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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
- 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() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
- AbstractSameDiffLayer(AbstractSameDiffLayer.Builder) - 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
- ACCURACY - org.deeplearning4j.eval.Evaluation.Metric
-
Deprecated.
- 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(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
- activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer
- 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(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.Deconvolution3DLayer
- 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(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(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(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
- activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer
- activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
- activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
- activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
- 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(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
- 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(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
- activate(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.recurrent.TimeDistributedLayer
- 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(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- activate(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- activate(INDArray, INDArray, boolean, LayerWorkspaceMgr) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils
- activate(INDArray, INDArray, LayerWorkspaceMgr) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils
- activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Equivalent to
MultiLayerNetwork.output(INDArray)
using the input set viaMultiLayerNetwork.setInput(INDArray)
- 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(INDArray, boolean, double, double, double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
- activate(INDArray, boolean, double, double, double, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper
- activate(INDArray, boolean, int[], int[], int[], PoolingType, ConvolutionMode, int[], CNN2DFormat, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper
- activate(INDArray, boolean, int[], int[], int[], PoolingType, ConvolutionMode, int[], CNN2DFormat, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper
- 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(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
- activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
- activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
- activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
- activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer
- activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
- 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(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
Deprecated.
- 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(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer
- activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.TimeDistributedLayer
- activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- activate(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Equivalent to
MultiLayerNetwork.output(INDArray, TrainingMode)
- activate(INDArray, IActivation, boolean) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
- activate(INDArray, IActivation, boolean) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
- activate(INDArray, INDArray) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils
-
Essentially: just apply activation functions...
- activate(INDArray, INDArray, boolean) - Static method in class org.deeplearning4j.nn.layers.objdetect.YoloUtils
- 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 - Variable in class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder
-
The activation function to use with ocnn
- activation(String) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder
-
Deprecated.Use
ActivationLayer.Builder.activation(Activation)
or {@link @activation(IActivation)} - activation(Activation) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder
- 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(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(Activation) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
Activation function / neuron non-linearity
- activation(IActivation) - 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(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(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_GRAD - org.deeplearning4j.nn.workspace.ArrayType
- ACTIVATION_GRADIENTS - org.deeplearning4j.nn.conf.memory.MemoryType
- 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
- ActivationLayer - Class in org.deeplearning4j.nn.layers
- ActivationLayer(ActivationLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.ActivationLayer
- ActivationLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.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.Builder - Class in org.deeplearning4j.nn.conf.layers
- ACTIVATIONS - org.deeplearning4j.nn.conf.memory.MemoryType
- ACTIVATIONS - org.deeplearning4j.nn.workspace.ArrayType
- 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.
- Active - org.deeplearning4j.nn.api.MaskState
- ADADELTA - org.deeplearning4j.nn.conf.Updater
- ADAGRAD - org.deeplearning4j.nn.conf.Updater
- ADAM - org.deeplearning4j.nn.conf.Updater
- ADAMAX - org.deeplearning4j.nn.conf.Updater
- adapt2dMask(INDArray, INDArray, CNN2DFormat, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
- AdaptiveThresholdAlgorithm - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold
- 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 targetAdaptiveThresholdAlgorithm.DEFAULT_MIN_SPARSITY_TARGET
, default maximum sparsity targetAdaptiveThresholdAlgorithm.DEFAULT_MAX_SPARSITY_TARGET
, and default decay rateAdaptiveThresholdAlgorithm.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 targetAdaptiveThresholdAlgorithm.DEFAULT_MAX_SPARSITY_TARGET
, and default decay rateAdaptiveThresholdAlgorithm.DEFAULT_DECAY_RATE
- AdaptiveThresholdAlgorithm(double, double, double, double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm
- add(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- 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 - org.deeplearning4j.nn.conf.graph.ElementWiseVertex.Op
- Add - org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op
- ADD - org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Mode
- addAll(Collection<? extends E>) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- addBiasParam(String, @lombok.NonNull 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(String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
- addInputs(Collection<String>) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
Specify the inputs to the network, and their associated labels.
- ADDITIVE - org.deeplearning4j.optimize.listeners.SleepyTrainingListener.TimeMode
-
In this mode, listener will be trying to match specified time for a given invocation method.
- 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, 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.conf.ComputationGraphConfiguration.GraphBuilder
-
Add a layer and an
InputPreProcessor
, with the specified name and specified inputs. - addLayer(String, Layer, InputPreProcessor, String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
-
Add a layer with a specified preprocessor
- addLayer(Layer) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder
-
Add layers to the net Required if layers are removed.
- 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(boolean, boolean, InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
Add preprocessors automatically, given the specified types of inputs for the network.
- addPreProcessors(boolean, InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
Add preprocessors automatically, given the specified types of inputs for the network.
- addPreProcessors(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
Add preprocessors automatically, given the specified types of inputs for the network.
- addScore(long, double) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener.ScoreStat
- 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, @lombok.NonNull 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
- ALGO_0 - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo
- ALGO_0 - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo
- ALGO_1 - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo
- ALGO_1 - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo
- ALGO_3 - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo
- ALL - org.deeplearning4j.gradientcheck.GradientCheckUtil.PrintMode
- allocate(boolean, DataType, long...) - Method in class org.deeplearning4j.nn.layers.samediff.DL4JSameDiffMemoryMgr
- allocate(boolean, LongShapeDescriptor) - Method in class org.deeplearning4j.nn.layers.samediff.DL4JSameDiffMemoryMgr
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution3D.Builder
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- allowCausal() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
- alpha(double) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
- 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(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.AlphaDropout
- AlphaDropout(double, ISchedule, double, double) - Constructor for class org.deeplearning4j.nn.conf.dropout.AlphaDropout
- AlphaDropout(ISchedule) - 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
- ANY - org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType
- 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(Model, INDArray[], INDArray[], 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
- apply(INDArray...) - Method in class org.deeplearning4j.nn.adapters.ArgmaxAdapter
-
This method does conversion from INDArrays to int[], where each element will represents position of the highest element in output INDArray I.e.
- apply(INDArray...) - Method in class org.deeplearning4j.nn.adapters.Regression2dAdapter
- apply(INDArray...) - Method in class org.deeplearning4j.nn.adapters.YoloModelAdapter
- 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, 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.AlphaDropout
- applyDropout(INDArray, INDArray, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.conf.dropout.Dropout
- 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, boolean) - 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 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.EncodedGradientsAccumulator
-
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
- 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
- ArrayEmbeddingInitializer(INDArray) - Constructor for class org.deeplearning4j.nn.weights.embeddings.ArrayEmbeddingInitializer
- ArrayType - Enum in org.deeplearning4j.nn.workspace
- 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
- atomicBoundary - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
- AttentionVertex - Class in org.deeplearning4j.nn.conf.graph
- AttentionVertex(AttentionVertex.Builder) - Constructor for class org.deeplearning4j.nn.conf.graph.AttentionVertex
- AttentionVertex.Builder - Class in org.deeplearning4j.nn.conf.graph
- AUC - org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.Metric
- AUPRC - org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.Metric
- AutoEncoder - Class in org.deeplearning4j.nn.conf.layers
- AutoEncoder - Class in org.deeplearning4j.nn.layers.feedforward.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
- 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
- Average - org.deeplearning4j.nn.conf.graph.ElementWiseVertex.Op
- Average - org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op
- AVERAGE - org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Mode
- AVG - org.deeplearning4j.nn.conf.layers.PoolingType
- AVG - org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.PoolingType
- AVG - org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType
- 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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- backprop(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
- 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(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.preprocessors.KerasFlattenRnnPreprocessor
- backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.preprocessors.PermutePreprocessor
- backprop(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.preprocessors.ReshapePreprocessor
- 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.AlphaDropout
- backprop(INDArray, INDArray, int, int) - Method in class org.deeplearning4j.nn.conf.dropout.Dropout
- 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
- backpropDropOutIfPresent(INDArray) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- 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...) - 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 interface org.deeplearning4j.nn.api.Layer
-
Calculate the gradient relative to the error in the next layer
- 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, 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.Deconvolution3DLayer
- 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, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.PReLU
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
- backpropGradient(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
- 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(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.recurrent.TimeDistributedLayer
- 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
- backpropGradient(INDArray, INDArray, double, double, double, double, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
- backpropGradient(INDArray, INDArray, double, double, double, double, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper
- backpropGradient(INDArray, INDArray, int[], int[], int[], PoolingType, ConvolutionMode, int[], CNN2DFormat, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper
- backpropGradient(INDArray, INDArray, int[], int[], int[], PoolingType, ConvolutionMode, int[], CNN2DFormat, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper
- backpropGradient(INDArray, INDArray, long[], INDArray, INDArray, INDArray, INDArray, double, CNN2DFormat, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
- backpropGradient(INDArray, INDArray, long[], INDArray, INDArray, INDArray, INDArray, double, CNN2DFormat, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
- backpropGradient(INDArray, INDArray, INDArray, INDArray, int[], int[], int[], INDArray, INDArray, IActivation, ConvolutionLayer.AlgoMode, ConvolutionLayer.BwdFilterAlgo, ConvolutionLayer.BwdDataAlgo, ConvolutionMode, int[], CNN2DFormat, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
- backpropGradient(INDArray, INDArray, INDArray, INDArray, int[], int[], int[], INDArray, INDArray, IActivation, ConvolutionLayer.AlgoMode, ConvolutionLayer.BwdFilterAlgo, ConvolutionLayer.BwdDataAlgo, ConvolutionMode, int[], CNN2DFormat, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
- 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 - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
- backpropType - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
- backpropType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
- backpropType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
- backpropType - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- backpropType(BackpropType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
The type of backprop.
- backpropType(BackpropType) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- backpropType(BackpropType) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
The type of backprop.
- backpropType(BackpropType) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
The type of backprop.
- BackpropType - Enum in org.deeplearning4j.nn.conf
- BackTrackLineSearch - Class in org.deeplearning4j.optimize.solvers
- BackTrackLineSearch(Model, ConvexOptimizer) - Constructor for class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
- BackTrackLineSearch(Model, StepFunction, ConvexOptimizer) - Constructor for class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
- BACKWARD_PASS - org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType
- 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() - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- BaseConvBuilder(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[], 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[], 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[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- BaseConvBuilder(int, int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- BaseEarlyStoppingTrainer<T extends Model> - Class in org.deeplearning4j.earlystopping.trainer
- 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.
- BaseEvaluation() - Constructor for class org.deeplearning4j.eval.BaseEvaluation
-
Deprecated.
- BaseGraphVertex - Class in org.deeplearning4j.nn.graph.vertex
- 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
- BaseIEvaluationScoreCalculator(DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator
- BaseIEvaluationScoreCalculator(MultiDataSetIterator) - 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<LayerConfT extends BaseLayer> - Class in org.deeplearning4j.nn.layers
-
A layer with parameters
- BaseLayer(BaseLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseLayer
- 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
- BaseMKLDNNHelper() - Constructor for class org.deeplearning4j.nn.layers.mkldnn.BaseMKLDNNHelper
- BaseMLNScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc.base
- BaseMLNScoreCalculator(DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.base.BaseMLNScoreCalculator
- BaseMultiLayerUpdater<T extends Model> - Class in org.deeplearning4j.nn.updater
- 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
- 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<LayerConfT extends BaseOutputLayer> - Class in org.deeplearning4j.nn.layers
- BaseOutputLayer(BaseOutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
- 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<LayerConfT extends BasePretrainNetwork> - Class in org.deeplearning4j.nn.layers
- BasePretrainNetwork(BasePretrainNetwork.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
- 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<LayerConfT extends BaseRecurrentLayer> - Class in org.deeplearning4j.nn.layers.recurrent
- BaseRecurrentLayer(BaseRecurrentLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer
- 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(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- BaseSubsamplingBuilder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- BaseSubsamplingBuilder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- BaseSubsamplingBuilder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- BaseSubsamplingBuilder(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.SubsamplingLayer.BaseSubsamplingBuilder
- BaseSubsamplingBuilder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- BaseSubsamplingBuilder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- BaseSubsamplingBuilder(PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.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.Subsampling3DLayer.BaseSubsamplingBuilder
- BaseSubsamplingBuilder(PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- BaseSubsamplingBuilder(Subsampling3DLayer.PoolingType) - 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[]) - 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(SubsamplingLayer.PoolingType) - 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[]) - 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
- BaseTrainingListener - Class in org.deeplearning4j.optimize.api
- 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
- BaseWrapperLayer - Class in org.deeplearning4j.nn.layers.wrapper
- BaseWrapperLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
- BaseWrapperLayer(Layer) - Constructor for class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- BaseWrapperLayer(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
- BaseWrapperLayer(Layer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
- BaseWrapperVertex - Class in org.deeplearning4j.nn.graph.vertex
- BaseWrapperVertex(GraphVertex) - Constructor for class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
- BasicGradientsAccumulator - Class in org.deeplearning4j.optimize.solvers.accumulation
- BasicGradientsAccumulator(int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
-
Creates new GradientsAccumulator with starting threshold of 1e-3
- BasicGradientsAccumulator(int, MessageHandler) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
-
Creates new GradientsAccumulator with custom starting threshold
- BATCH_NORM_CUDNN_HELPER_CLASS_NAME - Static variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
- BatchNormalization - Class in org.deeplearning4j.nn.conf.layers
- BatchNormalization - Class in org.deeplearning4j.nn.layers.normalization
- BatchNormalization() - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization
- 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
- BatchNormalizationParamInitializer - Class in org.deeplearning4j.nn.params
- 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
- 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
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
- beta(double) - Method 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.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.Deconvolution3DParamInitializer
- 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 - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- biasInit - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- biasInit(double) - Method 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.NeuralNetConfiguration.Builder
-
Constant for bias initialization.
- 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 - 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 - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
- biasUpdater - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- biasUpdater - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- biasUpdater(IUpdater) - Method 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.samediff.AbstractSameDiffLayer.Builder
-
Gradient updater configuration, for the biases only.
- biasUpdater(IUpdater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Gradient updater configuration, for the biases only.
- 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(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
- BidirectionalLayer(NeuralNetConfiguration, Layer, Layer, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- BidirectionalParamInitializer - Class in org.deeplearning4j.nn.params
- BidirectionalParamInitializer(Bidirectional) - Constructor for class org.deeplearning4j.nn.params.BidirectionalParamInitializer
- BINARY - org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.ROCType
- BinomialDistribution - Class in org.deeplearning4j.nn.conf.distribution
- 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.SpaceToDepthLayer.Builder
- blocks(int...) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.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
- BoundingBoxesDeserializer - Class in org.deeplearning4j.nn.conf.layers.objdetect
- BoundingBoxesDeserializer() - Constructor for class org.deeplearning4j.nn.conf.layers.objdetect.BoundingBoxesDeserializer
- boundingBoxPriors(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder
-
Bounding box priors dimensions [width, height].
- BP_WORKING_MEM - org.deeplearning4j.nn.workspace.ArrayType
- 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
- BUCKET_LENGTH - Static variable in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener.ScoreStat
- 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.Deconvolution3D.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() - Static method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- builder() - Static method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
- 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() - Constructor for class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
- Builder() - 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() - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.CapsuleStrengthLayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution3D.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.DenseLayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.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() - 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() - Constructor for class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
- Builder() - 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() - Constructor for class org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.PReLULayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
- Builder() - 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() - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.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() - Constructor for class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
- Builder() - Constructor for class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr.Builder
- Builder() - Constructor for class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder
- Builder() - Constructor for class org.deeplearning4j.optimize.Solver.Builder
- Builder(@lombok.NonNull int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder
- Builder(@lombok.NonNull int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder
- Builder(@lombok.NonNull int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder
- Builder(@lombok.NonNull int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder
- Builder(boolean) - Constructor for class org.deeplearning4j.nn.conf.layers.BatchNormalization.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(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(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(double, double, double) - Constructor for class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
- Builder(double, double, double, double) - Constructor for class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.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) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder
- Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder
- Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
- Builder(int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.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(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) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
-
This
- Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
- Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
- Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
- Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
- Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
- Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
- Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
- Builder(int...) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
- Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer.Builder
- Builder(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder
- Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
- Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
- Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
- Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
- Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
- Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
- Builder(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
- Builder(int[], int[][]) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
- Builder(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.ConvolutionLayer.Builder
- Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
- Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
- Builder(int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
- Builder(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.SubsamplingLayer.Builder
- Builder(int[], int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
- Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder
- Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
- Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D.Builder
- Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder
- Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
- Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
- Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer.Builder
- Builder(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder
- Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder
- Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
- Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D.Builder
- Builder(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.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[]) - 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[], int[], int[]) - 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[], int[], ConvolutionMode) - Constructor for class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
- Builder(int, int, 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.ZeroPaddingLayer.Builder
- Builder(int, int, int, 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.ZeroPadding3DLayer.Builder
-
Explicit padding of left and right boundaries in depth, height and width dimensions
- Builder(int, CNN2DFormat) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
- Builder(int, SpaceToDepthLayer.DataFormat) - Constructor for class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
-
Deprecated.
- Builder(File) - Constructor for class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder
- Builder(String) - Constructor for class org.deeplearning4j.optimize.listeners.CheckpointListener.Builder
- Builder(String, Class<?>, InputType, InputType) - Constructor for class org.deeplearning4j.nn.conf.memory.LayerMemoryReport.Builder
- Builder(IDropout) - Constructor for class org.deeplearning4j.nn.conf.layers.DropoutLayer.Builder
- Builder(Convolution3D.DataFormat) - Constructor for class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder
- Builder(Convolution3D.DataFormat, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder
- Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder
- Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
- Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
- Builder(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
- Builder(PoolingType, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
- Builder(PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.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.Subsampling3DLayer.Builder
- Builder(PoolingType, int[], int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
- Builder(PoolingType, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
- Builder(Subsampling3DLayer.PoolingType) - 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[]) - 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(SubsamplingLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
- Builder(SubsamplingLayer.PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
- Builder(SubsamplingLayer.PoolingType, int) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
- Builder(SubsamplingLayer.PoolingType, int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
- Builder(SubsamplingLayer.PoolingType, int[], 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(SubsamplingLayer.PoolingType, 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(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- Builder(MultiLayerNetwork) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder
-
Multilayer Network to tweak for transfer learning
- Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
- Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
- Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
- Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
- Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder
- Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
- Builder(ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder
- Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
- Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
- Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
- Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
- Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer.Builder
- Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
- Builder(LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder
- BUSY - org.deeplearning4j.optimize.listeners.SleepyTrainingListener.SleepMode
-
Busy-lock will be used, to guarantee 100% thread use
- 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
- CACHED_MEMORY_FIXED - org.deeplearning4j.nn.conf.memory.MemoryType
- CACHED_MEMORY_VARIABLE - org.deeplearning4j.nn.conf.memory.MemoryType
- 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 - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
- cacheMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
- cacheMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
- cacheMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- 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
- 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(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(CacheMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- CacheMode - Enum in org.deeplearning4j.nn.conf
- 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(ComputationGraph) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG
-
Deprecated.
- calculateScore(T) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator
- calculateScore(T) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
- 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
- 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
- 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
- CapsuleUtils() - Constructor for class org.deeplearning4j.util.CapsuleUtils
- Causal - org.deeplearning4j.nn.conf.ConvolutionMode
- CENTER_KEY - Static variable in class org.deeplearning4j.nn.params.CenterLossParamInitializer
- CenterLossOutputLayer - Class in org.deeplearning4j.nn.conf.layers
- CenterLossOutputLayer - Class in org.deeplearning4j.nn.layers.training
- CenterLossOutputLayer(CenterLossOutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
- 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
- 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(GradientCheckUtil.GraphConfig) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
- checkGradients(GradientCheckUtil.MLNConfig) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
- checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
-
Deprecated.
- checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray, INDArray, INDArray, boolean, int, Set<String>, Integer) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
-
Deprecated.
- 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
- Checkpoint() - Constructor for class org.deeplearning4j.optimize.listeners.Checkpoint
- CheckpointListener - Class in org.deeplearning4j.optimize.listeners
- 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.LayerHelper
- checkSupported() - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
- checkSupported() - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
- checkSupported() - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
- checkSupported() - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper
- checkSupported(double, boolean) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
- checkSupported(double, boolean) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
- checkSupported(double, double, double, double) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
- 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
- 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
- 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.
SeeComputationGraph.setLayerMaskArrays(INDArray[], INDArray[])
for details on mask arrays. - clearLayerMaskArrays() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Remove the mask arrays from all layers.
SeeMultiLayerNetwork.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
- ClipElementWiseAbsoluteValue - org.deeplearning4j.nn.conf.GradientNormalization
- ClipL2PerLayer - org.deeplearning4j.nn.conf.GradientNormalization
- ClipL2PerParamType - org.deeplearning4j.nn.conf.GradientNormalization
- 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.AttentionVertex
- 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.Deconvolution3D
- 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.SameDiffLambdaVertex
- 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
- clone() - Method in class org.deeplearning4j.preprocessors.KerasFlattenRnnPreprocessor
- close() - Method in interface org.deeplearning4j.nn.api.Model
- close() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Close the network and deallocate all native memory, including: parameters, gradients, updater memory and workspaces Note that the network should not be used again for any purpose after it has been closed
- close() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- close() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- close() - Method in class org.deeplearning4j.nn.layers.samediff.DL4JSameDiffMemoryMgr
- close() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- close() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- close() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Close the network and deallocate all native memory, including: parameters, gradients, updater memory and workspaces Note that the network should not be used again for any purpose after it has been closed
- CNN - org.deeplearning4j.nn.conf.inputs.InputType.Type
- 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.
- cnn2dDataFormat - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
- cnn2dDataFormat - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- cnn2DFormat - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
- cnn2DFormat - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
- cnn2DFormat - Variable in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
- cnn2DFormat - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
-
Configure the 2d data format
- CNN2DFormat - Enum in org.deeplearning4j.nn.conf
- 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.
- CNN3D - org.deeplearning4j.nn.conf.inputs.InputType.Type
- Cnn3DLossLayer - Class in org.deeplearning4j.nn.conf.layers
- Cnn3DLossLayer - Class in org.deeplearning4j.nn.layers.convolution
- 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
- Cnn3DToFeedForwardPreProcessor() - 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(long, long, long, long, boolean) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.Cnn3DToFeedForwardPreProcessor
- CNNFlat - org.deeplearning4j.nn.conf.inputs.InputType.Type
- CnnLossLayer - Class in org.deeplearning4j.nn.conf.layers
- CnnLossLayer - Class in org.deeplearning4j.nn.layers.convolution
- 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
- CnnToFeedForwardPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
- CnnToFeedForwardPreProcessor(long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
- CnnToFeedForwardPreProcessor(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
- CnnToFeedForwardPreProcessor(long, long, long, CNN2DFormat) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
- CnnToRnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
- CnnToRnnPreProcessor(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
- CnnToRnnPreProcessor(long, long, long, RNNFormat) - 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
- collapseThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
- CollectScoresIterationListener - Class in org.deeplearning4j.optimize.listeners
- 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.
- CollectScoresIterationListener.ScoreStat - Class in org.deeplearning4j.optimize.listeners
- CollectScoresListener - Class in org.deeplearning4j.optimize.listeners
- 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
- ComposableInputPreProcessor(InputPreProcessor...) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
- ComposableIterationListener - Class in org.deeplearning4j.optimize.listeners
-
Deprecated.
- ComposableIterationListener(Collection<TrainingListener>) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener
-
Deprecated.
- ComposableIterationListener(TrainingListener...) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener
-
Deprecated.
- CompositeReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational
- CompositeReconstructionDistribution(int[], ReconstructionDistribution[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
- CompositeReconstructionDistribution.Builder - Class in org.deeplearning4j.nn.conf.layers.variational
- ComputationGraph - Class in org.deeplearning4j.nn.graph
- ComputationGraph(ComputationGraphConfiguration) - Constructor for class org.deeplearning4j.nn.graph.ComputationGraph
- ComputationGraphConfiguration - Class in org.deeplearning4j.nn.conf
- 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 - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- ComputationGraphUpdater - Class in org.deeplearning4j.nn.updater.graph
- ComputationGraphUpdater(ComputationGraph) - Constructor for class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
- ComputationGraphUpdater(ComputationGraph, INDArray) - Constructor for class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
- ComputationGraphUtil - Class in org.deeplearning4j.nn.graph.util
- computeGradient(INDArray, INDArray, IActivation, INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction
- computeGradientAndScore() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- computeGradientAndScore() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- computeGradientAndScore(LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Model
-
Update the score
- computeGradientAndScore(LayerWorkspaceMgr) - 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(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(INDArray, INDArray, IActivation, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction
- 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(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.
- computeScore(INDArray, INDArray, IActivation, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction
- 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.
- CONCAT - org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Mode
- conf - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
- conf - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- conf - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- 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() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- conf() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- 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
- 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 - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
- confs(List<NeuralNetConfiguration>) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
- confs(List<NeuralNetConfiguration>) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- 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.
- ConfusionMatrix() - Constructor for class org.deeplearning4j.eval.ConfusionMatrix
-
Deprecated.Use
ConfusionMatrix
- ConfusionMatrix(List<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix
-
Deprecated.Use
ConfusionMatrix
- ConfusionMatrix(ConfusionMatrix<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix
-
Deprecated.Use
ConfusionMatrix
- CONJUGATE_GRADIENT - org.deeplearning4j.nn.api.OptimizationAlgorithm
- ConjugateGradient - Class in org.deeplearning4j.optimize.solvers
- 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
- 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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- constraints(List<LayerConstraint>) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
Set constraints to be applied to all layers.
- 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.
- convertMultipleTypes(InputType[]) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
-
Convert multiple types when multiple are found.
- ConvexOptimizer - Interface in org.deeplearning4j.optimize.api
- Convolution1D - Class in org.deeplearning4j.nn.conf.layers
- Convolution1D() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution1D
- Convolution1DLayer - Class in org.deeplearning4j.nn.conf.layers
- Convolution1DLayer - Class in org.deeplearning4j.nn.layers.convolution
- 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
- Convolution2D - Class in org.deeplearning4j.nn.conf.layers
- Convolution2D() - Constructor for class org.deeplearning4j.nn.conf.layers.Convolution2D
- Convolution3D - Class in org.deeplearning4j.nn.conf.layers
- 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
- Convolution3DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer
- Convolution3DParamInitializer - Class in org.deeplearning4j.nn.params
- Convolution3DParamInitializer() - Constructor for class org.deeplearning4j.nn.params.Convolution3DParamInitializer
- Convolution3DUtils - Class in org.deeplearning4j.util
- convolutional(int, int, int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder
- 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(long, long, long, CNN2DFormat) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
- CONVOLUTIONAL - org.deeplearning4j.nn.api.Layer.Type
- convolutional3D(long, long, long, long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
- 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 - CONVOLUTIONAL3D - org.deeplearning4j.nn.api.Layer.Type
- convolutionalFlat(int, int, int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder
- 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.
- convolutionDim - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- ConvolutionHelper - Interface in org.deeplearning4j.nn.layers.convolution
- ConvolutionLayer - Class in org.deeplearning4j.nn.conf.layers
- ConvolutionLayer - Class in org.deeplearning4j.nn.layers.convolution
- 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(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
, andConvolutionLayer.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 - Variable 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 - Variable 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 - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- convolutionMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
- convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
- convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
- convolutionMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
- convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
-
Set the convolution mode for the Convolution layer.
- convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
-
Set the convolution mode for the Convolution layer.
- convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution3D.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(ConvolutionMode) - Method 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.SubsamplingLayer.BaseSubsamplingBuilder
-
Set the convolution mode for the Convolution layer.
- 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(ConvolutionMode) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
Sets the convolution mode for convolutional layers, which impacts padding and output sizes.
- ConvolutionMode - Enum in org.deeplearning4j.nn.conf
- ConvolutionParamInitializer - Class in org.deeplearning4j.nn.params
- ConvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.ConvolutionParamInitializer
- ConvolutionUtils - Class in org.deeplearning4j.util
- copyToLegacy(IEvaluation<?>, Class<T>) - Static method in class org.deeplearning4j.eval.EvaluationUtils
-
Deprecated.
- corruptionLevel - Variable in class org.deeplearning4j.nn.conf.layers.AutoEncoder
- corruptionLevel(double) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder
-
Level of corruption - 0.0 (none) to 1.0 (all values corrupted)
- COUNT - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo
- COUNT - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo
- COUNT - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo
- 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
- createBias(long, double, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
- 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(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(long, double, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
- createGain(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
- createGradient(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
- createHelper(String, String, Class<? extends LayerHelper>, String, Object...) - Static method in class org.deeplearning4j.nn.layers.HelperUtils
-
Creates a
LayerHelper
for use with platform specific code. - 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(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(long, long, IWeightInit, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.EmbeddingLayerParamInitializer
- 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.Deconvolution3DParamInitializer
- 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(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
- Cropping1D - Class in org.deeplearning4j.nn.conf.layers.convolutional
- Cropping1D(int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D
- 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(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
- Cropping1DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer
- Cropping2D - Class in org.deeplearning4j.nn.conf.layers.convolutional
- Cropping2D(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
- 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(CNN2DFormat, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
- Cropping2D(CNN2DFormat, int, int, int, 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
- Cropping2DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer
- Cropping3D - Class in org.deeplearning4j.nn.conf.layers.convolutional
- Cropping3D(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D
- 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(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
- Cropping3DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer
- CUDA_CNN_HELPER_CLASS_NAME - Static variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
- CUDNN_DROPOUT_HELPER_CLASS_NAME - Static variable in class org.deeplearning4j.nn.conf.dropout.Dropout
- CUDNN_LSTM_CLASS_NAME - Static variable in class org.deeplearning4j.nn.layers.recurrent.LSTM
- CUDNN_SUBSAMPLING_HELPER_CLASS_NAME - Static variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
- 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 - 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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
-
Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
- cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.conf.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.
- 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 - 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 - 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 - 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 - 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 - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
Deprecated.
- cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
-
Deprecated.
- cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
-
Deprecated.
- cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- cudnnAllowFallback(boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- cudnnBwdDataAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- cudnnBwdDataAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
- cudnnBwdDataMode(ConvolutionLayer.BwdDataAlgo) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- cudnnBwdFilterAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- cudnnBwdFilterAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
- cudnnBwdFilterMode(ConvolutionLayer.BwdFilterAlgo) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- cudnnFwdAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- cudnnFwdAlgo - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
- cudnnFwdMode(ConvolutionLayer.FwdAlgo) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- currentConsumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
- currentConsumers - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- currentStep - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- currentThreshold - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
- CUSTOM - org.deeplearning4j.nn.conf.Updater
-
Deprecated.
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 - Variable in class org.deeplearning4j.nn.conf.layers.Convolution3D
- dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
- dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
- dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
- dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
- dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
-
Data format for input activations.
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
- dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder
- dataFormat - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling3D
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
Set the input and output array data format.
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D.Builder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D.Builder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.SeparableConvolution2D.Builder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- dataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer.Builder
-
Set the data format for the CNN activations - NCHW (channels first) or NHWC (channels last).
- 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(Convolution3D.DataFormat) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution3D.Builder
- 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(Convolution3D.DataFormat) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling3D.Builder
-
Sets the DataFormat.
- dataFormat(SpaceToDepthLayer.DataFormat) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.Builder
-
Deprecated.
- dataFormat(RNNFormat) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder
- dataFormat(RNNFormat) - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder
- dataFormat(RNNFormat) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
- dataFormat(RNNFormat) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder
- DataFormat - Interface in org.deeplearning4j.nn.conf
- DataFormatDeserializer - Class in org.deeplearning4j.nn.conf.serde.format
- DataFormatDeserializer() - Constructor for class org.deeplearning4j.nn.conf.serde.format.DataFormatDeserializer
- DataFormatSerializer - Class in org.deeplearning4j.nn.conf.serde.format
- DataFormatSerializer() - Constructor for class org.deeplearning4j.nn.conf.serde.format.DataFormatSerializer
- DataSetLossCalculator - Class in org.deeplearning4j.earlystopping.scorecalc
- 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.
- 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 - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
- dataType - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- 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
- 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(DataType) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Set the DataType for the network parameters and activations.
- dataType(DataType) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
- 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.
- 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 - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- decoderLayerSizes(int...) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder
-
Size of the decoder layers, in units.
- decodeUpdates(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
-
Deprecated.
- Deconvolution2D - Class in org.deeplearning4j.nn.conf.layers
- 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
- Deconvolution2DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Deconvolution2DLayer
- Deconvolution3D - Class in org.deeplearning4j.nn.conf.layers
- Deconvolution3D(Deconvolution3D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Deconvolution3D
-
Deconvolution3D 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
- Deconvolution3D.Builder - Class in org.deeplearning4j.nn.conf.layers
- Deconvolution3DLayer - Class in org.deeplearning4j.nn.layers.convolution
- Deconvolution3DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.Deconvolution3DLayer
- Deconvolution3DParamInitializer - Class in org.deeplearning4j.nn.params
- Deconvolution3DParamInitializer() - Constructor for class org.deeplearning4j.nn.params.Deconvolution3DParamInitializer
- 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) - Constructor for exception org.deeplearning4j.exception.DeepLearningException
- DeepLearningException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DeepLearningException
- DeepLearningException(String, Throwable, boolean, boolean) - 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_FORMAT - Static variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- 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_MERGE_DIM - Static variable in class org.deeplearning4j.nn.conf.graph.MergeVertex
- 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
- 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
- DefaultParamInitializer() - Constructor for class org.deeplearning4j.nn.params.DefaultParamInitializer
- DefaultStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions
- DefaultStepFunction - Class in org.deeplearning4j.optimize.stepfunctions
- DefaultStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction
- 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(int) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams
-
Define the inputs to the DL4J SameDiff vertex with generated names.
- defineInputs(String...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams
-
Define the inputs to the DL4J SameDiff Vertex with specific names
- defineLayer(SameDiff, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleStrengthLayer
- defineLayer(SameDiff, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer
-
The defineLayer method is used to define the forward pass for the layer
- defineLayer(SameDiff, SDVariable) - Method in class org.deeplearning4j.nn.layers.util.IdentityLayer
- defineLayer(SameDiff, SDVariable, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer
- 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, 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, Map<String, SDVariable>, SDVariable) - Method in class org.deeplearning4j.nn.conf.layers.SelfAttentionLayer
- defineLayer(SameDiff, SDVariable, SDVariable, Map<String, SDVariable>) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer
-
Define the output layer
- 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, 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
- 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
- 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
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D
- 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.DepthwiseConvolution2D.Builder
-
Set channels multiplier for depth-wise 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
- 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
- DepthwiseConvolution2DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.DepthwiseConvolution2DLayer
- DepthwiseConvolutionParamInitializer - Class in org.deeplearning4j.nn.params
- 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.layers.objdetect.BoundingBoxesDeserializer
- 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.format.DataFormatDeserializer
- 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
- DetectedObject(int, double, double, double, double, INDArray, double) - Constructor for class org.deeplearning4j.nn.layers.objdetect.DetectedObject
- DEVICE - org.deeplearning4j.nn.conf.CacheMode
-
Device memory will be used for cache (if current backend support such differentiation)
- dilation - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
-
Kernel dilation.
- dilation - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
- dilation - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- dilation - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
- dilation - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- dilation(int) - Method in class org.deeplearning4j.nn.conf.layers.LocallyConnected1D.Builder
- dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
-
Set dilation size for 3D convolutions in (depth, height, width) order
- dilation(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
-
Kernel dilation.
- 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(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
-
Kernel dilation.
- dilation(int, int, int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- dimension - Variable in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
- dimensionNames() - Method in enum org.deeplearning4j.nn.conf.CNN2DFormat
-
Returns a string that explains the dimensions:
NCHW -> returns "[minibatch, channels, height, width]"
NHWC -> returns "[minibatch, height, width, channels]" - dimensions - Variable in class org.deeplearning4j.nn.conf.constraint.BaseConstraint
- DIRECT - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo
- 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.
- dist(Distribution) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
Deprecated.
- Distribution - Class in org.deeplearning4j.nn.conf.distribution
- Distribution() - Constructor for class org.deeplearning4j.nn.conf.distribution.Distribution
- DISTRIBUTION - org.deeplearning4j.nn.weights.WeightInit
- 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
- divideByMinibatch(boolean, Gradient, int) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
- DL4JException - Exception in org.deeplearning4j.exception
- 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
- 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
- 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
- DL4JSameDiffMemoryMgr - Class in org.deeplearning4j.nn.layers.samediff
- DL4JSameDiffMemoryMgr(String, String, WorkspaceConfiguration, WorkspaceConfiguration) - Constructor for class org.deeplearning4j.nn.layers.samediff.DL4JSameDiffMemoryMgr
- 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(DataSetIterator, T...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Perform evaluation on the given data (DataSetIterator) 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 interface org.deeplearning4j.nn.api.NeuralNetwork
-
This method executes evaluation of the model against given iterator and evaluation implementations
- doEvaluation(MultiDataSetIterator, T...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Perform evaluation on the given data (MultiDataSetIterator) with the given
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(long, INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
- drainTo(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
- 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
- DropConnect - Class in org.deeplearning4j.nn.conf.weightnoise
- 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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- dropout(IDropout) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
Set the dropout
- dropOut(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Dropout probability.
- dropOut(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Dropout probability.
- dropOut(double) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.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(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 - Class in org.deeplearning4j.nn.conf.dropout
- Dropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.Dropout
- Dropout(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.Dropout
- Dropout(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.Dropout
- dropoutApplied - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
- DropoutHelper - Interface in org.deeplearning4j.nn.conf.dropout
- DropoutLayer - Class in org.deeplearning4j.nn.conf.layers
- DropoutLayer - Class in org.deeplearning4j.nn.layers
- DropoutLayer(double) - Constructor for class org.deeplearning4j.nn.conf.layers.DropoutLayer
- DropoutLayer(IDropout) - Constructor for class org.deeplearning4j.nn.conf.layers.DropoutLayer
- 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
- DummyConfig() - Constructor for class org.deeplearning4j.nn.conf.misc.DummyConfig
- DuplicateToTimeSeriesVertex - Class in org.deeplearning4j.nn.conf.graph.rnn
- DuplicateToTimeSeriesVertex - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn
- DuplicateToTimeSeriesVertex(String) - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
- 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
- EarlyStoppingConfiguration.Builder<T extends Model> - Class in org.deeplearning4j.earlystopping
- EarlyStoppingGraphTrainer - Class in org.deeplearning4j.earlystopping.trainer
- 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
- EarlyStoppingModelSaver<T extends Model> - Interface in org.deeplearning4j.earlystopping
- EarlyStoppingResult<T extends Model> - Class in org.deeplearning4j.earlystopping
- 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
- 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.ConvolutionUtils
- effectiveKernelSize(int, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils
- element() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- ElementWiseMultiplicationLayer - Class in org.deeplearning4j.nn.conf.layers.misc
- ElementWiseMultiplicationLayer - Class in org.deeplearning4j.nn.layers.feedforward.elementwise
- 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(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
- ElementWiseParamInitializer() - Constructor for class org.deeplearning4j.nn.params.ElementWiseParamInitializer
- ElementWiseVertex - Class in org.deeplearning4j.nn.conf.graph
- ElementWiseVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- ElementWiseVertex(ElementWiseVertex.Op) - Constructor for class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
- 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
- EmbeddingLayer - Class in org.deeplearning4j.nn.conf.layers
- EmbeddingLayer - Class in org.deeplearning4j.nn.layers.feedforward.embedding
- EmbeddingLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
- EmbeddingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
- EmbeddingLayerParamInitializer - Class in org.deeplearning4j.nn.params
- EmbeddingLayerParamInitializer() - Constructor for class org.deeplearning4j.nn.params.EmbeddingLayerParamInitializer
- EmbeddingSequenceLayer - Class in org.deeplearning4j.nn.conf.layers
- EmbeddingSequenceLayer - Class in org.deeplearning4j.nn.layers.feedforward.embedding
- 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
- ENABLED - org.deeplearning4j.nn.conf.WorkspaceMode
- encode(INDArray, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
- EncodedGradientsAccumulator - Class in org.deeplearning4j.optimize.solvers.accumulation
- EncodedGradientsAccumulator(int, double) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
- EncodedGradientsAccumulator(int, MessageHandler, long, int, Integer, boolean) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
- EncodedGradientsAccumulator(int, ThresholdAlgorithm, ResidualPostProcessor, 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 - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- encoderLayerSizes(int...) - Method in class org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder
-
Size of the encoder layers, in units.
- 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 - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
- encodingDebugMode - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
- encodingDebugMode(boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
- EncodingHandler - Class in org.deeplearning4j.optimize.solvers.accumulation
- EncodingHandler(ThresholdAlgorithm, ResidualPostProcessor, Integer, boolean) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
- EPOCH_END - org.deeplearning4j.optimize.api.InvocationType
-
Iterator will be called on end of epoch
- EPOCH_END - org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType
- EPOCH_START - org.deeplearning4j.optimize.api.InvocationType
-
Iterator will be called on start of epoch.
- EPOCH_START - org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType
- 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 - org.deeplearning4j.earlystopping.EarlyStoppingResult.TerminationReason
- EpochTerminationCondition - Interface in org.deeplearning4j.earlystopping.termination
- 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
- epochTerminationConditions(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 https://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 - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- 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 https://arxiv.org/pdf/1502.03167v3.pdf) to reduce/avoid underflow issues.
Default: 1e-5 - eps(double) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- 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
- Error - org.deeplearning4j.earlystopping.EarlyStoppingResult.TerminationReason
- 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.multilayer.MultiLayerNetwork
-
Evaluate the network (classification performance)
- evaluate(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Evaluate the network (classification performance).
- evaluate(DataSetIterator) - 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(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.graph.ComputationGraph
-
Evaluate the network (for classification) on the provided data set, with top N accuracy in addition to standard accuracy.
- 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.
- evaluate(DataSetIterator, Map<Integer, T[]>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Perform evaluation for networks with multiple outputs.
- evaluate(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Evaluate the network (classification performance - 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(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(MultiDataSetIterator, Map<Integer, T[]>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Perform evaluation for networks with multiple outputs.
- 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(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Evaluate the 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) - 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.multilayer.MultiLayerNetwork
-
Evaluate the network for regression performance Can only be used with MultiDataSetIterator instances with a single input/output array
- evaluateRegression(MultiDataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Evaluate the (single output layer only) 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) - 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.graph.ComputationGraph
-
Evaluate the network (must be a binary classifier) on the specified data, using the
ROC
class - evaluateROC(DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Evaluate the network (must be a binary classifier) on the specified data, using the
ROC
class - 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 - 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) - 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.graph.ComputationGraph
-
Evaluate the network on the specified data, using the
ROCMultiClass
class - evaluateROCMultiClass(DataSetIterator, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Evaluate the network on the specified data, using the
ROCMultiClass
class - evaluateROCMultiClass(MultiDataSetIterator, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
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.
- Evaluation() - 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(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(List<String>, int) - 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(Map<Integer, String>) - 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.Metric - Enum in org.deeplearning4j.eval
-
Deprecated.
- EvaluationAveraging - Enum in org.deeplearning4j.eval
-
Deprecated.
- EvaluationBinary - Class in org.deeplearning4j.eval
-
Deprecated.
- EvaluationBinary(int, Integer) - Constructor for class org.deeplearning4j.eval.EvaluationBinary
-
Deprecated.
- EvaluationBinary(INDArray) - Constructor for class org.deeplearning4j.eval.EvaluationBinary
-
Deprecated.
- EvaluationCalibration - Class in org.deeplearning4j.eval
-
Deprecated.
- EvaluationCalibration() - Constructor for class org.deeplearning4j.eval.EvaluationCalibration
-
Deprecated.
- EvaluationCalibration(int, int) - Constructor for class org.deeplearning4j.eval.EvaluationCalibration
-
Deprecated.
- EvaluationCalibration(int, int, boolean) - Constructor for class org.deeplearning4j.eval.EvaluationCalibration
-
Deprecated.
- EvaluationCallback - Interface in org.deeplearning4j.optimize.listeners.callbacks
- evaluations - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
- EvaluationUtils - Class in org.deeplearning4j.eval
-
Deprecated.
- EvaluationUtils() - Constructor for class org.deeplearning4j.eval.EvaluationUtils
-
Deprecated.
- EvaluativeListener - Class in org.deeplearning4j.optimize.listeners
- 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(DataSetIterator, int, InvocationType, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener
-
Evaluation will be launched after each *frequency* iteration
- EvaluativeListener(DataSetIterator, int, IEvaluation...) - Constructor for class org.deeplearning4j.optimize.listeners.EvaluativeListener
-
Evaluation will be launched after each *frequency* iteration
- 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(MultiDataSetIterator, 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(DataSet, 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) - 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
- ExponentialReconstructionDistribution() - Constructor for class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
- ExponentialReconstructionDistribution(String) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.ExponentialReconstructionDistribution
-
Deprecated.
- 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(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
- 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
- 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.
- F1 - org.deeplearning4j.eval.Evaluation.Metric
-
Deprecated.
- f1Score(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Returns the f1 score for the given examples.
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Returns the f1 score for the given examples.
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
-
Returns the f1 score for the given examples.
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
-
Returns the f1 score for the given examples.
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Returns the f1 score for the given examples.
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
-
Returns the f1 score for the given examples.
- 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(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Perform inference and then calculate the F1 score of the output(input) vs.
- 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(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(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
- f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
- 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(DataSet) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
- f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
- f1Score(DataSet) - 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
- fa - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
- FAILURES_ONLY - org.deeplearning4j.gradientcheck.GradientCheckUtil.PrintMode
- FailureTestingListener - Class in org.deeplearning4j.optimize.listeners
- 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
- 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(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
- 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
- FEED_FORWARD - org.deeplearning4j.nn.api.Layer.Type
- feedForward() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Conduct forward pass using the stored inputs, at test time
- 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(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Conduct forward pass using the stored inputs
- 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(boolean, boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- feedForward(boolean, int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Conduct forward pass using the stored inputs
- feedForward(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder
- feedForward(long) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
-
InputType for feed forward network data
- feedForward(long, DataFormat) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
- 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[], 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(INDArray[], int, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Conduct forward pass using an array of inputs
- feedForward(INDArray[], int, boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Conduct forward pass using an array of 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.multilayer.MultiLayerNetwork
-
Compute all layer activations, from input to output of the output layer.
- feedForward(INDArray, int, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Conduct forward pass using a single input array.
- 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
- 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.recurrent.TimeDistributedLayer
- 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
- FeedForwardToCnn3DPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor
- FeedForwardToCnn3DPreProcessor(int, int, int, int, boolean) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnn3DPreProcessor
- FeedForwardToCnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
- FeedForwardToCnnPreProcessor(long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
- FeedForwardToCnnPreProcessor(long, long, long) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
-
Reshape to a channels x rows x columns tensor
- 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. - 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. - FeedForwardToRnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
- FeedForwardToRnnPreProcessor(RNNFormat) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
- FF - org.deeplearning4j.nn.conf.inputs.InputType.Type
- FF_CACHE - org.deeplearning4j.nn.workspace.ArrayType
- FF_WORKING_MEM - org.deeplearning4j.nn.workspace.ArrayType
- FFT - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo
- FFT - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo
- FFT - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo
- FFT_TILING - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo
- FFT_TILING - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo
- FFT_TILING - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo
- 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(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. - 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. - finalize() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.AutoencoderScoreCalculator
- finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
- finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
- finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
- finalScore(double, int, int) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
- finalScore(Evaluation) - Method in class org.deeplearning4j.earlystopping.scorecalc.ClassificationScoreCalculator
- finalScore(IEvaluation) - Method in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
- finalScore(RegressionEvaluation) - Method in class org.deeplearning4j.earlystopping.scorecalc.RegressionScoreCalculator
- finalScore(U) - Method in class org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator
- 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 - Class in org.deeplearning4j.nn.transferlearning
- FineTuneConfiguration() - Constructor for class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- 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() - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
- fit() - Method in interface org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer
-
Conduct early stopping training
- fit() - Method in interface org.deeplearning4j.nn.api.Model
-
Deprecated.
- fit() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- fit() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- fit() - Method in class org.deeplearning4j.nn.layers.BaseLayer
- fit() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
- fit() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
- fit() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
- fit() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
- fit() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
- fit() - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
- fit() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- fit() - Method in class org.deeplearning4j.nn.layers.RepeatVector
- fit() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- fit() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- fit() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- fit(boolean) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
- 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(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, 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(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, 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(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(INDArray, int[]) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Fit the model
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Fit the model
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Fit the model for one iteration on the provided data
- fit(INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Model
-
Fit the model to the given data
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
- 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(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.LossLayer
- 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(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.RepeatVector
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- fit(INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- fit(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Fit the model
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Fit the model
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- 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(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(DataSet) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
This method fits model with a given DataSet
- fit(DataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Fit the ComputationGraph using a DataSet.
- fit(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Fit the model
- fit(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
- fit(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
- fit(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Fit the model
- fit(DataSet) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
- fit(DataSet) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
- fit(DataSet) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- fit(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Fit the model for one iteration on the provided data
- fit(DataSetIterator) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Train the model based on the datasetiterator
- fit(DataSetIterator) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
This method fits model with a given DataSetIterator
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.LossLayer
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- 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(MultiDataSetIterator) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
This method fits model with a given MultiDataSetIterator
- 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(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) - fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
- fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
- fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
- fit(MultiDataSet) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
This method fits model with a given MultiDataSet
- fit(MultiDataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Fit the ComputationGraph using a MultiDataSet
- fit(MultiDataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
- fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
- fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
- fitFeaturized(DataSetIterator) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
- fitFeaturized(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
-
Fit from a featurized dataset.
- fitFeaturized(DataSet) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
- fitFeaturized(MultiDataSet) - 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
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM
- forgetGateBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.AbstractLSTM.Builder
-
Set forget gate bias initalizations.
- forgetGateBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder
-
Deprecated.Set forget gate bias initalizations.
- format - Variable in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
- format - Variable in class org.deeplearning4j.nn.conf.layers.CnnLossLayer
- format - Variable in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D.Builder
- format - Variable in class org.deeplearning4j.nn.conf.layers.LocallyConnected2D
- format - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
- format - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
- format - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder
- format - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling2D
- format - Variable in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
- format(double) - Static method in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
- format(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
- formatter - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
- formatter2 - Static variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
- FORWARD_PASS - org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType
- 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 - Class in org.deeplearning4j.nn.layers
- FrozenLayer(Layer) - Constructor for class org.deeplearning4j.nn.layers.FrozenLayer
- FrozenLayer(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
- FrozenLayer.Builder - Class in org.deeplearning4j.nn.conf.layers.misc
- FrozenLayerParamInitializer - Class in org.deeplearning4j.nn.params
- 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 - 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
- FrozenLayerWithBackprop(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop
- FrozenLayerWithBackpropParamInitializer - Class in org.deeplearning4j.nn.params
- FrozenLayerWithBackpropParamInitializer() - Constructor for class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
- FrozenVertex - Class in org.deeplearning4j.nn.conf.graph
- FrozenVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- FrozenVertex(GraphVertex) - Constructor for class org.deeplearning4j.nn.conf.graph.FrozenVertex
- 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
- FwdPassReturn() - Constructor for class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
- FwdPassType - Enum in org.deeplearning4j.nn.api
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
- gainInit - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- gainInit(double) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
-
Gain initialization value, for layers with Layer Normalization.
- gamma - Variable 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(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
Used only when 'true' is passed to
BatchNormalization.Builder.lockGammaBeta(boolean)
. - 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(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.AbstractLSTM.Builder
-
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.AbstractLSTM.Builder
-
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.
- 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
- GaussianDropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianDropout
- GaussianDropout(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianDropout
- GaussianDropout(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianDropout
- GaussianNoise - Class in org.deeplearning4j.nn.conf.dropout
- GaussianNoise(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianNoise
- GaussianNoise(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianNoise
- GaussianNoise(ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.GaussianNoise
- GaussianReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational
- 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
- GEMM - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo
- 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() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- getComputationGraphUpdater(boolean) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
- getComputationGraphUpdater(boolean) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- getConf() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
- getConf() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- getConf(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
- 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
- getDataFormat() - Method in class org.deeplearning4j.nn.conf.layers.misc.RepeatVector.Builder
- getDataFormat() - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
- getDataFormat() - Method in class org.deeplearning4j.nn.layers.RepeatVector
- getDeconvolution3DOutputSize(INDArray, int[], int[], int[], int[], ConvolutionMode, Convolution3D.DataFormat) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get the output size of a deconvolution operation for given input data.
- getDeconvolutionOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[], CNN2DFormat) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get the output size of a deconvolution operation for given input data.
- getDefaultCNN2DFormat() - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
- 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.
- getEffectiveIndexes() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener.ScoreStat
- getEffectiveScores() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener.ScoreStat
- 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(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
- getFileForCheckpoint(Checkpoint) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener
-
Get the model file for the given checkpoint.
- 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
- getFormat() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling1D
- getFormat() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
- getFormatForLayer(Layer) - Static method in class org.deeplearning4j.util.Convolution3DUtils
-
Returns the
Convolution3D.DataFormat
for the associated layer. - getFormatForLayer(Layer) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get the format for a given layer.
- getFormatFromRnnLayer(Layer) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
-
Get the
RNNFormat
from the RNN layer, accounting for the presence of wrapper layers like Bidirectional, LastTimeStep, etc - 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.Deconvolution3DParamInitializer
- 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(int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get the height and width for an image
- getHeightAndWidth(NeuralNetConfiguration) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get the height and width from the configuration
- 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
- getIndexes() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener.ScoreStat
- 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() - 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
- 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
- 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() - 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
- getInputs(SameDiff) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex
- 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 (seeGraphVertex.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.Deconvolution3DParamInitializer
- 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.EmbeddingLayerParamInitializer
- 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() - 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
- getInstance(long[], long[]) - Static method in class org.deeplearning4j.nn.params.PReLUParamInitializer
- getIntConfig(int[], int) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Return the configuration for a given value for values like stride, dilation, kernel size that require 2 values If the input is already length 2, return that if the length is only 1, return the value specified twice otherwise return the default value duplicated twice
- 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() - Method in class org.deeplearning4j.nn.layers.LossLayer
- getLabels2d(LayerWorkspaceMgr, ArrayType) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
- 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() - 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.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(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- getLayer(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get a given layer by name.
- getLayer(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- 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 usingComputationGraphConfiguration.GraphBuilder.setInputTypes(InputType...)
first - 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 usingNeuralNetConfiguration.ListBuilder.setInputType(InputType)
first - getLayerActivationTypes(boolean, 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(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(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(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.
- 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(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Get the current learning rate, for the specified layer, from the network.
- getLearningRate(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the current learning rate, for the specified layer, from the network.
- getLearningRate(ComputationGraph, String) - Static method in class org.deeplearning4j.util.NetworkUtils
-
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.
- 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
TrainingListener
s 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) - 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.LayerMemoryReport
- 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(boolean, FeedForwardLayer, InputType) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers
- 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.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(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.samediff.SameDiffVertex
- 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
- getMergeAxis() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
- 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() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Get the output layer - i.e., the last layer in the netwok
- getOutputLayer(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the specified output layer, by index.
- getOutputLayerIndices() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- getOutputSize(long, 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
-
Deprecated.
- getOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils
- getOutputSize(INDArray, int[], int[], int[], ConvolutionMode, int[], CNN2DFormat) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get the output size (height/width) for the given input data and CNN configuration
- getOutputSize(INDArray, int, int, int, ConvolutionMode) - Static method in class org.deeplearning4j.util.Convolution1DUtils
- 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
- 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.Deconvolution3D
- 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.recurrent.TimeDistributed
- 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.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(int, InputType) - Method in class org.deeplearning4j.nn.layers.util.IdentityLayer
- 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(int, InputType...) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
- 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(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(InputType) - Method in class org.deeplearning4j.preprocessors.KerasFlattenRnnPreprocessor
- getOutputType(InputType) - Method in class org.deeplearning4j.preprocessors.PermutePreprocessor
- getOutputType(InputType) - Method in class org.deeplearning4j.preprocessors.ReshapePreprocessor
- getOutputTypeCnn1DLayers(InputType, int, int, int, int, ConvolutionMode, long, long, String, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
- getOutputTypeCnn3DLayers(InputType, Convolution3D.DataFormat, 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
- getOutputTypeCnnLayers(InputType, int[], int[], int[], int[], ConvolutionMode, long, long, String, CNN2DFormat, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
- getOutputTypeDeconv3dLayer(InputType, int[], int[], int[], int[], ConvolutionMode, Convolution3D.DataFormat, 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 returnedDetectedObject
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.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.Deconvolution3D
- 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 appropriateInputPreProcessor
for this layer, such as aCnnToFeedForwardPreProcessor
- 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.recurrent.TimeDistributed
- 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
andSubsamplingLayer
- 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
andSubsamplingLayer
- getPreprocessorForInputTypeRnnLayers(InputType, RNNFormat, 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
- getRNNDataFormat() - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional
- getRnnFormatFromLayer(Layer) - Static method in class org.deeplearning4j.util.Convolution1DUtils
-
Get the
RNNFormat
for the given layer. - getSameModeBottomRightPadding(int[], int[], int[], int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get bottom and right padding for same mode only.
- getSameModeBottomRightPadding(int, int, int, int, int) - Static method in class org.deeplearning4j.util.Convolution1DUtils
- getSameModeTopLeftPadding(int[], int[], int[], int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get top and left 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.
- getScoreCalculator() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration
- getScores() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener.ScoreStat
- getScoreVsIter() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
- 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
-
Returns the shape of this InputType
- 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) - 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.LayerMemoryReport
- 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.
- 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() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Get the updater for this MultiLayerNetwork
- getUpdater() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
- getUpdater() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- getUpdater(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the ComputationGraphUpdater for this network
- getUpdater(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- getUpdater(boolean) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
- getUpdater(boolean) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- getUpdater(Model) - Static method in class org.deeplearning4j.nn.updater.UpdaterCreator
- 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
- GlobalPoolingLayer - Class in org.deeplearning4j.nn.layers.pooling
- GlobalPoolingLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
- GlobalPoolingLayer(PoolingType) - Constructor for class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer
- 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.
- GMEASURE - org.deeplearning4j.eval.Evaluation.Metric
-
Deprecated.
- goldLabel() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
- gradient - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
- gradient - Variable in class org.deeplearning4j.nn.layers.BaseLayer
- gradient - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- gradient - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- gradient - Variable in class org.deeplearning4j.optimize.listeners.SharedGradient
- gradient() - Method in interface org.deeplearning4j.nn.api.Model
-
Get the gradient.
- gradient() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
- gradient() - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
The full gradient as one flat vector
- gradient() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- gradient() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- 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() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- gradient() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- gradient() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- gradient(List<String>) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
- gradient(List<String>) - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
The full gradient as one flat vector
- 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 - Interface in org.deeplearning4j.nn.gradient
- GRADIENT_CALC - org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
- gradientCheck(boolean) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
- GradientCheckUtil - Class in org.deeplearning4j.gradientcheck
- GradientCheckUtil.GraphConfig - Class in org.deeplearning4j.gradientcheck
- GradientCheckUtil.MLNConfig - Class in org.deeplearning4j.gradientcheck
- GradientCheckUtil.PrintMode - Enum in org.deeplearning4j.gradientcheck
- gradientForVariable() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
- gradientForVariable() - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
Gradient look up table
- gradientNormalization - Variable 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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- gradientNormalization(GradientNormalization) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
-
Gradient normalization strategy.
- 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 - Enum in org.deeplearning4j.nn.conf
- 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 - 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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- 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(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 - 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
- GradientStepFunction - Class in org.deeplearning4j.optimize.stepfunctions
- GradientStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction
- 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() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Create a GraphBuilder (for creating a ComputationGraphConfiguration).
- GraphBuilder(ComputationGraphConfiguration, NeuralNetConfiguration.Builder) - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
- GraphBuilder(NeuralNetConfiguration.Builder) - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
- 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
- GraphConfig() - Constructor for class org.deeplearning4j.gradientcheck.GradientCheckUtil.GraphConfig
- 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
- GraphIndices() - Constructor for class org.deeplearning4j.nn.graph.util.GraphIndices
- GraphVertex - Class in org.deeplearning4j.nn.conf.graph
- GraphVertex - Interface in org.deeplearning4j.nn.graph.vertex
- GraphVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.GraphVertex
- GraphVertexMixin() - Constructor for class org.deeplearning4j.nn.conf.serde.legacy.LegacyJsonFormat.GraphVertexMixin
- GravesBidirectionalLSTM - Class in org.deeplearning4j.nn.conf.layers
-
Deprecated.
- GravesBidirectionalLSTM - Class in org.deeplearning4j.nn.layers.recurrent
- 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
- GravesBidirectionalLSTMParamInitializer() - Constructor for class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
- GravesLSTM - Class in org.deeplearning4j.nn.conf.layers
-
Deprecated.
- GravesLSTM - Class in org.deeplearning4j.nn.layers.recurrent
-
Deprecated.
- 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
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
- hasBias - Variable 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.BaseOutputLayer
- hasBias() - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D
- hasBias() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
- hasBias() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D
- hasBias() - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution3D
- hasBias() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
- hasBias() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer
- hasBias() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer
- 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(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
-
If true (default): include bias parameters in the model.
- hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.CapsuleLayer.Builder
-
Sets whether to use bias.
- hasBias(boolean) - Method 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.DenseLayer.Builder
-
If true (default): include bias parameters in the model.
- hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder
-
If true: include bias parameters in the layer.
- hasBias(boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder
-
If true: include bias parameters in the layer.
- 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(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() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
- 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(boolean) - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer.Builder
- hasLayerNorm(boolean) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn.Builder
- 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?
- hasRnnDataFormat(Layer) - Static method in class org.deeplearning4j.util.Convolution1DUtils
-
Returns true if the given layer has an
RNNFormat
. - hasSomething - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
- 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
- headSize(long) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.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 - 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 - Variable in class org.deeplearning4j.nn.conf.layers.AbstractLSTM
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM
-
Deprecated.
- 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(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(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(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(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
- HelperUtils - Class in org.deeplearning4j.nn.layers
-
Simple meta helper util class for instantiating platform specific layer helpers that handle interaction with lower level libraries like cudnn and onednn.
- HelperUtils() - Constructor for class org.deeplearning4j.nn.layers.HelperUtils
- 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.
- Histogram(String, double, double, int[]) - Constructor for class org.deeplearning4j.eval.curves.Histogram
-
Deprecated.Use
Histogram
- HOST - org.deeplearning4j.nn.conf.CacheMode
-
Host memory will be used for cache
- 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
- IDENTITY - org.deeplearning4j.nn.weights.WeightInit
- IdentityLayer - Class in org.deeplearning4j.nn.layers.util
- IdentityLayer(String) - Constructor for class org.deeplearning4j.nn.layers.util.IdentityLayer
- idropOut - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- iDropout - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
- iDropout - Variable in class org.deeplearning4j.nn.conf.layers.Layer
- IDropout - Interface in org.deeplearning4j.nn.conf.dropout
- IEarlyStoppingTrainer<T extends Model> - Interface in org.deeplearning4j.earlystopping.trainer
- IEvaluation<T extends IEvaluation> - Interface in org.deeplearning4j.eval
-
Deprecated.
- ILLEGAL_STATE - org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureMode
- ILossFunctionMixin() - Constructor for class org.deeplearning4j.nn.conf.serde.legacy.LegacyJsonFormat.ILossFunctionMixin
- IMPLICIT_GEMM - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo
- IMPLICIT_PRECOMP_GEMM - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo
- 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
- IndexedTail(int) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
- IndexedTail(int, boolean, long[]) - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
- INFERENCE - org.deeplearning4j.nn.conf.memory.MemoryUseMode
- inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
- inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
- inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
- inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- inferenceWorkspaceMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- inferenceWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.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) - 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
- INFINITE_SLEEP - org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureMode
- 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() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Initialize the ComputationGraph network
- init() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
-
Init the model
- init() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
-
Init the model
- init() - Method in class org.deeplearning4j.nn.layers.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() - 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
- init(NeuralNetConfiguration, INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.ParamInitializer
-
Initialize the parameters
- init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
- 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.Deconvolution3DParamInitializer
- 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(INDArray, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Initialize the ComputationGraph, optionally with an existing parameters array.
- init(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Initialize the MultiLayerNetwork, optionally with an existing parameters array.
- 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.Deconvolution3D
- 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(double, double, int[], WeightInit, Distribution, char, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil
-
Deprecated.
- initWeights(double, double, int[], WeightInit, Distribution, 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
- 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(int, int, WeightInit, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer
- InMemoryModelSaver<T extends Model> - Class in org.deeplearning4j.earlystopping.saver
- InMemoryModelSaver() - Constructor for class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
- input - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
- input - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- input - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- 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() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- input() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- input() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- input() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- input() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- INPUT - org.deeplearning4j.nn.workspace.ArrayType
- 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(Integer, InputPreProcessor) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
Specify the processors.
- inputPreProcessor(Integer, InputPreProcessor) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- 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 theComputationGraphConfiguration.GraphBuilder.addLayer(String, Layer, InputPreProcessor, String...)
method. - InputPreProcessor - Interface in org.deeplearning4j.nn.conf
- 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 - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
- inputPreProcessors(Map<Integer, InputPreProcessor>) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
- inputPreProcessors(Map<Integer, InputPreProcessor>) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- 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 - 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 - Class in org.deeplearning4j.nn.conf.inputs
- InputType() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType
- 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
- InputTypeConvolutional(long, long, long, CNN2DFormat) - 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, DataFormat) - 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
- InputTypeRecurrent(long, long, RNNFormat) - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
- InputTypeRecurrent(long, RNNFormat) - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
- InputTypeUtil - Class in org.deeplearning4j.nn.conf.layers
- inputVars - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
- InputVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- 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(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.Deconvolution3D
- 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.recurrent.TimeDistributed
- 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(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
- 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(ComputationGraph, String, int, INDArray, boolean, DataType) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
- intializeConfigurations() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- InvalidInputTypeException - Exception in org.deeplearning4j.nn.conf.inputs
- 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
- InvalidScoreIterationTerminationCondition() - Constructor for class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition
- InvalidStepException - Exception in org.deeplearning4j.exception
- 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(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.
- 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).
- invocationCount - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
- invocationType - Variable in class org.deeplearning4j.optimize.listeners.EvaluativeListener
- InvocationType - Enum in org.deeplearning4j.optimize.api
- 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
- isBiasParam(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams
- isBiasParam(Layer, String) - Method in interface org.deeplearning4j.nn.api.ParamInitializer
-
Is the specified parameter a bias?
- 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
- 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.Deconvolution3DLayer
- 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(String) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams
- isWeightParam(Layer, String) - Method in interface org.deeplearning4j.nn.api.ParamInitializer
-
Is the specified parameter a weight?
- 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
- ITER_DONE - org.deeplearning4j.optimize.listeners.FailureTestingListener.CallType
- ITERATION_END - org.deeplearning4j.optimize.api.InvocationType
-
Iterator will be called on iteration end
- 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.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.
- IterationListener() - Constructor for class org.deeplearning4j.optimize.api.IterationListener
-
Deprecated.
- iterations - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
- IterationTerminationCondition - org.deeplearning4j.earlystopping.EarlyStoppingResult.TerminationReason
- IterationTerminationCondition - Interface in org.deeplearning4j.earlystopping.termination
- 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
- IWeightNoise - Interface in org.deeplearning4j.nn.conf.weightnoise
- iz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
J
- JsonMappers - Class in org.deeplearning4j.nn.conf.serde
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
- k(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
-
LRN scaling constant k.
- 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). - KerasFlattenRnnPreprocessor - Class in org.deeplearning4j.preprocessors
- KerasFlattenRnnPreprocessor(long, long) - Constructor for class org.deeplearning4j.preprocessors.KerasFlattenRnnPreprocessor
- kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
- kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
- kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- kernelSize - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- 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.LocallyConnected1D.Builder
- kernelSize(int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
-
Kernel size
- kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
-
Set kernel size for 3D convolutions in (depth, height, width) order
- 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(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.Deconvolution3D.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.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.Subsampling3DLayer.Builder
-
Kernel size
- kernelSize(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
-
Kernel size
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 viaNeuralNetConfiguration.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 viaFineTuneConfiguration.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 viaNeuralNetConfiguration.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 viaFineTuneConfiguration.Builder.weightDecayBias(double,boolean)
should be preferred to L2 regularization. - L2NormalizeVertex - Class in org.deeplearning4j.nn.conf.graph
- L2NormalizeVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- L2NormalizeVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
- L2NormalizeVertex(int[], double) - Constructor for class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
- 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 - Class in org.deeplearning4j.nn.graph.vertex.impl
- 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(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 - Variable in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
- lambda(double) - Method in class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer.Builder
- 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(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep
- LastTimeStepLayer - Class in org.deeplearning4j.nn.layers.recurrent
- LastTimeStepLayer(Layer) - Constructor for class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer
- LastTimeStepVertex - Class in org.deeplearning4j.nn.conf.graph.rnn
- LastTimeStepVertex - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn
- LastTimeStepVertex(String) - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer
- layer - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- layer - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
- layer(int, Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- 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(Layer) - Method in class org.deeplearning4j.nn.conf.layers.misc.FrozenLayer.Builder
- layer(Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Layer class.
- layer(Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- Layer - Class in org.deeplearning4j.nn.conf.layers
-
A neural network layer.
- Layer - Interface in org.deeplearning4j.nn.api
- Layer(Layer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Layer
- 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.convolution.Convolution1DLayer
- layerConf() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- LayerConstraint - Interface in org.deeplearning4j.nn.api.layers
- layerHasConvolution3DLayout(Layer) - Static method in class org.deeplearning4j.util.Convolution3DUtils
-
Returns true if any of the layers are 3d convolution, pooling, or upsampling layers including:
Convolution3D
,Deconvolution3D
,Subsampling3DLayer
,Upsampling3D
- layerHasConvolutionLayout(Layer) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Returns true if a layer has a
CNN2DFormat
property. - 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(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. - 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. - layerMap - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- LayerMemoryReport - Class in org.deeplearning4j.nn.conf.memory
- 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(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. - 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. - LayerUpdater - Class in org.deeplearning4j.nn.updater
- 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
- LayerVertex - Class in org.deeplearning4j.nn.conf.graph
- LayerVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- LayerVertex(NeuralNetConfiguration, InputPreProcessor) - Constructor for class org.deeplearning4j.nn.conf.graph.LayerVertex
- 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
- 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 - org.deeplearning4j.nn.api.OptimizationAlgorithm
- LBFGS(NeuralNetConfiguration, StepFunction, Collection<TrainingListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LBFGS
- LearnedSelfAttentionLayer - Class in org.deeplearning4j.nn.conf.layers
- LearnedSelfAttentionLayer(LearnedSelfAttentionLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.LearnedSelfAttentionLayer
- LearnedSelfAttentionLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
- LECUN_NORMAL - org.deeplearning4j.nn.weights.WeightInit
- LECUN_UNIFORM - org.deeplearning4j.nn.weights.WeightInit
- LegacyDistributionDeserializer - Class in org.deeplearning4j.nn.conf.distribution.serde
- LegacyDistributionDeserializer() - Constructor for class org.deeplearning4j.nn.conf.distribution.serde.LegacyDistributionDeserializer
- LegacyDistributionHelper - Class in org.deeplearning4j.nn.conf.distribution.serde
- LegacyIntArrayDeserializer - Class in org.deeplearning4j.nn.conf.serde.legacy
- LegacyIntArrayDeserializer() - Constructor for class org.deeplearning4j.nn.conf.serde.legacy.LegacyIntArrayDeserializer
- LegacyJsonFormat - Class in org.deeplearning4j.nn.conf.serde.legacy
- 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
- LINE_GRADIENT_DESCENT - org.deeplearning4j.nn.api.OptimizationAlgorithm
- LineGradientDescent - Class in org.deeplearning4j.optimize.solvers
- 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
- 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) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- ListBuilder(NeuralNetConfiguration.Builder, Map<Integer, 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)
orModelSerializer
- load(File, boolean) - Static method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Restore a MultiLayerNetwork to a file, saved using
MultiLayerNetwork.save(File)
orModelSerializer
- 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
- 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(Checkpoint) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener
-
Load a ComputationGraph for the given checkpoint
- loadCheckpointMLN(int) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener
-
Load a MultiLayerNetwork for the given checkpoint number
- loadCheckpointMLN(File, int) - Static 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(Checkpoint) - Method in class org.deeplearning4j.optimize.listeners.CheckpointListener
-
Load a MultiLayerNetwork for the given checkpoint
- 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
- LOCAL_RESPONSE_NORM_CUDNN_HELPER_CLASS_NAME - Static variable in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
- LocalFileGraphSaver - Class in org.deeplearning4j.earlystopping.saver
- 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
- 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
- LocalHandler() - Constructor for class org.deeplearning4j.optimize.solvers.accumulation.LocalHandler
- LocallyConnected1D - Class in org.deeplearning4j.nn.conf.layers
- 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
- 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
- LocalResponseNormalization - Class in org.deeplearning4j.nn.layers.normalization
- 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
- 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)
andBatchNormalization.Builder.beta(double)
. - lockGammaBeta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
- 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)
andBatchNormalization.Builder.beta(double)
. - locks - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
- 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(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
- logTestMode(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
- lossFunction - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
- 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.
- 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(ILossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
- lossFunction(LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
- lossFunction(LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
- lossFunctionExpectsProbability(ILossFunction) - Static method in class org.deeplearning4j.util.OutputLayerUtil
- LossFunctionWrapper - Class in org.deeplearning4j.nn.conf.layers.variational
- LossFunctionWrapper(Activation, ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
- LossFunctionWrapper(IActivation, ILossFunction) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper
- LossLayer - Class in org.deeplearning4j.nn.conf.layers
- LossLayer - Class in org.deeplearning4j.nn.layers
- LossLayer(LossLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer
- 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 - Class in org.deeplearning4j.nn.layers.recurrent
- 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
- LSTMHelpers - Class in org.deeplearning4j.nn.layers.recurrent
- LSTMParamInitializer - Class in org.deeplearning4j.nn.params
- LSTMParamInitializer() - Constructor for class org.deeplearning4j.nn.params.LSTMParamInitializer
M
- Macro - org.deeplearning4j.eval.EvaluationAveraging
-
Deprecated.
- MAE - org.deeplearning4j.eval.RegressionEvaluation.Metric
-
Deprecated.
- 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
- mapToMode(ConvolutionMode) - Static method in enum org.deeplearning4j.nn.conf.ConvolutionMode
- 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
- MaskLayer - Class in org.deeplearning4j.nn.conf.layers.util
- MaskLayer - Class in org.deeplearning4j.nn.layers.util
- MaskLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskLayer
- MaskLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.util.MaskLayer
- maskShape - Variable in class org.deeplearning4j.nn.conf.graph.ReshapeVertex
- maskState - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
- MaskState - Enum in org.deeplearning4j.nn.api
- maskValue(double) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder
- MaskZeroLayer - Class in org.deeplearning4j.nn.conf.layers.util
- MaskZeroLayer - Class in org.deeplearning4j.nn.layers.recurrent
- MaskZeroLayer(Layer, double) - Constructor for class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer
- MaskZeroLayer(Layer, double) - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
- MaskZeroLayer(MaskZeroLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer
- MaskZeroLayer.Builder - Class in org.deeplearning4j.nn.conf.layers.util
- matthewsCorrelation(EvaluationAveraging) - Method in class org.deeplearning4j.eval.Evaluation
-
Deprecated.
- Max - org.deeplearning4j.nn.conf.graph.ElementWiseVertex.Op
- Max - org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op
- MAX - org.deeplearning4j.nn.conf.layers.PoolingType
- MAX - org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.PoolingType
- MAX - org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType
- maxAppliedIndexEverywhere() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
- MaxEpochsTerminationCondition - Class in org.deeplearning4j.earlystopping.termination
- MaxEpochsTerminationCondition(int) - Constructor for class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition
- MaxNormConstraint - Class in org.deeplearning4j.nn.conf.constraint
- MaxNormConstraint(double, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MaxNormConstraint
-
Apply to weights but not biases by default
- MaxNormConstraint(double, Set<String>, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MaxNormConstraint
- maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
- maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- maxNumLineSearchIterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Maximum number of line search iterations.
- maxNumLineSearchIterations(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
- MaxScoreIterationTerminationCondition - Class in org.deeplearning4j.earlystopping.termination
- 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
- MCC - org.deeplearning4j.eval.Evaluation.Metric
-
Deprecated.
- 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.multilayer.MultiLayerNetwork
-
Generate information regarding memory use for the network, for the given input type and minibatch size.
- 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.
- 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
- MemoryReport() - Constructor for class org.deeplearning4j.nn.conf.memory.MemoryReport
- MemoryType - Enum in org.deeplearning4j.nn.conf.memory
- MemoryUseMode - Enum in org.deeplearning4j.nn.conf.memory
- 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
- mergeAxis - Variable in class org.deeplearning4j.nn.conf.graph.MergeVertex
- MergeVertex - Class in org.deeplearning4j.nn.conf.graph
- MergeVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- MergeVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.MergeVertex
- MergeVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], DataType, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
- MergeVertex(ComputationGraph, String, int, DataType, int) - 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
- 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
- Micro - org.deeplearning4j.eval.EvaluationAveraging
-
Deprecated.
- 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 - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
- miniBatch - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- miniBatch(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Process input as minibatch vs full dataset.
- 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. - minibatchCount - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
- minimize - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- minimize - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
- minimize - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- minimize() - Method in enum org.deeplearning4j.eval.RegressionEvaluation.Metric
-
Deprecated.
- 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(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
- 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
- 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
- MinMaxNormConstraint(double, double, int...) - Constructor for class org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint
-
Apply to weights but not biases by default
- 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
- MKLDNNBatchNormHelper(DataType) - Constructor for class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
- MKLDNNConvHelper - Class in org.deeplearning4j.nn.layers.mkldnn
- 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
- MKLDNNLocalResponseNormalizationHelper(DataType) - Constructor for class org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper
- MKLDNNSubsamplingHelper - Class in org.deeplearning4j.nn.layers.mkldnn
- MKLDNNSubsamplingHelper(DataType) - Constructor for class org.deeplearning4j.nn.layers.mkldnn.MKLDNNSubsamplingHelper
- MLNConfig() - Constructor for class org.deeplearning4j.gradientcheck.GradientCheckUtil.MLNConfig
- mode - Variable in class org.deeplearning4j.nn.conf.layers.Convolution3D
- mode(Bidirectional.Mode) - Method in class org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Builder
- model - Variable in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
- model - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- model(Model) - Method in class org.deeplearning4j.optimize.Solver.Builder
- Model - Interface in org.deeplearning4j.nn.api
- ModelAdapter<T> - Interface in org.deeplearning4j.nn.api
- 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
- 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
- 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
- ModelSerializer - Class in org.deeplearning4j.util
- modified - Variable in class org.deeplearning4j.nn.conf.graph.MergeVertex
- movingAverage(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
-
Calculate a moving average given the length
- MSE - org.deeplearning4j.eval.RegressionEvaluation.Metric
-
Deprecated.
- MUL - org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional.Mode
- MULTICLASS - org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.ROCType
- MULTILAYER - org.deeplearning4j.nn.api.Layer.Type
- MultiLayerConfiguration - Class in org.deeplearning4j.nn.conf
- 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(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) - Constructor for class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- 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(MultiLayerNetwork) - Constructor for class org.deeplearning4j.nn.updater.MultiLayerUpdater
- MultiLayerUpdater(MultiLayerNetwork, INDArray) - Constructor for class org.deeplearning4j.nn.updater.MultiLayerUpdater
N
- n - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
- n(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
-
Number of adjacent kernel maps to use when doing LRN.
- NADAM - org.deeplearning4j.nn.conf.Updater
- name() - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer.OCNNLossFunction
- 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
- NCDHW - org.deeplearning4j.nn.conf.layers.Convolution3D.DataFormat
- NCHW - org.deeplearning4j.nn.conf.CNN2DFormat
- NCHW - org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.DataFormat
-
Deprecated.
- NCHW_NHWC_ERROR_MSG - Static variable in class org.deeplearning4j.util.ConvolutionUtils
- NCW - org.deeplearning4j.nn.conf.RNNFormat
- NDHWC - org.deeplearning4j.nn.conf.layers.Convolution3D.DataFormat
- 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
- NegativeDefaultStepFunction - Class in org.deeplearning4j.optimize.stepfunctions
- NegativeDefaultStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction
- NegativeDefaultStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
- NegativeGradientStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions
- NegativeGradientStepFunction - Class in org.deeplearning4j.optimize.stepfunctions
- NegativeGradientStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction
- 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)
- NESTEROVS - org.deeplearning4j.nn.conf.Updater
- 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
- 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
- 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
- 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
- NHWC - org.deeplearning4j.nn.conf.CNN2DFormat
- NHWC - org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.DataFormat
-
Deprecated.
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
- 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(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(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
- 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).
- 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, 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(int, int, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder
-
Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call. - nInReplace(int, int, WeightInit, Distribution) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.Builder
-
Modify the architecture of a vertex layer by changing nIn of the specified layer.
Note that only the specified layer will be modified - all other layers will not be changed by this call. - nInReplace(String, int, 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. - 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. - 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
- NO_WORKSPACE - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode
- noLeverageOverride - Variable in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
- None - org.deeplearning4j.nn.conf.GradientNormalization
- NONE - org.deeplearning4j.nn.conf.CacheMode
-
Cache won't be used during training
- NONE - org.deeplearning4j.nn.conf.Updater
- NONE - org.deeplearning4j.nn.conf.WorkspaceMode
- NonNegativeConstraint - Class in org.deeplearning4j.nn.conf.constraint
- NonNegativeConstraint() - Constructor for class org.deeplearning4j.nn.conf.constraint.NonNegativeConstraint
- NoOpResidualPostProcessor - Class in org.deeplearning4j.optimize.solvers.accumulation.encoding.residual
- 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
- NORMAL - org.deeplearning4j.nn.weights.WeightInit
- 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
- NORMALIZATION - org.deeplearning4j.nn.api.Layer.Type
- NORMALIZER_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer
- notifyDead() - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
- 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(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(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
- nOut(long) - Method in class org.deeplearning4j.nn.conf.graph.AttentionVertex.Builder
-
Output Size
- 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).
- 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, 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, 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(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, 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, 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(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, Distribution, WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
- 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, WeightInit, Distribution) - 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() - Static method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
- noWorkspaces(Map<String, Pointer>) - 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() - Method in interface org.deeplearning4j.nn.api.Trainable
- numParams() - 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() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
The number of parameters for the model
- 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() - 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() - 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 interface org.deeplearning4j.nn.api.Model
-
the number of parameters for the model
- 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(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- numParams(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- numParams(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
- numParams(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- numParams(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- numParams(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- numParams(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Returns the number of parameters in the network
- numParams(Layer) - Method in interface org.deeplearning4j.nn.api.ParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.BidirectionalParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.Convolution3DParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.Deconvolution3DParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.ElementWiseParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.PReLUParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.SameDiffParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.SimpleRnnParamInitializer
- numParams(Layer) - Method in class org.deeplearning4j.nn.params.WrapperLayerParamInitializer
- numParams(NeuralNetConfiguration) - Method in interface org.deeplearning4j.nn.api.ParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
- numParams(NeuralNetConfiguration) - 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(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.Deconvolution3DParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.DepthwiseConvolutionParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.FrozenLayerParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.FrozenLayerWithBackpropParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.LSTMParamInitializer
- numParams(NeuralNetConfiguration) - 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(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
- numParams(NeuralNetConfiguration) - 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
- numSamples - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- 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.
- NWC - org.deeplearning4j.nn.conf.RNNFormat
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
- OCNNOutputLayer - Class in org.deeplearning4j.nn.layers.ocnn
- OCNNOutputLayer(int, double, IActivation, int, double, boolean) - Constructor for class org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer
- OCNNOutputLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
- OCNNOutputLayer(OCNNOutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.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
- 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)
orComputationGraph.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)
orComputationGraph.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
- ONES - org.deeplearning4j.nn.weights.WeightInit
- 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, 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, List<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.api.BaseTrainingListener
- 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, Map<String, INDArray>) - Method in class org.deeplearning4j.optimize.listeners.FailureTestingListener
- 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
- OOM - org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureMode
- op - Variable in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
- optimizationAlgo - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- optimizationAlgo - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
- optimizationAlgo - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- optimizationAlgo(OptimizationAlgorithm) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Optimization algorithm to use.
- optimizationAlgo(OptimizationAlgorithm) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
- OptimizationAlgorithm - Enum in org.deeplearning4j.nn.api
-
Optimization algorithm to use
- optimize(LayerWorkspaceMgr) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
Calls optimize
- optimize(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.Solver
- optimize(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
Optimize call.
- optimize(LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
- optimize(INDArray, INDArray, INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.optimize.api.LineOptimizer
-
Line optimizer
- optimize(INDArray, INDArray, INDArray, LayerWorkspaceMgr) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
- 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.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.format - package org.deeplearning4j.nn.conf.serde.format
- 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.preprocessors - package org.deeplearning4j.preprocessors
- org.deeplearning4j.util - package org.deeplearning4j.util
- OrthogonalDistribution - Class in org.deeplearning4j.nn.conf.distribution
-
Orthogonal distribution, with gain parameter.
See https://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(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 useComputationGraph.output(boolean, INDArray...)
or similar methods, unless they are doing non-standard operations (like providing the input arrays externally) - 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(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, 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...) - 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(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(List<String>, boolean, INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the activations for the specific layers only
- output(Model, INDArray) - Static method in class org.deeplearning4j.util.NetworkUtils
-
Currently supports
MultiLayerNetwork
andComputationGraph
models. - 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.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.VAEReconProbScoreCalculator
- 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.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.VAEReconProbScoreCalculator
- 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(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(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(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, 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) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Calculate the output of the network, with masking arrays.
- 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, 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(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(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Equivalent to
MultiLayerNetwork.output(DataSetIterator, boolean)
with train=false - 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(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(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
- outputDataFormat(RNNFormat) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder
- 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
- OutputLayer - Class in org.deeplearning4j.nn.layers
- OutputLayer(OutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.OutputLayer
- 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
- 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, @lombok.NonNull 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(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(boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
A convenience method that returns a single INDArray, instead of an INDArray[].
- outputSingle(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
A convenience method that returns a single INDArray, instead of an INDArray[].
- 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.
- overrideNinUponBuild - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
- overrideNinUponBuild(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
Whether to over ride the nIn configuration forcibly upon construction.
- overrideNinUponBuild(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- ownCounter - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
- oz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
P
- padding - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- padding - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
- padding - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
-
A 2d array, with format [[padTop, padBottom], [padLeft, padRight]]
- padding - Variable in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer
- padding - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- padding - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
- padding - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- padding - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- 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.LocallyConnected1D.Builder
- padding(int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
-
Padding
- padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
-
Set padding size for 3D convolutions in (depth, height, width) order
- 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(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
- padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution3D.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.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(int...) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.Builder
-
Padding
- padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
-
Padding
- padding(int[][]) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.Builder
- paddingModeForConvolutionMode(ConvolutionMode) - Static method in class org.deeplearning4j.util.ConvolutionUtils
- PARAMATER_GRADIENTS - org.deeplearning4j.nn.conf.memory.MemoryType
- PARAMETERS - org.deeplearning4j.nn.conf.memory.MemoryType
- 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 - Variable in class org.deeplearning4j.nn.conf.constraint.BaseConstraint
- params - Variable in class org.deeplearning4j.nn.layers.BaseLayer
- params - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
- params - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
- params - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- params - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- 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() - 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() - 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() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
- params() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
-
Returns the parameters of the neural network as a flattened row vector
- params() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
-
Returns the parameters of the neural network as a flattened row vector
- params() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- params() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- 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.
SeeMultiLayerNetwork.getParam(String)
andMultiLayerNetwork.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(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Deprecated.To be removed. Use
ComputationGraph.params()
- params(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Deprecated.To be removed. Use
MultiLayerNetwork.params()
instead - PARAMS_KEY - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- paramsFlattened - Variable in class org.deeplearning4j.nn.layers.BaseLayer
- paramsFlattened - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- ParamState() - Constructor for class org.deeplearning4j.nn.updater.UpdaterBlock.ParamState
- paramTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
- paramTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
- paramTable - Variable in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- paramTable() - Method in interface org.deeplearning4j.nn.api.Model
-
The param table
- paramTable() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- paramTable() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- paramTable() - Method in class org.deeplearning4j.nn.layers.BaseLayer
- paramTable() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- paramTable() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
- paramTable() - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- paramTable() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- paramTable() - 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 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(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(boolean) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
- paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
- paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffGraphVertex
- paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
- paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- paramTable(boolean) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- 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. - paramWeightInit - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer.Builder
- paramWeightInit - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer
- parent() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
- parent(Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Returns the parent of the passed in tree via traversal
- PARK - org.deeplearning4j.optimize.listeners.SleepyTrainingListener.SleepMode
-
In this mode parkNanos() call will be used, to make process really idle
- 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
- Passthrough - org.deeplearning4j.nn.api.MaskState
- PC - org.deeplearning4j.eval.RegressionEvaluation.Metric
-
Deprecated.
- peek() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- PerformanceListener - Class in org.deeplearning4j.optimize.listeners
- 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
- permuteAxes(int, int) - Method in class org.deeplearning4j.nn.layers.recurrent.TimeDistributedLayer
- permuteIfNWC(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
- PermutePreprocessor - Class in org.deeplearning4j.preprocessors
- PermutePreprocessor(int...) - Constructor for class org.deeplearning4j.preprocessors.PermutePreprocessor
- pnorm - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- pnorm - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- pnorm(int) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder
-
P-norm constant.
- pnorm(int) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- PNORM - org.deeplearning4j.nn.conf.layers.PoolingType
- PNORM - org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType
- 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
- PoolHelperVertex - Class in org.deeplearning4j.nn.conf.graph
- PoolHelperVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- PoolHelperVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.PoolHelperVertex
- PoolHelperVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex
- PoolHelperVertex(ComputationGraph, String, int, 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 - Variable 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 - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- poolingType(PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder
- poolingType(PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- poolingType(PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- poolingType(Subsampling3DLayer.PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- poolingType(SubsamplingLayer.PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- 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 - 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.
- PRECISION - org.deeplearning4j.eval.Evaluation.Metric
-
Deprecated.
- PrecisionRecallCurve - Class in org.deeplearning4j.eval.curves
-
Deprecated.
- PrecisionRecallCurve(double[], double[], double[], int[], int[], int[], int) - Constructor for class org.deeplearning4j.eval.curves.PrecisionRecallCurve
-
Deprecated.
- 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(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Returns the predictions for each example in the dataset
- predict(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
- predict(INDArray) - 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(INDArray) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
- predict(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
- predict(INDArray) - 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 interface org.deeplearning4j.nn.api.Classifier
-
Takes in a DataSet of examples For each row, returns a label
- predict(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Return predicted label names
- predict(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
- predict(DataSet) - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
- predict(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Return predicted label names
- predict(DataSet) - Method in class org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer
- predict(DataSet) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
- predict(DataSet) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- 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() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
- Prediction - Class in org.deeplearning4j.eval.meta
- Prediction(int, int, Object) - Constructor for class org.deeplearning4j.eval.meta.Prediction
- PREFER_FASTEST - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode
- PReLU - Class in org.deeplearning4j.nn.layers.feedforward
- PReLU(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.feedforward.PReLU
- PReLULayer - Class in org.deeplearning4j.nn.conf.layers
- PReLULayer.Builder - Class in org.deeplearning4j.nn.conf.layers
- PReLUParamInitializer - Class in org.deeplearning4j.nn.params
- PReLUParamInitializer(long[], long[]) - Constructor for class org.deeplearning4j.nn.params.PReLUParamInitializer
- preOutput - Variable in class org.deeplearning4j.nn.layers.AbstractLayer
- preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
- preOutput(boolean, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer
- 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, boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.RepeatVector
- preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.BaseLayer
- preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Convolution3DLayer
- preOutput(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.convolution.Deconvolution3DLayer
- 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(boolean, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- preOutput(INDArray, boolean, long[], INDArray, INDArray, INDArray, INDArray, double, double, CNN2DFormat, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper
- preOutput(INDArray, boolean, long[], INDArray, INDArray, INDArray, INDArray, double, double, CNN2DFormat, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
- preOutput(INDArray, Layer.TrainingMode, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
- preOutput(INDArray, INDArray, INDArray, int[], int[], int[], ConvolutionLayer.AlgoMode, ConvolutionLayer.FwdAlgo, ConvolutionMode, int[], CNN2DFormat, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
- preOutput(INDArray, INDArray, INDArray, int[], int[], int[], ConvolutionLayer.AlgoMode, ConvolutionLayer.FwdAlgo, ConvolutionMode, int[], CNN2DFormat, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.layers.mkldnn.MKLDNNConvHelper
- 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
- preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.preprocessors.KerasFlattenRnnPreprocessor
- preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.preprocessors.PermutePreprocessor
- preProcess(INDArray, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.preprocessors.ReshapePreprocessor
- 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 - Class in org.deeplearning4j.nn.graph.vertex.impl
- PreprocessorVertex(InputPreProcessor) - Constructor for class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
- 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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- pretrain() - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
- pretrain() - Method in interface org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer
- pretrain(boolean) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
- pretrain(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Perform layerwise pretraining for one epoch - see
ComputationGraph.pretrain(DataSetIterator, int)
- 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.graph.ComputationGraph
-
Pretrain network with a single input and single output.
- 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(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(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
- pretrain(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
- pretrain(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
- pretrain(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
- pretrain(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
- pretrain(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
- 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. - 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(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
- 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
- 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
- Product - org.deeplearning4j.nn.conf.graph.ElementWiseVertex.Op
- Product - org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op
- 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(INDArray) - Method in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
-
This mehtod adds update, with optional collapse
- put(E) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- 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 - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- 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). - 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
- R2 - org.deeplearning4j.eval.RegressionEvaluation.Metric
-
Deprecated.
- 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.
- RECALL - org.deeplearning4j.eval.Evaluation.Metric
-
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 - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- 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 - Interface in org.deeplearning4j.nn.conf.layers.variational
- 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., anILossFunction
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 usingVariationalAutoencoder.hasLossFunction()
Consequently, the reconstruction error is a simple deterministic function (no Monte-Carlo sampling is required, unlikeVariationalAutoencoder.reconstructionProbability(INDArray, int)
andVariationalAutoencoder.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.
SeeVariationalAutoencoder.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(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder.InputTypeBuilder
- 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(long, long, RNNFormat) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
- recurrent(long, RNNFormat) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
- RECURRENT - org.deeplearning4j.nn.api.Layer.Type
- 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
- 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
- RECURSIVE - org.deeplearning4j.nn.api.Layer.Type
- 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
- Regression2dAdapter() - Constructor for class org.deeplearning4j.nn.adapters.Regression2dAdapter
- RegressionEvaluation - Class in org.deeplearning4j.eval
-
Deprecated.
- 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
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
- regularization - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder
- 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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
- regularization - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- 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(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(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. - regularizationBias - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
-
Regularization for the bias parameters only
- regularizationBias - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
- regularizationBias - Variable in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder
- 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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
- regularizationBias - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- regularizationBias(List<Regularization>) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
-
Set the regularization for the biases only - for example
WeightDecay
- 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(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. - release(INDArray) - Method in class org.deeplearning4j.nn.layers.samediff.DL4JSameDiffMemoryMgr
- ReliabilityDiagram - Class in org.deeplearning4j.eval.curves
-
Deprecated.
- ReliabilityDiagram(String, @lombok.NonNull double[], @lombok.NonNull double[]) - Constructor for class org.deeplearning4j.eval.curves.ReliabilityDiagram
-
Deprecated.
- relocatable - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
- RELU - org.deeplearning4j.nn.weights.WeightInit
- RELU_UNIFORM - org.deeplearning4j.nn.weights.WeightInit
- remainingCapacity() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- 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
- RenormalizeL2PerLayer - org.deeplearning4j.nn.conf.GradientNormalization
- RenormalizeL2PerParamType - org.deeplearning4j.nn.conf.GradientNormalization
- RepeatVector - Class in org.deeplearning4j.nn.conf.layers.misc
- RepeatVector - Class in org.deeplearning4j.nn.layers
- RepeatVector(RepeatVector.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.misc.RepeatVector
- 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.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.
- reshape(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.TimeDistributedLayer
- reshape2dTo3d(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
- reshape2dTo3d(INDArray, long, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
- reshape2dTo4d(INDArray, long[], CNN2DFormat, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
- reshape2dTo5d(Convolution3D.DataFormat, INDArray, long, long, long, long, long, 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, CNN2DFormat, 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, CNN2DFormat, LayerWorkspaceMgr, ArrayType) - Static method in class org.deeplearning4j.util.ConvolutionUtils
- 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
- ReshapePreprocessor - Class in org.deeplearning4j.preprocessors
- ReshapePreprocessor(long[], long[], boolean) - Constructor for class org.deeplearning4j.preprocessors.ReshapePreprocessor
- ReshapePreprocessor(long[], long[], boolean, DataFormat) - Constructor for class org.deeplearning4j.preprocessors.ReshapePreprocessor
- 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
- ReshapeVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- ReshapeVertex(char, int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.graph.ReshapeVertex
- ReshapeVertex(int...) - Constructor for class org.deeplearning4j.nn.conf.graph.ReshapeVertex
-
Reshape with the default reshape order of 'c'
- 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
- reshapeWeightArrayOrGradientForFormat(INDArray, RNNFormat) - Static method in class org.deeplearning4j.util.Convolution1DUtils
-
Reshapes the given weight array or weight gradient to work with the specified
RNNFormat
- reshapeWeights(int[], INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil
-
Reshape the parameters view, without modifying the paramsView array values.
- reshapeWeights(int[], INDArray, char) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil
-
Reshape the parameters view, without modifying the paramsView array values.
- reshapeWeights(long[], INDArray) - 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
- 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 - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
- 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
- resolve(DeserializationContext) - Method in class org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer
- 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
- restoreComputationGraph(InputStream) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a computation graph from a InputStream
- restoreComputationGraph(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a computation graph from a InputStream
- 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
- restoreComputationGraphAndNormalizer(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Restore a ComputationGraph and Normalizer (if present - null if not) 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.
- 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) - 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(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(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(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Restore a MultiLayerNetwork and Normalizer (if present - null if not) 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.
- 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
- reverseTimeSeries(INDArray, LayerWorkspaceMgr, ArrayType, RNNFormat) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
- 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 - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn
- 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(ComputationGraph, String, int, String, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.ReverseTimeSeriesVertex
- revertReshape(INDArray, long) - Method in class org.deeplearning4j.nn.layers.recurrent.TimeDistributedLayer
- RMSE - org.deeplearning4j.eval.RegressionEvaluation.Metric
-
Deprecated.
- RMSPROP - org.deeplearning4j.nn.conf.Updater
- RNN - org.deeplearning4j.nn.conf.inputs.InputType.Type
- RNN_ACTIVATE_WITH_STORED_STATE - org.deeplearning4j.nn.api.FwdPassType
- RNN_BP_LOOP_WORKING_MEM - org.deeplearning4j.nn.workspace.ArrayType
- RNN_FF_LOOP_WORKING_MEM - org.deeplearning4j.nn.workspace.ArrayType
- RNN_TIMESTEP - org.deeplearning4j.nn.api.FwdPassType
- rnnActivateUsingStoredState(INDArray[], boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Similar to rnnTimeStep and feedForward() methods.
- rnnActivateUsingStoredState(INDArray, boolean, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Similar to rnnTimeStep and feedForward() methods.
- 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, 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
- 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).
- rnnDataFormat - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder
-
Set the format of data expected by the RNN.
- rnnDataFormat - Variable in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer
- rnnDataFormat(RNNFormat) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer.Builder
- RNNFormat - Enum in org.deeplearning4j.nn.conf
- 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() - 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.graph.ComputationGraph
-
Get the state of the RNN layer, as used in
ComputationGraph.rnnTimeStep(INDArray...)
. - rnnGetPreviousState(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Get the state of the RNN layer, as used in rnnTimeStep().
- rnnGetPreviousState(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the state of the RNN layer, as used in
ComputationGraph.rnnTimeStep(INDArray...)
. - 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
- RnnLossLayer - Class in org.deeplearning4j.nn.layers.recurrent
- 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
- RnnOutputLayer - Class in org.deeplearning4j.nn.layers.recurrent
- RnnOutputLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
- RnnOutputLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
- 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(int, Map<String, INDArray>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Set the state of the RNN layer.
- 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 interface org.deeplearning4j.nn.api.layers.RecurrentLayer
-
Set the stateMap (stored history).
- 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
- 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(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) - 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...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
If this ComputationGraph contains one or more RNN layers: conduct forward pass (prediction) but using previous stored state for any RNN layers.
- rnnTimeStep(INDArray, 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, 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, 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
- RnnToCnnPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
- RnnToCnnPreProcessor(int, int, int, RNNFormat) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
- RnnToFeedForwardPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
- RnnToFeedForwardPreProcessor(RNNFormat) - 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.
- ROC - org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator.ROCType
- 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.
- ROCBinary - Class in org.deeplearning4j.eval
-
Deprecated.
- 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.
- RocCurve(double[], double[], double[]) - Constructor for class org.deeplearning4j.eval.curves.RocCurve
-
Deprecated.Use
RocCurve
- ROCMultiClass - Class in org.deeplearning4j.eval
-
Deprecated.
- 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
- 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(ROCScoreCalculator.ROCType, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
- ROCScoreCalculator(ROCScoreCalculator.ROCType, 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.
- RSE - org.deeplearning4j.eval.RegressionEvaluation.Metric
-
Deprecated.
S
- Same - org.deeplearning4j.nn.conf.ConvolutionMode
- 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
- 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() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer
- SameDiffLambdaVertex - Class in org.deeplearning4j.nn.conf.layers.samediff
- 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
- SameDiffLayer - Class in org.deeplearning4j.nn.layers.samediff
- SameDiffLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer
- SameDiffLayer(SameDiffLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer
- 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
- SameDiffOutputLayer - Class in org.deeplearning4j.nn.conf.layers.samediff
- SameDiffOutputLayer - Class in org.deeplearning4j.nn.layers.samediff
- SameDiffOutputLayer() - Constructor for class org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer
- 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
- 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) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Save the MultiLayerNetwork to a file.
- save(File, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Save the ComputationGraph 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(ComputationGraph, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
- saveBestModel(MultiLayerNetwork, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
- 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
- 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(ComputationGraph, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
- saveLatestModel(MultiLayerNetwork, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
- 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
- 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
- ScaleVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- ScaleVertex(double) - Constructor for class org.deeplearning4j.nn.conf.graph.ScaleVertex
- 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 - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
- score - Variable in class org.deeplearning4j.nn.layers.BaseLayer
- score - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- score - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- score - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- score() - Method in interface org.deeplearning4j.nn.api.Model
-
The score for the model
- score() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- score() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- 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() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- score() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- 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() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- 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 toComputationGraph.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(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 toMultiLayerNetwork.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_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
- scoreExamples(DataSet, 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(MultiDataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Calculate the score for each example in a DataSet individually.
- 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
- 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
- ScoreIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.ScoreIterationListener
-
Default constructor printing every 10 iterations
- ScoreIterationListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.ScoreIterationListener
- 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.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.VAEReconProbScoreCalculator
- 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.VAEReconErrorScoreCalculator
- scoreMinibatch(Model, INDArray, INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.earlystopping.scorecalc.VAEReconProbScoreCalculator
- 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
- ScoreStat() - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener.ScoreStat
- scoreSum - Variable in class org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator
- SDLayerParams - Class in org.deeplearning4j.nn.conf.layers.samediff
- 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() - 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 - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
- seed - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- seed(int) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
RNG seed for reproducibility
- seed(long) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Random number generator seed.
- seed(long) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
RNG seed for reproducibility
- SelfAttentionLayer - Class in org.deeplearning4j.nn.conf.layers
- 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
- 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
- SeparableConvolution2DLayer(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.SeparableConvolution2DLayer
- SeparableConvolutionParamInitializer - Class in org.deeplearning4j.nn.params
- SeparableConvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer
- SEPARATE - org.deeplearning4j.nn.conf.WorkspaceMode
-
Deprecated.Use
WorkspaceMode.ENABLED
instead - serialize(DataFormat, JsonGenerator, SerializerProvider) - Method in class org.deeplearning4j.nn.conf.serde.format.DataFormatSerializer
- 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
- 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
- setConvolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- setConvolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- setConvolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- 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
- setDataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
- setDataFormat(CNN2DFormat) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.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.
- setDefaultCNN2DFormat(CNN2DFormat) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
- 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.Deconvolution3D.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) - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
Update gradient for the given variable
- setGradientFor(String, INDArray, Character) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
- 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(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) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Set the input array for the network
- setInput(INDArray, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Layer
-
Set the layer input.
- 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, 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(String...) - Method in class org.deeplearning4j.nn.transferlearning.TransferLearning.GraphBuilder
-
Sets new inputs for the computation graph.
- 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
- 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.Deconvolution3D.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.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
- setLabels(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Set all labels for the ComputationGraph network
- 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(double) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Set the learning rate for all layers in the network to the specified value.
- 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 thatMultiLayerNetwork.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(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 thatComputationGraph.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(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, 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 thatsetLearningRate(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. - 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(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, 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 thatsetLearningRate(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(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(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(ISchedule) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Set the learning rate schedule for all layers in the network to the specified schedule.
- 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(Collection<TrainingListener>) - Method in interface org.deeplearning4j.nn.api.Layer
-
Set the
TrainingListener
s 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(Collection<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(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- setListeners(Collection<TrainingListener>) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- setListeners(Collection<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
- setListeners(TrainingListener...) - Method in interface org.deeplearning4j.nn.api.Layer
-
Set the
TrainingListener
s for this model. - setListeners(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 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.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(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- setListeners(TrainingListener...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- 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.recurrent.TimeDistributedLayer
- 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
- setMergeAxis(int) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
- setNIn(long) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder
- setNIn(long) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
- setNIn(long) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
- 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(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Convolution1DLayer
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution3D
- 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.recurrent.TimeDistributed
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RecurrentAttentionLayer
- 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.Upsampling2D
- 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
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer
- setNoLeverageOverride(String) - Method in class org.deeplearning4j.nn.workspace.LayerWorkspaceMgr
- setNOut(long) - Method in class org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer.Builder
- setNOut(long) - Method in class org.deeplearning4j.nn.conf.layers.CnnLossLayer.Builder
- setNOut(long) - Method in class org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
- setNOut(long) - 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.Deconvolution3D.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.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
- setPadding(int[][]) - Method in class org.deeplearning4j.nn.conf.layers.SpaceToBatchLayer.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
- 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) - 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) - 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.
- setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.BaseLayer
- setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffLayer
- setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.samediff.SameDiffOutputLayer
- 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 class org.deeplearning4j.nn.graph.ComputationGraph
- 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.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
- 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(Trainable, INDArray, boolean) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
- setStateViewArray(INDArray) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
-
Set the view array.
- 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.Deconvolution3D.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(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
- setUpdater(ComputationGraphUpdater) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Set the computationGraphUpdater for the network
- 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
- SGD - org.deeplearning4j.nn.conf.Updater
- shape - Variable in class org.deeplearning4j.optimize.solvers.accumulation.BasicGradientsAccumulator
- shape - Variable in class org.deeplearning4j.optimize.solvers.accumulation.IndexedTail
- shape() - Method in class org.deeplearning4j.nn.weights.embeddings.WeightInitEmbedding
- 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
- ShiftVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- ShiftVertex(double) - Constructor for class org.deeplearning4j.nn.conf.graph.ShiftVertex
- 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
- SIGMOID_UNIFORM - org.deeplearning4j.nn.weights.WeightInit
- SIMPLE - org.deeplearning4j.optimize.listeners.SleepyTrainingListener.TimeMode
-
In this mode, listener will just call
- SimpleRnn - Class in org.deeplearning4j.nn.conf.layers.recurrent
- SimpleRnn - Class in org.deeplearning4j.nn.layers.recurrent
- SimpleRnn(SimpleRnn.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn
- 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
- SINGLE - org.deeplearning4j.nn.conf.WorkspaceMode
-
Deprecated.Use
WorkspaceMode.ENABLED
instead - 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 - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling1D
- size - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling2D
- size - Variable in class org.deeplearning4j.nn.conf.layers.Upsampling3D
- size() - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- size(int) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling1D.Builder
-
Upsampling size
- 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.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.Upsampling1D.Builder
-
Upsampling size int array with a single element.
- size(int[]) - Method in class org.deeplearning4j.nn.conf.layers.Upsampling2D.Builder
-
Upsampling size array
- 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
- 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
- SLEEP - org.deeplearning4j.optimize.listeners.SleepyTrainingListener.SleepMode
-
In this mode Thread.sleep() call will be used, to make sleep traceable via profiler
- sleepMode - Variable in class org.deeplearning4j.optimize.listeners.SleepyTrainingListener
- SleepyTrainingListener - Class in org.deeplearning4j.optimize.listeners
- 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
- 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
- Solver() - Constructor for class org.deeplearning4j.optimize.Solver
- Solver.Builder - Class in org.deeplearning4j.optimize
- SpaceToBatch - Class in org.deeplearning4j.nn.layers.convolution
- SpaceToBatch(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
- SpaceToBatchLayer - Class in org.deeplearning4j.nn.conf.layers
- 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
- SpaceToDepth(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
- SpaceToDepthLayer - Class in org.deeplearning4j.nn.conf.layers
- 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
-
Deprecated.Use
CNN2DFormat
instead - sparsity - Variable in class org.deeplearning4j.nn.conf.layers.AutoEncoder
- sparsity(double) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder
-
Autoencoder sparity parameter
- SpatialDropout - Class in org.deeplearning4j.nn.conf.dropout
- SpatialDropout(double) - Constructor for class org.deeplearning4j.nn.conf.dropout.SpatialDropout
- SpatialDropout(double, ISchedule) - Constructor for class org.deeplearning4j.nn.conf.dropout.SpatialDropout
- SpatialDropout(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 - Class in org.deeplearning4j.nn.graph.vertex.impl
- StackVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.StackVertex
- StackVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
- StackVertex(ComputationGraph, String, int, DataType) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
- Standard - org.deeplearning4j.nn.conf.BackpropType
-
Default option.
- STANDARD - org.deeplearning4j.nn.api.FwdPassType
- 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 - Variable in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- step - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- step() - Method in interface org.deeplearning4j.optimize.api.StepFunction
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
- step(INDArray, INDArray) - Method in interface org.deeplearning4j.optimize.api.StepFunction
-
Step with no parameters
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
- step(INDArray, INDArray, double) - Method in interface org.deeplearning4j.optimize.api.StepFunction
-
Step with the given parameters
- step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
-
Does x = x + stepSize * line
- step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
- step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
- step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
- stepFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- stepFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
- stepFunction - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- stepFunction - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- stepFunction(StepFunction) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Deprecated.
- stepFunction(StepFunction) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
- StepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions
- StepFunction - Interface in org.deeplearning4j.optimize.api
- StepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.StepFunction
- StepFunctions - Class in org.deeplearning4j.optimize.stepfunctions
- stepMax - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- STOCHASTIC_GRADIENT_DESCENT - org.deeplearning4j.nn.api.OptimizationAlgorithm
- StochasticGradientDescent - Class in org.deeplearning4j.optimize.solvers
- 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
- Strict - org.deeplearning4j.nn.conf.ConvolutionMode
- stride - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
- stride - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
- stride - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer.BaseSubsamplingBuilder
- stride - Variable in class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
- stride - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
- stride - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- 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.LocallyConnected1D.Builder
- stride(int) - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer.Builder
-
Stride
- stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Convolution3D.Builder
-
Set stride size for 3D convolutions in (depth, height, width) order
- 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(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution2D.Builder
- stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.Deconvolution3D.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.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.Subsampling3DLayer.Builder
-
Stride
- stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
-
Stride
- SUBSAMPLING - org.deeplearning4j.nn.api.Layer.Type
- Subsampling1DLayer - Class in org.deeplearning4j.nn.conf.layers
- Subsampling1DLayer - Class in org.deeplearning4j.nn.layers.convolution.subsampling
- 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
- Subsampling3DLayer - Class in org.deeplearning4j.nn.layers.convolution.subsampling
- Subsampling3DLayer(Subsampling3DLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
- 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
- SubsamplingLayer - Class in org.deeplearning4j.nn.layers.convolution.subsampling
- SubsamplingLayer(SubsamplingLayer.BaseSubsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
- 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 - Class in org.deeplearning4j.nn.graph.vertex.impl
- SubsetVertex(int, int) - Constructor for class org.deeplearning4j.nn.conf.graph.SubsetVertex
- 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
- Subtract - org.deeplearning4j.nn.conf.graph.ElementWiseVertex.Op
- Subtract - org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op
- SUM - org.deeplearning4j.nn.conf.layers.PoolingType
- SUM - org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType
- summary() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
String detailing the architecture of the computation graph.
- summary() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
String detailing the architecture of the multilayernetwork.
- summary(InputType) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
String detailing the architecture of the multilayernetwork.
- summary(InputType...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
String detailing the architecture of the computation graph.
- synchronize(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
- synchronize(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.FancyBlockingQueue
- synchronize(int, boolean) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
- synchronizeIterEpochCounts() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- synchronizeIterEpochCounts() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- SYSTEM_EXIT_1 - org.deeplearning4j.optimize.listeners.FailureTestingListener.FailureMode
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
- 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 targetTargetSparsityThresholdAlgorithm.DEFAULT_SPARSITY_TARGET
and default decay rateTargetSparsityThresholdAlgorithm.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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- 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 - 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 - tBPTTBackwardLength(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- 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 - tBPTTForwardLength(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
- tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
- tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
- tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
- tbpttFwdLength - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- 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 - 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 - tBPTTLength(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- 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(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(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.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(int, double, boolean) - Method in class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition
- terminate(int, double, boolean) - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
- TEST - org.deeplearning4j.nn.api.Layer.TrainingMode
- 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 - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodingHandler
- 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
- ThresholdAlgorithmReducer - Interface in org.deeplearning4j.optimize.solvers.accumulation.encoding
- throwable - Variable in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator
- TimeDistributed - Class in org.deeplearning4j.nn.conf.layers.recurrent
- TimeDistributed(Layer, RNNFormat) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.TimeDistributed
- TimeDistributed(Layer) - Constructor for class org.deeplearning4j.nn.conf.layers.recurrent.TimeDistributed
- timeDistributedFormat - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
- TimeDistributedLayer - Class in org.deeplearning4j.nn.layers.recurrent
- TimeDistributedLayer(Layer, RNNFormat) - Constructor for class org.deeplearning4j.nn.layers.recurrent.TimeDistributedLayer
- TimeIterationListener - Class in org.deeplearning4j.optimize.listeners
- TimeIterationListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.TimeIterationListener
-
Constructor
- TimeIterationListener(int, 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
- 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
- toFormat() - Method in enum org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer.DataFormat
-
Deprecated.
- 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
- TRAIN - org.deeplearning4j.nn.api.Layer.TrainingMode
- Trainable - Interface in org.deeplearning4j.nn.api
- TRAINING - org.deeplearning4j.nn.conf.memory.MemoryUseMode
- TrainingConfig - Interface in org.deeplearning4j.nn.api
- TrainingListener - Interface in org.deeplearning4j.optimize.api
- 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 - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
- trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- trainingWorkspaceMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- trainingWorkspaceMode(WorkspaceMode) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.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)
- TransferLearning - Class in org.deeplearning4j.nn.transferlearning
- 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
- TransferLearningHelper(ComputationGraph) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
-
Expects a computation graph where some vertices are frozen
- 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(MultiLayerNetwork) - Constructor for class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
-
Expects a MLN where some layers 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
- Tree - Class in org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive
- Tree(List<String>) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
- 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
- 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
- Truncate - org.deeplearning4j.nn.conf.ConvolutionMode
- TruncatedBPTT - org.deeplearning4j.nn.conf.BackpropType
-
Truncated BackPropagation Through Time.
- TruncatedNormalDistribution - Class in org.deeplearning4j.nn.conf.distribution
- TruncatedNormalDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.TruncatedNormalDistribution
-
Create a truncated normal distribution with the given mean and std
- type - Variable in class org.deeplearning4j.earlystopping.scorecalc.ROCScoreCalculator
- type() - Method in interface org.deeplearning4j.nn.api.Layer
-
Returns the layer type
- type() - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- type() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.Cnn3DLossLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.CnnLossLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping1DLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping2DLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.Cropping3DLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToBatch
- type() - Method in class org.deeplearning4j.nn.layers.convolution.SpaceToDepth
- type() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling3DLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D
- type() - Method in class org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling3D
- type() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPadding3DLayer
- type() - Method in class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
- type() - Method in class org.deeplearning4j.nn.layers.DropoutLayer
- type() - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingSequenceLayer
- type() - Method in class org.deeplearning4j.nn.layers.feedforward.PReLU
- type() - Method in class org.deeplearning4j.nn.layers.LossLayer
- type() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
- type() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
- type() - Method in class org.deeplearning4j.nn.layers.ocnn.OCNNOutputLayer
- type() - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
- type() - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- type() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
- type() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
Deprecated.
- type() - Method in class org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer
- type() - Method in class org.deeplearning4j.nn.layers.recurrent.LSTM
- type() - Method in class org.deeplearning4j.nn.layers.recurrent.MaskZeroLayer
- type() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnLossLayer
- type() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
- type() - Method in class org.deeplearning4j.nn.layers.RepeatVector
- type() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- type() - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- type() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
U
- underlying - Variable in class org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
- underlying - Variable in class org.deeplearning4j.nn.graph.vertex.BaseWrapperVertex
- underlying - Variable in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- underlying(Layer) - Method in class org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer.Builder
- unfrozenGraph() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
-
Returns the unfrozen subset of the original computation graph as a computation graph Note that with each call to featurizedFit the parameters to the original computation graph are also updated
- unfrozenMLN() - Method in class org.deeplearning4j.nn.transferlearning.TransferLearningHelper
-
Returns the unfrozen layers of the MultiLayerNetwork as a multilayernetwork Note that with each call to featurizedFit the parameters to the original MLN are also updated
- UNIFORM - org.deeplearning4j.nn.weights.WeightInit
- 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
- 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 toFeedForwardLayer.Builder.nOut(int)
- UnstackVertex - Class in org.deeplearning4j.nn.conf.graph
- UnstackVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
- UnstackVertex(int, int) - Constructor for class org.deeplearning4j.nn.conf.graph.UnstackVertex
- 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(int, int) - Method in class org.deeplearning4j.nn.updater.UpdaterBlock
-
Update the gradient for this block
- update(Trainable, Gradient, int, int, int, LayerWorkspaceMgr) - Method in interface org.deeplearning4j.nn.api.Updater
-
Updater: updates the model
- update(Trainable, Gradient, int, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
- update(Gradient) - Method in interface org.deeplearning4j.nn.api.Model
-
Update layer weights and biases with gradient change
- update(Gradient) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- update(Gradient) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- update(Gradient) - Method in class org.deeplearning4j.nn.layers.BaseLayer
- update(Gradient) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
- update(Gradient) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
- update(Gradient) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- update(Gradient) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- update(Gradient) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- update(Gradient) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- update(Gradient, int, int, int, LayerWorkspaceMgr) - Method in class org.deeplearning4j.nn.updater.BaseMultiLayerUpdater
-
Update the gradient for the model.
- update(INDArray, String) - Method in interface org.deeplearning4j.nn.api.Model
-
Perform one update applying the gradient
- update(INDArray, String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.AbstractLayer
- 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(INDArray, String) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
- update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.FrozenLayerWithBackprop
- update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.recurrent.BidirectionalLayer
- update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.RepeatVector
- update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- update(INDArray, String) - Method in class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer
- update(INDArray, String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- update(Task) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- 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 - Variable 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 - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- updater - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
- updater(Updater) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
-
Deprecated.
- updater(Updater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Deprecated.
- updater(Updater) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
Deprecated.
- updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
-
Gradient updater.
- updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder
-
Gradient updater.
- updater(IUpdater) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Gradient updater configuration.
- updater(IUpdater) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
Gradient updater configuration.
- Updater - Enum in org.deeplearning4j.nn.conf
-
All the possible different updaters
- Updater - Interface in org.deeplearning4j.nn.api
-
Update the model
- UPDATER_BIN - Static variable in class org.deeplearning4j.util.ModelSerializer
- UPDATER_STATE - org.deeplearning4j.nn.conf.memory.MemoryType
- UPDATER_WORKING_MEM - org.deeplearning4j.nn.workspace.ArrayType
- UpdaterBlock - Class in org.deeplearning4j.nn.updater
- 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
- 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(int) - Method in class org.deeplearning4j.optimize.solvers.accumulation.EncodedGradientsAccumulator.Builder
-
This method enables optional limit for max number of updates per message Default value: Integer.MAX_VALUE (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
- UPSAMPLING - org.deeplearning4j.nn.api.Layer.Type
- Upsampling1D - Class in org.deeplearning4j.nn.conf.layers
- Upsampling1D - Class in org.deeplearning4j.nn.layers.convolution.upsampling
- Upsampling1D(BaseUpsamplingLayer.UpsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling1D
- 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
- Upsampling2D - Class in org.deeplearning4j.nn.layers.convolution.upsampling
- Upsampling2D(BaseUpsamplingLayer.UpsamplingBuilder) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling2D
- 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
- Upsampling3D - Class in org.deeplearning4j.nn.layers.convolution.upsampling
- Upsampling3D(Upsampling3D.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Upsampling3D
- 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 - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
- 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.
- USER_SPECIFIED - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode
- useReLU() - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
-
Use a ReLU activation on the 2d convolution
- useReLU(boolean) - Method in class org.deeplearning4j.nn.conf.layers.PrimaryCapsules.Builder
-
Whether to 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
- VAEReconErrorScoreCalculator(RegressionEvaluation.Metric, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.VAEReconErrorScoreCalculator
-
Constructor for reconstruction *ERROR*
- VAEReconProbScoreCalculator - Class in org.deeplearning4j.earlystopping.scorecalc
- 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, int, boolean, String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
- validateArrayWorkspaces(LayerWorkspaceMgr, INDArray, ArrayType, String, boolean, String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
- 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)
orModelSerializer.writeModel(Model, File, boolean)
, to be read withComputationGraph.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(long) - 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)
orModelSerializer.writeModel(Model, File, boolean)
, to be read withMultiLayerNetwork.load(File, boolean)
- 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.
- validateNonNegative(int, String) - Static method in class org.deeplearning4j.util.ValidationUtils
-
Checks that the values is >= 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 - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
- validateOutputLayerConfig - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
- validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
Enabled by default.
- validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
Enabled by default.
- validateOutputLayerConfig(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- 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.ConvolutionUtils
- validateShapes(INDArray, int, int, int, ConvolutionMode, int, int, boolean) - Static method in class org.deeplearning4j.util.Convolution1DUtils
- validateTbpttConfig - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
- validateTbpttConfig - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
- validateTbpttConfig(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
Enabled by default.
- validateTbpttConfig(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
Enabled by default.
- validateTbpttConfig(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
- 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.gradientcheck.GradientCheckUtil.PrintMode
-
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.CNN2DFormat
-
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
-
Deprecated.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.RNNFormat
-
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.gradientcheck.GradientCheckUtil.PrintMode
-
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.CNN2DFormat
-
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
-
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.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.RNNFormat
-
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.
- VAR_SCALING_NORMAL_FAN_AVG - org.deeplearning4j.nn.weights.WeightInit
- VAR_SCALING_NORMAL_FAN_IN - org.deeplearning4j.nn.weights.WeightInit
- VAR_SCALING_NORMAL_FAN_OUT - org.deeplearning4j.nn.weights.WeightInit
- VAR_SCALING_UNIFORM_FAN_AVG - org.deeplearning4j.nn.weights.WeightInit
- VAR_SCALING_UNIFORM_FAN_IN - org.deeplearning4j.nn.weights.WeightInit
- VAR_SCALING_UNIFORM_FAN_OUT - org.deeplearning4j.nn.weights.WeightInit
- 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
- VariationalAutoencoder - Class in org.deeplearning4j.nn.layers.variational
- 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
- 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() - 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 - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
- visibleBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
- 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.Deconvolution3DParamInitializer
- 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 - seeWeightDecay
for more details. - 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 - seeWeightDecay
for more details. - 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 - seeWeightDecay
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) - 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 - seeWeightDecay
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, boolean) - Method in class org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer.Builder
-
Add weight decay regularization for the network parameters (excluding biases).
- weightDecay(double, boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Add weight decay regularization for the network parameters (excluding biases).
- 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) - 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) - 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.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.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.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, 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 - Variable in class org.deeplearning4j.nn.conf.graph.AttentionVertex
- weightInit - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer.Builder
- weightInit - Variable in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer
- weightInit(String, IWeightInit) - Method in class org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer.Builder
- weightInit(Distribution) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
-
Set weight initialization scheme to random sampling via the specified distribution.
- weightInit(Distribution) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Set weight initialization scheme to random sampling via the specified distribution.
- weightInit(Distribution) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
Set weight initialization scheme to random sampling via the specified distribution.
- 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(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(IWeightInit) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
-
Weight initialization scheme to use, for initial weight values
- weightInit(IWeightInit) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder
- weightInit(IWeightInit) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder
- 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(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.conf.graph.AttentionVertex.Builder
-
Weight initialization scheme
- weightInit(WeightInit) - 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.samediff.SameDiffLayer.Builder
- 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(WeightInit) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
Weight initialization scheme to use, for initial weight values
- weightInit(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer.Builder
-
Initialize the embedding layer using values from the specified array.
- weightInit(INDArray) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer.Builder
-
Initialize the embedding layer using values from the specified array.
- WeightInit - Enum in org.deeplearning4j.nn.weights
- WeightInitConstant - Class in org.deeplearning4j.nn.weights
- 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
- WeightInitDistribution(Distribution) - Constructor for class org.deeplearning4j.nn.weights.WeightInitDistribution
- WeightInitEmbedding - Class in org.deeplearning4j.nn.weights.embeddings
- 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
- WeightInitIdentity(Double) - 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(Distribution) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer.Builder
-
Set the weight initialization for the recurrent weights, based on the specified distribution.
- weightInitRecurrent(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.
- WeightInitRelu - Class in org.deeplearning4j.nn.weights
- WeightInitRelu() - Constructor for class org.deeplearning4j.nn.weights.WeightInitRelu
- WeightInitReluUniform - Class in org.deeplearning4j.nn.weights
- WeightInitReluUniform() - Constructor for class org.deeplearning4j.nn.weights.WeightInitReluUniform
- WeightInitSigmoidUniform - Class in org.deeplearning4j.nn.weights
- WeightInitSigmoidUniform() - Constructor for class org.deeplearning4j.nn.weights.WeightInitSigmoidUniform
- WeightInitUniform - Class in org.deeplearning4j.nn.weights
- 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
- WeightInitVarScalingNormalFanAvg(Double) - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanAvg
- WeightInitVarScalingNormalFanIn - Class in org.deeplearning4j.nn.weights
- WeightInitVarScalingNormalFanIn(Double) - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanIn
- WeightInitVarScalingNormalFanOut - Class in org.deeplearning4j.nn.weights
- WeightInitVarScalingNormalFanOut(Double) - 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(Double) - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingUniformFanAvg
- WeightInitVarScalingUniformFanIn - Class in org.deeplearning4j.nn.weights
- WeightInitVarScalingUniformFanIn(Double) - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingUniformFanIn
- WeightInitVarScalingUniformFanOut - Class in org.deeplearning4j.nn.weights
- WeightInitVarScalingUniformFanOut(Double) - Constructor for class org.deeplearning4j.nn.weights.WeightInitVarScalingUniformFanOut
- WeightInitXavier - Class in org.deeplearning4j.nn.weights
- 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
andWeightNoise
) for this layer - weightNoise - Variable in class org.deeplearning4j.nn.conf.layers.BaseLayer
- weightNoise - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
- weightNoise - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
- weightNoise(IWeightNoise) - Method in class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
-
Set the weight noise (such as
DropConnect
andWeightNoise
) for this layer - weightNoise(IWeightNoise) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Set the weight noise (such as
DropConnect
andWeightNoise
) 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(IWeightNoise) - Method in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration.Builder
-
Set the weight noise (such as
DropConnect
andWeightNoise
) - WeightNoise - Class in org.deeplearning4j.nn.conf.weightnoise
- 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
- 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.
- WINOGRAD - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo
- WINOGRAD - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo
- WINOGRAD - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo
- WINOGRAD_NONFUSED - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo
- WINOGRAD_NONFUSED - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo
- WINOGRAD_NONFUSED - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo
- 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
- WORKING_MEMORY_FIXED - org.deeplearning4j.nn.conf.memory.MemoryType
- WORKING_MEMORY_VARIABLE - org.deeplearning4j.nn.conf.memory.MemoryType
- 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
- 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, 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
- writeModel(Model, String, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Write a model to a file path
- 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
- XAVIER - org.deeplearning4j.nn.weights.WeightInit
- XAVIER_FAN_IN - org.deeplearning4j.nn.weights.WeightInit
- XAVIER_LEGACY - org.deeplearning4j.nn.weights.WeightInit
- XAVIER_UNIFORM - org.deeplearning4j.nn.weights.WeightInit
- 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
- Yolo2OutputLayer - Class in org.deeplearning4j.nn.layers.objdetect
- 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
- YoloModelAdapter() - Constructor for class org.deeplearning4j.nn.adapters.YoloModelAdapter
- YoloUtils - Class in org.deeplearning4j.nn.layers.objdetect
- YoloUtils() - Constructor for class org.deeplearning4j.nn.layers.objdetect.YoloUtils
Z
- ZERO - org.deeplearning4j.nn.weights.WeightInit
- zeroedPretrainParamGradients - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
- ZeroPadding1DLayer - Class in org.deeplearning4j.nn.conf.layers
- ZeroPadding1DLayer - Class in org.deeplearning4j.nn.layers.convolution
- ZeroPadding1DLayer(int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
- ZeroPadding1DLayer(int[]) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
- ZeroPadding1DLayer(int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer
- 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
- ZeroPadding3DLayer - Class in org.deeplearning4j.nn.layers.convolution
- 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
- ZeroPaddingLayer - Class in org.deeplearning4j.nn.layers.convolution
- 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(NeuralNetConfiguration, DataType) - Constructor for class org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer
- ZeroPaddingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
- ZEROS - org.deeplearning4j.gradientcheck.GradientCheckUtil.PrintMode
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