTBackwardLength(int) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

When doing truncated BPTT: how many steps of backward should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
This is the k2 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf

tBPTTBackwardLength(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

When doing truncated BPTT: how many steps of backward should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
This is the k2 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf

tBPTTForwardLength(int) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

When doing truncated BPTT: how many steps of forward pass should we do before doing (truncated) backprop?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter, but may be larger than it in some circumstances (but never smaller)
Ideally your training data time series length should be divisible by this This is the k1 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf

tBPTTForwardLength(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

When doing truncated BPTT: how many steps of forward pass should we do before doing (truncated) backprop?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter, but may be larger than it in some circumstances (but never smaller)
Ideally your training data time series length should be divisible by this This is the k1 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf

tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

tbpttFwdLength(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

When doing truncated BPTT: how many steps of forward pass should we do before doing (truncated) backprop?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter, but may be larger than it in some circumstances (but never smaller)
Ideally your training data time series length should be divisible by this This is the k1 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf

tbpttFwdLength - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

tBPTTLength(int) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

When doing truncated backpropagation through time (tBPTT): how many steps should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
See: http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf

tBPTTLength(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

When doing truncated BPTT: how many steps should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
See: http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf

tBpttStateMap - Variable in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer

State map for use specifically in truncated BPTT training.

template - Variable in class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback

terminate(int, double, boolean) - Method in class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition

terminate(int, double, boolean) - Method in interface org.deeplearning4j.earlystopping.termination.EpochTerminationCondition

Should the early stopping training terminate at this epoch, based on the calculated score and the epoch number? Returns true if training should terminated, or false otherwise

terminate(double) - Method in class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition

terminate(double) - Method in interface org.deeplearning4j.earlystopping.termination.IterationTerminationCondition

Should early stopping training terminate at this iteration, based on the score for the last iteration? return true if training should be terminated immediately, or false otherwise

terminate(int, double, boolean) - Method in class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition

terminate(double) - Method in class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition

terminate(double) - Method in class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition

terminate(int, double, boolean) - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition

THRESHOLD_LOG_FREQ_MS - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

thresholdAlgorithm - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

thresholdAlgorithm(ThresholdAlgorithm) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

This method allows to set the ThresholdAlgorithm to be used for determining the threshold

ThresholdAlgorithm - Interface in org.deeplearning4j.optimize.solvers.accumulation.encoding

ThresholdAlgorithm is responsible for determining the threshold to use when encoding updates for distributed training.

thresholdAlgorithm - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

ThresholdAlgorithmReducer - Interface in org.deeplearning4j.optimize.solvers.accumulation.encoding

Used to combine ThresholdAlgorithm implementations in in a way that is useful in a distributed training setting.

throwable - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

TimeIterationListener - Class in org.deeplearning4j.optimize.listeners

Time Iteration Listener.

TimeIterationListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.TimeIterationListener

Constructor

timeMode - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

timerBP - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

timerEE - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

timerES - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

timerFF - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

timerIteration - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener

TimeSeriesUtils - Class in org.deeplearning4j.util

Basic time series utils

TimeSinceInitializedTrigger(long) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.TimeSinceInitializedTrigger

toArray() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

toArray(T[]) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

toComputationGraph() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Convert this MultiLayerNetwork to a ComputationGraph

toComputationGraph(MultiLayerNetwork) - Static method in class org.deeplearning4j.util.NetworkUtils

Convert a MultiLayerNetwork to a ComputationGraph

toFileString() - Method in class org.deeplearning4j.optimize.listeners.Checkpoint

toJson() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

toJson() - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport

toJson() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

toJson() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

Return this configuration as json

toJson() - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

toMultiDataSet(DataSet) - Static method in class org.deeplearning4j.nn.graph.util.ComputationGraphUtil

Convert a DataSet to the equivalent MultiDataSet

toMultiDataSetIterator(DataSetIterator) - Static method in class org.deeplearning4j.nn.graph.util.ComputationGraphUtil

Convert a DataSetIterator to a MultiDataSetIterator, via an adaptor class

toNd4j() - Method in enum org.deeplearning4j.eval.Evaluation.Metric

Deprecated.

toNd4j() - Method in enum org.deeplearning4j.eval.EvaluationAveraging

Deprecated.

toNd4j() - Method in enum org.deeplearning4j.eval.RegressionEvaluation.Metric

Deprecated.

topologicalOrder - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

topologicalOrder - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

Indexes of graph vertices, in topological order.

topologicalOrderStr - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

topologicalSortOrder() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

Calculate a topological sort order for the vertices in the graph.

toPoolingType() - Method in enum org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.PoolingType

toPoolingType() - Method in enum org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType

toString() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingResult

toString() - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver

toString() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver

toString() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver

toString() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator

toString() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG

Deprecated.

toString() - Method in class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition

toString() - Method in class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition

toString() - Method in class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition

toString() - Method in class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition

toString() - Method in class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition

toString() - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition

toString() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

toString() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution

toString() - Method in class org.deeplearning4j.nn.conf.distribution.ConstantDistribution

toString() - Method in class org.deeplearning4j.nn.conf.distribution.LogNormalDistribution

toString() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution

toString() - Method in class org.deeplearning4j.nn.conf.distribution.OrthogonalDistribution

toString() - Method in class org.deeplearning4j.nn.conf.distribution.TruncatedNormalDistribution

toString() - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution

toString() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex

toString() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex

toString() - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex

toString() - Method in class org.deeplearning4j.nn.conf.graph.StackVertex

toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional

toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional3D

toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat

toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward

toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent

toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType

toString() - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer

toString() - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer

toString() - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution

toString() - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution

toString() - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution

toString() - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper

toString() - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport

toString() - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport

toString() - Method in class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport

toString() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

toString() - Method in class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction

toString() - Method in class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction

toString() - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction

toString() - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction

toString() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient

toString() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex

toString() - Method in class org.deeplearning4j.nn.graph.vertex.VertexIndices

toString() - Method in class org.deeplearning4j.nn.layers.BaseLayer

toString() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

toString() - Method in class org.deeplearning4j.nn.layers.objdetect.DetectedObject

toString() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex

toString() - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

toString() - Method in class org.deeplearning4j.optimize.listeners.ScoreIterationListener

toString() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm

toString() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm

touch() - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

This method does initialization of given worker wrt Thread-Device Affinity

touch() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

This method does initialization of given worker wrt Thread-Device Affinity

touch() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator

This method does initialization of given worker wrt Thread-Device Affinity

toYaml() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

toYaml() - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport

toYaml() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

toYaml() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

Return this configuration as json

toYaml() - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

Trainable - Interface in org.deeplearning4j.nn.api

Trainable: an interface common to Layers and GraphVertices that have trainable parameters

TrainingConfig - Interface in org.deeplearning4j.nn.api

Simple interface for the training configuration (updater, L1/L2 values, etc) for trainable layers/vertices.

TrainingListener - Interface in org.deeplearning4j.optimize.api

A listener interface for training DL4J models.
The methods here will be called at various points during training, and only during training.
Note that users can extend BaseTrainingListener and selectively override the required methods, instead of implementing TrainingListener directly and having a number of no-op methods.

trainingListeners - Variable in class org.deeplearning4j.nn.layers.AbstractLayer

trainingListeners - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

trainingListeners - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

trainingListeners - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

trainingWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Deprecated.

Use NeuralNetConfiguration.Builder.trainingWorkspaceMode(WorkspaceMode)

trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration

trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

trainingWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

This method defines Workspace mode being used during training:
NONE: workspace won't be used
ENABLED: workspaces will be used for training (reduced memory and better performance)

trainingWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

This method defines Workspace mode being used during training: NONE: workspace won't be used ENABLED: workspaces will be used for training (reduced memory and better performance)

trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

TransferLearning - Class in org.deeplearning4j.nn.transferlearning

The transfer learning API can be used to modify the architecture or the learning parameters of an existing multilayernetwork or computation graph.

TransferLearning() - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearning

TransferLearning.Builder - Class in org.deeplearning4j.nn.transferlearning

TransferLearning.GraphBuilder - Class in org.deeplearning4j.nn.transferlearning

TransferLearningHelper - Class in org.deeplearning4j.nn.transferlearning

This class is intended for use with the transfer learning API.

TransferLearningHelper(ComputationGraph, String...) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

Will modify the given comp graph (in place!) to freeze vertices from input to the vertex specified.

TransferLearningHelper(ComputationGraph) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

Expects a computation graph where some vertices are frozen

TransferLearningHelper(MultiLayerNetwork, int) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

Will modify the given MLN (in place!) to freeze layers (hold params constant during training) specified and below

TransferLearningHelper(MultiLayerNetwork) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

Expects a MLN where some layers are frozen

Tree - Class in org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive

Tree for a recursive neural tensor network based on Socher et al's work.

Tree(Tree) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Clone constructor (all but the children)

Tree(Tree, List<String>) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Tree(List<String>) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

triggerEpochListeners(boolean, Model, int) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer

triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.And

triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureTrigger

If true: trigger the failure.

triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.HostNameTrigger

triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.IterationEpochTrigger

triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.Or

triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.RandomProb

triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.TimeSinceInitializedTrigger

triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.UserNameTrigger

triggers - Variable in class org.deeplearning4j.optimize.listeners.FailureTestingListener.And

TruncatedNormalDistribution - Class in org.deeplearning4j.nn.conf.distribution

A truncated normal distribution, with 2 parameters: mean and standard deviation
This distribution is a standard normal/Gaussian distribtion, however values are "truncated" in the sense that any values that fall outside the range [mean - 2 * stdev, mean + 2 * stdev] are re-sampled.

TruncatedNormalDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.TruncatedNormalDistribution

Create a truncated normal distribution with the given mean and std

type - Variable in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator

type() - Method in interface org.deeplearning4j.nn.api.Layer

Returns the layer type

type() - Method in class org.deeplearning4j.nn.layers.AbstractLayer

type() - Method in class org.deeplearning4j.nn.layers.ActivationLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

type() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

type() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

type() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

type() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer

type() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer

type() - Method in class org.deeplearning4j.nn.layers.DropoutLayer

type() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer

type() - Method in class org.deeplearning4j.nn.layers.feedforward.PReLU

type() - Method in class org.deeplearning4j.nn.layers.LossLayer

type() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

type() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

type() - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer

type() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer

type() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

type() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM

type() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM

Deprecated.

type() - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer

type() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM

type() - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer

type() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer

type() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer

type() - Method in class org.deeplearning4j.nn.layers.RepeatVector

type() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

type() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

type() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

U

underlying - Variable in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer

underlying - Variable in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

underlying - Variable in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

unfrozenGraph() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

Returns the unfrozen subset of the original computation graph as a computation graph Note that with each call to featurizedFit the parameters to the original computation graph are also updated

unfrozenMLN() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper

Returns the unfrozen layers of the MultiLayerNetwork as a multilayernetwork Note that with each call to featurizedFit the parameters to the original MLN are also updated

UniformDistribution - Class in org.deeplearning4j.nn.conf.distribution

A uniform distribution, with two parameters: lower and upper - i.e., U(lower,upper)

UniformDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.UniformDistribution

Create a uniform real distribution using the given lower and upper bounds.

UnitNormConstraint - Class in org.deeplearning4j.nn.conf.constraint

Constrain the L2 norm of the incoming weights for each unit to be 1.0

UnitNormConstraint(int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.UnitNormConstraint

Apply to weights but not biases by default

UnitNormConstraint(Set<String>, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.UnitNormConstraint

units(int) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

Set the number of units / layer size for this layer.
This is equivalent to FeedForwardLayer.Builder.nOut(int)

UnstackVertex - Class in org.deeplearning4j.nn.conf.graph

UnstackVertex allows for unstacking of inputs so that they may be forwarded through a network.

UnstackVertex(int, int) - Constructor for class org.deeplearning4j.nn.conf.graph.UnstackVertex

UnstackVertex - Class in org.deeplearning4j.nn.graph.vertex.impl

UnstackVertex allows for unstacking of inputs so that they may be forwarded through a network.

UnstackVertex(ComputationGraph, String, int, int, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex

UnstackVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], int, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex

update(Gradient) - Method in interface org.deeplearning4j.nn.api.Model

Update layer weights and biases with gradient change

update(INDArray, String) - Method in interface org.deeplearning4j.nn.api.Model

Perform one update applying the gradient

update(Trainable, Gradient, int, int, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Updater

Updater: updates the model

update(INDArray, String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

update(Gradient) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

update(Gradient) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

update(Gradient) - Method in class org.deeplearning4j.nn.layers.BaseLayer

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.BaseLayer

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

update(Gradient) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.FrozenLayer

update(Gradient) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop

update(Gradient) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.RepeatVector

update(Gradient) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

update(Gradient) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

update(INDArray, String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

update(Gradient) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

update(Task) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

update(Trainable, Gradient, int, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

update(Gradient, int, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

Update the gradient for the model.

update(int, int) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock

Update the gradient for this block

updateBuilder(ComputationGraphConfiguration.GraphBuilder, String, int, int[][], String) - Method in interface org.deeplearning4j.nn.conf.module.GraphBuilderModule

Add a layer to the collection of layers being generated by this module.

updateExternalGradient(int, int, INDArray, INDArray) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock

updateGradientAccordingToParams(Gradient, Model, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer

Update the gradient according to the configuration such as adagrad, momentum, and sparsity

updateGradientAccordingToParams(Gradient, Model, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer

Updater - Interface in org.deeplearning4j.nn.api

Update the model

updater(Updater) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Deprecated.

updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Gradient updater.

updater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder

Gradient updater.

updater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

updater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex

updater(Updater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Deprecated.

Use NeuralNetConfiguration.Builder.updater(IUpdater)

updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Gradient updater configuration.

Updater - Enum in org.deeplearning4j.nn.conf

All the possible different updaters

updater(IUpdater) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Gradient updater configuration.

updater(Updater) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Deprecated.

Use FineTuneConfiguration.Builder.updater(IUpdater)

updater - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

updater - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer

UPDATER_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer

UpdaterBlock - Class in org.deeplearning4j.nn.updater

UpdaterBlock: used in BaseMultiLayerUpdater, this class implements updating (i.e., Adam, RMSProp, Momentum, etc) across multiple contiguous layers/parameters, as described in the BaseMultiLayerUpdater javadoc.

UpdaterBlock(int, int, int, int, List<UpdaterBlock.ParamState>) - Constructor for class org.deeplearning4j.nn.updater.UpdaterBlock

UpdaterBlock.ParamState - Class in org.deeplearning4j.nn.updater

updaterBlocks - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

updaterConfigurationsEquals(Trainable, String, Trainable, String) - Static method in class org.deeplearning4j.nn.updater.UpdaterUtils

UpdaterCreator - Class in org.deeplearning4j.nn.updater

updaterDivideByMinibatch(String) - Method in interface org.deeplearning4j.nn.api.Trainable

DL4J layers typically produce the sum of the gradients during the backward pass for each layer, and if required (if minibatch=true) then divide by the minibatch size.
However, there are some exceptions, such as the batch norm mean/variance estimate parameters: these "gradients" are actually not gradients, but are updates to be applied directly to the parameter vector.

updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex

updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.layers.AbstractLayer

updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer

updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

updateRnnStateWithTBPTTState() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

updaterState() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork

This method returns updater state (if applicable), null otherwise

updaterState() - Method in class org.deeplearning4j.nn.graph.ComputationGraph

updaterState() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

updaterStateViewArray - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater

UpdaterUtils - Class in org.deeplearning4j.nn.updater

Created by Alex on 14/04/2017.

UpdaterUtils() - Constructor for class org.deeplearning4j.nn.updater.UpdaterUtils

updates - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

updates - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

updatesApplied - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

updatesBoundary(double) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder

This method enables optional limit for max number of updates per message Default value: 1.0 (no limit)

updatesCounter - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

updatesLock - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator

updatesSize() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail

This method returns actual number of updates stored within tail

Upsampling1D - Class in org.deeplearning4j.nn.conf.layers

Upsampling 1D layer
Repeats each step size times along the temporal/sequence axis (dimension 2)
For input shape [minibatch, channels, sequenceLength] output has shape [minibatch, channels, size * sequenceLength]
Example:

Upsampling1D(BaseUpsamplingLayer.UpsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling1D

Upsampling1D - Class in org.deeplearning4j.nn.layers.convolution.upsampling

1D Upsampling layer.

Upsampling1D(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D

Upsampling1D(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D

Upsampling1D.Builder - Class in org.deeplearning4j.nn.conf.layers

Upsampling2D - Class in org.deeplearning4j.nn.conf.layers

Upsampling 2D layer
Repeats each value (or rather, set of depth values) in the height and width dimensions by size[0] and size[1] times respectively.
If input has shape [minibatch, channels, height, width] then output has shape [minibatch, channels, height*size[0], width*size[1]]
Example:

Upsampling2D(BaseUpsamplingLayer.UpsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling2D

Upsampling2D - Class in org.deeplearning4j.nn.layers.convolution.upsampling

2D Upsampling layer.

Upsampling2D(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

Upsampling2D(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

Upsampling2D.Builder - Class in org.deeplearning4j.nn.conf.layers

Upsampling3D - Class in org.deeplearning4j.nn.conf.layers

Upsampling 3D layer
Repeats each value (all channel values for each x/y/z location) by size[0], size[1] and size[2]
If input has shape [minibatch, channels, depth, height, width] then output has shape [minibatch, channels, size[0] * depth, size[1] * height, size[2] * width]

Upsampling3D(BaseUpsamplingLayer.UpsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling3D

Upsampling3D - Class in org.deeplearning4j.nn.layers.convolution.upsampling

3D Upsampling layer.

Upsampling3D(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

Upsampling3D(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D

Upsampling3D.Builder - Class in org.deeplearning4j.nn.conf.layers

UpsamplingBuilder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer.UpsamplingBuilder

A single size integer is used for upsampling in all spatial dimensions

UpsamplingBuilder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer.UpsamplingBuilder

An int array to specify upsampling dimensions, the length of which has to equal to the number of spatial dimensions (e.g.

UserNameTrigger(String) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.UserNameTrigger

V

V_KEY - Static variable in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

VAEReconErrorScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc

Score function for variational autoencoder reconstruction error for a MultiLayerNetwork or ComputationGraph.
VariationalAutoencoder layer must be first layer in the network

VAEReconErrorScoreCalculator(RegressionEvaluation.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator

Constructor for reconstruction *ERROR*

VAEReconProbScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc

Score calculator for variational autoencoder reconstruction probability or reconstruction log probability for a MultiLayerNetwork or ComputationGraph.

VAEReconProbScoreCalculator(DataSetIterator, int, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

Constructor for average reconstruction probability

VAEReconProbScoreCalculator(DataSetIterator, int, boolean, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator

Constructor for reconstruction probability

validate() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration

validate() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

Check the configuration, make sure it is valid

validate(boolean, boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

Check the configuration, make sure it is valid

validateArrayLocation(ArrayType, INDArray, boolean, boolean) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr

validateArrayWorkspaces(LayerWorkspaceMgr, INDArray, ArrayType, String, boolean, String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph

validateArrayWorkspaces(LayerWorkspaceMgr, INDArray, ArrayType, int, boolean, String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

validateCnn1DKernelStridePadding(int, int, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils

Perform validation on the CNN layer kernel/stride/padding.

validateCnn3DKernelStridePadding(int[], int[], int[]) - Static method in class org.deeplearning4j.util.Convolution3DUtils

Perform validation on the CNN3D layer kernel/stride/padding.

validateCnnKernelStridePadding(int[], int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Perform validation on the CNN layer kernel/stride/padding.

validateConvolutionModePadding(ConvolutionMode, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils

Check that the convolution mode is consistent with the padding specification

validateConvolutionModePadding(ConvolutionMode, int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils

Check that the convolution mode is consistent with the padding specification

validateInputDepth(int) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

validateInputRank() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer

validateOutputConfig - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

validateOutputConfig - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

validateOutputLayer(String, Layer) - Static method in class org.deeplearning4j.util.OutputLayerUtil

Validate the output layer (or loss layer) configuration, to detect invalid consfiugrations.

validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Enabled by default.

validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Enabled by default.

validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder

validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder

validateOutputLayerConfiguration(String, long, boolean, IActivation, ILossFunction) - Static method in class org.deeplearning4j.util.OutputLayerUtil

Validate the output layer (or loss layer) configuration, to detect invalid consfiugrations.

validateShapes(INDArray, int, int, int, ConvolutionMode, int, int, boolean) - Static method in class org.deeplearning4j.util.Convolution1DUtils

validateShapes(INDArray, int[], int[], int[], ConvolutionMode, int[], int[], boolean) - Static method in class org.deeplearning4j.util.ConvolutionUtils

value() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

valueOf(String) - Static method in enum org.deeplearning4j.earlystopping.EarlyStoppingResult.TerminationReason

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.Metric

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.ROCType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.eval.Evaluation.Metric

Deprecated.

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.eval.EvaluationAveraging

Deprecated.

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.eval.RegressionEvaluation.Metric

Deprecated.

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.api.FwdPassType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.api.Layer.TrainingMode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.api.Layer.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.api.MaskState

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.api.OptimizationAlgorithm

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.BackpropType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.CacheMode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.ConvolutionMode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.GradientNormalization

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.graph.ElementWiseVertex.Op

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.inputs.InputType.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.Convolution3D.DataFormat

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.PoolingType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Mode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.DataFormat

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.PoolingType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.memory.MemoryType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.memory.MemoryUseMode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.Updater

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.WorkspaceMode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.weights.WeightInit

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.nn.workspace.ArrayType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.optimize.api.InvocationType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureMode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.optimize.listeners.SleepyTrainingListener.SleepMode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.deeplearning4j.optimize.listeners.SleepyTrainingListener.TimeMode

Returns the enum constant of this type with the specified name.

values() - Static method in enum org.deeplearning4j.earlystopping.EarlyStoppingResult.TerminationReason

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.Metric

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.ROCType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.eval.Evaluation.Metric

Deprecated.

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.eval.EvaluationAveraging

Deprecated.

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.eval.RegressionEvaluation.Metric

Deprecated.

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.api.FwdPassType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.api.Layer.TrainingMode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.api.Layer.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.api.MaskState

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.api.OptimizationAlgorithm

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.BackpropType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.CacheMode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.ConvolutionMode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.GradientNormalization

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.graph.ElementWiseVertex.Op

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.inputs.InputType.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.layers.Convolution3D.DataFormat

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.layers.PoolingType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Mode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.DataFormat

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.PoolingType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.memory.MemoryType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.memory.MemoryUseMode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.Updater

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.conf.WorkspaceMode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.weights.WeightInit

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.nn.workspace.ArrayType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.optimize.api.InvocationType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureMode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.optimize.listeners.SleepyTrainingListener.SleepMode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.deeplearning4j.optimize.listeners.SleepyTrainingListener.TimeMode

Returns an array containing the constants of this enum type, in the order they are declared.

variables - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

variables() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

variables(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration

VariationalAutoencoder - Class in org.deeplearning4j.nn.conf.layers.variational

Variational Autoencoder layer

VariationalAutoencoder - Class in org.deeplearning4j.nn.layers.variational

Variational Autoencoder layer

VariationalAutoencoder(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

VariationalAutoencoder.Builder - Class in org.deeplearning4j.nn.conf.layers.variational

VariationalAutoencoderParamInitializer - Class in org.deeplearning4j.nn.params

Parameter initializer for the Variational Autoencoder model.

VariationalAutoencoderParamInitializer() - Constructor for class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

vector() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

vertexIndex - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

The index of this vertex

VertexIndices - Class in org.deeplearning4j.nn.graph.vertex

VertexIndices defines a pair of integers: the index of a vertex, and the edge number of that vertex.

VertexIndices() - Constructor for class org.deeplearning4j.nn.graph.vertex.VertexIndices

vertexInputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Key: graph node.

vertexInputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

VertexInputs(SameDiff) - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex.VertexInputs

vertexName - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex

vertices - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

vertices - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration

vertices - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

All GraphVertex objects in the network.

verticesMap - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

Map of vertices by name

VISIBLE_BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.PretrainParamInitializer

visibleBiasInit - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder

visibleBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder

visibleBiasInit - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork

W

W_KEY - Static variable in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.CenterLossParamInitializer

WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.Convolution3DParamInitializer

WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.DefaultParamInitializer

WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.PReLUParamInitializer

WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

WEIGHT_KEY_SUFFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

weightConstraints - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder

weightConstraints - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

weightInit - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

weightInit(WeightInit) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Weight initialization scheme to use, for initial weight values

weightInit(Distribution) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Set weight initialization scheme to random sampling via the specified distribution.

weightInit - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

weightInit - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer.Builder

weightInit(WeightInit) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer.Builder

weightInit - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer

weightInit - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

weightInit(WeightInit) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Weight initialization scheme.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

weightInit(Distribution) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Set weight initialization scheme to random sampling via the specified distribution.

weightInit(WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Weight initialization scheme

weightInit(Distribution) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Set weight initialization scheme to random sampling via the specified distribution.
Equivalent to: .weightInit(WeightInit.DISTRIBUTION).dist(distribution)

weightInit - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

WeightInit - Enum in org.deeplearning4j.nn.weights

Weight initialization scheme

weightInitRecurrent - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder

weightInitRecurrent(WeightInit) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder

Set the weight initialization for the recurrent weights.

weightInitRecurrent(Distribution) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder

Set the weight initialization for the recurrent weights, based on the specified distribution.

weightInitRecurrent - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer

WeightInitUtil - Class in org.deeplearning4j.nn.weights

Weight initialization utility

weightKeys(Layer) - Method in interface org.deeplearning4j.nn.api.ParamInitializer

Weight parameter keys given the layer configuration

weightKeys(Layer) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer

weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer

weightNoise - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

weightNoise(IWeightNoise) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

Set the weight noise (such as DropConnect and WeightNoise) for this layer

weightNoise - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer

weightNoise - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

weightNoise(IWeightNoise) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Set the weight noise (such as DropConnect and WeightNoise) for the layers in this network.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

WeightNoise - Class in org.deeplearning4j.nn.conf.weightnoise

Apply noise of the specified distribution to the weights at training time.

WeightNoise(Distribution) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.WeightNoise

WeightNoise(Distribution, boolean) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.WeightNoise

WeightNoise(Distribution, boolean, boolean) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.WeightNoise

weightNoise(IWeightNoise) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder

Set the weight noise (such as DropConnect and WeightNoise)

weightNoise - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration

weightNoiseParams - Variable in class org.deeplearning4j.nn.layers.BaseLayer

weightNoiseParams - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

windowSize - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

windowSize(int) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder

The number of examples to use for computing the quantile for the r value update.

with(ArrayType, String, WorkspaceConfiguration) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder

Configure the workspace (name, configuration) for the specified array type

workersCounter - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

workingMemory(long, long, long, long) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder

Report the working memory size, for both inference and training

workingMemory(long, long, Map<CacheMode, Long>, Map<CacheMode, Long>) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder

Report the working memory requirements, for both inference and training.

WorkspaceMode - Enum in org.deeplearning4j.nn.conf

Workspace mode to use.

workspaces - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

WrapperLayerParamInitializer - Class in org.deeplearning4j.nn.params

writeMemoryCrashDump(Model, Throwable) - Static method in class org.deeplearning4j.util.CrashReportingUtil

Generate and write the crash dump to the crash dump root directory (by default, the working directory).

writeModel(Model, File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Write a model to a file

writeModel(Model, File, boolean, DataNormalization) - Static method in class org.deeplearning4j.util.ModelSerializer

Write a model to a file

writeModel(Model, String, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Write a model to a file path

writeModel(Model, OutputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer

Write a model to an output stream

writeModel(Model, OutputStream, boolean, DataNormalization) - Static method in class org.deeplearning4j.util.ModelSerializer

Write a model to an output stream

WS_ALL_LAYERS_ACT - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph

Workspace for storing all layers' activations - used only to store activations (layer inputs) as part of backprop Not used for inference

WS_ALL_LAYERS_ACT - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Workspace for storing all layers' activations - used only to store activations (layer inputs) as part of backprop Not used for inference

WS_ALL_LAYERS_ACT_CONFIG - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph

WS_ALL_LAYERS_ACT_CONFIG - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

WS_LAYER_ACT_1 - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Next 2 workspaces: used for: (a) Inference: holds activations for one layer only (b) Backprop: holds activation gradients for one layer only In both cases, they are opened and closed on every second layer

WS_LAYER_ACT_2 - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

WS_LAYER_ACT_X_CONFIG - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

WS_LAYER_ACT_X_CONFIG - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

WS_LAYER_WORKING_MEM - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph

Workspace for working memory for a single layer: forward pass and backward pass Note that this is opened/closed once per op (activate/backpropGradient call)

WS_LAYER_WORKING_MEM - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Workspace for working memory for a single layer: forward pass and backward pass Note that this is opened/closed once per op (activate/backpropGradient call)

WS_LAYER_WORKING_MEM_CONFIG - Variable in class org.deeplearning4j.nn.graph.ComputationGraph

WS_LAYER_WORKING_MEM_CONFIG - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

WS_OUTPUT_MEM - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph

Workspace for output methods that use OutputAdapter

WS_OUTPUT_MEM - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Workspace for output methods that use OutputAdapter

WS_RNN_LOOP_WORKING_MEM - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph

Workspace for working memory in RNNs - opened and closed once per RNN time step

WS_RNN_LOOP_WORKING_MEM - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

Workspace for working memory in RNNs - opened and closed once per RNN time step

WS_RNN_LOOP_WORKING_MEM_CONFIG - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph

WS_RNN_LOOP_WORKING_MEM_CONFIG - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

X

xHat - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

xMu - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

Y

yield() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree

Returns all of the labels for this node and all of its children (recursively)

Yolo2OutputLayer - Class in org.deeplearning4j.nn.conf.layers.objdetect

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.

Yolo2OutputLayer - Class in org.deeplearning4j.nn.layers.objdetect

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.

Yolo2OutputLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer

Yolo2OutputLayer.Builder - Class in org.deeplearning4j.nn.conf.layers.objdetect

YoloUtils - Class in org.deeplearning4j.nn.layers.objdetect

Functionality to interpret the network output of Yolo2OutputLayer.

YoloUtils() - Constructor for class org.deeplearning4j.nn.layers.objdetect.YoloUtils

Z

zeroedPretrainParamGradients - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

ZeroPadding1DLayer - Class in org.deeplearning4j.nn.conf.layers

Zero padding 1D layer for convolutional neural networks.

ZeroPadding1DLayer(int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

ZeroPadding1DLayer(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

ZeroPadding1DLayer(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer

ZeroPadding1DLayer - Class in org.deeplearning4j.nn.layers.convolution

Zero padding 1D layer for convolutional neural networks.

ZeroPadding1DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer

ZeroPadding1DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

ZeroPadding3DLayer - Class in org.deeplearning4j.nn.conf.layers

Zero padding 3D layer for convolutional neural networks.

ZeroPadding3DLayer - Class in org.deeplearning4j.nn.layers.convolution

Zero padding 3D layer for convolutional neural networks.

ZeroPadding3DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer

ZeroPadding3DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

ZeroPaddingLayer - Class in org.deeplearning4j.nn.conf.layers

Zero padding layer for convolutional neural networks (2D CNNs).

ZeroPaddingLayer(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer

ZeroPaddingLayer(int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer

ZeroPaddingLayer - Class in org.deeplearning4j.nn.layers.convolution

Zero padding layer for convolutional neural networks.

ZeroPaddingLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer

ZeroPaddingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

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