Index (deeplearning4j-nn 0.9.1 API)

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

AbstractDataSetIterator<T> - Class in org.deeplearning4j.datasets.iterator: This is simple DataSetIterator implementation, that builds DataSetIterator out of INDArray/float[]/double[] pairs.
AbstractDataSetIterator(Iterable<Pair<T, T>>, int) - Constructor for class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
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
acceptResult(String) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
acceptsInput(String) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
accumulateScore(double) - Method in interface org.deeplearning4j.nn.api.Model: Sets a rolling tally for the score.
accumulateScore(double) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
accumulateScore(double) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
accumulateScore(double) - Method in class org.deeplearning4j.nn.layers.BaseLayer
accumulateScore(double) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
accumulateScore(double) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
accumulateScore(double) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
accumulateScore(double) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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
accuracy() - Method in class org.deeplearning4j.eval.Evaluation: Accuracy: (TP + TN) / (P + N)
accuracy(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Get the accuracy for the specified output
activate(Layer.TrainingMode) - Method in interface org.deeplearning4j.nn.api.Layer: Trigger an activation with the last specified input
activate(INDArray, Layer.TrainingMode) - Method in interface org.deeplearning4j.nn.api.Layer: Initialize the layer with the given input and return the activation for this layer given this input
activate(boolean) - Method in interface org.deeplearning4j.nn.api.Layer: Trigger an activation with the last specified input
activate(INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.Layer: Initialize the layer with the given input and return the activation for this layer given this input
activate() - Method in interface org.deeplearning4j.nn.api.Layer: Trigger an activation with the last specified input
activate(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer: Initialize the layer with the given input and return the activation for this layer given this input
activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
activate(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
activate() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
activate(boolean) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.BaseLayer
activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.BaseLayer
activate(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
activate(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
activate() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
activate(INDArray, IActivation) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
activate(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
activate(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer
activate(INDArray, boolean, int[], int[], int[], PoolingType, ConvolutionMode) - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper
activate(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
activate(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
activate(boolean) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
activate(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
activate(boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
activate() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
activate(boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
activate(boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Reconstructs the visible INPUT.
activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
activate(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
activate() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
activate(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
activate(boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer
activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer
activate(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
activate() - Method in class org.deeplearning4j.nn.layers.LossLayer
activate(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
activate(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
activate(INDArray, boolean, double, double, double, double) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper
activate(boolean) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
activate(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
activate(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
activate() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
activate(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
activate(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
activate() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
activate(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
activate(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
activate() - 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) - Method in interface org.deeplearning4j.nn.layers.recurrent.LSTMHelper
activate(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
activate(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
activate() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
activate(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
activate() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Triggers the activation of the last hidden layer ie: not logistic regression
activate(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Triggers the activation for a given layer
activate(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
activate(int, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Triggers the activation of the given layer
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) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
activate(INDArray, boolean) - 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) - 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(String) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
Use BaseLayer.Builder.activation(Activation) or @activation(IActivation)
activation(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
activation(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
activation(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
Use NeuralNetConfiguration.Builder.activation(Activation) or @activation(IActivation)
activation(IActivation) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Activation function / neuron non-linearity
activation(Activation) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Activation function / neuron non-linearity
activation(Activation) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
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) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Calculate activation from previous layer including pre processing where necessary
ActivationLayer - Class in org.deeplearning4j.nn.conf.layers
ActivationLayer(ActivationLayer.Builder) - 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
activationMean() - Method in interface org.deeplearning4j.nn.api.Layer: Deprecated.
As of 0.7.3 - Feb 2017. No longer used.
activationMean() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
activationMean() - Method in class org.deeplearning4j.nn.layers.BaseLayer
activationMean() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
activationMean() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
activationMean() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
activationMean() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
activationMean() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
activationMean() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
activationMean() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
activationMean() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
activationMean() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
activationMean() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
activations(INDArray) - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
adamMeanDecay - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer: Deprecated.
adamMeanDecay - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
adamMeanDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
use .updater(Adam.builder().beta1(adamMeanDecay).build()) intead
adamMeanDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Mean decay rate for Adam updater.
adamMeanDecay - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
adamMeanDecay(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
use .updater(Adam.builder().beta1(adamMeanDecay).build()) intead
adamMeanDecay - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration: Deprecated.
adamVarDecay - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer: Deprecated.
adamVarDecay - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
adamVarDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
use .updater(Adam.builder().beta2(adamVarDecay).build()) intead
adamVarDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Variance decay rate for Adam updater.
adamVarDecay - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
adamVarDecay(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
use .updater(Adam.builder().beta2(adamVarDecay).build()) intead
adamVarDecay - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration: Deprecated.
add(T, T) - Method in class org.deeplearning4j.eval.ConfusionMatrix: Increments the entry specified by actual and predicted by one.
add(T, T, int) - Method in class org.deeplearning4j.eval.ConfusionMatrix: Increments the entry specified by actual and predicted by count.
add(ConfusionMatrix<T>) - Method in class org.deeplearning4j.eval.ConfusionMatrix: Adds the entries from another confusion matrix to this one.
add(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
add(E) - Method in class org.deeplearning4j.util.DiskBasedQueue
add(Object, int) - Method in class org.deeplearning4j.util.Index
add(Object) - Method in class org.deeplearning4j.util.Index
add(Pair<K, V>) - Method in class org.deeplearning4j.util.MultiDimensionalSet: Adds the specified element to this applyTransformToDestination if it is not already present (optional operation).
add(K, V) - Method in class org.deeplearning4j.util.MultiDimensionalSet
addAll(Collection<? extends E>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
addAll(Collection<? extends E>) - Method in class org.deeplearning4j.util.DiskBasedQueue
addAll(Collection<? extends Pair<K, V>>) - Method in class org.deeplearning4j.util.MultiDimensionalSet: Adds all of the elements in the specified collection to this applyTransformToDestination if they're not already present (optional operation).
addColumn(List<String>) - Method in class org.deeplearning4j.util.StringGrid
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(IterationListener...) - Method in interface org.deeplearning4j.nn.api.Model: This method ADDS additional IterationListener to existing listeners
addListeners(IterationListener...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: This method ADDS additional IterationListener to existing listeners
addListeners(IterationListener...) - Method in class org.deeplearning4j.nn.layers.AbstractLayer: This method ADDS additional IterationListener to existing listeners
addListeners(IterationListener...) - Method in class org.deeplearning4j.nn.layers.FrozenLayer: This method ADDS additional IterationListener to existing listeners
addListeners(IterationListener...) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder: This method ADDS additional IterationListener to existing listeners
addListeners(IterationListener...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: This method ADDS additional IterationListener to existing listeners
addNormalizerToModel(File, Normalizer<?>) - Static method in class org.deeplearning4j.util.ModelSerializer: This method appends normalizer to a given persisted model.
addPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.CombinedMultiDataSetPreProcessor.Builder
addPreProcessor(int, MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.CombinedMultiDataSetPreProcessor.Builder: Inserts the specified preprocessor at the specified position to the list of preprocessors to be applied
addPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.CombinedPreProcessor.Builder
addPreProcessor(int, DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.CombinedPreProcessor.Builder
addPreProcessors(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: Add preprocessors automatically, given the specified types of inputs for the network.
addRow(List<String>) - Method in class org.deeplearning4j.util.StringGrid
addSourceIterator(DataSetIterator) - Method in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator.Builder
addToConfusion(Integer, Integer) - Method in class org.deeplearning4j.eval.Evaluation: Adds to the confusion matrix
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
adjustedrSquared(double, int, int) - Static method in class org.deeplearning4j.util.MathUtils: This calculates the adjusted r^2 including degrees of freedom.
ALF - Variable in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
allDepleted - Variable in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
allFalse() - Method in class org.deeplearning4j.datasets.iterator.parallel.MultiBoolean: This method returns true if ALL states are false.
allTrue() - Method in class org.deeplearning4j.datasets.iterator.parallel.MultiBoolean: This method returns true if ALL states are true.
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.
ancestor(int, Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Returns the ancestor of the given tree
appendTo(String, File) - Static method in class org.deeplearning4j.util.FileOperations
appendToEach(String, int) - Method in class org.deeplearning4j.util.StringGrid
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
applyDropConnect(Layer, String) - Static method in class org.deeplearning4j.util.Dropout: Apply drop connect to the given variable
applyDropout(INDArray, double) - Static method in class org.deeplearning4j.util.Dropout: Apply dropout to the given input and return the drop out mask used
applyDropOutIfNecessary(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
applyDropOutIfNecessary(boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
applyLearningRateScoreDecay() - Method in interface org.deeplearning4j.nn.api.Model: Update learningRate using for this model.
applyLearningRateScoreDecay() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
applyLearningRateScoreDecay() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
applyLearningRateScoreDecay() - Method in class org.deeplearning4j.nn.layers.BaseLayer
applyLearningRateScoreDecay() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
applyLearningRateScoreDecay() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
applyLearningRateScoreDecay() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
applyLrDecayPolicy(LearningRatePolicy, int) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock: Apply learning rate decay, based on the configuration
applyMask(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
applyMask(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
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) - 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) - 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) - 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
ArchiveUtils - Class in org.deeplearning4j.util
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.InputTypeConvolutionalFlat
arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward
arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
arrayToString(String[]) - Static method in class org.deeplearning4j.util.StringUtils: Given an array of strings, return a comma-separated list of its elements.
asciify(String) - Method in class org.deeplearning4j.util.FingerPrintKeyer
assertNInNOutSet(String, String, int, int, int) - Static method in class org.deeplearning4j.util.LayerValidation: Asserts that the layer nIn and nOut values are set for the layer
AsyncDataSetIterator - Class in org.deeplearning4j.datasets.iterator: Async prefetching iterator wrapper for MultiDataSetIterator implementations
AsyncDataSetIterator() - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
AsyncDataSetIterator(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
AsyncDataSetIterator(DataSetIterator, int, BlockingQueue<DataSet>) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
AsyncDataSetIterator(DataSetIterator, int) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
AsyncDataSetIterator(DataSetIterator, int, boolean) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
AsyncDataSetIterator(DataSetIterator, int, boolean, Integer) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
AsyncDataSetIterator(DataSetIterator, int, boolean, DataSetCallback) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
AsyncDataSetIterator(DataSetIterator, int, BlockingQueue<DataSet>, boolean) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
AsyncDataSetIterator(DataSetIterator, int, BlockingQueue<DataSet>, boolean, DataSetCallback) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
AsyncDataSetIterator(DataSetIterator, int, BlockingQueue<DataSet>, boolean, DataSetCallback, Integer) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
AsyncDataSetIterator.AsyncPrefetchThread - Class in org.deeplearning4j.datasets.iterator
asyncIterators - Variable in class org.deeplearning4j.datasets.iterator.parallel.FileSplitParallelDataSetIterator
asyncIterators - Variable in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator
AsyncMultiDataSetIterator - Class in org.deeplearning4j.datasets.iterator: Async prefetching iterator wrapper for MultiDataSetIterator implementations
AsyncMultiDataSetIterator() - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
AsyncMultiDataSetIterator(MultiDataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
AsyncMultiDataSetIterator(MultiDataSetIterator, int, BlockingQueue<MultiDataSet>) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
AsyncMultiDataSetIterator(MultiDataSetIterator, int) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
AsyncMultiDataSetIterator(MultiDataSetIterator, int, boolean) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
AsyncMultiDataSetIterator(MultiDataSetIterator, int, boolean, Integer) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
AsyncMultiDataSetIterator(MultiDataSetIterator, int, BlockingQueue<MultiDataSet>, boolean) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
AsyncMultiDataSetIterator(MultiDataSetIterator, int, BlockingQueue<MultiDataSet>, boolean, DataSetCallback) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
AsyncMultiDataSetIterator(MultiDataSetIterator, int, BlockingQueue<MultiDataSet>, boolean, DataSetCallback, Integer) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
AsyncMultiDataSetIterator.AsyncPrefetchThread - Class in org.deeplearning4j.datasets.iterator
AsyncPrefetchThread(BlockingQueue<DataSet>, DataSetIterator, DataSet, MemoryWorkspace) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator.AsyncPrefetchThread
AsyncPrefetchThread(BlockingQueue<MultiDataSet>, MultiDataSetIterator, MultiDataSet) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator.AsyncPrefetchThread
AsyncShieldDataSetIterator - Class in org.deeplearning4j.datasets.iterator: This wrapper takes your existing DataSetIterator implementation and prevents asynchronous prefetch
AsyncShieldDataSetIterator(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator
AsyncShieldMultiDataSetIterator - Class in org.deeplearning4j.datasets.iterator: This wrapper takes your existing MultiDataSetIterator implementation and prevents asynchronous prefetch
AsyncShieldMultiDataSetIterator(MultiDataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncShieldMultiDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Does this DataSetIterator support asynchronous prefetching of multiple DataSet objects? Most DataSetIterators do, but in some cases it may not make sense to wrap this iterator in an iterator that does asynchronous prefetching.
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator: Does this DataSetIterator support asynchronous prefetching of multiple DataSet objects? Most DataSetIterators do, but in some cases it may not make sense to wrap this iterator in an iterator that does asynchronous prefetching.
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Does this DataSetIterator support asynchronous prefetching of multiple DataSet objects? PLEASE NOTE: This iterator ALWAYS returns FALSE
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldMultiDataSetIterator: /** Does this DataSetIterator support asynchronous prefetching of multiple DataSet objects? PLEASE NOTE: This iterator ALWAYS returns FALSE
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationMultiDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
atomicBoundary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
attachThread(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
AutoEncoder - Class in org.deeplearning4j.nn.conf.layers: Autoencoder.
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
averageAccuracy() - Method in class org.deeplearning4j.eval.EvaluationBinary
averagecorrelationR2() - Method in class org.deeplearning4j.eval.RegressionEvaluation: Average R2 across all columns
averageF1() - Method in class org.deeplearning4j.eval.EvaluationBinary
averageF1NumClassesExcluded() - Method in class org.deeplearning4j.eval.Evaluation: When calculating the (macro) average F1, how many classes are excluded from the average due to no predictions – i.e., F1 would be calculated from a precision or recall of 0/0
averageFBetaNumClassesExcluded() - Method in class org.deeplearning4j.eval.Evaluation: When calculating the (macro) average FBeta, how many classes are excluded from the average due to no predictions – i.e., FBeta would be calculated from a precision or recall of 0/0
averageMeanAbsoluteError() - Method in class org.deeplearning4j.eval.RegressionEvaluation: Average MAE across all columns
averageMeanSquaredError() - Method in class org.deeplearning4j.eval.RegressionEvaluation: Average MSE across all columns
averagePrecision() - Method in class org.deeplearning4j.eval.EvaluationBinary
averagePrecisionNumClassesExcluded() - Method in class org.deeplearning4j.eval.Evaluation: When calculating the (macro) average precision, how many classes are excluded from the average due to no predictions – i.e., precision would be the edge case of 0/0
averageRecall() - Method in class org.deeplearning4j.eval.EvaluationBinary
averageRecallNumClassesExcluded() - Method in class org.deeplearning4j.eval.Evaluation: When calculating the (macro) average Recall, how many classes are excluded from the average due to no predictions – i.e., recall would be the edge case of 0/0
averagerelativeSquaredError() - Method in class org.deeplearning4j.eval.RegressionEvaluation: Average RSE across all columns
averagerootMeanSquaredError() - Method in class org.deeplearning4j.eval.RegressionEvaluation: Average RMSE across all columns

B

backedIterator - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
backedIterator - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
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
backprop(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Whether to do back prop (standard supervised learning) or not
backprop(INDArray, int) - 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
backprop(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Whether to do back prop or not
backprop(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.BinomialSamplingPreProcessor
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.UnitVarianceProcessor
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanAndUnitVariancePreProcessor
backprop(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanPrePreProcessor
backprop() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Calculate and set gradients for MultiLayerNetwork, based on OutputLayer and labels
backprop - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
backpropGradient(INDArray) - 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) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
backpropGradient(INDArray, INDArray, INDArray, int[], int[], int[], INDArray, INDArray, IActivation, ConvolutionLayer.AlgoMode, ConvolutionLayer.BwdFilterAlgo, ConvolutionLayer.BwdDataAlgo, ConvolutionMode) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer
backpropGradient(INDArray, INDArray, int[], int[], int[], PoolingType, ConvolutionMode) - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
backpropGradient(INDArray, INDArray, int[], INDArray, INDArray, INDArray, double) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
backpropGradient(INDArray, INDArray, double, double, double, double) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
backpropGradient(INDArray) - 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) - Method in interface org.deeplearning4j.nn.layers.recurrent.LSTMHelper
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer
backpropGradient(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
backpropGradient(INDArray) - 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) - 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
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
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
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...) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseConvBuilder() - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseCurve - Class in org.deeplearning4j.eval.curves: Abstract class for ROC and Precision recall curves
BaseCurve() - Constructor for class org.deeplearning4j.eval.curves.BaseCurve
BaseDataFetcher - Class in org.deeplearning4j.datasets.fetchers: A base class for assisting with creation of matrices with the data applyTransformToDestination fetcher
BaseDataFetcher() - Constructor for class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
BaseDatasetIterator - Class in org.deeplearning4j.datasets.iterator: Baseline implementation includes control over the data fetcher and some basic getters for metadata
BaseDatasetIterator(int, int, BaseDataFetcher) - Constructor for class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
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: BaseEvaluation implement common evaluation functionality (for time series, etc) for Evaluation, RegressionEvaluation, ROC, ROCMultiClass etc.
BaseEvaluation() - Constructor for class org.deeplearning4j.eval.BaseEvaluation
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
BaseHistogram - Class in org.deeplearning4j.eval.curves: Created by Alex on 06/07/2017.
BaseHistogram() - Constructor for class org.deeplearning4j.eval.curves.BaseHistogram
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
BaseMultiLayerUpdater<T extends Model> - Class in org.deeplearning4j.nn.updater: BaseMultiLayerUpdater - core functionality for applying updaters to MultiLayerNetwork and ComputationGraph.
BaseMultiLayerUpdater(T) - Constructor for class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
BaseMultiLayerUpdater(T, INDArray) - 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<IterationListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.BaseOptimizer
BaseOptimizer(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Collection<TerminationCondition>, 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
BaseParallelDataSetIterator - Class in org.deeplearning4j.datasets.iterator.parallel
BaseParallelDataSetIterator(int) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
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
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
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
batch() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: Batch size
batch() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Batch size
batch() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Batch size
batch - Variable in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
batch - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: Batch size
batch() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: Batch size
batch() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
batch() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
batchedDS - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
BatchNormalization - Class in org.deeplearning4j.nn.conf.layers: Batch normalization configuration
BatchNormalization - Class in org.deeplearning4j.nn.layers.normalization: Batch normalization layer.
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.FrozenLayer
batchSize() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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
BenchmarkDataSetIterator - Class in org.deeplearning4j.datasets.iterator.impl: Dataset iterator for simulated inputs, or input derived from a DataSet example.
BenchmarkDataSetIterator(int[], int, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
BenchmarkDataSetIterator(DataSet, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
BenchmarkMultiDataSetIterator - Class in org.deeplearning4j.datasets.iterator.impl: MultiDataset iterator for simulated inputs, or input derived from a MultiDataSet example.
BenchmarkMultiDataSetIterator(int[][], int[], int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator
BenchmarkMultiDataSetIterator(MultiDataSet, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator
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(String) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution: Deprecated.
Use BernoulliReconstructionDistribution.BernoulliReconstructionDistribution(Activation)
BernoulliReconstructionDistribution(Activation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
BernoulliReconstructionDistribution(IActivation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
bernoullis(double, double, double) - Static method in class org.deeplearning4j.util.MathUtils: This will return the bernoulli trial for the given event.
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
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
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.CenterLossParamInitializer
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.GravesLSTMParamInitializer
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.LSTMParamInitializer
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
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
biasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
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
biasLearningRate - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
biasLearningRate - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
biasLearningRate(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Bias learning rate.
biasLearningRate(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Bias learning rate.
biasLearningRate - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
biasLearningRate(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Bias learning rate.
biasLearningRate - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
BinaryClassificationResult - Class in org.deeplearning4j.nn.simple.binary: Created by agibsonccc on 4/28/17.
BinaryClassificationResult() - Constructor for class org.deeplearning4j.nn.simple.binary.BinaryClassificationResult
binaryDecisionThreshold - Variable in class org.deeplearning4j.eval.Evaluation
binomial(RandomGenerator, int, double) - Static method in class org.deeplearning4j.util.MathUtils: Generates a binomial distributed number using the given rng
BinomialDistribution - Class in org.deeplearning4j.nn.conf.distribution: A binomial distribution.
BinomialDistribution(int, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.BinomialDistribution: Create a distribution
BinomialSamplingPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: Binomial sampling pre processor
BinomialSamplingPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.BinomialSamplingPreProcessor
bitmapMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
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
broadcastUpdates(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
broadcastUpdates(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.LocalHandler
broadcastUpdates(INDArray) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.MessageHandler: This method does broadcast of given update message across network
buffer - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
buffer - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
buffer - Static variable in class org.deeplearning4j.util.OneTimeLogger
bufferSizePerDevice - Variable in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator
build() - Method in class org.deeplearning4j.datasets.iterator.CombinedMultiDataSetPreProcessor.Builder
build() - Method in class org.deeplearning4j.datasets.iterator.CombinedPreProcessor.Builder
build() - Method in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator.Builder
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.Convolution1DLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer.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.GlobalPoolingLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Layer.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.FrozenLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.RBM.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.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.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.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.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.datasets.iterator.CombinedMultiDataSetPreProcessor.Builder
Builder() - Constructor for class org.deeplearning4j.datasets.iterator.CombinedPreProcessor.Builder
Builder(InequalityHandling) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator.Builder
Builder(List<DataSetIterator>, InequalityHandling) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator.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() - 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() - 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
Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.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() - Constructor for class org.deeplearning4j.nn.conf.layers.DenseLayer.Builder
Builder(double) - 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.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
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Layer.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.FrozenLayer.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(RBM.HiddenUnit, RBM.VisibleUnit) - Constructor for class org.deeplearning4j.nn.conf.layers.RBM.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.RBM.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(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(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() - 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, 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.transferlearning.FineTuneConfiguration.Builder
Builder(MultiLayerNetwork) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Multilayer Network to tweak for transfer learning
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
byteDesc(long) - Static method in class org.deeplearning4j.util.StringUtils: Return an abbreviated English-language desc of the byte length
byteToHexString(byte[], int, int) - Static method in class org.deeplearning4j.util.StringUtils: Given an array of bytes it will convert the bytes to a hex string representation of the bytes
byteToHexString(byte[]) - Static method in class org.deeplearning4j.util.StringUtils: Same as byteToHexString(bytes, 0, bytes.length).
ByteUtil - Class in org.deeplearning4j.util

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
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, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Do backprop (gradient calculation)
calcBackpropGradients(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Calculate gradients and errors.
calcGradient(Gradient, INDArray) - Method in interface org.deeplearning4j.nn.api.Layer: Deprecated.
As of 0.7.3 - Feb 2017. No longer used.
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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.ConvolutionLayer
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
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.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.GravesBidirectionalLSTM
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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.ConvolutionLayer
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
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.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.GravesBidirectionalLSTM
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
calcL2(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
calculateArea() - Method in class org.deeplearning4j.eval.curves.BaseCurve
calculateArea(double[], double[]) - Method in class org.deeplearning4j.eval.curves.BaseCurve
calculateAUC() - Method in class org.deeplearning4j.eval.curves.RocCurve: Calculate and return the area under ROC curve
calculateAUC() - Method in class org.deeplearning4j.eval.ROC: Calculate the AUROC - Area Under ROC Curve
Utilizes trapezoidal integration internally
calculateAUC(int) - Method in class org.deeplearning4j.eval.ROCBinary: Calculate the AUC - Area Under (ROC) Curve
Utilizes trapezoidal integration internally
calculateAUC(int) - Method in class org.deeplearning4j.eval.ROCMultiClass: Calculate the AUC - Area Under ROC Curve
Utilizes trapezoidal integration internally
calculateAUCPR() - Method in class org.deeplearning4j.eval.ROC: Calculate the area under the precision/recall curve - aka AUCPR
calculateAUCPR(int) - Method in class org.deeplearning4j.eval.ROCMultiClass: Calculate the AUPRC - Area Under Curve Precision Recall
Utilizes trapezoidal integration internally
calculateAUPRC() - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve
calculateAverageAuc() - Method in class org.deeplearning4j.eval.ROCBinary: Macro-average AUC for all outcomes
calculateAverageAUC() - Method in class org.deeplearning4j.eval.ROCMultiClass: Calculate the macro-average (one-vs-all) AUC for all classes
calculateScore(MultiLayerNetwork) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
calculateScore(ComputationGraph) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG
calculateScore(T) - Method in interface org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator: Calculate the score for the given MultiLayerNetwork
call(DataSet) - Method in interface org.deeplearning4j.datasets.iterator.callbacks.DataSetCallback
call(MultiDataSet) - Method in interface org.deeplearning4j.datasets.iterator.callbacks.DataSetCallback
call(File) - Method in class org.deeplearning4j.datasets.iterator.callbacks.DataSetDeserializer
call(DataSet) - Method in class org.deeplearning4j.datasets.iterator.callbacks.DefaultCallback
call(MultiDataSet) - Method in class org.deeplearning4j.datasets.iterator.callbacks.DefaultCallback
call(File) - Method in interface org.deeplearning4j.datasets.iterator.callbacks.FileCallback
call(DataSet) - Method in class org.deeplearning4j.datasets.iterator.callbacks.InterleavedDataSetCallback
call(MultiDataSet) - Method in class org.deeplearning4j.datasets.iterator.callbacks.InterleavedDataSetCallback
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
callback - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
callback - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
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 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 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(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[]) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil: Check backprop gradients for a ComputationGraph
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...
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
checkTerminalConditions(INDArray, double, double, int) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: Check termination conditions setup a search state
checkTerminalConditions(INDArray, double, double, int) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
children() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
choleskyFromMatrix(RealMatrix) - Method in class org.deeplearning4j.util.MathUtils: This will return the cholesky decomposition of the given matrix
clamp(int, int, int) - Static method in class org.deeplearning4j.util.MathUtils: Clamps the value to a discrete value
classCount(Integer) - Method in class org.deeplearning4j.eval.Evaluation: Returns the number of times the given label has actually occurred
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.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 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.BaseOutputLayer
clear() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
clear() - Method in class org.deeplearning4j.nn.layers.LossLayer
clear() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
clear() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Clear the inputs.
clear() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
clear() - Method in class org.deeplearning4j.util.DiskBasedQueue
clear() - Method in class org.deeplearning4j.util.MultiDimensionalMap: Removes all of the mappings from this map (optional operation).
clear() - Method in class org.deeplearning4j.util.MultiDimensionalSet: Removes all of the elements from this applyTransformToDestination (optional operation).
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
clearVariables() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
clearVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
clearVertex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: This method clears inpjut for this vertex
clone() - Method in class org.deeplearning4j.eval.ROC.CountsForThreshold
clone() - Method in interface org.deeplearning4j.nn.api.Layer: Clone the layer
clone() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
clone() - Method in class org.deeplearning4j.nn.conf.distribution.Distribution
clone() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
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.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.BatchNormalization
clone() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
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.FrozenLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
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.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.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.preprocessor.UnitVarianceProcessor
clone() - Method in class org.deeplearning4j.nn.conf.stepfunctions.StepFunction
clone() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
clone() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
clone() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
clone() - Method in class org.deeplearning4j.nn.layers.BaseLayer
clone() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
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.FrozenLayer
clone() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
clone() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
clone() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
clone() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
clone() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Clones the multilayernetwork
clone() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
clusterColumn(int) - Method in class org.deeplearning4j.util.StringGrid
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 depth or featureMaps referenced in different literature
CnnToFeedForwardPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
CnnToFeedForwardPreProcessor(int, int) - 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(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
collapseDimensions(boolean) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder: Whether to collapse dimensions when pooling or not.
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.
combination(double, double) - Static method in class org.deeplearning4j.util.MathUtils: This returns the combination of n choose r
combineColumns(int, Integer[]) - Method in class org.deeplearning4j.util.StringGrid: Combine the column based on a template and a number of template variable columns.
combineColumns(int, int[]) - Method in class org.deeplearning4j.util.StringGrid: Combine the column based on a template and a number of template variable columns.
CombinedMultiDataSetPreProcessor - Class in org.deeplearning4j.datasets.iterator: Combines various multidataset preprocessors Applied in the order they are specified to in the builder
CombinedMultiDataSetPreProcessor.Builder - Class in org.deeplearning4j.datasets.iterator
CombinedPreProcessor - Class in org.deeplearning4j.datasets.iterator: This is special preProcessor, that allows to combine multiple prerpocessors, and apply them to data sequentially.
CombinedPreProcessor.Builder - Class in org.deeplearning4j.datasets.iterator
COMMA - Static variable in class org.deeplearning4j.util.StringUtils
compare(Map<String, Integer>, Map<String, Integer>) - Method in class org.deeplearning4j.util.StringCluster.SizeComparator
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: A group of listeners
ComposableIterationListener(IterationListener...) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener
ComposableIterationListener(Collection<IterationListener>) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener
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) - 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
computeGradientAndScore() - Method in interface org.deeplearning4j.nn.api.Model: Update the score
computeGradientAndScore() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.BaseLayer
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.LossLayer
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
computeGradientAndScore() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
computeLossFunctionScoreArray(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
computeScore(double, double, boolean) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer: Compute score after labels and input have been set.
computeScore(double, double, boolean) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Compute score after labels and input have been set.
computeScore(double, double, boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer: Compute score after labels and input have been set.
computeScore(double, double, boolean) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer: Compute score after labels and input have been set.
computeScoreForExamples(double, double) - 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) - 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) - 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) - 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) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer: Compute the score for each example individually, after labels and input have been set.
computeZ(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: * Compute input linear transformation (z) of the output layer
computeZ(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Compute activations from input to output of the output layer
concurrentSkipListSet() - Static method in class org.deeplearning4j.util.MultiDimensionalSet
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.FrozenLayer
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.multilayer.MultiLayerNetwork
conf - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
configuration - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
configure(NeuralNetConfiguration) - Method in class org.deeplearning4j.optimize.Solver.Builder
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() - Constructor for class org.deeplearning4j.eval.curves.PrecisionRecallCurve.Confusion
confusion - Variable in class org.deeplearning4j.eval.Evaluation
ConfusionMatrix<T extends Comparable<? super T>> - Class in org.deeplearning4j.eval
ConfusionMatrix(List<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix: Creates an empty confusion Matrix
ConfusionMatrix() - Constructor for class org.deeplearning4j.eval.ConfusionMatrix
ConfusionMatrix(ConfusionMatrix<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix: Creates a new ConfusionMatrix initialized with the contents of another ConfusionMatrix.
ConfusionMatrixDeserializer - Class in org.deeplearning4j.eval.serde: A JSON deserializer for ConfusionMatrix<Integer> instances, used in Evaluation
ConfusionMatrixDeserializer() - Constructor for class org.deeplearning4j.eval.serde.ConfusionMatrixDeserializer
confusionMatrixMetaData - Variable in class org.deeplearning4j.eval.Evaluation
ConfusionMatrixSerializer - Class in org.deeplearning4j.eval.serde: A JSON serializer for ConfusionMatrix<Integer> instances, used in Evaluation
ConfusionMatrixSerializer() - Constructor for class org.deeplearning4j.eval.serde.ConfusionMatrixSerializer
confusionToString() - Method in class org.deeplearning4j.eval.Evaluation: Get a String representation of the confusion matrix
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<IterationListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.ConjugateGradient
ConjugateGradient(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Collection<TerminationCondition>, 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
consumeOnce(DataSet, boolean) - Method in class org.deeplearning4j.util.TestDataSetConsumer: This method consumes single DataSet, and spends delay time simulating execution of this dataset
consumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
consumeWhileHasNext(boolean) - Method in class org.deeplearning4j.util.TestDataSetConsumer: This method cycles through iterator, whie iterator.hasNext() returns true.
contains(Object) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
contains(Object) - Method in class org.deeplearning4j.util.DiskBasedQueue
contains(K, T) - Method in class org.deeplearning4j.util.MultiDimensionalMap
contains(Object) - Method in class org.deeplearning4j.util.MultiDimensionalSet: Returns true if this applyTransformToDestination contains the specified element.
contains(K, V) - Method in class org.deeplearning4j.util.MultiDimensionalSet
containsAll(Collection<?>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
containsAll(Collection<?>) - Method in class org.deeplearning4j.util.DiskBasedQueue
containsAll(Collection<?>) - Method in class org.deeplearning4j.util.MultiDimensionalSet: Returns true if this applyTransformToDestination contains all of the elements of the specified collection.
containsKey(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap: Returns true if this map contains a mapping for the specified key.
containsValue(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap: Returns true if this map maps one or more keys to the specified value.
contrastiveDivergence() - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Deprecated.
No longer used; use fit methods in MultiLayerNetwork
ConvexOptimizer - Interface in org.deeplearning4j.optimize.api: Convex optimizer.
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
convolutional(int, int, int) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: Input type for convolutional (CNN) data, that is 4d with shape [miniBatchSize, depth, height, width].
convolutionalFlat(int, int, int) - 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.
ConvolutionHelper - Interface in org.deeplearning4j.nn.layers.convolution: Helper for the convolution layer.
ConvolutionLayer - Class in org.deeplearning4j.nn.conf.layers
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 depth 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 - 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(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Set the convolution mode for the Convolution layer.
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
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
convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
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
coordSplit(double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the coordinate split in a list of coordinates such that the values for ret[0] are the x values and ret[1] are the y values
coordSplit(List<Double>) - Static method in class org.deeplearning4j.util.MathUtils: This returns the coordinate split in a list of coordinates such that the values for ret[0] are the x values and ret[1] are the y values
correlation(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils: Returns the correlation coefficient of two double vectors.
correlationR2(int) - Method in class org.deeplearning4j.eval.RegressionEvaluation
corruptionLevel(double) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder
corruptionLevel - Variable in class org.deeplearning4j.nn.conf.layers.AutoEncoder
costArray - Variable in class org.deeplearning4j.eval.Evaluation
counter - Variable in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
CountsForThreshold(double) - Constructor for class org.deeplearning4j.eval.ROC.CountsForThreshold
createAppendingOutputStream(File) - Static method in class org.deeplearning4j.util.FileOperations
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(int, double, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createCenterLossMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer
createDistribution(Distribution) - Static method in class org.deeplearning4j.nn.conf.distribution.Distributions
createGradient(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
createInputMatrix(int) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher: Creates a feature vector
createOutputMatrix(int) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
createOutputVector(int) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher: Creates an output label matrix
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.params.ConvolutionParamInitializer
createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createWeightMatrix(int, int, WeightInit, Distribution, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
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(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: 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.
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
curr - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
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
cursor - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
cursor() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
cursor() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: The current cursor if applicable
cursor() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: The current cursor if applicable
cursor() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: The current cursor if applicable
cursor() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
cursor() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher: Deprecated.

Direct access to a number represenative of iterating through a dataset
cursor() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
cursor() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
cursor() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
cursor() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
cursor() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
cursor() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
cursor() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
cursor() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: The current cursor if applicable
cursor() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
cursor() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: The current cursor if applicable
cursor() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
cursor() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
CUSTOM_FUNCTIONALITY - Static variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: System property for custom layers, preprocessors, graph vertices etc.

D

dampingFactor - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
DataSetCallback - Interface in org.deeplearning4j.datasets.iterator.callbacks
DataSetDeserializer - Class in org.deeplearning4j.datasets.iterator.callbacks: This callback does DataSet deserialization of a given file.
DataSetDeserializer() - Constructor for class org.deeplearning4j.datasets.iterator.callbacks.DataSetDeserializer
DataSetFetcher - Interface in org.deeplearning4j.datasets.iterator: Deprecated.
Retained for legacy purposes, will be removed in a future release
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)
DataSetLossCalculatorCG - Class in org.deeplearning4j.earlystopping.scorecalc: Given a DataSetIterator: calculate the total loss for the model on that data set.
DataSetLossCalculatorCG(DataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG: 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: Calculate the score (loss function value) on a given data set (usually a test set)
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) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
decode(INDArray) - Method in class org.deeplearning4j.util.Viterbi: Decodes the given labels, assuming its a binary label matrix
decode(INDArray, boolean) - Method in class org.deeplearning4j.util.Viterbi: Decodes a series of labels
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.
dedupeByCluster(int) - Method in class org.deeplearning4j.util.StringGrid: Deduplicate based on the column clustering signature
dedupeByClusterAll() - Method in class org.deeplearning4j.util.StringGrid
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
DeepLearningIOUtil - Class in org.deeplearning4j.util
DEFAULT_EDGE_VALUE - Static variable in class org.deeplearning4j.eval.Evaluation
DEFAULT_EDGE_VALUE - Static variable in class org.deeplearning4j.eval.EvaluationBinary
DEFAULT_EPS - Static variable in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
DEFAULT_FLATTENING_ORDER - Static variable in class org.deeplearning4j.nn.gradient.DefaultGradient
DEFAULT_FORMAT_PREC - Static variable in class org.deeplearning4j.eval.curves.BaseCurve
DEFAULT_HISTOGRAM_NUM_BINS - Static variable in class org.deeplearning4j.eval.EvaluationCalibration
DEFAULT_PATTERN - Static variable in class org.deeplearning4j.datasets.iterator.parallel.FileSplitParallelDataSetIterator
DEFAULT_PRECISION - Static variable in class org.deeplearning4j.eval.EvaluationBinary
DEFAULT_PRECISION - Static variable in class org.deeplearning4j.eval.RegressionEvaluation
DEFAULT_RELIABILITY_DIAG_NUM_BINS - Static variable in class org.deeplearning4j.eval.EvaluationCalibration
DEFAULT_STATS_PRECISION - Static variable in class org.deeplearning4j.eval.ROCBinary
DEFAULT_STATS_PRECISION - Static variable in class org.deeplearning4j.eval.ROCMultiClass
DEFAULT_WEIGHT_INIT_ORDER - Static variable in class org.deeplearning4j.nn.weights.WeightInitUtil: Default order for the arrays created by WeightInitUtil.
DefaultCallback - Class in org.deeplearning4j.datasets.iterator.callbacks: This callback ensures that memory on device is up-to-date with host memory.
DefaultCallback() - Constructor for class org.deeplearning4j.datasets.iterator.callbacks.DefaultCallback
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
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
DenseLayer - Class in org.deeplearning4j.nn.conf.layers: Dense layer: fully connected feed forward layer trainable by backprop.
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 depth 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
derivativeActivation(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer: Deprecated.
As of 0.7.3 - Feb 2017. No longer used.
derivativeActivation(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer: Deprecated.
derivativeActivation(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
derivativeActivation(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
derivativeActivation(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.eval.serde.ConfusionMatrixDeserializer
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.MultiLayerConfigurationDeserializer
determinationCoefficient(double[], double[], int) - Static method in class org.deeplearning4j.util.MathUtils: This returns the determination coefficient of two vectors given a length
deviceId - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
deviceId - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
difference(Collection<? extends T>, Collection<? extends T>) - Static method in class org.deeplearning4j.util.SetUtils: Return is s1 \ s2
dimension - Variable in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
discretize(double, double, double, int) - Static method in class org.deeplearning4j.util.MathUtils: Discretize the given value
DiskBasedQueue<E> - Class in org.deeplearning4j.util: Naive disk based queue for storing items on disk.
DiskBasedQueue() - Constructor for class org.deeplearning4j.util.DiskBasedQueue
DiskBasedQueue(String) - Constructor for class org.deeplearning4j.util.DiskBasedQueue
DiskBasedQueue(File) - Constructor for class org.deeplearning4j.util.DiskBasedQueue
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(Distribution) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Distribution to sample initial weights from.
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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
distanceFinderZValue(double[]) - Static method in class org.deeplearning4j.util.MathUtils: This will translate a vector in to an equivalent integer
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 method for instantiating an nd4j distribution from a configuration object.
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
Dl4jReflection - Class in org.deeplearning4j.util
DL4JSubTypesScanner - Class in org.deeplearning4j.util.reflections: Custom Reflections library scanner for finding DL4J subtypes (custom layers, graph vertices, etc)
DL4JSubTypesScanner(List<Class<?>>, List<Class<?>>) - Constructor for class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
doBackward(boolean) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Do backward pass
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
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
doForward(boolean) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Do forward pass using the stored inputs
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
doTruncatedBPTT(INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the network using truncated BPTT
doTruncatedBPTT(INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
DoublesDataSetIterator - Class in org.deeplearning4j.datasets.iterator
DoublesDataSetIterator(Iterable<Pair<double[], double[]>>, int) - Constructor for class org.deeplearning4j.datasets.iterator.DoublesDataSetIterator
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
dropOut(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Dropout.
dropOut - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
dropOut(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder: Dropout.
dropOut - Variable in class org.deeplearning4j.nn.conf.layers.Layer
dropOut - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
dropOut(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Dropout probability.
dropOut - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
Dropout - Class in org.deeplearning4j.util
dropoutApplied - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
DropoutLayer - Class in org.deeplearning4j.nn.conf.layers
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
dropoutMask - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
ds - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
ds - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
dsIterator - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
DummyPreProcessor - Class in org.deeplearning4j.datasets.iterator: This is special dummy preProcessor, that does nothing.
DummyPreProcessor() - Constructor for class org.deeplearning4j.datasets.iterator.DummyPreProcessor
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
EarlyTerminationDataSetIterator - Class in org.deeplearning4j.datasets.iterator: Builds an iterator that terminates once the number of minibatches returned with .next() is equal to a specified number Note that a call to .next(num) is counted as a call to return a minibatch regardless of the value of num This essentially restricts the data to this specified number of minibatches.
EarlyTerminationDataSetIterator(DataSetIterator, int) - Constructor for class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator: Constructor takes the iterator to wrap and the number of minibatches after which the call to hasNext() will return false
EarlyTerminationMultiDataSetIterator - Class in org.deeplearning4j.datasets.iterator: Builds an iterator that terminates once the number of minibatches returned with .next() is equal to a specified number Note that a call to .next(num) is counted as a call to return a minibatch regardless of the value of num This essentially restricts the data to this specified number of minibatches.
EarlyTerminationMultiDataSetIterator(MultiDataSetIterator, int) - Constructor for class org.deeplearning4j.datasets.iterator.EarlyTerminationMultiDataSetIterator: Constructor takes the iterator to wrap and the number of minibatches after which the call to hasNext() will return false
element() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
element() - Method in class org.deeplearning4j.util.DiskBasedQueue
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 or multiplication.
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.
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
EmptyParamInitializer - Class in org.deeplearning4j.nn.params
EmptyParamInitializer() - Constructor for class org.deeplearning4j.nn.params.EmptyParamInitializer
emptyStringArray - Static variable in class org.deeplearning4j.util.StringUtils
encode(INDArray, boolean) - 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(double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
EncodedGradientsAccumulator(int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
EncodedGradientsAccumulator(int, double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
EncodedGradientsAccumulator(int, MessageHandler, long, int, Double) - 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(INDArray) - Method 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() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler: This method builds new EncodingHandler instance with initial threshold of 1e-3
EncodingHandler(double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler: This method builds new EncodingHandler instance
EncodingHandler(double, Double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler: This method builds new EncodingHandler instance
EncodingHandler(double, double, double, double, int, int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler: This method builds new EncodingHandler instance
EncodingHandler(double, double, double, double, int, int, Double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler: This method builds new EncodingHandler instance
encodingThreshold(double) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder: This method allows to set encoding threshold for this accumulator instance Default value: 1e-3
enforceSingleDevice(boolean) - Method in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator.Builder
enforceSingleDevice - Variable in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator
entropy(double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the entropy (information gain, or uncertainty of a random variable).
Entry(K, T, V) - Constructor for class org.deeplearning4j.util.MultiDimensionalMap.Entry
entrySet() - Method in class org.deeplearning4j.util.MultiDimensionalMap: Returns a Set view of the mappings contained in this map.
EnumUtil - Class in org.deeplearning4j.util: Created by agibsonccc on 9/3/14.
epochs - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
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.layers.BaseLayer.Builder: Deprecated.
epsilon(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
Use use .updater(Adam.builder().epsilon(epsilon).build()) or similar instead
epsilon - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer: Deprecated.
epsilon(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Epsilon value for updaters: Adagrad and Adadelta.
epsilon - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
epsilon(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
Use use .updater(Adam.builder().epsilon(epsilon).build()) or similar instead
epsilon - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
epsilon - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
epsilon() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
epsilon - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration: Deprecated.
EpsTermination - Class in org.deeplearning4j.optimize.terminations: Epsilon termination (absolute change based on tolerance)
EpsTermination(double, double) - Constructor for class org.deeplearning4j.optimize.terminations.EpsTermination
EpsTermination() - Constructor for class org.deeplearning4j.optimize.terminations.EpsTermination
equals(Object) - Method in class org.deeplearning4j.eval.BaseEvaluation
equals(Object) - Method in class org.deeplearning4j.eval.ConfusionMatrix
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.distribution.UniformDistribution
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.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.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
equals(Object) - Method in class org.deeplearning4j.util.Index
equals(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap
error(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer: Deprecated.
As of 0.7.3 - Feb 2017. No longer used.
error(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
error(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
error(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
error() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Returns the prediction error for this node
error(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
error(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
error(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
error(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
error(Logger, String, Object...) - Static method in class org.deeplearning4j.util.OneTimeLogger
errorFor(double, double) - Static method in class org.deeplearning4j.util.MathUtils
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
ESCAPE_CHAR - Static variable in class org.deeplearning4j.util.StringUtils
escapeHTML(String) - Static method in class org.deeplearning4j.util.StringUtils: Escapes HTML Special characters present in the string.
escapeString(String) - Static method in class org.deeplearning4j.util.StringUtils: Escape commas in the string using the default escape char
escapeString(String, char, char) - Static method in class org.deeplearning4j.util.StringUtils: Escape charToEscape in the string with the escape char escapeChar
escapeString(String, char, char[]) - Static method in class org.deeplearning4j.util.StringUtils
esConfig - Variable in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
euclideanDistance(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the distance of two vectors sum(i=1,n) (q_i - p_i)^2
euclideanDistance(float[], float[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the distance of two vectors sum(i=1,n) (q_i - p_i)^2
eval(INDArray, INDArray, List<? extends Serializable>) - Method in class org.deeplearning4j.eval.BaseEvaluation
eval(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.eval.BaseEvaluation
eval(INDArray, INDArray, ComputationGraph) - Method in class org.deeplearning4j.eval.Evaluation: Evaluate the output using the given true labels, the input to the multi layer network and the multi layer network to use for evaluation
eval(INDArray, INDArray, MultiLayerNetwork) - Method in class org.deeplearning4j.eval.Evaluation: Evaluate the output using the given true labels, the input to the multi layer network and the multi layer network to use for evaluation
eval(INDArray, INDArray) - Method in class org.deeplearning4j.eval.Evaluation: Collects statistics on the real outcomes vs the guesses.
eval(INDArray, INDArray, List<? extends Serializable>) - Method in class org.deeplearning4j.eval.Evaluation: Evaluate the network, with optional metadata
eval(int, int) - Method in class org.deeplearning4j.eval.Evaluation: Evaluate a single prediction (one prediction at a time)
eval(INDArray, INDArray) - Method in class org.deeplearning4j.eval.EvaluationBinary
eval(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.eval.EvaluationBinary
eval(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.eval.EvaluationCalibration
eval(INDArray, INDArray) - Method in class org.deeplearning4j.eval.EvaluationCalibration
eval(INDArray, INDArray) - Method in interface org.deeplearning4j.eval.IEvaluation
eval(INDArray, INDArray, List<? extends Serializable>) - Method in interface org.deeplearning4j.eval.IEvaluation
eval(INDArray, INDArray, INDArray) - Method in interface org.deeplearning4j.eval.IEvaluation
eval(INDArray, INDArray) - Method in class org.deeplearning4j.eval.RegressionEvaluation
eval(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.eval.RegressionEvaluation
eval(INDArray, INDArray) - Method in class org.deeplearning4j.eval.ROC: Evaluate (collect statistics for) the given minibatch of data.
eval(INDArray, INDArray) - Method in class org.deeplearning4j.eval.ROCBinary
eval(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.eval.ROCBinary
eval(INDArray, INDArray) - Method in class org.deeplearning4j.eval.ROCMultiClass: Evaluate (collect statistics for) the given minibatch of data.
evalAtIndex(IEvaluation, INDArray[], INDArray[], int) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener
evalTimeSeries(INDArray, INDArray) - Method in class org.deeplearning4j.eval.BaseEvaluation
evalTimeSeries(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.eval.BaseEvaluation
evalTimeSeries(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.eval.EvaluationBinary
evalTimeSeries(INDArray, INDArray) - Method in interface org.deeplearning4j.eval.IEvaluation
evalTimeSeries(INDArray, INDArray, INDArray) - Method in interface org.deeplearning4j.eval.IEvaluation
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) - 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, 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, 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, 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, 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, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Evaluate the network on the specified data, using the ROCMultiClass class
Evaluation - Class in org.deeplearning4j.eval: Evaluation metrics:
- precision, recall, f1, fBeta, accuracy, Matthews correlation coefficient, gMeasure
- Top N accuracy (if using constructor Evaluation.Evaluation(List, int))
- Custom binary evaluation decision threshold (use constructor Evaluation.Evaluation(double) (default if not set is argmax / 0.5)
- Custom cost array, using Evaluation.Evaluation(INDArray) or Evaluation.Evaluation(List, INDArray) for multi-class

Note that setting a custom binary decision threshold is only possible for the binary case (1 or 2 outputs) and cannot be used if the number of classes exceeds 2.
Evaluation() - Constructor for class org.deeplearning4j.eval.Evaluation
Evaluation(int) - Constructor for class org.deeplearning4j.eval.Evaluation: The number of classes to account for in the evaluation
Evaluation(List<String>) - Constructor for class org.deeplearning4j.eval.Evaluation: The labels to include with the evaluation.
Evaluation(Map<Integer, String>) - Constructor for class org.deeplearning4j.eval.Evaluation: Use a map to generate labels Pass in a label index with the actual label you want to use for output
Evaluation(List<String>, int) - Constructor for class org.deeplearning4j.eval.Evaluation: Constructor to use for top N accuracy
Evaluation(double) - Constructor for class org.deeplearning4j.eval.Evaluation: Create an evaluation instance with a custom binary decision threshold.
Evaluation(INDArray) - Constructor for class org.deeplearning4j.eval.Evaluation: Created evaluation instance with the specified cost array.
Evaluation(List<String>, INDArray) - Constructor for class org.deeplearning4j.eval.Evaluation: Created evaluation instance with the specified cost array.
EvaluationAveraging - Enum in org.deeplearning4j.eval: The averaging approach for binary valuation measures when applied to multiclass classification problems.
EvaluationBinary - Class in org.deeplearning4j.eval: EvaluationBinary: used for evaluating networks with binary classification outputs.
EvaluationBinary(INDArray) - Constructor for class org.deeplearning4j.eval.EvaluationBinary: Create an EvaulationBinary instance with an optional decision threshold array.
EvaluationBinary(int, Integer) - Constructor for class org.deeplearning4j.eval.EvaluationBinary: This constructor allows for ROC to be calculated in addition to the standard evaluation metrics, when the rocBinarySteps arg is non-null.
EvaluationCalibration - Class in org.deeplearning4j.eval: EvaluationCalibration is an evaluation class designed to analyze the calibration of a classifier.
It provides a number of tools for this purpose: - Counts of the number of labels and predictions for each class
- Reliability diagram (or reliability curve)
- Residual plot (histogram)
- Histograms of probabilities, including probabilities for each class separately

References:
- Reliability diagram: see for example Niculescu-Mizil and Caruana 2005, Predicting Good Probabilities With Supervised Learning
- Residual plot: see Wallace and Dahabreh 2012, Class Probability Estimates are Unreliable for Imbalanced Data (and How to Fix Them)
EvaluationCalibration() - Constructor for class org.deeplearning4j.eval.EvaluationCalibration: Create an EvaluationCalibration instance with the default number of bins
EvaluationCalibration(int, int) - Constructor for class org.deeplearning4j.eval.EvaluationCalibration: Create an EvaluationCalibration instance with the specified number of bins
EvaluationCalibration(int, int, boolean) - Constructor for class org.deeplearning4j.eval.EvaluationCalibration: Create an EvaluationCalibration instance with the specified number of bins
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: Utility methods for performing evaluation
EvaluationUtils() - Constructor for class org.deeplearning4j.eval.EvaluationUtils
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* iteration
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* iteration
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
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
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
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
ExistingDataSetIterator - Class in org.deeplearning4j.datasets.iterator: This wrapper provides DataSetIterator interface to existing java Iterable and Iterator
ExistingDataSetIterator(Iterator<DataSet>) - Constructor for class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
ExistingDataSetIterator(Iterator<DataSet>, List<String>) - Constructor for class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
ExistingDataSetIterator(Iterable<DataSet>) - Constructor for class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
ExistingDataSetIterator(Iterable<DataSet>, List<String>) - Constructor for class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
ExistingDataSetIterator(Iterable<DataSet>, int, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
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
externalCall() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
externalCall() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
externalSource - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
extractNonMaskedTimeSteps(INDArray, INDArray, INDArray) - Static method in class org.deeplearning4j.eval.EvaluationUtils

F

f1(int) - Method in class org.deeplearning4j.eval.Evaluation: Calculate f1 score for a given class
f1() - Method in class org.deeplearning4j.eval.Evaluation: Calculate the (macro) average F1 score across all classes TP: true positive FP: False Positive FN: False Negative F1 score: 2 * TP / (2TP + FP + FN)
f1(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the average F1 score across all classes, using macro or micro averaging
f1(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Get the F1 score for the specified output
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.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(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.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
factorial(double) - Static method in class org.deeplearning4j.util.MathUtils: This will return the factorial of the given number n.
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
falseAlarmRate() - Method in class org.deeplearning4j.eval.Evaluation: False Alarm Rate (FAR) reflects rate of misclassified to classified records http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1058&context=isw
falseNegativeRate(Integer) - Method in class org.deeplearning4j.eval.Evaluation: Returns the false negative rate for a given label
falseNegativeRate(Integer, double) - Method in class org.deeplearning4j.eval.Evaluation: Returns the false negative rate for a given label
falseNegativeRate() - Method in class org.deeplearning4j.eval.Evaluation: False negative rate based on guesses so far Takes into account all known classes and outputs average fnr across all of them
falseNegativeRate(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the average false negative rate for all classes - can specify whether macro or micro averaging should be used
falseNegativeRate(Integer) - Method in class org.deeplearning4j.eval.EvaluationBinary: Returns the false negative rate for a given label
falseNegativeRate(Integer, double) - Method in class org.deeplearning4j.eval.EvaluationBinary: Returns the false negative rate for a given label
falseNegativeRate(long, long, double) - Static method in class org.deeplearning4j.eval.EvaluationUtils: Calculate the false negative rate from the false negative counts and true positive count
falseNegatives - Variable in class org.deeplearning4j.eval.Evaluation
falseNegatives() - Method in class org.deeplearning4j.eval.Evaluation: False negatives: correctly rejected
falseNegatives(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Get the false negatives count for the specified output
falsePositiveRate(int) - Method in class org.deeplearning4j.eval.Evaluation: Returns the false positive rate for a given label
falsePositiveRate(int, double) - Method in class org.deeplearning4j.eval.Evaluation: Returns the false positive rate for a given label
falsePositiveRate() - Method in class org.deeplearning4j.eval.Evaluation: False positive rate based on guesses so far Takes into account all known classes and outputs average fpr across all of them
falsePositiveRate(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the average false positive rate across all classes.
falsePositiveRate(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Returns the false positive rate for a given label
falsePositiveRate(int, double) - Method in class org.deeplearning4j.eval.EvaluationBinary: Returns the false positive rate for a given label
falsePositiveRate(long, long, double) - Static method in class org.deeplearning4j.eval.EvaluationUtils: Calculate the false positive rate from the false positive count and true negative count
falsePositives - Variable in class org.deeplearning4j.eval.Evaluation
falsePositives() - Method in class org.deeplearning4j.eval.Evaluation: False positive: wrong guess
falsePositives(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Get the false positives count for the specified output
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, int) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the f_beta for a given class, where f_beta is defined as:
(1+beta^2) * (precision * recall) / (beta^2 * precision + recall).
F1 is a special case of f_beta, with beta=1.0
fBeta(double, int, double) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the f_beta for a given class, where f_beta is defined as:
(1+beta^2) * (precision * recall) / (beta^2 * precision + recall).
F1 is a special case of f_beta, with beta=1.0
fBeta(double, EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the average F_beta score across all classes, using macro or micro averaging
fBeta(double, int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Calculate the F-beta value for the given output
fBeta(double, long, long, long) - Static method in class org.deeplearning4j.eval.EvaluationUtils: Calculate the F beta value from counts
fBeta(double, double, double) - Static method in class org.deeplearning4j.eval.EvaluationUtils: Calculate the F-beta value from precision and recall
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(int) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: InputType for feed forward network data
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() - 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) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
feedForward(boolean, boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
feedForward(boolean, boolean, boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: PLEASE NEVER USE THIS METHOD IF YOU"RE NOT SURE WHAT YOU'll GET
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() - 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.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.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.FrozenLayer
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.GravesBidirectionalLSTM
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
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.RnnOutputLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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.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
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 depth or featureMaps referenced in different literature
FeedForwardToCnnPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor: Reshape to a channels x rows x columns tensor
FeedForwardToCnnPreProcessor(int, int) - 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
fetch(int) - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher: Deprecated.

Fetches the next dataset.
fetch(int) - Method in class org.deeplearning4j.datasets.iterator.impl.MovingWindowDataSetFetcher: Fetches the next dataset.
fetcher - Variable in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
FileCallback - Interface in org.deeplearning4j.datasets.iterator.callbacks
FileOperations - Class in org.deeplearning4j.util
FileSplitDataSetIterator - Class in org.deeplearning4j.datasets.iterator: Simple iterator working with list of files.
FileSplitDataSetIterator(List<File>, FileCallback) - Constructor for class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
FileSplitParallelDataSetIterator - Class in org.deeplearning4j.datasets.iterator.parallel
FileSplitParallelDataSetIterator(File, String, FileCallback) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.FileSplitParallelDataSetIterator
FileSplitParallelDataSetIterator(File, String, FileCallback, int) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.FileSplitParallelDataSetIterator
FileSplitParallelDataSetIterator(File, String, FileCallback, int, InequalityHandling) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.FileSplitParallelDataSetIterator
FileSplitParallelDataSetIterator(File, String, FileCallback, int, int, InequalityHandling) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.FileSplitParallelDataSetIterator
fillDown(String, int) - Method in class org.deeplearning4j.util.StringGrid
fillQueue() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
filterBySimilarity(double, int, int) - Method in class org.deeplearning4j.util.StringGrid
filterResultsBy(Predicate<String>) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
filterRowsByColumn(int, Collection<String>) - Method in class org.deeplearning4j.util.StringGrid
findNext(String, char, char, int, StringBuilder) - Static method in class org.deeplearning4j.util.StringUtils: Finds the first occurrence of the separator character ignoring the escaped separators starting from the index.
finetune() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Run SGD based on the given labels
FineTuneConfiguration - Class in org.deeplearning4j.nn.transferlearning: Created by Alex on 21/02/2017.
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
FingerPrintKeyer - Class in org.deeplearning4j.util: Copied from google refine: takes the key and gets rid of all punctuation, transforms to lower case and alphabetic sorts the words
FingerPrintKeyer() - Constructor for class org.deeplearning4j.util.FingerPrintKeyer
firstChild() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
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: All models have a fit method
fit(INDArray) - 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 interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
fit(DataSetIterator) - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
fit(DataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the ComputationGraph using a DataSet.
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the ComputationGraph using a DataSetIterator.
fit(MultiDataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the ComputationGraph using a MultiDataSet
fit(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the ComputationGraph using a MultiDataSetIterator
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) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
fit(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
fit() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
fit(INDArray) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
fit() - Method in class org.deeplearning4j.nn.layers.BaseLayer
fit(INDArray) - 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) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Fit the model to the given data
fit(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
fit() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
fit(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
fit(INDArray) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
fit(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer
fit() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
fit(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
fit(INDArray) - 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) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
fit(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
fit(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
fit() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Fit the unsupervised model
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) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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
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
FloatsDataSetIterator - Class in org.deeplearning4j.datasets.iterator: float[] wrapper for DataSetIterator impementation.
FloatsDataSetIterator(Iterable<Pair<float[], float[]>>, int) - Constructor for class org.deeplearning4j.datasets.iterator.FloatsDataSetIterator
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: Set forget gate bias initalizations.
format(double, int) - Method in class org.deeplearning4j.eval.curves.BaseCurve
formatPercent(double, int) - Static method in class org.deeplearning4j.util.StringUtils: Format a percentage for presentation to the user.
formatTime(long) - Static method in class org.deeplearning4j.util.StringUtils: Given the time in long milliseconds, returns a String in the format Xhrs, Ymins, Z sec.
formatTimeDiff(long, long) - Static method in class org.deeplearning4j.util.StringUtils: Given a finish and start time in long milliseconds, returns a String in the format Xhrs, Ymins, Z sec, for the time difference between two times.
frequency - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
from - Variable in class org.deeplearning4j.nn.conf.graph.UnstackVertex
fromFile(String, String) - Static method in class org.deeplearning4j.util.StringGrid
fromInput(InputStream, String) - Static method in class org.deeplearning4j.util.StringGrid
fromJson(String, Class<T>) - Static method in class org.deeplearning4j.eval.BaseEvaluation
fromJson(String, Class<T>) - Static method in class org.deeplearning4j.eval.curves.BaseCurve
fromJson(String, Class<T>) - Static method in class org.deeplearning4j.eval.curves.BaseHistogram
fromJson(String) - Static method in class org.deeplearning4j.eval.curves.Histogram
fromJson(String) - Static method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve
fromJson(String) - Static method in class org.deeplearning4j.eval.curves.RocCurve
fromJson(String) - Static method in class org.deeplearning4j.eval.Evaluation
fromJson(String) - Static method in class org.deeplearning4j.eval.EvaluationBinary
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
fromString(String, String) - Static method in class org.deeplearning4j.util.MathUtils: This will take a given string and separator and convert it to an equivalent double array.
fromYaml(String, Class<T>) - Static method in class org.deeplearning4j.eval.BaseEvaluation
fromYaml(String, Class<T>) - Static method in class org.deeplearning4j.eval.curves.BaseCurve
fromYaml(String, Class<T>) - Static method in class org.deeplearning4j.eval.curves.BaseHistogram
fromYaml(String) - Static method in class org.deeplearning4j.eval.curves.Histogram
fromYaml(String) - Static method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve
fromYaml(String) - Static method in class org.deeplearning4j.eval.curves.RocCurve
fromYaml(String) - Static method in class org.deeplearning4j.eval.Evaluation
fromYaml(String) - Static method in class org.deeplearning4j.eval.EvaluationBinary
fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: Create a neural net configuration from json
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: Created by Alex on 10/07/2017.
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
FrozenLayerParamInitializer - Class in org.deeplearning4j.nn.params: Parameter initializer for FrozenLayer instances.
FrozenLayerParamInitializer() - Constructor for class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
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
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
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: Activation function for the LSTM gates.
gateActivationFunction(Activation) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder: Activation function for the LSTM gates.
gateActivationFunction(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder: Activation function for the LSTM gates.
GaussianDistribution - Class in org.deeplearning4j.nn.conf.distribution: A normal distribution.
GaussianDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.GaussianDistribution: Create a gaussian distribution (equivalent to normal) with the given mean and std
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(String) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution: Deprecated.
Use GaussianReconstructionDistribution.GaussianReconstructionDistribution(Activation)
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, boolean, boolean, Double, Double, Double, Double, Double, Distribution) - Static method in class org.deeplearning4j.nn.conf.layers.LayerValidation
generalValidation(String, Layer, boolean, boolean, double, double, double, double, double, Distribution) - 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)
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) - 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)
generateUniform(int) - Static method in class org.deeplearning4j.util.MathUtils: This will generate a series of uniformally distributed numbers between l times
get(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.MultiBoolean: Gets current state for specified entry
get(int) - Method in class org.deeplearning4j.util.Index
get(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap: Returns the value to which the specified key is mapped, or null if this map contains no mapping for the key.
get(K, T) - Method in class org.deeplearning4j.util.MultiDimensionalMap
getActualTotal(T) - Method in class org.deeplearning4j.eval.ConfusionMatrix: Computes the total number of times the class actually appeared in the data.
getAllFields(Class<?>) - Static method in class org.deeplearning4j.util.Dl4jReflection
getAllWithSimilarity(double, int, int) - Method in class org.deeplearning4j.util.StringGrid
getAlpha() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
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
getBinCounts() - Method in class org.deeplearning4j.eval.curves.BaseHistogram
getBinLowerBounds() - Method in class org.deeplearning4j.eval.curves.BaseHistogram
getBinLowerBounds() - Method in class org.deeplearning4j.eval.curves.Histogram
getBinMidValues() - Method in class org.deeplearning4j.eval.curves.BaseHistogram
getBinMidValues() - Method in class org.deeplearning4j.eval.curves.Histogram
getBinUpperBounds() - Method in class org.deeplearning4j.eval.curves.BaseHistogram
getBinUpperBounds() - Method in class org.deeplearning4j.eval.curves.Histogram
getBytesPerElement(DataBuffer.Type) - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport
getChildren() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
getClass(T) - Static method in class org.deeplearning4j.util.ReflectionUtils: Return the correctly-typed Class of the given object.
getClasses() - Method in class org.deeplearning4j.eval.ConfusionMatrix: Gives the applyTransformToDestination of all classes in the confusion matrix.
getClassLabel(Integer) - Method in class org.deeplearning4j.eval.Evaluation
getClusters() - Method in class org.deeplearning4j.util.StringCluster
getColumn(int) - Method in class org.deeplearning4j.util.StringGrid
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
getConfiguration() - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
getConfiguration() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: This method returns configuration of this ComputationGraph
getConfusionMatrix() - Method in class org.deeplearning4j.eval.Evaluation: Returns the confusion matrix variable
getConfusionMatrixAtPoint(int) - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve: Get the binary confusion matrix for the given position.
getConfusionMatrixAtThreshold(double) - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve: Get the binary confusion matrix for the given threshold.
getCorruptedInput(INDArray, double) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork: Corrupts the given input by doing a binomial sampling given the corruption level
getCount(T, T) - Method in class org.deeplearning4j.eval.ConfusionMatrix: Gives the count of the number of times the "predicted" class was predicted for the "actual" class.
getCount() - Method in class org.deeplearning4j.util.TestDataSetConsumer
getCountActualNegative(int) - Method in class org.deeplearning4j.eval.ROCBinary: Get the actual negative count (accounting for any masking) for the specified output/column
getCountActualNegative(int) - Method in class org.deeplearning4j.eval.ROCMultiClass: Get the actual negative count (accounting for any masking) for the specified output/column
getCountActualPositive(int) - Method in class org.deeplearning4j.eval.ROCBinary: Get the actual positive count (accounting for any masking) for the specified output/column
getCountActualPositive(int) - Method in class org.deeplearning4j.eval.ROCMultiClass: Get the actual positive count (accounting for any masking) for the specified class
getCurrentProducerIndex() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
getDefaultConfiguration() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getDefaultStepFunctionForOptimizer(Class<? extends ConvexOptimizer>) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getEmptyConstructor(Class<?>) - Static method in class org.deeplearning4j.util.Dl4jReflection: Gets the empty constructor from a class
getEnd() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
getEps() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getEpsilon() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
getEpsilon() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the epsilon/error (i.e., dL/dOutput) array previously set for this GraphVertex
getFalsePositiveRate(int) - Method in class org.deeplearning4j.eval.curves.RocCurve
getFieldsAsProperties(Object, Class<?>[]) - Static method in class org.deeplearning4j.util.Dl4jReflection: Get fields as properties
getFirstKey() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
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
getFormattedTimeWithDiff(DateFormat, long, long) - Static method in class org.deeplearning4j.util.StringUtils: Formats time in ms and appends difference (finishTime - startTime) as returned by formatTimeDiff().
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
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.params.BatchNormalizationParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
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.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.PretrainParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
getGradientsViewArray() - Method in interface org.deeplearning4j.nn.api.Model
getGradientsViewArray() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
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.FrozenLayer
getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getGradientsViewArray() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getGradientUpdater() - Method in class org.deeplearning4j.nn.updater.UpdaterBlock
getHardwareUID() - Static method in class org.deeplearning4j.util.UIDProvider
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
getHostname() - Static method in class org.deeplearning4j.util.StringUtils: Return hostname without throwing exception.
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.FrozenLayer
getIndex() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
getIndex() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getIndex() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getInnerConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
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.FrozenLayer
getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getInputPreProcess(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
getInputs() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the previously set inputs for the ComputationGraph
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 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
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.ConvolutionParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.DefaultParamInitializer
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.GravesBidirectionalLSTMParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.LSTMParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.PretrainParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
getIterationCount(Model) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getIUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer: Get the updater for the given parameter.
getIUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
getIUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
getIUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer: Get the updater for the given parameter.
getIUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getIUpdaterWithDefaultConfig() - Method in enum org.deeplearning4j.nn.conf.Updater
getJVMUID() - Static method in class org.deeplearning4j.util.UIDProvider
getKey() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry: Returns the key corresponding to this entry.
getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM
getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
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
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.SubsamplingLayer
getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM
getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
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
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.SubsamplingLayer
getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
getLabelCountsEachClass() - Method in class org.deeplearning4j.eval.EvaluationCalibration
getLabelMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the previously set label/output mask arrays for the ComputationGraph
getLabelName(int) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
getLabels() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: Get dataset iterator record reader labels
getLabels() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Get dataset iterator record reader labels
getLabels() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Get dataset iterator record reader labels
getLabels() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
getLabels() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
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() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
getLabels2d() - Method in class org.deeplearning4j.nn.layers.LossLayer
getLabels2d() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
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 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.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(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLayer(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLayerNames() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLayers() - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
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
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer: Get the (initial) learning rate coefficient for the given parameter.
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
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.
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 IterationListeners for the ComputationGraph
getListeners() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getListeners() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
getListeners() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
getListeners() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getListeners() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLogMetaInstability() - Method in class org.deeplearning4j.util.Viterbi
getLogOfDiangnalTProb() - Method in class org.deeplearning4j.util.Viterbi
getLogPCorrect() - Method in class org.deeplearning4j.util.Viterbi
getLogPIncorrect() - Method in class org.deeplearning4j.util.Viterbi
getLogStates() - Method in class org.deeplearning4j.util.Viterbi
getLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer: Deprecated.
As of 0.6.0. Use #getLossFn() instead
getLower() - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
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.FrozenLayer
getMaskArray() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getMaskArray() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getMax() - Method in class org.deeplearning4j.util.SummaryStatistics
getMaxIterations() - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
getMean() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
getMean() - Method in class org.deeplearning4j.util.SummaryStatistics
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.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.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.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.GlobalPoolingLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM
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.FrozenLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.RBM
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
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
getMetaStability() - Method in class org.deeplearning4j.util.Viterbi
getMin() - Method in class org.deeplearning4j.util.SummaryStatistics
getModuleName() - Method in interface org.deeplearning4j.nn.conf.module.GraphBuilderModule: A module should return its name.
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
getnLayers() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the number of layers in the network
getNumberOfTrials() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
getNumClasses() - Method in class org.deeplearning4j.eval.ROCMultiClass
getNumColumns() - Method in class org.deeplearning4j.util.StringGrid
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 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 interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the number of outgoing connections from this GraphVertex.
getNumRowCounter() - Method in class org.deeplearning4j.eval.Evaluation
getOptimalBufferSize(int, 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.FrozenLayer
getOptimizer() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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
getOutputSize(INDArray, int[], int[], int[], ConvolutionMode) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Get the output size (height/width) for the given inpud 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.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.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.Convolution1DLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
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.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.RnnOutputLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.BinomialSamplingPreProcessor
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.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(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.UnitVarianceProcessor
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanAndUnitVariancePreProcessor
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanPrePreProcessor
getOutputTypeCnnLayers(InputType, int[], int[], int[], ConvolutionMode, int, int, 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 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.subsampling.SubsamplingLayer
getParam(String) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
getParam(String) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
getParam(String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getParam(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getParams() - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
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
getpCorrect() - Method in class org.deeplearning4j.util.Viterbi
getPnorm() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getPointAtPrecision(double) - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve: Get the point (index, threshold, precision, recall) at the given precision.
Specifically, return the points at the lowest threshold that has precision equal to or greater than the requested precision.
getPointAtRecall(double) - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve: Get the point (index, threshold, precision, recall) at the given recall.
Specifically, return the points at the highest threshold that has recall equal to or greater than the requested recall.
getPointAtThreshold(double) - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve: Get the point (index, threshold, precision, recall) at the given threshold.
Note that if the threshold is not found exactly, the next highest threshold exceeding the requested threshold is returned
getPossibleLabels() - Method in class org.deeplearning4j.util.Viterbi
getPrecision(int) - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve
getPrecisionRecallCurve() - Method in class org.deeplearning4j.eval.ROC: Get the precision recall curve as array.
getPrecisionRecallCurve(int) - Method in class org.deeplearning4j.eval.ROCBinary: Get the Precision-Recall curve for the specified output
getPrecisionRecallCurve(int) - Method in class org.deeplearning4j.eval.ROCMultiClass: Get the (one vs.
getPredictedTotal(T) - Method in class org.deeplearning4j.eval.ConfusionMatrix: Computes the total number of times the class was predicted by the classifier.
getPredictionByPredictedClass(int) - Method in class org.deeplearning4j.eval.Evaluation: Get a list of predictions, for all data with the specified predicted class, regardless of the actual data class.
getPredictionCountsEachClass() - Method in class org.deeplearning4j.eval.EvaluationCalibration
getPredictionErrors() - Method in class org.deeplearning4j.eval.Evaluation: Get a list of prediction errors, on a per-record basis
getPredictions(int, int) - Method in class org.deeplearning4j.eval.Evaluation: Get a list of predictions in the specified confusion matrix entry (i.e., for the given actua/predicted class pair)
getPredictionsByActualClass(int) - Method in class org.deeplearning4j.eval.Evaluation: Get a list of predictions, for all data with the specified actual class, regardless of the predicted class.
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Returns preprocessors, if defined
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Returns preprocessors, if defined
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldMultiDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationMultiDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
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.BatchNormalization
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer
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.LocalResponseNormalization
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
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
getProbabilityHistogram(int) - Method in class org.deeplearning4j.eval.EvaluationCalibration: Return a probability histogram of the specified label class index.
getProbabilityHistogramAllClasses() - Method in class org.deeplearning4j.eval.EvaluationCalibration: Return a probability histogram for all predictions/classes.
getProbabilityOfSuccess() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
getProbAndLabelUsed() - Method in class org.deeplearning4j.eval.ROC
getRecall(int) - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve
getRecordMetaData(Class<T>) - Method in class org.deeplearning4j.eval.meta.Prediction: Convenience method for getting the record meta data as a particular class (as an alternative to casting it manually).
getReliabilityDiagram(int) - Method in class org.deeplearning4j.eval.EvaluationCalibration: Get the reliability diagram for the specified class
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
getResidualPlot(int) - Method in class org.deeplearning4j.eval.EvaluationCalibration: Get the residual plot, only for examples of the specified class..
getResidualPlotAllClasses() - Method in class org.deeplearning4j.eval.EvaluationCalibration: Get the residual plot for all classes combined.
getROCBinary() - Method in class org.deeplearning4j.eval.EvaluationBinary: Returns the ROCBinary instance, if present
getRocCurve() - Method in class org.deeplearning4j.eval.ROC: Get the ROC curve, as a set of (threshold, falsePositive, truePositive) points
getRocCurve(int) - Method in class org.deeplearning4j.eval.ROCBinary: Get the ROC curve for the specified output
getRocCurve(int) - Method in class org.deeplearning4j.eval.ROCMultiClass: Get the (one vs.
getRow(int) - Method in class org.deeplearning4j.util.StringGrid
getRowsWithColumnValues(Collection<String>, int) - Method in class org.deeplearning4j.util.StringGrid
getRowsWithDuplicateValuesInColumn(int) - Method in class org.deeplearning4j.util.StringGrid
getRowWithOnlyOneOccurrence(int) - Method in class org.deeplearning4j.util.StringGrid
getSameModeBottomRightPadding(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[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Get top and left padding for same mode only.
getScore() - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
getScoreVsIter() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
getSecondKey() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
getShape(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
getStates() - Method in class org.deeplearning4j.util.Viterbi
getStateViewArray() - Method in interface org.deeplearning4j.nn.api.Updater
getStateViewArray() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
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
getStore() - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
getStringCollection(String) - Static method in class org.deeplearning4j.util.StringUtils: Returns a collection of strings.
getStrings(String) - Static method in class org.deeplearning4j.util.StringUtils: Returns an arraylist of strings.
getSum() - Method in class org.deeplearning4j.util.SummaryStatistics
getThreshold(int) - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve
getThreshold(int) - Method in class org.deeplearning4j.eval.curves.RocCurve
getTitle() - Method in class org.deeplearning4j.eval.curves.BaseCurve
getTitle() - Method in class org.deeplearning4j.eval.curves.BaseHistogram
getTitle() - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve
getTitle() - Method in class org.deeplearning4j.eval.curves.ReliabilityDiagram
getTitle() - Method in class org.deeplearning4j.eval.curves.RocCurve
getTokens() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
getTopNCorrectCount() - Method in class org.deeplearning4j.eval.Evaluation: Return the number of correct predictions according to top N value.
getTopNTotalCount() - Method in class org.deeplearning4j.eval.Evaluation: Return the total number of top N evaluations.
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
getTrimmedStringCollection(String) - Static method in class org.deeplearning4j.util.StringUtils: Splits a comma separated value String, trimming leading and trailing whitespace on each value.
getTrimmedStrings(String) - Static method in class org.deeplearning4j.util.StringUtils: Splits a comma separated value String, trimming leading and trailing whitespace on each value.
getTruePositiveRate(int) - Method in class org.deeplearning4j.eval.curves.RocCurve
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.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
getUnflattenedType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
getUniqueRows() - Method in class org.deeplearning4j.util.StringGrid
getUpdater() - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
getUpdater() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the ComputationGraphUpdater for the network
getUpdater() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the updater for this 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 class org.deeplearning4j.nn.conf.layers.BaseLayer: Deprecated.
Use BaseLayer.getIUpdaterByParam(String)
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer: Deprecated.
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer: Deprecated.
Use Layer.getIUpdaterByParam(String)
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getUpper() - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
getValue() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
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 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 interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the name/label of the GraphVertex
getVertices() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Returns an array of all GraphVertex objects.
getX() - Method in class org.deeplearning4j.eval.curves.BaseCurve
getX() - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve
getX() - Method in class org.deeplearning4j.eval.curves.ReliabilityDiagram
getX() - Method in class org.deeplearning4j.eval.curves.RocCurve
getY() - Method in class org.deeplearning4j.eval.curves.BaseCurve
getY() - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve
getY() - Method in class org.deeplearning4j.eval.curves.ReliabilityDiagram
getY() - Method in class org.deeplearning4j.eval.curves.RocCurve
gibbhVh(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Gibbs sampling step: hidden ---> visible ---> hidden
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 - 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.
GlobalPoolingLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
GlobalPoolingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
gMeasure(int) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the G-measure for the given output
gMeasure(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Calculates the average G measure for all outputs using micro or macro averaging
gMeasure(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Calculate the G-measure for the given output
gMeasure(double, double) - Static method in class org.deeplearning4j.eval.EvaluationUtils: Calculate the G-measure from precision and recall
goldLabel() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
gr(double, double) - Static method in class org.deeplearning4j.util.MathUtils: Tests if a is greater than b.
gradient() - Method in interface org.deeplearning4j.nn.api.Model: Calculate a 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.subsampling.SubsamplingLayer
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.GravesBidirectionalLSTM
gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
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 - 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.training.CenterLossOutputLayer
gradientAndScore() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
gradientAndScore() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
gradientAndScore() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: The gradient and score for this optimizer
gradientAndScore() - 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(GradientNormalization) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Gradient normalization strategy.
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 - 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(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.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.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.
gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
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
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
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.
GraphVertex - Class in org.deeplearning4j.nn.conf.graph: A GraphVertex is a vertex in the computation graph.
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: LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
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
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: LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
GravesLSTM - Class in org.deeplearning4j.nn.layers.recurrent: LSTM layer implementation.
GravesLSTM(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
GravesLSTM(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
GravesLSTM.Builder - Class in org.deeplearning4j.nn.conf.layers
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(Layer[]) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
hasDepleted - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
hasDepleted - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
hashCode() - Method in class org.deeplearning4j.eval.ConfusionMatrix
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.distribution.UniformDistribution
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.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.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
hashCode() - Method in class org.deeplearning4j.util.Index
hashCode() - Method in class org.deeplearning4j.util.MultiDimensionalMap
hashSet() - Static method in class org.deeplearning4j.util.MultiDimensionalSet
hashSet - Static variable in class org.deeplearning4j.util.OneTimeLogger
hasIterator(DataSetIterator) - Method in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator.Builder
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.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
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?
hasMore() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
hasMore() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher: Deprecated.

Whether the dataset has more to load
hasNext() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: Returns true if the iteration has more elements.
hasNext() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Returns true if the iteration has more elements.
hasNext() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator: Returns true if the iteration has more elements.
hasNext() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Returns true if the iteration has more elements.
hasNext() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldMultiDataSetIterator: Returns true if the iteration has more elements.
hasNext() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationMultiDataSetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator: Returns true if the iteration has more elements.
hasNext() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator: Returns true if the iteration has more elements.
hasNext() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
hasNext() - Method in class org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: Returns true if the iteration has more elements.
hasNext() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: Returns true if the iteration has more elements.
hasNext() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
hasNext() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
hasNextFor() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
hasNextFor(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
hasNextFor(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.FileSplitParallelDataSetIterator
hasNextFor(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator
hasSomething - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
head(int) - Method in class org.deeplearning4j.util.StringGrid
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
hexStringToByte(String) - Static method in class org.deeplearning4j.util.StringUtils: Given a hexstring this will return the byte array corresponding to the string
hiddenSigma - Variable in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Deprecated.
hiddenUnit(RBM.HiddenUnit) - Method in class org.deeplearning4j.nn.conf.layers.RBM.Builder
hiddenUnit - Variable in class org.deeplearning4j.nn.conf.layers.RBM
Histogram - Class in org.deeplearning4j.eval.curves: A simple histogram used in evaluation classes, such as EvaluationCalibration
Histogram(String, double, double, int[]) - Constructor for class org.deeplearning4j.eval.curves.Histogram
humanReadableInt(long) - Static method in class org.deeplearning4j.util.StringUtils: Given an integer, return a string that is in an approximate, but human readable format.
hypotenuse(double, double) - Static method in class org.deeplearning4j.util.MathUtils: sqrt(a^2 + b^2) without under/overflow.

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
idf(double, double) - Static method in class org.deeplearning4j.util.MathUtils: Inverse document frequency: the total docs divided by the number of times the word appeared in a document
IEarlyStoppingTrainer<T extends Model> - Interface in org.deeplearning4j.earlystopping.trainer: Interface for early stopping trainers
IEvaluation<T extends IEvaluation> - Interface in org.deeplearning4j.eval: A general purpose interface for evaluating neural networks - methods are shared by implemetations such as Evaluation, RegressionEvaluation, ROC, ROCMultiClass
incrementFalseNegatives(Integer) - Method in class org.deeplearning4j.eval.Evaluation
incrementFalsePositive(long) - Method in class org.deeplearning4j.eval.ROC.CountsForThreshold
incrementFalsePositives(Integer) - Method in class org.deeplearning4j.eval.Evaluation
incrementIterationCount(Model, int) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
incrementTrueNegatives(Integer) - Method in class org.deeplearning4j.eval.Evaluation
incrementTruePositive(long) - Method in class org.deeplearning4j.eval.ROC.CountsForThreshold
incrementTruePositives(Integer) - Method in class org.deeplearning4j.eval.Evaluation
INDArrayDataSetIterator - Class in org.deeplearning4j.datasets.iterator
INDArrayDataSetIterator(Iterable<Pair<INDArray, INDArray>>, int) - Constructor for class org.deeplearning4j.datasets.iterator.INDArrayDataSetIterator
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
Index - Class in org.deeplearning4j.util: An index is a transform of objects augmented with a list and a reverse lookup table for fast lookups.
Index() - Constructor for class org.deeplearning4j.util.Index
indexOf(Object) - Method in class org.deeplearning4j.util.Index
inequalityHandling - Variable in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
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: This method defines Workspace mode being used during inference: NONE: workspace won't be used SINGLE: one workspace will be used during whole iteration loop SEPARATE: separate workspaces will be used for feedforward and backprop iteration loops
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 SINGLE: one workspace will be used during whole iteration loop SEPARATE: separate workspaces will be used for feedforward and backprop iteration loops
inferInputType(INDArray) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
inferInputTypes(INDArray...) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
info(Logger, String, Object...) - Static method in class org.deeplearning4j.util.OneTimeLogger
information(double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the entropy for a given vector of probabilities.
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() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder: Init the model
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.CenterLossParamInitializer
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.EmptyParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
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.PretrainParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
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(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Sets the input and labels from this dataset
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
initializeCurrFromList(List<DataSet>) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher: Initializes this data transform fetcher from the passed in datasets
initializeIterators(List<DataSetIterator>) - Method in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator
initializeLayers(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Base class for initializing the neuralNets based on the input.
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.ConvolutionLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
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.GlobalPoolingLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM
initializer() - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM
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.FrozenLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.RBM
initializer() - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
initializer() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
initializeWorkspaces(long) - Method in class org.deeplearning4j.datasets.iterator.callbacks.InterleavedDataSetCallback
initialMemory - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
initialMemory - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
initOptimizer() - Method in class org.deeplearning4j.optimize.Solver
initParams() - Method in interface org.deeplearning4j.nn.api.Model: Deprecated.
As of 0.7.3 - Feb 2017. Not used; neural network params are initialized by the parameter initializaters. Furthermore, most implementations are unsupported or no-op.
initParams() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
initParams() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
initParams() - Method in class org.deeplearning4j.nn.layers.BaseLayer
initParams() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
initParams() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
initParams() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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: 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
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.FrozenLayer
input - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
input() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
input - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
input() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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
inputColumns - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
inputColumns() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: Input columns for the dataset
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Input columns for the dataset
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Input columns for the dataset
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
inputColumns() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher: Deprecated.

The length of a feature vector for an individual example
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: Input columns for the dataset
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: Input columns for the dataset
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
inputColumns() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
inputMaskArray - Variable in class org.deeplearning4j.nn.layers.BaseOutputLayer
inputMaskArrayState - Variable in class org.deeplearning4j.nn.layers.BaseOutputLayer
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.graph.vertex.BaseGraphVertex
InputSplit - Class in org.deeplearning4j.util
inputStreamFromPath(String) - Static method in class org.deeplearning4j.util.DeepLearningIOUtil
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.InputTypeConvolutional - 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: Convolutional neural n
InputTypeConvolutional() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional
InputTypeConvolutionalFlat() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
InputTypeFeedForward() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward
InputTypeRecurrent(int) - 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
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
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.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.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<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Layer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LSTM
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RBM
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
InterleavedDataSetCallback - Class in org.deeplearning4j.datasets.iterator.callbacks: This callback migrates incoming datasets in round-robin manner, to ensure TDA for ParallelWrapper
InterleavedDataSetCallback(int) - Constructor for class org.deeplearning4j.datasets.iterator.callbacks.InterleavedDataSetCallback
intersection(Collection<T>, Collection<T>) - Static method in class org.deeplearning4j.util.SetUtils
intersectionP(Set<? extends T>, Set<? extends T>) - Static method in class org.deeplearning4j.util.SetUtils
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.
inverse(RBM.HiddenUnit) - Static method in class org.deeplearning4j.util.RBMUtil
inverse(RBM.VisibleUnit) - Static method in class org.deeplearning4j.util.RBMUtil
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
invoke() - Method in interface org.deeplearning4j.optimize.api.IterationListener: Change invoke to true
invoke() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
invoke() - Method in class org.deeplearning4j.optimize.listeners.ComposableIterationListener
invoke() - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener: Change invoke to true
invoke() - Method in class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener
invoke() - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener
invoke() - Method in class org.deeplearning4j.optimize.listeners.ScoreIterationListener
invoke() - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
invoke() - Method in class org.deeplearning4j.optimize.listeners.TimeIterationListener
invoked() - Method in interface org.deeplearning4j.optimize.api.IterationListener: Get if listener invoked
invoked() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
invoked() - Method in class org.deeplearning4j.optimize.listeners.ComposableIterationListener
invoked() - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener: Get if listener invoked
invoked() - Method in class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener
invoked() - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener
invoked() - Method in class org.deeplearning4j.optimize.listeners.ScoreIterationListener
invoked() - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
invoked() - Method in class org.deeplearning4j.optimize.listeners.TimeIterationListener
invokeListener(Model) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener
IOutputLayer - Interface in org.deeplearning4j.nn.api.layers: Interface for output layers (those that calculate gradients with respect to a labels array)
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
isEligible(String) - Static method in class org.deeplearning4j.util.OneTimeLogger
isEmpty() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
isEmpty() - Method in class org.deeplearning4j.util.DiskBasedQueue
isEmpty() - Method in class org.deeplearning4j.util.MultiDimensionalMap: Returns true if this map contains no key-value mappings.
isEmpty() - Method in class org.deeplearning4j.util.MultiDimensionalSet: Returns true if this applyTransformToDestination contains no elements.
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 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
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.ElementWiseVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
isPreTerminal() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Node has one child that is a leaf
isPretrainLayer() - Method in interface org.deeplearning4j.nn.api.Layer: Returns true if the layer can be trained in an unsupervised/pretrain manner (VAE, RBMs 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.ConvolutionLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
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.embedding.EmbeddingLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
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.pooling.GlobalPoolingLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
isPretrainLayer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
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.SubsamplingLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
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
iter - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
iterate(INDArray) - Method in interface org.deeplearning4j.nn.api.Model: Run one iteration
iterate(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer: iterate one iteration of the network
iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer: iterate one iteration of the network
iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Deprecated.
No longer used
iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
iterate(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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.optimize.listeners.EvaluativeListener
iterationDone(Model, int) - Method in interface org.deeplearning4j.optimize.api.IterationListener: Event listener for each iteration
iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.ComposableIterationListener
iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener: Event listener for each iteration
iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener
iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener
iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.ScoreIterationListener
iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.TimeIterationListener
IterationListener - Interface in org.deeplearning4j.optimize.api: Each iteration the listener is called, mainly used for debugging or visualizations
iterationListeners - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
iterationListeners - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
iterationListeners - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
iterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Number of optimization iterations.
iterations(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
iterations - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
iterationsCounter - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
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.datasets.iterator.MultiDataSetWrapperIterator
iterator - Variable in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
iterator() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
iterator() - Method in class org.deeplearning4j.util.DiskBasedQueue
iterator() - Method in class org.deeplearning4j.util.MultiDimensionalSet: Returns an iterator over the elements in this applyTransformToDestination.
IteratorDataSetIterator - Class in org.deeplearning4j.datasets.iterator: A DataSetIterator that works on an Iterator, combining and splitting the input DataSet objects as required to get a consistent batch size.
IteratorDataSetIterator(Iterator<DataSet>, int) - Constructor for class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
IteratorMultiDataSetIterator - Class in org.deeplearning4j.datasets.iterator: A DataSetIterator that works on an Iterator, combining and splitting the input DataSet objects as required to get a consistent batch size.
IteratorMultiDataSetIterator(Iterator<MultiDataSet>, int) - Constructor for class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
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
iUpdater - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
iz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

J

join(CharSequence, Iterable<String>) - Static method in class org.deeplearning4j.util.StringUtils: Concatenates strings, using a separator.
join(Iterable<String>) - Static method in class org.deeplearning4j.util.StringUtils: Concatenates strings, using whitespaces as separator.
joinObjects(CharSequence, Iterable<? extends Object>) - Static method in class org.deeplearning4j.util.StringUtils: Concatenates stringified objects, using a separator.
JointParallelDataSetIterator - Class in org.deeplearning4j.datasets.iterator.parallel
JointParallelDataSetIterator(List<DataSetIterator>, boolean, int, InequalityHandling) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator
JointParallelDataSetIterator.Builder - Class in org.deeplearning4j.datasets.iterator.parallel

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(int) - Method in class org.deeplearning4j.nn.conf.layers.RBM.Builder
k - Variable in class org.deeplearning4j.nn.conf.layers.RBM
kernelSize(int) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder: Size of the convolution
kernelSize - Variable 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.Subsampling1DLayer.Builder: Kernel size
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
KernelValidationUtil - Class in org.deeplearning4j.nn.layers.convolution: Confirm calculations to reduce the shape of the input based on convolution or subsampling transformation
key(String, Object...) - Method in class org.deeplearning4j.util.FingerPrintKeyer
keys() - Static method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
keySet() - Method in class org.deeplearning4j.util.MultiDimensionalMap: Returns a Set view of the keys contained in this map.
kroneckerDelta(double, double) - Static method in class org.deeplearning4j.util.MathUtils: This returns the kronecker delta of two doubles.

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(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: L1 regularization coefficient (weights only).
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.
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(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: L1 regularization coefficient for the bias.
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.
l1Bias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
l1ByParam - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
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(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: L2 regularization coefficient (weights only).
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.
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(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: L2 regularization coefficient for the bias.
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.
l2Bias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
l2ByParam - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
L2NormalizeVertex - Class in org.deeplearning4j.nn.conf.graph: L2NormalizeVertex performs L2 normalization on a single input.
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 least squares error of two inputs.
L2Vertex() - Constructor for class org.deeplearning4j.nn.conf.graph.L2Vertex
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.LossLayer: Returns the probabilities for each label for each example row wise
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.LossLayer
labels - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
labelsList - Variable in class org.deeplearning4j.eval.Evaluation
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
lastAct - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
lastBatch - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
lastBP - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
lastChild() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
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
lastMemCell - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
lastStep - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
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 - 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.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
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.variational.VariationalAutoencoder
layerIndex - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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
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) - Constructor for class org.deeplearning4j.nn.updater.LayerUpdater
LayerValidation - Class in org.deeplearning4j.nn.conf.layers: Created by Alex on 22/02/2017.
LayerValidation() - Constructor for class org.deeplearning4j.nn.conf.layers.LayerValidation
LayerValidation - Class in org.deeplearning4j.util: Created by Alex on 12/11/2016.
LayerValidation() - Constructor for class org.deeplearning4j.util.LayerValidation
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
LBFGS - Class in org.deeplearning4j.optimize.solvers: LBFGS
LBFGS(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LBFGS
LBFGS(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Collection<TerminationCondition>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LBFGS
leakyreluAlpha - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
leakyreluAlpha(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
Use NeuralNetConfiguration.Builder.activation(IActivation) with leaky relu, setting alpha value directly in constructor.
leakyreluAlpha - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: Deprecated.
learningRate - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
learningRate(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Learning rate.
learningRate - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
learningRate(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Learning rate.
learningRate - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
learningRate(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Learning rate.
learningRate - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
learningRateByParam - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
learningRateDecayPolicy(LearningRatePolicy) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Learning rate decay policy.
learningRateDecayPolicy(LearningRatePolicy) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Learning rate decay policy.
learningRateDecayPolicy(LearningRatePolicy) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Learning rate decay policy.
learningRatePolicy - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
LearningRatePolicy - Enum in org.deeplearning4j.nn.conf: Learning Rate Policy How to decay learning rate during training.
learningRatePolicy - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
learningRatePolicy - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
learningRatePolicy - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
learningRateSchedule - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
learningRateSchedule(Map<Integer, Double>) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Learning rate schedule.
learningRateSchedule - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
learningRateSchedule(Map<Integer, Double>) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Learning rate schedule.
learningRateSchedule - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
learningRateSchedule(Map<Integer, Double>) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Learning rate schedule.
learningRateSchedule - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
learningRateScoreBasedDecayRate(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Rate to decrease learningRate by when the score stops improving.
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.
leverageTo(String) - Method in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn: This method is OPTIONAL, and written mostly for future use
limitDecimalTo2(double) - Static method in class org.deeplearning4j.util.StringUtils
LineGradientDescent - Class in org.deeplearning4j.optimize.solvers: Stochastic Gradient Descent with Line Search
LineGradientDescent(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LineGradientDescent
LineGradientDescent(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Collection<TerminationCondition>, 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
ListDataSetIterator<T extends org.nd4j.linalg.dataset.DataSet> - Class in org.deeplearning4j.datasets.iterator.impl: Wraps a data applyTransformToDestination collection
ListDataSetIterator(Collection<T>, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
ListDataSetIterator(Collection<T>) - Constructor for class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator: Initializes with a batch of 5
listener(IterationListener...) - Method in class org.deeplearning4j.optimize.Solver.Builder
listeners - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
listeners(Collection<IterationListener>) - Method in class org.deeplearning4j.optimize.Solver.Builder
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(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
LocalResponseNormalization - Class in org.deeplearning4j.nn.conf.layers: Created by nyghtowl on 10/29/15.
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
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.datasets.fetchers.BaseDataFetcher
log - Static variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
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
log2 - Static variable in class org.deeplearning4j.util.MathUtils: The natural logarithm of 2.
log2(double) - Static method in class org.deeplearning4j.util.MathUtils: Returns the logarithm of a for base 2.
logger - Static variable in class org.deeplearning4j.util.TestDataSetConsumer
logs2probs(double[]) - Static method in class org.deeplearning4j.util.MathUtils: Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logTestMode(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
logTestMode(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
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.LossLayer
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.
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(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
lrPolicyDecayRate - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
lrPolicyDecayRate(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set the decay rate for the learning rate decay policy.
lrPolicyDecayRate - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
lrPolicyDecayRate - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
lrPolicyPower - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
lrPolicyPower(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set the power used for learning rate inverse policy.
lrPolicyPower - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
lrPolicyPower - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
lrPolicySteps - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
lrPolicySteps(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set the number of steps used for learning decay rate steps policy.
lrPolicySteps - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
lrPolicySteps - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
lrSchedulesEqual(Layer, String, Layer, String) - Static method in class org.deeplearning4j.nn.updater.UpdaterUtils
lrScoreBasedDecay - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
lrScoreBasedDecay - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
LSTM - Class in org.deeplearning4j.nn.conf.layers: LSTM recurrent net 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

main(String[]) - Static method in class org.deeplearning4j.eval.ConfusionMatrix
manhattanDistance(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils: This will calculate the Manhattan distance between two sets of points.
mapByPrimaryKey(int) - Method in class org.deeplearning4j.util.StringGrid
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
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, boolean, int) - Static method in class org.deeplearning4j.util.MaskedReductionUtil
maskedPoolingEpsilonCnn(PoolingType, INDArray, INDArray, INDArray, boolean, 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.
MaskedReductionUtil() - Constructor for class org.deeplearning4j.util.MaskedReductionUtil
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
MathUtils - Class in org.deeplearning4j.util: This is a math utils class.
MathUtils() - Constructor for class org.deeplearning4j.util.MathUtils
matthewsCorrelation(int) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the binary Mathews correlation coefficient, for the specified class.
MCC = (TP*TN - FP*FN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN))
matthewsCorrelation(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the average binary Mathews correlation coefficient, using macro or micro averaging.
MCC = (TP*TN - FP*FN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN))
Note: This is NOT the same as the multi-class Matthews correlation coefficient
matthewsCorrelation(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Calculate the Matthews correlation coefficient for the specified output
matthewsCorrelation(long, long, long, long) - Static method in class org.deeplearning4j.eval.EvaluationUtils: Calculate the binary Matthews correlation coefficient from counts
max(double[]) - Static method in class org.deeplearning4j.util.MathUtils
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
maxIndex(double[]) - Static method in class org.deeplearning4j.util.MathUtils: Returns index of maximum element in a given array of doubles.
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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
maxOutcomeForRow(int) - Method in class org.deeplearning4j.nn.simple.multiclass.RankClassificationResult: Get the max index for the given row
maxOutcomes() - Method in class org.deeplearning4j.nn.simple.multiclass.RankClassificationResult
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.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.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
mds - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
mdsIterator - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
mean(double[]) - Static method in class org.deeplearning4j.util.MathUtils: Computes the mean for an array of doubles.
meanAbsoluteError(int) - Method in class org.deeplearning4j.eval.RegressionEvaluation
meanSquaredError(int) - Method in class org.deeplearning4j.eval.RegressionEvaluation
memCellActivations - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
memCellState - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
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(Evaluation) - Method in class org.deeplearning4j.eval.Evaluation: Merge the other evaluation object into this one.
merge(EvaluationBinary) - Method in class org.deeplearning4j.eval.EvaluationBinary
merge(EvaluationCalibration) - Method in class org.deeplearning4j.eval.EvaluationCalibration
merge(T) - Method in interface org.deeplearning4j.eval.IEvaluation
merge(RegressionEvaluation) - Method in class org.deeplearning4j.eval.RegressionEvaluation
merge(ROC) - Method in class org.deeplearning4j.eval.ROC: Merge this ROC instance with another.
merge(ROCBinary) - Method in class org.deeplearning4j.eval.ROCBinary
merge(ROCMultiClass) - Method in class org.deeplearning4j.eval.ROCMultiClass: Merge this ROCMultiClass instance with another.
merge(Layer, int) - Method in interface org.deeplearning4j.nn.api.Layer: Deprecated.
As of 0.7.3 - Feb 2017. No longer used. Merging (for parameter averaging) done via alternative means
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer: Averages the given logistic regression from a mini batch into this layer
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.BaseLayer: Averages the given logistic regression from a mini batch into this layer
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.LossLayer
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
merge(Layer, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Deprecated.
Not supported and not used
merge(MultiLayerNetwork, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Deprecated.
As of 0.7.3 - Feb 2017. No longer used; parameter averaging is performed via alternative means/methods
merge(int, int) - Method in class org.deeplearning4j.util.StringGrid
mergeCoords(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils: This will merge the coordinates of the given coordinate system.
mergeCoords(List<Double>, List<Double>) - Static method in class org.deeplearning4j.util.MathUtils: This will merge the coordinates of the given coordinate system.
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
migrate(MultiDataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
migrate(DataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
min(double[]) - Static method in class org.deeplearning4j.util.MathUtils
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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
minThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
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.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
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
momentum - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
momentum(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
Use .updater(new Nesterov(momentum)) instead
momentum - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer: Deprecated.
momentum(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Momentum rate.
momentum - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
momentum(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
Use .updater(new Nesterov(momentum)) instead
momentum - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration: Deprecated.
momentumAfter - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
momentumAfter(Map<Integer, Double>) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
Use .updater(Nesterov.builder().momentumSchedule(schedule).build()) instead
momentumAfter(Map<Integer, Double>) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Momentum schedule.
momentumAfter(Map<Integer, Double>) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
Use .updater(Nesterov.builder().momentumSchedule(schedule).build()) instead
momentumSchedule - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer: Deprecated.
momentumSchedule - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
momentumSchedule - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration: Deprecated.
movingAverage(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Calculate a moving average given the length
MovingWindowBaseDataSetIterator - Class in org.deeplearning4j.datasets.iterator: DataSetIterator for moving window (rotating matrices)
MovingWindowBaseDataSetIterator(int, int, DataSet, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.MovingWindowBaseDataSetIterator
MovingWindowDataSetFetcher - Class in org.deeplearning4j.datasets.iterator.impl: Moving window data fetcher.
MovingWindowDataSetFetcher(DataSet, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.MovingWindowDataSetFetcher
MovingWindowMatrix - Class in org.deeplearning4j.util: Moving window on a matrix (usually used for images) Given a: This is a list of flattened arrays: 1 1 1 1 1 1 2 2 2 2 2 2 ----> 1 1 2 2 3 3 3 3 3 3 4 4 4 4 4 4 3 3 4 4
MovingWindowMatrix(INDArray, int, int, boolean) - Constructor for class org.deeplearning4j.util.MovingWindowMatrix
MovingWindowMatrix(INDArray, int, int) - Constructor for class org.deeplearning4j.util.MovingWindowMatrix: Same as calling new MovingWindowMatrix(toSlice,windowRowSize,windowColumnSize,false)
MultiBoolean - Class in org.deeplearning4j.datasets.iterator.parallel: This is utility class, that allows easy handling of multiple joint boolean states.
MultiBoolean(int) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.MultiBoolean
MultiBoolean(int, boolean) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.MultiBoolean
MultiBoolean(int, boolean, boolean) - Constructor for class org.deeplearning4j.datasets.iterator.parallel.MultiBoolean
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
MultiDataSetWrapperIterator - Class in org.deeplearning4j.datasets.iterator: This class is simple wrapper that takes single-input MultiDataSets and converts them to DataSets on the fly PLEASE NOTE: This only works if number of features/labels/masks is 1
MultiDataSetWrapperIterator(MultiDataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
MultiDimensionalMap<K,T,V> - Class in org.deeplearning4j.util: Multiple key map
MultiDimensionalMap(Map<Pair<K, T>, V>) - Constructor for class org.deeplearning4j.util.MultiDimensionalMap
MultiDimensionalMap.Entry<K,T,V> - Class in org.deeplearning4j.util
MultiDimensionalSet<K,V> - Class in org.deeplearning4j.util: Created by agibsonccc on 4/29/14.
MultiDimensionalSet(Set<Pair<K, V>>) - Constructor for class org.deeplearning4j.util.MultiDimensionalSet
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.
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) - Constructor for class org.deeplearning4j.nn.updater.MultiLayerUpdater
MultiLayerUtil - Class in org.deeplearning4j.util: Various cooccurrences for manipulating a multi layer network
MultipleEpochsIterator - Class in org.deeplearning4j.datasets.iterator: A dataset iterator for doing multiple passes over a dataset
MultipleEpochsIterator(int, DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
MultipleEpochsIterator(int, DataSetIterator, int) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: Deprecated.
MultipleEpochsIterator(DataSetIterator, int, long) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: Deprecated.
MultipleEpochsIterator(DataSetIterator, long) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
MultipleEpochsIterator(int, DataSet) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
MultiThreadUtils - Class in org.deeplearning4j.util

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.ConvolutionLayer.Builder: Layer name assigns layer string name.
name(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder: Layer name assigns layer string name.
negative() - Method in class org.deeplearning4j.eval.Evaluation: Total negatives true negatives + false negatives
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
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.ListBuilder - Class in org.deeplearning4j.nn.conf: Fluent interface for building a list of configurations
NeuralNetwork - Interface in org.deeplearning4j.nn.api
NeuralNetworkPrototype - Interface in org.deeplearning4j.nn.api
newEpoch - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
newExecutorService() - Static method in class org.deeplearning4j.util.MultiThreadUtils
newHashBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap: Thread safe hash map impl
newShape - Variable in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
newThreadSafeHashBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap: Thread safe hash map implementation
newThreadSafeTreeBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap: Thread safe sorted map implementation
newTreeBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap: Tree map implementation
next() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
next(int) - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: Like the standard next method but allows a customizable number of examples returned
next() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: Returns the next element in the iteration.
next(int) - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Like the standard next method but allows a customizable number of examples returned
next() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Returns the next element in the iteration.
next(int) - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator: Like the standard next method but allows a customizable number of examples returned
next() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator: Returns the next element in the iteration.
next(int) - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Like the standard next method but allows a customizable number of examples returned
next() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Returns the next element in the iteration.
next(int) - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldMultiDataSetIterator: Fetch the next 'num' examples.
next() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldMultiDataSetIterator: Returns the next element in the iteration.
next() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
next() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher: Deprecated.

Returns the next data applyTransformToDestination
next(int) - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
next() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationMultiDataSetIterator
next() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationMultiDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
next() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
next() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
next() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator: Returns the next element in the iteration.
next(int) - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator
next() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator: Returns the next element in the iteration.
next() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
next() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
next(int) - Method in class org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator
next() - Method in class org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator
next() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
next() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
next() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: Like the standard next method but allows a customizable number of examples returned
next() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
next() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: Like the standard next method but allows a customizable number of examples returned
next() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: Returns the next element in the iteration.
next() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
next(int) - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
next() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
nextElement - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
nextElement - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
nextFor() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
nextFor(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
nextFor(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.FileSplitParallelDataSetIterator
nextFor(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator
nextPowOf2(long) - Static method in class org.deeplearning4j.util.MathUtils: See: http://stackoverflow.com/questions/466204/rounding-off-to-nearest-power-of-2
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
nIn - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
nIn - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
NONE - Static variable in class org.deeplearning4j.util.StringGrid
Norm2Termination - Class in org.deeplearning4j.optimize.terminations: Terminate if the norm2 of the gradient is < a certain tolerance
Norm2Termination(double) - Constructor for class org.deeplearning4j.optimize.terminations.Norm2Termination
NormalDistribution - Class in org.deeplearning4j.nn.conf.distribution: A normal distribution.
NormalDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.NormalDistribution: Create a normal distribution with the given mean and std
normalize(double, double, double) - Static method in class org.deeplearning4j.util.MathUtils: Normalize a value (val - min) / (max - min)
normalize(double[], double) - Static method in class org.deeplearning4j.util.MathUtils: Normalizes the doubles in the array using the given value.
NORMALIZER_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer
normalizeToOne(double[]) - Static method in class org.deeplearning4j.util.MathUtils
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
nOut - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
nOut - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Set the size of the VAE state Z.
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
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
numClasses() - Method in class org.deeplearning4j.eval.EvaluationCalibration
numColumns() - Method in class org.deeplearning4j.eval.RegressionEvaluation
numElementsDrained - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
numElementsReady - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
numEpochs - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: Total number of examples in the dataset
numExamples() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Total number of examples in the dataset
numExamples() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Total number of examples in the dataset
numExamples - Variable in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: Total number of examples in the dataset
numExamples() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: Total number of examples in the dataset
numExamples() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
numExamples() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
numFeatureMap(NeuralNetConfiguration) - Static method in class org.deeplearning4j.util.ConvolutionUtils
numIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
numIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
numIterations - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
numLabels() - Method in class org.deeplearning4j.eval.EvaluationBinary: Returns the number of labels - (i.e., size of the prediction/labels arrays) - if known.
numLabels() - Method in class org.deeplearning4j.eval.ROCBinary: Returns the number of labels - (i.e., size of the prediction/labels arrays) - if known.
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.LossLayer: Returns the number of possible labels
numLabels() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Returns the number of possible labels
numOutcomes - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
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(boolean) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
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.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() - 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.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.subsampling.SubsamplingLayer
numParams() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
numParams(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
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.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.CenterLossParamInitializer
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.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.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.PretrainParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
numPoints() - Method in class org.deeplearning4j.eval.curves.BaseCurve
numPoints() - Method in class org.deeplearning4j.eval.curves.BaseHistogram
numPoints() - Method in class org.deeplearning4j.eval.curves.Histogram
numPoints() - Method in class org.deeplearning4j.eval.curves.PrecisionRecallCurve
numPoints() - Method in class org.deeplearning4j.eval.curves.ReliabilityDiagram
numPoints() - Method in class org.deeplearning4j.eval.curves.RocCurve
numProducers - Variable in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
numRowCounter - Variable in class org.deeplearning4j.eval.Evaluation
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
offer(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
offer(E, long, TimeUnit) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
offer(E) - Method in class org.deeplearning4j.util.DiskBasedQueue
OLD_UPDATER_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer
oldScore - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
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.EvaluativeListener
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 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.EvaluativeListener
onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
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.SleepyTrainingListener
OneTimeLogger - Class in org.deeplearning4j.util
OneTimeLogger() - Constructor for class org.deeplearning4j.util.OneTimeLogger
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.EvaluativeListener
onForwardPass(Model, Map<String, INDArray>) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener
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 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.EvaluativeListener
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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
OptimizationAlgorithm - Enum in org.deeplearning4j.nn.api: Optimization algorithm to use
optimize() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: Calls optimize
optimize(INDArray, INDArray, INDArray) - Method in interface org.deeplearning4j.optimize.api.LineOptimizer: Line optimizer
optimize() - Method in class org.deeplearning4j.optimize.Solver
optimize(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
optimize() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer: Optimize call.
optimize() - 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
orderedLayers - Variable in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
ordering - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
org.deeplearning4j.datasets.fetchers - package org.deeplearning4j.datasets.fetchers
org.deeplearning4j.datasets.iterator - package org.deeplearning4j.datasets.iterator
org.deeplearning4j.datasets.iterator.callbacks - package org.deeplearning4j.datasets.iterator.callbacks
org.deeplearning4j.datasets.iterator.impl - package org.deeplearning4j.datasets.iterator.impl
org.deeplearning4j.datasets.iterator.parallel - package org.deeplearning4j.datasets.iterator.parallel
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.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.eval.serde - package org.deeplearning4j.eval.serde
org.deeplearning4j.exception - package org.deeplearning4j.exception
org.deeplearning4j.gradientcheck - package org.deeplearning4j.gradientcheck
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.distribution - package org.deeplearning4j.nn.conf.distribution
org.deeplearning4j.nn.conf.distribution.serde - package org.deeplearning4j.nn.conf.distribution.serde
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.misc - package org.deeplearning4j.nn.conf.layers.misc
org.deeplearning4j.nn.conf.layers.variational - package org.deeplearning4j.nn.conf.layers.variational
org.deeplearning4j.nn.conf.memory - package org.deeplearning4j.nn.conf.memory
org.deeplearning4j.nn.conf.module - package org.deeplearning4j.nn.conf.module
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.stepfunctions - package org.deeplearning4j.nn.conf.stepfunctions
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.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.embedding - package org.deeplearning4j.nn.layers.feedforward.embedding
org.deeplearning4j.nn.layers.feedforward.rbm - package org.deeplearning4j.nn.layers.feedforward.rbm
org.deeplearning4j.nn.layers.normalization - package org.deeplearning4j.nn.layers.normalization
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.training - package org.deeplearning4j.nn.layers.training
org.deeplearning4j.nn.layers.variational - package org.deeplearning4j.nn.layers.variational
org.deeplearning4j.nn.multilayer - package org.deeplearning4j.nn.multilayer
org.deeplearning4j.nn.params - package org.deeplearning4j.nn.params
org.deeplearning4j.nn.simple.binary - package org.deeplearning4j.nn.simple.binary
org.deeplearning4j.nn.simple.multiclass - package org.deeplearning4j.nn.simple.multiclass
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.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.stepfunctions - package org.deeplearning4j.optimize.stepfunctions
org.deeplearning4j.optimize.terminations - package org.deeplearning4j.optimize.terminations
org.deeplearning4j.util - package org.deeplearning4j.util
org.deeplearning4j.util.reflections - package org.deeplearning4j.util.reflections
output(INDArray) - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
output(INDArray...) - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
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(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
output(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
output(boolean) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Classify input
output(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer
output(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
output(boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer: Classify input
output(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
output(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
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) - 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: Label the probabilities of the input
output(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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
OutputLayer - Class in org.deeplearning4j.nn.conf.layers: Output layer with different objective co-occurrences for different objectives.
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
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[].
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
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 - Variable 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.Subsampling1DLayer.Builder: Padding
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
parallelTasks(List<Runnable>, ExecutorService) - Static method in class org.deeplearning4j.util.MultiThreadUtils
ParamAndGradientIterationListener - Class in org.deeplearning4j.optimize.listeners: An iteration listener that provides details on parameters and gradients at each iteration during traning.
ParamAndGradientIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener: 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: Full constructor with all options.
ParamInitializer - Interface in org.deeplearning4j.nn.api: Param initializer for a layer
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(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.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.subsampling.SubsamplingLayer
params() - Method in class org.deeplearning4j.nn.layers.DropoutLayer
params() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
params() - Method in class org.deeplearning4j.nn.layers.LossLayer
params() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
params - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
params() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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
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() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
paramTable(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
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.FrozenLayer
paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
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.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
parse(String, Class<E>) - Static method in class org.deeplearning4j.util.EnumUtil
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
partitionVariable(List<Double>, int) - Static method in class org.deeplearning4j.util.MathUtils: Deprecated.
not thread-safe, unused, see https://github.com/deeplearning4j/deeplearning4j/issues/3797
peek() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
peek() - Method in class org.deeplearning4j.util.DiskBasedQueue
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
permutation(double, double) - Static method in class org.deeplearning4j.util.MathUtils: This returns the permutation of n choose r.
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() - Constructor for class org.deeplearning4j.eval.curves.PrecisionRecallCurve.Point
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.util.DiskBasedQueue
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
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: Created by Alex on 17/01/2017.
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
positive() - Method in class org.deeplearning4j.eval.Evaluation: Returns all of the positive guesses: true positive + false negative
postApply(Layer, 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(Layer, Gradient, int) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater: Pre-apply: Apply gradient normalization/clipping
precision(Integer) - Method in class org.deeplearning4j.eval.Evaluation: Returns the precision for a given label
precision(Integer, double) - Method in class org.deeplearning4j.eval.Evaluation: Returns the precision for a given label
precision() - Method in class org.deeplearning4j.eval.Evaluation: Precision based on guesses so far Takes into account all known classes and outputs average precision across all of them.
precision(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the average precision for all classes.
precision(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Get the precision (tp / (tp + fp)) for the specified output
precision(long, long, double) - Static method in class org.deeplearning4j.eval.EvaluationUtils: Calculate the precision from true positive and false positive counts
PrecisionRecallCurve - Class in org.deeplearning4j.eval.curves: Precision recall curve: A set of (recall, precision) points and different thresholds
PrecisionRecallCurve(double[], double[], double[], int[], int[], int[], int) - Constructor for class org.deeplearning4j.eval.curves.PrecisionRecallCurve
PrecisionRecallCurve.Confusion - Class in org.deeplearning4j.eval.curves
PrecisionRecallCurve.Point - Class in org.deeplearning4j.eval.curves
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.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.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: Prediction: a prediction for classification, used with the Evaluation class.
Prediction() - Constructor for class org.deeplearning4j.eval.meta.Prediction
prediction() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
prefetchSize - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
prefetchSize - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
preOutput(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer: Raw activations
preOutput(INDArray, Layer.TrainingMode) - Method in interface org.deeplearning4j.nn.api.Layer: Raw activations
preOutput(INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.Layer: Raw activations
preOutput - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
preOutput(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer: Classify input
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
preOutput(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.BaseLayer
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
preOutput(INDArray, INDArray, INDArray, int[], int[], int[], ConvolutionLayer.AlgoMode, ConvolutionLayer.FwdAlgo, ConvolutionMode) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
preOutput(boolean, boolean) - 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) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
preOutput(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
preOutput(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
preOutput(INDArray, boolean, int[], INDArray, INDArray, INDArray, INDArray, double, double) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
preOutput(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
preOutput(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
preOutput(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
preOutput2d(boolean) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
preOutput2d(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
preOutput4d(boolean, boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
preOutput4d(boolean, boolean) - 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
prependToEach(String, int) - Method in class org.deeplearning4j.util.StringGrid
preProcess(MultiDataSet) - Method in class org.deeplearning4j.datasets.iterator.CombinedMultiDataSetPreProcessor
preProcess(DataSet) - Method in class org.deeplearning4j.datasets.iterator.CombinedPreProcessor: Pre process a dataset sequentially
preProcess(DataSet) - Method in class org.deeplearning4j.datasets.iterator.DummyPreProcessor: Pre process a dataset
preProcess(INDArray, int) - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor: Pre preProcess input/activations for a multi layer network
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.BinomialSamplingPreProcessor
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.UnitVarianceProcessor
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanAndUnitVariancePreProcessor
preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanPrePreProcessor
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 - Variable in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
preProcessor - Variable in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
preProcessor - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
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(DataSetIterator) - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
pretrain - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
pretrain(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Whether to do layerwise pre training or not
pretrain - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
pretrain - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
pretrain(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Whether to do pre train or not
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
pretrain - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
pretrain(DataSetIterator) - 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(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform layerwise pretraining on all pre-trainable layers in the network (VAEs, RBMs, Autoencoders, etc)
Note that pretraining will be performed on one layer after the other, resetting the DataSetIterator between iterations.
For multiple epochs per layer, appropriately wrap the iterator (for example, a MultipleEpochsIterator) or train each layer manually using MultiLayerNetwork.pretrainLayer(int, DataSetIterator)
pretrain(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Deprecated.
use MultiLayerNetwork.pretrain(DataSetIterator) or MultiLayerNetwork.pretrainLayer(int, DataSetIterator) or MultiLayerNetwork.pretrainLayer(int, INDArray). Pretraining each layer in a row on a single minibatch (as per this method) instead of N epochs per layer is not advisable.
pretrain - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
preTrainIterations - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
preTrainIterations(int) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
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: Perform layerwise unsupervised training on a single pre-trainable layer in the network (VAEs, RBMs, Autoencoders, etc)
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, RBMs, 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
printThreadInfo(PrintWriter, String) - Static method in class org.deeplearning4j.util.ReflectionUtils: Print all of the thread's information and stack traces.
probRound(double, Random) - Static method in class org.deeplearning4j.util.MathUtils: Rounds a double to the next nearest integer value in a probabilistic fashion (e.g.
probToLogOdds(double) - Static method in class org.deeplearning4j.util.MathUtils: Returns the log-odds for a given probability.
producerAffinity - Variable in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
propDown(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Calculates the activation of the hidden: activation(h * W + vbias)
propUp(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Calculates the activation of the visible : sigmoid(v * W + hbias)
propUp(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Calculates the activation of the visible : sigmoid(v * W + hbias)
propUpDerivative(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
put(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
put(Pair<K, T>, V) - Method in class org.deeplearning4j.util.MultiDimensionalMap: Associates the specified value with the specified key in this map (optional operation).
put(K, T, V) - Method in class org.deeplearning4j.util.MultiDimensionalMap
putAll(Map<? extends Pair<K, T>, ? extends V>) - Method in class org.deeplearning4j.util.MultiDimensionalMap: Copies all of the mappings from the specified map to this map (optional operation).
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(String) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Deprecated.
Use VariationalAutoencoder.Builder.pzxActivationFunction(Activation)
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

randomDoubleBetween(double, double) - Static method in class org.deeplearning4j.util.MathUtils
randomFloatBetween(float, float) - Static method in class org.deeplearning4j.util.MathUtils
randomNumberBetween(double, double) - Static method in class org.deeplearning4j.util.MathUtils: Generates a random integer between the specified numbers
randomNumberBetween(double, double, RandomGenerator) - Static method in class org.deeplearning4j.util.MathUtils: Generates a random integer between the specified numbers
RankClassificationResult - Class in org.deeplearning4j.nn.simple.multiclass: A RankClassificationResult is an abstraction over an activation matrix for ranking classes.
RankClassificationResult(INDArray) - Constructor for class org.deeplearning4j.nn.simple.multiclass.RankClassificationResult: Takes in just a classification matrix and initializes the labels to just be indices
RankClassificationResult(INDArray, List<String>) - Constructor for class org.deeplearning4j.nn.simple.multiclass.RankClassificationResult: Takes in a classification matrix and the labels for each column
RBM - Class in org.deeplearning4j.nn.conf.layers: Restricted Boltzmann Machine.
RBM - Class in org.deeplearning4j.nn.layers.feedforward.rbm: Restricted Boltzmann Machine.
RBM(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
RBM(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
RBM.Builder - Class in org.deeplearning4j.nn.conf.layers
RBM.HiddenUnit - Enum in org.deeplearning4j.nn.conf.layers
RBM.VisibleUnit - Enum in org.deeplearning4j.nn.conf.layers
RBMUtil - Class in org.deeplearning4j.util: Handles various cooccurrences for RBM specific cooccurrences
readObject(File) - Static method in class org.deeplearning4j.util.SerializationUtils
readObject(InputStream) - Static method in class org.deeplearning4j.util.SerializationUtils: Reads an object from the given input stream
readString(DataInputStream, int) - Static method in class org.deeplearning4j.util.ByteUtil
recall(int) - Method in class org.deeplearning4j.eval.Evaluation: Returns the recall for a given label
recall(int, double) - Method in class org.deeplearning4j.eval.Evaluation: Returns the recall for a given label
recall() - Method in class org.deeplearning4j.eval.Evaluation: Recall based on guesses so far Takes into account all known classes and outputs average recall across all of them
recall(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Calculate the average recall for all classes - can specify whether macro or micro averaging should be used NOTE: if any classes have tp=0 and fn=0, (recall=0/0) these are excluded from the average
recall(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Get the recall (tp / (tp + fn)) for the specified output
recall(long, long, double) - Static method in class org.deeplearning4j.eval.EvaluationUtils: Calculate the recall from true positive and false negative counts
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.
ReconstructionDataSetIterator - Class in org.deeplearning4j.datasets.iterator: Wraps a data applyTransformToDestination iterator setting the first (feature matrix) as the labels.
ReconstructionDataSetIterator(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
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).
recurrent(int) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: InputType for recurrent neural network (time series) data
recurrent(int, int) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: InputType for recurrent neural network (time series) data
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_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
RecurrentLayer - Interface in org.deeplearning4j.nn.api.layers: Created by Alex on 28/08/2016.
ReflectionsHelper - Class in org.deeplearning4j.nn.conf: Original credit: https://gist.github.com/nonrational/287ed109bb0852f982e8
ReflectionsHelper() - Constructor for class org.deeplearning4j.nn.conf.ReflectionsHelper
ReflectionUtils - Class in org.deeplearning4j.util: General reflection utils
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
registerUrlTypes() - Static method in class org.deeplearning4j.nn.conf.ReflectionsHelper: OSX contains file:// resources on the classpath including .mar and .jnilib files.
RegressionEvaluation - Class in org.deeplearning4j.eval: Evaluation method for the evaluation of regression algorithms.
Provides the following metrics, for each column:
- MSE: mean squared error
- MAE: mean absolute error
- RMSE: root mean squared error
- RSE: relative squared error
- correlation coefficient
See for example: http://www.saedsayad.com/model_evaluation_r.htm For classification, see Evaluation
RegressionEvaluation() - Constructor for class org.deeplearning4j.eval.RegressionEvaluation
RegressionEvaluation(int) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Create a regression evaluation object with the specified number of columns, and default precision for the stats() method.
RegressionEvaluation(int, int) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Create a regression evaluation object with the specified number of columns, and specified precision for the stats() method.
RegressionEvaluation(String...) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Create a regression evaluation object with default precision for the stats() method
RegressionEvaluation(List<String>) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Create a regression evaluation object with default precision for the stats() method
RegressionEvaluation(List<String>, int) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Create a regression evaluation object with specified precision for the stats() method
regularization(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Whether to use regularization (l1, l2, dropout, etc
regularization(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
reinitMapperWithSubtypes(Collection<NamedType>) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: Reinitialize and return the Jackson/json ObjectMapper with additional named types.
relativeSquaredError(int) - Method in class org.deeplearning4j.eval.RegressionEvaluation
ReliabilityDiagram - Class in org.deeplearning4j.eval.curves: Created by Alex on 05/07/2017.
ReliabilityDiagram(String, double[], double[]) - Constructor for class org.deeplearning4j.eval.curves.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.AbstractDataSetIterator: Removes from the underlying collection the last element returned by this iterator (optional operation).
remove() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Removes from the underlying collection the last element returned by this iterator (optional operation).
remove() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator: Removes from the underlying collection the last element returned by this iterator (optional operation).
remove() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Removes from the underlying collection the last element returned by this iterator (optional operation).
remove() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldMultiDataSetIterator: Removes from the underlying collection the last element returned by this iterator (optional operation).
remove() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationMultiDataSetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator: Removes from the underlying collection the last element returned by this iterator (optional operation).
remove() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator: Removes from the underlying collection the last element returned by this iterator (optional operation).
remove() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
remove() - Method in class org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: Removes from the underlying collection the last element returned by this iterator (optional operation).
remove() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: Removes from the underlying collection the last element returned by this iterator (optional operation).
remove() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
remove() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
remove() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
remove(Object) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
remove(Object) - Method in class org.deeplearning4j.util.DiskBasedQueue
remove() - Method in class org.deeplearning4j.util.DiskBasedQueue
remove(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap: Removes the mapping for a key from this map if it is present (optional operation).
remove(Object) - Method in class org.deeplearning4j.util.MultiDimensionalSet: Removes the specified element from this applyTransformToDestination if it is present (optional operation).
removeAll(Collection<?>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
removeAll(Collection<?>) - Method in class org.deeplearning4j.util.DiskBasedQueue
removeAll(Collection<?>) - Method in class org.deeplearning4j.util.MultiDimensionalSet: Removes from this applyTransformToDestination all of its elements that are contained in the specified collection (optional operation).
removeColumns(Integer...) - Method in class org.deeplearning4j.util.StringGrid: Removes the specified columns from the grid
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.
removeRowsWithEmptyColumn(int) - Method in class org.deeplearning4j.util.StringGrid: Removes all rows with a column of NONE
removeRowsWithEmptyColumn(int, String) - Method in class org.deeplearning4j.util.StringGrid: Removes all rows with a column of missingValue
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.
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
reset() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
reset() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: Resets the iterator back to the beginning
reset() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Resets the iterator back to the beginning
reset() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator: Resets the iterator back to the beginning
reset() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Resets the iterator back to the beginning
reset() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldMultiDataSetIterator: Resets the iterator back to the beginning
reset() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
reset() - Method in interface org.deeplearning4j.datasets.iterator.callbacks.DataSetCallback
reset() - Method in class org.deeplearning4j.datasets.iterator.callbacks.DefaultCallback
reset() - Method in class org.deeplearning4j.datasets.iterator.callbacks.InterleavedDataSetCallback
reset() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher: Deprecated.

Returns the fetcher back to the beginning of the dataset
reset() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationMultiDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
reset() - Method in class org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: Resets the iterator back to the beginning
reset() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
reset(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
reset(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.FileSplitParallelDataSetIterator
reset(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: Resets the iterator back to the beginning
reset() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
reset() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
reset() - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
reset() - Method in class org.deeplearning4j.eval.Evaluation
reset() - Method in class org.deeplearning4j.eval.EvaluationBinary
reset() - Method in class org.deeplearning4j.eval.EvaluationCalibration
reset() - Method in interface org.deeplearning4j.eval.IEvaluation
reset() - Method in class org.deeplearning4j.eval.RegressionEvaluation
reset() - Method in class org.deeplearning4j.eval.ROC
reset() - Method in class org.deeplearning4j.eval.ROCBinary
reset() - Method in class org.deeplearning4j.eval.ROCMultiClass
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)
reset() - Static method in class org.deeplearning4j.util.OneTimeLogger
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.AbstractDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Is resetting supported by this DataSetIterator? Many DataSetIterators do support resetting, but some don't
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator: Is resetting supported by this DataSetIterator? Many DataSetIterators do support resetting, but some don't
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Is resetting supported by this DataSetIterator? Many DataSetIterators do support resetting, but some don't
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldMultiDataSetIterator: Is resetting supported by this DataSetIterator? Many DataSetIterators do support resetting, but some don't
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationMultiDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
resetTracker - Variable in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
resetVariables() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
reshape2dTo3d(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
reshape3dTo2d(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
reshapePerOutputTimeSeriesMaskTo2d(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
reshapeTimeSeriesMaskToVector(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Reshape time series mask arrays.
reshapeTimeSeriesTo2d(INDArray) - Static method in class org.deeplearning4j.eval.EvaluationUtils
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
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, int[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
ReshapeVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], 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(int[], INDArray, char) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil: Reshape the parameters view, without modifying the paramsView array values.
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
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 a file
restoreMultiLayerNetwork(InputStream) - Static method in class org.deeplearning4j.util.ModelSerializer
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
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
retainAll(Collection<?>) - Method in class org.deeplearning4j.util.DiskBasedQueue
retainAll(Collection<?>) - Method in class org.deeplearning4j.util.MultiDimensionalSet: Retains only the elements in this applyTransformToDestination that are contained in the specified collection (optional operation).
rho - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
rho(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
use .updater(new AdaDelta(rho,epsilon)) intead
rho - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer: Deprecated.
rho(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Ada delta coefficient, rho.
rho - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
rho(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
use .updater(new AdaDelta(rho,epsilon)) intead
rho - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration: Deprecated.
rmsDecay - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
rmsDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
use .updater(new RmsProp(rmsDecay)) instead
rmsDecay - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer: Deprecated.
rmsDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Decay rate for RMSProp.
rmsDecay - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
rmsDecay(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
use .updater(new RmsProp(rmsDecay)) intead
rmsDecay - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration: Deprecated.
rnnActivateUsingStoredState(INDArray, boolean, boolean) - 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) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
rnnActivateUsingStoredState(INDArray, boolean, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
rnnActivateUsingStoredState(INDArray, boolean, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
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 interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
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.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(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the state of the RNN layer, as used in rnnTimeStep().
rnnGetPreviousStates() - Method in interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
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
RnnOutputLayer - Class in org.deeplearning4j.nn.conf.layers
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(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
rnnTimeStep(INDArray) - 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 interface org.deeplearning4j.nn.api.NeuralNetworkPrototype
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(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
rnnTimeStep(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
rnnTimeStep(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
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.
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: ROC (Receiver Operating Characteristic) for binary classifiers.
ROC has 2 modes of operation: (a) Thresholded (default, less memory)
(b) Exact (use numSteps == 0.
ROC() - Constructor for class org.deeplearning4j.eval.ROC
ROC(int) - Constructor for class org.deeplearning4j.eval.ROC
ROC(int, boolean) - Constructor for class org.deeplearning4j.eval.ROC
ROC(int, boolean, int) - Constructor for class org.deeplearning4j.eval.ROC
ROC.CountsForThreshold - Class in org.deeplearning4j.eval
ROCArraySerializer - Class in org.deeplearning4j.eval.serde: Custom Jackson serializer for ROC[].
ROCArraySerializer() - Constructor for class org.deeplearning4j.eval.serde.ROCArraySerializer
ROCBinary - Class in org.deeplearning4j.eval: ROC (Receiver Operating Characteristic) for multi-task binary classifiers.
ROCBinary() - Constructor for class org.deeplearning4j.eval.ROCBinary
ROCBinary(int) - Constructor for class org.deeplearning4j.eval.ROCBinary
ROCBinary(int, boolean) - Constructor for class org.deeplearning4j.eval.ROCBinary
RocCurve - Class in org.deeplearning4j.eval.curves: ROC curve: a set of (false positive, true positive) tuples at different thresholds
RocCurve(double[], double[], double[]) - Constructor for class org.deeplearning4j.eval.curves.RocCurve
ROCMultiClass - Class in org.deeplearning4j.eval: ROC (Receiver Operating Characteristic) for multi-class classifiers.
ROCMultiClass() - Constructor for class org.deeplearning4j.eval.ROCMultiClass
ROCMultiClass(int) - Constructor for class org.deeplearning4j.eval.ROCMultiClass
ROCMultiClass(int, boolean) - Constructor for class org.deeplearning4j.eval.ROCMultiClass
ROCSerializer - Class in org.deeplearning4j.eval.serde: Custom Jackson serializer for ROC.
ROCSerializer() - Constructor for class org.deeplearning4j.eval.serde.ROCSerializer
rootFolder - Variable in class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback
rootMeanSquaredError(int) - Method in class org.deeplearning4j.eval.RegressionEvaluation
rootMeansSquaredError(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the root mean squared error of two data sets
round(double) - Static method in class org.deeplearning4j.util.MathUtils: Rounds a double to the next nearest integer value.
roundDouble(double, int) - Static method in class org.deeplearning4j.util.MathUtils: Rounds a double to the given number of decimal places.
roundFloat(float, int) - Static method in class org.deeplearning4j.util.MathUtils: Rounds a double to the given number of decimal places.
run() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator.AsyncPrefetchThread
run() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator.AsyncPrefetchThread

S

sampleDoublesInInterval(double[][], int) - Static method in class org.deeplearning4j.util.MathUtils
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
sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Binomial sampling of the hidden values given visible
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
sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Guess the visible values given the hidden
SamplingDataSetIterator - Class in org.deeplearning4j.datasets.iterator: A wrapper for a dataset to sample from.
SamplingDataSetIterator(DataSet, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
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
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
saveObject(Object, File) - Static method in class org.deeplearning4j.util.SerializationUtils
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
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
scan(Object) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
scan(Vfs.File, Object) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
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.subsampling.SubsamplingLayer
score() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
score - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
score() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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==true.
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<T>) - 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.
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
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(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Random number generator seed.
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(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
seed(long) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
seed - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
select(int, String) - Method in class org.deeplearning4j.util.StringGrid
sendMessage(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler: This method does loops encoded data back to updates queue
SerializationUtils - Class in org.deeplearning4j.util: Serialization utils for saving and reading serializable objects
serialize(ConfusionMatrix<Integer>, JsonGenerator, SerializerProvider) - Method in class org.deeplearning4j.eval.serde.ConfusionMatrixSerializer
serialize(ROC[], JsonGenerator, SerializerProvider) - Method in class org.deeplearning4j.eval.serde.ROCArraySerializer
serialize(ROC, JsonGenerator, SerializerProvider) - Method in class org.deeplearning4j.eval.serde.ROCSerializer
serializeWithType(ROC, JsonGenerator, SerializerProvider, TypeSerializer) - Method in class org.deeplearning4j.eval.serde.ROCSerializer
set(boolean, int) - Method in class org.deeplearning4j.datasets.iterator.parallel.MultiBoolean: Sets specified entry to specified state
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 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.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.variational.VariationalAutoencoder
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
setBufferSizePerSplit(int) - Method in class org.deeplearning4j.datasets.iterator.parallel.JointParallelDataSetIterator.Builder
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.variational.VariationalAutoencoder
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.FrozenLayer
setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setConfiguration(Configuration) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
setContentionTracing(boolean) - Static method in class org.deeplearning4j.util.ReflectionUtils
setEnd(int) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setEpsilon(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
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(Queue<INDArray>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
setExternalSource(Queue<INDArray>) - 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(Queue<INDArray>) - 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
setFirstKey(K) - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
setFrequency(int) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder: Desired IterationListener 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 PLEASE NOTE: Do not use this method unless you understand how to use GradientsAccumulator & updates sharing.
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
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.FrozenLayer
setIndex(int) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
setIndex(int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setIndex(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setInput(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer: Get the layer input.
setInput(int, INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the specified input for the ComputationGraph
setInput(int, INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
setInput(int, INDArray) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Set the input activations.
setInput(int, INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
setInput(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setInput(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
setInput(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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
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.FrozenLayer
setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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 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.
setInputType(InputType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
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 interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Sets the input vertices.
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
setLabelNames(List<String>) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher: Sets a list of label names to the curr dataset
setLabelNames(List<String>) - Method in class org.deeplearning4j.eval.EvaluationBinary: Set the label names, for printing via EvaluationBinary.stats()
setLabelNames(List<String>) - Method in class org.deeplearning4j.eval.ROCBinary: Set the label names, for printing via ROCBinary.stats()
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.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 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
setLayerParamLR(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
setLayers(Layer[]) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setLayerWiseConfigurations(MultiLayerConfiguration) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setLearningRateByParam(String, double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
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(IterationListener...) - Method in interface org.deeplearning4j.nn.api.Layer: Set the iteration listeners for this layer.
setListeners(Collection<IterationListener>) - Method in interface org.deeplearning4j.nn.api.Layer: Set the iteration listeners for this layer.
setListeners(Collection<IterationListener>) - Method in interface org.deeplearning4j.nn.api.Model: Set the IterationListeners for the ComputationGraph (and all layers in the network)
setListeners(IterationListener...) - Method in interface org.deeplearning4j.nn.api.Model: Set the IterationListeners for the ComputationGraph (and all layers in the network)
setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the IterationListeners for the ComputationGraph (and all layers in the network)
setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the IterationListeners for the ComputationGraph (and all layers in the network)
setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setListeners(Collection<IterationListener>) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.optimize.Solver
setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
setLogMetaInstability(double) - Method in class org.deeplearning4j.util.Viterbi
setLogOfDiangnalTProb(double) - Method in class org.deeplearning4j.util.Viterbi
setLogPCorrect(double) - Method in class org.deeplearning4j.util.Viterbi
setLogPIncorrect(double) - Method in class org.deeplearning4j.util.Viterbi
setLogStates(double) - Method in class org.deeplearning4j.util.Viterbi
setLower(double) - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
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.FrozenLayer
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.multilayer.MultiLayerNetwork
setMax(double) - Method in class org.deeplearning4j.util.SummaryStatistics
setMaxIterations(int) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
setMean(double) - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
setMean(double) - Method in class org.deeplearning4j.util.SummaryStatistics
setMetaStability(double) - Method in class org.deeplearning4j.util.Viterbi
setMin(double) - Method in class org.deeplearning4j.util.SummaryStatistics
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.Convolution1DLayer
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 depth for CNNs) based on the given input type
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.RnnOutputLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
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
setOutputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
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.FrozenLayer
setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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(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.subsampling.SubsamplingLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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.FrozenLayer
setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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.FrozenLayer
setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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
setpCorrect(double) - Method in class org.deeplearning4j.util.Viterbi
setPossibleLabels(INDArray) - Method in class org.deeplearning4j.util.Viterbi
setPrediction(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: Set a pre processor
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Set a pre processor
setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator: Set the preprocessor to be applied to each MultiDataSet, before each MultiDataSet is returned.
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Set a pre processor
setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldMultiDataSetIterator: Set the preprocessor to be applied to each MultiDataSet, before each MultiDataSet is returned.
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationMultiDataSetIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkMultiDataSetIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
setProbabilityOfSuccess(double) - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
setProperties(Object, Properties) - Static method in class org.deeplearning4j.util.Dl4jReflection: Sets the properties of the given object
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) - 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
setSecondKey(T) - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
setStates(int) - Method in class org.deeplearning4j.util.Viterbi
setStateViewArray(Layer, 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(Layer, 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
setStore(Multimap<String, String>) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
setSum(double) - Method in class org.deeplearning4j.util.SummaryStatistics
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
setType(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
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
setUpper(double) - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
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
SetUtils - Class in org.deeplearning4j.util
setValue(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setValue(V) - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry: Replaces the value corresponding to this entry with the specified value (optional operation).
setVector(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
shakeFrequency - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
shape - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
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
One could use it to add a bias or as part of some other calculation.
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
shouldWork - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
shouldWork - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
shuffleArray(int[], long) - Static method in class org.deeplearning4j.util.MathUtils
shuffleArray(int[], Random) - Static method in class org.deeplearning4j.util.MathUtils
shutdown() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator.AsyncPrefetchThread
shutdown() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: This method will terminate background thread AND will destroy attached workspace (if any) PLEASE NOTE: After shutdown() call, this instance can't be used anymore
shutdown() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator.AsyncPrefetchThread
shutdown() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator: This method will terminate background thread AND will destroy attached workspace (if any) PLEASE NOTE: After shutdown() call, this instance can't be used anymore
sigma - Variable in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Deprecated.
sigmoid(double) - Static method in class org.deeplearning4j.util.MathUtils: 1 / 1 + exp(-x)
silentOutput(boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
silentOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
silentOutput(INDArray, boolean, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
simpleHostname(String) - Static method in class org.deeplearning4j.util.StringUtils: Given a full hostname, return the word upto the first dot.
SingletonMultiDataSetIterator - Class in org.deeplearning4j.datasets.iterator.impl: A very simple adapter class for converting a single MultiDataSet to a MultiDataSetIterator.
SingletonMultiDataSetIterator(MultiDataSet) - Constructor for class org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator
size() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
size() - Method in class org.deeplearning4j.util.DiskBasedQueue
size() - Method in class org.deeplearning4j.util.Index
size() - Method in class org.deeplearning4j.util.MultiDimensionalMap: Returns the number of key-value mappings in this map.
size() - Method in class org.deeplearning4j.util.MultiDimensionalSet: Returns the number of elements in this applyTransformToDestination (its cardinality).
SizeComparator() - Constructor for class org.deeplearning4j.util.StringCluster.SizeComparator
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.
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
slope(double, double, double, double) - Method in class org.deeplearning4j.util.MathUtils: This returns the slope of the given points.
sm(double, double) - Static method in class org.deeplearning4j.util.MathUtils: Tests if a is smaller than b.
SMALL - Static variable in class org.deeplearning4j.util.MathUtils: The small deviation allowed in double comparisons.
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
sort() - Method in class org.deeplearning4j.util.StringCluster
sortBy(int) - Method in class org.deeplearning4j.util.StringGrid
sortColumnsByWordLikelihoodIncluded(int) - Method in class org.deeplearning4j.util.StringGrid
sparsity(double) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder
sparsity - Variable in class org.deeplearning4j.nn.conf.layers.AutoEncoder
sparsity(double) - Method in class org.deeplearning4j.nn.conf.layers.RBM.Builder
sparsity - Variable in class org.deeplearning4j.nn.conf.layers.RBM
split(int, String) - Method in class org.deeplearning4j.util.StringGrid
split(String) - Static method in class org.deeplearning4j.util.StringUtils: Split a string using the default separator
split(String, char, char) - Static method in class org.deeplearning4j.util.StringUtils: Split a string using the given separator
splitInputs(INDArray, INDArray, List<Pair<INDArray, INDArray>>, List<Pair<INDArray, INDArray>>, double) - Static method in class org.deeplearning4j.util.InputSplit
splitInputs(List<Pair<INDArray, INDArray>>, List<Pair<INDArray, INDArray>>, List<Pair<INDArray, INDArray>>, double) - Static method in class org.deeplearning4j.util.InputSplit
splitOnCharWithQuoting(String, char, char, char) - Static method in class org.deeplearning4j.util.StringUtils: This function splits the String s into multiple Strings using the splitChar.
squaredLoss(double[], double[], double, double) - Static method in class org.deeplearning4j.util.MathUtils: This will return the squared loss of the given points
ssError(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils: How much of the variance is NOT explained by the regression
ssReg(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils: How much of the variance is explained by the regression
ssTotal(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils: Total variance in target attribute
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
STATE_KEY_PREV_ACTIVATION - Static variable in class org.deeplearning4j.nn.layers.recurrent.LSTM
STATE_KEY_PREV_MEMCELL - Static variable in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
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.
states - Variable in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
stats() - Method in class org.deeplearning4j.eval.Evaluation
stats(boolean) - Method in class org.deeplearning4j.eval.Evaluation: Method to obtain the classification report as a String
stats() - Method in class org.deeplearning4j.eval.EvaluationBinary: Get a String representation of the EvaluationBinary class, using the default precision
stats(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Get a String representation of the EvaluationBinary class, using the specified precision
stats() - Method in class org.deeplearning4j.eval.EvaluationCalibration
stats() - Method in interface org.deeplearning4j.eval.IEvaluation
stats() - Method in class org.deeplearning4j.eval.RegressionEvaluation
stats() - Method in class org.deeplearning4j.eval.ROC
stats() - Method in class org.deeplearning4j.eval.ROCBinary
stats(int) - Method in class org.deeplearning4j.eval.ROCBinary
stats() - Method in class org.deeplearning4j.eval.ROCMultiClass
stats(int) - Method in class org.deeplearning4j.eval.ROCMultiClass
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
stepDelay - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
stepForward() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
stepFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
stepFunction(StepFunction) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Step function to apply for back track line search.
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 - 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
stepTrigger - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
StochasticGradientDescent - Class in org.deeplearning4j.optimize.solvers: Stochastic Gradient Descent Standard fix step size No line search
StochasticGradientDescent(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
StochasticGradientDescent(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Collection<TerminationCondition>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
storage - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
storeUpdate(INDArray) - 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) - 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) - 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 - Variable 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.Subsampling1DLayer.Builder: Stride
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
string2long(String) - Static method in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix: Convert a string to long.
StringCluster - Class in org.deeplearning4j.util: Clusters strings based on fingerprint: for example Two words and TWO words or WORDS TWO would be put together
StringCluster(List<String>) - Constructor for class org.deeplearning4j.util.StringCluster
StringCluster.SizeComparator - Class in org.deeplearning4j.util
StringGrid - Class in org.deeplearning4j.util: String matrix
StringGrid(StringGrid) - Constructor for class org.deeplearning4j.util.StringGrid
StringGrid(String, int) - Constructor for class org.deeplearning4j.util.StringGrid
StringGrid(String, Collection<String>) - Constructor for class org.deeplearning4j.util.StringGrid
stringifyException(Throwable) - Static method in class org.deeplearning4j.util.StringUtils: Make a string representation of the exception.
stringSimilarity(String...) - Static method in class org.deeplearning4j.util.MathUtils: Calculate string similarity with tfidf weights relative to each character frequency and how many times a character appears in a given string
stringToURI(String[]) - Static method in class org.deeplearning4j.util.StringUtils
StringUtils - Class in org.deeplearning4j.util: General string utils
StringUtils.TraditionalBinaryPrefix - Enum in org.deeplearning4j.util: The traditional binary prefixes, kilo, mega, ..., exa, which can be represented by a 64-bit integer.
stripDuplicateRows() - Method in class org.deeplearning4j.util.StringGrid
Subsampling1DLayer - Class in org.deeplearning4j.nn.conf.layers: 1D (temporal) subsampling layer.
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
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 NON
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
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
sum(double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the sum of the given array.
summary() - 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.
SummaryStatistics - Class in org.deeplearning4j.util
summaryStats(INDArray) - Static method in class org.deeplearning4j.util.SummaryStatistics
summaryStatsString(INDArray) - Static method in class org.deeplearning4j.util.SummaryStatistics
sumOfMeanDifferences(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils: Used for calculating top part of simple regression for beta 1
sumOfMeanDifferencesOnePoint(double[]) - Static method in class org.deeplearning4j.util.MathUtils: Used for calculating top part of simple regression for beta 1
sumOfProducts(double[]...) - Static method in class org.deeplearning4j.util.MathUtils: This returns the sum of products for the given numbers.
sumOfSquares(double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the sum of squares for the given vector.
swap(int, int) - Method in class org.deeplearning4j.util.StringGrid
symbol - Variable in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix
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

T

tags() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
take() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
tbpttBackpropGradient(INDArray, int) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer: Truncated BPTT equivalent of Layer.backpropGradient().
tbpttBackpropGradient(INDArray, int) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
tbpttBackpropGradient(INDArray, int) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
tbpttBackpropGradient(INDArray, int) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
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) - Method in class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition
terminate(int, double) - 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) - 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) - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
terminate(double, double, Object[]) - Method in interface org.deeplearning4j.optimize.api.TerminationCondition: Whether to terminate based on the given metadata
terminate(double, double, Object[]) - Method in class org.deeplearning4j.optimize.terminations.EpsTermination
terminate(double, double, Object[]) - Method in class org.deeplearning4j.optimize.terminations.Norm2Termination
terminate(double, double, Object[]) - Method in class org.deeplearning4j.optimize.terminations.ZeroDirection
TerminationCondition - Interface in org.deeplearning4j.optimize.api: Created by agibsonccc on 12/24/14.
terminationConditions - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
TerminationConditions - Class in org.deeplearning4j.optimize.terminations: Created by agibsonccc on 12/24/14.
TerminationConditions() - Constructor for class org.deeplearning4j.optimize.terminations.TerminationConditions
terminator - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
terminator - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
TestDataSetConsumer - Class in org.deeplearning4j.util: Class that consumes DataSets with specified delays, suitable for testing
TestDataSetConsumer(long) - Constructor for class org.deeplearning4j.util.TestDataSetConsumer
TestDataSetConsumer(DataSetIterator, long) - Constructor for class org.deeplearning4j.util.TestDataSetConsumer
tf(int, int) - Static method in class org.deeplearning4j.util.MathUtils: Term frequency: 1+ log10(count)
tfidf(double, double) - Static method in class org.deeplearning4j.util.MathUtils: Return td * idf
thread - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
thread - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
threshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
threshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
thresholdStep - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
throwable - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
throwable - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
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
times(double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the product of all numbers in the given array.
TimeSeriesUtils - Class in org.deeplearning4j.util: Basic time series utils
toArray() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
toArray(T[]) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
toArray() - Method in class org.deeplearning4j.util.DiskBasedQueue
toArray(T[]) - Method in class org.deeplearning4j.util.DiskBasedQueue
toArray() - Method in class org.deeplearning4j.util.MultiDimensionalSet: Returns an array containing all of the elements in this applyTransformToDestination.
toArray(T[]) - Method in class org.deeplearning4j.util.MultiDimensionalSet: Returns an array containing all of the elements in this applyTransformToDestination; the runtime type of the returned array is that of the specified array.
toByteArray(Serializable) - Static method in class org.deeplearning4j.util.SerializationUtils: Converts the given object to a byte array
toCSV() - Method in class org.deeplearning4j.eval.ConfusionMatrix: Outputs the ConfusionMatrix as comma-separated values for easy import into spreadsheets
toDecimal(String) - Static method in class org.deeplearning4j.util.MathUtils: This will convert the given binary string to a decimal based integer
toHTML() - Method in class org.deeplearning4j.eval.ConfusionMatrix: Outputs Confusion Matrix in an HTML table.
toJson() - Method in class org.deeplearning4j.eval.BaseEvaluation
toJson() - Method in class org.deeplearning4j.eval.curves.BaseCurve
toJson() - Method in class org.deeplearning4j.eval.curves.BaseHistogram
toJson() - Method in interface org.deeplearning4j.eval.IEvaluation
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
toLines() - Method in class org.deeplearning4j.util.StringGrid
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
topN - Variable in class org.deeplearning4j.eval.Evaluation
topNAccuracy() - Method in class org.deeplearning4j.eval.Evaluation: Top N accuracy of the predictions so far.
topNCorrectCount - Variable in class org.deeplearning4j.eval.Evaluation
topNTotalCount - Variable in class org.deeplearning4j.eval.Evaluation
topologicalOrder - Variable in class org.deeplearning4j.nn.graph.ComputationGraph: Indexes of graph vertices, in topological order.
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.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
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.eval.BaseEvaluation
toString() - Method in class org.deeplearning4j.eval.ConfusionMatrix
toString() - Method in class org.deeplearning4j.eval.meta.Prediction
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.NormalDistribution
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.StackVertex
toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional
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.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.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.util.Index
toString() - Method in class org.deeplearning4j.util.MultiDimensionalMap
toString() - Method in class org.deeplearning4j.util.SummaryStatistics
totalCount(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Get the total number of values for the specified column, accounting for any masking
totalExamples - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
totalExamples() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: Total examples in the iterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: Total examples in the iterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: Total examples in the iterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
totalExamples() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher: Deprecated.

The total number of examples
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: Total examples in the iterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: Total examples in the iterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
totalExamples() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
totalIterations - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
totalOutcomes() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator: The number of labels for the dataset
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator: The number of labels for the dataset
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator: The number of labels for the dataset
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
totalOutcomes() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher: Deprecated.

The number of labels for a dataset
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.FileSplitDataSetIterator
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.impl.BenchmarkDataSetIterator
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator: The number of labels for the dataset
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.parallel.BaseParallelDataSetIterator
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator: The number of labels for the dataset
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
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.eval.BaseEvaluation
toYaml() - Method in class org.deeplearning4j.eval.curves.BaseCurve
toYaml() - Method in class org.deeplearning4j.eval.curves.BaseHistogram
toYaml() - Method in interface org.deeplearning4j.eval.IEvaluation
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
trackEpochs() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
TrainingListener - Interface in org.deeplearning4j.optimize.api: TrainingListener: an extension of IterationListener that adds onEpochStart, onEpochEnd, onForwardPass and onBackwardPass methods
trainingListeners - Variable in class org.deeplearning4j.nn.layers.BasePretrainNetwork
trainingListeners - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
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: This method defines Workspace mode being used during training: NONE: workspace won't be used SINGLE: one workspace will be used during whole iteration loop SEPARATE: separate workspaces will be used for feedforward and backprop iteration loops
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 SINGLE: one workspace will be used during whole iteration loop SEPARATE: separate workspaces will be used for feedforward and backprop iteration loops
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
transpose() - Method in interface org.deeplearning4j.nn.api.Layer: Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)
transpose() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
transpose() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
transpose() - Method in class org.deeplearning4j.nn.layers.BaseLayer
transpose() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
transpose() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
transpose() - Method in class org.deeplearning4j.nn.layers.DropoutLayer
transpose() - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Deprecated.
transpose() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
transpose() - Method in class org.deeplearning4j.nn.layers.LossLayer
transpose() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
transpose() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
transpose() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
transpose() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
transpose() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
transpose() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
transpose() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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
treeSet() - Static method in class org.deeplearning4j.util.MultiDimensionalSet
trueNegatives - Variable in class org.deeplearning4j.eval.Evaluation
trueNegatives() - Method in class org.deeplearning4j.eval.Evaluation: True negatives: correctly rejected
trueNegatives(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Get the true negatives count for the specified output
truePositives - Variable in class org.deeplearning4j.eval.Evaluation
truePositives() - Method in class org.deeplearning4j.eval.Evaluation: True positives: correctly rejected
truePositives(int) - Method in class org.deeplearning4j.eval.EvaluationBinary: Get the true positives count for the specified output
truncatedBPTTGradient() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Equivalent to backprop(), but calculates gradient for truncated BPTT instead.
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.ConvolutionLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
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.FrozenLayer
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.pooling.GlobalPoolingLayer
type() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
type() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
type() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
type() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
type() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
type() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

U

UIDProvider - Class in org.deeplearning4j.util: Static methods for obtaining unique identifiers for both the machine (hardware) and the JVM.
unEscapeString(String) - Static method in class org.deeplearning4j.util.StringUtils: Unescape commas in the string using the default escape char
unEscapeString(String, char, char) - Static method in class org.deeplearning4j.util.StringUtils: Unescape charToEscape in the string with the escape char escapeChar
unEscapeString(String, char, char[]) - Static method in class org.deeplearning4j.util.StringUtils
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
uniform(Random, double, double) - Static method in class org.deeplearning4j.util.MathUtils: Generate a uniform random number from the given rng
UniformDistribution - Class in org.deeplearning4j.nn.conf.distribution: A uniform distribution.
UniformDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.UniformDistribution: Create a uniform real distribution using the given lower and upper bounds.
union(Set<? extends T>, Set<? extends T>) - Static method in class org.deeplearning4j.util.SetUtils
UnitVarianceProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: Unit variance operation
UnitVarianceProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.UnitVarianceProcessor
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
unzipFileTo(String, String) - Static method in class org.deeplearning4j.util.ArchiveUtils: Extracts files to the specified destination
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(Layer, Gradient, int, int) - 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.subsampling.SubsamplingLayer
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.variational.VariationalAutoencoder
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
update(MultiLayerNetwork) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Assigns the parameters of this model to the ones specified by this network.
update(INDArray, String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
update(Gradient) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
update(Layer, Gradient, int, int) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
update(Gradient, int, int) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater: Update the gradient for the model.
update(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, INDArray, INDArray) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock
updateGradientAccordingToParams(Gradient, Model, int) - 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) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
Updater - Interface in org.deeplearning4j.nn.api: Update the model
updater - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
updater(Updater) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Gradient updater.
updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Gradient updater.
updater - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer: Deprecated.
updater(Updater) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Gradient updater.
updater - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
updater(Updater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Gradient updater.
updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Gradient updater.
Updater - Enum in org.deeplearning4j.nn.conf: All the possible different updaters
updater(IUpdater) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
updater(Updater) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
updater - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration: Deprecated.
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(Layer, String, Layer, String) - Static method in class org.deeplearning4j.nn.updater.UpdaterUtils
UpdaterCreator - Class in org.deeplearning4j.nn.updater
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
updaterValidation(String, Layer, Double, Double, Map<Integer, Double>, Double, Double, Double, Double, Double) - Static method in class org.deeplearning4j.nn.conf.layers.LayerValidation: Validate the updater configuration - setting the default updater values, if necessary
updaterValidation(String, BaseLayer, double, double, Map<Integer, Double>, double, double, double, double, double) - Static method in class org.deeplearning4j.nn.conf.layers.LayerValidation: Validate the updater configuration - setting the default updater values, if necessary
updates - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
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)
updatesLock - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
uriToString(URI[]) - Static method in class org.deeplearning4j.util.StringUtils
useDropConnect - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
useDropConnect(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Use drop connect: multiply the weight by a binomial sampling wrt the dropout probability.
useDropConnect - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
useDropConnect - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
useRegularization - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
useRegularization - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
useRegularization - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
useWorkspace - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
useWorkspaces - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator

V

validate() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: Check the configuration, make sure it is valid
validateCnnKernelStridePadding(int[], int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Perform validation on the CNN layer kernel/stride/padding.
validateInput() - Method in interface org.deeplearning4j.nn.api.Model: Deprecated.
As of 0.7.3 - Feb 2017. No longer used, most implementations are unsupported or no-op.
validateInput() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
validateInput() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
validateInput() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
validateInput() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
validateInput() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
validateShapes(int, int, int, int, int, int, int, int) - Static method in class org.deeplearning4j.nn.layers.convolution.KernelValidationUtil
value() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
value - Variable in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix
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.eval.EvaluationAveraging: 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.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.RBM.HiddenUnit: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.RBM.VisibleUnit: 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.LearningRatePolicy: 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.optimize.api.InvocationType: 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.
valueOf(String) - Static method in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix: Returns the enum constant of this type with the specified name.
valueOf(char) - Static method in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix
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.eval.EvaluationAveraging: 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.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.RBM.HiddenUnit: 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.RBM.VisibleUnit: 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.LearningRatePolicy: 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.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.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.
values() - Method in class org.deeplearning4j.util.MultiDimensionalMap: Returns a Collection view of the values contained in this map.
values() - Static method in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix: 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
variance(double[]) - Static method in class org.deeplearning4j.util.MathUtils
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
vectorLength(double[]) - Static method in class org.deeplearning4j.util.MathUtils: Returns the vector length (sqrt(sum(x_i))
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
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
visibleUnit(RBM.VisibleUnit) - Method in class org.deeplearning4j.nn.conf.layers.RBM.Builder
visibleUnit - Variable in class org.deeplearning4j.nn.conf.layers.RBM
Viterbi - Class in org.deeplearning4j.util: Based on the impl from: https://gist.github.com/rmcgibbo/3915977
Viterbi(INDArray) - Constructor for class org.deeplearning4j.util.Viterbi: The possible outcomes for the chain.

W

w_0(double[], double[], int) - Static method in class org.deeplearning4j.util.MathUtils
w_1(double[], double[], int) - Static method in class org.deeplearning4j.util.MathUtils
warn(Logger, String, Object...) - Static method in class org.deeplearning4j.util.OneTimeLogger
WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.CenterLossParamInitializer
WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.DefaultParamInitializer
WEIGHT_KEY_SUFFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
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.
weightInit - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
weightInit(WeightInit) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Weight initialization scheme.
weightInit - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
weightInit(WeightInit) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Weight initialization scheme.
weightInit - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
WeightInit - Enum in org.deeplearning4j.nn.weights: Weight initialization scheme
WeightInitUtil - Class in org.deeplearning4j.nn.weights: Weight initialization utility
weightMatrices(MultiLayerNetwork) - Static method in class org.deeplearning4j.util.MultiLayerUtil: Return the weight matrices for a multi layer network
weightsFor(List<Double>) - Static method in class org.deeplearning4j.util.MathUtils: This returns the minimized loss values for a given vector.
weightsFor(double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the minimized loss values for a given vector.
windows() - Method in class org.deeplearning4j.util.MovingWindowMatrix: Returns a list of non flattened moving window matrices
windows(boolean) - Method in class org.deeplearning4j.util.MovingWindowMatrix: Moving window, capture a row x column moving window of a given matrix
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.
workspaceBackProp - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
workspaceCache - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
workspaceConfigurationCache - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
workspaceConfigurationExternal - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
workspaceConfigurationExternal - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
workspaceConfigurationFeedForward - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
workspaceConfigurationFeedForward - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
workspaceConfigurationLSTM - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
workspaceConfigurationTBPTT - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
workspaceConfigurationTBPTT - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
workspaceExternal - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
workspaceExternal - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
workspaceFeedForward - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
workspaceFeedForward - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
workspaceId - Variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
workspaceId - Variable in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
workspaceLSTM - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
WorkspaceMode - Enum in org.deeplearning4j.nn.conf
workspacePretrain - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
workspaces - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
WorkspacesShieldDataSetIterator - Class in org.deeplearning4j.datasets.iterator: This iterator detaches/migrates DataSets coming out from backed DataSetIterator, thus providing "safe" DataSets.
WorkspacesShieldDataSetIterator(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.WorkspacesShieldDataSetIterator
workspaceTBPTT - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
workspaceTBPTT - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
writeLinesTo(String) - Method in class org.deeplearning4j.util.StringGrid
writeModel(Model, File, boolean) - 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
writeObject(Serializable, OutputStream) - Static method in class org.deeplearning4j.util.SerializationUtils: Writes the object to the output stream THIS DOES NOT FLUSH THE STREAMMultiLayerNetwork

X

xHat - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
xMu - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
xVals(double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the x values of the given vector.

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)
yVals(double[]) - Static method in class org.deeplearning4j.util.MathUtils: This returns the odd indexed values for the given vector

Z

ZeroDirection - Class in org.deeplearning4j.optimize.terminations: Absolute magnitude of gradient is 0
ZeroDirection() - Constructor for class org.deeplearning4j.optimize.terminations.ZeroDirection
zeroedPretrainParamGradients - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
ZeroMeanAndUnitVariancePreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: Zero mean and unit variance operation
ZeroMeanAndUnitVariancePreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanAndUnitVariancePreProcessor
ZeroMeanPrePreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: Zero mean and unit variance operation
ZeroMeanPrePreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanPrePreProcessor
ZeroPaddingLayer - Class in org.deeplearning4j.nn.conf.layers: Zero padding layer for convolutional neural networks.
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
zFromPrevLayer(int, INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Compute input linear transformation (z) from previous layer Apply pre processing transformation where necessary

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