Index (deeplearning4j-nn 1.0.0-beta5 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

a(double) - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout
AbstractLayer<LayerConfT extends Layer> - Class in org.deeplearning4j.nn.layers: A layer with input and output, no parameters or gradients
AbstractLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.AbstractLayer
AbstractLSTM - Class in org.deeplearning4j.nn.conf.layers: LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
AbstractLSTM(AbstractLSTM.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.AbstractLSTM
AbstractLSTM.Builder<T extends AbstractLSTM.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers
AbstractSameDiffLayer - Class in org.deeplearning4j.nn.conf.layers.samediff
AbstractSameDiffLayer(AbstractSameDiffLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
AbstractSameDiffLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
AbstractSameDiffLayer.Builder<T extends AbstractSameDiffLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers.samediff
accumulator - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
accumulator - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
accumulator - Variable in class org.deeplearning4j.optimize.solvers.accumulation.LocalHandler
accumulator - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
activate(boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Layer: Perform forward pass and return the activations array with the last set input
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Layer: Perform forward pass and return the activations array with the specified input
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
activate(INDArray, IActivation, boolean) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
activate(INDArray, boolean, int[], int[], int[], PoolingType, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.PReLU
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer
activate(INDArray, IActivation, boolean) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
activate(INDArray, boolean, double, double, double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
activate(INDArray, boolean, int[], int[], int[], PoolingType, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
activate(INDArray, boolean, double, double, double, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
activate(INDArray, INDArray) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils: Essentially: just apply activation functions...
activate(INDArray, INDArray, LayerWorkspaceMgr) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
activate(Layer, NeuralNetConfiguration, IActivation, INDArray, INDArray, INDArray, INDArray, boolean, INDArray, INDArray, boolean, boolean, String, INDArray, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.recurrent.LSTMHelper
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.RepeatVector
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.util.MaskLayer
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Equivalent to MultiLayerNetwork.output(INDArray) using the input set via MultiLayerNetwork.setInput(INDArray)
activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Equivalent to #output(INDArray, TrainingMode)
activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
activateHelper(BaseRecurrentLayer, NeuralNetConfiguration, IActivation, INDArray, INDArray, INDArray, INDArray, boolean, INDArray, INDArray, boolean, boolean, String, INDArray, boolean, LSTMHelper, CacheMode, LayerWorkspaceMgr, boolean) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers: Returns FwdPassReturn object with activations/INDArrays.
activateSelectedLayers(int, int, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Calculate activation for few layers at once.
activation(String) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder: Deprecated.
Use ActivationLayer.Builder.activation(Activation) or @activation(IActivation)
activation(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder
activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder
activation(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Set the activation function for the layer.
activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Set the activation function for the layer, from an Activation enumeration value.
activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer.Builder
activation(IActivation) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Activation function / neuron non-linearity
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
activation(Activation) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Activation function / neuron non-linearity
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
activation - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: The activation function to use with ocnn
activation(IActivation) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: The activation function to use with ocnn
activation(IActivation) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Activation function / neuron non-linearity
activation(Activation) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Activation function / neuron non-linearity
activationExceedsZeroOneRange(IActivation, boolean) - Static method in class org.deeplearning4j.util.OutputLayerUtil
activationFn - Variable in class org.deeplearning4j.nn.conf.layers.ActivationLayer
activationFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
activationFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Set the activation function for the layer.
activationFn - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
activationFn - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
activationFromPrevLayer(int, INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
ActivationLayer - Class in org.deeplearning4j.nn.conf.layers: Activation layer is a simple layer that applies the specified activation function to the input activations
ActivationLayer(ActivationLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.ActivationLayer
ActivationLayer(Activation) - Constructor for class org.deeplearning4j.nn.conf.layers.ActivationLayer
ActivationLayer(IActivation) - Constructor for class org.deeplearning4j.nn.conf.layers.ActivationLayer
ActivationLayer - Class in org.deeplearning4j.nn.layers: Activation Layer Used to apply activation on input and corresponding derivative on epsilon.
ActivationLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.ActivationLayer
ActivationLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
activationsVertexName() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer: Output layers should terminate in a single scalar value (i.e., a score) - however, sometimes the output activations (such as softmax probabilities) need to be returned.
adapt2dMask(INDArray, INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
AdaptiveThresholdAlgorithm - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold: An adaptive threshold algorithm used to determine the encoding threshold for distributed training.
The idea: the threshold can be too high or too low for optimal training - both cases are bad.
So instead, we'll define a range of "acceptable" sparsity ratio values (default: 1e-4 to 1e-2).
The sparsity ratio is defined as numValues(encodedUpdate)/numParameters

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

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

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

A sparsity ratio of 1.0 means all values are present in the encoded update vector.
A sparsity ratio of 0.0 means all values were excluded from the encoded update vector.
AdaptiveThresholdAlgorithm() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm: Create the adaptive threshold algorithm with the default initial threshold AdaptiveThresholdAlgorithm.DEFAULT_INITIAL_THRESHOLD, default minimum sparsity target AdaptiveThresholdAlgorithm.DEFAULT_MIN_SPARSITY_TARGET, default maximum sparsity target AdaptiveThresholdAlgorithm.DEFAULT_MAX_SPARSITY_TARGET, and default decay rate AdaptiveThresholdAlgorithm.DEFAULT_DECAY_RATE
AdaptiveThresholdAlgorithm(double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm: Create the adaptive threshold algorithm with the specified initial threshold, but defaults for the other values: default minimum sparsity target AdaptiveThresholdAlgorithm.DEFAULT_MIN_SPARSITY_TARGET, default maximum sparsity target AdaptiveThresholdAlgorithm.DEFAULT_MAX_SPARSITY_TARGET, and default decay rate AdaptiveThresholdAlgorithm.DEFAULT_DECAY_RATE
AdaptiveThresholdAlgorithm(double, double, double, double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm
add(ThresholdAlgorithm) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer
add(ThresholdAlgorithm) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithmReducer: Add a ThresholdAlgorithm instance to the reducer
add(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
addAll(Collection<? extends E>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
addBiasParam(String, long...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams: Add a bias parameter to the layer, with the specified shape.
addDistribution(int, ReconstructionDistribution) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution.Builder: Add another distribution to the composite distribution.
addInputs(String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Specify the inputs to the network, and their associated labels.
addInputs(Collection<String>) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Specify the inputs to the network, and their associated labels.
addInputs(String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
addLayer(String, Layer, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Add a layer, with no InputPreProcessor, with the specified name and specified inputs.
addLayer(String, Layer, InputPreProcessor, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Add a layer and an InputPreProcessor, with the specified name and specified inputs.
addLayer(Layer) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Add layers to the net Required if layers are removed.
addLayer(String, Layer, String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Add a layer of the specified configuration to the computation graph
addLayer(String, Layer, InputPreProcessor, String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Add a layer with a specified preprocessor
addListeners(TrainingListener...) - Method in interface org.deeplearning4j.nn.api.Model: This method ADDS additional TrainingListener to existing listeners
addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: This method ADDS additional TrainingListener to existing listeners
addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.AbstractLayer: This method ADDS additional TrainingListener to existing listeners
addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder: This method ADDS additional TrainingListener to existing listeners
addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
addListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: This method ADDS additional TrainingListener to existing listeners
addNormalizerToModel(File, Normalizer<?>) - Static method in class org.deeplearning4j.util.ModelSerializer: This method appends normalizer to a given persisted model.
addObjectToFile(File, String, Object) - Static method in class org.deeplearning4j.util.ModelSerializer: Add an object to the (already existing) model file using Java Object Serialization.
addPreProcessors(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: Add preprocessors automatically, given the specified types of inputs for the network.
addVariable(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
addVertex(String, GraphVertex, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Add a GraphVertex to the network configuration.
addVertex(String, GraphVertex, String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Add a vertex of the given configuration to the computation graph
addWeightParam(String, long...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams: Add a weight parameter to the layer, with the specified shape.
ALF - Variable in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
allowCollapse - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
allowDisconnected - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
allowDisconnected(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Used only during validation after building.
If true: don't throw an exception on configurations containing vertices that are 'disconnected'.
allowInputModification(boolean) - Method in interface org.deeplearning4j.nn.api.Layer: A performance optimization: mark whether the layer is allowed to modify its input array in-place.
allowInputModification(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
allowInputModification(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
allowInputModification(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
allowInputModification(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
allowInputModification(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
allowNoOutput - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
allowNoOutput(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Used only during validation after building.
If true: don't throw an exception on configurations without any outputs.
allParamConstraints - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
allParamConstraints - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
allThreadThresholdAlgorithms - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
alpha - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
alpha - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
alpha(double) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
alpha - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
alpha(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder: LRN scaling constant alpha.
AlphaDropout - Class in org.deeplearning4j.nn.conf.dropout: AlphaDropout is a dropout technique proposed by Klaumbauer et al.
AlphaDropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.AlphaDropout
AlphaDropout(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.AlphaDropout
AlphaDropout(double, ISchedule, double, double) - Constructor for class org.deeplearning4j.nn.conf.dropout.AlphaDropout
ancestor(int, Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Returns the ancestor of the given tree
And(FailureTestingListener.FailureTrigger...) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.And
appendLayer(String, Layer) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Add a layer, with no InputPreProcessor, with the specified name and input from the last added layer/vertex.
appendLayer(String, Layer, InputPreProcessor) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Add a layer and an InputPreProcessor, with the specified name and input from the last added layer/vertex.
appendVertex(String, GraphVertex) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Add a GraphVertex to the network configuration, with input from the last added vertex/layer.
appliedConfiguration - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
appliedNeuralNetConfiguration(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
appliedNeuralNetConfigurationBuilder() - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
apply(INDArray...) - Method in class org.deeplearning4j.nn.adapters.ArgmaxAdapter: This method does conversion from INDArrays to int[], where each element will represents position of the highest element in output INDArray I.e.
apply(INDArray...) - Method in class org.deeplearning4j.nn.adapters.Regression2dAdapter
apply(Model, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.adapters.YoloModelAdapter
apply(INDArray...) - Method in class org.deeplearning4j.nn.adapters.YoloModelAdapter
apply(Model, INDArray[], INDArray[], INDArray[]) - Method in interface org.deeplearning4j.nn.api.ModelAdapter: This method invokes model internally, and does convertion to T
apply(INDArray) - Method in class org.deeplearning4j.nn.conf.constraint.BaseConstraint
apply(INDArray) - Method in class org.deeplearning4j.nn.conf.constraint.MaxNormConstraint
apply(INDArray) - Method in class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint
apply(INDArray) - Method in class org.deeplearning4j.nn.conf.constraint.NonNegativeConstraint
apply(INDArray) - Method in class org.deeplearning4j.nn.conf.constraint.UnitNormConstraint
applyConstraint(Layer, int, int) - Method in interface org.deeplearning4j.nn.api.layers.LayerConstraint: Apply a given constraint to a layer at each iteration in the provided epoch, after parameters have been updated.
applyConstraint(Layer, int, int) - Method in class org.deeplearning4j.nn.conf.constraint.BaseConstraint
applyConstraints(int, int) - Method in interface org.deeplearning4j.nn.api.Model: Apply any constraints to the model
applyConstraints(int, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
applyConstraints(int, int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
applyConstraints(int, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
applyConstraints(Model) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout
applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.Dropout
applyDropout(INDArray, INDArray, double) - Method in interface org.deeplearning4j.nn.conf.dropout.DropoutHelper: Apply the dropout during forward pass
applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.GaussianDropout
applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.GaussianNoise
applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.conf.dropout.IDropout
applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.SpatialDropout
applyDropOutIfNecessary(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
applyDropOutIfNecessary(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
applyDropOutIfNecessary(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
applyGlobalConfig(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
applyGlobalConfig(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
applyGlobalConfigToLayer(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D
applyGlobalConfigToLayer(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D
applyGlobalConfigToLayer(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer
applyGlobalConfigToLayer(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
applyGlobalConfigToLayer(NeuralNetConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
applyMask(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
applyMask(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
applyPostProcessor(int, int, Double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
applyPreprocessorAndSetInput(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
applyRegularization(Regularization.ApplyStep, Trainable, String, INDArray, INDArray, int, int, double) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock: Apply L1 and L2 regularization, if necessary.
applyRegularizationAllVariables(Regularization.ApplyStep, int, int, boolean, INDArray, INDArray) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock
applyToComputationGraphConfiguration(ComputationGraphConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
applyToMultiLayerConfiguration(MultiLayerConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
applyToNeuralNetConfiguration(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
applyUpdate(StepFunction, INDArray, INDArray, boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator: This method applies accumulated updates via given StepFunction
applyUpdate(StepFunction, INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator: This method applies accumulated updates via given StepFunction
applyUpdate(StepFunction, INDArray, INDArray, boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator: This method applies accumulated updates via given StepFunction
applyUpdate(StepFunction, INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator: This method applies accumulated updates via given StepFunction
applyUpdate(StepFunction, INDArray, INDArray, boolean) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator: This method applies accumulated updates via given StepFunction
applyUpdate(StepFunction, INDArray, INDArray, double) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator: This method applies accumulated updates via given StepFunction
ArgmaxAdapter - Class in org.deeplearning4j.nn.adapters: This OutputAdapter implementation is suited for silent conversion of 2D SoftMax output
ArgmaxAdapter() - Constructor for class org.deeplearning4j.nn.adapters.ArgmaxAdapter
arr(INDArray) - Static method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType
arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional
arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional3D
arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward
arrayElementsPerExample() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
ArrayEmbeddingInitializer - Class in org.deeplearning4j.nn.weights.embeddings: Embedding layer initialization from a specified array
ArrayEmbeddingInitializer(INDArray) - Constructor for class org.deeplearning4j.nn.weights.embeddings.ArrayEmbeddingInitializer
ArrayType - Enum in org.deeplearning4j.nn.workspace: Array type enumeration for use with LayerWorkspaceMgr

Array types:
INPUT: The array set to the input field of a layer (i.e., input activations)
ACTIVATIONS: The output activations for a layer's feed-forward method
ACTIVATION_GRAD: Activation gradient arrays - aka "epsilons" - output from a layer's backprop method
FF_WORKING_MEM: Working memory during feed-forward.
assertInputSet(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
assertInputSet(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
assertNInNOutSet(String, String, long, long, long) - Static method in class org.deeplearning4j.nn.conf.layers.LayerValidation: Asserts that the layer nIn and nOut values are set for the layer
assertNOutSet(String, String, long, long) - Static method in class org.deeplearning4j.nn.conf.layers.LayerValidation: Asserts that the layer nOut value is set for the layer
asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
atomicBoundary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
AttentionVertex - Class in org.deeplearning4j.nn.conf.graph: Implements Dot Product Attention using the given inputs.
AttentionVertex(AttentionVertex.Builder) - Constructor for class org.deeplearning4j.nn.conf.graph.AttentionVertex
AttentionVertex.Builder - Class in org.deeplearning4j.nn.conf.graph
AutoEncoder - Class in org.deeplearning4j.nn.conf.layers: Autoencoder layer.
AutoEncoder - Class in org.deeplearning4j.nn.layers.feedforward.autoencoder: Autoencoder.
AutoEncoder(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
AutoEncoder.Builder - Class in org.deeplearning4j.nn.conf.layers
AutoencoderScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc: Score function for a MultiLayerNetwork or ComputationGraph with a single AutoEncoder layer.
AutoencoderScoreCalculator(RegressionEvaluation.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
availableCheckpoints() - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener: List all available checkpoints.
availableCheckpoints(File) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener: List all available checkpoints.
average - Variable in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
avgPoolIncludePadInDivisor - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
avgPoolIncludePadInDivisor - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
avgPoolIncludePadInDivisor(boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder: When doing average pooling, should the padding values be included in the divisor or not?
Not applicable for max and p-norm pooling.
Users should not usually set this - instead, leave it as the default (false).

B

b(double) - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout
backingQueue - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout
backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.Dropout
backprop(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.conf.dropout.DropoutHelper: Perform backpropagation.
backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.GaussianDropout
backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.GaussianNoise
backprop(INDArray, INDArray, int, int) - Method in interface org.deeplearning4j.nn.conf.dropout.IDropout: Perform backprop.
backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.SpatialDropout
backprop(INDArray, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor: Reverse the preProcess during backprop.
backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor
backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
backprop - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
backprop(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
backpropDropOutIfPresent(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Layer: Calculate the gradient relative to the error in the next layer
backpropGradient(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Calculate the gradient of the network with respect to some external errors.
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer
backpropGradient(INDArray, INDArray, INDArray, INDArray, int[], int[], int[], INDArray, INDArray, IActivation, ConvolutionLayer.AlgoMode, ConvolutionLayer.BwdFilterAlgo, ConvolutionLayer.BwdDataAlgo, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
backpropGradient(INDArray, INDArray, int[], int[], int[], PoolingType, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.PReLU
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer
backpropGradient(INDArray, INDArray, int[], INDArray, INDArray, INDArray, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
backpropGradient(INDArray, INDArray, INDArray, INDArray, int[], int[], int[], INDArray, INDArray, IActivation, ConvolutionLayer.AlgoMode, ConvolutionLayer.BwdFilterAlgo, ConvolutionLayer.BwdDataAlgo, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
backpropGradient(INDArray, INDArray, double, double, double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
backpropGradient(INDArray, INDArray, int[], int[], int[], PoolingType, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
backpropGradient(INDArray, INDArray, int[], INDArray, INDArray, INDArray, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
backpropGradient(INDArray, INDArray, double, double, double, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
backpropGradient(NeuralNetConfiguration, IActivation, INDArray, INDArray, INDArray, INDArray, boolean, int, FwdPassReturn, boolean, String, String, String, Map<String, INDArray>, INDArray, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.recurrent.LSTMHelper
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.RepeatVector
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.util.MaskLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
backpropGradientHelper(BaseRecurrentLayer, NeuralNetConfiguration, IActivation, INDArray, INDArray, INDArray, INDArray, boolean, int, FwdPassReturn, boolean, String, String, String, Map<String, INDArray>, INDArray, boolean, LSTMHelper, LayerWorkspaceMgr, boolean) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers
BackpropType - Enum in org.deeplearning4j.nn.conf: Defines the type of backpropagation.
backpropType - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
backpropType - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
backpropType(BackpropType) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: The type of backprop.
backpropType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
backpropType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
backpropType(BackpropType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: The type of backprop.
backpropType - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
backpropType(BackpropType) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: The type of backprop.
BackTrackLineSearch - Class in org.deeplearning4j.optimize.solvers
BackTrackLineSearch(Model, StepFunction, ConvexOptimizer) - Constructor for class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
BackTrackLineSearch(Model, ConvexOptimizer) - Constructor for class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
BACKWARD_PREFIX - Static variable in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
barrier - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
barrier - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
barrier - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
BaseConstraint - Class in org.deeplearning4j.nn.conf.constraint
BaseConstraint() - Constructor for class org.deeplearning4j.nn.conf.constraint.BaseConstraint
BaseConstraint(Set<String>, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.BaseConstraint
BaseConvBuilder(int[], int[], int[], int[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseConvBuilder(int[], int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseConvBuilder(int[], int[], int[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseConvBuilder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseConvBuilder(int[], int[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseConvBuilder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseConvBuilder(int, int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseConvBuilder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseConvBuilder() - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
BaseEarlyStoppingTrainer<T extends Model> - Class in org.deeplearning4j.earlystopping.trainer: Base/abstract class for conducting early stopping training locally (single machine).
Can be used to train a MultiLayerNetwork or a ComputationGraph via early stopping
BaseEarlyStoppingTrainer(EarlyStoppingConfiguration<T>, T, DataSetIterator, MultiDataSetIterator, EarlyStoppingListener<T>) - Constructor for class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
BaseEvaluation<T extends BaseEvaluation> - Class in org.deeplearning4j.eval: Deprecated.
Use BaseEvaluation
BaseEvaluation() - Constructor for class org.deeplearning4j.eval.BaseEvaluation: Deprecated.
BaseGraphVertex - Class in org.deeplearning4j.nn.graph.vertex: BaseGraphVertex defines a set of common functionality for GraphVertex instances.
BaseGraphVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
BaseIEvaluationScoreCalculator<T extends Model,U extends IEvaluation> - Class in org.deeplearning4j.earlystopping.scorecalc.base: Base score function based on an IEvaluation instance.
BaseIEvaluationScoreCalculator(MultiDataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator
BaseIEvaluationScoreCalculator(DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator
BaseInputPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
BaseInputPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor
BaseLayer - Class in org.deeplearning4j.nn.conf.layers: A neural network layer.
BaseLayer(BaseLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseLayer
BaseLayer<LayerConfT extends BaseLayer> - Class in org.deeplearning4j.nn.layers: A layer with parameters
BaseLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.BaseLayer
BaseLayer.Builder<T extends BaseLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers
BaseMKLDNNHelper - Class in org.deeplearning4j.nn.layers.mkldnn: Base class for MKL-DNN Helpers
BaseMKLDNNHelper() - Constructor for class org.deeplearning4j.nn.layers.mkldnn.BaseMKLDNNHelper
BaseMLNScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc.base: Abstract score calculator for MultiLayerNetwonk
BaseMLNScoreCalculator(DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseMLNScoreCalculator
BaseMultiLayerUpdater<T extends Model> - Class in org.deeplearning4j.nn.updater: BaseMultiLayerUpdater - core functionality for applying updaters to MultiLayerNetwork and ComputationGraph.
BaseMultiLayerUpdater(T) - 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<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.BaseOptimizer
BaseOutputLayer - Class in org.deeplearning4j.nn.conf.layers
BaseOutputLayer(BaseOutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
BaseOutputLayer<LayerConfT extends BaseOutputLayer> - Class in org.deeplearning4j.nn.layers: Output layer with different objective in co-occurrences for different objectives.
BaseOutputLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.BaseOutputLayer
BaseOutputLayer.Builder<T extends BaseOutputLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers
BasePretrainNetwork - Class in org.deeplearning4j.nn.conf.layers
BasePretrainNetwork(BasePretrainNetwork.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
BasePretrainNetwork<LayerConfT extends BasePretrainNetwork> - Class in org.deeplearning4j.nn.layers: Baseline class for any Neural Network used as a layer in a deep network *
BasePretrainNetwork(NeuralNetConfiguration, DataType) - 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, DataType) - Constructor for class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
BaseRecurrentLayer.Builder<T extends BaseRecurrentLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers
BaseScoreCalculator<T extends Model> - Class in org.deeplearning4j.earlystopping.scorecalc.base
BaseScoreCalculator(DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
BaseScoreCalculator(MultiDataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
BaseSubsamplingBuilder(Subsampling3DLayer.PoolingType, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(Subsampling3DLayer.PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(Subsampling3DLayer.PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(Subsampling3DLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(SubsamplingLayer.PoolingType, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(SubsamplingLayer.PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(SubsamplingLayer.PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(SubsamplingLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
BaseSubsamplingBuilder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
BaseTrainingListener - Class in org.deeplearning4j.optimize.api: A no-op implementation of a TrainingListener to be used as a starting point for custom training callbacks.
BaseTrainingListener() - Constructor for class org.deeplearning4j.optimize.api.BaseTrainingListener
BaseUpsamplingLayer - Class in org.deeplearning4j.nn.conf.layers: Upsampling base layer
BaseUpsamplingLayer(BaseUpsamplingLayer.UpsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer
BaseUpsamplingLayer.UpsamplingBuilder<T extends BaseUpsamplingLayer.UpsamplingBuilder<T>> - Class in org.deeplearning4j.nn.conf.layers
BaseWrapperLayer - Class in org.deeplearning4j.nn.conf.layers.wrapper: Base wrapper layer: the idea is to pass through all methods to the underlying layer, and selectively override them as required.
BaseWrapperLayer(Layer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
BaseWrapperLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
BaseWrapperLayer(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
BaseWrapperLayer - Class in org.deeplearning4j.nn.layers.wrapper: Abstract wrapper layer.
BaseWrapperLayer(Layer) - Constructor for class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
BaseWrapperVertex - Class in org.deeplearning4j.nn.graph.vertex: A base class for wrapper vertices: i.e., those vertices that have another vertex inside.
BaseWrapperVertex(GraphVertex) - Constructor for class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
BasicGradientsAccumulator - Class in org.deeplearning4j.optimize.solvers.accumulation: This class provides accumulation for gradients for both input (i.e.
BasicGradientsAccumulator(int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator: Creates new GradientsAccumulator with starting threshold of 1e-3
BasicGradientsAccumulator(int, MessageHandler) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator: Creates new GradientsAccumulator with custom starting threshold
BatchNormalization - Class in org.deeplearning4j.nn.conf.layers: Batch normalization layer
See: Ioffe and Szegedy, 2014, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift https://arxiv.org/abs/1502.03167
BatchNormalization() - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization
BatchNormalization - Class in org.deeplearning4j.nn.layers.normalization: Batch normalization layer.
Rerences:
http://arxiv.org/pdf/1502.03167v3.pdf
http://arxiv.org/pdf/1410.7455v8.pdf
https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html Batch normalization should be applied between the output of a layer (with identity activation) and the activation function.
BatchNormalization(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.normalization.BatchNormalization
BatchNormalization.Builder - Class in org.deeplearning4j.nn.conf.layers
BatchNormalizationHelper - Interface in org.deeplearning4j.nn.layers.normalization: Helper for the batch normalization layer.
BatchNormalizationParamInitializer - Class in org.deeplearning4j.nn.params: Batch normalization variable init
BatchNormalizationParamInitializer() - Constructor for class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
batchSize() - Method in interface org.deeplearning4j.nn.api.Model: The current inputs batch size
batchSize() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
batchSize() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
batchSize() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
batchSize() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
batchSize() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
batchSize() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
batchSize() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: The batch size for the optimizer
batchSize() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
BernoulliReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational: Bernoulli reconstruction distribution for variational autoencoder.
Outputs are modelled by a Bernoulli distribution - i.e., the Bernoulli distribution should be used for binary data (all values 0 or 1); the VAE models the probability of the output being 0 or 1.
Consequently, the sigmoid activation function should be used to bound activations to the range of 0 to 1.
BernoulliReconstructionDistribution() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution: Create a BernoulliReconstructionDistribution with the default Sigmoid activation function
BernoulliReconstructionDistribution(Activation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
BernoulliReconstructionDistribution(IActivation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
BestScoreEpochTerminationCondition - Class in org.deeplearning4j.earlystopping.termination: Created by Sadat Anwar on 3/26/16.
BestScoreEpochTerminationCondition(double) - Constructor for class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition
BestScoreEpochTerminationCondition(double, boolean) - Constructor for class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition: Deprecated.
"lessBetter" argument no longer used
beta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
beta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: Used only when 'true' is passed to BatchNormalization.Builder.lockGammaBeta(boolean).
beta(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: Used only when 'true' is passed to BatchNormalization.Builder.lockGammaBeta(boolean).
beta - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
beta(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder: Scaling constant beta.
BETA - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
betaConstraints - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: Set constraints to be applied to the beta parameter of this batch normalisation layer.
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.CenterLossParamInitializer
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.Convolution3DParamInitializer
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.DefaultParamInitializer
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.LSTMParamInitializer
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
BIAS_KEY_BACKWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
BIAS_KEY_FORWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
BIAS_KEY_SUFFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
biasConstraints - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
biasConstraints - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
biasInit - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
biasInit - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Bias initialization value, for layers with biases.
biasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Bias initialization value, for layers with biases.
biasInit - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
biasInit(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Constant for bias initialization.
biasInit - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
biasInit(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Constant for bias initialization.
biasKeys(Layer) - Method in interface org.deeplearning4j.nn.api.ParamInitializer: Bias parameter keys given the layer configuration
biasKeys(Layer) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
biasKeys(Layer) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
biasUpdater - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
biasUpdater - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Gradient updater configuration, for the biases only.
biasUpdater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Gradient updater configuration, for the biases only.
biasUpdater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
biasUpdater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: Gradient updater configuration, for the biases only.
biasUpdater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: Gradient updater configuration, for the biases only.
biasUpdater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
biasUpdater - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
biasUpdater(IUpdater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Gradient updater configuration, for the biases only.
biasUpdater - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
biasUpdater(IUpdater) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Gradient updater configuration, for the biases only.
Bidirectional - Class in org.deeplearning4j.nn.conf.layers.recurrent: Bidirectional is a "wrapper" layer: it wraps any uni-directional RNN layer to make it bidirectional.
Note that multiple different modes are supported - these specify how the activations should be combined from the forward and backward RNN networks.
Bidirectional(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional: Create a Bidirectional wrapper, with the default Mode (CONCAT) for the specified layer
Bidirectional(Bidirectional.Mode, Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional: Create a Bidirectional wrapper for the specified layer
Bidirectional.Builder - Class in org.deeplearning4j.nn.conf.layers.recurrent
Bidirectional.Mode - Enum in org.deeplearning4j.nn.conf.layers.recurrent: This Mode enumeration defines how the activations for the forward and backward networks should be combined.
ADD: out = forward + backward (elementwise addition)
MUL: out = forward * backward (elementwise multiplication)
AVERAGE: out = 0.5 * (forward + backward)
CONCAT: Concatenate the activations.
Where 'forward' is the activations for the forward RNN, and 'backward' is the activations for the backward RNN.
BidirectionalLayer - Class in org.deeplearning4j.nn.layers.recurrent: Bidirectional is a "wrapper" layer: it wraps any uni-directional RNN layer to make it bidirectional.
Note that multiple different modes are supported - these specify how the activations should be combined from the forward and backward RNN networks.
BidirectionalLayer(NeuralNetConfiguration, Layer, Layer, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
BidirectionalParamInitializer - Class in org.deeplearning4j.nn.params: Parameter initializer for bidirectional wrapper layer
BidirectionalParamInitializer(Bidirectional) - Constructor for class org.deeplearning4j.nn.params.BidirectionalParamInitializer
BinomialDistribution - Class in org.deeplearning4j.nn.conf.distribution: A binomial distribution, with 2 parameters: number of trials, and probability of success
BinomialDistribution(int, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.BinomialDistribution: Create a distribution
bitmapMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
blocks - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
blocks - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder: Block size for SpaceToBatch layer.
blocks(int...) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
blocks(int) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
blockSize - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
blockSize - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
boundary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
boundary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
boundary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
boundingBoxPriors(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder: Bounding box priors dimensions [width, height].
broadcastUpdates(INDArray, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
broadcastUpdates(INDArray, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.LocalHandler
broadcastUpdates(INDArray, int, int) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.MessageHandler: This method does broadcast of given update message across network
build() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder: Create the early stopping configuration
build() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Create the ComputationGraphConfiguration from the Builder pattern
build() - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder
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.CapsuleLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.CapsuleStrengthLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder: Deprecated.
build() - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder: Deprecated.
build() - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.LSTM.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder
build() - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder
build() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
build() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Return a configuration based on this builder
build() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder: Build the multi layer network based on this neural network and overr ridden parameters
build() - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder
build() - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
build() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Returns a model with the fine tune configuration and specified architecture changes.
build() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Returns a computation graph build to specifications.
build() - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder
build() - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder
build() - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder: This method returns configured PerformanceListener instance
build() - Method in class org.deeplearning4j.optimize.Solver.Builder
build() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
Builder() - Constructor for class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder
Builder(double) - Constructor for class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder: Builder - sets the level of corruption - 0.0 (none) to 1.0 (all values corrupted)
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder
Builder(double, boolean) - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
Builder(double, double) - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
Builder(double, double, boolean) - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
Builder(boolean) - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder
Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.CapsuleStrengthLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
Builder(Convolution3D.DataFormat) - Constructor for class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder: Constructor with specified kernel size, stride of 1, padding of 0
Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
Builder(int[], int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder
Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder
Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder
Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder
Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder
Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder
Builder(int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder
Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder
Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder
Builder(int, int, int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder
Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.DenseLayer.Builder
Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
Builder(double) - Constructor for class org.deeplearning4j.nn.conf.layers.DropoutLayer.Builder: Create a dropout layer with standard Dropout, with the specified probability of retaining the input activation.
Builder(IDropout) - Constructor for class org.deeplearning4j.nn.conf.layers.DropoutLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder
Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder: Deprecated.
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder: Deprecated.
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Layer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
Builder(double, double, double, double) - Constructor for class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
Builder(double, double, double) - Constructor for class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.LSTM.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer.Builder
Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder
Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder
Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.PReLULayer.Builder
Builder(int, int, int[], int[], int[], int[], ConvolutionMode) - Constructor for class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
Builder(int, int, int[], int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
Builder(int, int, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
Builder(int, int, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
Builder(int, int, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder
Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder
Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer.Builder
Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
Builder(int[], int[][]) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
Builder(int, SpaceToDepthLayer.DataFormat) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
Builder(SubsamplingLayer.PoolingType, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder(SubsamplingLayer.PoolingType, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder(PoolingType, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder(SubsamplingLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder(PoolingType, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder(SubsamplingLayer.PoolingType, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
Builder(Subsampling3DLayer.PoolingType, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
Builder(Subsampling3DLayer.PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
Builder(Subsampling3DLayer.PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
Builder(PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
Builder(PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
Builder(Subsampling3DLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
Builder(SubsamplingLayer.PoolingType, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
Builder(SubsamplingLayer.PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
Builder(SubsamplingLayer.PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
Builder(PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
Builder(PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
Builder(SubsamplingLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling1D.Builder
Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder
Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder
Builder(Convolution3D.DataFormat, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder
Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder
Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder
Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder
Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder
Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder: Use same padding for left and right boundaries in depth, height and width.
Builder(int, int, int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder: Explicit padding of left and right boundaries in depth, height and width dimensions
Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder
Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder
Builder(int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder
Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder
Builder(String, Class<?>, InputType, InputType) - Constructor for class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
Builder(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
Builder() - Constructor for class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder
builder() - Static method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
Builder() - Constructor for class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
Builder(MultiLayerNetwork) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Multilayer Network to tweak for transfer learning
builder() - Static method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
Builder() - Constructor for class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder
Builder(String) - Constructor for class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder
Builder(File) - Constructor for class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder
Builder() - Constructor for class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder
Builder() - Constructor for class org.deeplearning4j.optimize.Solver.Builder
Builder(int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder: This
bypassMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
bypassMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue

C

CACHE_MODE_ALL_ZEROS - Static variable in class org.deeplearning4j.nn.conf.memory.MemoryReport: A simple Map containing all zeros for each CacheMode key
cachedFwdPass - Variable in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
cachedFwdPass - Variable in class org.deeplearning4j.nn.layers.recurrent.LSTM
cachedPassBackward - Variable in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
cachedPassForward - Variable in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
cacheMemory(long, long) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder: Reports the cached/cacheable memory requirements.
cacheMemory(Map<CacheMode, Long>, Map<CacheMode, Long>) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder: Reports the cached/cacheable memory requirements.
CacheMode - Enum in org.deeplearning4j.nn.conf
cacheMode - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
cacheMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
cacheMode(CacheMode) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: This method defines how/if preOutput cache is handled: NONE: cache disabled (default value) HOST: Host memory will be used DEVICE: GPU memory will be used (on CPU backends effect will be the same as for HOST)
cacheMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
cacheMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
cacheMode(CacheMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: This method defines how/if preOutput cache is handled: NONE: cache disabled (default value) HOST: Host memory will be used DEVICE: GPU memory will be used (on CPU backends effect will be the same as for HOST)
cacheMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
cacheMode - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
cacheMode - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
cacheModeMapFor(long) - Static method in class org.deeplearning4j.nn.conf.memory.MemoryReport: Get a map of CacheMode with all keys associated with the specified value
calcBackpropGradients(boolean, boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Do backprop (gradient calculation)
calcBackpropGradients(INDArray, boolean, boolean, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Calculate gradients and errors.
calcRegularizationScore(boolean) - Method in interface org.deeplearning4j.nn.api.Layer: Calculate the regularization component of the score, for the parameters in this layer
For example, the L1, L2 and/or weight decay components of the loss function
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.RepeatVector
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
calcRegularizationScore(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
calculateGradients(INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Calculate parameter gradients and input activation gradients given the input and labels, and optionally mask arrays
calculateIndices() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Calculate the indices needed for the network:
(a) topological sort order
(b) Map: vertex index -> vertex name
(c) Map: vertex name -> vertex index
calculateScore(T) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator
calculateScore(T) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
calculateScore(ComputationGraph) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG: Deprecated.
calculateScore(T) - Method in interface org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator: Calculate the score for the given MultiLayerNetwork
calculateThreshold(int, int, Double, Boolean, Double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm
calculateThreshold(int, int, Double, Boolean, Double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm
calculateThreshold(int, int, Double, Boolean, Double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm
calculateThreshold(int, int, Double, Boolean, Double, INDArray) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithm
call(EvaluativeListener, Model, long, IEvaluation[]) - Method in interface org.deeplearning4j.optimize.listeners.callbacks.EvaluationCallback
call(EvaluativeListener, Model, long, IEvaluation[]) - Method in class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback
call(FailureTestingListener.CallType, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener
callback - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener: This callback will be invoked after evaluation finished
candidates - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
canDoBackward() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
canDoBackward() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
canDoBackward() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Whether the GraphVertex can do backward pass.
canDoBackward() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
canDoForward() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
canDoForward() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
canDoForward() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Whether the GraphVertex can do forward pass.
capsuleDimensions(int) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder: Set the number dimensions of each capsule
capsuleDimensions(int) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Sets the number of dimensions to use in the capsules.
CapsuleLayer - Class in org.deeplearning4j.nn.conf.layers: An implementation of the DigiCaps layer from Dynamic Routing Between Capsules Input should come from a PrimaryCapsules layer and be of shape [mb, inputCaps, inputCapDims].
CapsuleLayer(CapsuleLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.CapsuleLayer
CapsuleLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
capsules(int) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder: Set the number of capsules to use.
capsules(int) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Usually inferred automatically.
CapsuleStrengthLayer - Class in org.deeplearning4j.nn.conf.layers: An layer to get the "strength" of each capsule, that is, the probability of it being in the input.
CapsuleStrengthLayer(CapsuleStrengthLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.CapsuleStrengthLayer
CapsuleStrengthLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
CapsuleUtils - Class in org.deeplearning4j.util: Utilities for CapsNet Layers
CapsuleUtils() - Constructor for class org.deeplearning4j.util.CapsuleUtils
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, DataType) - 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
channels(int) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Sets the number of channels to use in the 2d convolution.
checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil: Check backprop gradients for a MultiLayerNetwork.
checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, Set<String>) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray, boolean, int) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray, boolean, int, Set<String>) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray, boolean, int, Set<String>, Integer) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray, boolean, int, Set<String>, Consumer<MultiLayerNetwork>) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[]) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil: Check backprop gradients for a ComputationGraph
checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[], INDArray[], INDArray[]) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[], INDArray[], INDArray[], Set<String>) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[], INDArray[], INDArray[], Set<String>, Integer) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[], INDArray[], INDArray[], Set<String>, Consumer<ComputationGraph>) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
checkGradientsPretrainLayer(Layer, double, double, double, boolean, boolean, INDArray, int) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil: Check backprop gradients for a pretrain layer NOTE: gradient checking pretrain layers can be difficult...
Checkpoint - Class in org.deeplearning4j.optimize.listeners: A model checkpoint, used with CheckpointListener
Checkpoint() - Constructor for class org.deeplearning4j.optimize.listeners.Checkpoint
CheckpointListener - Class in org.deeplearning4j.optimize.listeners: CheckpointListener: The goal of this listener is to periodically save a copy of the model during training..
Model saving may be done:
1.
CheckpointListener.Builder - Class in org.deeplearning4j.optimize.listeners
checkSupported() - Method in interface org.deeplearning4j.nn.conf.dropout.DropoutHelper
checkSupported() - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
checkSupported() - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper
checkSupported(double, boolean) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
checkSupported() - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
checkSupported(double, double, double, double) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
checkSupported() - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper
checkSupported(double, boolean) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
checkSupported(double, double, double, double) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper
checkSupported(IActivation, IActivation, boolean) - Method in interface org.deeplearning4j.nn.layers.recurrent.LSTMHelper
children() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
ClassificationScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc: Score function for evaluating a MultiLayerNetwork according to an evaluation metric (Evaluation.Metric such as accuracy, F1 score, etc.
ClassificationScoreCalculator(Evaluation.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator
ClassificationScoreCalculator(Evaluation.Metric, MultiDataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator
Classifier - Interface in org.deeplearning4j.nn.api: A classifier (this is for supervised learning)
clear() - Method in interface org.deeplearning4j.nn.api.Model: Clear input
clear() - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout
clear() - Method in class org.deeplearning4j.nn.conf.dropout.Dropout
clear() - Method in class org.deeplearning4j.nn.conf.dropout.GaussianDropout
clear() - Method in class org.deeplearning4j.nn.conf.dropout.GaussianNoise
clear() - Method in interface org.deeplearning4j.nn.conf.dropout.IDropout: Clear the internal state (for example, dropout mask) if any is present
clear() - Method in class org.deeplearning4j.nn.conf.dropout.SpatialDropout
clear() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams: Clear any previously set weight/bias parameters (including their shapes)
clear() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
clear() - Method in interface org.deeplearning4j.nn.gradient.Gradient: Clear residual parameters (useful for returning a gradient and then clearing old objects)
clear() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
clear() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
clear() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
clear() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Clear the internal state (if any) of the GraphVertex.
clear() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
clear() - Method in class org.deeplearning4j.nn.layers.BaseLayer
clear() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
clear() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
clear() - Method in class org.deeplearning4j.nn.layers.LossLayer
clear() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
clear() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
clear() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
clear() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Clear the inputs.
clear() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
clearLayerMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Remove the mask arrays from all layers.
See ComputationGraph.setLayerMaskArrays(INDArray[], INDArray[]) for details on mask arrays.
clearLayerMaskArrays() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Remove the mask arrays from all layers.
See MultiLayerNetwork.setLayerMaskArrays(INDArray, INDArray) for details on mask arrays.
clearLayersStates() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: This method just makes sure there's no state preserved within layers
clearLayersStates() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: This method just makes sure there's no state preserved within layers
clearNoiseWeightParams() - Method in interface org.deeplearning4j.nn.api.Layer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.BaseLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.RepeatVector
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.util.MaskLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
clearNoiseWeightParams() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
clearTbpttState - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
clearTbpttState - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
clearVariables() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
clearVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
clearVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
clearVertex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: This method clears inpjut for this vertex
clearVertex() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
clone() - Method in class org.deeplearning4j.eval.ROC.CountsForThreshold: Deprecated.
clone() - Method in interface org.deeplearning4j.nn.api.layers.LayerConstraint
clone() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
clone() - Method in class org.deeplearning4j.nn.conf.constraint.BaseConstraint
clone() - Method in class org.deeplearning4j.nn.conf.constraint.MaxNormConstraint
clone() - Method in class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint
clone() - Method in class org.deeplearning4j.nn.conf.constraint.NonNegativeConstraint
clone() - Method in class org.deeplearning4j.nn.conf.constraint.UnitNormConstraint
clone() - Method in class org.deeplearning4j.nn.conf.distribution.Distribution
clone() - Method in class org.deeplearning4j.nn.conf.dropout.AlphaDropout
clone() - Method in class org.deeplearning4j.nn.conf.dropout.Dropout
clone() - Method in class org.deeplearning4j.nn.conf.dropout.GaussianDropout
clone() - Method in class org.deeplearning4j.nn.conf.dropout.GaussianNoise
clone() - Method in interface org.deeplearning4j.nn.conf.dropout.IDropout
clone() - Method in class org.deeplearning4j.nn.conf.dropout.SpatialDropout
clone() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
clone() - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
clone() - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D
clone() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D
clone() - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D
clone() - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.Layer
clone() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
clone() - Method in class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
clone() - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector
clone() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
clone() - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D
clone() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D
clone() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D
clone() - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D
clone() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
clone() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
clone() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: Creates and returns a deep copy of the configuration.
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.stepfunctions.StepFunction
clone() - Method in class org.deeplearning4j.nn.conf.weightnoise.DropConnect
clone() - Method in interface org.deeplearning4j.nn.conf.weightnoise.IWeightNoise
clone() - Method in class org.deeplearning4j.nn.conf.weightnoise.WeightNoise
clone() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
clone() - Method in class org.deeplearning4j.nn.layers.BaseLayer
clone() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer
clone() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer
clone() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer
clone() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer
clone() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer
clone() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
clone() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
clone() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
clone() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
clone() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
clone() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
clone() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
clone() - Method in class org.deeplearning4j.nn.layers.util.MaskLayer
clone() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Clone the MultiLayerNetwork
clone() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
clone() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.NoOpResidualPostProcessor
clone() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.ResidualClippingPostProcessor
clone() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ResidualPostProcessor
clone() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm
clone() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm
clone() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm
clone() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithm
cnn1dMaskReduction(INDArray, int, int, int, int, ConvolutionMode) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Given a mask array for a 1D CNN layer of shape [minibatch, sequenceLength], reduce the mask according to the 1D CNN layer configuration.
cnn2dMaskReduction(INDArray, int[], int[], int[], int[], ConvolutionMode) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Reduce a 2d CNN layer mask array (of 0s and 1s) according to the layer configuration.
Cnn3DLossLayer - Class in org.deeplearning4j.nn.conf.layers: 3D Convolutional Neural Network Loss Layer.
Handles calculation of gradients etc for various loss (objective) functions.
NOTE: Cnn3DLossLayer does not have any parameters.
Cnn3DLossLayer - Class in org.deeplearning4j.nn.layers.convolution: 3D Convolutional Neural Network Loss Layer.
Handles calculation of gradients etc for various objective functions.
NOTE: Cnn3DLossLayer does not have any parameters.
Cnn3DLossLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
Cnn3DLossLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
Cnn3DToFeedForwardPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: A preprocessor to allow CNN and standard feed-forward network layers to be used together.
For example, CNN3D -> Denselayer
This does two things:
(b) Reshapes 5d activations out of CNN layer, with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) into 2d activations (with shape [numExamples, inputDepth*inputHeight*inputWidth*numChannels]) for use in feed forward layer (a) Reshapes epsilons (weights*deltas) out of FeedFoward layer (which is 2D or 3D with shape [numExamples, inputDepth* inputHeight*inputWidth*numChannels]) into 5d epsilons (with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) suitable to feed into CNN layers.
Note: numChannels is equivalent to featureMaps referenced in different literature
Cnn3DToFeedForwardPreProcessor(long, long, long, long, boolean) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
Cnn3DToFeedForwardPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
Cnn3DToFeedForwardPreProcessor(int, int, int, int, Convolution3D.DataFormat) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
Cnn3DToFeedForwardPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
CnnLossLayer - Class in org.deeplearning4j.nn.conf.layers: Convolutional Neural Network Loss Layer.
Handles calculation of gradients etc for various loss (objective) functions.
NOTE: CnnLossLayer does not have any parameters.
CnnLossLayer - Class in org.deeplearning4j.nn.layers.convolution: Convolutional Neural Network Loss Layer.
Handles calculation of gradients etc for various objective functions.
NOTE: CnnLossLayer does not have any parameters.
CnnLossLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
CnnLossLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
CnnToFeedForwardPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: A preprocessor to allow CNN and standard feed-forward network layers to be used together.
For example, CNN -> Denselayer
This does two things:
(b) Reshapes 4d activations out of CNN layer, with shape [numExamples, numChannels, inputHeight, inputWidth]) into 2d activations (with shape [numExamples, inputHeight*inputWidth*numChannels]) for use in feed forward layer (a) Reshapes epsilons (weights*deltas) out of FeedFoward layer (which is 2D or 3D with shape [numExamples, inputHeight*inputWidth*numChannels]) into 4d epsilons (with shape [numExamples, numChannels, inputHeight, inputWidth]) suitable to feed into CNN layers.
Note: numChannels is equivalent to channels or featureMaps referenced in different literature
CnnToFeedForwardPreProcessor(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
CnnToFeedForwardPreProcessor(long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
CnnToFeedForwardPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
CnnToRnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: A preprocessor to allow CNN and RNN layers to be used together.
For example, ConvolutionLayer -> GravesLSTM Functionally equivalent to combining CnnToFeedForwardPreProcessor + FeedForwardToRnnPreProcessor
Specifically, this does two things:
(a) Reshape 4d activations out of CNN layer, with shape [timeSeriesLength*miniBatchSize, numChannels, inputHeight, inputWidth]) into 3d (time series) activations (with shape [numExamples, inputHeight*inputWidth*numChannels, timeSeriesLength]) for use in RNN layers
(b) Reshapes 3d epsilons (weights.*deltas) out of RNN layer (with shape [miniBatchSize,inputHeight*inputWidth*numChannels,timeSeriesLength]) into 4d epsilons with shape [miniBatchSize*timeSeriesLength, numChannels, inputHeight, inputWidth] suitable to feed into CNN layers.
CnnToRnnPreProcessor(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
COEFFICIENTS_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer
collapseDimensions(boolean) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder: Whether to collapse dimensions when pooling or not.
collapsedIndex - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
collapsedMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
collapsedMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
collapseThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
CollectScoresIterationListener - Class in org.deeplearning4j.optimize.listeners: CollectScoresIterationListener simply stores the model scores internally (along with the iteration) every 1 or N iterations (this is configurable).
CollectScoresIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener: Constructor for collecting scores with default saving frequency of 1
CollectScoresIterationListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener: Constructor for collecting scores with the specified frequency.
CollectScoresListener - Class in org.deeplearning4j.optimize.listeners: A simple listener that collects scores to a list every N iterations.
CollectScoresListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresListener
CollectScoresListener(int, boolean) - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresListener
ComposableInputPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: Composable input pre processor
ComposableInputPreProcessor(InputPreProcessor...) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
ComposableIterationListener - Class in org.deeplearning4j.optimize.listeners: Deprecated.
Not required - DL4J networks can use multiple listeners simultaneously
ComposableIterationListener(TrainingListener...) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener: Deprecated.
ComposableIterationListener(Collection<TrainingListener>) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener: Deprecated.
CompositeReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational: CompositeReconstructionDistribution is a reconstruction distribution built from multiple other ReconstructionDistribution instances.
The typical use is to combine for example continuous and binary data in the same model, or to combine different distributions for continuous variables.
CompositeReconstructionDistribution(int[], ReconstructionDistribution[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
CompositeReconstructionDistribution.Builder - Class in org.deeplearning4j.nn.conf.layers.variational
compressor - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
ComputationGraph - Class in org.deeplearning4j.nn.graph: A ComputationGraph network is a neural network with arbitrary (directed acyclic graph) connection structure.
ComputationGraph(ComputationGraphConfiguration) - Constructor for class org.deeplearning4j.nn.graph.ComputationGraph
ComputationGraphConfiguration - Class in org.deeplearning4j.nn.conf: ComputationGraphConfiguration is a configuration object for neural networks with arbitrary connection structure.
ComputationGraphConfiguration() - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
ComputationGraphConfiguration.GraphBuilder - Class in org.deeplearning4j.nn.conf
ComputationGraphConfigurationDeserializer - Class in org.deeplearning4j.nn.conf.serde
ComputationGraphConfigurationDeserializer(JsonDeserializer<?>) - Constructor for class org.deeplearning4j.nn.conf.serde.ComputationGraphConfigurationDeserializer
ComputationGraphUpdater - Class in org.deeplearning4j.nn.updater.graph: Gradient updater for ComputationGraph.
ComputationGraphUpdater(ComputationGraph) - Constructor for class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
ComputationGraphUpdater(ComputationGraph, INDArray) - Constructor for class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
computationGraphUpdater - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
ComputationGraphUtil - Class in org.deeplearning4j.nn.graph.util
computeGradient(INDArray, INDArray, IActivation, INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction
computeGradientAndScore(LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Model: Update the score
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
computeGradientAndScore() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
computeGradientAndScore(INDArray, INDArray, IActivation, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
computeGradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
computeGradientAndScore() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
computeLossFunctionScoreArray(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
computeOutputSize() - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D
computeOutputSize() - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D
computeScore(double, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer: Compute score after labels and input have been set.
computeScore(double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
computeScore(double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Compute score after labels and input have been set.
computeScore(double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
computeScore(double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
computeScore(double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer: Compute score after labels and input have been set.
computeScore(double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
computeScore(double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer: Compute score after labels and input have been set.
computeScore(INDArray, INDArray, IActivation, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction
computeScore(double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
computeScore(double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
computeScore(double, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer: Compute score after labels and input have been set.
computeScoreArray(INDArray, INDArray, IActivation, INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
computeScoreForExamples(double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer: Compute the score for each example individually, after labels and input have been set.
conf() - Method in interface org.deeplearning4j.nn.api.Model: The configuration for the neural network
conf() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
conf - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
conf() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
conf() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
conf - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
conf() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
conf() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
conf() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
conf - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
config - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
configuration - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
CONFIGURATION_JSON - Static variable in class org.deeplearning4j.util.ModelSerializer
configure(NeuralNetConfiguration) - Method in class org.deeplearning4j.optimize.Solver.Builder
configureR - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: Whether to use the specified OCNNOutputLayer.Builder.initialRValue or use the weight initialization with the neural network for the r value
configureR(boolean) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: Whether to use the specified OCNNOutputLayer.Builder.initialRValue or use the weight initialization with the neural network for the r value
confs - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
confs(List<NeuralNetConfiguration>) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
confs - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
Confusion(PrecisionRecallCurve.Point, int, int, int, int) - Constructor for class org.deeplearning4j.eval.curves.PrecisionRecallCurve.Confusion: Deprecated.
ConfusionMatrix<T extends Comparable<? super T>> - Class in org.deeplearning4j.eval: Deprecated.
Use ConfusionMatrix
ConfusionMatrix(List<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix: Deprecated.
Use ConfusionMatrix
ConfusionMatrix() - Constructor for class org.deeplearning4j.eval.ConfusionMatrix: Deprecated.
Use ConfusionMatrix
ConfusionMatrix(ConfusionMatrix<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix: Deprecated.
Use ConfusionMatrix
ConjugateGradient - Class in org.deeplearning4j.optimize.solvers: Originally based on cc.mallet.optimize.ConjugateGradient Rewritten based on Conjugate Gradient algorithm in Bengio et al., Deep Learning (in preparation) Ch8.
ConjugateGradient(NeuralNetConfiguration, StepFunction, Collection<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.ConjugateGradient
connect(List<Tree>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Connects the given trees and sets the parents of the children
ConstantDistribution - Class in org.deeplearning4j.nn.conf.distribution: Constant distribution: a "distribution" where all values are set to the specified constant
ConstantDistribution(double) - Constructor for class org.deeplearning4j.nn.conf.distribution.ConstantDistribution: Create a Constant distribution with given value
constrainAllParameters(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder: Set constraints to be applied to this layer.
constrainAllParameters(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set constraints to be applied to all layers.
constrainBeta(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: Set constraints to be applied to the beta parameter of this batch normalisation layer.
constrainBias(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder: Set constraints to be applied to bias parameters of this layer.
constrainBias(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set constraints to be applied to all layers.
constrainGamma(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: Set constraints to be applied to the gamma parameter of this batch normalisation layer.
constrainInputWeights(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder: Set constraints to be applied to the RNN input weight parameters of this layer.
constrainPointWise(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder: Set constraints to be applied to the point-wise convolution weight parameters of this layer.
constrainRecurrent(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder: Set constraints to be applied to the RNN recurrent weight parameters of this layer.
constraints - Variable in class org.deeplearning4j.nn.conf.layers.Layer
constraints(List<LayerConstraint>) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Set constraints to be applied to all layers.
constraints - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
constrainWeights(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder: Set constraints to be applied to the weight parameters of this layer.
constrainWeights(LayerConstraint...) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set constraints to be applied to all layers.
consumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
contains(Object) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
containsAll(Collection<?>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
context - Variable in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
context - Variable in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
context - Variable in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
context - Variable in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper
contextBwd - Variable in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
convertDataType(DataType) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return a copy of the network with the parameters and activations set to use the specified (floating point) data type.
convertDataType(DataType) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Return a copy of the network with the parameters and activations set to use the specified (floating point) data type.
ConvexOptimizer - Interface in org.deeplearning4j.optimize.api: Convex optimizer.
Convolution1D - Class in org.deeplearning4j.nn.conf.layers: 1D convolution layer.
Convolution1D() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1D
Convolution1DLayer - Class in org.deeplearning4j.nn.conf.layers: 1D (temporal) convolutional layer.
Convolution1DLayer - Class in org.deeplearning4j.nn.layers.convolution: 1D (temporal) convolutional layer.
Convolution1DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
Convolution1DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
Convolution1DUtils - Class in org.deeplearning4j.util: Shape utilities for 1D convolution layers
Convolution2D - Class in org.deeplearning4j.nn.conf.layers: 2D convolution layer
Convolution2D() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution2D
Convolution3D - Class in org.deeplearning4j.nn.conf.layers: 3D convolution layer configuration
Convolution3D(Convolution3D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D: 3-dimensional convolutional layer configuration nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the depth The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size
Convolution3D.Builder - Class in org.deeplearning4j.nn.conf.layers
Convolution3D.DataFormat - Enum in org.deeplearning4j.nn.conf.layers: An optional dataFormat: "NDHWC" or "NCDHW".
Convolution3DLayer - Class in org.deeplearning4j.nn.layers.convolution: 3D convolution layer implementation.
Convolution3DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer
Convolution3DParamInitializer - Class in org.deeplearning4j.nn.params: Initialize 3D convolution parameters.
Convolution3DParamInitializer() - Constructor for class org.deeplearning4j.nn.params.Convolution3DParamInitializer
Convolution3DUtils - Class in org.deeplearning4j.util: Shape utilities for 3D convolution layers
convolutional(long, long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: Input type for convolutional (CNN) data, that is 4d with shape [miniBatchSize, channels, height, width].
convolutional(int, int, int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder: See InputType.convolutional(long, long, long)
convolutional3D(long, long, long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: Deprecated.
Use InputType.convolutional3D(Convolution3D.DataFormat, long, long, long, long)
convolutional3D(Convolution3D.DataFormat, long, long, long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: Input type for 3D convolutional (CNN3D) 5d data:
If NDHWC format [miniBatchSize, depth, height, width, channels]
If NDCWH
convolutionalFlat(long, long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: Input type for convolutional (CNN) data, where the data is in flattened (row vector) format.
convolutionalFlat(int, int, int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder: * See InputType.convolutionalFlat(long, long, long)
convolutionDim - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
ConvolutionHelper - Interface in org.deeplearning4j.nn.layers.convolution: Helper for the convolution layer.
ConvolutionLayer - Class in org.deeplearning4j.nn.conf.layers: 2D Convolution layer (for example, spatial convolution over images).
ConvolutionLayer(ConvolutionLayer.BaseConvBuilder<?>) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer: ConvolutionLayer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the channels The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size
ConvolutionLayer - Class in org.deeplearning4j.nn.layers.convolution: Convolution layer
ConvolutionLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
ConvolutionLayer.AlgoMode - Enum in org.deeplearning4j.nn.conf.layers: The "PREFER_FASTEST" mode will pick the fastest algorithm for the specified parameters from the ConvolutionLayer.FwdAlgo, ConvolutionLayer.BwdFilterAlgo, and ConvolutionLayer.BwdDataAlgo lists, but they may be very memory intensive, so if weird errors occur when using cuDNN, please try the "NO_WORKSPACE" mode.
ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>> - Class in org.deeplearning4j.nn.conf.layers
ConvolutionLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
ConvolutionLayer.BwdDataAlgo - Enum in org.deeplearning4j.nn.conf.layers: The backward data algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".
ConvolutionLayer.BwdFilterAlgo - Enum in org.deeplearning4j.nn.conf.layers: The backward filter algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".
ConvolutionLayer.FwdAlgo - Enum in org.deeplearning4j.nn.conf.layers: The forward algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".
ConvolutionMode - Enum in org.deeplearning4j.nn.conf: ConvolutionMode defines how convolution operations should be executed for Convolutional and Subsampling layers, for a given input size and network configuration (specifically stride/padding/kernel sizes).
Currently, 3 modes are provided:

Strict: Output size for Convolutional and Subsampling layers are calculated as follows, in each dimension: outputSize = (inputSize - kernelSize + 2*padding) / stride + 1.
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: Set the convolution mode for the Convolution layer.
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: Set the convolution mode for the Convolution layer.
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder: Set the convolution mode for the Convolution layer.
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: The convolution mode to use in the 2d convolution
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder: Set the convolution mode for the Convolution layer.
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder: Set the convolution mode for the Convolution layer.
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder: Set the convolution mode for the Convolution layer.
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder: Set the convolution mode for the Convolution layer.
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
convolutionMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Sets the convolution mode for convolutional layers, which impacts padding and output sizes.
convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Sets the convolution mode for convolutional layers, which impacts padding and output sizes.
convolutionMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
ConvolutionParamInitializer - Class in org.deeplearning4j.nn.params: Initialize convolution params.
ConvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.ConvolutionParamInitializer
ConvolutionUtils - Class in org.deeplearning4j.util: Convolutional shape utilities
copyToLegacy(IEvaluation<?>, Class<T>) - Static method in class org.deeplearning4j.eval.EvaluationUtils: Deprecated.
corruptionLevel(double) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder: Level of corruption - 0.0 (none) to 1.0 (all values corrupted)
corruptionLevel - Variable in class org.deeplearning4j.nn.conf.layers.AutoEncoder
CountsForThreshold(double) - Constructor for class org.deeplearning4j.eval.ROC.CountsForThreshold: Deprecated.
CountsForThreshold(double, long, long) - Constructor for class org.deeplearning4j.eval.ROC.CountsForThreshold: Deprecated.
crashDumpOutputDirectory(File) - Static method in class org.deeplearning4j.util.CrashReportingUtil: Method that can be use to customize the output directory for memory crash reporting.
crashDumpsEnabled(boolean) - Static method in class org.deeplearning4j.util.CrashReportingUtil: Method that can be used to enable or disable memory crash reporting.
CrashReportingUtil - Class in org.deeplearning4j.util: A utility for generating crash reports when an out of memory error occurs.
createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createBias(long, double, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
createCenterLossMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer
createDepthWiseWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
createDepthWiseWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
createDistribution(Distribution) - Static method in class org.deeplearning4j.nn.conf.distribution.Distributions
createGain(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createGain(long, double, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createGradient(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
createPointWiseWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
createStepFunction(StepFunction) - Static method in class org.deeplearning4j.optimize.stepfunctions.StepFunctions
createVisibleBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer
createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer
createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DeconvolutionParamInitializer
createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createWeightMatrix(long, long, IWeightInit, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createWeightMatrix(long, long, IWeightInit, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer
createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
Cropping1D - Class in org.deeplearning4j.nn.conf.layers.convolutional: Cropping layer for convolutional (1d) neural networks.
Cropping1D(int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D
Cropping1D(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D
Cropping1D(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D
Cropping1D(Cropping1D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D
Cropping1D.Builder - Class in org.deeplearning4j.nn.conf.layers.convolutional
Cropping1DLayer - Class in org.deeplearning4j.nn.layers.convolution: Zero cropping layer for 1D convolutional neural networks.
Cropping1DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer
Cropping2D - Class in org.deeplearning4j.nn.conf.layers.convolutional: Cropping layer for convolutional (2d) neural networks.
Cropping2D(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
Cropping2D(int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
Cropping2D(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
Cropping2D(Cropping2D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
Cropping2D.Builder - Class in org.deeplearning4j.nn.conf.layers.convolutional
Cropping2DLayer - Class in org.deeplearning4j.nn.layers.convolution: Zero cropping layer for convolutional neural networks.
Cropping2DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer
Cropping3D - Class in org.deeplearning4j.nn.conf.layers.convolutional: Cropping layer for convolutional (3d) neural networks.
Cropping3D(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D
Cropping3D(int, int, int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D
Cropping3D(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D
Cropping3D(Cropping3D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D
Cropping3D.Builder - Class in org.deeplearning4j.nn.conf.layers.convolutional
Cropping3DLayer - Class in org.deeplearning4j.nn.layers.convolution: Cropping layer for 3D convolutional neural networks.
Cropping3DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer
CUDNN_WORKSPACE_KEY - Static variable in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
cudnnAlgoMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
cudnnAlgoMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer: Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
cudnnAlgoMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage of cuDNN.
cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage of cuDNN.
cudnnAlgoMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.
cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: Deprecated.
Use BatchNormalization.Builder.helperAllowFallback(boolean)
cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.
cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: Deprecated.
Use ConvolutionLayer.BaseConvBuilder.helperAllowFallback(boolean)
cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder: When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.
cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder: Deprecated.
Use LocalResponseNormalization.Builder.helperAllowFallback(boolean)
cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder: When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.
cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder: Deprecated.
Use Subsampling3DLayer.BaseSubsamplingBuilder.helperAllowFallback(boolean)
cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder: When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.
cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder: Deprecated.
Use SubsamplingLayer.BaseSubsamplingBuilder.helperAllowFallback(boolean)
cudnnAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
cudnnBwdDataAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
cudnnBwdDataAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
cudnnBwdDataMode(ConvolutionLayer.BwdDataAlgo) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
cudnnBwdFilterAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
cudnnBwdFilterAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
cudnnBwdFilterMode(ConvolutionLayer.BwdFilterAlgo) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
cudnnFwdAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
cudnnFwdAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
cudnnFwdMode(ConvolutionLayer.FwdAlgo) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
currentConsumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
currentConsumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
currentStep - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
currentThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler

D

dampingFactor - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder: Format of the input/output data.
dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer
dataFormat(Convolution3D.DataFormat) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder: The data format for input and output activations.
NCDHW: activations (in/out) should have shape [minibatch, channels, depth, height, width]
NDHWC: activations (in/out) should have shape [minibatch, depth, height, width, channels]
dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder: Data format for input activations.
dataFormat(SpaceToDepthLayer.DataFormat) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder: The data format for input and output activations.
NCDHW: activations (in/out) should have shape [minibatch, channels, depth, height, width]
NDHWC: activations (in/out) should have shape [minibatch, depth, height, width, channels]
dataFormat(Convolution3D.DataFormat) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder: The data format for input and output activations.
NCDHW: activations (in/out) should have shape [minibatch, channels, depth, height, width]
NDHWC: activations (in/out) should have shape [minibatch, depth, height, width, channels]
dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder
dataFormat(Convolution3D.DataFormat) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder: Sets the DataFormat.
dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling3D
DataSetLossCalculator - Class in org.deeplearning4j.earlystopping.scorecalc: Given a DataSetIterator: calculate the total loss for the model on that data set.
DataSetLossCalculator(DataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator: Calculate the score (loss function value) on a given data set (usually a test set)
DataSetLossCalculator(MultiDataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator: Calculate the score (loss function value) on a given data set (usually a test set)
DataSetLossCalculatorCG - Class in org.deeplearning4j.earlystopping.scorecalc: Deprecated.
Use DataSetLossCalculator instead for both MultiLayerNetwork and ComputationGraph
DataSetLossCalculatorCG(DataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG: Deprecated.

Calculate the score (loss function value) on a given data set (usually a test set)
DataSetLossCalculatorCG(MultiDataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG: Deprecated.

Calculate the score (loss function value) on a given data set (usually a test set)
dataType - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
dataType - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
dataType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
dataType(DataType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Set the DataType for the network parameters and activations for all layers in the network.
dataType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
dataType - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
dataType(DataType) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set the DataType for the network parameters and activations.
dataType - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
dataType - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
dataType - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
dataType - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
dead - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
decay - Variable 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(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: At test time: we can use a global estimate of the mean and variance, calculated using a moving average of the batch means/variances.
decay - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
decode(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
DECODER_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
decoderLayerSizes(int...) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Size of the decoder layers, in units.
decoderLayerSizes - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
decodeUpdates(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler: Deprecated.
decompressionThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
Deconvolution2D - Class in org.deeplearning4j.nn.conf.layers: 2D deconvolution layer configuration
Deconvolutions are also known as transpose convolutions or fractionally strided convolutions.
Deconvolution2D(ConvolutionLayer.BaseConvBuilder<?>) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D: Deconvolution2D layer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the channels The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size
Deconvolution2D.Builder - Class in org.deeplearning4j.nn.conf.layers
Deconvolution2DLayer - Class in org.deeplearning4j.nn.layers.convolution: 2D deconvolution layer implementation.
Deconvolution2DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer
DeconvolutionParamInitializer - Class in org.deeplearning4j.nn.params
DeconvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.DeconvolutionParamInitializer
DeepLearningException - Exception in org.deeplearning4j.exception
DeepLearningException() - Constructor for exception org.deeplearning4j.exception.DeepLearningException
DeepLearningException(String, Throwable, boolean, boolean) - Constructor for exception org.deeplearning4j.exception.DeepLearningException
DeepLearningException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DeepLearningException
DeepLearningException(String) - Constructor for exception org.deeplearning4j.exception.DeepLearningException
DeepLearningException(Throwable) - Constructor for exception org.deeplearning4j.exception.DeepLearningException
DEFAULT_ALPHA - Static variable in class org.deeplearning4j.nn.conf.dropout.AlphaDropout
DEFAULT_DECAY_RATE - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm
DEFAULT_DECAY_RATE - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm
DEFAULT_EDGE_VALUE - Static variable in class org.deeplearning4j.eval.EvaluationBinary: Deprecated.
DEFAULT_EPS - Static variable in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
DEFAULT_EPSILON - Static variable in class org.deeplearning4j.nn.conf.constraint.BaseConstraint
DEFAULT_FLATTENING_ORDER - Static variable in class org.deeplearning4j.nn.gradient.DefaultGradient
DEFAULT_INITIAL_MEMORY - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
DEFAULT_INITIAL_THRESHOLD - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm
DEFAULT_INITIAL_THRESHOLD - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm
DEFAULT_LAMBDA - Static variable in class org.deeplearning4j.nn.conf.dropout.AlphaDropout
DEFAULT_MAX_SPARSITY_TARGET - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm
DEFAULT_MIN_SPARSITY_TARGET - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm
DEFAULT_PRECISION - Static variable in class org.deeplearning4j.eval.EvaluationBinary: Deprecated.
DEFAULT_RATE - Static variable in class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint
DEFAULT_RESHAPE_ORDER - Static variable in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
DEFAULT_SPARSITY_TARGET - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm
DEFAULT_STATS_PRECISION - Static variable in class org.deeplearning4j.eval.ROCBinary: Deprecated.
Use ROCBinary
DEFAULT_STATS_PRECISION - Static variable in class org.deeplearning4j.eval.ROCMultiClass: Deprecated.
Use ROCMultiClass
DEFAULT_WEIGHT_INIT_ORDER - Static variable in interface org.deeplearning4j.nn.weights.IWeightInit
DEFAULT_WEIGHT_INIT_ORDER - Static variable in class org.deeplearning4j.nn.weights.WeightInitUtil: Default order for the arrays created by WeightInitUtil.
defaultConfiguration - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
defaultConfiguration - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
defaultDeserializer - Variable in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
DefaultGradient - Class in org.deeplearning4j.nn.gradient: Default gradient implementation.
DefaultGradient() - Constructor for class org.deeplearning4j.nn.gradient.DefaultGradient
DefaultGradient(INDArray) - Constructor for class org.deeplearning4j.nn.gradient.DefaultGradient
defaultNoWorkspace() - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder: Set the default to be scoped out for all array types.
DefaultParamInitializer - Class in org.deeplearning4j.nn.params: Static weight initializer with just a weight matrix and a bias
DefaultParamInitializer() - Constructor for class org.deeplearning4j.nn.params.DefaultParamInitializer
DefaultStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions: Default step function
DefaultStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction
DefaultStepFunction - Class in org.deeplearning4j.optimize.stepfunctions: Default step function
DefaultStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
defaultWorkspace(String, WorkspaceConfiguration) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder: Set the default workspace for all array types to the specified workspace name/configuration NOTE: This will NOT override any settings previously set.
defineInputs(String...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams: Define the inputs to the DL4J SameDiff Vertex with specific names
defineInputs(int) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams: Define the inputs to the DL4J SameDiff vertex with generated names.
defineLayer(SameDiff, SDVariable, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer
defineLayer(SameDiff, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleStrengthLayer
defineLayer(SameDiff, SDVariable, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer
defineLayer(SameDiff, SDVariable, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D
defineLayer(SameDiff, SDVariable, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D
defineLayer(SameDiff, SDVariable, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules
defineLayer(SameDiff, SDVariable, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer
defineLayer(SameDiff, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer: The defineLayer method is used to define the foward pass for the layer
defineLayer(SameDiff, SDVariable, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer
defineLayer(SameDiff, SDVariable, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer: Define the layer
defineLayer(SameDiff, SDVariable, SDVariable, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer: Define the output layer
defineLayer(SameDiff, SDVariable, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer
defineLayer(SameDiff, SDVariable) - Method in class org.deeplearning4j.nn.layers.util.IdentityLayer
defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer
defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer
defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D
defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D
defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules
defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer
defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer: Define the parameters for the network.
defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer
defineParameters(SDLayerParams) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer
defineParametersAndInputs(SDVertexParams) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex
defineParametersAndInputs(SDVertexParams) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex
defineParametersAndInputs(SDVertexParams) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex: Define the parameters - and inputs - for the network.
defineVertex(SameDiff, Map<String, SDVariable>, Map<String, SDVariable>, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex
defineVertex(SameDiff, SameDiffLambdaVertex.VertexInputs) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex: The defineVertex method is used to define the foward pass for the vertex
defineVertex(SameDiff, Map<String, SDVariable>, Map<String, SDVariable>, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex
defineVertex(SameDiff, Map<String, SDVariable>, Map<String, SDVariable>, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex: Define the vertex
deleteExisting(boolean) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: If the checkpoint listener is set to save to a non-empty directory, should the CheckpointListener-related content be deleted?
This is disabled by default (and instead, an exception will be thrown if existing data is found)
WARNING: Be careful when enabling this, as it deletes all saved checkpoint models in the specified directory!
DenseLayer - Class in org.deeplearning4j.nn.conf.layers: Dense layer: a standard fully connected feed forward layer
DenseLayer - Class in org.deeplearning4j.nn.layers.feedforward.dense
DenseLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer
DenseLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
depth() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Finds the channels of the tree.
depth(Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Returns the distance between this node and the specified subnode
DEPTH_WISE_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
depthMultiplier - Variable in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder: Set channels multiplier for depth-wise convolution
depthMultiplier(int) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder: Set channels multiplier for depth-wise convolution
depthMultiplier - Variable in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder: Set channels multiplier of channels-wise step in separable convolution
depthMultiplier(int) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder: Set channels multiplier of channels-wise step in separable convolution
DepthwiseConvolution2D - Class in org.deeplearning4j.nn.conf.layers: 2D depth-wise convolution layer configuration.
DepthwiseConvolution2D(DepthwiseConvolution2D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D
DepthwiseConvolution2D.Builder - Class in org.deeplearning4j.nn.conf.layers
DepthwiseConvolution2DLayer - Class in org.deeplearning4j.nn.layers.convolution: 2D depth-wise convolution layer configuration.
DepthwiseConvolution2DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer
DepthwiseConvolutionParamInitializer - Class in org.deeplearning4j.nn.params: Initialize depth-wise convolution parameters.
DepthwiseConvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.distribution.serde.LegacyDistributionDeserializer
deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.ComputationGraphConfigurationDeserializer
deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.legacy.LegacyIntArrayDeserializer
deserialize(JsonParser, DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.MultiLayerConfigurationDeserializer
DetectedObject - Class in org.deeplearning4j.nn.layers.objdetect: A detected object, by an object detection algorithm.
DetectedObject(int, double, double, double, double, INDArray, double) - Constructor for class org.deeplearning4j.nn.layers.objdetect.DetectedObject
dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder: Set dilation size for 3D convolutions in (depth, height, width) order
dilation - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: Kernel dilation.
dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: Kernel dilation.
dilation - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
dilation(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Sets the dilation of the 2d convolution
dilation - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
dilation(int, int, int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
dilation - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder: Kernel dilation.
dilation - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
dimension - Variable in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
dimensions - Variable in class org.deeplearning4j.nn.conf.constraint.BaseConstraint
dist(Distribution) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
dist(Distribution) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
Use NeuralNetConfiguration.Builder.weightInit(Distribution)
dist(Distribution) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Deprecated.
Distribution - Class in org.deeplearning4j.nn.conf.distribution: An abstract distribution.
Distribution() - Constructor for class org.deeplearning4j.nn.conf.distribution.Distribution
distributionInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
distributionInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
distributionInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
distributionInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution
distributionInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
distributionInputSize(int) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution: Get the number of distribution parameters for the given input data size.
Distributions - Class in org.deeplearning4j.nn.conf.distribution: Static methods for instantiating an nd4j distribution from a DL4J distribution configuration object.
divideByMinibatch(boolean, Gradient, int) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
DL4JException - Exception in org.deeplearning4j.exception: Base exception for DL4J
DL4JException() - Constructor for exception org.deeplearning4j.exception.DL4JException
DL4JException(String) - Constructor for exception org.deeplearning4j.exception.DL4JException
DL4JException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JException
DL4JException(Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JException
DL4JInvalidConfigException - Exception in org.deeplearning4j.exception: Exception signifying that the specified configuration is invalid
DL4JInvalidConfigException() - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException
DL4JInvalidConfigException(String) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException
DL4JInvalidConfigException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException
DL4JInvalidConfigException(Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException
DL4JInvalidInputException - Exception in org.deeplearning4j.exception: DL4J Exception thrown when invalid input is provided (wrong rank, wrong size, etc)
DL4JInvalidInputException() - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException
DL4JInvalidInputException(String) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException
DL4JInvalidInputException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException
DL4JInvalidInputException(Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException
DL4JModelValidator - Class in org.deeplearning4j.util: A utility for validating Deeplearning4j Serialized model file formats
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Do backward pass
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
doBackward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
doEvaluation(DataSetIterator, T...) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork: This method executes evaluation of the model against given iterator and evaluation implementations
doEvaluation(MultiDataSetIterator, T...) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork: This method executes evaluation of the model against given iterator and evaluation implementations
doEvaluation(DataSetIterator, T...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Perform evaluation on the given data (DataSetIterator) with the given IEvaluation instance
doEvaluation(MultiDataSetIterator, T...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Perform evaluation on the given data (MultiDataSetIterator) with the given IEvaluation instance
doEvaluation(DataSetIterator, T...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform evaluation using an arbitrary IEvaluation instance.
doEvaluation(MultiDataSetIterator, T[]) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
doEvaluationHelper(DataSetIterator, T...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
doForward(boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Do forward pass using the stored inputs
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
doForward(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
doInit() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
doInit() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
doInit() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
doTruncatedBPTT(INDArray[], INDArray[], INDArray[], INDArray[], LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the network using truncated BPTT
doTruncatedBPTT(INDArray, INDArray, INDArray, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
drainTo(Collection<? super E>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
drainTo(Collection<? super E>, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
drainTo(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
drainTo(long, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
DropConnect - Class in org.deeplearning4j.nn.conf.weightnoise: DropConnect, based on Wan et.
DropConnect(double) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.DropConnect
DropConnect(double, boolean) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.DropConnect
DropConnect(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.DropConnect
DropConnect(ISchedule, boolean) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.DropConnect
Dropout - Class in org.deeplearning4j.nn.conf.dropout: Implements standard (inverted) dropout.

Regarding dropout probability.
Dropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.Dropout
Dropout(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.Dropout
Dropout(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.Dropout
dropOut(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder: Dropout probability.
dropOut(IDropout) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder: Set the dropout for all layers in this network
dropOut(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Dropout probability.
dropOut(IDropout) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set the dropout for all layers in this network
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
dropout(IDropout) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Set the dropout
dropOut(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Dropout probability.
dropout - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
dropoutApplied - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
DropoutHelper - Interface in org.deeplearning4j.nn.conf.dropout: A helper interface for native dropout implementations
DropoutLayer - Class in org.deeplearning4j.nn.conf.layers: Dropout layer.
DropoutLayer(double) - Constructor for class org.deeplearning4j.nn.conf.layers.DropoutLayer
DropoutLayer(IDropout) - Constructor for class org.deeplearning4j.nn.conf.layers.DropoutLayer
DropoutLayer - Class in org.deeplearning4j.nn.layers: Created by davekale on 12/7/16.
DropoutLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.DropoutLayer
DropoutLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
ds - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
dsIterator - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
dummyBias - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
dummyBiasGrad - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
DummyConfig - Class in org.deeplearning4j.nn.conf.misc: A 'dummy' training configuration for use in frozen layers
DummyConfig() - Constructor for class org.deeplearning4j.nn.conf.misc.DummyConfig
DuplicateToTimeSeriesVertex - Class in org.deeplearning4j.nn.conf.graph.rnn: DuplicateToTimeSeriesVertex is a vertex that goes from 2d activations to a 3d time series activations, by means of duplication.
DuplicateToTimeSeriesVertex(String) - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
DuplicateToTimeSeriesVertex - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn: DuplicateToTimeSeriesVertex is a vertex that goes from 2d activations to a 3d time series activations, by means of duplication.
DuplicateToTimeSeriesVertex(ComputationGraph, String, int, String, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
DuplicateToTimeSeriesVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], String, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex

E

EarlyStoppingConfiguration<T extends Model> - Class in org.deeplearning4j.earlystopping: Early stopping configuration: Specifies the various configuration options for running training with early stopping.
Users need to specify the following:
(a) EarlyStoppingModelSaver: How models will be saved (to disk, to memory, etc) (Default: in memory)
(b) Termination conditions: at least one termination condition must be specified
(i) Iteration termination conditions: calculated once for each minibatch.
EarlyStoppingConfiguration.Builder<T extends Model> - Class in org.deeplearning4j.earlystopping
EarlyStoppingGraphTrainer - Class in org.deeplearning4j.earlystopping.trainer: Class for conducting early stopping training locally (single machine).
Can be used to train a ComputationGraph
EarlyStoppingGraphTrainer(EarlyStoppingConfiguration<ComputationGraph>, ComputationGraph, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
EarlyStoppingGraphTrainer(EarlyStoppingConfiguration<ComputationGraph>, ComputationGraph, DataSetIterator, EarlyStoppingListener<ComputationGraph>) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer: Constructor for training using a DataSetIterator
EarlyStoppingGraphTrainer(EarlyStoppingConfiguration<ComputationGraph>, ComputationGraph, MultiDataSetIterator, EarlyStoppingListener<ComputationGraph>) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer: Constructor for training using a MultiDataSetIterator
EarlyStoppingListener<T extends Model> - Interface in org.deeplearning4j.earlystopping.listener: EarlyStoppingListener is a listener interface for conducting early stopping training.
EarlyStoppingModelSaver<T extends Model> - Interface in org.deeplearning4j.earlystopping: Interface for saving MultiLayerNetworks learned during early stopping, and retrieving them again later
EarlyStoppingResult<T extends Model> - Class in org.deeplearning4j.earlystopping: EarlyStoppingResult: contains the results of the early stopping training, such as: - Why the training was terminated - Score vs.
EarlyStoppingResult(EarlyStoppingResult.TerminationReason, String, Map<Integer, Double>, int, double, int, T) - Constructor for class org.deeplearning4j.earlystopping.EarlyStoppingResult
EarlyStoppingResult.TerminationReason - Enum in org.deeplearning4j.earlystopping
EarlyStoppingTrainer - Class in org.deeplearning4j.earlystopping.trainer: Class for conducting early stopping training locally (single machine), for training a MultiLayerNetwork.
EarlyStoppingTrainer(EarlyStoppingConfiguration<MultiLayerNetwork>, MultiLayerConfiguration, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
EarlyStoppingTrainer(EarlyStoppingConfiguration<MultiLayerNetwork>, MultiLayerNetwork, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
EarlyStoppingTrainer(EarlyStoppingConfiguration<MultiLayerNetwork>, MultiLayerNetwork, DataSetIterator, EarlyStoppingListener<MultiLayerNetwork>) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
effectiveKernelSize(int, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils
effectiveKernelSize(int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils
element() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
ElementWiseMultiplicationLayer - Class in org.deeplearning4j.nn.conf.layers.misc: Elementwise multiplication layer with weights: implements out = activationFn(input .* w + b) where:
- w is a learnable weight vector of length nOut
- ".*" is element-wise multiplication
- b is a bias vector

Note that the input and output sizes of the element-wise layer are the same for this layer
ElementWiseMultiplicationLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer
ElementWiseMultiplicationLayer(ElementWiseMultiplicationLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer
ElementWiseMultiplicationLayer - Class in org.deeplearning4j.nn.layers.feedforward.elementwise: Elementwise multiplication layer with weights: implements out = activationFn(input .* w + b) where:
- w is a learnable weight vector of length nOut
- ".*" is element-wise multiplication
- b is a bias vector

Note that the input and output sizes of the element-wise layer are the same for this layer
ElementWiseMultiplicationLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer
ElementWiseMultiplicationLayer.Builder - Class in org.deeplearning4j.nn.conf.layers.misc
ElementWiseParamInitializer - Class in org.deeplearning4j.nn.params: created by jingshu
ElementWiseParamInitializer() - Constructor for class org.deeplearning4j.nn.params.ElementWiseParamInitializer
ElementWiseVertex - Class in org.deeplearning4j.nn.conf.graph: An ElementWiseVertex is used to combine the activations of two or more layer in an element-wise manner
For example, the activations may be combined by addition, subtraction, multiplication (product), average or by selecting the maximum.
Addition, Average, Max and Product may use an arbitrary number of input arrays.
ElementWiseVertex(ElementWiseVertex.Op) - Constructor for class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
ElementWiseVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: An ElementWiseVertex is used to combine the activations of two or more layer in an element-wise manner
For example, the activations may be combined by addition, subtraction or multiplication or by selecting the maximum.
ElementWiseVertex(ComputationGraph, String, int, ElementWiseVertex.Op, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
ElementWiseVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], ElementWiseVertex.Op, DataType) - 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
EmbeddingInitializer - Interface in org.deeplearning4j.nn.weights.embeddings: An interface implemented by things like Word2Vec etc that allows them to be used as weight
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, DataType) - Constructor for class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
EmbeddingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
EmbeddingSequenceLayer - Class in org.deeplearning4j.nn.conf.layers: Embedding layer for sequences: feed-forward layer that expects fixed-length number (inputLength) of integers/indices per example as input, ranged from 0 to numClasses - 1.
EmbeddingSequenceLayer - Class in org.deeplearning4j.nn.layers.feedforward.embedding: Embedding layer for sequences: feed-forward layer that expects fixed-length number (inputLength) of integers/indices per example as input, ranged from 0 to numClasses - 1.
EmbeddingSequenceLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
EmbeddingSequenceLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
EmptyParamInitializer - Class in org.deeplearning4j.nn.params
EmptyParamInitializer() - Constructor for class org.deeplearning4j.nn.params.EmptyParamInitializer
encode(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
EncodedGradientsAccumulator - Class in org.deeplearning4j.optimize.solvers.accumulation: This GradientsAccumulator is suited for CUDA backend.
EncodedGradientsAccumulator(int, double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
EncodedGradientsAccumulator(int, ThresholdAlgorithm, ResidualPostProcessor, boolean) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
EncodedGradientsAccumulator(int, MessageHandler, long, int, Double, boolean) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
EncodedGradientsAccumulator.Builder - Class in org.deeplearning4j.optimize.solvers.accumulation
ENCODER_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
encoderLayerSizes(int...) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Size of the encoder layers, in units.
encoderLayerSizes - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
encodeUpdates(int, int, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
encodingDebugMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
encodingDebugMode(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
encodingDebugMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
encodingDebugMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
EncodingHandler - Class in org.deeplearning4j.optimize.solvers.accumulation: This MessageHandler implementation is suited for debugging mostly, but still can be used in production environment if you really want that.
EncodingHandler(ThresholdAlgorithm, ResidualPostProcessor, Double, boolean) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
epochCount - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
epochCount - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
epochCount - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
epochCount - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
epochCount - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
EpochTerminationCondition - Interface in org.deeplearning4j.earlystopping.termination: Interface for termination conditions to be evaluated once per epoch (i.e., once per pass of the full data set), based on a score and epoch number
epochTerminationConditions(EpochTerminationCondition...) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder: Termination conditions to be evaluated every N epochs, with N set by evaluateEveryNEpochs option
epochTerminationConditions(List<EpochTerminationCondition>) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder: Termination conditions to be evaluated every N epochs, with N set by evaluateEveryNEpochs option
eps - Variable in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
eps - Variable in class org.deeplearning4j.nn.conf.graph.L2Vertex
eps - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: 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(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: Epsilon value for batch normalization; small floating point value added to variance (algorithm 1 in http://arxiv.org/pdf/1502.03167v3.pdf) to reduce/avoid underflow issues.
Default: 1e-5
eps - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
eps - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
eps(double) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
eps - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
epsilon - Variable in class org.deeplearning4j.nn.conf.constraint.BaseConstraint
epsilon - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
equals(Object) - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
equals(Object) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams
equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction
equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction
equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction
equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction
equals(Object) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
equals(Object) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
error() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Returns the prediction error for this node
errorIfGraphIfMLN() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
errorSum() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Returns the total prediction error for this tree and its children
esConfig - Variable in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
evalAtIndex(IEvaluation, INDArray[], INDArray[], int) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener
evaluate(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the network (classification performance - single output ComputationGraphs only)
evaluate(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the network (classification performance - single output ComputationGraphs only)
evaluate(DataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the network on the provided data set (single output ComputationGraphs only).
evaluate(MultiDataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the network on the provided data set (single output ComputationGraphs only).
evaluate(DataSetIterator, List<String>, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the network (for classification) on the provided data set, with top N accuracy in addition to standard accuracy.
evaluate(MultiDataSetIterator, List<String>, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the network (for classification) on the provided data set, with top N accuracy in addition to standard accuracy.
evaluate(DataSetIterator, Map<Integer, T[]>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Perform evaluation for networks with multiple outputs.
evaluate(MultiDataSetIterator, Map<Integer, T[]>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Perform evaluation for networks with multiple outputs.
evaluate(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Evaluate the network (classification performance)
evaluate(DataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Evaluate the network on the provided data set.
evaluate(DataSetIterator, List<String>, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Evaluate the network (for classification) on the provided data set, with top N accuracy in addition to standard accuracy.
evaluateEveryNEpochs(int) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder: How frequently should evaluations be conducted (in terms of epochs)? Defaults to every (1) epochs.
evaluateRegression(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the (single output layer only) network for regression performance
evaluateRegression(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the (single output layer only) network for regression performance
evaluateRegression(DataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the (single output layer only) network for regression performance
evaluateRegression(MultiDataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the (single output layer only) network for regression performance
evaluateRegression(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Evaluate the network for regression performance
evaluateROC(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Deprecated.
To be removed - use ComputationGraph.evaluateROC(DataSetIterator, int) to enforce selection of appropriate ROC/threshold configuration
evaluateROC(DataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the network (must be a binary classifier) on the specified data, using the ROC class
evaluateROC(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Deprecated.
To be removed - use ComputationGraph.evaluateROC(DataSetIterator, int) to enforce selection of appropriate ROC/threshold configuration
evaluateROC(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the network (must be a binary classifier) on the specified data, using the ROC class
evaluateROC(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Deprecated.
To be removed - use MultiLayerNetwork.evaluateROC(DataSetIterator, int) to enforce selection of appropriate ROC/threshold configuration
evaluateROC(DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Evaluate the network (must be a binary classifier) on the specified data, using the ROC class
evaluateROCMultiClass(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Deprecated.
To be removed - use ComputationGraph.evaluateROCMultiClass(DataSetIterator, int) to enforce selection of appropriate ROC/threshold configuration
evaluateROCMultiClass(DataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the network on the specified data, using the ROCMultiClass class
evaluateROCMultiClass(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Evaluate the network on the specified data, using the ROCMultiClass class
evaluateROCMultiClass(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Deprecated.
To be removed - use MultiLayerNetwork.evaluateROCMultiClass(DataSetIterator, int) to enforce selection of appropriate ROC/threshold configuration
evaluateROCMultiClass(DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Evaluate the network on the specified data, using the ROCMultiClass class
evaluation - Variable in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
evaluation - Variable in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
Evaluation - Class in org.deeplearning4j.eval: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation() - Constructor for class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation(int) - Constructor for class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation(int, Integer) - Constructor for class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation(List<String>) - Constructor for class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation(Map<Integer, String>) - Constructor for class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation(List<String>, int) - Constructor for class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation(double) - Constructor for class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation(double, Integer) - Constructor for class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation(INDArray) - Constructor for class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation(List<String>, INDArray) - Constructor for class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
Evaluation.Metric - Enum in org.deeplearning4j.eval: Deprecated.
EvaluationAveraging - Enum in org.deeplearning4j.eval: Deprecated.
Use EvaluationAveraging
EvaluationBinary - Class in org.deeplearning4j.eval: Deprecated.
EvaluationBinary(INDArray) - Constructor for class org.deeplearning4j.eval.EvaluationBinary: Deprecated.
EvaluationBinary(int, Integer) - Constructor for class org.deeplearning4j.eval.EvaluationBinary: Deprecated.
EvaluationCalibration - Class in org.deeplearning4j.eval: Deprecated.
Use EvaluationCalibration
EvaluationCalibration() - Constructor for class org.deeplearning4j.eval.EvaluationCalibration: Deprecated.
Use EvaluationCalibration
EvaluationCalibration(int, int) - Constructor for class org.deeplearning4j.eval.EvaluationCalibration: Deprecated.
Use EvaluationCalibration
EvaluationCalibration(int, int, boolean) - Constructor for class org.deeplearning4j.eval.EvaluationCalibration: Deprecated.
Use EvaluationCalibration
EvaluationCallback - Interface in org.deeplearning4j.optimize.listeners.callbacks: This interface describes callback, which can be used with EvaluativeListener, to extend its functionality.
evaluations - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
EvaluationUtils - Class in org.deeplearning4j.eval: Deprecated.
Use EvaluationUtils
EvaluationUtils() - Constructor for class org.deeplearning4j.eval.EvaluationUtils: Deprecated.
EvaluativeListener - Class in org.deeplearning4j.optimize.listeners: This TrainingListener implementation provides simple way for model evaluation during training.
EvaluativeListener(DataSetIterator, int) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener: Evaluation will be launched after each *frequency* iterations, with Evaluation datatype
EvaluativeListener(DataSetIterator, int, InvocationType) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener
EvaluativeListener(MultiDataSetIterator, int) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener: Evaluation will be launched after each *frequency* iterations, with Evaluation datatype
EvaluativeListener(MultiDataSetIterator, int, InvocationType) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener
EvaluativeListener(DataSetIterator, int, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener: Evaluation will be launched after each *frequency* iteration
EvaluativeListener(DataSetIterator, int, InvocationType, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener: Evaluation will be launched after each *frequency* iteration
EvaluativeListener(MultiDataSetIterator, int, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener: Evaluation will be launched after each *frequency* iteration
EvaluativeListener(MultiDataSetIterator, int, InvocationType, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener: Evaluation will be launched after each *frequency* iteration
EvaluativeListener(DataSet, int, InvocationType) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener
EvaluativeListener(MultiDataSet, int, InvocationType) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener
EvaluativeListener(DataSet, int, InvocationType, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener
EvaluativeListener(MultiDataSet, int, InvocationType, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener
exampleCount - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
exampleNegLogProbability(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
exampleNegLogProbability(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
exampleNegLogProbability(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
exampleNegLogProbability(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution
exampleNegLogProbability(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
exampleNegLogProbability(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution: Calculate the negative log probability for each example individually
expectedConsumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
ExponentialReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational: Exponential reconstruction distribution.
Supports data in range [0,infinity)
ExponentialReconstructionDistribution() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
ExponentialReconstructionDistribution(String) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution: Deprecated.
Use ExponentialReconstructionDistribution.ExponentialReconstructionDistribution(Activation)
ExponentialReconstructionDistribution(Activation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
ExponentialReconstructionDistribution(IActivation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
exportScores(OutputStream) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener: Export the scores in tab-delimited (one per line) UTF-8 format.
exportScores(OutputStream, String) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener: Export the scores in delimited (one per line) UTF-8 format with the specified delimiter
exportScores(File) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener: Export the scores to the specified file in delimited (one per line) UTF-8 format, tab delimited
exportScores(File, String) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener: Export the scores to the specified file in delimited (one per line) UTF-8 format, using the specified delimiter
extCounter - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
externalSource - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
externalUpdatesAvailable - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator

F

f1(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Deprecated.
f1Score(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier: Sets the input and labels and returns a score for the prediction wrt true labels
f1Score(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier: Returns the f1 score for the given examples.
f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Sets the input and labels and returns a score for the prediction wrt true labels
f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Returns the f1 score for the given examples.
f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer: Returns the f1 score for the given examples.
f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer: Returns the f1 score for the given examples.
f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer: Sets the input and labels and returns a score for the prediction wrt true labels
f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer: Returns the f1 score for the given examples.
f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer: Returns the f1 score for the given examples.
f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer: Returns the f1 score for the given examples.
f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer: Returns the f1 score for the given examples.
f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
f1Score(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Sets the input and labels and returns the F1 score for the prediction with respect to the true labels
f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform inference and then calculate the F1 score of the output(input) vs.
fa - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
FailureTestingListener - Class in org.deeplearning4j.optimize.listeners: WARNING: THIS LISTENER SHOULD ONLY BE USED FOR MANUAL TESTING PURPOSES
It intentionally causes various types of failures according to some criteria, in order to test the response to it.
This is useful for example in: (a) Testing Spark fault tolerance
(b) Testing OOM exception crash dump information
Generally it should not be used in unit tests either, depending on how it is configured.

Two aspects need to be configured to use this listener: 1.
FailureTestingListener(FailureTestingListener.FailureMode, FailureTestingListener.FailureTrigger) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener
FailureTestingListener.And - Class in org.deeplearning4j.optimize.listeners
FailureTestingListener.CallType - Enum in org.deeplearning4j.optimize.listeners
FailureTestingListener.FailureMode - Enum in org.deeplearning4j.optimize.listeners
FailureTestingListener.FailureTrigger - Class in org.deeplearning4j.optimize.listeners
FailureTestingListener.HostNameTrigger - Class in org.deeplearning4j.optimize.listeners
FailureTestingListener.IterationEpochTrigger - Class in org.deeplearning4j.optimize.listeners
FailureTestingListener.Or - Class in org.deeplearning4j.optimize.listeners
FailureTestingListener.RandomProb - Class in org.deeplearning4j.optimize.listeners
FailureTestingListener.TimeSinceInitializedTrigger - Class in org.deeplearning4j.optimize.listeners
FailureTestingListener.UserNameTrigger - Class in org.deeplearning4j.optimize.listeners
FailureTrigger() - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureTrigger
fallbackToSingleConsumerMode(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
fallbackToSingleConsumerMode(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
fallbackToSingleConsumerMode(boolean) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.Registerable: This method enables/disables bypass mode
falseNegativeRate(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Deprecated.
falsePositiveRate(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Deprecated.
FancyBlockingQueue<E> - Class in org.deeplearning4j.optimize.solvers.accumulation: This BlockingQueue implementation is suited only for symmetric gradients updates, and should NOT be used anywhere else.
FancyBlockingQueue(BlockingQueue<E>) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
FancyBlockingQueue(BlockingQueue<E>, int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
fBeta(double, EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Deprecated.
featurize(MultiDataSet) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: During training frozen vertices/layers can be treated as "featurizing" the input The forward pass through these frozen layer/vertices can be done in advance and the dataset saved to disk to iterate quickly on the smaller unfrozen part of the model Currently does not support datasets with feature masks
featurize(DataSet) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: During training frozen vertices/layers can be treated as "featurizing" the input The forward pass through these frozen layer/vertices can be done in advance and the dataset saved to disk to iterate quickly on the smaller unfrozen part of the model Currently does not support datasets with feature masks
feedForward(long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: InputType for feed forward network data
feedForward(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder: See InputType.feedForward(long)
feedForward(INDArray, int, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Conduct forward pass using a single input array.
feedForward(INDArray[], int, boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Conduct forward pass using an array of inputs.
feedForward(INDArray[], int, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Conduct forward pass using an array of inputs
feedForward(boolean, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Conduct forward pass using the stored inputs
feedForward(INDArray, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Conduct forward pass using a single input array.
feedForward(INDArray[], boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Conduct forward pass using an array of inputs
feedForward(INDArray[], boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Conduct forward pass using an array of inputs.
feedForward() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Conduct forward pass using the stored inputs, at test time
feedForward(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Conduct forward pass using the stored inputs
feedForward(boolean, boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
feedForward(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Compute all layer activations, from input to output of the output layer.
feedForward(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Compute activations from input to output of the output layer.
feedForward(boolean, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform feed-forward, optionally (not) clearing the layer input arrays.
Note: when using clearInputs=false, there can be some performance and memory overhead: this is because the arrays are defined outside of workspaces (which are enabled by default) - otherwise, old/invalidated arrays could still be accessed after calling this method.
feedForward() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Compute activations of all layers from input (inclusive) to output of the final/output layer.
feedForward(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Compute activations of all layers from input (inclusive) to output of the final/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 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.layers.LearnedSelfAttentionLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
feedForwardMaskArray(INDArray, MaskState, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
feedForwardMaskArrays(INDArray[], MaskState, int) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
FeedForwardToCnn3DPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: A preprocessor to allow 3D CNN and standard feed-forward network layers to be used together.
For example, DenseLayer -> Convolution3D
This does two things:
(a) Reshapes activations out of FeedFoward layer (which is 2D with shape [numExamples, inputDepth*inputHeight*inputWidth*numChannels]) into 5d activations (with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) suitable to feed into CNN layers.
(b) Reshapes 5d epsilons from 3D CNN layer, with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) into 2d epsilons (with shape [numExamples, inputDepth*inputHeight*inputWidth*numChannels]) for use in feed forward layer
FeedForwardToCnn3DPreProcessor(int, int, int, int, boolean) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor
FeedForwardToCnn3DPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor
FeedForwardToCnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: A preprocessor to allow CNN and standard feed-forward network layers to be used together.
For example, DenseLayer -> CNN
This does two things:
(a) Reshapes activations out of FeedFoward layer (which is 2D or 3D with shape [numExamples, inputHeight*inputWidth*numChannels]) into 4d activations (with shape [numExamples, numChannels, inputHeight, inputWidth]) suitable to feed into CNN layers.
(b) Reshapes 4d epsilons (weights*deltas) from CNN layer, with shape [numExamples, numChannels, inputHeight, inputWidth]) into 2d epsilons (with shape [numExamples, inputHeight*inputWidth*numChannels]) for use in feed forward layer Note: numChannels is equivalent to channels or featureMaps referenced in different literature
FeedForwardToCnnPreProcessor(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor: Reshape to a channels x rows x columns tensor
FeedForwardToCnnPreProcessor(long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
feedForwardToLayer(int, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Compute the activations from the input to the specified layer.
To compute activations for all layers, use feedForward(...) methods
Note: output list includes the original input.
feedForwardToLayer(int, INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Compute the activations from the input to the specified layer.
To compute activations for all layers, use feedForward(...) methods
Note: output list includes the original input.
feedForwardToLayer(int, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Compute the activations from the input to the specified layer, using the currently set input for the network.
To compute activations for all layers, use feedForward(...) methods
Note: output list includes the original input.
FeedForwardToRnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: A preprocessor to allow RNN and feed-forward network layers to be used together.
For example, DenseLayer -> GravesLSTM
This does two things:
(a) Reshapes activations out of FeedFoward layer (which is 2D with shape [miniBatchSize*timeSeriesLength,layerSize]) into 3d activations (with shape [miniBatchSize,layerSize,timeSeriesLength]) suitable to feed into RNN layers.
(b) Reshapes 3d epsilons (weights*deltas from RNN layer, with shape [miniBatchSize,layerSize,timeSeriesLength]) into 2d epsilons (with shape [miniBatchSize*timeSeriesLength,layerSize]) for use in feed forward layer
FeedForwardToRnnPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
ffToLayerActivationsDetached(boolean, FwdPassType, boolean, int, int[], INDArray[], INDArray[], INDArray[], boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Feed-forward through the network - returning all array activations detached from any workspace.
ffToLayerActivationsDetached(boolean, FwdPassType, boolean, int, INDArray, INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Feed-forward through the network - returning all array activations in a list, detached from any workspace.
ffToLayerActivationsInWS(boolean, int, int[], FwdPassType, boolean, INDArray[], INDArray[], INDArray[], boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Feed-forward through the network - if workspaces are used, all returned activations will be present in workspace WS_ALL_LAYERS_ACT.
Note: if using workspaces for training, requires that WS_ALL_LAYERS_ACT is open externally.
ffToLayerActivationsInWs(int, FwdPassType, boolean, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Feed-forward through the network at training time - returning a list of all activations in a workspace (WS_ALL_LAYERS_ACT) if workspaces are enabled for training; or detached if no workspaces are used.
Note: if using workspaces for training, this method requires that WS_ALL_LAYERS_ACT is open externally.
If using NO workspaces, requires that no external workspace is open
Note that this method does NOT clear the inputs to each layer - instead, they are in the WS_ALL_LAYERS_ACT workspace for use in later backprop.
finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
finalScore(U) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator
finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
finalScore(Evaluation) - Method in class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator
finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
finalScore(RegressionEvaluation) - Method in class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator
finalScore(IEvaluation) - Method in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
FineTuneConfiguration - Class in org.deeplearning4j.nn.transferlearning: Configuration for fine tuning.
FineTuneConfiguration() - Constructor for class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
fineTuneConfiguration(FineTuneConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Fine tune configurations specified will overwrite the existing configuration if any Usage example: specify a learning rate will set specified learning rate on all layers Refer to the fineTuneConfiguration class for more details
fineTuneConfiguration(FineTuneConfiguration) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Set parameters to selectively override existing learning parameters Usage eg.
FineTuneConfiguration.Builder - Class in org.deeplearning4j.nn.transferlearning
firstChild() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
firstNotAppliedIndexEverywhere() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
firstOne - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
fit() - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
fit(boolean) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
fit() - Method in interface org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer: Conduct early stopping training
fit(DataSetIterator) - Method in interface org.deeplearning4j.nn.api.Classifier: Train the model based on the datasetiterator
fit(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier: Fit the model
fit(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier: Fit the model
fit(INDArray, int[]) - Method in interface org.deeplearning4j.nn.api.Classifier: Fit the model
fit() - Method in interface org.deeplearning4j.nn.api.Model: Deprecated.
fit(INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Model: Fit the model to the given data
fit(DataSet) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork: This method fits model with a given DataSet
fit(MultiDataSet) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork: This method fits model with a given MultiDataSet
fit(DataSetIterator) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork: This method fits model with a given DataSetIterator
fit(MultiDataSetIterator) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork: This method fits model with a given MultiDataSetIterator
fit(DataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the ComputationGraph using a DataSet.
fit(DataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Perform minibatch training on all minibatches in the DataSetIterator, for the specified number of epochs.
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the ComputationGraph using a DataSetIterator.
Note that this method can only be used with ComputationGraphs with 1 input and 1 output
Method doesn't do layerwise pretraining.
For pretraining use method pretrain..
fit(MultiDataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the ComputationGraph using a MultiDataSet
fit(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Perform minibatch training on all minibatches in the MultiDataSetIterator, for the specified number of epochs.
fit(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the ComputationGraph using a MultiDataSetIterator Method doesn't do layerwise pretraining.
For pretraining use method pretrain..
fit(INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the ComputationGraph given arrays of inputs and labels.
fit(INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Fit the ComputationGraph using the specified inputs and labels (and mask arrays)
fit() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
fit() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
fit() - Method in class org.deeplearning4j.nn.layers.BaseLayer
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Fit the model
fit(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Fit the model
fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Fit the model
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
fit(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
fit(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
fit() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
fit() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
fit() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
fit() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer
fit() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
fit() - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.LossLayer
fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer: Fit the model
fit(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer: Fit the model
fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.LossLayer: Fit the model
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
fit(DataSet) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
fit() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
fit(DataSet) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
fit() - Method in class org.deeplearning4j.nn.layers.RepeatVector
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.RepeatVector
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
fit(DataSet) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
fit() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
fit() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
fit(DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform minibatch training on all minibatches in the DataSetIterator, for the specified number of epochs.
fit(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform minibatch training on all minibatches in the DataSetIterator for 1 epoch.
Note that this method does not do layerwise pretraining.
For pretraining use method pretrain..
fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Fit the model for one iteration on the provided data
fit(INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Fit the model for one iteration on the provided data
fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
fit(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Fit the model for one iteration on the provided data
fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Fit the model for one iteration on the provided data
fit() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
fit(MultiDataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
fit(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform minibatch training on all minibatches in the MultiDataSetIterator, for the specified number of epochs.
fit(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform minibatch training on all minibatches in the MultiDataSetIterator.
Note: The MultiDataSets in the MultiDataSetIterator must have exactly 1 input and output array (as MultiLayerNetwork only supports 1 input and 1 output)
fitFeaturized(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: Fit from a featurized dataset.
fitFeaturized(MultiDataSet) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
fitFeaturized(DataSet) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
fitFeaturized(DataSetIterator) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
FixedAlgorithmThresholdReducer() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer
FixedThresholdAlgorithm - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold: A simple fixed threshold algorithm, not adaptive in any way.
FixedThresholdAlgorithm() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm
FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold
flattenedGradients - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
flattenedGradients - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
flattenedParams - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
flattenedParams - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
flatteningOrderForVariable(String) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
flatteningOrderForVariable(String) - Method in interface org.deeplearning4j.nn.gradient.Gradient: Return the gradient flattening order for the specified variable, or null if it is not explicitly set
fn - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
fn - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
forgetGateBiasInit - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder: Set forget gate bias initalizations.
forgetGateBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder: Set forget gate bias initalizations.
forgetGateBiasInit - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM
forgetGateBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder: Deprecated.

Set forget gate bias initalizations.
format(double) - Static method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
formatter - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
formatter2 - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
FORWARD_PREFIX - Static variable in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
frequency - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
from - Variable in class org.deeplearning4j.nn.conf.graph.UnstackVertex
fromFileString(String) - Static method in class org.deeplearning4j.optimize.listeners.Checkpoint
fromIActivation(IActivation) - Static method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayerUtils
fromJson(String) - Static method in class org.deeplearning4j.eval.curves.Histogram: Deprecated.
Use Histogram
fromJson(String) - Static method in class org.deeplearning4j.eval.curves.RocCurve: Deprecated.
Use RocCurve
fromJson(String) - Static method in class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
fromJson(String) - Static method in class org.deeplearning4j.eval.EvaluationBinary: Deprecated.
fromJson(String) - Static method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: Create a computation graph configuration from json
fromJson(String) - Static method in class org.deeplearning4j.nn.conf.memory.MemoryReport
fromJson(String) - Static method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration: Create a neural net configuration from json
fromJson(String) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: Create a neural net configuration from json
fromJson(String) - Static method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
fromYaml(String) - Static method in class org.deeplearning4j.eval.curves.Histogram: Deprecated.
Use Histogram
fromYaml(String) - Static method in class org.deeplearning4j.eval.curves.RocCurve: Deprecated.
Use RocCurve
fromYaml(String) - Static method in class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
fromYaml(String) - Static method in class org.deeplearning4j.eval.EvaluationBinary: Deprecated.
fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: Create a neural net configuration from YAML
fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.memory.MemoryReport
fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration: Create a neural net configuration from json
fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: Create a neural net configuration from json
fromYaml(String) - Static method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
FrozenLayer - Class in org.deeplearning4j.nn.conf.layers.misc: FrozenLayer is used for the purposes of transfer learning.
A frozen layer wraps another DL4J Layer within it.
FrozenLayer(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
FrozenLayer - Class in org.deeplearning4j.nn.layers: For purposes of transfer learning A frozen layers wraps another dl4j layer within it.
FrozenLayer(Layer) - Constructor for class org.deeplearning4j.nn.layers.FrozenLayer
FrozenLayer.Builder - Class in org.deeplearning4j.nn.conf.layers.misc
FrozenLayerParamInitializer - Class in org.deeplearning4j.nn.params: Parameter initializer for FrozenLayer instances.
FrozenLayerParamInitializer() - Constructor for class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
FrozenLayerWithBackprop - Class in org.deeplearning4j.nn.conf.layers.misc: Frozen layer freezes parameters of the layer it wraps, but allows the backpropagation to continue.
FrozenLayerWithBackprop(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
FrozenLayerWithBackprop - Class in org.deeplearning4j.nn.layers: Frozen layer freezes parameters of the layer it wraps, but allows the backpropagation to continue.
FrozenLayerWithBackprop(Layer) - Constructor for class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
FrozenLayerWithBackpropParamInitializer - Class in org.deeplearning4j.nn.params: Parameter initializer for FrozenLayer instances.
FrozenLayerWithBackpropParamInitializer() - Constructor for class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
FrozenVertex - Class in org.deeplearning4j.nn.conf.graph: FrozenVertex is used for the purposes of transfer learning.
A frozen vertex wraps another DL4J GraphVertex within it.
FrozenVertex(GraphVertex) - Constructor for class org.deeplearning4j.nn.conf.graph.FrozenVertex
FrozenVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: FrozenVertex is used for the purposes of transfer learning A frozen layers wraps another DL4J GraphVertex within it.
FrozenVertex(GraphVertex) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.FrozenVertex
fwdPassOutput - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
fwdPassOutputAsArrays - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
FwdPassReturn - Class in org.deeplearning4j.nn.layers.recurrent: Created by benny on 12/31/15.
FwdPassReturn() - Constructor for class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
FwdPassType - Enum in org.deeplearning4j.nn.api: Type of forward pass to do.
fz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

G

ga - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
GAIN_KEY - Static variable in class org.deeplearning4j.nn.params.DefaultParamInitializer
GAIN_KEY - Static variable in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
gainInit - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Gain initialization value, for layers with Layer Normalization.
gainInit(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Gain initialization value, for layers with Layer Normalization.
gainInit - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
gainInit - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
gamma - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: Used only when 'true' is passed to BatchNormalization.Builder.lockGammaBeta(boolean).
gamma(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: Used only when 'true' is passed to BatchNormalization.Builder.lockGammaBeta(boolean).
gamma - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
GAMMA - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
gammaConstraints - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: Set constraints to be applied to the gamma parameter of this batch normalisation layer.
gateActivationFn - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder: Activation function for the LSTM gates.
gateActivationFn - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM
gateActivationFunction(String) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder: Activation function for the LSTM gates.
gateActivationFunction(Activation) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder: Activation function for the LSTM gates.
gateActivationFunction(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder: Activation function for the LSTM gates.
gateActivationFunction(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder: Deprecated.

Activation function for the LSTM gates.
gateActivationFunction(Activation) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder: Deprecated.

Activation function for the LSTM gates.
gateActivationFunction(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder: Deprecated.

Activation function for the LSTM gates.
GaussianDistribution - Class in org.deeplearning4j.nn.conf.distribution: Deprecated.
Use NormalDistribution which is identical to this implementation
GaussianDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.GaussianDistribution: Deprecated.

Create a gaussian distribution (equivalent to normal) with the given mean and std
GaussianDropout - Class in org.deeplearning4j.nn.conf.dropout: Gaussian dropout.
GaussianDropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianDropout
GaussianDropout(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianDropout
GaussianDropout(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianDropout
GaussianNoise - Class in org.deeplearning4j.nn.conf.dropout: Applies additive, mean-zero Gaussian noise to the input - i.e., x = x + N(0,stddev).
Note that this differs from GaussianDropout, which applies multiplicative mean-1 N(1,s) noise.
Note also that schedules for the standard deviation value can also be used.
GaussianNoise(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianNoise
GaussianNoise(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianNoise
GaussianNoise(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianNoise
GaussianReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational: Gaussian reconstruction distribution for variational autoencoder.
Outputs are modelled by a Gaussian distribution, with the mean and variances (diagonal covariance matrix) for each output determined by the network forward pass.
GaussianReconstructionDistribution() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution: Create a GaussianReconstructionDistribution with the default identity activation function.
GaussianReconstructionDistribution(Activation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution
GaussianReconstructionDistribution(IActivation) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution
generalValidation(String, Layer, IDropout, List<Regularization>, List<Regularization>, List<LayerConstraint>, List<LayerConstraint>, List<LayerConstraint>) - Static method in class org.deeplearning4j.nn.conf.layers.LayerValidation
generateAtMean(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
generateAtMean(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
generateAtMean(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
generateAtMean(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution
generateAtMean(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
generateAtMean(INDArray) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution: Generate a sample from P(x|z), where x = E[P(x|z)] i.e., return the mean value for the distribution
generateAtMeanGivenZ(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder: Given a specified values for the latent space as input (latent space being z in p(z|data)), generate output from P(x|z), where x = E[P(x|z)]
i.e., return the mean value for the distribution P(x|z)
generateMemoryStatus(Model, int, InputType...) - Static method in class org.deeplearning4j.util.CrashReportingUtil: Generate memory/system report as a String, for the specified network.
generateRandom(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
generateRandom(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
generateRandom(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
generateRandom(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution
generateRandom(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
generateRandom(INDArray) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution: Randomly sample from P(x|z) using the specified distribution parameters
generateRandomGivenZ(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder: Given a specified values for the latent space as input (latent space being z in p(z|data)), randomly generate output x, where x ~ P(x|z)
get0(INDArray[]) - Static method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
get3DOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[], boolean) - Static method in class org.deeplearning4j.util.Convolution3DUtils: Get the output size (depth/height/width) for the given input data and CNN3D configuration
get3DSameModeTopLeftPadding(int[], int[], int[], int[], int[]) - Static method in class org.deeplearning4j.util.Convolution3DUtils: Get top and left padding for same mode only for 3d convolutions
getAlpha() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
getAverageThresholdAlgorithm() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler: This should ONLY be called once all training threads have completed
getBegin() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
getBestModel() - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver: Retrieve the best model that was previously saved
getBestModel() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingResult
getBestModel() - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
getBestModel() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
getBestModel() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
getBiasParameterKeys() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams
getBottomRightXY() - Method in class org.deeplearning4j.nn.layers.objdetect.DetectedObject: Get the bottom right X/Y coordinates of the detected object
getBroadcastDims(int[], int) - Static method in class org.deeplearning4j.nn.conf.constraint.BaseConstraint
getBytesPerElement(DataType) - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport
getChildren() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
getComputationGraphUpdater() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
getComputationGraphUpdater(boolean) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
getComputationGraphUpdater() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getComputationGraphUpdater(boolean) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getConf(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
getConf() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
getConf() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getConfidenceMatrix(INDArray, int, int) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer: Get the confidence matrix (confidence for all x/y positions) for the specified bounding box, from the network output activations array
getConfig() - Method in interface org.deeplearning4j.nn.api.Trainable
getConfig() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
getConfig() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getConfig() - Method in class org.deeplearning4j.nn.graph.vertex.impl.FrozenVertex
getConfig() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
getConfig() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getConfig() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
getConfig() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getConfig() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
getConfig() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getConfig() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getConfig() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getConfiguration() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: This method returns configuration of this ComputationGraph
getCorruptedInput(INDArray, double) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork: Corrupts the given input by doing a binomial sampling given the corruption level
getDeconvolutionOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Get the output size of a deconvolution operation for given input data.
getDefaultConfiguration() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Intended for internal/developer use
getDefaultStepFunctionForOptimizer(Class<? extends ConvexOptimizer>) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getDelta() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
getDelta(long) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
getDepth() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional: Deprecated.
getEnd() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
getEpoch(Model) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener
getEpochCount() - Method in interface org.deeplearning4j.nn.api.Layer
getEpochCount() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
getEpochCount() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Returns the number of epochs that the ComputationGraph has done.
getEpochCount() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getEpochCount() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getEpochCount() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getEpochCount() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getEpochCount(Model) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getEps() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getEpsilon() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
getEpsilon() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getEpsilon() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the epsilon/error (i.e., dL/dOutput) array previously set for this GraphVertex
getExternalSource() - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
getExternalSource() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
getExternalSource() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator
getFileForCheckpoint(Checkpoint) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Get the model file for the given checkpoint.
getFileForCheckpoint(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Get the model file for the given checkpoint number.
getFileForCheckpoint(File, int) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener
getFileHeader() - Static method in class org.deeplearning4j.optimize.listeners.Checkpoint
getFinalResult() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer
getFinalResult() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithmReducer
getFlattenedGradientsView() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
getFlattenedGradientsView() - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
getFlattenedGradientsView() - Method in class org.deeplearning4j.nn.updater.LayerUpdater
getFlattenedGradientsView() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
getFlattenedSize() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
getGlobalPosition() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
getGradientCheck() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
getGradientFor(String) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
getGradientFor(String) - Method in interface org.deeplearning4j.nn.gradient.Gradient: The gradient for the given variable
getGradientNormalization() - Method in interface org.deeplearning4j.nn.api.TrainingConfig
getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer
getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer
getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer
getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
getGradientNormalization() - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig
getGradientNormalizationThreshold() - Method in interface org.deeplearning4j.nn.api.TrainingConfig
getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer
getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer
getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
getGradientNormalizationThreshold() - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig
getGradientsAccumulator() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: This method returns GradientsAccumulator instance used in this optimizer.
getGradientsAccumulator() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in interface org.deeplearning4j.nn.api.ParamInitializer: Return a map of gradients (in their standard non-flattened representation), taken from the flattened (row vector) gradientView array.
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.DeconvolutionParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer: Return a map of gradients (in their standard non-flattened representation), taken from the flattened (row vector) gradientView array.
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
getGradientsViewArray() - Method in interface org.deeplearning4j.nn.api.Model
getGradientsViewArray() - Method in interface org.deeplearning4j.nn.api.Trainable
getGradientsViewArray() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
getGradientsViewArray() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
getGradientsViewArray() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getGradientsViewArray() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.BaseLayer
getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getGradientsViewArray() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getGradientsViewArray() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getGradientUpdater() - Method in class org.deeplearning4j.nn.updater.UpdaterBlock
getHeadWord() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
getHeightAndWidth(NeuralNetConfiguration) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Get the height and width from the configuration
getHeightAndWidth(int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Get the height and width for an image
getHelper() - Method in interface org.deeplearning4j.nn.api.Layer
getHelper() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getHelper() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
getHelper() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
getHelper() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
getHelper() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
getHelper() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getHelper() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
getHelper() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getHelper() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getHelper() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getHelperWorkspace(String) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr: Get the pointer to the helper memory.
getHWDFromInputType(InputType) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Get heigh/width/channels as length 3 int[] from the InputType
getIndex() - Method in interface org.deeplearning4j.nn.api.Layer: Get the layer index.
getIndex() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getIndex() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
getIndex() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getIndex() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getIndex() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getIndex() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getInnerConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getInnerConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
getInput(int) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex.VertexInputs
getInput(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the previously set input for the ComputationGraph
getInput() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getInput() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
getInput() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getInputMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the previously set feature/input mask arrays for the ComputationGraph
getInputMiniBatchSize() - Method in interface org.deeplearning4j.nn.api.Layer: Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)
getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getInputPreProcess(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
getInputs(SameDiff) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex
getInputs() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the previously set inputs for the ComputationGraph
getInputs() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getInputs() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the array of inputs previously set for this GraphVertex
getInputVertices() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex: A representation of the vertices that are inputs to this vertex (inputs duing forward pass)
Specifically, if inputVertices[X].getVertexIndex() = Y, and inputVertices[X].getVertexEdgeNumber() = Z then the Zth output of vertex Y is the Xth input to this vertex
getInputVertices() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getInputVertices() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: A representation of the vertices that are inputs to this vertex (inputs duing forward pass)
Specifically, if inputVertices[X].getVertexIndex() = Y, and inputVertices[X].getVertexEdgeNumber() = Z then the Zth output connection (see GraphVertex.getNumOutputConnections() of vertex Y is the Xth input to this vertex
getInsideLayer() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
getInsideLayer() - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
getInstance() - Static method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.CenterLossParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.DeconvolutionParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.DefaultParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.EmptyParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.LSTMParamInitializer
getInstance(long[], long[]) - Static method in class org.deeplearning4j.nn.params.PReLUParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.PretrainParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
getInstance() - Static method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
getIter(Model) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener
getIterationCount() - Method in interface org.deeplearning4j.nn.api.Layer
getIterationCount() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Returns the number of iterations (parameter updates) that the ComputationGraph has done
getIterationCount() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getIterationCount() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getIterationCount() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getIterationCount(Model) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getIUpdaterWithDefaultConfig() - Method in enum org.deeplearning4j.nn.conf.Updater
getLabelMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the previously set label/output mask arrays for the ComputationGraph
getLabels() - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer: Get the labels array previously set with IOutputLayer.setLabels(INDArray)
getLabels() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
getLabels() - Method in class org.deeplearning4j.nn.layers.LossLayer
getLabels() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
getLabels2d() - Method in class org.deeplearning4j.nn.layers.LossLayer
getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.OutputLayer
getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer
getLambda() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
getLastEtlTime() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: This method returns ETL time field value
getLastEtlTime() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the last ETL time.
getLatestModel() - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver: Retrieve the most recent model that was previously saved
getLatestModel() - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
getLatestModel() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
getLatestModel() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
getLayer(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the layer by the number of that layer, in range 0 to getNumLayers()-1 NOTE: This is different from the internal GraphVertex index for the layer
getLayer(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get a given layer by name.
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getLayer() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the Layer (if any).
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
getLayer() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
getLayer(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLayer(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLayerActivationTypes(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: For the given input shape/type for the network, return a map of activation sizes for each layer and vertex in the graph.
getLayerActivationTypes(boolean, InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: For the given input shape/type for the network, return a map of activation sizes for each layer and vertex in the graph.
getLayerActivationTypes() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: For the (perhaps partially constructed) network configuration, return a map of activation sizes for each layer and vertex in the graph.
Note 1: The network configuration may be incomplete, but the inputs have been added to the layer already.
Note 2: To use this method, the network input types must have been set using ComputationGraphConfiguration.GraphBuilder.setInputTypes(InputType...) first
getLayerActivationTypes(InputType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration: For the given input shape/type for the network, return a list of activation sizes for each layer in the network.
i.e., list.get(i) is the output activation sizes for layer i
getLayerActivationTypes() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder: For the (perhaps partially constructed) network configuration, return a list of activation sizes for each layer in the network.
Note: To use this method, the network input type must have been set using NeuralNetConfiguration.ListBuilder.setInputType(InputType) first
getLayerActivationWSConfig(int) - Static method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLayerName() - Method in interface org.deeplearning4j.nn.api.TrainingConfig
getLayerName() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
getLayerName() - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig
getLayerNames() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLayerParams() - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
getLayers() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get all layers in the ComputationGraph
getLayers() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLayerwise() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
getLayerWiseConfigurations() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the configuration for the network
getLayerWorkingMemWSConfig(int) - Static method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getLearningRate(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the current learning rate, for the specified layer, from the network.
getLearningRate(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the current learning rate, for the specified layer, from the network.
getLearningRate(MultiLayerNetwork, int) - Static method in class org.deeplearning4j.util.NetworkUtils: Get the current learning rate, for the specified layer, fromthe network.
getLearningRate(ComputationGraph, String) - Static method in class org.deeplearning4j.util.NetworkUtils: Get the current learning rate, for the specified layer, from the network.
getLeaves() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Gets the leaves of the tree.
getLeaves(List<T>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Gets the leaves of the tree.
getLegacyMapper() - Static method in class org.deeplearning4j.nn.conf.serde.JsonMappers
getListeners() - Method in interface org.deeplearning4j.nn.api.Layer: Get the iteration listeners for this layer.
getListeners() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the trainingListeners for the ComputationGraph
getListeners() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getListeners() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
getListeners() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getListeners() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getListeners() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getListeners() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the TrainingListeners set for this network, if any
getLocalPosition() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
getLocalPosition(long) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
getLossFn() - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer
getMapper() - Static method in class org.deeplearning4j.nn.conf.serde.JsonMappers
getMapper100alpha() - Static method in class org.deeplearning4j.nn.conf.serde.legacy.LegacyJsonFormat: Get a mapper (minus general config) suitable for loading old format JSON - 1.0.0-alpha and before
getMapperYaml() - Static method in class org.deeplearning4j.nn.conf.serde.JsonMappers
getMask() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getMaskArray() - Method in interface org.deeplearning4j.nn.api.Layer
getMaskArray() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getMaskArray() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getMaskArray() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getMaskArray() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getMaskArray() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getMaxIterations() - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
getMean() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
getMeanCache(DataType) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
getMeanCache(DataType) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
getMemoryBytes(MemoryType, int, MemoryUseMode, CacheMode, DataType) - 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, DataType) - 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, DataType) - Method in class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: Get a MemoryReport for the given computation graph configuration.
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex: This is a report of the estimated memory consumption for the given vertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM: Deprecated.
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM: Deprecated.
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Layer: This is a report of the estimated memory consumption for the given layer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LSTM
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer: This is a report of the estimated memory consumption for the given layer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
getMemoryReport(InputType...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
getMemoryReport(InputType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration: Get a MemoryReport for the given MultiLayerConfiguration.
getMemoryReport(AbstractLSTM, InputType) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers
getMemoryReport(GravesBidirectionalLSTM, InputType) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers
getMemoryReport(boolean, FeedForwardLayer, InputType) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers
getMinibatchDivisionSubsets(INDArray) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
getModelType(Model) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener
getModuleName() - Method in interface org.deeplearning4j.nn.conf.module.GraphBuilderModule: A module should return its name.
getN() - Method in class org.deeplearning4j.nn.layers.RepeatVector
getName() - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport
getName() - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport: Name of the object that the memory report was generated for
getName() - Method in class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport
getNIn() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
getnLayers() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the number of layers in the network
getNOut() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
getNOut() - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer
getNumberOfTrials() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
getNumInputArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: The number of inputs to this network
getNumInputArrays() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
getNumInputArrays() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getNumInputArrays() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the number of input arrays.
getNumLayers() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Returns the number of layers in the ComputationGraph
getNumOutputArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: The number of output (arrays) for this network
getNumOutputConnections() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
getNumOutputConnections() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getNumOutputConnections() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the number of outgoing connections from this GraphVertex.
getObjectFromFile(File, String) - Static method in class org.deeplearning4j.util.ModelSerializer: Get an object with the specified key from the model file, that was previously added to the file using ModelSerializer.addObjectToFile(File, String, Object)
getOptimalBufferSize(long, int, int) - Static method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator: This method returns optimal bufferSize for a given model We know, that updates are guaranteed to have MAX size of params / 16.
getOptimalBufferSize(Model, int, int) - Static method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
getOptimizer() - Method in interface org.deeplearning4j.nn.api.Model: Returns this models optimizer
getOptimizer() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork: This method returns Optimizer used for training
getOptimizer() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
getOptimizer() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getOptimizer() - Method in class org.deeplearning4j.nn.layers.BaseLayer
getOptimizer() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getOptimizer() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getOptimizer() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getOptimizer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getOptimizer() - Method in class org.deeplearning4j.optimize.Solver
getOrderedLayers() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
getOrderedLayers() - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
getOrderedLayers() - Method in class org.deeplearning4j.nn.updater.LayerUpdater
getOrderedLayers() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
getOutputLayer(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the specified output layer, by index.
getOutputLayer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the output layer - i.e., the last layer in the netwok
getOutputLayerIndices() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
getOutputSize(INDArray, int, int, int, ConvolutionMode) - Static method in class org.deeplearning4j.util.Convolution1DUtils
getOutputSize(int, int, int, int, ConvolutionMode, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils: Get the output size (height) for the given input data and CNN1D configuration
getOutputSize(INDArray, int, int, int, ConvolutionMode, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils: Get the output size (height) for the given input data and CNN1D configuration
getOutputSize(INDArray, int[], int[], int[], ConvolutionMode) - Static method in class org.deeplearning4j.util.ConvolutionUtils
getOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Get the output size (height/width) for the given input data and CNN configuration
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex: Determine the type of output for this GraphVertex, given the specified inputs.
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
getOutputType(InputType) - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor: For a given type of input to this preprocessor, what is the type of the output?
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleStrengthLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Layer: For a given type of input to this layer, what is the type of the output?
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer
getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.layers.util.IdentityLayer
getOutputTypeCnn1DLayers(InputType, int, int, int, int, ConvolutionMode, long, long, String, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
getOutputTypeCnn3DLayers(InputType, int[], int[], int[], int[], ConvolutionMode, long, long, String, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
getOutputTypeCnnLayers(InputType, int[], int[], int[], int[], ConvolutionMode, long, long, String, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
getOutputTypeDeconvLayer(InputType, int[], int[], int[], int[], ConvolutionMode, long, long, String, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
getOutputVertices() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex: A representation of the vertices that this vertex is connected to (outputs duing forward pass) Specifically, if outputVertices[X].getVertexIndex() = Y, and outputVertices[X].getVertexEdgeNumber() = Z then the Xth output of this vertex is connected to the Zth input of vertex Y
getOutputVertices() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getOutputVertices() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: A representation of the vertices that this vertex is connected to (outputs duing forward pass) Specifically, if outputVertices[X].getVertexIndex() = Y, and outputVertices[X].getVertexEdgeNumber() = Z then the Xth output of this vertex is connected to the Zth input of vertex Y
getParam(String) - Method in interface org.deeplearning4j.nn.api.Model: Get the parameter
getParam(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
getParam(String) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
getParam(String) - Method in class org.deeplearning4j.nn.layers.BaseLayer
getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
getParam(String) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
getParam(String) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
getParam(String) - Method in class org.deeplearning4j.nn.layers.RepeatVector
getParam(String) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
getParam(String) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
getParam(String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getParam(String) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
getParam(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get one parameter array for the network.
In MultiLayerNetwork, parameters are keyed like "0_W" and "0_b" to mean "weights of layer index 0" and "biases of layer index 0" respectively.
getParameter(Layer, String, int, int, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.weightnoise.DropConnect
getParameter(Layer, String, int, int, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.conf.weightnoise.IWeightNoise: Get the parameter, after applying weight noise
getParameter(Layer, String, int, int, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.weightnoise.WeightNoise
getParameterKeys() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams
getParams() - Method in interface org.deeplearning4j.nn.api.layers.LayerConstraint
getParams() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
getParams() - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
getParams() - Method in class org.deeplearning4j.nn.updater.LayerUpdater
getParams() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
getParamShapes() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams: Get the parameter shapes for all parameters
getParamWithNoise(String, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer: Get the parameter, after applying any weight noise (such as DropConnect) if necessary.
getParamWithNoise(String, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
getPnorm() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getPredictedClass() - Method in class org.deeplearning4j.nn.layers.objdetect.DetectedObject: Get the index of the predicted class (based on maximum predicted probability)
getPredictedObjects(INDArray, double) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
getPredictedObjects(INDArray, INDArray, double, double) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils: Given the network output and a detection threshold (in range 0 to 1) determine the objects detected by the network.
Supports minibatches - the returned DetectedObject instances have an example number index.
Note that the dimensions are grid cell units - for example, with 416x416 input, 32x downsampling by the network (before getting to the Yolo2OutputLayer) we have 13x13 grid cells (each corresponding to 32 pixels in the input image).
getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
getPreProcessor() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer
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.LearnedSelfAttentionLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer
getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
getPreProcessorForInputTypeCnn3DLayers(InputType, String) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil: Utility method for determining the appropriate preprocessor for CNN layers, such as ConvolutionLayer and SubsamplingLayer
getPreProcessorForInputTypeCnnLayers(InputType, String) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil: Utility method for determining the appropriate preprocessor for CNN layers, such as ConvolutionLayer and SubsamplingLayer
getPreprocessorForInputTypeRnnLayers(InputType, String) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
getProbabilityMatrix(INDArray, int, int) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer: Get the probability matrix (probability of the specified class, assuming an object is present, for all x/y positions), from the network output activations array
getProbabilityOfSuccess() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
getRegularizationByParam(String) - Method in interface org.deeplearning4j.nn.api.TrainingConfig: Get the regularization types (l1/l2/weight decay) for the given parameter.
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer: Get the regularization types (l1/l2/weight decay) for the given parameter.
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig
getRegularizationByParam(String) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer
getRepetitionFactor() - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder: Set repetition factor for RepeatVector layer
getReportClass() - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport
getReportClass() - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport
getReportClass() - Method in class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport
getSameModeBottomRightPadding(int, int, int, int, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils
getSameModeBottomRightPadding(int[], int[], int[], int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Get bottom and right padding for same mode only.
getSameModeTopLeftPadding(int, int, int, int, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils: Get top padding for same mode only.
getSameModeTopLeftPadding(int[], int[], int[], int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Get top and left padding for same mode only.
getScoreCalculator() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration
getScoreVsIter() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType: Returns the shape of this InputType
getShape() - Method in class org.deeplearning4j.nn.conf.inputs.InputType: Returns the shape of this InputType without minibatch dimension in the returned array
getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional
getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional3D
getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward
getShape(boolean) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
getShape(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
getSize() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D
getSize() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
getSize() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
getStateViewArray() - Method in interface org.deeplearning4j.nn.api.Updater
getStateViewArray() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
getStateViewArrayCopy() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater: A synchronized version of BaseMultiLayerUpdater.getStateViewArray() that duplicates the view array internally.
getStd() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
getStepFunction() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: This method returns StepFunction defined within this Optimizer instance
getStepMax() - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
getTokens() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
getTopLeftXY() - Method in class org.deeplearning4j.nn.layers.objdetect.DetectedObject: Get the top left X/Y coordinates of the detected object
getTotalMemoryBytes(int, MemoryUseMode, CacheMode, DataType) - 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, DataType) - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport: Get the total memory use in bytes for the given configuration
getTotalMemoryBytes(int, MemoryUseMode, CacheMode, DataType) - Method in class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport
getTrainingListeners() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Deprecated.
Use MultiLayerNetwork.getListeners()
getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType
getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional
getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional3D
getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward
getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
getType() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: The type of node; mainly extra meta data
getUnderlying() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep
getUnflattenedType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
getUpdater() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the ComputationGraphUpdater for the network.
getUpdater(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the ComputationGraphUpdater for this network
getUpdater() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the updater for this MultiLayerNetwork
getUpdater(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
getUpdater(Model) - Static method in class org.deeplearning4j.nn.updater.UpdaterCreator
getUpdater() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
getUpdater(boolean) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
getUpdater() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getUpdater(boolean) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
getUpdaterByParam(String) - Method in interface org.deeplearning4j.nn.api.TrainingConfig: Get the updater for the given parameter.
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer: Get the updater for the given parameter.
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer: Get the updater for the given parameter.
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional: Get the updater for the given parameter.
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig
getVarCache(DataType) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
getVarCache(DataType) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
getVertex(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return a given GraphVertex by name, or null if no vertex with that name exists
getVertexEdgeNumber() - Method in class org.deeplearning4j.nn.graph.vertex.VertexIndices: The edge number.
getVertexIndex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
getVertexIndex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getVertexIndex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the index of the GraphVertex
getVertexIndex() - Method in class org.deeplearning4j.nn.graph.vertex.VertexIndices: Index of the vertex
getVertexName() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
getVertexName() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
getVertexName() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the name/label of the GraphVertex
getVertexParams() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
getVertices() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Returns an array of all GraphVertex objects.
getWeightInitFunction() - Method in enum org.deeplearning4j.nn.weights.WeightInit: Create an instance of the weight initialization function
getWeightInitFunction(Distribution) - Method in enum org.deeplearning4j.nn.weights.WeightInit: Create an instance of the weight initialization function
getWeightParameterKeys() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams
GLOBAL_LOG_STD - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
GLOBAL_MEAN - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
GLOBAL_VAR - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
globalConfiguration - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
GlobalPoolingLayer - Class in org.deeplearning4j.nn.conf.layers: Global pooling layer - used to do pooling over time for RNNs, and 2d pooling for CNNs.
Supports the following PoolingTypes: SUM, AVG, MAX, PNORM
Global pooling layer can also handle mask arrays when dealing with variable length inputs.
GlobalPoolingLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
GlobalPoolingLayer(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
GlobalPoolingLayer - Class in org.deeplearning4j.nn.layers.pooling: Global pooling layer - used to do pooling over time for RNNs, and 2d pooling for CNNs.
Supports the following PoolingTypes: SUM, AVG, MAX, PNORM
Global pooling layer can also handle mask arrays when dealing with variable length inputs.
mask arrays are assumed to be 2d, and are fed forward through the network during training or post-training forward pass:
- Time series (RNNs, 1d CNNs): mask arrays are shape [miniBatchSize, maxTimeSeriesLength] and contain values 0 or 1 only
- CNNs (2d): mask have shape [miniBatchSize, 1, height, 1] or [miniBatchSize, 1, 1, width] or [minibatch, 1, height, width].
GlobalPoolingLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
GlobalPoolingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
gMeasure(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Deprecated.
goldLabel() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
gradient() - Method in interface org.deeplearning4j.nn.api.Model: Get the gradient.
gradient(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
gradient(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
gradient(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
gradient(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution
gradient(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
gradient(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution: Calculate the gradient of the negative log probability with respect to the preOutDistributionParams
gradient(List<String>) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
gradient() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
Gradient - Interface in org.deeplearning4j.nn.gradient: Generic gradient
gradient(List<String>) - Method in interface org.deeplearning4j.nn.gradient.Gradient: The full gradient as one flat vector
gradient() - Method in interface org.deeplearning4j.nn.gradient.Gradient: The full gradient as one flat vector
gradient - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
gradient() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
gradient() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
gradient - Variable in class org.deeplearning4j.nn.layers.BaseLayer
gradient() - Method in class org.deeplearning4j.nn.layers.BaseLayer
gradient() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Gets the gradient from one training iteration
gradient() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
gradient() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
gradient() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
gradient() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
gradient() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
gradient() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
gradient() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
gradient() - Method in class org.deeplearning4j.nn.layers.LossLayer: Gets the gradient from one training iteration
gradient() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
gradient() - Method in class org.deeplearning4j.nn.layers.RepeatVector
gradient() - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer: Gets the gradient from one training iteration
gradient - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
gradient() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
gradient() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
gradient - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
gradient() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
gradient - Variable in class org.deeplearning4j.optimize.listeners.SharedGradient
GRADIENT_KEY - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
gradientAndScore() - Method in interface org.deeplearning4j.nn.api.Model: Get the gradient and score
gradientAndScore() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
gradientAndScore() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
gradientAndScore() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
gradientAndScore() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
gradientAndScore() - Method in class org.deeplearning4j.nn.layers.LossLayer
gradientAndScore() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
gradientAndScore() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
gradientAndScore() - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer
gradientAndScore() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
gradientAndScore() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
gradientAndScore() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
gradientAndScore(LayerWorkspaceMgr) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: The gradient and score for this optimizer
gradientAndScore(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
gradientCheck - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
gradientCheck(boolean) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
gradientCheck - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
GradientCheckUtil - Class in org.deeplearning4j.gradientcheck: A utility for numerically checking gradients.
gradientForVariable() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
gradientForVariable() - Method in interface org.deeplearning4j.nn.gradient.Gradient: Gradient look up table
GradientNormalization - Enum in org.deeplearning4j.nn.conf: Gradient normalization strategies.
gradientNormalization - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Gradient normalization strategy.
gradientNormalization(GradientNormalization) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Gradient normalization strategy.
gradientNormalization - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
gradientNormalization - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
gradientNormalization - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
gradientNormalization - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
gradientNormalization(GradientNormalization) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Gradient normalization strategy.
gradientNormalization(GradientNormalization) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Gradient normalization strategy.
gradientNormalization - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: 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(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer, GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue
Not used otherwise.
L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping.
gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
gradientNormalizationThreshold(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer, GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue
Not used otherwise.
L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
gradientNormalizationThreshold(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer, GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue
Not used otherwise.
L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping
gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
gradients - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
gradients - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
gradients - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
gradients - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
GradientsAccumulator - Interface in org.deeplearning4j.optimize.solvers.accumulation
gradientsFlattened - Variable in class org.deeplearning4j.nn.layers.BaseLayer
gradientsFlattened - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
gradientsForMinibatchDivision - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
GradientStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions: Normal gradient step function
GradientStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction
GradientStepFunction - Class in org.deeplearning4j.optimize.stepfunctions: Normal gradient step function
GradientStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
gradientViews - Variable in class org.deeplearning4j.nn.layers.BaseLayer
gradientViews - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
gradTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
gradTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
gradTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
graph - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
GraphBuilder(NeuralNetConfiguration.Builder) - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
GraphBuilder(ComputationGraphConfiguration, NeuralNetConfiguration.Builder) - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
graphBuilder() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Create a GraphBuilder (for creating a ComputationGraphConfiguration).
GraphBuilder(ComputationGraph) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Computation Graph to tweak for transfer learning
GraphBuilderModule - Interface in org.deeplearning4j.nn.conf.module: GraphBuilderModule for nn layers.
graphIndices - Variable in class org.deeplearning4j.nn.graph.ComputationGraph: Topological sort and vertex index/name + name/index mapping
GraphIndices - Class in org.deeplearning4j.nn.graph.util: Simple helper class for ComputationGraph topological sort and vertex index/name + name/index mapping
GraphIndices() - Constructor for class org.deeplearning4j.nn.graph.util.GraphIndices
GraphVertex - Class in org.deeplearning4j.nn.conf.graph: A GraphVertex is a vertex in the computation graph type of neural network.
GraphVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.GraphVertex
GraphVertex - Interface in org.deeplearning4j.nn.graph.vertex: A GraphVertex is a vertex in the computation graph.
GraphVertexMixin() - Constructor for class org.deeplearning4j.nn.conf.serde.legacy.LegacyJsonFormat.GraphVertexMixin
GravesBidirectionalLSTM - Class in org.deeplearning4j.nn.conf.layers: Deprecated.
use Bidirectional instead. With the Bidirectional layer wrapper you can make any recurrent layer bidirectional, in particular GravesLSTM. Note that this layer adds the output of both directions, which translates into "ADD" mode in Bidirectional. Usage: .layer(new Bidirectional(Bidirectional.Mode.ADD, new GravesLSTM.Builder()....build()))
GravesBidirectionalLSTM - Class in org.deeplearning4j.nn.layers.recurrent: RNN tutorial: https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent READ THIS FIRST Bdirectional LSTM layer implementation.
GravesBidirectionalLSTM(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
GravesBidirectionalLSTM.Builder - Class in org.deeplearning4j.nn.conf.layers: Deprecated.
GravesBidirectionalLSTMParamInitializer - Class in org.deeplearning4j.nn.params: LSTM Parameter initializer, for LSTM based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
GravesBidirectionalLSTMParamInitializer() - Constructor for class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
GravesLSTM - Class in org.deeplearning4j.nn.conf.layers: Deprecated.
Will be eventually removed. Use LSTM instead, which has similar prediction accuracy, but supports CuDNN for faster network training on CUDA (Nvidia) GPUs
GravesLSTM - Class in org.deeplearning4j.nn.layers.recurrent: Deprecated.
Will be eventually removed. Use LSTM instead, which has similar prediction accuracy, but supports CuDNN for faster network training on CUDA (Nvidia) GPUs
GravesLSTM(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
GravesLSTM.Builder - Class in org.deeplearning4j.nn.conf.layers: Deprecated.
GravesLSTMParamInitializer - Class in org.deeplearning4j.nn.params: LSTM Parameter initializer, for LSTM based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
GravesLSTMParamInitializer() - Constructor for class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
gz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

H

handleActivationBackwardCompatibility(BaseLayer, ObjectNode) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
handleL1L2BackwardCompatibility(BaseLayer, ObjectNode) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
handleLossBackwardCompatibility(BaseOutputLayer, ObjectNode) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
handler - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
handler - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
handler - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
handleUpdaterBackwardCompatibility(BaseLayer, ObjectNode) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
handleWeightInitBackwardCompatibility(BaseLayer, ObjectNode) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
hasAFrozenLayer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
hasAnything() - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
hasAnything() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
hasAnything() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator: This method checks if there are any (probably external) updates available
hasAnything() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
hasAnything(long) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder: If true (default): include bias parameters in the model.
hasBias - Variable in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
hasBias() - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder: Sets whether to use bias.
hasBias() - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D
hasBias - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: If true (default): include bias parameters in the model.
hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: If true (default): include bias parameters in the model.
hasBias - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
hasBias() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
hasBias() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D
hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer.Builder: If true (default): include bias parameters in the model.
hasBias() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder: If true: include bias parameters in the layer.
hasBias() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer
hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder: If true: include bias parameters in the layer.
hasBias() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer
hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer.Builder
hasBias() - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D
hasBias() - Method in class org.deeplearning4j.nn.layers.BaseLayer: Does this layer have no bias term? Many layers (dense, convolutional, output, embedding) have biases by default, but no-bias versions are possible via configuration
hasBias() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
hasBias() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
hasBias() - Method in class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer
hasBias() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
hasBias() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
hasBias(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
hashCode() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
hashCode() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
hashCode() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams
hashCode() - Method in class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction
hashCode() - Method in class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction
hashCode() - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction
hashCode() - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction
hashCode() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
hashCode() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
hasLayer() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Whether the GraphVertex contains a Layer object or not
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
hasLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
hasLayer() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
hasLayerNorm(boolean) - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer.Builder
hasLayerNorm() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
hasLayerNorm(boolean) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn.Builder
hasLayerNorm() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn
hasLayerNorm() - Method in class org.deeplearning4j.nn.layers.BaseLayer: Does this layer support and is it enabled layer normalization? Only Dense and SimpleRNN Layers support layer normalization.
hasLayerNorm() - Method in class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer
hasLayerNorm() - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn
hasLayerNorm(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
hasLayerNorm(Layer) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
hasLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
hasLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
hasLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
hasLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution
hasLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
hasLossFunction() - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution: Does this reconstruction distribution has a standard neural network loss function (such as mean squared error, which is deterministic) or is it a standard VAE with a probabilistic reconstruction distribution?
hasLossFunction() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder: Does the reconstruction distribution have a loss function (such as mean squared error) or is it a standard probabilistic reconstruction distribution?
hasNext() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
hasSomething - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
headSize(long) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder: Size of Attention Heads
headSize(int) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer.Builder: Size of attention heads
headSize(int) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer.Builder: Size of attention heads
headSize(int) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer.Builder: Size of attention heads
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.normalization.LocalResponseNormalization
helper - Variable in class org.deeplearning4j.nn.layers.recurrent.LSTM
helperAllowFallback - Variable in class org.deeplearning4j.nn.conf.dropout.Dropout: When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.
helperAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.dropout.Dropout: When using a helper (CuDNN or MKLDNN in some cases) and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user.
helperAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder: When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.
helperAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder: When using a helper (CuDNN or MKLDNN in some cases) and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user.
helperAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM
helperAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: When using CuDNN or MKLDNN and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user.
helperAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: When using CuDNN or MKLDNN and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user.
helperAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder: Deprecated.

When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.
helperAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder: Deprecated.

When using a helper (CuDNN or MKLDNN in some cases) and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user.
helperAllowFallback - Variable in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM: Deprecated.
helperAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder: When using CuDNN or MKLDNN and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user.
helperAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder: When using CuDNN or MKLDNN and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user.
helperAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder: When using CuDNN or MKLDNN and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user.
helperCountFail - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
helperCountFail - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
helperCountFail - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
helperCountFail - Variable in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
helperCountFail - Variable in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
helperMemoryUse() - Method in interface org.deeplearning4j.nn.layers.LayerHelper: Return the currently allocated memory for the helper.
(a) Excludes: any shared memory used by multiple helpers/layers
(b) Excludes any temporary memory (c) Includes all memory that persists for longer than the helper method
This is mainly used for debugging and reporting purposes.
helperMemoryUse() - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
helperMemoryUse() - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
helperMemoryUse() - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
helperMemoryUse() - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper
helperWorkspacePointers - Variable in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
helperWorkspaces - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
helperWorkspaces - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
hiddenLayerSize - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: The hidden layer size for the one class neural network.
hiddenLayerSize(int) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: The hidden layer size for the one class neural network.
Histogram - Class in org.deeplearning4j.eval.curves: Deprecated.
Use Histogram
Histogram(String, double, double, int[]) - Constructor for class org.deeplearning4j.eval.curves.Histogram: Deprecated.
Use Histogram
HostNameTrigger(String) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.HostNameTrigger

I

i2d - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
ia - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
IActivationMixin() - Constructor for class org.deeplearning4j.nn.conf.serde.legacy.LegacyJsonFormat.IActivationMixin
id - Variable in class org.deeplearning4j.optimize.listeners.SharedGradient
IdentityLayer - Class in org.deeplearning4j.nn.layers.util: Identity layer, passes data through unaltered.
IdentityLayer(String) - Constructor for class org.deeplearning4j.nn.layers.util.IdentityLayer
IDropout - Interface in org.deeplearning4j.nn.conf.dropout: IDropout instances operate on an activations array, modifying or dropping values at training time only.
iDropout - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
iDropout - Variable in class org.deeplearning4j.nn.conf.layers.Layer
idropOut - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
IEarlyStoppingTrainer<T extends Model> - Interface in org.deeplearning4j.earlystopping.trainer: Interface for early stopping trainers
IEvaluation<T extends IEvaluation> - Interface in org.deeplearning4j.eval: Deprecated.
Use IEvaluation
ILossFunctionMixin() - Constructor for class org.deeplearning4j.nn.conf.serde.legacy.LegacyJsonFormat.ILossFunctionMixin
incrementEpochCount() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Increment the epoch count (in the underlying ComputationGraphConfiguration by 1).
incrementEpochCount() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Increment the epoch count (in the underlying MultiLayerConfiguration by 1).
incrementIterationCount(Model, int) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
index - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
index - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
index - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
index - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
IndexedTail - Class in org.deeplearning4j.optimize.solvers.accumulation: This class provides queue-like functionality for multiple readers/multiple writers, with transparent duplication and collapsing ability
IndexedTail(int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
IndexedTail(int, boolean, long[]) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
inferenceWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Deprecated.
Use NeuralNetConfiguration.Builder.inferenceWorkspaceMode(WorkspaceMode)
inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
inferenceWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: This method defines Workspace mode being used during inference:
NONE: workspace won't be used
ENABLED: workspaces will be used for inference (reduced memory and better performance)
inferenceWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: This method defines Workspace mode being used during inference:
NONE: workspace won't be used
ENABLED: workspaces will be used for inference (reduced memory and better performance)
inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
inferInputLength(boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder: Set input sequence inference mode for embedding layer.
inferInputType(INDArray) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
inferInputTypes(INDArray...) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
init() - Method in interface org.deeplearning4j.nn.api.Model: Init the model
init() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork: This method does initialization of model PLEASE NOTE: All implementations should track own state, to avoid double spending
init(NeuralNetConfiguration, INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.ParamInitializer: Initialize the parameters
init() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Initialize the ComputationGraph network
init(INDArray, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Initialize the ComputationGraph, optionally with an existing parameters array.
init() - Method in class org.deeplearning4j.nn.layers.AbstractLayer: Init the model
init() - Method in class org.deeplearning4j.nn.layers.FrozenLayer: Init the model
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
init() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
init() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder: Init the model
init() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
init() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Initialize the MultiLayerNetwork.
init(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Initialize the MultiLayerNetwork, optionally with an existing parameters array.
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer: Initialize the parameters
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
init() - Method in class org.deeplearning4j.nn.updater.UpdaterBlock
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.embeddings.WeightInitEmbedding
init(double, double, long[], char, INDArray) - Method in interface org.deeplearning4j.nn.weights.IWeightInit: Initialize parameters in the given view.
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitConstant
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitDistribution
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitIdentity
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitLecunUniform
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitNormal
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitRelu
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitReluUniform
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitSigmoidUniform
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitUniform
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanAvg
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanIn
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanOut
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitVarScalingUniformFanAvg
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitVarScalingUniformFanIn
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitVarScalingUniformFanOut
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitXavier
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitXavierLegacy
init(double, double, long[], char, INDArray) - Method in class org.deeplearning4j.nn.weights.WeightInitXavierUniform
initCalled - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
initCalled - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
initDone - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
initGradientsView() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: This method: initializes the flattened gradients array (used in backprop) and sets the appropriate subset in all layers.
initGradientsView() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: This method: initializes the flattened gradients array (used in backprop) and sets the appropriate subset in all layers.
initialize() - Method in class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition
initialize() - Method in interface org.deeplearning4j.earlystopping.termination.EpochTerminationCondition: Initialize the epoch termination condition (often a no-op)
initialize() - Method in class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition
initialize() - Method in interface org.deeplearning4j.earlystopping.termination.IterationTerminationCondition: Initialize the iteration termination condition (sometimes a no-op)
initialize() - Method in class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition
initialize() - Method in class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition
initialize() - Method in class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition
initialize() - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.And
initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureTrigger
initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.HostNameTrigger
initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.RandomProb
initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.TimeSinceInitializedTrigger
initialize() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.UserNameTrigger
initialize(GradientsAccumulator) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
initialize(GradientsAccumulator) - Method in class org.deeplearning4j.optimize.solvers.accumulation.LocalHandler
initialize(GradientsAccumulator) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.MessageHandler: This method does initial configuration of given MessageHandler instance
initializeConstraints(Layer.Builder<?>) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM: Deprecated.
initializeConstraints(Layer.Builder<?>) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM: Deprecated.
initializeConstraints(Layer.Builder<?>) - Method in class org.deeplearning4j.nn.conf.layers.Layer: Initialize the weight constraints.
initializeConstraints(Layer.Builder<?>) - Method in class org.deeplearning4j.nn.conf.layers.LSTM
initializeConstraints(Layer.Builder<?>) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D
initialized() - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureTrigger
initializedMinibatchDivision - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
initializeHelper(DataType) - Method in class org.deeplearning4j.nn.conf.dropout.Dropout: Initialize the CuDNN dropout helper, if possible
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer: Set the initial parameter values for this layer, if required
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex: Set the initial parameter values for this layer, if required
initializeParameters(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder
initializer() - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
initializer() - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D
initializer() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D
initializer() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D
initializer() - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM: Deprecated.
initializer() - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM: Deprecated.
initializer() - Method in class org.deeplearning4j.nn.conf.layers.Layer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
initializer() - Method in class org.deeplearning4j.nn.conf.layers.LossLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.LSTM
initializer() - Method in class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
initializer() - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector
initializer() - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
initializer() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn
initializer() - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D
initializer() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
initializer() - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
initializer() - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer
initializer() - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer
initialMemory - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
initialMemory - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
initialResidualPostProcessor - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
initialRValue - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: The initial r value to use for ocnn for definition, see the paper, note this is only active when OCNNOutputLayer.Builder.configureR is specified as true
initialRValue(double) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: The initial r value to use for ocnn for definition, see the paper, note this is only active when OCNNOutputLayer.Builder.configureR is specified as true
initialThresholdAlgorithm - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
initOptimizer() - Method in class org.deeplearning4j.optimize.Solver
initWeights(int, int, WeightInit, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
initWeights(double, double, int[], WeightInit, Distribution, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil: Deprecated.
initWeights(double, double, long[], WeightInit, Distribution, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil: Initializes a matrix with the given weight initialization scheme.
initWeights(double, double, int[], WeightInit, Distribution, char, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil: Deprecated.
initWeights(double, double, long[], WeightInit, Distribution, char, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil
InMemoryModelSaver<T extends Model> - Class in org.deeplearning4j.earlystopping.saver: Save the best (and latest) models for early stopping training to memory for later retrieval Note: Assumes that network is cloneable via .clone() method
InMemoryModelSaver() - Constructor for class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
input() - Method in interface org.deeplearning4j.nn.api.Model: The input/feature matrix for the model
input() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
input - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
input() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
input() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
input - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
input() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
input() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
input - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
input() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
INPUT_KEY - Static variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
INPUT_KEY - Static variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
INPUT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
INPUT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.LSTMParamInitializer
INPUT_WEIGHT_KEY_BACKWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
INPUT_WEIGHT_KEY_FORWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
inputCapsuleDimensions(int) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder: Usually inferred automatically.
inputCapsules(int) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder: Usually inferred automatically.
inputDepth - Variable in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
inputHeight - Variable in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
inputHeight - Variable in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
inputLength(int) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder: Set input sequence length for this embedding layer.
inputMaskArray - Variable in class org.deeplearning4j.nn.layers.BaseOutputLayer
inputMaskArrayState - Variable in class org.deeplearning4j.nn.layers.BaseOutputLayer
inputModificationAllowed - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
inputPreProcessor(String, InputPreProcessor) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Specify the processors for a given layer These are used at each layer for doing things like normalization and shaping of input.
Note: preprocessors can also be defined using the ComputationGraphConfiguration.GraphBuilder.addLayer(String, Layer, InputPreProcessor, String...) method.
InputPreProcessor - Interface in org.deeplearning4j.nn.conf: Input pre processor used for pre processing input before passing it to the neural network.
inputPreProcessor(Integer, InputPreProcessor) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Specify the processors.
InputPreProcessorMixin() - Constructor for class org.deeplearning4j.nn.conf.serde.legacy.LegacyJsonFormat.InputPreProcessorMixin
inputPreProcessors - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
inputPreProcessors - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
inputPreProcessors(Map<Integer, InputPreProcessor>) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
inputPreProcessors - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
inputs - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex
inputs - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams
inputs - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
inputShape(int...) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder: Usually inferred automatically.
inputShape(long...) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer.Builder: Explicitly set input shape of incoming activations so that parameters can be initialized properly.
InputType - Class in org.deeplearning4j.nn.conf.inputs: The InputType class is used to track and define the types of activations etc used in a ComputationGraph.
InputType() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType
inputType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
inputType() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder: A convenience method for setting input types: note that for example .inputType().convolutional(h,w,d) is equivalent to .setInputType(InputType.convolutional(h,w,d))
InputType.InputTypeConvolutional - Class in org.deeplearning4j.nn.conf.inputs
InputType.InputTypeConvolutional3D - Class in org.deeplearning4j.nn.conf.inputs
InputType.InputTypeConvolutionalFlat - Class in org.deeplearning4j.nn.conf.inputs
InputType.InputTypeFeedForward - Class in org.deeplearning4j.nn.conf.inputs
InputType.InputTypeRecurrent - Class in org.deeplearning4j.nn.conf.inputs
InputType.Type - Enum in org.deeplearning4j.nn.conf.inputs: The type of activations in/out of a given GraphVertex
FF: Standard feed-foward (2d minibatch, 1d per example) data
RNN: Recurrent neural network (3d minibatch) time series data
CNN: 2D Convolutional neural network (4d minibatch, [miniBatchSize, channels, height, width]) CNNFlat: Flattened 2D conv net data (2d minibatch, [miniBatchSize, height * width * channels]) CNN3D: 3D convolutional neural network (5d minibatch, [miniBatchSize, channels, height, width, channels])
InputTypeBuilder() - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder
InputTypeConvolutional(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional
InputTypeConvolutional3D(Convolution3D.DataFormat, long, long, long, long) - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional3D
InputTypeConvolutionalFlat(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
InputTypeFeedForward(long) - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward
InputTypeRecurrent(long) - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
InputTypeRecurrent(long, long) - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
InputTypeUtil - Class in org.deeplearning4j.nn.conf.layers: Utilities for calculating input types
InputTypeUtil() - Constructor for class org.deeplearning4j.nn.conf.layers.InputTypeUtil
inputVars - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
InputVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: An InputVertex simply defines the location (and connection structure) of inputs to the ComputationGraph.
InputVertex(ComputationGraph, String, int, VertexIndices[], DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
inputVertices - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex: A representation of the vertices that are inputs to this vertex (inputs during forward pass) Specifically, if inputVertices[X].getVertexIndex() = Y, and inputVertices[X].getVertexEdgeNumber() = Z then the Zth output of vertex Y is the Xth input to this vertex
inputWeightConstraints - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder: Set constraints to be applied to the RNN input weight parameters of this layer.
inputWidth - Variable in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
inputWidth - Variable in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - 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, DataType) - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM: Deprecated.
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM: Deprecated.
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.Layer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.LSTM
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer
instantiate(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
instantiate(NeuralNetConfiguration, Collection<TrainingListener>, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer
intializeConfigurations() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
InvalidInputTypeException - Exception in org.deeplearning4j.nn.conf.inputs: InvalidInputTypeException: Thrown if the GraphVertex cannot handle the type of input provided.
InvalidInputTypeException(String) - Constructor for exception org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException
InvalidInputTypeException(String, Throwable) - Constructor for exception org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException
InvalidInputTypeException(Throwable) - Constructor for exception org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException
InvalidScoreIterationTerminationCondition - Class in org.deeplearning4j.earlystopping.termination: Terminate training at this iteration if score is NaN or Infinite for the last minibatch
InvalidScoreIterationTerminationCondition() - Constructor for class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition
InvalidStepException - Exception in org.deeplearning4j.exception: Created by agibsonccc on 8/20/14.
InvalidStepException(String) - Constructor for exception org.deeplearning4j.exception.InvalidStepException: Constructs a new exception with the specified detail message.
InvalidStepException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.InvalidStepException: Constructs a new exception with the specified detail message and cause.
InvalidStepException(Throwable) - Constructor for exception org.deeplearning4j.exception.InvalidStepException: Constructs a new exception with the specified cause and a detail message of (cause==null ? null : cause.toString()) (which typically contains the class and detail message of cause).
InvalidStepException(String, Throwable, boolean, boolean) - Constructor for exception org.deeplearning4j.exception.InvalidStepException: Constructs a new exception with the specified detail message, cause, suppression enabled or disabled, and writable stack trace enabled or disabled.
invocationCount - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
InvocationType - Enum in org.deeplearning4j.optimize.api: This enum holds options for TrainingListener invocation scheme
invocationType - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
invokeListener(Model) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener
iou(DetectedObject, DetectedObject) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils: Returns intersection over union (IOU) between o1 and o2.
IOutputLayer - Interface in org.deeplearning4j.nn.api.layers: Interface for output layers (those that calculate gradients with respect to a labels array)
isBiasParam(Layer, String) - Method in interface org.deeplearning4j.nn.api.ParamInitializer: Is the specified parameter a bias?
isBiasParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
isBiasParam(Layer, String) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
isDead() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
isDebug - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
isDebug - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
isDone - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
isDone - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
isEmpty() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
isEmpty() - Method in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
isFirst - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
isFirst - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
isInference() - Method in enum org.deeplearning4j.nn.conf.memory.MemoryType
isInitCalled() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
isInputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
isInputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
isInputVertex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Whether the GraphVertex is an input vertex
isInputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
isLeaf() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Returns whether the node has any children or not
isMinibatch - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: If doing minibatch training or not.
isMinibatch - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
isMiniBatch() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
isMiniBatch() - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
isMiniBatch() - Method in class org.deeplearning4j.nn.updater.LayerUpdater
isMiniBatch() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
isNCDHW - Variable in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
isOutputVertex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Whether the GraphVertex is an output vertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex
isPreTerminal() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Node has one child that is a leaf
isPretrainLayer() - Method in interface org.deeplearning4j.nn.api.Layer: Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.DropoutLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer: Returns true if the layer can be trained in an unsupervised/pretrain manner (VAE, RBMs etc)
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.feedforward.PReLU
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.LossLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.RepeatVector
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.util.MaskLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
isPretrainLayer() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
isPretrainLayer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
isPretrainParam(String) - Method in interface org.deeplearning4j.nn.api.TrainingConfig: Is the specified parameter a layerwise pretraining only parameter?
For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop.
Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer: Is the specified parameter a layerwise pretraining only parameter?
For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop.
Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer
isPretrainParam(String) - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig
isPretrainParam(String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
isPretrainUpdaterBlock() - Method in class org.deeplearning4j.nn.updater.UpdaterBlock
isSingleLayerUpdater() - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
isSingleLayerUpdater() - Method in class org.deeplearning4j.nn.updater.LayerUpdater
isWeightParam(Layer, String) - Method in interface org.deeplearning4j.nn.api.ParamInitializer: Is the specified parameter a weight?
isWeightParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
isWeightParam(Layer, String) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
iter - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator
iterationCount - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
iterationCount - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
iterationCount - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
iterationCount - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
iterationCount - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
iterationCount - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.api.IterationListener: Deprecated.

Event listener for each iteration
iterationDone(Model, int, int) - Method in interface org.deeplearning4j.optimize.api.TrainingListener: Event listener for each iteration.
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresListener
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.ComposableIterationListener: Deprecated.
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener: Event listener for each iteration
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener: Deprecated.
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.ScoreIterationListener
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
iterationDone(Model, int, int) - Method in class org.deeplearning4j.optimize.listeners.TimeIterationListener
IterationEpochTrigger(boolean, int) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.IterationEpochTrigger
IterationListener - Class in org.deeplearning4j.optimize.api: Deprecated.
Use TrainingListener instead
IterationListener() - Constructor for class org.deeplearning4j.optimize.api.IterationListener: Deprecated.
iterations - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
IterationTerminationCondition - Interface in org.deeplearning4j.earlystopping.termination: Interface for termination conditions to be evaluated once per iteration (i.e., once per minibatch).
iterationTerminationConditions(IterationTerminationCondition...) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder: Termination conditions to be evaluated every iteration (minibatch)
iterator - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator
iterator - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
iterator() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
iupdater - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Gradient updater.
iUpdater - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
iUpdater - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
IWeightInit - Interface in org.deeplearning4j.nn.weights: Interface for weight initialization.
IWeightNoise - Interface in org.deeplearning4j.nn.conf.weightnoise: IWeightNoise instances operate on an weight array(s), modifying values at training time or test time, before they are used.
iz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

J

JsonMappers - Class in org.deeplearning4j.nn.conf.serde: JSON mappers for deserializing neural net configurations, etc.
JsonMappers() - Constructor for class org.deeplearning4j.nn.conf.serde.JsonMappers
jsonSerializable() - Method in class org.deeplearning4j.nn.weights.embeddings.ArrayEmbeddingInitializer
jsonSerializable() - Method in interface org.deeplearning4j.nn.weights.embeddings.EmbeddingInitializer

K

k(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder: LRN scaling constant k.
k - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
K_KEY - Static variable in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
keepAll() - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: Keep all model checkpoints - i.e., don't delete any.
keepLast(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: Keep only the last N most recent model checkpoint files.
keepLastAndEvery(int, int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: Keep the last N most recent model checkpoint files, and every M checkpoint files.
For example: suppose you save every 100 iterations, for 2050 iteration, and use keepLastAndEvery(3,5).
kernelSize(int) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder: Size of the convolution
kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder: Set kernel size for 3D convolutions in (depth, height, width) order
kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Size of the convolution rows/columns
kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder: Size of the convolution rows/columns
kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder: Size of the convolution rows/columns
kernelSize(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Sets the kernel size of the 2d convolution
kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder: Size of the convolution rows/columns (height/width)
kernelSize(int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder: Kernel size
kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder: Kernel size
kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder: Kernel size
kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer

L

l1(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: L1 regularization coefficient (weights only).
l1(double) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: L1 regularization coefficient (weights only).
l1(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: L1 regularization coefficient for the weights (excluding biases).
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
l1(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: L1 regularization coefficient for the weights (excluding biases)
l1Bias(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: L1 regularization coefficient for the bias.
l1Bias(double) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: L1 regularization coefficient for the bias.
l1Bias(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: L1 regularization coefficient for the bias.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
l1Bias(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: L1 regularization coefficient for the bias parameters
l2(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: L2 regularization coefficient (weights only).
l2(double) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: L2 regularization coefficient (weights only).
l2(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: L2 regularization coefficient for the weights (excluding biases).
Note: Generally, WeightDecay (set via NeuralNetConfiguration.Builder.weightDecay(double) should be preferred to L2 regularization.
l2(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: L2 regularization coefficient for the weights (excluding biases)
Note: Generally, WeightDecay (set via FineTuneConfiguration.Builder.weightDecay(double,boolean) should be preferred to L2 regularization.
l2Bias(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: L2 regularization coefficient for the bias.
l2Bias(double) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: L2 regularization coefficient for the bias.
l2Bias(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: L2 regularization coefficient for the bias.
Note: Generally, WeightDecay (set via NeuralNetConfiguration.Builder.weightDecayBias(double,boolean) should be preferred to L2 regularization.
l2Bias(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: L2 regularization coefficient for the bias parameters
Note: Generally, WeightDecay (set via FineTuneConfiguration.Builder.weightDecayBias(double,boolean) should be preferred to L2 regularization.
L2NormalizeVertex - Class in org.deeplearning4j.nn.conf.graph: L2NormalizeVertex performs L2 normalization on a single input, along the specified dimensions.
L2NormalizeVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
L2NormalizeVertex(int[], double) - Constructor for class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
L2NormalizeVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: L2NormalizeVertex performs L2 normalization on a single input.
L2NormalizeVertex(ComputationGraph, String, int, int[], double, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
L2NormalizeVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], int[], double, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
L2Vertex - Class in org.deeplearning4j.nn.conf.graph: L2Vertex calculates the L2 (Euclidean) least squares error of two inputs, on a per-example basis.
L2Vertex() - Constructor for class org.deeplearning4j.nn.conf.graph.L2Vertex: Constructor with default epsilon value of 1e-8
L2Vertex(double) - Constructor for class org.deeplearning4j.nn.conf.graph.L2Vertex
L2Vertex - Class in org.deeplearning4j.nn.graph.vertex.impl: L2Vertex calculates the L2 least squares error of two inputs.
L2Vertex(ComputationGraph, String, int, double, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
L2Vertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], double, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
label() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
labels - Variable in class org.deeplearning4j.nn.layers.BaseOutputLayer
labels - Variable in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
labels - Variable in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
labels - Variable in class org.deeplearning4j.nn.layers.LossLayer
labels - Variable in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
labels - Variable in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
labels - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
labels - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
LABELS_KEY - Static variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
labelsRequired() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer: Whether labels are required for calculating the score.
lambda - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
lambda(double) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
lambda - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
lambdaCoord(double) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder: Loss function coefficient for position and size/scale components of the loss function.
lambdaNoObj(double) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder: Loss function coefficient for the "no object confidence" components of the loss function.
lastAct - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
lastAdded - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
lastBP - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
lastCheckpoint() - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Return the most recent checkpoint, if one exists - otherwise returns null
lastCheckpoint(File) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Return the most recent checkpoint, if one exists - otherwise returns null
lastChild() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
lastDeletedIndex - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
lastEE - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
lastES - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
lastEtlTime - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
lastEtlTime - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
lastFF - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
lastIteration - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
lastIterWasDense - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
lastMemCell - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
lastSparsityRatio - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
lastStep - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
lastThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
lastThresholdLogTime - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
LastTimeStep - Class in org.deeplearning4j.nn.conf.layers.recurrent: LastTimeStep is a "wrapper" layer: it wraps any RNN (or CNN1D) layer, and extracts out the last time step during forward pass, and returns it as a row vector (per example).
LastTimeStep(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep
LastTimeStepLayer - Class in org.deeplearning4j.nn.layers.recurrent: LastTimeStep is a "wrapper" layer: it wraps any RNN layer, and extracts out the last time step during forward pass, and returns it as a row vector (per example).
LastTimeStepLayer(Layer) - Constructor for class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer
LastTimeStepVertex - Class in org.deeplearning4j.nn.conf.graph.rnn: LastTimeStepVertex is used in the context of recurrent neural network activations, to go from 3d (time series) activations to 2d activations, by extracting out the last time step of activations for each example.
This can be used for example in sequence to sequence architectures, and potentially for sequence classification.
LastTimeStepVertex(String) - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
LastTimeStepVertex - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn: LastTimeStepVertex is used in the context of recurrent neural network activations, to go from 3d (time series) activations to 2d activations, by extracting out the last time step of activations for each example.
This can be used for example in sequence to sequence architectures, and potentially for sequence classification.
LastTimeStepVertex(ComputationGraph, String, int, String, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
LastTimeStepVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], String, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
Layer - Interface in org.deeplearning4j.nn.api: Interface for a layer of a neural network.
layer(int, Layer, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Add a layer, with no InputPreProcessor, with the specified name and specified inputs.
layer(String, Layer, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Add a layer, with no InputPreProcessor, with the specified name and specified inputs.
layer(String, Layer, InputPreProcessor, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Add a layer and an InputPreProcessor, with the specified name and specified inputs.
Layer - Class in org.deeplearning4j.nn.conf.layers: A neural network layer.
Layer(Layer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Layer
layer(Layer) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer.Builder
layer - Variable in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
layer - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
layer(Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Layer class.
layer - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
layer(int, Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
layer(Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
Layer.Builder<T extends Layer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers
Layer.TrainingMode - Enum in org.deeplearning4j.nn.api
Layer.Type - Enum in org.deeplearning4j.nn.api
layerConf() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
layerConf() - Method in class org.deeplearning4j.nn.layers.BaseLayer
layerConf() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
LayerConstraint - Interface in org.deeplearning4j.nn.api.layers
LayerHelper - Interface in org.deeplearning4j.nn.layers
layerId() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
layerId() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
layerId() - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
layerId() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
layerIndex - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
layerInputSize(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return the input size (number of inputs) for the specified layer.
Note that the meaning of the "input size" can depend on the type of layer.
layerInputSize(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return the input size (number of inputs) for the specified layer.
Note that the meaning of the "input size" can depend on the type of layer.
layerInputSize(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Return the input size (number of inputs) for the specified layer.
Note that the meaning of the "input size" can depend on the type of layer.
layerMap - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
LayerMemoryReport - Class in org.deeplearning4j.nn.conf.memory: A MemoryReport Designed to report estimated memory use for a single layer or graph vertex.
LayerMemoryReport(LayerMemoryReport.Builder) - Constructor for class org.deeplearning4j.nn.conf.memory.LayerMemoryReport
LayerMemoryReport.Builder - Class in org.deeplearning4j.nn.conf.memory
LayerMixin() - Constructor for class org.deeplearning4j.nn.conf.serde.legacy.LegacyJsonFormat.LayerMixin
layerName - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
layerName - Variable in class org.deeplearning4j.nn.conf.layers.Layer
layers - Variable in class org.deeplearning4j.nn.graph.ComputationGraph: A list of layers.
layers - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
layersByName - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
layerSize(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return the layer size (number of units) for the specified layer.
layerSize(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return the layer size (number of units) for the specified layer.
Note that the meaning of the "layer size" can depend on the type of layer.
layerSize(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Return the layer size (number of units) for the specified layer.
Note that the meaning of the "layer size" can depend on the type of layer.
LayerUpdater - Class in org.deeplearning4j.nn.updater: Updater for a single layer, excluding MultiLayerNetwork (which also implements the Layer interface)
LayerUpdater(Layer) - Constructor for class org.deeplearning4j.nn.updater.LayerUpdater
LayerUpdater(Layer, INDArray) - Constructor for class org.deeplearning4j.nn.updater.LayerUpdater
LayerValidation - Class in org.deeplearning4j.nn.conf.layers: Utility methods for validating layer configurations
LayerVertex - Class in org.deeplearning4j.nn.conf.graph: LayerVertex is a GraphVertex with a neural network Layer (and, optionally an InputPreProcessor) in it
LayerVertex(NeuralNetConfiguration, InputPreProcessor) - Constructor for class org.deeplearning4j.nn.conf.graph.LayerVertex
LayerVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: LayerVertex is a GraphVertex with a neural network Layer (and, optionally an InputPreProcessor) in it
LayerVertex(ComputationGraph, String, int, Layer, InputPreProcessor, boolean, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex: Create a network input vertex:
LayerVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], Layer, InputPreProcessor, boolean, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
layerWiseConfigurations - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
LayerWorkspaceMgr - Class in org.deeplearning4j.nn.workspace: WorkspaceMgr for DL4J layers.
LayerWorkspaceMgr(Set<ArrayType>, Map<ArrayType, WorkspaceConfiguration>, Map<ArrayType, String>) - Constructor for class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
LayerWorkspaceMgr.Builder - Class in org.deeplearning4j.nn.workspace
LBFGS - Class in org.deeplearning4j.optimize.solvers: LBFGS
LBFGS(NeuralNetConfiguration, StepFunction, Collection<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LBFGS
LearnedSelfAttentionLayer - Class in org.deeplearning4j.nn.conf.layers: Implements Dot Product Self Attention with learned queries Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using learned queries.
LearnedSelfAttentionLayer(LearnedSelfAttentionLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer
LearnedSelfAttentionLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
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.
LegacyIntArrayDeserializer - Class in org.deeplearning4j.nn.conf.serde.legacy: Deserialize either an int[] to an int[], or a single int x to int[]{x,x} Used when supporting a configuration format change from single int value to int[], as for Upsampling2D between 1.0.0-alpha and 1.0.0-beta
LegacyIntArrayDeserializer() - Constructor for class org.deeplearning4j.nn.conf.serde.legacy.LegacyIntArrayDeserializer
LegacyJsonFormat - Class in org.deeplearning4j.nn.conf.serde.legacy: This class defines a set of Jackson Mixins - which are a way of using a proxy class with annotations to override the existing annotations.
LegacyJsonFormat.GraphVertexMixin - Class in org.deeplearning4j.nn.conf.serde.legacy
LegacyJsonFormat.IActivationMixin - Class in org.deeplearning4j.nn.conf.serde.legacy
LegacyJsonFormat.ILossFunctionMixin - Class in org.deeplearning4j.nn.conf.serde.legacy
LegacyJsonFormat.InputPreProcessorMixin - Class in org.deeplearning4j.nn.conf.serde.legacy
LegacyJsonFormat.LayerMixin - Class in org.deeplearning4j.nn.conf.serde.legacy
LegacyJsonFormat.ReconstructionDistributionMixin - Class in org.deeplearning4j.nn.conf.serde.legacy
leverageTo(String) - Method in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn: This method is OPTIONAL, and written mostly for future use
leverageTo(ArrayType, INDArray) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
LineGradientDescent - Class in org.deeplearning4j.optimize.solvers: Stochastic Gradient Descent with Line Search
LineGradientDescent(NeuralNetConfiguration, StepFunction, Collection<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LineGradientDescent
lineMaximizer - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
LineOptimizer - Interface in org.deeplearning4j.optimize.api: Line optimizer interface adapted from mallet
list() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Create a ListBuilder (for creating a MultiLayerConfiguration)
Usage:
list(Layer...) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Create a ListBuilder (for creating a MultiLayerConfiguration) with the specified layers
Usage:
ListBuilder(NeuralNetConfiguration.Builder, Map<Integer, NeuralNetConfiguration.Builder>) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
ListBuilder(NeuralNetConfiguration.Builder) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
listener(TrainingListener...) - Method in class org.deeplearning4j.optimize.Solver.Builder
listeners - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
listeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.optimize.Solver.Builder
listObjectsInFile(File) - Static method in class org.deeplearning4j.util.ModelSerializer: List the keys of all objects added using the method ModelSerializer.addObjectToFile(File, String, Object)
load(File, boolean) - Static method in class org.deeplearning4j.nn.graph.ComputationGraph: Restore a ComputationGraph to a file, saved using ComputationGraph.save(File) or ModelSerializer
load(File, boolean) - Static method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Restore a MultiLayerNetwork to a file, saved using MultiLayerNetwork.save(File) or ModelSerializer
loadCheckpointCG(Checkpoint) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Load a ComputationGraph for the given checkpoint
loadCheckpointCG(File, Checkpoint) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Load a ComputationGraph for the given checkpoint from the specified root direcotry
loadCheckpointCG(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Load a ComputationGraph for the given checkpoint
loadCheckpointCG(File, int) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Load a ComputationGraph for the given checkpoint that resides in the specified root directory
loadCheckpointMLN(Checkpoint) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Load a MultiLayerNetwork for the given checkpoint
loadCheckpointMLN(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Load a MultiLayerNetwork for the given checkpoint number
loadCheckpointMLN(File, Checkpoint) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Load a MultiLayerNetwork for the given checkpoint that resides in the specified root directory
loadCheckpointMLN(File, int) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Load a MultiLayerNetwork for the given checkpoint number
loadLastCheckpointCG(File) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Load the last (most recent) checkpoint from the specified root directory
loadLastCheckpointMLN(File) - Static method in class org.deeplearning4j.optimize.listeners.CheckpointListener: Load the last (most recent) checkpoint from the specified root directory
loadWeightsInto(INDArray) - Method in class org.deeplearning4j.nn.weights.embeddings.ArrayEmbeddingInitializer
loadWeightsInto(INDArray) - Method in interface org.deeplearning4j.nn.weights.embeddings.EmbeddingInitializer: Load the weights into the specified INDArray
LocalFileGraphSaver - Class in org.deeplearning4j.earlystopping.saver: Save the best (and latest/most recent) ComputationGraphs learned during early stopping training to the local file system.
Instances of this class will save 3 files for best (and optionally, latest) models:
(a) The network configuration: bestGraphConf.json
(b) The network parameters: bestGraphParams.bin
(c) The network updater: bestGraphUpdater.bin

NOTE: The model updater is an object that contains the internal state for training features such as AdaGrad, Momentum and RMSProp.
The updater is not required to use the network at test time; it is saved in case further training is required.
LocalFileGraphSaver(String) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver: Constructor that uses default character set for configuration (json) encoding
LocalFileGraphSaver(String, Charset) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
LocalFileModelSaver - Class in org.deeplearning4j.earlystopping.saver: Save the best (and latest/most recent) models learned during early stopping training to the local file system.
Instances of this class will save 3 files for best (and optionally, latest) models:
(a) The network configuration: bestModelConf.json
(b) The network parameters: bestModelParams.bin
(c) The network updater: bestModelUpdater.bin

NOTE: The model updater is an object that contains the internal state for training features such as AdaGrad, Momentum and RMSProp.
The updater is not required to use the network at test time; it is saved in case further training is required.
LocalFileModelSaver(File) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
LocalFileModelSaver(String) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver: Constructor that uses default character set for configuration (json) encoding
LocalFileModelSaver(String, Charset) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
LocalHandler - Class in org.deeplearning4j.optimize.solvers.accumulation: MessageHandler implementation suited for ParallelWrapper running on single box PLEASE NOTE: This handler does NOT provide any network connectivity.
LocalHandler() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.LocalHandler
LocallyConnected1D - Class in org.deeplearning4j.nn.conf.layers: SameDiff version of a 1D locally connected layer.
LocallyConnected1D(LocallyConnected1D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.LocallyConnected1D
LocallyConnected1D.Builder - Class in org.deeplearning4j.nn.conf.layers
LocallyConnected2D - Class in org.deeplearning4j.nn.conf.layers: SameDiff version of a 2D locally connected layer.
LocallyConnected2D(LocallyConnected2D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.LocallyConnected2D
LocallyConnected2D.Builder - Class in org.deeplearning4j.nn.conf.layers
LocalResponseNormalization - Class in org.deeplearning4j.nn.conf.layers: Local response normalization layer
See section 3.3 of http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
LocalResponseNormalization - Class in org.deeplearning4j.nn.layers.normalization: Deep neural net normalization approach normalizes activations between layers "brightness normalization" Used for nets like AlexNet
LocalResponseNormalization(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
LocalResponseNormalization.Builder - Class in org.deeplearning4j.nn.conf.layers
LocalResponseNormalizationHelper - Interface in org.deeplearning4j.nn.layers.normalization: Helper for the local response normalization layer.
lock - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
lock - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
lockGammaBeta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: 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(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: If set to true: lock the gamma and beta parameters to the values for each activation, specified by BatchNormalization.Builder.gamma(double) and BatchNormalization.Builder.beta(double).
lockGammaBeta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
locks - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
log - Static variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
log - Static variable in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
log - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
LogNormalDistribution - Class in org.deeplearning4j.nn.conf.distribution: A log-normal distribution, with two parameters: mean and standard deviation.
LogNormalDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.LogNormalDistribution: Create a log-normal distribution with the given mean and std
logProb - Variable in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
logSaving(boolean) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: If true (the default) log a message every time a model is saved
logTestMode(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
logTestMode(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
logTestMode(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
logTestMode(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
logThresholdIfReq(boolean, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
lossClassPredictions(ILossFunction) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder: Loss function for the class predictions - defaults to L2 loss (i.e., sum of squared errors, as per the paper), however Loss MCXENT could also be used (which is more common for classification).
lossFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder: Loss function for the output layer
lossFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
lossFn - Variable in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer
lossFn - Variable in class org.deeplearning4j.nn.conf.layers.CnnLossLayer
lossFn - Variable in class org.deeplearning4j.nn.conf.layers.LossLayer
lossFn - Variable in class org.deeplearning4j.nn.conf.layers.RnnLossLayer
lossFunction(LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
lossFunction(ILossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
lossFunction - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
lossFunction(LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
lossFunction - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
lossFunction(IActivation, LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Configure the VAE to use the specified loss function for the reconstruction, instead of a ReconstructionDistribution.
lossFunction(Activation, LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Configure the VAE to use the specified loss function for the reconstruction, instead of a ReconstructionDistribution.
lossFunction(IActivation, ILossFunction) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Configure the VAE to use the specified loss function for the reconstruction, instead of a ReconstructionDistribution.
lossFunctionExpectsProbability(ILossFunction) - Static method in class org.deeplearning4j.util.OutputLayerUtil
LossFunctionWrapper - Class in org.deeplearning4j.nn.conf.layers.variational: LossFunctionWrapper allows training of a VAE model with a standard (possibly deterministic) neural network loss function for the reconstruction, instead of using a ReconstructionDistribution as would normally be done with a VAE model.
LossFunctionWrapper(IActivation, ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
LossFunctionWrapper(Activation, ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
LossLayer - Class in org.deeplearning4j.nn.conf.layers: LossLayer is a flexible output layer that performs a loss function on an input without MLP logic.
LossLayer is similar to OutputLayer in that both perform loss calculations for network outputs vs.
LossLayer(LossLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer
LossLayer - Class in org.deeplearning4j.nn.layers: LossLayer is a flexible output "layer" that performs a loss function on an input without MLP logic.
LossLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.LossLayer
LossLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
lossPositionScale(ILossFunction) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder: Loss function for position/scale component of the loss function
LSTM - Class in org.deeplearning4j.nn.conf.layers: LSTM recurrent neural network layer without peephole connections.
LSTM - Class in org.deeplearning4j.nn.layers.recurrent: LSTM layer implementation.
LSTM(NeuralNetConfiguration, DataType) - 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: https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent READ THIS FIRST if you want to understand what the heck is happening here.
LSTMParamInitializer - Class in org.deeplearning4j.nn.params: LSTM Parameter initializer, for LSTM based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
LSTMParamInitializer() - Constructor for class org.deeplearning4j.nn.params.LSTMParamInitializer

M

maintenance() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail: This method does maintenance of updates within
mapper() - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: Object mapper for serialization of configurations
mapperYaml() - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: Object mapper for serialization of configurations
markExternalUpdates(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
markExternalUpdates(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
markExternalUpdates(boolean) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator: This method allows to highlight early availability of updates
mask - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
MASK_KEY - Static variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
maskArray - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
maskArray - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
maskArrays - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
maskedPoolingConvolution(PoolingType, INDArray, INDArray, int, DataType) - Static method in class org.deeplearning4j.util.MaskedReductionUtil
maskedPoolingEpsilonCnn(PoolingType, INDArray, INDArray, INDArray, int, DataType) - 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, DataType) - Static method in class org.deeplearning4j.util.MaskedReductionUtil
MaskedReductionUtil - Class in org.deeplearning4j.util: This is a TEMPORARY class for implementing global pooling with masking.
MaskLayer - Class in org.deeplearning4j.nn.conf.layers.util: MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through this layer.
MaskLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskLayer
MaskLayer - Class in org.deeplearning4j.nn.layers.util: MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through this layer.
MaskLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.util.MaskLayer
maskShape - Variable in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
MaskState - Enum in org.deeplearning4j.nn.api: MaskState: specifies whether a mask should be applied or not.
maskState - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
maskValue(double) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder
MaskZeroLayer - Class in org.deeplearning4j.nn.conf.layers.util: Wrapper which masks timesteps with activation equal to the specified masking value (0.0 default).
MaskZeroLayer(MaskZeroLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
MaskZeroLayer(Layer, double) - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
MaskZeroLayer - Class in org.deeplearning4j.nn.layers.recurrent: Masks timesteps with activation equal to the specified masking value, defaulting to 0.0.
MaskZeroLayer(Layer, double) - Constructor for class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer
MaskZeroLayer.Builder - Class in org.deeplearning4j.nn.conf.layers.util
matthewsCorrelation(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Deprecated.
maxAppliedIndexEverywhere() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
MaxEpochsTerminationCondition - Class in org.deeplearning4j.earlystopping.termination: Terminate training if the number of epochs exceeds the maximum number of epochs
MaxEpochsTerminationCondition(int) - Constructor for class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition
MaxNormConstraint - Class in org.deeplearning4j.nn.conf.constraint: Constrain the maximum L2 norm of the incoming weights for each unit to be less than or equal to the specified value.
MaxNormConstraint(double, Set<String>, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MaxNormConstraint
MaxNormConstraint(double, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MaxNormConstraint: Apply to weights but not biases by default
maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
maxNumLineSearchIterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Maximum number of line search iterations.
maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
maxNumLineSearchIterations(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
MaxScoreIterationTerminationCondition - Class in org.deeplearning4j.earlystopping.termination: Iteration termination condition for terminating training if the minibatch score exceeds a certain value.
MaxScoreIterationTerminationCondition(double) - Constructor for class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition
MaxTimeIterationTerminationCondition - Class in org.deeplearning4j.earlystopping.termination: Terminate training based on max time.
MaxTimeIterationTerminationCondition(long, TimeUnit) - Constructor for class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
maxVertexInputs() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
mds - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
mdsIterator - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
mdsIterator - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
memCellActivations - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
memCellState - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
memoryInfo(int, InputType...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Generate information regarding memory use for the network, for the given input types and minibatch size.
memoryInfo(int, InputType) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Generate information regarding memory use for the network, for the given input type and minibatch size.
memoryParameters(long, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder: This method allows to define buffer memory parameters for this GradientsAccumulator Default values: 100MB initialMemory, 5 queueSize
MemoryReport - Class in org.deeplearning4j.nn.conf.memory: A MemoryReport is designed to represent the estimated memory usage of a model, as a function of:
- Training vs.
MemoryReport() - Constructor for class org.deeplearning4j.nn.conf.memory.MemoryReport
MemoryType - Enum in org.deeplearning4j.nn.conf.memory: Type of memory
MemoryUseMode - Enum in org.deeplearning4j.nn.conf.memory: This simple enumeration defines the memory is used during inference or training.
merge(ThresholdAlgorithmReducer) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer
merge(ThresholdAlgorithmReducer) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithmReducer: Combine two reducers and return the result
MergeVertex - Class in org.deeplearning4j.nn.conf.graph: A MergeVertex is used to combine the activations of two or more layers/GraphVertex by means of concatenation/merging.
Exactly how this is done depends on the type of input.
For 2d (feed forward layer) inputs: MergeVertex([numExamples,layerSize1],[numExamples,layerSize2]) -> [numExamples,layerSize1 + layerSize2]
For 3d (time series) inputs: MergeVertex([numExamples,layerSize1,timeSeriesLength],[numExamples,layerSize2,timeSeriesLength]) -> [numExamples,layerSize1 + layerSize2,timeSeriesLength]
For 4d (convolutional) inputs: MergeVertex([numExamples,depth1,width,height],[numExamples,depth2,width,height]) -> [numExamples,depth1 + depth2,width,height]
MergeVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.MergeVertex
MergeVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: A MergeVertex is used to combine the activations of two or more layers/GraphVertex by means of concatenation/merging.
Exactly how this is done depends on the type of input.
For 2d (feed forward layer) inputs: MergeVertex([numExamples,layerSize1],[numExamples,layerSize2]) -> [numExamples,layerSize1 + layerSize2]
For 3d (time series) inputs: MergeVertex([numExamples,layerSize1,timeSeriesLength],[numExamples,layerSize2,timeSeriesLength]) -> [numExamples,layerSize1 + layerSize2,timeSeriesLength]
For 4d (convolutional) inputs: MergeVertex([numExamples,depth1,width,height],[numExamples,depth2,width,height]) -> [numExamples,depth1 + depth2,width,height]
MergeVertex(ComputationGraph, String, int, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
MergeVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
messageHandler(MessageHandler) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder: This method allows to specify MessageHandler instance Default value: EncodingHandler
MessageHandler - Interface in org.deeplearning4j.optimize.solvers.accumulation: This interface describes communication primitive for GradientsAccumulator PLEASE NOTE: All implementations of this interface must be thread-safe.
messages - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
metric - Variable in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
metric - Variable in class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator
metric - Variable in class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator
metric - Variable in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
metric - Variable in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
minibatch(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: If doing minibatch training or not.
miniBatch - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
miniBatch(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Process input as minibatch vs full dataset.
miniBatch - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
miniBatch(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Whether scores and gradients should be divided by the minibatch size.
Most users should leave this ast he default value of true.
miniBatch - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
minibatchCount - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
minimize() - Method in enum org.deeplearning4j.eval.RegressionEvaluation.Metric: Deprecated.
minimize - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
minimize(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Objective function to minimize or maximize cost function Default set to minimize true.
minimize - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
minimize(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
minimize - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator
minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG: Deprecated.
minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator
minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
minimizeScore() - Method in interface org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator
minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
minimizeScore() - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
MinMaxNormConstraint - Class in org.deeplearning4j.nn.conf.constraint: Constrain the minimum AND maximum L2 norm of the incoming weights for each unit to be between the specified values.
MinMaxNormConstraint(double, double, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint: Apply to weights but not biases by default
MinMaxNormConstraint(double, double, double, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint: Apply to weights but not biases by default
MinMaxNormConstraint(double, double, double, Set<String>, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
minVertexInputs() - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
MKLDNNBatchNormHelper - Class in org.deeplearning4j.nn.layers.mkldnn: MKL-DNN batch normalization helper implementation
MKLDNNBatchNormHelper(DataType) - Constructor for class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
MKLDNNConvHelper - Class in org.deeplearning4j.nn.layers.mkldnn: MKL-DNN Convolution (2d) helper
MKLDNNConvHelper(DataType) - Constructor for class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
mklDnnEnabled() - Static method in class org.deeplearning4j.nn.layers.mkldnn.BaseMKLDNNHelper
MKLDNNLocalResponseNormalizationHelper - Class in org.deeplearning4j.nn.layers.mkldnn: MKL-DNN Local response normalization helper
MKLDNNLocalResponseNormalizationHelper(DataType) - Constructor for class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
MKLDNNSubsamplingHelper - Class in org.deeplearning4j.nn.layers.mkldnn: MKL-DNN Subsampling (2d) helper
MKLDNNSubsamplingHelper(DataType) - Constructor for class org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper
mode(Bidirectional.Mode) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder
model - Variable in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
Model - Interface in org.deeplearning4j.nn.api: A Model is meant for predicting something from data.
model(Model) - Method in class org.deeplearning4j.optimize.Solver.Builder
model - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
ModelAdapter<T> - Interface in org.deeplearning4j.nn.api: This interface describes abstraction that uses provided model to convert INDArrays to some specific output
modelSaver(EarlyStoppingModelSaver<T>) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder: How should models be saved? (Default: in memory)
ModelSavingCallback - Class in org.deeplearning4j.optimize.listeners.callbacks: This callback will save model after each EvaluativeListener invocation.
ModelSavingCallback(String) - Constructor for class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback: This constructor will create ModelSavingCallback instance that will save models in current folder PLEASE NOTE: Make sure you have write access to the current folder
ModelSavingCallback(File, String) - Constructor for class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback: This constructor will create ModelSavingCallback instance that will save models in specified folder PLEASE NOTE: Make sure you have write access to the target folder
ModelSerializer - Class in org.deeplearning4j.util: Utility class suited to save/restore neural net models
movingAverage(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Calculate a moving average given the length
MultiDataSetIteratorAdapter - Class in org.deeplearning4j.datasets.iterator.impl: Iterator that adapts a DataSetIterator to a MultiDataSetIterator
MultiDataSetIteratorAdapter(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
MultiLayerConfiguration - Class in org.deeplearning4j.nn.conf: Configuration for a multi layer network
MultiLayerConfiguration() - Constructor for class org.deeplearning4j.nn.conf.MultiLayerConfiguration
MultiLayerConfiguration.Builder - Class in org.deeplearning4j.nn.conf
MultiLayerConfigurationDeserializer - Class in org.deeplearning4j.nn.conf.serde
MultiLayerConfigurationDeserializer(JsonDeserializer<?>) - Constructor for class org.deeplearning4j.nn.conf.serde.MultiLayerConfigurationDeserializer
MultiLayerNetwork - Class in org.deeplearning4j.nn.multilayer: MultiLayerNetwork is a neural network with multiple layers in a stack, and usually an output layer.
For neural networks with a more complex connection architecture, use ComputationGraph which allows for an arbitrary directed acyclic graph connection structure between layers.
MultiLayerNetwork(MultiLayerConfiguration) - Constructor for class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
MultiLayerNetwork(String, INDArray) - Constructor for class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Initialize the network based on the configuration (a MultiLayerConfiguration in JSON format) and parameters array
MultiLayerNetwork(MultiLayerConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Initialize the network based on the configuration and parameters array
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

N

n(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder: Number of adjacent kernel maps to use when doing LRN.
n - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
name(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder: Layer name assigns layer string name.
name(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
name(String) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
name() - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction
needsLabels() - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer: Returns true if labels are required for this output layer
needsLabels() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
needsLabels() - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
needsLabels() - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
needsLabels() - Method in class org.deeplearning4j.nn.layers.LossLayer
needsLabels() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
needsLabels() - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
needsLabels() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
needsLabels() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
NegativeDefaultStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions: Inverse step function
NegativeDefaultStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction
NegativeDefaultStepFunction - Class in org.deeplearning4j.optimize.stepfunctions: Inverse step function
NegativeDefaultStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
NegativeGradientStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions: Subtract the line
NegativeGradientStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction
NegativeGradientStepFunction - Class in org.deeplearning4j.optimize.stepfunctions: Subtract the line
NegativeGradientStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
negLogProbability(INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
negLogProbability(INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
negLogProbability(INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
negLogProbability(INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution
negLogProbability(INDArray, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
negLogProbability(INDArray, INDArray, boolean) - Method in interface org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution: Calculate the negative log probability (summed or averaged over each example in the minibatch)
network - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
networkInputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
networkInputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: List of inputs to the network, by name
networkInputTypes - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
NetworkMemoryReport - Class in org.deeplearning4j.nn.conf.memory: Network memory reports is a class that is used to store/represent the memory requirements of a MultiLayerNetwork or ComputationGraph, composed of multiple layers and/or vertices.
NetworkMemoryReport(Map<String, MemoryReport>, Class<?>, String, InputType...) - Constructor for class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport
networkOutputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
networkOutputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: List of network outputs, by name
NetworkUtils - Class in org.deeplearning4j.util
NeuralNetConfiguration - Class in org.deeplearning4j.nn.conf: A Serializable configuration for neural nets that covers per layer parameters
NeuralNetConfiguration() - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration
NeuralNetConfiguration.Builder - Class in org.deeplearning4j.nn.conf: NeuralNetConfiguration builder, used as a starting point for creating a MultiLayerConfiguration or ComputationGraphConfiguration.
Note that values set here on the layer will be applied to all relevant layers - unless the value is overridden on a layer's configuration
NeuralNetConfiguration.ListBuilder - Class in org.deeplearning4j.nn.conf: Fluent interface for building a list of configurations
NeuralNetConfiguration.ListBuilder.InputTypeBuilder - Class in org.deeplearning4j.nn.conf: Helper class for setting input types
NeuralNetwork - Interface in org.deeplearning4j.nn.api
newEval() - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator
newEval() - Method in class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator
newEval() - Method in class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator
newEval() - Method in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
newReducer() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm
newReducer() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm
newReducer() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm
newReducer() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ThresholdAlgorithm: Create a new ThresholdAlgorithmReducer.
newShape - Variable in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
next(int) - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
next() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
nHeads(int) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder: Number of Attention Heads
nHeads(int) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer.Builder: Number of Attention Heads
nHeads(int) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer.Builder: Number of Attention Heads
nHeads(int) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer.Builder: Number of Attention Heads
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
nIn - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder: Number of inputs for the layer (usually the size of the last layer).
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder: Number of inputs for the layer (usually the size of the last layer).
nIn(long) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder: Number of inputs for the layer (usually the size of the last layer).
nIn - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer.Builder
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer.Builder
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer.Builder
nInKeys(long) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder: Size of Keys
nInQueries(long) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder: Size of Queries
nInReplace(int, int, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call.
nInReplace(int, int, WeightInit, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call.
nInReplace(int, int, IWeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call.
nInReplace(String, int, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call.
nInReplace(String, int, WeightInit, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call.
nInReplace(String, int, IWeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call.
nInValues(long) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder: Size of Values
nms(List<DetectedObject>, double) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils: Performs non-maximum suppression (NMS) on objects, using their IOU with threshold to match pairs.
NO_PARAMS_MARKER - Static variable in class org.deeplearning4j.util.ModelSerializer
noLeverageOverride - Variable in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
NonNegativeConstraint - Class in org.deeplearning4j.nn.conf.constraint: Constrain the parameters to be non-negative.
NonNegativeConstraint() - Constructor for class org.deeplearning4j.nn.conf.constraint.NonNegativeConstraint
NoOpResidualPostProcessor - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.residual: This residual post process is a "no op" post processor.
NoOpResidualPostProcessor() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.NoOpResidualPostProcessor
NoParamLayer - Class in org.deeplearning4j.nn.conf.layers
NoParamLayer(Layer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.NoParamLayer
NormalDistribution - Class in org.deeplearning4j.nn.conf.distribution: A normal (Gaussian) distribution, with two parameters: mean and standard deviation
NormalDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.NormalDistribution: Create a normal distribution with the given mean and std
NORMALIZER_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer
notifyDead() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
nOut(long) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder: Output Size
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
nOut - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder: Number of inputs for the layer (usually the size of the last layer).
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder: Number of outputs - used to set the layer size (number of units/nodes for the current layer).
nOut(long) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder: Number of outputs - used to set the layer size (number of units/nodes for the current layer).
nOut - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer.Builder
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Sets the number of channels to use in the 2d convolution.
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer.Builder
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer.Builder
nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Set the size of the VAE state Z.
nOut(int) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder
nOutReplace(int, int, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer
nOutReplace(int, int, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer
nOutReplace(int, int, WeightInit, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer Can specify different weight init schemes for the specified layer and the layer that follows it.
nOutReplace(int, int, Distribution, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer Can specify different weight init schemes for the specified layer and the layer that follows it.
nOutReplace(int, int, WeightInit, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer Can specify different weight init schemes for the specified layer and the layer that follows it.
nOutReplace(int, int, Distribution, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer Can specify different weight init schemes for the specified layer and the layer that follows it.
nOutReplace(int, int, IWeightInit, IWeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Modify the architecture of a layer by changing nOut Note this will also affect the layer that follows the layer specified, unless it is the output layer Can specify different weight init schemes for the specified layer and the layer that follows it.
nOutReplace(String, int, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Modify the architecture of a vertex layer by changing nOut Note this will also affect the vertex layer that follows the layer specified, unless it is the output layer Currently does not support modifying nOut of layers that feed into non-layer vertices like merge, subset etc To modify nOut for such vertices use remove vertex, followed by add vertex Can specify different weight init schemes for the specified layer and the layer that follows it.
nOutReplace(String, int, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Modify the architecture of a vertex layer by changing nOut Note this will also affect the vertex layer that follows the layer specified, unless it is the output layer Currently does not support modifying nOut of layers that feed into non-layer vertices like merge, subset etc To modify nOut for such vertices use remove vertex, followed by add vertex Can specify different weight init schemes for the specified layer and the layer that follows it.
nOutReplace(String, int, Distribution, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Modified nOut of specified layer.
nOutReplace(String, int, WeightInit, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
nOutReplace(String, int, Distribution, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
nOutReplace(String, int, WeightInit, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
noWorkspaceFor(ArrayType) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder: Specify that no workspace should be used for array of the specified type - i.e., these arrays should all be scoped out.
noWorkspaces(Map<String, Pointer>) - Static method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
noWorkspaces() - Static method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
noWorkspacesImmutable() - Static method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
nQueries(int) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer.Builder: Number of queries to learn
nu - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: For nu definition see the paper
nu(double) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: For nu definition see the paper
NU_KEY - Static variable in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
numChannels - Variable in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
numChannels - Variable in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
numChannels(int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Returns the number of feature maps for a given shape (must be at least 3 dimensions
numElementsDrained - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
numElementsReady - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
numFeatureMap(NeuralNetConfiguration) - Static method in class org.deeplearning4j.util.ConvolutionUtils
numLabels() - Method in interface org.deeplearning4j.nn.api.Classifier: Deprecated.
Will be removed in a future release
numLabels() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Returns the number of possible labels
numLabels() - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
numLabels() - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
numLabels() - Method in class org.deeplearning4j.nn.layers.LossLayer: Returns the number of possible labels
numLabels() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
numLabels() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
numLabels() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
numLabels() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Deprecated.
Will be removed in a future release
numParams() - Method in interface org.deeplearning4j.nn.api.Model: the number of parameters for the model
numParams(boolean) - Method in interface org.deeplearning4j.nn.api.Model: the number of parameters for the model
numParams(NeuralNetConfiguration) - Method in interface org.deeplearning4j.nn.api.ParamInitializer
numParams(Layer) - Method in interface org.deeplearning4j.nn.api.ParamInitializer
numParams() - Method in interface org.deeplearning4j.nn.api.Trainable
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.FrozenVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.ScaleVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.ShiftVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
numParams(boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
numParams() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
numParams(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
numParams() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
numParams() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
numParams() - Method in class org.deeplearning4j.nn.layers.AbstractLayer: The number of parameters for the model
numParams(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
numParams() - Method in class org.deeplearning4j.nn.layers.BaseLayer: The number of parameters for the model
numParams(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
numParams() - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork: The number of parameters for the model, for backprop (i.e., excluding visible bias)
numParams() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
numParams() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
numParams() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
numParams() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
numParams() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
numParams() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
numParams() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
numParams(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
numParams() - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer
numParams() - Method in class org.deeplearning4j.nn.layers.RepeatVector
numParams() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
numParams() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
numParams() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
numParams(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
numParams() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
numParams(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
numParams() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Returns the number of parameters in the network
numParams(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Returns the number of parameters in the network
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
numParams(Layer) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
numSamples(int) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Set the number of samples per data point (from VAE state Z) used when doing pretraining.
numSamples - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

O

oa - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
OCNNLossFunction() - Constructor for class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction
OCNNOutputLayer - Class in org.deeplearning4j.nn.conf.ocnn: An implementation of one class neural networks from: https://arxiv.org/pdf/1802.06360.pdf The one class neural network approach is an extension of the standard output layer with a single set of weights, an activation function, and a bias to: 2 sets of weights, a learnable "r" parameter that is held static 1 traditional set of weights.
OCNNOutputLayer(OCNNOutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer
OCNNOutputLayer(int, double, IActivation, int, double, boolean) - Constructor for class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer
OCNNOutputLayer - Class in org.deeplearning4j.nn.layers.ocnn: Layer implementation for OCNNOutputLayer See OCNNOutputLayer for details.
OCNNOutputLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
OCNNOutputLayer.Builder - Class in org.deeplearning4j.nn.conf.ocnn
OCNNOutputLayer.OCNNLossFunction - Class in org.deeplearning4j.nn.layers.ocnn
OCNNParamInitializer - Class in org.deeplearning4j.nn.layers.ocnn: Param initializer for OCNNOutputLayer
OCNNParamInitializer() - Constructor for class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
offer(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
offer(E, long, TimeUnit) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
oldScore - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
onBackwardPass(Model) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener
onBackwardPass(Model) - Method in interface org.deeplearning4j.optimize.api.TrainingListener: Called once per iteration (backward pass) after gradients have been calculated, and updated Gradients are available via Model.gradient().
onBackwardPass(Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener
onBackwardPass(Model) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
onCompletion(EarlyStoppingResult<T>) - Method in interface org.deeplearning4j.earlystopping.listener.EarlyStoppingListener: Method that is called at the end of early stopping training
ONE_ON_2LOGE_10 - Static variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
onEpoch(int, double, EarlyStoppingConfiguration<T>, T) - Method in interface org.deeplearning4j.earlystopping.listener.EarlyStoppingListener: Method that is called at the end of each epoch completed during early stopping training
onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener
onEpochEnd(Model) - Method in interface org.deeplearning4j.optimize.api.TrainingListener: Called once at the end of each epoch, when using methods such as MultiLayerNetwork.fit(DataSetIterator), ComputationGraph.fit(DataSetIterator) or ComputationGraph.fit(MultiDataSetIterator)
onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener
onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener
onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener
onEpochEnd(Model) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
onEpochStart(Model) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener
onEpochStart(Model) - Method in interface org.deeplearning4j.optimize.api.TrainingListener: Called once at the start of each epoch, when using methods such as MultiLayerNetwork.fit(DataSetIterator), ComputationGraph.fit(DataSetIterator) or ComputationGraph.fit(MultiDataSetIterator)
onEpochStart(Model) - Method in class org.deeplearning4j.optimize.listeners.EvaluativeListener
onEpochStart(Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener
onEpochStart(Model) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
onesMaskForInput(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer: This method generates an "all ones" mask array for use in the SameDiff model when none is provided.
onForwardPass(Model, List<INDArray>) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener
onForwardPass(Model, Map<String, INDArray>) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener
onForwardPass(Model, List<INDArray>) - Method in interface org.deeplearning4j.optimize.api.TrainingListener: Called once per iteration (forward pass) for activations (usually for a MultiLayerNetwork), only at training time
onForwardPass(Model, Map<String, INDArray>) - Method in interface org.deeplearning4j.optimize.api.TrainingListener: Called once per iteration (forward pass) for activations (usually for a ComputationGraph), only at training time
onForwardPass(Model, List<INDArray>) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener
onForwardPass(Model, Map<String, INDArray>) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener
onForwardPass(Model, List<INDArray>) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
onForwardPass(Model, Map<String, INDArray>) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
onGradientCalculation(Model) - Method in class org.deeplearning4j.optimize.api.BaseTrainingListener
onGradientCalculation(Model) - Method in interface org.deeplearning4j.optimize.api.TrainingListener: Called once per iteration (backward pass) before the gradients are updated Gradients are available via Model.gradient().
onGradientCalculation(Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener
onGradientCalculation(Model) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
onStart(EarlyStoppingConfiguration<T>, T) - Method in interface org.deeplearning4j.earlystopping.listener.EarlyStoppingListener: Method to be called when early stopping training is first started
op - Variable in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
optimizationAlgo - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
optimizationAlgo(OptimizationAlgorithm) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Optimization algorithm to use.
optimizationAlgo - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
optimizationAlgo(OptimizationAlgorithm) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
optimizationAlgo - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
OptimizationAlgorithm - Enum in org.deeplearning4j.nn.api: Optimization algorithm to use
optimize(LayerWorkspaceMgr) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: Calls optimize
optimize(INDArray, INDArray, INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.optimize.api.LineOptimizer: Line optimizer
optimize(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.Solver
optimize(INDArray, INDArray, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
optimize(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer: Optimize call.
optimize(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
optimizer - Variable in class org.deeplearning4j.nn.layers.BaseLayer
optimizer - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
Or(FailureTestingListener.FailureTrigger...) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.Or
orderedLayers - Variable in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
ordering - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
org.deeplearning4j.datasets.iterator.impl - package org.deeplearning4j.datasets.iterator.impl
org.deeplearning4j.earlystopping - package org.deeplearning4j.earlystopping
org.deeplearning4j.earlystopping.listener - package org.deeplearning4j.earlystopping.listener
org.deeplearning4j.earlystopping.saver - package org.deeplearning4j.earlystopping.saver
org.deeplearning4j.earlystopping.scorecalc - package org.deeplearning4j.earlystopping.scorecalc
org.deeplearning4j.earlystopping.scorecalc.base - package org.deeplearning4j.earlystopping.scorecalc.base
org.deeplearning4j.earlystopping.termination - package org.deeplearning4j.earlystopping.termination
org.deeplearning4j.earlystopping.trainer - package org.deeplearning4j.earlystopping.trainer
org.deeplearning4j.eval - package org.deeplearning4j.eval
org.deeplearning4j.eval.curves - package org.deeplearning4j.eval.curves
org.deeplearning4j.eval.meta - package org.deeplearning4j.eval.meta
org.deeplearning4j.exception - package org.deeplearning4j.exception
org.deeplearning4j.gradientcheck - package org.deeplearning4j.gradientcheck
org.deeplearning4j.nn.adapters - package org.deeplearning4j.nn.adapters
org.deeplearning4j.nn.api - package org.deeplearning4j.nn.api
org.deeplearning4j.nn.api.layers - package org.deeplearning4j.nn.api.layers
org.deeplearning4j.nn.conf - package org.deeplearning4j.nn.conf
org.deeplearning4j.nn.conf.constraint - package org.deeplearning4j.nn.conf.constraint
org.deeplearning4j.nn.conf.distribution - package org.deeplearning4j.nn.conf.distribution
org.deeplearning4j.nn.conf.distribution.serde - package org.deeplearning4j.nn.conf.distribution.serde
org.deeplearning4j.nn.conf.dropout - package org.deeplearning4j.nn.conf.dropout
org.deeplearning4j.nn.conf.graph - package org.deeplearning4j.nn.conf.graph
org.deeplearning4j.nn.conf.graph.rnn - package org.deeplearning4j.nn.conf.graph.rnn
org.deeplearning4j.nn.conf.inputs - package org.deeplearning4j.nn.conf.inputs
org.deeplearning4j.nn.conf.layers - package org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.convolutional - package org.deeplearning4j.nn.conf.layers.convolutional
org.deeplearning4j.nn.conf.layers.misc - package org.deeplearning4j.nn.conf.layers.misc
org.deeplearning4j.nn.conf.layers.objdetect - package org.deeplearning4j.nn.conf.layers.objdetect
org.deeplearning4j.nn.conf.layers.recurrent - package org.deeplearning4j.nn.conf.layers.recurrent
org.deeplearning4j.nn.conf.layers.samediff - package org.deeplearning4j.nn.conf.layers.samediff
org.deeplearning4j.nn.conf.layers.util - package org.deeplearning4j.nn.conf.layers.util
org.deeplearning4j.nn.conf.layers.variational - package org.deeplearning4j.nn.conf.layers.variational
org.deeplearning4j.nn.conf.layers.wrapper - package org.deeplearning4j.nn.conf.layers.wrapper
org.deeplearning4j.nn.conf.memory - package org.deeplearning4j.nn.conf.memory
org.deeplearning4j.nn.conf.misc - package org.deeplearning4j.nn.conf.misc
org.deeplearning4j.nn.conf.module - package org.deeplearning4j.nn.conf.module
org.deeplearning4j.nn.conf.ocnn - package org.deeplearning4j.nn.conf.ocnn
org.deeplearning4j.nn.conf.preprocessor - package org.deeplearning4j.nn.conf.preprocessor
org.deeplearning4j.nn.conf.serde - package org.deeplearning4j.nn.conf.serde
org.deeplearning4j.nn.conf.serde.legacy - package org.deeplearning4j.nn.conf.serde.legacy
org.deeplearning4j.nn.conf.stepfunctions - package org.deeplearning4j.nn.conf.stepfunctions
org.deeplearning4j.nn.conf.weightnoise - package org.deeplearning4j.nn.conf.weightnoise
org.deeplearning4j.nn.gradient - package org.deeplearning4j.nn.gradient
org.deeplearning4j.nn.graph - package org.deeplearning4j.nn.graph
org.deeplearning4j.nn.graph.util - package org.deeplearning4j.nn.graph.util
org.deeplearning4j.nn.graph.vertex - package org.deeplearning4j.nn.graph.vertex
org.deeplearning4j.nn.graph.vertex.impl - package org.deeplearning4j.nn.graph.vertex.impl
org.deeplearning4j.nn.graph.vertex.impl.rnn - package org.deeplearning4j.nn.graph.vertex.impl.rnn
org.deeplearning4j.nn.layers - package org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.convolution - package org.deeplearning4j.nn.layers.convolution
org.deeplearning4j.nn.layers.convolution.subsampling - package org.deeplearning4j.nn.layers.convolution.subsampling
org.deeplearning4j.nn.layers.convolution.upsampling - package org.deeplearning4j.nn.layers.convolution.upsampling
org.deeplearning4j.nn.layers.feedforward - package org.deeplearning4j.nn.layers.feedforward
org.deeplearning4j.nn.layers.feedforward.autoencoder - package org.deeplearning4j.nn.layers.feedforward.autoencoder
org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive - package org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive
org.deeplearning4j.nn.layers.feedforward.dense - package org.deeplearning4j.nn.layers.feedforward.dense
org.deeplearning4j.nn.layers.feedforward.elementwise - package org.deeplearning4j.nn.layers.feedforward.elementwise
org.deeplearning4j.nn.layers.feedforward.embedding - package org.deeplearning4j.nn.layers.feedforward.embedding
org.deeplearning4j.nn.layers.mkldnn - package org.deeplearning4j.nn.layers.mkldnn
org.deeplearning4j.nn.layers.normalization - package org.deeplearning4j.nn.layers.normalization
org.deeplearning4j.nn.layers.objdetect - package org.deeplearning4j.nn.layers.objdetect
org.deeplearning4j.nn.layers.ocnn - package org.deeplearning4j.nn.layers.ocnn
org.deeplearning4j.nn.layers.pooling - package org.deeplearning4j.nn.layers.pooling
org.deeplearning4j.nn.layers.recurrent - package org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.layers.samediff - package org.deeplearning4j.nn.layers.samediff
org.deeplearning4j.nn.layers.training - package org.deeplearning4j.nn.layers.training
org.deeplearning4j.nn.layers.util - package org.deeplearning4j.nn.layers.util
org.deeplearning4j.nn.layers.variational - package org.deeplearning4j.nn.layers.variational
org.deeplearning4j.nn.layers.wrapper - package org.deeplearning4j.nn.layers.wrapper
org.deeplearning4j.nn.multilayer - package org.deeplearning4j.nn.multilayer
org.deeplearning4j.nn.params - package org.deeplearning4j.nn.params
org.deeplearning4j.nn.transferlearning - package org.deeplearning4j.nn.transferlearning
org.deeplearning4j.nn.updater - package org.deeplearning4j.nn.updater
org.deeplearning4j.nn.updater.graph - package org.deeplearning4j.nn.updater.graph
org.deeplearning4j.nn.weights - package org.deeplearning4j.nn.weights
org.deeplearning4j.nn.weights.embeddings - package org.deeplearning4j.nn.weights.embeddings
org.deeplearning4j.nn.workspace - package org.deeplearning4j.nn.workspace
org.deeplearning4j.optimize - package org.deeplearning4j.optimize
org.deeplearning4j.optimize.api - package org.deeplearning4j.optimize.api
org.deeplearning4j.optimize.listeners - package org.deeplearning4j.optimize.listeners
org.deeplearning4j.optimize.listeners.callbacks - package org.deeplearning4j.optimize.listeners.callbacks
org.deeplearning4j.optimize.solvers - package org.deeplearning4j.optimize.solvers
org.deeplearning4j.optimize.solvers.accumulation - package org.deeplearning4j.optimize.solvers.accumulation
org.deeplearning4j.optimize.solvers.accumulation.encoding - package org.deeplearning4j.optimize.solvers.accumulation.encoding
org.deeplearning4j.optimize.solvers.accumulation.encoding.residual - package org.deeplearning4j.optimize.solvers.accumulation.encoding.residual
org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold - package org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold
org.deeplearning4j.optimize.stepfunctions - package org.deeplearning4j.optimize.stepfunctions
org.deeplearning4j.util - package org.deeplearning4j.util
OrthogonalDistribution - Class in org.deeplearning4j.nn.conf.distribution: Orthogonal distribution, with gain parameter.
See http://arxiv.org/abs/1312.6120 for details
OrthogonalDistribution(double) - Constructor for class org.deeplearning4j.nn.conf.distribution.OrthogonalDistribution: Create a log-normal distribution with the given mean and std
output(Model, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
output(Model, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
output(MultiLayerNetwork, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseMLNScoreCalculator
output(MultiLayerNetwork, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseMLNScoreCalculator
output(T, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
output(T, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
output(Model, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
output(Model, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
output(Model, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
output(Model, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
output(Model, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
output(Model, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
output(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return an array of network outputs (predictions) at test time, given the specified network inputs Network outputs are for output layers only.
output(boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return an array of network outputs (predictions), given the specified network inputs Network outputs are for output layers only.
output(boolean, MemoryWorkspace, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return an array of network outputs (predictions), given the specified network inputs Network outputs are for output layers only.
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.
output(boolean, INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return an array of network outputs (predictions), given the specified network inputs Network outputs are for output layers only.
output(boolean, INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return an array of network outputs (predictions), given the specified network inputs Network outputs are for output layers only.
output(INDArray[], INDArray[], INDArray[], OutputAdapter<T>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: This method uses provided OutputAdapter to return custom object built from INDArray PLEASE NOTE: This method uses dedicated Workspace for output generation to avoid redundant allocations
output(boolean, INDArray[], INDArray[], INDArray[], MemoryWorkspace) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Return an array of network outputs (predictions), given the specified network inputs Network outputs are for output layers only.
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.
output(boolean, boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: An output method for the network, with optional clearing of the layer inputs.
Note: most users should use ComputationGraph.output(boolean, INDArray...) or similar methods, unless they are doing non-standard operations (like providing the input arrays externally)
output(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Generate the output for all examples/batches in the input iterator, and concatenate them into a single array per network output
output(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Generate the output for all examples/batches in the input iterator, and concatenate them into a single array per network output
output(List<String>, boolean, INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the activations for the specific layers only
output(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform inference on the provided input/features - i.e., perform forward pass using the provided input/features and return the output of the final layer.
output(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform inference on the provided input/features - i.e., perform forward pass using the provided input/features and return the output of the final layer.
output(INDArray, boolean, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Calculate the output of the network, with masking arrays.
output(INDArray, boolean, MemoryWorkspace) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the network output, which is optionally placed in the specified memory workspace.
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.
output(INDArray, boolean, INDArray, INDArray, MemoryWorkspace) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the network output, which is optionally placed in the specified memory workspace.
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.
output(INDArray, INDArray, INDArray, OutputAdapter<T>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: This method uses provided OutputAdapter to return custom object built from INDArray PLEASE NOTE: This method uses dedicated Workspace for output generation to avoid redundant allocations
output(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform inference on the provided input/features - i.e., perform forward pass using the provided input/features and return the output of the final layer.
output(DataSetIterator, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Generate the output for all examples/batches in the input iterator, and concatenate them into a single array.
output(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Equivalent to MultiLayerNetwork.output(DataSetIterator, boolean) with train=false
output(Model, INDArray) - Static method in class org.deeplearning4j.util.NetworkUtils: Currently supports MultiLayerNetwork and ComputationGraph models.
outputFromFeaturized(INDArray[]) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: Use to get the output from a featurized input
outputFromFeaturized(INDArray) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: Use to get the output from a featurized input
outputKey - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
outputKey - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
outputKey - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
OutputLayer - Class in org.deeplearning4j.nn.conf.layers: Output layer used for training via backpropagation based on labels and a specified loss function.
OutputLayer(OutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer
OutputLayer - Class in org.deeplearning4j.nn.layers: Output layer with different objective incooccurrences for different objectives.
OutputLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.OutputLayer
OutputLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
OutputLayerUtil - Class in org.deeplearning4j.util: Utility methods for output layer configuration/validation
outputOfLayerDetached(boolean, FwdPassType, int, INDArray, INDArray, INDArray, MemoryWorkspace) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Provide the output of the specified layer, detached from any workspace.
outputOfLayersDetached(boolean, FwdPassType, int[], INDArray[], INDArray[], INDArray[], boolean, boolean, MemoryWorkspace) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Provide the output of the specified layers, detached from any workspace.
outputSingle(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: A convenience method that returns a single INDArray, instead of an INDArray[].
outputSingle(boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: A convenience method that returns a single INDArray, instead of an INDArray[].
outputSingle(boolean, boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Identical to ComputationGraph.outputSingle(boolean, boolean, INDArray...) but has the option of not clearing the input arrays (useful when later backpropagating external errors).
outputSingle(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Generate the output for all examples/batches in the input iterator, and concatenate them into a single array.
outputSingle(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Generate the output for all examples/batches in the input iterator, and concatenate them into a single array.
outputVar - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
outputVar - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
outputVar - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
outputVertex - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
outputVertices - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex: A representation of the vertices that this vertex is connected to (outputs duing forward pass) Specifically, if outputVertices[X].getVertexIndex() = Y, and outputVertices[X].getVertexEdgeNumber() = Z then the output of this vertex (there is only one output) is connected to the Zth input of vertex Y
overlap(double, double, double, double) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils: Returns overlap between lines [x1, x2] and [x3.
ownCounter - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
oz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn

P

padding(int) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder: Padding value for the convolution.
padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder: Set padding size for 3D convolutions in (depth, height, width) order
padding - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
padding - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder: Padding of the convolution in rows/columns (height/width) dimensions
padding(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Sets the padding of the 2d convolution
padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder: Padding - rows/columns (height/width)
padding - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder: A 2d array, with format [[padTop, padBottom], [padLeft, padRight]]
padding(int[][]) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
padding - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
padding(int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder: Padding
padding - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder: Padding
padding - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
padding - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder: Padding
padding - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
ParamAndGradientIterationListener - Class in org.deeplearning4j.optimize.listeners: Deprecated.
StatsListener can be used instead, storing data using FileStatsStorage - UI is not required
ParamAndGradientIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener: Deprecated.

Default constructor for output to console only every iteration, tab delimited
ParamAndGradientIterationListener(int, boolean, boolean, boolean, boolean, boolean, boolean, boolean, File, String) - Constructor for class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener: Deprecated.

Full constructor with all options.
ParamInitializer - Interface in org.deeplearning4j.nn.api: Param initializer for a layer
paramKeys(Layer) - Method in interface org.deeplearning4j.nn.api.ParamInitializer: Get a list of all parameter keys given the layer configuration
paramKeys(Layer) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
paramKeys(Layer) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
paramReshapeOrder(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer: Returns the memory layout ('c' or 'f' order - i.e., row/column major) of the parameters.
paramReshapeOrder(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
params() - Method in interface org.deeplearning4j.nn.api.Model: Parameters of the model (if any)
params() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork: This method returns model parameters as single INDArray
params() - Method in interface org.deeplearning4j.nn.api.Trainable
params - Variable in class org.deeplearning4j.nn.conf.constraint.BaseConstraint
params(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Deprecated.
To be removed. Use ComputationGraph.params()
params() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
params() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
params() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
params() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
params() - Method in class org.deeplearning4j.nn.layers.AbstractLayer: Returns the parameters of the neural network as a flattened row vector
params() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
params - Variable in class org.deeplearning4j.nn.layers.BaseLayer
params() - Method in class org.deeplearning4j.nn.layers.BaseLayer: Returns the parameters of the neural network as a flattened row vector
params() - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
params() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
params() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
params() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
params() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
params() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
params() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
params() - Method in class org.deeplearning4j.nn.layers.DropoutLayer
params() - Method in class org.deeplearning4j.nn.layers.LossLayer
params() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
params() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
params() - Method in class org.deeplearning4j.nn.layers.RepeatVector
params - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
params() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
params - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
params() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer: Returns the parameters of the neural network as a flattened row vector
params - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
params() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer: Returns the parameters of the neural network as a flattened row vector
params - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
params() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
params() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
params(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Deprecated.
To be removed. Use MultiLayerNetwork.params() instead
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 parameters in the network.
See MultiLayerNetwork.getParam(String) and MultiLayerNetwork.paramTable() for a more useful/interpretable representation of the parameters.
Note that the parameter vector is not a copy, and changes to the returned INDArray will impact the network parameters.
PARAMS_KEY - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
paramsFlattened - Variable in class org.deeplearning4j.nn.layers.BaseLayer
paramsFlattened - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
paramsOrder - Variable in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
paramsShape - Variable in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
ParamState() - Constructor for class org.deeplearning4j.nn.updater.UpdaterBlock.ParamState
paramTable() - Method in interface org.deeplearning4j.nn.api.Model: The param table
paramTable(boolean) - Method in interface org.deeplearning4j.nn.api.Model: Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters
paramTable(boolean) - Method in interface org.deeplearning4j.nn.api.Trainable
paramTable() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
paramTable(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
paramTable(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
paramTable(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
paramTable(boolean) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Get the parameter table for the vertex
paramTable(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
paramTable() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
paramTable() - Method in class org.deeplearning4j.nn.layers.BaseLayer
paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
paramTable() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
paramTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
paramTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
paramTable() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
paramTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
paramTable() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
paramTable() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
paramTable() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
paramTable() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Return a map of all parameters in the network.
paramTable(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Returns a map of all parameters in the network as per MultiLayerNetwork.paramTable().
Optionally (with backpropParamsOnly=true) only the 'backprop' parameters are returned - that is, any parameters involved only in unsupervised layerwise pretraining not standard inference/backprop are excluded from the returned list.
parent(Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Returns the parent of the passed in tree via traversal
parent() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
parties - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
parties - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
parties - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
peek() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
PerformanceListener - Class in org.deeplearning4j.optimize.listeners: Simple IterationListener that tracks time spend on training per iteration.
PerformanceListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.PerformanceListener
PerformanceListener(int, boolean) - Constructor for class org.deeplearning4j.optimize.listeners.PerformanceListener
PerformanceListener(int, boolean, boolean) - Constructor for class org.deeplearning4j.optimize.listeners.PerformanceListener
PerformanceListener.Builder - Class in org.deeplearning4j.optimize.listeners
pnorm(int) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder: P-norm constant.
pnorm - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
pnorm(int) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
pnorm - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
Point(int, double, double, double) - Constructor for class org.deeplearning4j.eval.curves.PrecisionRecallCurve.Point: Deprecated.
POINT_WISE_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
pointWiseConstraints - Variable in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder: Set constraints to be applied to the point-wise convolution weight parameters of this layer.
poll() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
poll(long, TimeUnit) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
poll() - Method in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
PoolHelperVertex - Class in org.deeplearning4j.nn.conf.graph: Removes the first column and row from an input.
PoolHelperVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
PoolHelperVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: A custom layer for removing the first column and row from an input.
PoolHelperVertex(ComputationGraph, String, int, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
PoolHelperVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
Pooling1D - Class in org.deeplearning4j.nn.conf.layers: 1D Pooling (subsampling) layer.
Pooling1D() - Constructor for class org.deeplearning4j.nn.conf.layers.Pooling1D
Pooling2D - Class in org.deeplearning4j.nn.conf.layers: 2D Pooling (subsampling) layer.
Pooling2D() - Constructor for class org.deeplearning4j.nn.conf.layers.Pooling2D
poolingDimensions(int...) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder: Pooling dimensions.
poolingType(PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder
PoolingType - Enum in org.deeplearning4j.nn.conf.layers: Pooling type:
MAX: Max pooling - output is the maximum value of the input values
AVG: Average pooling - output is the average value of the input values
SUM: Sum pooling - output is the sum of the input values
PNORM: P-norm pooling
poolingType - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
poolingType(Subsampling3DLayer.PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
poolingType(PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
poolingType - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
poolingType - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
poolingType(SubsamplingLayer.PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
poolingType(PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
poolingType - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
positions - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
postFirstStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
postStep(INDArray) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: After the step has been made, do an action
postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer: Post step to update searchDirection with new gradient and parameter information
postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.ConjugateGradient
postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.LBFGS
postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.LineGradientDescent
postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
preApply(Trainable, Gradient, int) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater: Pre-apply: Apply gradient normalization/clipping
precision(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Deprecated.
PrecisionRecallCurve - Class in org.deeplearning4j.eval.curves: Deprecated.
Use ReliabilityDiagram
PrecisionRecallCurve(double[], double[], double[], int[], int[], int[], int) - Constructor for class org.deeplearning4j.eval.curves.PrecisionRecallCurve: Deprecated.
Use ReliabilityDiagram
PrecisionRecallCurve.Confusion - Class in org.deeplearning4j.eval.curves: Deprecated.
PrecisionRecallCurve.Point - Class in org.deeplearning4j.eval.curves: Deprecated.
predict(INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier: Takes in a list of examples For each row, returns a label
predict(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier: Takes in a DataSet of examples For each row, returns a label
predict(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Returns the predictions for each example in the dataset
predict(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Return predicted label names
predict(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
predict(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
predict(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
predict(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
predict(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer: Returns the predictions for each example in the dataset
predict(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer: Return predicted label names
predict(INDArray) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
predict(DataSet) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
predict(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
predict(DataSet) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
predict(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
predict(DataSet) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
predict(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Usable only for classification networks in conjunction with OutputLayer.
predict(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: As per MultiLayerNetwork.predict(INDArray) but the returned values are looked up from the list of label names in the provided DataSet
Prediction - Class in org.deeplearning4j.eval.meta: Deprecated.
Use Prediction
Prediction(int, int, Object) - Constructor for class org.deeplearning4j.eval.meta.Prediction: Deprecated.
prediction() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
PReLU - Class in org.deeplearning4j.nn.layers.feedforward: Parametrized Rectified Linear Unit (PReLU) f(x) = alpha * x for x < 0, f(x) = x for x >= 0 alpha has the same shape as x and is a learned parameter.
PReLU(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.feedforward.PReLU
PReLULayer - Class in org.deeplearning4j.nn.conf.layers: Parametrized Rectified Linear Unit (PReLU)
PReLULayer.Builder - Class in org.deeplearning4j.nn.conf.layers
PReLUParamInitializer - Class in org.deeplearning4j.nn.params: PReLU weight initializer.
PReLUParamInitializer(long[], long[]) - Constructor for class org.deeplearning4j.nn.params.PReLUParamInitializer
preOutput - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer
preOutput(INDArray, INDArray, INDArray, int[], int[], int[], ConvolutionLayer.AlgoMode, ConvolutionLayer.FwdAlgo, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer: PreOutput method that also returns the im2col2d array (if being called for backprop), as this can be re-used instead of being calculated again.
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer
preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
preOutput(INDArray, boolean, int[], INDArray, INDArray, INDArray, INDArray, double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
preOutput(INDArray, INDArray, INDArray, int[], int[], int[], ConvolutionLayer.AlgoMode, ConvolutionLayer.FwdAlgo, ConvolutionMode, int[], LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
preOutput(INDArray, Layer.TrainingMode, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
preOutput(INDArray, boolean, int[], INDArray, INDArray, INDArray, INDArray, double, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.RepeatVector
preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
preOutput2d(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
preOutput2d(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
preOutput2d(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
preOutput4d(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
preOutput4d(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer: preOutput4d: Used so that ConvolutionLayer subclasses (such as Convolution1DLayer) can maintain their standard non-4d preOutput method, while overriding this to return 4d activations (for use in backprop) without modifying the public API
preOutputWithPreNorm(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer
preProcess(INDArray, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor: Pre preProcess input/activations for a multi layer network
preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor
preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
preProcessLine() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: Pre preProcess a line before an iteration
preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer: Pre preProcess to setup initial searchDirection approximation
preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.ConjugateGradient
preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.LBFGS
preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.LineGradientDescent
preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
PREPROCESSOR_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer
PreprocessorVertex - Class in org.deeplearning4j.nn.conf.graph: PreprocessorVertex is a simple adaptor class that allows a InputPreProcessor to be used in a ComputationGraph GraphVertex, without it being associated with a layer.
PreprocessorVertex(InputPreProcessor) - Constructor for class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
PreprocessorVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: PreprocessorVertex is a simple adaptor class that allows a InputPreProcessor to be used in a ComputationGraph GraphVertex, without it being associated with a layer.
PreprocessorVertex(ComputationGraph, String, int, InputPreProcessor, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
PreprocessorVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], InputPreProcessor, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
pretrain(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
pretrain(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
pretrain() - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
pretrain(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
pretrain(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
pretrain(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
pretrain(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
pretrain() - Method in interface org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer
pretrain(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Perform layerwise pretraining for one epoch - see ComputationGraph.pretrain(DataSetIterator, int)
pretrain(DataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Pretrain network with a single input and single output.
pretrain(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Pretrain network with multiple inputs and/or outputs
pretrain(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Pretrain network with multiple inputs and/or outputs
This method performs layerwise pretraining on all pre-trainable layers in the network (VAEs, Autoencoders, etc), for the specified number of epochs each.
pretrain(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform layerwise pretraining for one epoch - see MultiLayerNetwork.pretrain(DataSetIterator, int)
pretrain(DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform layerwise unsupervised training on all pre-trainable layers in the network (VAEs, Autoencoders, etc), for the specified number of epochs each.
pretrain(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
pretrain - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
pretrainLayer(String, DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Pretrain a specified layer with the given DataSetIterator
pretrainLayer(String, MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Pretrain a specified layer with the given MultiDataSetIterator
pretrainLayer(int, DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Fit for one epoch - see MultiLayerNetwork.pretrainLayer(int, DataSetIterator, int)
pretrainLayer(int, DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform layerwise unsupervised training on a single pre-trainable layer in the network (VAEs, Autoencoders, etc) for the specified number of epochs
If the specified layer index (0 to numLayers - 1) is not a pretrainable layer, this is a no-op.
pretrainLayer(int, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Perform layerwise unsupervised training on a single pre-trainable layer in the network (VAEs, Autoencoders, etc)
If the specified layer index (0 to numLayers - 1) is not a pretrainable layer, this is a no-op.
PretrainParamInitializer - Class in org.deeplearning4j.nn.params: Pretrain weight initializer.
PretrainParamInitializer() - Constructor for class org.deeplearning4j.nn.params.PretrainParamInitializer
prevAct - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
prevMemCell - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
PrimaryCapsules - Class in org.deeplearning4j.nn.conf.layers: An implementation of the PrimaryCaps layer from Dynamic Routing Between Capsules Is a reshaped 2D convolution, and the input should be 2D convolutional ([mb, c, h, w]).
PrimaryCapsules(PrimaryCapsules.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.PrimaryCapsules
PrimaryCapsules.Builder - Class in org.deeplearning4j.nn.conf.layers
processResidual(int, int, double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.NoOpResidualPostProcessor
processResidual(int, int, double, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.ResidualClippingPostProcessor
processResidual(int, int, double, INDArray) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.encoding.ResidualPostProcessor
projectInput(boolean) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder: Toggle to enable / disable projection of inputs (key, values, queries).
projectInput(boolean) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer.Builder: Project input before applying attention or not.
projectInput(boolean) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer.Builder: Project input before applying attention or not.
projectInput(boolean) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer.Builder: Project input before applying attention or not.
pullLastTimeSteps(INDArray, INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Extract out the last time steps (2d array from 3d array input) accounting for the mask layer, if present.
pullLastTimeSteps(INDArray, INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Extract out the last time steps (2d array from 3d array input) accounting for the mask layer, if present.
purge() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
put(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
put(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail: This mehtod adds update, with optional collapse
put(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
PXZ_B - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer: Key for bias parameters connecting the last decoder layer and p(data|z) (according to whatever ReconstructionDistribution is set for the VAE)
PXZ_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
PXZ_W - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer: Key for weight parameters connecting the last decoder layer and p(data|z) (according to whatever ReconstructionDistribution is set for the VAE)
PZX_LOGSTD2_B - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer: Key for bias parameters for log(sigma^2) in p(z|data)
PZX_LOGSTD2_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
PZX_LOGSTD2_W - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer: Key for weight parameters connecting the last encoder layer and the log(sigma^2) values for p(z|data)
PZX_MEAN_B - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer: Key for bias parameters for the mean values for p(z|data)
PZX_MEAN_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
PZX_MEAN_W - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer: Key for weight parameters connecting the last encoder layer and the mean values for p(z|data)
PZX_PREFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
pzxActivationFn(IActivation) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Activation function for the input to P(z|data).
Care should be taken with this, as some activation functions (relu, etc) are not suitable due to being bounded in range [0,infinity).
pzxActivationFn - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
pzxActivationFunction(Activation) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Activation function for the input to P(z|data).
Care should be taken with this, as some activation functions (relu, etc) are not suitable due to being bounded in range [0,infinity).

Q

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

R

R_KEY - Static variable in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
RandomProb(FailureTestingListener.CallType, double) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.RandomProb
rebuildUpdaterStateArray(INDArray, List<UpdaterBlock>, List<UpdaterBlock>) - Static method in class org.deeplearning4j.util.NetworkUtils: Rebuild the updater state after a learning rate change.
recall(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation: Deprecated.
receiveUpdate(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator: This method accepts updates suitable for StepFunction and puts them to the queue, which is used in backpropagation loop PLEASE NOTE: array is expected to be ready for use and match params dimensionality
receiveUpdate(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator: This method accepts updates suitable for StepFunction and puts them to the queue, which is used in backpropagation loop
receiveUpdate(INDArray) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator: This method accepts updates suitable for StepFunction and puts them to the queue, which is used in backpropagation loop PLEASE NOTE: array is expected to be ready for use and match params dimensionality
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
ReconstructionDistributionMixin() - Constructor for class org.deeplearning4j.nn.conf.serde.legacy.LegacyJsonFormat.ReconstructionDistributionMixin
reconstructionError(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder: Return the reconstruction error for this variational autoencoder.
NOTE (important): This method is used ONLY for VAEs that have a standard neural network loss function (i.e., an ILossFunction instance such as mean squared error) instead of using a probabilistic reconstruction distribution P(x|z) for the reconstructions (as presented in the VAE architecture by Kingma and Welling).
You can check if the VAE has a loss function using VariationalAutoencoder.hasLossFunction()
Consequently, the reconstruction error is a simple deterministic function (no Monte-Carlo sampling is required, unlike VariationalAutoencoder.reconstructionProbability(INDArray, int) and VariationalAutoencoder.reconstructionLogProbability(INDArray, int))
reconstructionLogProbability(INDArray, int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder: Return the log reconstruction probability given the specified number of samples.
See VariationalAutoencoder.reconstructionLogProbability(INDArray, int) for more details
reconstructionProbability(INDArray, int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder: Calculate the reconstruction probability, as described in An & Cho, 2015 - "Variational Autoencoder based Anomaly Detection using Reconstruction Probability" (Algorithm 4)
The authors describe it as follows: "This is essentially the probability of the data being generated from a given latent variable drawn from the approximate posterior distribution."

Specifically, for each example x in the input, calculate p(x).
reconstructionProbNumSamples - Variable in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
recurrent(long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: InputType for recurrent neural network (time series) data
recurrent(long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType: InputType for recurrent neural network (time series) data
recurrent(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder: See InputType.recurrent(long)}
RECURRENT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer: Weights for previous time step -> current time step connections
RECURRENT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.LSTMParamInitializer: Weights for previous time step -> current time step connections
RECURRENT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
RECURRENT_WEIGHT_KEY_BACKWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
RECURRENT_WEIGHT_KEY_FORWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer: Weights for previous time step -> current time step connections
RecurrentAttentionLayer - Class in org.deeplearning4j.nn.conf.layers: Implements Recurrent Dot Product Attention Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using the hidden state as the query and all time steps as keys/values.
RecurrentAttentionLayer(RecurrentAttentionLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer
RecurrentAttentionLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
recurrentConstraints - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder: Set constraints to be applied to the RNN recurrent weight parameters of this layer.
RecurrentLayer - Interface in org.deeplearning4j.nn.api.layers: Created by Alex on 28/08/2016.
Registerable - Interface in org.deeplearning4j.optimize.solvers.accumulation
registerConsumers(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
registerConsumers(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
registerConsumers(int) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.Registerable: This method notifies producer about number of consumers for the current consumption cycle
registered - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
Regression2dAdapter - Class in org.deeplearning4j.nn.adapters: This OutputAdapter implementation takes single 2D nn output in, and returns JVM double[][] array
Regression2dAdapter() - Constructor for class org.deeplearning4j.nn.adapters.Regression2dAdapter
RegressionEvaluation - Class in org.deeplearning4j.eval: Deprecated.
Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation
RegressionEvaluation() - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Deprecated.
Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation
RegressionEvaluation(long) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Deprecated.
Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation
RegressionEvaluation(long, long) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Deprecated.
Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation
RegressionEvaluation(String...) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Deprecated.
Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation
RegressionEvaluation(List<String>) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Deprecated.
Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation
RegressionEvaluation(List<String>, long) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation: Deprecated.
Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation
RegressionEvaluation.Metric - Enum in org.deeplearning4j.eval: Deprecated.
Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation.Metric
RegressionScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc: Calculate the regression score of the network (MultiLayerNetwork or ComputationGraph) on a test set, using the specified regression metric - RegressionEvaluation.Metric
RegressionScoreCalculator(RegressionEvaluation.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator
regularization - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Regularization for the parameters (excluding biases).
regularization(List<Regularization>) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Set the regularization for the parameters (excluding biases) - for example WeightDecay
regularization - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
regularization - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder
regularization(List<Regularization>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: Set the regularization for the parameters (excluding biases) - for example WeightDecay
regularization - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
regularization - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
regularization - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
regularization(List<Regularization>) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set the regularization for the parameters (excluding biases) - for example WeightDecay
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
regularization - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
regularization - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
regularizationBias - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Regularization for the bias parameters only
regularizationBias(List<Regularization>) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Set the regularization for the biases only - for example WeightDecay
regularizationBias - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
regularizationBias - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder
regularizationBias(List<Regularization>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: Set the regularization for the biases only - for example WeightDecay
regularizationBias - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
regularizationBias - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
regularizationBias - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
regularizationBias(List<Regularization>) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set the regularization for the biases only - for example WeightDecay
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
regularizationBias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
regularizationBias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
ReliabilityDiagram - Class in org.deeplearning4j.eval.curves: Deprecated.
Use ReliabilityDiagram
ReliabilityDiagram(String, double[], double[]) - Constructor for class org.deeplearning4j.eval.curves.ReliabilityDiagram: Deprecated.
Use ReliabilityDiagram
relocatable - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
remainingCapacity() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
remove() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
remove() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
remove(Object) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
removeAll(Collection<?>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
removeInstances(List<?>, Class<?>) - Static method in class org.deeplearning4j.util.NetworkUtils: Remove any instances of the specified type from the list.
removeInstancesWithWarning(List<?>, Class<?>, String) - Static method in class org.deeplearning4j.util.NetworkUtils
removeL1 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
removeL1 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
removeL1Bias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
removeL1Bias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
removeL2 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
removeL2 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
removeL2Bias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
removeL2Bias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
removeLayersFromOutput(int) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Remove last "n" layers of the net At least an output layer must be added back in
removeOutputLayer() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Helper method to remove the outputLayer of the net.
removeVertex(String) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Intended for use with the transfer learning API.
removeVertex(String, boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Intended for use with the transfer learning API.
removeVertexAndConnections(String) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Remove specified vertex and it's connections from the computation graph
removeVertexKeepConnections(String) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Remove the specified vertex from the computation graph but keep it's connections.
removeWD - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
removeWD - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
removeWDBias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
removeWDBias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
RepeatVector - Class in org.deeplearning4j.nn.conf.layers.misc: RepeatVector layer configuration.
RepeatVector(RepeatVector.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.RepeatVector
RepeatVector - Class in org.deeplearning4j.nn.layers: RepeatVector layer.
RepeatVector(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.RepeatVector
RepeatVector.Builder<T extends RepeatVector.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers.misc
repetitionFactor(int) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder: Set repetition factor for RepeatVector layer
reportBatch(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder: This method defines, if batches/sec should be reported together with other data
reportETL(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder: This method defines, if ETL time per iteration should be reported together with other data
reportIteration(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder: This method defines, if iteration number should be reported together with other data
reportSample(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder: This method defines, if samples/sec should be reported together with other data
reportScore(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder: This method defines, if score should be reported together with other data
reportTime(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder: This method defines, if time per iteration should be reported together with other data
requiresActivationFromLegacy(Layer[]) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
requiresDropoutFromLegacy(Layer[]) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
requiresIUpdaterFromLegacy(Layer[]) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
requiresLegacyLossHandling(Layer[]) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
requiresRegularizationFromLegacy(Layer[]) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
requiresWeightInitFromLegacy(Layer[]) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
reset() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
reset() - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
reset() - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
reset() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
reset() - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
reset() - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
reset() - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
reset() - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator: This method resets all accumulated updates (if any)
reset() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator: This method resets all accumulated updates (if any)
reset() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator: This method resets all accumulated updates (if any)
resetLayerDefaultConfig() - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer: Reset the learning related configs of the layer to default.
resetLayerDefaultConfig() - Method in class org.deeplearning4j.nn.conf.layers.Layer: Reset the learning related configs of the layer to default.
resetSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
reshape2dTo3d(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
reshape2dTo3d(INDArray, int, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
reshape2dTo4d(INDArray, int[], LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
reshape2dTo5d(Convolution3D.DataFormat, INDArray, int, int, int, int, int, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
reshape3dMask(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
reshape3dTo2d(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
reshape3dTo2d(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
reshape4dMask(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
reshape4dTo2d(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
reshape5dTo2d(Convolution3D.DataFormat, INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
reshapeCnn3dMask(Convolution3D.DataFormat, INDArray, INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
reshapeCnnMaskToTimeSeriesMask(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Reshape CNN-style 4d mask array of shape [seqLength*minibatch,1,1,1] to time series mask [mb,seqLength] This should match the assumptions (f order, etc) in RnnOutputLayer
reshapeMaskIfRequired(INDArray, INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
reshapeOrder - Variable in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
reshapePerOutputTimeSeriesMaskTo2d(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
reshapePerOutputTimeSeriesMaskTo2d(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
reshapeTimeSeriesMaskToCnn4dMask(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Reshape time series mask arrays.
reshapeTimeSeriesMaskToVector(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Reshape time series mask arrays.
reshapeTimeSeriesMaskToVector(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Reshape time series mask arrays.
reshapeVectorToTimeSeriesMask(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Reshape time series mask arrays.
ReshapeVertex - Class in org.deeplearning4j.nn.conf.graph: Adds the ability to reshape and flatten the tensor in the computation graph.
NOTE: This class should only be used if you know exactly what you are doing with reshaping activations.
ReshapeVertex(int...) - Constructor for class org.deeplearning4j.nn.conf.graph.ReshapeVertex: Reshape with the default reshape order of 'c'
ReshapeVertex(char, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.graph.ReshapeVertex
ReshapeVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: Adds the ability to reshape and flatten the tensor in the computation graph.
ReshapeVertex(ComputationGraph, String, int, char, int[], int[], DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
ReshapeVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], char, int[], int[], DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
reshapeWeights(int[], INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil: Reshape the parameters view, without modifying the paramsView array values.
reshapeWeights(long[], INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil: Reshape the parameters view, without modifying the paramsView array values.
reshapeWeights(int[], INDArray, char) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil: Reshape the parameters view, without modifying the paramsView array values.
reshapeWeights(long[], INDArray, char) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil: Reshape the parameters view, without modifying the paramsView array values.
ResidualClippingPostProcessor - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.residual: Residual clipping post processor clips the values of a residual every N iterations as follows:
For residual vector R, and C = thresholdMultipleClipValue, T is the current encoding threshold
R[i] = C*T if R[i] > C*T
R[i] = -C*T if R[i] < -C*T
R[i] is unmodified otherwise

Note: Regarding the frequency, a value around 5 is suggested as a good balance between applying frequently enough, and minimizing the computational overhead.
ResidualClippingPostProcessor(double, int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.residual.ResidualClippingPostProcessor
residualDebugOutputIfRequired(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
residualPostProcessor - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
residualPostProcessor(ResidualPostProcessor) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder: Set the residual post processor
ResidualPostProcessor - Interface in org.deeplearning4j.optimize.solvers.accumulation.encoding: ResidualPostProcessor: is (as the name suggests) is used to post process the residual vector for DL4J's gradient sharing implementation.
residualPostProcessor - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
resolve(DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
restoreComputationGraph(String) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a computation graph from a file
restoreComputationGraph(String, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a computation graph from a file
restoreComputationGraph(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a computation graph from a InputStream
restoreComputationGraph(InputStream) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a computation graph from a InputStream
restoreComputationGraph(File) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a computation graph from a file
restoreComputationGraph(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a computation graph from a file
restoreComputationGraphAndNormalizer(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Restore a ComputationGraph and Normalizer (if present - null if not) from the InputStream.
restoreComputationGraphAndNormalizer(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Restore a ComputationGraph and Normalizer (if present - null if not) from a File
restoreMultiLayerNetwork(File) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a multi layer network from a file
restoreMultiLayerNetwork(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a multi layer network from a file
restoreMultiLayerNetwork(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a MultiLayerNetwork from InputStream from an input stream
Note: the input stream is read fully and closed by this method.
restoreMultiLayerNetwork(InputStream) - Static method in class org.deeplearning4j.util.ModelSerializer: Restore a multi layer network from an input stream
* Note: the input stream is read fully and closed by this method.
restoreMultiLayerNetwork(String) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a MultilayerNetwork model from a file
restoreMultiLayerNetwork(String, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Load a MultilayerNetwork model from a file
restoreMultiLayerNetworkAndNormalizer(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Restore a MultiLayerNetwork and Normalizer (if present - null if not) from the InputStream.
restoreMultiLayerNetworkAndNormalizer(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Restore a MultiLayerNetwork and Normalizer (if present - null if not) from a File
restoreNormalizerFromFile(File) - Static method in class org.deeplearning4j.util.ModelSerializer: This method restores normalizer from a given persisted model file PLEASE NOTE: File should be model file saved earlier with ModelSerializer with addNormalizerToModel being called
restoreNormalizerFromInputStream(InputStream) - Static method in class org.deeplearning4j.util.ModelSerializer: This method restores the normalizer form a persisted model file.
retainAll(Collection<?>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
reverseTimeSeries(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Reverse an input time series along the time dimension
reverseTimeSeries(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Reverse an input time series along the time dimension
reverseTimeSeriesMask(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Reverse a (per time step) time series mask, with shape [minibatch, timeSeriesLength]
reverseTimeSeriesMask(INDArray, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils: Reverse a (per time step) time series mask, with shape [minibatch, timeSeriesLength]
ReverseTimeSeriesVertex - Class in org.deeplearning4j.nn.conf.graph.rnn: ReverseTimeSeriesVertex is used in recurrent neural networks to revert the order of time series.
ReverseTimeSeriesVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex: Creates a new ReverseTimeSeriesVertex that doesn't pay attention to masks
ReverseTimeSeriesVertex(String) - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex: Creates a new ReverseTimeSeriesVertex that uses the mask array of a given input
ReverseTimeSeriesVertex - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn: ReverseTimeSeriesVertex is used in recurrent neural networks to revert the order of time series.
ReverseTimeSeriesVertex(ComputationGraph, String, int, String, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex
rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer: Similar to rnnTimeStep, this method is used for activations using the state stored in the stateMap as the initialization.
rnnActivateUsingStoredState(INDArray[], boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Similar to rnnTimeStep and feedForward() methods.
rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
rnnActivateUsingStoredState(INDArray, boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn
rnnActivateUsingStoredState(INDArray, boolean, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Similar to rnnTimeStep and feedForward() methods.
rnnClearPreviousState() - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer: Reset/clear the stateMap for rnnTimeStep() and tBpttStateMap for rnnActivateUsingStoredState()
rnnClearPreviousState() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Clear the previous state of the RNN layers (if any), used in ComputationGraph.rnnTimeStep(INDArray...)
rnnClearPreviousState() - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer: Reset/clear the stateMap for rnnTimeStep() and tBpttStateMap for rnnActivateUsingStoredState()
rnnClearPreviousState() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
rnnClearPreviousState() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Clear the previous state of the RNN layers (if any).
rnnGetPreviousState() - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer: Returns a shallow copy of the RNN stateMap (that contains the stored history for use in methods such as rnnTimeStep
rnnGetPreviousState(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the state of the RNN layer, as used in ComputationGraph.rnnTimeStep(INDArray...).
rnnGetPreviousState(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get the state of the RNN layer, as used in ComputationGraph.rnnTimeStep(INDArray...).
rnnGetPreviousState() - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer: Returns a shallow copy of the stateMap
rnnGetPreviousState() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
rnnGetPreviousState(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Get the state of the RNN layer, as used in rnnTimeStep().
rnnGetPreviousStates() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Get a map of states for ALL RNN layers, as used in ComputationGraph.rnnTimeStep(INDArray...).
rnnGetTBPTTState() - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer: Get the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer.
rnnGetTBPTTState() - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
rnnGetTBPTTState() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
rnnLayer(Layer) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder
RnnLossLayer - Class in org.deeplearning4j.nn.conf.layers: Recurrent Neural Network Loss Layer.
Handles calculation of gradients etc for various objective (loss) functions.
Note: Unlike RnnOutputLayer this RnnLossLayer does not have any parameters - i.e., there is no time distributed dense component here.
RnnLossLayer - Class in org.deeplearning4j.nn.layers.recurrent: Recurrent Neural Network Loss Layer.
Handles calculation of gradients etc for various objective functions.
NOTE: Unlike RnnOutputLayer this RnnLossLayer does not have any parameters - i.e., there is no time distributed dense component here.
RnnLossLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
RnnLossLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
RnnOutputLayer - Class in org.deeplearning4j.nn.conf.layers: A version of OutputLayer for recurrent neural networks.
RnnOutputLayer - Class in org.deeplearning4j.nn.layers.recurrent: Recurrent Neural Network Output Layer.
Handles calculation of gradients etc for various objective functions.
Functionally the same as OutputLayer, but handles output and label reshaping automatically.
Input and output activations are same as other RNN layers: 3 dimensions with shape [miniBatchSize,nIn,timeSeriesLength] and [miniBatchSize,nOut,timeSeriesLength] respectively.
RnnOutputLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
RnnOutputLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
rnnSetPreviousState(Map<String, INDArray>) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer: Set the stateMap (stored history).
rnnSetPreviousState(int, Map<String, INDArray>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the state of the RNN layer, for use in ComputationGraph.rnnTimeStep(INDArray...)
rnnSetPreviousState(String, Map<String, INDArray>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the state of the RNN layer, for use in ComputationGraph.rnnTimeStep(INDArray...)
rnnSetPreviousState(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer: Set the state map.
rnnSetPreviousState(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
rnnSetPreviousState(int, Map<String, INDArray>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the state of the RNN layer.
rnnSetPreviousStates(Map<String, Map<String, INDArray>>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the states for all RNN layers, for use in ComputationGraph.rnnTimeStep(INDArray...)
rnnSetTBPTTState(Map<String, INDArray>) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer: Set the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer.
rnnSetTBPTTState(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
rnnSetTBPTTState(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer: Do one or more time steps using the previous time step state stored in stateMap.
Can be used to efficiently do forward pass one or n-steps at a time (instead of doing forward pass always from t=0)
If stateMap is empty, default initialization (usually zeros) is used
Implementations also update stateMap at the end of this method
rnnTimeStep(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: If this ComputationGraph contains one or more RNN layers: conduct forward pass (prediction) but using previous stored state for any RNN layers.
rnnTimeStep(MemoryWorkspace, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: See ComputationGraph.rnnTimeStep(INDArray...) for details.
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.
rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
rnnTimeStep(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn
rnnTimeStep(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: If this MultiLayerNetwork contains one or more RNN layers: conduct forward pass (prediction) but using previous stored state for any RNN layers.
rnnTimeStep(INDArray, MemoryWorkspace) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: See MultiLayerNetwork.rnnTimeStep(INDArray) for details
If no memory workspace is provided, the output will be detached (not in any workspace).
If a memory workspace is provided, the output activation array (i.e., the INDArray returned by this method) will be placed in the specified workspace.
RnnToCnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: A preprocessor to allow RNN and CNN layers to be used together
For example, time series (video) input -> ConvolutionLayer, or conceivable GravesLSTM -> ConvolutionLayer
Functionally equivalent to combining RnnToFeedForwardPreProcessor + FeedForwardToCnnPreProcessor
Specifically, this does two things:
(a) Reshape 3d activations out of RNN layer, with shape [miniBatchSize, numChannels*inputHeight*inputWidth, timeSeriesLength]) into 4d (CNN) activations (with shape [numExamples*timeSeriesLength, numChannels, inputWidth, inputHeight])
(b) Reshapes 4d epsilons (weights.*deltas) out of CNN layer (with shape [numExamples*timeSeriesLength, numChannels, inputHeight, inputWidth]) into 3d epsilons with shape [miniBatchSize, numChannels*inputHeight*inputWidth, timeSeriesLength] suitable to feed into CNN layers.
RnnToCnnPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
RnnToFeedForwardPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: A preprocessor to allow RNN and feed-forward network layers to be used together.
For example, GravesLSTM -> OutputLayer or GravesLSTM -> DenseLayer
This does two things:
(a) Reshapes activations out of RNN layer (which is 3D with shape [miniBatchSize,layerSize,timeSeriesLength]) into 2d activations (with shape [miniBatchSize*timeSeriesLength,layerSize]) suitable for use in feed-forward layers.
(b) Reshapes 2d epsilons (weights*deltas from feed forward layer, with shape [miniBatchSize*timeSeriesLength,layerSize]) into 3d epsilons (with shape [miniBatchSize,layerSize,timeSeriesLength]) for use in RNN layer
RnnToFeedForwardPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
rnnUpdateStateWithTBPTTState() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Update the internal state of RNN layers after a truncated BPTT fit call
ROC - Class in org.deeplearning4j.eval: Deprecated.
Use ROC
ROC() - Constructor for class org.deeplearning4j.eval.ROC: Deprecated.
Use ROC
ROC(int) - Constructor for class org.deeplearning4j.eval.ROC: Deprecated.
Use ROC
ROC(int, boolean) - Constructor for class org.deeplearning4j.eval.ROC: Deprecated.
Use ROC
ROC(int, boolean, int) - Constructor for class org.deeplearning4j.eval.ROC: Deprecated.
Use ROC
ROC.CountsForThreshold - Class in org.deeplearning4j.eval: Deprecated.
Use ROC.CountsForThreshold
ROCBinary - Class in org.deeplearning4j.eval: Deprecated.
Use ROCBinary
ROCBinary() - Constructor for class org.deeplearning4j.eval.ROCBinary: Deprecated.
Use ROCBinary
ROCBinary(int) - Constructor for class org.deeplearning4j.eval.ROCBinary: Deprecated.
Use ROCBinary
ROCBinary(int, boolean) - Constructor for class org.deeplearning4j.eval.ROCBinary: Deprecated.
Use ROCBinary
RocCurve - Class in org.deeplearning4j.eval.curves: Deprecated.
Use RocCurve
RocCurve(double[], double[], double[]) - Constructor for class org.deeplearning4j.eval.curves.RocCurve: Deprecated.
Use RocCurve
ROCMultiClass - Class in org.deeplearning4j.eval: Deprecated.
Use ROCMultiClass
ROCMultiClass() - Constructor for class org.deeplearning4j.eval.ROCMultiClass: Deprecated.
Use ROCMultiClass
ROCMultiClass(int) - Constructor for class org.deeplearning4j.eval.ROCMultiClass: Deprecated.
Use ROCMultiClass
ROCMultiClass(int, boolean) - Constructor for class org.deeplearning4j.eval.ROCMultiClass: Deprecated.
Use ROCMultiClass
ROCScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc: Calculate ROC AUC (area under ROC curve) or AUCPR (area under precision recall curve) for a MultiLayerNetwork or ComputationGraph
ROCScoreCalculator(ROCScoreCalculator.ROCType, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
ROCScoreCalculator(ROCScoreCalculator.ROCType, MultiDataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
ROCScoreCalculator(ROCScoreCalculator.ROCType, ROCScoreCalculator.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
ROCScoreCalculator(ROCScoreCalculator.ROCType, ROCScoreCalculator.Metric, MultiDataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
ROCScoreCalculator.Metric - Enum in org.deeplearning4j.earlystopping.scorecalc
ROCScoreCalculator.ROCType - Enum in org.deeplearning4j.earlystopping.scorecalc
rootFolder - Variable in class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback
routings(int) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder: Set the number of dynamic routing iterations to use.

S

sameDiff - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
sameDiff - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
sameDiff - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
SameDiffGraphVertex - Class in org.deeplearning4j.nn.layers.samediff: Implementation of a SameDiff graph vertex.
SameDiffGraphVertex(SameDiffVertex, ComputationGraph, String, int, INDArray, boolean, DataType) - Constructor for class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
SameDiffLambdaLayer - Class in org.deeplearning4j.nn.conf.layers.samediff: SameDiffLambdaLayer is defined to be used as the base class for implementing lambda layers using SameDiff
Lambda layers are layers without parameters - and as a result, have a much simpler API - users need only extend SameDiffLambdaLayer and implement a single method
SameDiffLambdaLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer
SameDiffLambdaVertex - Class in org.deeplearning4j.nn.conf.layers.samediff: SameDiffLambdaVertex is defined to be used as the base class for implementing lambda vertices using SameDiff
Lambda vertices are vertices without parameters - and as a result, have a much simpler API - users need only extend SameDiffLambdaVertex and implement a single method to define their vertex
SameDiffLambdaVertex() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex
SameDiffLambdaVertex.VertexInputs - Class in org.deeplearning4j.nn.conf.layers.samediff
SameDiffLayer - Class in org.deeplearning4j.nn.conf.layers.samediff: A base layer used for implementing Deeplearning4j layers using SameDiff.
SameDiffLayer(SameDiffLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer
SameDiffLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer
SameDiffLayer - Class in org.deeplearning4j.nn.layers.samediff
SameDiffLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
SameDiffLayer.Builder<T extends SameDiffLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers.samediff
SameDiffLayerUtils - Class in org.deeplearning4j.nn.conf.layers.samediff: Utility methods for DL4J SameDiff layers
SameDiffOutputLayer - Class in org.deeplearning4j.nn.conf.layers.samediff: A base layer used for implementing Deeplearning4j Output layers using SameDiff.
SameDiffOutputLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer
SameDiffOutputLayer - Class in org.deeplearning4j.nn.layers.samediff
SameDiffOutputLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
SameDiffParamInitializer - Class in org.deeplearning4j.nn.params
SameDiffParamInitializer() - Constructor for class org.deeplearning4j.nn.params.SameDiffParamInitializer
SameDiffVertex - Class in org.deeplearning4j.nn.conf.layers.samediff: A SameDiff-based GraphVertex.
SameDiffVertex() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork: Sample the hidden distribution given the visible
sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork: Sample the visible distribution given the hidden
sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
save(File) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Save the ComputationGraph to a file.
save(File, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Save the ComputationGraph to a file.
save(File) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Save the MultiLayerNetwork to a file.
save(File, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Save the MultiLayerNetwork to a file.
save(Model, String) - Method in class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback: This method saves model
saveBestModel(T, double) - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver: Save the best model (so far) learned during early stopping training
saveBestModel(T, double) - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
saveBestModel(ComputationGraph, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
saveBestModel(MultiLayerNetwork, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
saveEvery(long, TimeUnit) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: Save a model periodically
saveEvery(long, TimeUnit, boolean) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: Save a model periodically (if sinceLast == false), or if the specified amount of time has elapsed since the last model was saved (if sinceLast == true)
saveEveryEpoch() - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: Save a model at the end of every epoch
saveEveryNEpochs(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: Save a model at the end of every N epochs
saveEveryNIterations(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: Save a model every N iterations
saveEveryNIterations(int, boolean) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder: Save a model every N iterations (if sinceLast == false), or if N iterations have passed since the last model vas saved (if sinceLast == true)
saveLastModel(boolean) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder: Save the last model? If true: save the most recent model at each epoch, in addition to the best model (whenever the best model improves).
saveLatestModel(T, double) - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver: Save the latest (most recent) model learned during early stopping
saveLatestModel(T, double) - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
saveLatestModel(ComputationGraph, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
saveLatestModel(MultiLayerNetwork, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
scale(int) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport: Multiply all memory usage by the specified scaling factor
scaleFactor - Variable in class org.deeplearning4j.nn.conf.graph.ScaleVertex
ScaleVertex - Class in org.deeplearning4j.nn.conf.graph: A ScaleVertex is used to scale the size of activations of a single layer: this is simply multiplication by a fixed scalar value
For example, ResNet activations can be scaled in repeating blocks to keep variance under control.
ScaleVertex(double) - Constructor for class org.deeplearning4j.nn.conf.graph.ScaleVertex
ScaleVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: A ScaleVertex is used to scale the size of activations of a single layer
For example, ResNet activations can be scaled in repeating blocks to keep variance under control.
ScaleVertex(ComputationGraph, String, int, double, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
ScaleVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], double, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
score() - Method in interface org.deeplearning4j.nn.api.Model: The score for the model
score - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
score(DataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Sets the input and labels and returns a score for the prediction with respect to the true labels
This is equivalent to ComputationGraph.score(DataSet, boolean) with training==true.
NOTE: this version of the score function can only be used with ComputationGraph networks that have a single input and a single output.
score(DataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Sets the input and labels and returns a score for the prediction with respect to the true labels
NOTE: this version of the score function can only be used with ComputationGraph networks that have a single input and a single output.
score(MultiDataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Score the network given the MultiDataSet, at test time
score(MultiDataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Sets the input and labels and returns a score for the prediction with respect to the true labels
score() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
score() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
score - Variable in class org.deeplearning4j.nn.layers.BaseLayer
score() - Method in class org.deeplearning4j.nn.layers.BaseLayer: Objective function: the specified objective
score() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
score() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
score() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
score() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
score() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
score() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
score() - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
score() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
score() - Method in class org.deeplearning4j.nn.layers.RepeatVector
score - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
score() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
score() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
score - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
score(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Sets the input and labels and calculates the score (value of the output layer loss function plus l1/l2 if applicable) for the prediction with respect to the true labels
This is equivalent to MultiLayerNetwork.score(DataSet, boolean) with training==false.
score(DataSet, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Sets the input and labels and calculates the score (value of the output layer loss function plus l1/l2 if applicable) for 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) - previously calculated on the last minibatch
score() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: The score for the optimizer so far
score - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
score() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
SCORE_KEY - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
scoreCalculator(ScoreCalculator) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder: Score calculator.
scoreCalculator(Supplier<ScoreCalculator>) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder: Score calculator.
ScoreCalculator<T extends Model> - Interface in org.deeplearning4j.earlystopping.scorecalc: ScoreCalculator interface is used to calculate a score for a neural network.
scoreExamples(DataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Calculate the score for each example in a DataSet individually.
scoreExamples(MultiDataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Calculate the score for each example in a DataSet individually.
scoreExamples(DataSetIterator, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: As per MultiLayerNetwork.scoreExamples(DataSet, boolean) - the outputs (example scores) for all DataSets in the iterator are concatenated
scoreExamples(DataSet, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Calculate the score for each example in a DataSet individually.
scoreForMetric(Evaluation.Metric) - Method in class org.deeplearning4j.eval.Evaluation: Deprecated.
Use ND4J Evaluation class, which has the same interface: Evaluation.Metric
scoreForMetric(RegressionEvaluation.Metric) - Method in class org.deeplearning4j.eval.RegressionEvaluation: Deprecated.
Use ND4J RegressionEvaluation class, which has the same interface: RegressionEvaluation
ScoreImprovementEpochTerminationCondition - Class in org.deeplearning4j.earlystopping.termination: Terminate training if best model score does not improve for N epochs
ScoreImprovementEpochTerminationCondition(int) - Constructor for class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
ScoreImprovementEpochTerminationCondition(int, double) - Constructor for class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
ScoreIterationListener - Class in org.deeplearning4j.optimize.listeners: Score iteration listener.
ScoreIterationListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.ScoreIterationListener
ScoreIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.ScoreIterationListener: Default constructor printing every 10 iterations
scoreMinibatch(Model, INDArray, INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
scoreMinibatch(Model, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
scoreMinibatch(MultiLayerNetwork, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseMLNScoreCalculator
scoreMinibatch(T, INDArray, INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
scoreMinibatch(T, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
scoreMinibatch(Model, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
scoreMinibatch(Model, INDArray, INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
scoreMinibatch(Model, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
scoreMinibatch(Model, INDArray, INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
scoreMinibatch(Model, INDArray[], INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
scoreSum - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
SDLayerParams - Class in org.deeplearning4j.nn.conf.layers.samediff: SDLayerParams is used to define the parameters for a Deeplearning4j SameDiff layer
SDLayerParams(Map<String, long[]>, Map<String, long[]>) - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams
SDVertexParams - Class in org.deeplearning4j.nn.conf.layers.samediff: SDVertexParams is used to define the inputs - and the parameters - for a SameDiff vertex
SDVertexParams() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams
SEARCH_DIR - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
searchState - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
secondary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
secondary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
seed - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
seed(long) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Random number generator seed.
seed - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
seed(long) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: RNG seed for reproducibility
seed(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: RNG seed for reproducibility
seed - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
SelfAttentionLayer - Class in org.deeplearning4j.nn.conf.layers: Implements Dot Product Self Attention Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using each timestep as the query.
SelfAttentionLayer(SelfAttentionLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer
SelfAttentionLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
sendMessage(INDArray, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler: This method does loops encoded data back to updates queue
SeparableConvolution2D - Class in org.deeplearning4j.nn.conf.layers: 2D Separable convolution layer configuration.
SeparableConvolution2D(SeparableConvolution2D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D: SeparableConvolution2D layer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the channels The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size
SeparableConvolution2D.Builder - Class in org.deeplearning4j.nn.conf.layers
SeparableConvolution2DLayer - Class in org.deeplearning4j.nn.layers.convolution: 2D Separable convolution layer implementation Separable convolutions split a regular convolution operation into two simpler operations, which are usually computationally more efficient.
SeparableConvolution2DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer
SeparableConvolutionParamInitializer - Class in org.deeplearning4j.nn.params: Initialize separable convolution params.
SeparableConvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
setAbsTolx(double) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch: Sets the tolerance of absolute diff in function value.
setBackpropGradientsViewArray(INDArray) - Method in interface org.deeplearning4j.nn.api.Model: Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
setBackpropGradientsViewArray(INDArray) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: See Model.setBackpropGradientsViewArray(INDArray)
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setBatchSize(int) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: Set the batch size for the optimizer
setBatchSize(int) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
setBegin(int) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setBlocks(int...) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.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.recurrent.BidirectionalLayer
setCacheMode(CacheMode) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setCacheMode(CacheMode) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setCacheMode(CacheMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: This method sets specified CacheMode for all layers within network
setConf(NeuralNetConfiguration) - Method in interface org.deeplearning4j.nn.api.Model: Setter for the configuration
setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setConstraints(List<LayerConstraint>) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
setConstraints(List<LayerConstraint>) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
setCropping(int...) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder
setCropping(int...) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder
setCropping(int...) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder
setDataType(DataType) - Method in interface org.deeplearning4j.nn.api.TrainingConfig
setDataType(DataType) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
setDataType(DataType) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
setDataType(DataType) - Method in class org.deeplearning4j.nn.conf.layers.Layer
setDataType(DataType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
setDataType(DataType) - Method in class org.deeplearning4j.nn.conf.misc.DummyConfig
setDecoderLayerSizes(int...) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Size of the decoder layers, in units.
setDepth(long) - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional: Deprecated.
setDilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
setDilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder: Set dilation size for 3D convolutions in (depth, height, width) order
setDilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Set dilation size for 3D convolutions in (depth, height, width) order
setDilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
setDilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
setDilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
setDilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
setDilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
setDilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder: Dilation
setDilation(int[]) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
setEncoderLayerSizes(int...) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder: Size of the encoder layers, in units.
setEnd(int) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setEpochCount(int) - Method in interface org.deeplearning4j.nn.api.Layer: Set the current epoch count (number of epochs passed ) for the layer/network
setEpochCount(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
setEpochCount(int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setEpochCount(int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setEpochCount(int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setEpochCount(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setEps(double) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
setEpsilon(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
setEpsilon(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
setEpsilon(INDArray) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Set the errors (epsilon - aka dL/dActivation) for this GraphVertex
setError(double) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setExternalSource(IndexedTail) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
setExternalSource(IndexedTail) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator: This method allows to pass external updates to accumulator, they will be populated across all workers using this GradientsAccumulator instance
setExternalSource(IndexedTail) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator: This method allows to pass external updates to accumulator, they will be populated across all workers using this GradientsAccumulator instance
setFeatureExtractor(int) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Specify a layer to set as a "feature extractor" The specified layer and the layers preceding it will be "frozen" with parameters staying constant
setFeatureExtractor(String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Specify a layer vertex to set as a "feature extractor" The specified layer vertex and the layers on the path from an input vertex to it will be "frozen" with parameters staying constant
setFrequency(int) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder: Desired TrainingListener activation frequency
setGoldLabel(int) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setGradientFor(String, INDArray) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
setGradientFor(String, INDArray, Character) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
setGradientFor(String, INDArray) - Method in interface org.deeplearning4j.nn.gradient.Gradient: Update gradient for the given variable
setGradientFor(String, INDArray, Character) - Method in interface org.deeplearning4j.nn.gradient.Gradient: Update gradient for the given variable; also (optionally) specify the order in which the array should be flattened to a row vector
setGradientsAccumulator(GradientsAccumulator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: This method allows you to specificy GradientsAccumulator instance to be used with this model
setGradientsAccumulator(GradientsAccumulator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: This method allows you to specificy GradientsAccumulator instance to be used with this model

PLEASE NOTE: Do not use this method unless you understand how to use GradientsAccumulator & updates sharing.
PLEASE NOTE: Do not use this method on standalone model
setGradientsAccumulator(GradientsAccumulator) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: This method specifies GradientsAccumulator instance to be used for updates sharing across multiple models
setGradientsAccumulator(GradientsAccumulator) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
setHeadWord(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setHelperWorkspace(String, Pointer) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr: Set the pointer to the helper memory.
setIndex(int) - Method in interface org.deeplearning4j.nn.api.Layer: Set the layer index.
setIndex(int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setIndex(int) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
setIndex(int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setIndex(int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setIndex(int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setIndex(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setInput(INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Layer: Set the layer input.
setInput(int, INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the specified input for the ComputationGraph
setInput(int, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
setInput(int, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
setInput(int, INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Set the input activations.
setInput(int, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
setInput(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setInput(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setInput(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setInput(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setInput(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the input array for the network
setInput(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setInputMiniBatchSize(int) - Method in interface org.deeplearning4j.nn.api.Layer: Set current/last input mini-batch size.
Used for score and gradient calculations.
setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setInputPreProcessor(int, InputPreProcessor) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder: Specify the preprocessor for the added layers for cases where they cannot be inferred automatically.
setInputs(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set all inputs for the ComputationGraph network
setInputs(INDArray...) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
setInputs(INDArray...) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Set all inputs for this GraphVertex
setInputs(String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Sets new inputs for the computation graph.
setInputSize(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder: Set input filter size for this locally connected 1D layer
setInputSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder: Set input filter size (h,w) for this locally connected 2D layer
setInputType(InputType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
setInputType(InputType) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
setInputTypes(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Specify the types of inputs to the network, so that:
(a) preprocessors can be automatically added, and
(b) the nIns (input size) for each layer can be automatically calculated and set
The order here is the same order as .addInputs().
setInputTypes(InputType...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Sets the input type of corresponding inputs.
setInputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
setInputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
setInputVertices(VertexIndices[]) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Sets the input vertices.
setIterationCount(int) - Method in interface org.deeplearning4j.nn.api.Layer: Set the current iteration count (number of parameter updates) for the layer/network
setIterationCount(int) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setIterationCount(int) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setIterationCount(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setIWM - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
setKernel(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
setKernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
setKernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder: Set kernel size for 3D convolutions in (depth, height, width) order
setKernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Set kernel size for 3D convolutions in (depth, height, width) order
setKernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
setKernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
setKernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
setKernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
setKernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder: Kernel size
setKernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
setKernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
setLabel(int, INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the specified label for the ComputationGraph
setLabel(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setLabels(INDArray) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer: Set the labels array for this output layer
setLabels(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set all labels for the ComputationGraph network
setLabels(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
setLabels(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
setLabels(INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
setLabels(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setLastEtlTime(long) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: This method allows to set ETL field time, useful for performance tracking
setLastEtlTime(long) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the last ETL time in milliseconds, for informational/reporting purposes.
setLayer(Layer) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder
setLayerAsFrozen() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
setLayerAsFrozen() - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
setLayerAsFrozen() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Only applies to layer vertices.
setLayerAsFrozen() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
setLayerMaskArrays(INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the mask arrays for features and labels.
setLayerMaskArrays(INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the mask arrays for features and labels.
setLayerName(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
setLayerName(String) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
setLayerName(String) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
setLayerName(String) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
setLayers(Layer[]) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setLayerWiseConfigurations(MultiLayerConfiguration) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: This method is intended for internal/developer use only.
setLearningRate(double) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the learning rate for all layers in the network to the specified value.
setLearningRate(ISchedule) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the learning rate schedule for all layers in the network to the specified schedule.
setLearningRate(String, double) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the learning rate for a single layer in the network to the specified value.
setLearningRate(String, ISchedule) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the learning rate schedule for a single layer in the network to the specified value.
Note also that ComputationGraph.setLearningRate(ISchedule) should also be used in preference, when all layers need to be set to a new LR schedule.
This schedule will replace any/all existing schedules, and also any fixed learning rate values.
Note also that the iteration/epoch counts will not be reset.
setLearningRate(double) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the learning rate for all layers in the network to the specified value.
setLearningRate(ISchedule) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the learning rate schedule for all layers in the network to the specified schedule.
setLearningRate(int, double) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the learning rate for a single layer in the network to the specified value.
setLearningRate(int, ISchedule) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the learning rate schedule for a single layer in the network to the specified value.
Note also that MultiLayerNetwork.setLearningRate(ISchedule) should also be used in preference, when all layers need to be set to a new LR schedule.
This schedule will replace any/all existing schedules, and also any fixed learning rate values.
Note also that the iteration/epoch counts will not be reset.
setLearningRate(MultiLayerNetwork, double) - Static method in class org.deeplearning4j.util.NetworkUtils: Set the learning rate for all layers in the network to the specified value.
setLearningRate(MultiLayerNetwork, ISchedule) - Static method in class org.deeplearning4j.util.NetworkUtils: Set the learning rate schedule for all layers in the network to the specified schedule.
setLearningRate(MultiLayerNetwork, int, double) - Static method in class org.deeplearning4j.util.NetworkUtils: Set the learning rate for a single layer in the network to the specified value.
setLearningRate(MultiLayerNetwork, int, ISchedule) - Static method in class org.deeplearning4j.util.NetworkUtils: Set the learning rate schedule for a single layer in the network to the specified value.
Note also that NetworkUtils.setLearningRate(MultiLayerNetwork, ISchedule) should also be used in preference, when all layers need to be set to a new LR schedule.
This schedule will replace any/all existing schedules, and also any fixed learning rate values.
Note also that the iteration/epoch counts will not be reset.
setLearningRate(ComputationGraph, double) - Static method in class org.deeplearning4j.util.NetworkUtils: Set the learning rate for all layers in the network to the specified value.
setLearningRate(ComputationGraph, ISchedule) - Static method in class org.deeplearning4j.util.NetworkUtils: Set the learning rate schedule for all layers in the network to the specified schedule.
setLearningRate(ComputationGraph, String, double) - Static method in class org.deeplearning4j.util.NetworkUtils: Set the learning rate for a single layer in the network to the specified value.
setLearningRate(ComputationGraph, String, ISchedule) - Static method in class org.deeplearning4j.util.NetworkUtils: Set the learning rate schedule for a single layer in the network to the specified value.
Note also that NetworkUtils.setLearningRate(ComputationGraph, ISchedule) should also be used in preference, when all layers need to be set to a new LR schedule.
This schedule will replace any/all existing schedules, and also any fixed learning rate values.
Note also that the iteration/epoch counts will not be reset.
setListener(EarlyStoppingListener<T>) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
setListener(EarlyStoppingListener<T>) - Method in interface org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer: Set the early stopping listener
setListeners(TrainingListener...) - Method in interface org.deeplearning4j.nn.api.Layer: Set the TrainingListeners for this model.
setListeners(Collection<TrainingListener>) - Method in interface org.deeplearning4j.nn.api.Layer: Set the TrainingListeners for this model.
setListeners(Collection<TrainingListener>) - Method in interface org.deeplearning4j.nn.api.Model: Set the trainingListeners for the ComputationGraph (and all layers in the network)
setListeners(TrainingListener...) - Method in interface org.deeplearning4j.nn.api.Model: Set the trainingListeners for the ComputationGraph (and all layers in the network)
setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the trainingListeners for the ComputationGraph (and all layers in the network)
setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the trainingListeners for the ComputationGraph (and all layers in the network)
setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setListeners(Collection<TrainingListener>) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.optimize.Solver
setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
setMask(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setMaskArray(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer: Set the mask array.
setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setMaskValue(double) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder
setMaxIterations(int) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
setMean(double) - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer
setNIn(int) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer
setNIn(int) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.DropoutLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Layer: Set the nIn value (number of inputs, or input channels for CNNs) based on the given input type
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.NoParamLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer
setNIn(int) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer
setNoLeverageOverride(String) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
setNOut(int) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder
setNOut(int) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
setNOut(int) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
setNOut(int) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder
setOutputs(String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Set the network output labels.
setOutputs(String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder: Set outputs to the computation graph, will add to ones that are existing Also determines the order, like in ComputationGraphConfiguration
setOutputVertex(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
setOutputVertex(boolean) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: Set the GraphVertex to be an output vertex
setOutputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
setOutputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
setOutputVertices(VertexIndices[]) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex: set the output vertices.
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder: Set padding size for 3D convolutions in (depth, height, width) order
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Set padding size for 3D convolutions in (depth, height, width) order
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
setPadding(int[][]) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder: Padding
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder: Padding
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder: [padLeftD, padRightD, padLeftH, padRightH, padLeftW, padRightW]
setPadding(int...) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder
setParam(String, INDArray) - Method in interface org.deeplearning4j.nn.api.Model: Set the parameter with a new ndarray
setParam(String, INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setParam(String, INDArray) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setParam(String, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the values of a single parameter.
setParameters(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: See MultiLayerNetwork.setParams(INDArray)
setParams(Set<String>) - Method in interface org.deeplearning4j.nn.api.layers.LayerConstraint: Set the parameters that this layer constraint should be applied to
setParams(INDArray) - Method in interface org.deeplearning4j.nn.api.Model: Set the parameters for this model.
setParams(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.BaseLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.RepeatVector
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setParams(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the parameters for this model.
setParamsViewArray(INDArray) - Method in interface org.deeplearning4j.nn.api.Model: Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
setParamTable(Map<String, INDArray>) - Method in interface org.deeplearning4j.nn.api.Model: Setter for the param table
setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.BaseLayer
setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the parameters of the netowrk.
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
setPnorm(int) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder
setPnorm(int) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
setPrediction(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
setProbabilityOfSuccess(double) - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
setRelTolx(double) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch: Sets the tolerance of relative diff in function value.
setRepetitionFactor(int) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder: Set repetition factor for RepeatVector layer
setScore(double) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
setScore(double) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Intended for developer/internal use
setScoreFor(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer
setSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D.Builder
setSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder
setSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder
setStateViewArray(Trainable, INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.Updater: Set the internal (historical) state view array for this updater
setStateViewArray(INDArray) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater: Set the view array.
setStateViewArray(Trainable, INDArray, boolean) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
setStd(double) - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
setStepMax(double) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder: Set stride size for 3D convolutions in (depth, height, width) order
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Set stride size for 3D convolutions in (depth, height, width) order
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder: Stride
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder: Stride
setStride(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
setTags(List<String>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setTokens(List<String>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setTWM - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
setType(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setUnderlying(Layer) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder
setUpdater(ComputationGraphUpdater) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Set the computationGraphUpdater for the network
setUpdater(Updater) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Set the updater for the MultiLayerNetwork
setUpdater(Updater) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
setUpdater(Updater) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
setUpdaterComputationGraph(ComputationGraphUpdater) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
setUpdaterComputationGraph(ComputationGraphUpdater) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
setupSearchState(Pair<Gradient, Double>) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: Based on the gradient and score setup a search state
setupSearchState(Pair<Gradient, Double>) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer: Setup the initial search state
setupSearchState(Pair<Gradient, Double>) - Method in class org.deeplearning4j.optimize.solvers.LBFGS
setValue(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setVector(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
setWeightInitFn(IWeightInit) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder
setWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
shape() - Method in class org.deeplearning4j.nn.weights.embeddings.WeightInitEmbedding
shape - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
shape - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
sharedAxes(long...) - Method in class org.deeplearning4j.nn.conf.layers.PReLULayer.Builder: Set the broadcasting axes of PReLU's alpha parameter.
SharedGradient - Class in org.deeplearning4j.optimize.listeners
SharedGradient() - Constructor for class org.deeplearning4j.optimize.listeners.SharedGradient
shiftFactor - Variable in class org.deeplearning4j.nn.conf.graph.ShiftVertex
ShiftVertex - Class in org.deeplearning4j.nn.conf.graph: A ShiftVertex is used to shift the activations of a single layer.
ShiftVertex(double) - Constructor for class org.deeplearning4j.nn.conf.graph.ShiftVertex
ShiftVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: A ShiftVertex is used to shift the activations of a single layer
One could use it to add a bias or as part of some other calculation.
ShiftVertex(ComputationGraph, String, int, double, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
ShiftVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], double, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
SimpleRnn - Class in org.deeplearning4j.nn.conf.layers.recurrent: Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer.
SimpleRnn(SimpleRnn.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn
SimpleRnn - Class in org.deeplearning4j.nn.layers.recurrent: Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer.
SimpleRnn(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.recurrent.SimpleRnn
SimpleRnn.Builder - Class in org.deeplearning4j.nn.conf.layers.recurrent
SimpleRnnParamInitializer - Class in org.deeplearning4j.nn.params
SimpleRnnParamInitializer() - Constructor for class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
size - Variable in class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer
size - Variable in class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer.UpsamplingBuilder: An int array to specify upsampling dimensions, the length of which has to equal to the number of spatial dimensions (e.g.
size(int) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D.Builder: Upsampling size
size(int[]) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D.Builder: Upsampling size int array with a single element.
size - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling1D
size(int) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder: Upsampling size int, used for both height and width
size(int[]) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder: Upsampling size array
size - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling2D
size(int) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder: Upsampling size as int, so same upsampling size is used for depth, width and height
size(int[]) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder: Upsampling size as int, so same upsampling size is used for depth, width and height
size - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling3D
size() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
skipDueToPretrainConfig(boolean) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock
sleep(long) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
sleep(AtomicLong, long) - Method in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
sleepMode - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
SleepyTrainingListener - Class in org.deeplearning4j.optimize.listeners: This TrainingListener implementation provides a way to "sleep" during specific Neural Network training phases.
Suitable for debugging/testing purposes only.
SleepyTrainingListener() - Constructor for class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
SleepyTrainingListener.SleepMode - Enum in org.deeplearning4j.optimize.listeners
SleepyTrainingListener.TimeMode - Enum in org.deeplearning4j.optimize.listeners
smartDecompress(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
smartDecompress(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
SmartFancyBlockingQueue - Class in org.deeplearning4j.optimize.solvers.accumulation: This class provides additional functionality to FancyBlockingQueue: it tracks memory use of stored compressed INDArrays, and if their size becomes too big, it: a) decompresses them into single INDArray b) removes original updates messages c) keeps updating single INDArray until it gets consumed d) once that happened - it automatically switches back to original behavior
SmartFancyBlockingQueue(int, INDArray) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
SmartFancyBlockingQueue(int, BlockingQueue<INDArray>, INDArray) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
smartLock - Variable in class org.deeplearning4j.optimize.solvers.accumulation.SmartFancyBlockingQueue
softmax(SameDiff, SDVariable, int, int) - Static method in class org.deeplearning4j.util.CapsuleUtils: Compute softmax along a given dimension
solver - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
solver - Variable in class org.deeplearning4j.nn.layers.BaseLayer
solver - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
solver - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
Solver - Class in org.deeplearning4j.optimize: Generic purpose solver
Solver() - Constructor for class org.deeplearning4j.optimize.Solver
Solver.Builder - Class in org.deeplearning4j.optimize
SpaceToBatch - Class in org.deeplearning4j.nn.layers.convolution: Space to batch utility layer for convolutional input types.
SpaceToBatch(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
SpaceToBatchLayer - Class in org.deeplearning4j.nn.conf.layers: Space to batch utility layer configuration for convolutional input types.
SpaceToBatchLayer(SpaceToBatchLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
SpaceToBatchLayer.Builder<T extends SpaceToBatchLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers
SpaceToDepth - Class in org.deeplearning4j.nn.layers.convolution: Space to channels utility layer for convolutional input types.
SpaceToDepth(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
SpaceToDepthLayer - Class in org.deeplearning4j.nn.conf.layers: Space to channels utility layer configuration for convolutional input types.
SpaceToDepthLayer(SpaceToDepthLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer
SpaceToDepthLayer.Builder<T extends SpaceToDepthLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers
SpaceToDepthLayer.DataFormat - Enum in org.deeplearning4j.nn.conf.layers
sparsity(double) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder: Autoencoder sparity parameter
sparsity - Variable in class org.deeplearning4j.nn.conf.layers.AutoEncoder
SpatialDropout - Class in org.deeplearning4j.nn.conf.dropout: Spatial dropout: can only be applied to 3D (time series), 4D (convolutional 2D) or 5D (convolutional 3D) activations.
SpatialDropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.SpatialDropout
SpatialDropout(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.SpatialDropout
SpatialDropout(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.SpatialDropout
squash(SameDiff, SDVariable, int) - Static method in class org.deeplearning4j.util.CapsuleUtils: Compute the squash operation used in CapsNet The formula is (||s||^2 / (1 + ||s||^2)) * (s / ||s||).
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, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
StackVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
standardMemory(long, long) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder: Report the standard memory
state - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
STATE_KEY_PREV_ACTIVATION - Static variable in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
STATE_KEY_PREV_ACTIVATION - Static variable in class org.deeplearning4j.nn.layers.recurrent.LSTM
STATE_KEY_PREV_ACTIVATION - Static variable in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn
STATE_KEY_PREV_MEMCELL - Static variable in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
STATE_KEY_PREV_MEMCELL - Static variable in class org.deeplearning4j.nn.layers.recurrent.LSTM
stateMap - Variable in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer: stateMap stores the INDArrays needed to do rnnTimeStep() forward pass.
std - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
step(INDArray, INDArray, double) - Method in interface org.deeplearning4j.optimize.api.StepFunction: Step with the given parameters
step(INDArray, INDArray) - Method in interface org.deeplearning4j.optimize.api.StepFunction: Step with no parameters
step() - Method in interface org.deeplearning4j.optimize.api.StepFunction
step - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
step - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction: Does x = x + stepSize * line
step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
step() - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
step() - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
step() - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
step() - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
stepFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
stepFunction(StepFunction) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
stepFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
StepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions: Custom step function for line search.
StepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.StepFunction
stepFunction(StepFunction) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
stepFunction - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
StepFunction - Interface in org.deeplearning4j.optimize.api: Custom step function for line search
stepFunction - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
StepFunctions - Class in org.deeplearning4j.optimize.stepfunctions
stepMax - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
StochasticGradientDescent - Class in org.deeplearning4j.optimize.solvers: Stochastic Gradient Descent Standard fix step size No line search
StochasticGradientDescent(NeuralNetConfiguration, StepFunction, Collection<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
storage - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
storeUpdate(INDArray, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator: This method accepts updates suitable for StepFunction, and accumulates/propagates it across all workers
storeUpdate(INDArray, int, int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator: This method accepts updates suitable for StepFunction, and accumulates/propagates it across all workers
storeUpdate(INDArray, int, int) - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator: This method accepts updates suitable for StepFunction, and accumulates/propagates it across all workers
stride(int) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder: Stride for the convolution.
stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder: Set stride size for 3D convolutions in (depth, height, width) order
stride - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
stride - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder: Stride of the convolution in rows/columns (height/width) dimensions
stride(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Sets the stride of the 2d convolution
stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder: Stride of the convolution rows/columns (height/width)
stride(int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder: Stride
stride - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder: Stride
stride - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
stride - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder: Stride
stride - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
Subsampling1DLayer - Class in org.deeplearning4j.nn.conf.layers: 1D (temporal) subsampling layer - also known as pooling layer.
Expects input of shape [minibatch, nIn, sequenceLength].
Subsampling1DLayer - Class in org.deeplearning4j.nn.layers.convolution.subsampling: 1D (temporal) subsampling layer.
Subsampling1DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer
Subsampling1DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
Subsampling3DLayer - Class in org.deeplearning4j.nn.conf.layers: 3D subsampling / pooling layer for convolutional neural networks
Subsampling3DLayer(Subsampling3DLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
Subsampling3DLayer - Class in org.deeplearning4j.nn.layers.convolution.subsampling: Subsampling 3D layer, used for downsampling a 3D convolution
Subsampling3DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
Subsampling3DLayer.BaseSubsamplingBuilder<T extends Subsampling3DLayer.BaseSubsamplingBuilder<T>> - Class in org.deeplearning4j.nn.conf.layers
Subsampling3DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
Subsampling3DLayer.PoolingType - Enum in org.deeplearning4j.nn.conf.layers
SubsamplingHelper - Interface in org.deeplearning4j.nn.layers.convolution.subsampling: Helper for the subsampling layer.
SubsamplingLayer - Class in org.deeplearning4j.nn.conf.layers: Subsampling layer also referred to as pooling in convolution neural nets Supports the following pooling types: MAX, AVG, SUM, PNORM
SubsamplingLayer(SubsamplingLayer.BaseSubsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
SubsamplingLayer - Class in org.deeplearning4j.nn.layers.convolution.subsampling: Subsampling layer.
SubsamplingLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
SubsamplingLayer.BaseSubsamplingBuilder<T extends SubsamplingLayer.BaseSubsamplingBuilder<T>> - Class in org.deeplearning4j.nn.conf.layers
SubsamplingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
SubsamplingLayer.PoolingType - Enum in org.deeplearning4j.nn.conf.layers
subsetAndReshape(List<String>, Map<String, long[]>, INDArray, AbstractSameDiffLayer, SameDiffVertex) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
SubsetVertex - Class in org.deeplearning4j.nn.conf.graph: SubsetVertex is used to select a subset of the activations out of another GraphVertex.
For example, a subset of the activations out of a layer.
Note that this subset is specifying by means of an interval of the original activations.
SubsetVertex(int, int) - Constructor for class org.deeplearning4j.nn.conf.graph.SubsetVertex
SubsetVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: SubsetVertex is used to select a subset of the activations out of another GraphVertex.
For example, a subset of the activations out of a layer.
Note that this subset is specifying by means of an interval of the original activations.
SubsetVertex(ComputationGraph, String, int, int, int, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
SubsetVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], int, int, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
summary() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: String detailing the architecture of the computation graph.
summary(InputType...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph: String detailing the architecture of the computation graph.
summary() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: String detailing the architecture of the multilayernetwork.
summary(InputType) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: String detailing the architecture of the multilayernetwork.
synchronize(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
synchronize(int, boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
synchronize(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
synchronizeIterEpochCounts() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
synchronizeIterEpochCounts() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

T

tags() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
take() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
TargetSparsityThresholdAlgorithm - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold: Targets a specific sparisty throughout training
TargetSparsityThresholdAlgorithm() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm: Create the adaptive threshold algorithm with the default initial threshold TargetSparsityThresholdAlgorithm.DEFAULT_INITIAL_THRESHOLD, default sparsity target TargetSparsityThresholdAlgorithm.DEFAULT_SPARSITY_TARGET and default decay rate TargetSparsityThresholdAlgorithm.DEFAULT_DECAY_RATE
TargetSparsityThresholdAlgorithm(double, double, double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm
taskByModel(Model) - Static method in class org.deeplearning4j.util.ModelSerializer
tbpttBackLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
tbpttBackLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
tbpttBackLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
tbpttBackLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
tbpttBackLength(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: When doing truncated BPTT: how many steps of backward should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
This is the k2 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
tbpttBackLength - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer: Truncated BPTT equivalent of Layer.backpropGradient().
tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
tbpttBackpropGradient(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.SimpleRnn
tBPTTBackwardLength(int) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: When doing truncated BPTT: how many steps of backward should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
This is the k2 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
tBPTTBackwardLength(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: When doing truncated BPTT: how many steps of backward should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
This is the k2 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
tBPTTForwardLength(int) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: When doing truncated BPTT: how many steps of forward pass should we do before doing (truncated) backprop?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter, but may be larger than it in some circumstances (but never smaller)
Ideally your training data time series length should be divisible by this This is the k1 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
tBPTTForwardLength(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: When doing truncated BPTT: how many steps of forward pass should we do before doing (truncated) backprop?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter, but may be larger than it in some circumstances (but never smaller)
Ideally your training data time series length should be divisible by this This is the k1 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
tbpttFwdLength(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: When doing truncated BPTT: how many steps of forward pass should we do before doing (truncated) backprop?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter, but may be larger than it in some circumstances (but never smaller)
Ideally your training data time series length should be divisible by this This is the k1 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
tbpttFwdLength - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
tBPTTLength(int) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: When doing truncated backpropagation through time (tBPTT): how many steps should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
See: http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
tBPTTLength(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: When doing truncated BPTT: how many steps should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
See: http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
tBpttStateMap - Variable in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer: State map for use specifically in truncated BPTT training.
template - Variable in class org.deeplearning4j.optimize.listeners.callbacks.ModelSavingCallback
terminate(int, double, boolean) - Method in class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition
terminate(int, double, boolean) - Method in interface org.deeplearning4j.earlystopping.termination.EpochTerminationCondition: Should the early stopping training terminate at this epoch, based on the calculated score and the epoch number? Returns true if training should terminated, or false otherwise
terminate(double) - Method in class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition
terminate(double) - Method in interface org.deeplearning4j.earlystopping.termination.IterationTerminationCondition: Should early stopping training terminate at this iteration, based on the score for the last iteration? return true if training should be terminated immediately, or false otherwise
terminate(int, double, boolean) - Method in class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition
terminate(double) - Method in class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition
terminate(double) - Method in class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition
terminate(int, double, boolean) - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
THRESHOLD_LOG_FREQ_MS - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
thresholdAlgorithm - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
thresholdAlgorithm(ThresholdAlgorithm) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder: This method allows to set the ThresholdAlgorithm to be used for determining the threshold
ThresholdAlgorithm - Interface in org.deeplearning4j.optimize.solvers.accumulation.encoding: ThresholdAlgorithm is responsible for determining the threshold to use when encoding updates for distributed training.
thresholdAlgorithm - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
ThresholdAlgorithmReducer - Interface in org.deeplearning4j.optimize.solvers.accumulation.encoding: Used to combine ThresholdAlgorithm implementations in a way that is useful in a distributed training setting.
throwable - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
TimeIterationListener - Class in org.deeplearning4j.optimize.listeners: Time Iteration Listener.
TimeIterationListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.TimeIterationListener: Constructor
timeMode - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
timerBP - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
timerEE - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
timerES - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
timerFF - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
timerIteration - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
TimeSeriesUtils - Class in org.deeplearning4j.util: Basic time series utils
TimeSinceInitializedTrigger(long) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.TimeSinceInitializedTrigger
toArray() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
toArray(T[]) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
toComputationGraph() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Convert this MultiLayerNetwork to a ComputationGraph
toComputationGraph(MultiLayerNetwork) - Static method in class org.deeplearning4j.util.NetworkUtils: Convert a MultiLayerNetwork to a ComputationGraph
toFileString() - Method in class org.deeplearning4j.optimize.listeners.Checkpoint
toJson() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
toJson() - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport
toJson() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
toJson() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: Return this configuration as json
toJson() - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
toMultiDataSet(DataSet) - Static method in class org.deeplearning4j.nn.graph.util.ComputationGraphUtil: Convert a DataSet to the equivalent MultiDataSet
toMultiDataSetIterator(DataSetIterator) - Static method in class org.deeplearning4j.nn.graph.util.ComputationGraphUtil: Convert a DataSetIterator to a MultiDataSetIterator, via an adaptor class
toNd4j() - Method in enum org.deeplearning4j.eval.Evaluation.Metric: Deprecated.
toNd4j() - Method in enum org.deeplearning4j.eval.EvaluationAveraging: Deprecated.
toNd4j() - Method in enum org.deeplearning4j.eval.RegressionEvaluation.Metric: Deprecated.
topologicalOrder - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
topologicalOrder - Variable in class org.deeplearning4j.nn.graph.ComputationGraph: Indexes of graph vertices, in topological order.
topologicalOrderStr - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
topologicalSortOrder() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Calculate a topological sort order for the vertices in the graph.
toPoolingType() - Method in enum org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.PoolingType
toPoolingType() - Method in enum org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType
toString() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingResult
toString() - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
toString() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
toString() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
toString() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
toString() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG: Deprecated.
toString() - Method in class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition
toString() - Method in class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition
toString() - Method in class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition
toString() - Method in class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition
toString() - Method in class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition
toString() - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
toString() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
toString() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
toString() - Method in class org.deeplearning4j.nn.conf.distribution.ConstantDistribution
toString() - Method in class org.deeplearning4j.nn.conf.distribution.LogNormalDistribution
toString() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
toString() - Method in class org.deeplearning4j.nn.conf.distribution.OrthogonalDistribution
toString() - Method in class org.deeplearning4j.nn.conf.distribution.TruncatedNormalDistribution
toString() - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
toString() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
toString() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
toString() - Method in class org.deeplearning4j.nn.conf.graph.rnn.ReverseTimeSeriesVertex
toString() - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional
toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional3D
toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward
toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType
toString() - Method in class org.deeplearning4j.nn.conf.layers.util.MaskLayer
toString() - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
toString() - Method in class org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution
toString() - Method in class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
toString() - Method in class org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution
toString() - Method in class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
toString() - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport
toString() - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport
toString() - Method in class org.deeplearning4j.nn.conf.memory.NetworkMemoryReport
toString() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
toString() - Method in class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction
toString() - Method in class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction
toString() - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction
toString() - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction
toString() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
toString() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2NormalizeVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ReshapeVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ScaleVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ShiftVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
toString() - Method in class org.deeplearning4j.nn.graph.vertex.VertexIndices
toString() - Method in class org.deeplearning4j.nn.layers.BaseLayer
toString() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
toString() - Method in class org.deeplearning4j.nn.layers.objdetect.DetectedObject
toString() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
toString() - Method in class org.deeplearning4j.optimize.listeners.ScoreIterationListener
toString() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm
toString() - Method in class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.TargetSparsityThresholdAlgorithm
touch() - Method in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator: This method does initialization of given worker wrt Thread-Device Affinity
touch() - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator: This method does initialization of given worker wrt Thread-Device Affinity
touch() - Method in interface org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator: This method does initialization of given worker wrt Thread-Device Affinity
toYaml() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
toYaml() - Method in class org.deeplearning4j.nn.conf.memory.MemoryReport
toYaml() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
toYaml() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: Return this configuration as json
toYaml() - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
Trainable - Interface in org.deeplearning4j.nn.api: Trainable: an interface common to Layers and GraphVertices that have trainable parameters
TrainingConfig - Interface in org.deeplearning4j.nn.api: Simple interface for the training configuration (updater, L1/L2 values, etc) for trainable layers/vertices.
TrainingListener - Interface in org.deeplearning4j.optimize.api: A listener interface for training DL4J models.
The methods here will be called at various points during training, and only during training.
Note that users can extend BaseTrainingListener and selectively override the required methods, instead of implementing TrainingListener directly and having a number of no-op methods.
trainingListeners - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
trainingListeners - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
trainingListeners - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
trainingListeners - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
trainingWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Deprecated.
Use NeuralNetConfiguration.Builder.trainingWorkspaceMode(WorkspaceMode)
trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
trainingWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: This method defines Workspace mode being used during training:
NONE: workspace won't be used
ENABLED: workspaces will be used for training (reduced memory and better performance)
trainingWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: This method defines Workspace mode being used during training: NONE: workspace won't be used ENABLED: workspaces will be used for training (reduced memory and better performance)
trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
TransferLearning - Class in org.deeplearning4j.nn.transferlearning: The transfer learning API can be used to modify the architecture or the learning parameters of an existing multilayernetwork or computation graph.
TransferLearning() - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearning
TransferLearning.Builder - Class in org.deeplearning4j.nn.transferlearning
TransferLearning.GraphBuilder - Class in org.deeplearning4j.nn.transferlearning
TransferLearningHelper - Class in org.deeplearning4j.nn.transferlearning: This class is intended for use with the transfer learning API.
TransferLearningHelper(ComputationGraph, String...) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: Will modify the given comp graph (in place!) to freeze vertices from input to the vertex specified.
TransferLearningHelper(ComputationGraph) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: Expects a computation graph where some vertices are frozen
TransferLearningHelper(MultiLayerNetwork, int) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: Will modify the given MLN (in place!) to freeze layers (hold params constant during training) specified and below
TransferLearningHelper(MultiLayerNetwork) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: Expects a MLN where some layers are frozen
Tree - Class in org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive: Tree for a recursive neural tensor network based on Socher et al's work.
Tree(Tree) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Clone constructor (all but the children)
Tree(Tree, List<String>) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
Tree(List<String>) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
triggerEpochListeners(boolean, Model, int) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.And
triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureTrigger: If true: trigger the failure.
triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.HostNameTrigger
triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.IterationEpochTrigger
triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.Or
triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.RandomProb
triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.TimeSinceInitializedTrigger
triggerFailure(FailureTestingListener.CallType, int, int, Model) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener.UserNameTrigger
triggers - Variable in class org.deeplearning4j.optimize.listeners.FailureTestingListener.And
TruncatedNormalDistribution - Class in org.deeplearning4j.nn.conf.distribution: A truncated normal distribution, with 2 parameters: mean and standard deviation
This distribution is a standard normal/Gaussian distribtion, however values are "truncated" in the sense that any values that fall outside the range [mean - 2 * stdev, mean + 2 * stdev] are re-sampled.
TruncatedNormalDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.TruncatedNormalDistribution: Create a truncated normal distribution with the given mean and std
type - Variable in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
type() - Method in interface org.deeplearning4j.nn.api.Layer: Returns the layer type
type() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
type() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
type() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
type() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
type() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
type() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer
type() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
type() - Method in class org.deeplearning4j.nn.layers.DropoutLayer
type() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
type() - Method in class org.deeplearning4j.nn.layers.feedforward.PReLU
type() - Method in class org.deeplearning4j.nn.layers.LossLayer
type() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
type() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
type() - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
type() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
type() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
type() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
type() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM: Deprecated.
type() - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer
type() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
type() - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer
type() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
type() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
type() - Method in class org.deeplearning4j.nn.layers.RepeatVector
type() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
type() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
type() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

U

underlying(Layer) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder
underlying - Variable in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
underlying - Variable in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
underlying - Variable in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
unfrozenGraph() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: Returns the unfrozen subset of the original computation graph as a computation graph Note that with each call to featurizedFit the parameters to the original computation graph are also updated
unfrozenMLN() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper: Returns the unfrozen layers of the MultiLayerNetwork as a multilayernetwork Note that with each call to featurizedFit the parameters to the original MLN are also updated
UniformDistribution - Class in org.deeplearning4j.nn.conf.distribution: A uniform distribution, with two parameters: lower and upper - i.e., U(lower,upper)
UniformDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.UniformDistribution: Create a uniform real distribution using the given lower and upper bounds.
UnitNormConstraint - Class in org.deeplearning4j.nn.conf.constraint: Constrain the L2 norm of the incoming weights for each unit to be 1.0
UnitNormConstraint(int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.UnitNormConstraint: Apply to weights but not biases by default
UnitNormConstraint(Set<String>, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.UnitNormConstraint
units(int) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder: Set the number of units / layer size for this layer.
This is equivalent to FeedForwardLayer.Builder.nOut(int)
UnstackVertex - Class in org.deeplearning4j.nn.conf.graph: UnstackVertex allows for unstacking of inputs so that they may be forwarded through a network.
UnstackVertex(int, int) - Constructor for class org.deeplearning4j.nn.conf.graph.UnstackVertex
UnstackVertex - Class in org.deeplearning4j.nn.graph.vertex.impl: UnstackVertex allows for unstacking of inputs so that they may be forwarded through a network.
UnstackVertex(ComputationGraph, String, int, int, int, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
UnstackVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], int, int, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
update(Gradient) - Method in interface org.deeplearning4j.nn.api.Model: Update layer weights and biases with gradient change
update(INDArray, String) - Method in interface org.deeplearning4j.nn.api.Model: Perform one update applying the gradient
update(Trainable, Gradient, int, int, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Updater: Updater: updates the model
update(INDArray, String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
update(Gradient) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
update(Gradient) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
update(Gradient) - Method in class org.deeplearning4j.nn.layers.BaseLayer
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.BaseLayer
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
update(Gradient) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
update(Gradient) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
update(Gradient) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.RepeatVector
update(Gradient) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
update(Gradient) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
update(INDArray, String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
update(Gradient) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
update(Task) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
update(Trainable, Gradient, int, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
update(Gradient, int, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater: Update the gradient for the model.
update(int, int) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock: Update the gradient for this block
updateBuilder(ComputationGraphConfiguration.GraphBuilder, String, int, int[][], String) - Method in interface org.deeplearning4j.nn.conf.module.GraphBuilderModule: Add a layer to the collection of layers being generated by this module.
updateExternalGradient(int, int, INDArray, INDArray) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock
updateGradientAccordingToParams(Gradient, Model, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer: Update the gradient according to the configuration such as adagrad, momentum, and sparsity
updateGradientAccordingToParams(Gradient, Model, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
Updater - Interface in org.deeplearning4j.nn.api: Update the model
updater(Updater) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Deprecated.
updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Gradient updater.
updater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: Gradient updater.
updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: Gradient updater.
updater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
updater - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
updater(Updater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Deprecated.
Use NeuralNetConfiguration.Builder.updater(IUpdater)
updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Gradient updater configuration.
Updater - Enum in org.deeplearning4j.nn.conf: All the possible different updaters
updater(IUpdater) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Gradient updater configuration.
updater(Updater) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Deprecated.
Use FineTuneConfiguration.Builder.updater(IUpdater)
updater - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
updater - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
UPDATER_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer
UpdaterBlock - Class in org.deeplearning4j.nn.updater: UpdaterBlock: used in BaseMultiLayerUpdater, this class implements updating (i.e., Adam, RMSProp, Momentum, etc) across multiple contiguous layers/parameters, as described in the BaseMultiLayerUpdater javadoc.
UpdaterBlock(int, int, int, int, List<UpdaterBlock.ParamState>) - Constructor for class org.deeplearning4j.nn.updater.UpdaterBlock
UpdaterBlock.ParamState - Class in org.deeplearning4j.nn.updater
updaterBlocks - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
updaterConfigurationsEquals(Trainable, String, Trainable, String) - Static method in class org.deeplearning4j.nn.updater.UpdaterUtils
UpdaterCreator - Class in org.deeplearning4j.nn.updater
updaterDivideByMinibatch(String) - Method in interface org.deeplearning4j.nn.api.Trainable: DL4J layers typically produce the sum of the gradients during the backward pass for each layer, and if required (if minibatch=true) then divide by the minibatch size.
However, there are some exceptions, such as the batch norm mean/variance estimate parameters: these "gradients" are actually not gradients, but are updates to be applied directly to the parameter vector.
updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
updaterDivideByMinibatch(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Intended for internal use
updateRnnStateWithTBPTTState() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Intended for internal/developer use
updaterState() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork: This method returns updater state (if applicable), null otherwise
updaterState() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
updaterState() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
updaterStateViewArray - Variable in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
UpdaterUtils - Class in org.deeplearning4j.nn.updater: Created by Alex on 14/04/2017.
UpdaterUtils() - Constructor for class org.deeplearning4j.nn.updater.UpdaterUtils
updates - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
updates - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
updatesApplied - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
updatesBoundary(double) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder: This method enables optional limit for max number of updates per message Default value: 1.0 (no limit)
updatesCounter - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
updatesLock - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
updatesSize() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail: This method returns actual number of updates stored within tail
Upsampling1D - Class in org.deeplearning4j.nn.conf.layers: Upsampling 1D layer
Repeats each step size times along the temporal/sequence axis (dimension 2)
For input shape [minibatch, channels, sequenceLength] output has shape [minibatch, channels, size * sequenceLength]
Example:
Upsampling1D(BaseUpsamplingLayer.UpsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling1D
Upsampling1D - Class in org.deeplearning4j.nn.layers.convolution.upsampling: 1D Upsampling layer.
Upsampling1D(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D
Upsampling1D.Builder - Class in org.deeplearning4j.nn.conf.layers
Upsampling2D - Class in org.deeplearning4j.nn.conf.layers: Upsampling 2D layer
Repeats each value (or rather, set of depth values) in the height and width dimensions by size[0] and size[1] times respectively.
If input has shape [minibatch, channels, height, width] then output has shape [minibatch, channels, height*size[0], width*size[1]]
Example:
Upsampling2D(BaseUpsamplingLayer.UpsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling2D
Upsampling2D - Class in org.deeplearning4j.nn.layers.convolution.upsampling: 2D Upsampling layer.
Upsampling2D(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
Upsampling2D.Builder - Class in org.deeplearning4j.nn.conf.layers
Upsampling3D - Class in org.deeplearning4j.nn.conf.layers: Upsampling 3D layer
Repeats each value (all channel values for each x/y/z location) by size[0], size[1] and size[2]
If input has shape [minibatch, channels, depth, height, width] then output has shape [minibatch, channels, size[0] * depth, size[1] * height, size[2] * width]
Upsampling3D(Upsampling3D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling3D
Upsampling3D - Class in org.deeplearning4j.nn.layers.convolution.upsampling: 3D Upsampling layer.
Upsampling3D(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
Upsampling3D.Builder - Class in org.deeplearning4j.nn.conf.layers
UpsamplingBuilder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer.UpsamplingBuilder: A single size integer is used for upsampling in all spatial dimensions
UpsamplingBuilder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseUpsamplingLayer.UpsamplingBuilder: An int array to specify upsampling dimensions, the length of which has to equal to the number of spatial dimensions (e.g.
useLeakyReLU(double) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Use a LeakyReLU activation on the 2d convolution
useLogStd - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: How should the moving average of variance be stored? Two different parameterizations are supported.
useLogStd(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder: How should the moving average of variance be stored? Two different parameterizations are supported.
useLogStd - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
useReLU(boolean) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Whether to use a ReLU activation on the 2d convolution
useReLU() - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder: Use a ReLU activation on the 2d convolution
UserNameTrigger(String) - Constructor for class org.deeplearning4j.optimize.listeners.FailureTestingListener.UserNameTrigger

V

V_KEY - Static variable in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
VAEReconErrorScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc: Score function for variational autoencoder reconstruction error for a MultiLayerNetwork or ComputationGraph.
VariationalAutoencoder layer must be first layer in the network
VAEReconErrorScoreCalculator(RegressionEvaluation.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator: Constructor for reconstruction *ERROR*
VAEReconProbScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc: Score calculator for variational autoencoder reconstruction probability or reconstruction log probability for a MultiLayerNetwork or ComputationGraph.
VAEReconProbScoreCalculator(DataSetIterator, int, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator: Constructor for average reconstruction probability
VAEReconProbScoreCalculator(DataSetIterator, int, boolean, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator: Constructor for reconstruction probability
validate() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration
validate() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: Check the configuration, make sure it is valid
validate(boolean, boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration: Check the configuration, make sure it is valid
validate1(int[], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a length 1 array.
validate1NonNegative(int[], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a length 1 array and checks that all values are >= 0.
validate2(int[], boolean, String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a length 2 array.
validate2NonNegative(int[], boolean, String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a length 2 array and checks that all values are >= 0.
validate2x2(int[][], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a 2x2 array.
validate2x2NonNegative(int[][], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a 2x2 array and checks that all values are >= 0.
validate3(int[], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a length 3 array.
validate3NonNegative(int[], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a length 3 array and checks that all values >= 0.
validate4(int[], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a length 4 array.
validate4NonNegative(int[], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a length 4 array and checks that all values >= 0.
validate6(int[], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a length 6 array.
validate6NonNegative(int[], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Reformats the input array to a length 6 array and checks that all values >= 0.
validateArrayLocation(ArrayType, INDArray, boolean, boolean) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
validateArrayWorkspaces(LayerWorkspaceMgr, INDArray, ArrayType, String, boolean, String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
validateArrayWorkspaces(LayerWorkspaceMgr, INDArray, ArrayType, int, boolean, String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
validateCnn1DKernelStridePadding(int, int, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils: Perform validation on the CNN layer kernel/stride/padding.
validateCnn3DKernelStridePadding(int[], int[], int[]) - Static method in class org.deeplearning4j.util.Convolution3DUtils: Perform validation on the CNN3D layer kernel/stride/padding.
validateCnnKernelStridePadding(int[], int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Perform validation on the CNN layer kernel/stride/padding.
validateComputationGraph(File) - Static method in class org.deeplearning4j.util.DL4JModelValidator: Validate whether the file represents a valid ComputationGraph saved previously with ComputationGraph.save(File) or ModelSerializer.writeModel(Model, File, boolean), to be read with ComputationGraph.load(File, boolean)
validateConvolutionModePadding(ConvolutionMode, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils: Check that the convolution mode is consistent with the padding specification
validateConvolutionModePadding(ConvolutionMode, int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils: Check that the convolution mode is consistent with the padding specification
validateInput(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer
validateInput(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer: Validate input arrays to confirm that they fulfill the assumptions of the layer.
validateInput(INDArray[]) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex: Validate input arrays to confirm that they fulfill the assumptions of the layer.
validateInputDepth(int) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
validateInputRank() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
validateMultiLayerNetwork(File) - Static method in class org.deeplearning4j.util.DL4JModelValidator: Validate whether the file represents a valid MultiLayerNetwork saved previously with MultiLayerNetwork.save(File) or ModelSerializer.writeModel(Model, File, boolean), to be read with MultiLayerNetwork.load(File, boolean)
validateNonNegative(int, String) - Static method in class org.deeplearning4j.util.ValidationUtils: Checks that the values is >= 0.
validateNonNegative(double, String) - Static method in class org.deeplearning4j.util.ValidationUtils: Checks that the values is >= 0.
validateNonNegative(int[], String) - Static method in class org.deeplearning4j.util.ValidationUtils: Checks that all values are >= 0.
validateOutputConfig - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
validateOutputConfig - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
validateOutputLayer(String, Layer) - Static method in class org.deeplearning4j.util.OutputLayerUtil: Validate the output layer (or loss layer) configuration, to detect invalid consfiugrations.
validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Enabled by default.
validateOutputLayerConfig - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Enabled by default.
validateOutputLayerConfig - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder
validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
validateOutputLayerConfiguration(String, long, boolean, IActivation, ILossFunction) - Static method in class org.deeplearning4j.util.OutputLayerUtil: Validate the output layer (or loss layer) configuration, to detect invalid consfiugrations.
validateOutputLayerForClassifierEvaluation(Layer, Class<? extends IEvaluation>) - Static method in class org.deeplearning4j.util.OutputLayerUtil: Validates if the output layer configuration is valid for classifier evaluation.
validateShapes(INDArray, int, int, int, ConvolutionMode, int, int, boolean) - Static method in class org.deeplearning4j.util.Convolution1DUtils
validateShapes(INDArray, int[], int[], int[], ConvolutionMode, int[], int[], boolean) - Static method in class org.deeplearning4j.util.ConvolutionUtils
validateTbpttConfig - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
validateTbpttConfig(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Enabled by default.
validateTbpttConfig - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
validateTbpttConfig(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Enabled by default.
ValidationUtils - Class in org.deeplearning4j.util: Validation methods for array sizes/shapes and value non-negativeness
value() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
valueOf(String) - Static method in enum org.deeplearning4j.earlystopping.EarlyStoppingResult.TerminationReason: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.Metric: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.ROCType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.eval.Evaluation.Metric: Deprecated.

Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.eval.EvaluationAveraging: Deprecated.

Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.eval.RegressionEvaluation.Metric: Deprecated.

Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.api.FwdPassType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.api.Layer.TrainingMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.api.Layer.Type: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.api.MaskState: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.api.OptimizationAlgorithm: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.BackpropType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.CacheMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.ConvolutionMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.GradientNormalization: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.graph.ElementWiseVertex.Op: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.inputs.InputType.Type: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.Convolution3D.DataFormat: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.PoolingType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Mode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.DataFormat: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.PoolingType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.memory.MemoryType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.memory.MemoryUseMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.Updater: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.WorkspaceMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.weights.WeightInit: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.nn.workspace.ArrayType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.optimize.api.InvocationType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.optimize.listeners.SleepyTrainingListener.SleepMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.deeplearning4j.optimize.listeners.SleepyTrainingListener.TimeMode: Returns the enum constant of this type with the specified name.
values() - Static method in enum org.deeplearning4j.earlystopping.EarlyStoppingResult.TerminationReason: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.Metric: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.ROCType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.eval.Evaluation.Metric: Deprecated.

Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.eval.EvaluationAveraging: Deprecated.

Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.eval.RegressionEvaluation.Metric: Deprecated.

Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.api.FwdPassType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.api.Layer.TrainingMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.api.Layer.Type: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.api.MaskState: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.api.OptimizationAlgorithm: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.BackpropType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.CacheMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.ConvolutionMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.GradientNormalization: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.graph.ElementWiseVertex.Op: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.inputs.InputType.Type: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.layers.Convolution3D.DataFormat: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.layers.PoolingType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Mode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.DataFormat: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.PoolingType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.memory.MemoryType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.memory.MemoryUseMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.Updater: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.conf.WorkspaceMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.weights.WeightInit: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.nn.workspace.ArrayType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.optimize.api.InvocationType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.optimize.listeners.SleepyTrainingListener.SleepMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.deeplearning4j.optimize.listeners.SleepyTrainingListener.TimeMode: Returns an array containing the constants of this enum type, in the order they are declared.
variables - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
variables() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
variables(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
VariationalAutoencoder - Class in org.deeplearning4j.nn.conf.layers.variational: Variational Autoencoder layer
VariationalAutoencoder - Class in org.deeplearning4j.nn.layers.variational: Variational Autoencoder layer
VariationalAutoencoder(NeuralNetConfiguration, DataType) - 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
vectorSize() - Method in class org.deeplearning4j.nn.weights.embeddings.ArrayEmbeddingInitializer
vectorSize() - Method in interface org.deeplearning4j.nn.weights.embeddings.EmbeddingInitializer
vertexIndex - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex: The index of this vertex
VertexIndices - Class in org.deeplearning4j.nn.graph.vertex: VertexIndices defines a pair of integers: the index of a vertex, and the edge number of that vertex.
VertexIndices() - Constructor for class org.deeplearning4j.nn.graph.vertex.VertexIndices
vertexInputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder: Key: graph node.
vertexInputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
VertexInputs(SameDiff) - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex.VertexInputs
vertexName - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
vertices - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
vertices - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
vertices - Variable in class org.deeplearning4j.nn.graph.ComputationGraph: All GraphVertex objects in the network.
verticesMap - Variable in class org.deeplearning4j.nn.graph.ComputationGraph: Map of vertices by name
VISIBLE_BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.PretrainParamInitializer
visibleBiasInit - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
visibleBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
visibleBiasInit - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
vocabSize() - Method in class org.deeplearning4j.nn.weights.embeddings.ArrayEmbeddingInitializer
vocabSize() - Method in interface org.deeplearning4j.nn.weights.embeddings.EmbeddingInitializer

W

W_KEY - Static variable in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.CenterLossParamInitializer
WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.Convolution3DParamInitializer
WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.DefaultParamInitializer
WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.PReLUParamInitializer
WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
WEIGHT_KEY_SUFFIX - Static variable in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
weightConstraints - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
weightConstraints - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
weightDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Add weight decay regularization for the network parameters (excluding biases).
This applies weight decay with multiplying the learning rate - see WeightDecay for more details.
weightDecay(double, boolean) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Add weight decay regularization for the network parameters (excluding biases).
weightDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: Add weight decay regularization for the network parameters (excluding biases).
This applies weight decay with multiplying the learning rate - see WeightDecay for more details.
weightDecay(double, boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: Add weight decay regularization for the network parameters (excluding biases).
weightDecay(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Add weight decay regularization for the network parameters (excluding biases).
This applies weight decay with multiplying the learning rate - see WeightDecay for more details.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
weightDecay(double, boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Add weight decay regularization for the network parameters (excluding biases).
weightDecay(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Add weight decay regularization for the network parameters (excluding biases).
This applies weight decay with multiplying the learning rate - see WeightDecay for more details.
weightDecay(double, boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Add weight decay regularization for the network parameters (excluding biases).
weightDecayBias(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Weight decay for the biases only - see BaseLayer.Builder.weightDecay(double) for more details.
weightDecayBias(double, boolean) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Weight decay for the biases only - see BaseLayer.Builder.weightDecay(double) for more details
weightDecayBias(double) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: Weight decay for the biases only - see AbstractSameDiffLayer.Builder.weightDecay(double) for more details.
weightDecayBias(double, boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder: Weight decay for the biases only - see AbstractSameDiffLayer.Builder.weightDecay(double) for more details
weightDecayBias(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Weight decay for the biases only - see NeuralNetConfiguration.Builder.weightDecay(double) for more details.
weightDecayBias(double, boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Weight decay for the biases only - see NeuralNetConfiguration.Builder.weightDecay(double) for more details
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
weightDecayBias(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Weight decay for the biases only - see FineTuneConfiguration.Builder.weightDecay(double) for more details.
weightDecayBias(double, boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Weight decay for the biases only - see FineTuneConfiguration.Builder.weightDecay(double) for more details
weightInit - Variable in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder: Weight initialization scheme
weightInit(WeightInit) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder: Weight initialization scheme
weightInit - Variable in class org.deeplearning4j.nn.conf.graph.AttentionVertex
weightInit(IWeightInit) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Weight initialization scheme to use, for initial weight values
weightInit(WeightInit) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Weight initialization scheme to use, for initial weight values
weightInit(Distribution) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Set weight initialization scheme to random sampling via the specified distribution.
weightInit(IWeightInit) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder
weightInit(EmbeddingInitializer) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder: Initialize the embedding layer using the specified EmbeddingInitializer - such as a Word2Vec instance
weightInit(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder: Initialize the embedding layer using values from the specified array.
weightInit(IWeightInit) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder
weightInit(EmbeddingInitializer) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder: Initialize the embedding layer using the specified EmbeddingInitializer - such as a Word2Vec instance
weightInit(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder: Initialize the embedding layer using values from the specified array.
weightInit - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer.Builder
weightInit(WeightInit) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer.Builder
weightInit - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer
weightInit(IWeightInit) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Weight initialization scheme to use, for initial weight values Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
weightInit(WeightInit) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Weight initialization scheme to use, for initial weight values Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
weightInit(Distribution) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set weight initialization scheme to random sampling via the specified distribution.
weightInit(IWeightInit) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Weight initialization scheme to use, for initial weight values
weightInit(WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Weight initialization scheme to use, for initial weight values
weightInit(Distribution) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Set weight initialization scheme to random sampling via the specified distribution.
WeightInit - Enum in org.deeplearning4j.nn.weights: Weight initialization scheme
WeightInitConstant - Class in org.deeplearning4j.nn.weights: Initialize to a constant value (deafult 0).
WeightInitConstant() - Constructor for class org.deeplearning4j.nn.weights.WeightInitConstant
WeightInitConstant(double) - Constructor for class org.deeplearning4j.nn.weights.WeightInitConstant
WeightInitDistribution - Class in org.deeplearning4j.nn.weights: Sample weights from a provided Distribution
Note that Distribution is not extendable as it is interpreted through Distributions.createDistribution(Distribution).
WeightInitDistribution(Distribution) - Constructor for class org.deeplearning4j.nn.weights.WeightInitDistribution
WeightInitEmbedding - Class in org.deeplearning4j.nn.weights.embeddings: Weight initialization for initializing the parameters of an EmbeddingLayer from a EmbeddingInitializer Note: WeightInitEmbedding supports both JSON serializable and non JSON serializable initializations.
WeightInitEmbedding(EmbeddingInitializer) - Constructor for class org.deeplearning4j.nn.weights.embeddings.WeightInitEmbedding
WeightInitEmbedding(EmbeddingInitializer, EmbeddingInitializer) - Constructor for class org.deeplearning4j.nn.weights.embeddings.WeightInitEmbedding
weightInitFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Weight initialization scheme to use, for initial weight values
weightInitFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
weightInitFn - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
weightInitFn - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
weightInitFnRecurrent - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder: Set the weight initialization for the recurrent weights.
weightInitFnRecurrent - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer
WeightInitIdentity - Class in org.deeplearning4j.nn.weights: Weights are set to an identity matrix.
WeightInitIdentity() - Constructor for class org.deeplearning4j.nn.weights.WeightInitIdentity
WeightInitLecunUniform - Class in org.deeplearning4j.nn.weights: Uniform U[-a,a] with a=3/sqrt(fanIn).
WeightInitLecunUniform() - Constructor for class org.deeplearning4j.nn.weights.WeightInitLecunUniform
WeightInitNormal - Class in org.deeplearning4j.nn.weights: Normal/Gaussian distribution, with mean 0 and standard deviation 1/sqrt(fanIn).
WeightInitNormal() - Constructor for class org.deeplearning4j.nn.weights.WeightInitNormal
weightInitRecurrent(IWeightInit) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder: Set the weight initialization for the recurrent weights.
weightInitRecurrent(WeightInit) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder: Set the weight initialization for the recurrent weights.
weightInitRecurrent(Distribution) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder: Set the weight initialization for the recurrent weights, based on the specified distribution.
WeightInitRelu - Class in org.deeplearning4j.nn.weights: : He et al.
WeightInitRelu() - Constructor for class org.deeplearning4j.nn.weights.WeightInitRelu
WeightInitReluUniform - Class in org.deeplearning4j.nn.weights: He et al.
WeightInitReluUniform() - Constructor for class org.deeplearning4j.nn.weights.WeightInitReluUniform
WeightInitSigmoidUniform - Class in org.deeplearning4j.nn.weights: A version of WeightInitXavierUniform for sigmoid activation functions.
WeightInitSigmoidUniform() - Constructor for class org.deeplearning4j.nn.weights.WeightInitSigmoidUniform
WeightInitUniform - Class in org.deeplearning4j.nn.weights: Uniform U[-a,a] with a=1/sqrt(fanIn).
WeightInitUniform() - Constructor for class org.deeplearning4j.nn.weights.WeightInitUniform
WeightInitUtil - Class in org.deeplearning4j.nn.weights: Weight initialization utility
WeightInitVarScalingNormalFanAvg - Class in org.deeplearning4j.nn.weights: Gaussian distribution with mean 0, variance 1.0/((fanIn + fanOut)/2)
WeightInitVarScalingNormalFanAvg() - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanAvg
WeightInitVarScalingNormalFanIn - Class in org.deeplearning4j.nn.weights: Gaussian distribution with mean 0, variance 1.0/(fanIn)
WeightInitVarScalingNormalFanIn() - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanIn
WeightInitVarScalingNormalFanOut - Class in org.deeplearning4j.nn.weights: Gaussian distribution with mean 0, variance 1.0/(fanOut)
WeightInitVarScalingNormalFanOut() - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanOut
WeightInitVarScalingUniformFanAvg - Class in org.deeplearning4j.nn.weights: Uniform U[-a,a] with a=3.0/((fanIn + fanOut)/2)
WeightInitVarScalingUniformFanAvg() - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingUniformFanAvg
WeightInitVarScalingUniformFanIn - Class in org.deeplearning4j.nn.weights: Uniform U[-a,a] with a=3.0/(fanIn)
WeightInitVarScalingUniformFanIn() - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingUniformFanIn
WeightInitVarScalingUniformFanOut - Class in org.deeplearning4j.nn.weights: Uniform U[-a,a] with a=3.0/(fanOut)
WeightInitVarScalingUniformFanOut() - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingUniformFanOut
WeightInitXavier - Class in org.deeplearning4j.nn.weights: As per Glorot and Bengio 2010: Gaussian distribution with mean 0, variance 2.0/(fanIn + fanOut)
WeightInitXavier() - Constructor for class org.deeplearning4j.nn.weights.WeightInitXavier
WeightInitXavierLegacy - Class in org.deeplearning4j.nn.weights: Xavier weight init in DL4J up to 0.6.0.
WeightInitXavierLegacy() - Constructor for class org.deeplearning4j.nn.weights.WeightInitXavierLegacy
WeightInitXavierUniform - Class in org.deeplearning4j.nn.weights: As per Glorot and Bengio 2010: Uniform distribution U(-s,s) with s = sqrt(6/(fanIn + fanOut))
WeightInitXavierUniform() - Constructor for class org.deeplearning4j.nn.weights.WeightInitXavierUniform
weightKeys(Layer) - Method in interface org.deeplearning4j.nn.api.ParamInitializer: Weight parameter keys given the layer configuration
weightKeys(Layer) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer
weightKeys(Layer) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
weightNoise - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Set the weight noise (such as DropConnect and WeightNoise) for this layer
weightNoise(IWeightNoise) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder: Set the weight noise (such as DropConnect and WeightNoise) for this layer
weightNoise - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
weightNoise - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
weightNoise(IWeightNoise) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder: Set the weight noise (such as DropConnect and WeightNoise) for the layers in this network.
Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
WeightNoise - Class in org.deeplearning4j.nn.conf.weightnoise: Apply noise of the specified distribution to the weights at training time.
WeightNoise(Distribution) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.WeightNoise
WeightNoise(Distribution, boolean) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.WeightNoise
WeightNoise(Distribution, boolean, boolean) - Constructor for class org.deeplearning4j.nn.conf.weightnoise.WeightNoise
weightNoise(IWeightNoise) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder: Set the weight noise (such as DropConnect and WeightNoise)
weightNoise - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
weightNoiseParams - Variable in class org.deeplearning4j.nn.layers.BaseLayer
weightNoiseParams - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
windowSize - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: The number of examples to use for computing the quantile for the r value update.
windowSize(int) - Method in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder: The number of examples to use for computing the quantile for the r value update.
with(ArrayType, String, WorkspaceConfiguration) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder: Configure the workspace (name, configuration) for the specified array type
workersCounter - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
workingMemory(long, long, long, long) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder: Report the working memory size, for both inference and training
workingMemory(long, long, Map<CacheMode, Long>, Map<CacheMode, Long>) - Method in class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder: Report the working memory requirements, for both inference and training.
WorkspaceMode - Enum in org.deeplearning4j.nn.conf: Workspace mode to use.
workspaces - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
WrapperLayerParamInitializer - Class in org.deeplearning4j.nn.params
writeMemoryCrashDump(Model, Throwable) - Static method in class org.deeplearning4j.util.CrashReportingUtil: Generate and write the crash dump to the crash dump root directory (by default, the working directory).
writeModel(Model, File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Write a model to a file
writeModel(Model, File, boolean, DataNormalization) - Static method in class org.deeplearning4j.util.ModelSerializer: Write a model to a file
writeModel(Model, String, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Write a model to a file path
writeModel(Model, OutputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer: Write a model to an output stream
writeModel(Model, OutputStream, boolean, DataNormalization) - Static method in class org.deeplearning4j.util.ModelSerializer: Write a model to an output stream
WS_ALL_LAYERS_ACT - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph: Workspace for storing all layers' activations - used only to store activations (layer inputs) as part of backprop Not used for inference
WS_ALL_LAYERS_ACT - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Workspace for storing all layers' activations - used only to store activations (layer inputs) as part of backprop Not used for inference
WS_ALL_LAYERS_ACT_CONFIG - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
WS_ALL_LAYERS_ACT_CONFIG - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
WS_LAYER_ACT_1 - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Next 2 workspaces: used for: (a) Inference: holds activations for one layer only (b) Backprop: holds activation gradients for one layer only In both cases, they are opened and closed on every second layer
WS_LAYER_ACT_2 - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
WS_LAYER_ACT_X_CONFIG - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
WS_LAYER_ACT_X_CONFIG - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
WS_LAYER_WORKING_MEM - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph: Workspace for working memory for a single layer: forward pass and backward pass Note that this is opened/closed once per op (activate/backpropGradient call)
WS_LAYER_WORKING_MEM - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Workspace for working memory for a single layer: forward pass and backward pass Note that this is opened/closed once per op (activate/backpropGradient call)
WS_LAYER_WORKING_MEM_CONFIG - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
WS_LAYER_WORKING_MEM_CONFIG - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
WS_OUTPUT_MEM - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph: Workspace for output methods that use OutputAdapter
WS_OUTPUT_MEM - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Workspace for output methods that use OutputAdapter
WS_RNN_LOOP_WORKING_MEM - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph: Workspace for working memory in RNNs - opened and closed once per RNN time step
WS_RNN_LOOP_WORKING_MEM - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Workspace for working memory in RNNs - opened and closed once per RNN time step
WS_RNN_LOOP_WORKING_MEM_CONFIG - Static variable in class org.deeplearning4j.nn.graph.ComputationGraph
WS_RNN_LOOP_WORKING_MEM_CONFIG - Static variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork

X

xHat - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
xMu - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization

Y

yield() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree: Returns all of the labels for this node and all of its children (recursively)
Yolo2OutputLayer - Class in org.deeplearning4j.nn.conf.layers.objdetect: Output (loss) layer for YOLOv2 object detection model, based on the papers: YOLO9000: Better, Faster, Stronger - Redmon & Farhadi (2016) - https://arxiv.org/abs/1612.08242
and
You Only Look Once: Unified, Real-Time Object Detection - Redmon et al.
Yolo2OutputLayer - Class in org.deeplearning4j.nn.layers.objdetect: Output (loss) layer for YOLOv2 object detection model, based on the papers: YOLO9000: Better, Faster, Stronger - Redmon & Farhadi (2016) - https://arxiv.org/abs/1612.08242
and
You Only Look Once: Unified, Real-Time Object Detection - Redmon et al.
Yolo2OutputLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
Yolo2OutputLayer.Builder - Class in org.deeplearning4j.nn.conf.layers.objdetect
YoloModelAdapter - Class in org.deeplearning4j.nn.adapters: This ModelAdapter implementation is suited for use of Yolo2 model with ParallelInference
YoloModelAdapter() - Constructor for class org.deeplearning4j.nn.adapters.YoloModelAdapter
YoloUtils - Class in org.deeplearning4j.nn.layers.objdetect: Functionality to interpret the network output of Yolo2OutputLayer.
YoloUtils() - Constructor for class org.deeplearning4j.nn.layers.objdetect.YoloUtils

Z

zeroedPretrainParamGradients - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
ZeroPadding1DLayer - Class in org.deeplearning4j.nn.conf.layers: Zero padding 1D layer for convolutional neural networks.
ZeroPadding1DLayer(int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
ZeroPadding1DLayer(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
ZeroPadding1DLayer(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
ZeroPadding1DLayer - Class in org.deeplearning4j.nn.layers.convolution: Zero padding 1D layer for convolutional neural networks.
ZeroPadding1DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer
ZeroPadding1DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
ZeroPadding3DLayer - Class in org.deeplearning4j.nn.conf.layers: Zero padding 3D layer for convolutional neural networks.
ZeroPadding3DLayer - Class in org.deeplearning4j.nn.layers.convolution: Zero padding 3D layer for convolutional neural networks.
ZeroPadding3DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer
ZeroPadding3DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
ZeroPaddingLayer - Class in org.deeplearning4j.nn.conf.layers: Zero padding layer for convolutional neural networks (2D CNNs).
ZeroPaddingLayer(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
ZeroPaddingLayer(int, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
ZeroPaddingLayer - Class in org.deeplearning4j.nn.layers.convolution: Zero padding layer for convolutional neural networks.
ZeroPaddingLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
ZeroPaddingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers

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