- able(Iterator<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
Wraps an iterator as an iterable
- abs(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- AbstractDataSetIterator<T> - Class in org.deeplearning4j.datasets.iterator
-
This is simple DataSetIterator implementation, that builds DataSetIterator out of INDArray/float[]/double[] pairs.
- AbstractDataSetIterator(Iterable<Pair<T, T>>, int) - Constructor for class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
- accept(T) - Method in interface org.deeplearning4j.berkeley.Filter
-
- acceptResult(String) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
-
- acceptsInput(String) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
-
- accumulateScore(double) - Method in interface org.deeplearning4j.nn.api.Model
-
Sets a rolling tally for the score.
- accumulateScore(double) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- accumulateScore(double) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- accumulateScore(double) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- accumulateScore(double) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- accuracy() - Method in class org.deeplearning4j.eval.Evaluation
-
Accuracy:
(TP + TN) / (P + N)
- activate(Layer.TrainingMode) - Method in interface org.deeplearning4j.nn.api.Layer
-
Trigger an activation with the last specified input
- activate(INDArray, Layer.TrainingMode) - Method in interface org.deeplearning4j.nn.api.Layer
-
Initialize the layer with the given input
and return the activation for this layer
given this input
- activate(boolean) - Method in interface org.deeplearning4j.nn.api.Layer
-
Trigger an activation with the last specified input
- activate(INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.Layer
-
Initialize the layer with the given input
and return the activation for this layer
given this input
- activate() - Method in interface org.deeplearning4j.nn.api.Layer
-
Trigger an activation with the last specified input
- activate(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
Initialize the layer with the given input
and return the activation for this layer
given this input
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
-
- activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- activate(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- activate() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- activate(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- activate() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- activate(INDArray, String) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- activate(INDArray, boolean, int[], int[], int[], SubsamplingLayer.PoolingType, ConvolutionMode) - Method in interface org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- activate(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- activate() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Reconstructs the visible INPUT.
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- activate(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- activate() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- activate(INDArray, boolean, double, double, double, double) - Method in interface org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper
-
- activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- activate(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- activate() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- activate(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- activate() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- activate(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
-
- activate() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Triggers the activation of the last hidden layer ie: not logistic regression
- activate(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Triggers the activation for a given layer
- activate(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- activate(int, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Triggers the activation of the given layer
- activate(Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- activate(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- activate(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- activate(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- activateHelper(Layer, NeuralNetConfiguration, INDArray, INDArray, INDArray, INDArray, boolean, INDArray, INDArray, boolean, boolean, String) - Static method in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers
-
Returns FwdPassReturn object with activations/INDArrays.
- activateSelectedLayers(int, int, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Calculate activation for few layers at once.
- activation(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Layer activation function.
- activation(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Activation function / neuron non-linearity
Typical values include:
"relu" (rectified linear), "tanh", "sigmoid", "softmax",
"hardtanh", "leakyrelu", "maxout", "softsign", "softplus"
- ACTIVATION_LAYER - Static variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- activationFromPrevLayer(int, INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Calculate activation from previous layer including pre processing where necessary
- activationFunction - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- activationFunction - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- activationFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- ActivationLayer - Class in org.deeplearning4j.nn.conf.layers
-
- ActivationLayer - Class in org.deeplearning4j.nn.layers
-
Activation Layer
Used to apply activation on input and corresponding derivative on epsilon.
- ActivationLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.ActivationLayer
-
- ActivationLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.ActivationLayer
-
- ActivationLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- activationMean() - Method in interface org.deeplearning4j.nn.api.Layer
-
Calculate the mean representation
for the activation for this layer
- activationMean() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- activationMean() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- activationMean() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- activationMean() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- activationMean() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- adamMeanDecay - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- adamMeanDecay - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- adamMeanDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Mean decay rate for Adam updater.
- adamMeanDecay - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- adamMeanDecay(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Mean decay rate for Adam updater.
- adamVarDecay - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- adamVarDecay - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- adamVarDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Variance decay rate for Adam updater.
- adamVarDecay - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- adamVarDecay(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Variance decay rate for Adam updater.
- add(E, double) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- add(T, T) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Increments the entry specified by actual and predicted by one.
- add(T, T, int) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Increments the entry specified by actual and predicted by count.
- add(ConfusionMatrix<T>) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Adds the entries from another confusion matrix to this one.
- add(E) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- add(Object, int) - Method in class org.deeplearning4j.util.Index
-
- add(Object) - Method in class org.deeplearning4j.util.Index
-
- add(Pair<K, V>) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Adds the specified element to this applyTransformToDestination if it is not already present
(optional operation).
- add(K, V) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
- addAll(Collection<? extends E>) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- addAll(Collection<? extends Pair<K, V>>) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Adds all of the elements in the specified collection to this applyTransformToDestination if
they're not already present (optional operation).
- addColumn(List<String>) - Method in class org.deeplearning4j.util.StringGrid
-
- addExp(float[], int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- addExp_Old(float[]) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Similar to logAdd, but without the final log.
- addInputs(String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
Specify the inputs to the network, and their associated labels.
- addLayer(String, Layer, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
Add a layer, with no
InputPreProcessor, with the specified name and specified inputs.
- addLayer(String, Layer, InputPreProcessor, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- addPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.CombinedPreProcessor.Builder
-
- addPreProcessor(int, DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.CombinedPreProcessor.Builder
-
- addPreProcessors(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
Add preprocessors automatically, given the specified types of inputs for the network.
- addRow(List<String>) - Method in class org.deeplearning4j.util.StringGrid
-
- addToConfusion(Integer, Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
Adds to the confusion matrix
- addVariable(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- addVertex(String, GraphVertex, String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- adjustedrSquared(double, int, int) - Static method in class org.deeplearning4j.util.MathUtils
-
This calculates the adjusted r^2 including degrees of freedom.
- ALF - Variable in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
-
- allMatches(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- alpha - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
-
- alpha(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
-
LRN scaling constant alpha.
- ancestor(int, Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Returns the ancestor of the given tree
- appendTo(String, File) - Static method in class org.deeplearning4j.util.FileOperations
-
- appendToEach(String, int) - Method in class org.deeplearning4j.util.StringGrid
-
- applyDropConnect(Layer, String) - Static method in class org.deeplearning4j.util.Dropout
-
Apply drop connect to the given variable
- applyDropout(INDArray, double) - Static method in class org.deeplearning4j.util.Dropout
-
Apply dropout to the given input
and return the drop out mask used
- applyDropOutIfNecessary(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- applyDropOutIfNecessary(boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
-
- applyLearningRateScoreDecay() - Method in interface org.deeplearning4j.nn.api.Model
-
Update learningRate using for this model.
- applyLearningRateScoreDecay() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- applyLearningRateScoreDecay() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- applyLearningRateScoreDecay() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- applyLrDecayPolicy(LearningRatePolicy, Layer, int, String) - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
Update learning rate based on policy
- applyMomentumDecayPolicy(Layer, int, String) - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
Update momentum if schedule exist
- approxEquals(Counter<E>, double) - Method in class org.deeplearning4j.berkeley.Counter
-
- approxExp(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- approxLog(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- approxPow(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- ArchiveUtils - Class in org.deeplearning4j.util
-
- argMax() - Method in class org.deeplearning4j.berkeley.Counter
-
Finds the key with maximum count.
- argMax() - Method in class org.deeplearning4j.berkeley.CounterMap
-
Finds the key with maximum count.
- argsToMap(String[]) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Parses command line arguments into a Map.
- argsToMap(String[], Map<String, Integer>) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Parses command line arguments into a Map.
- argsToProperties(String[]) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- argsToProperties(String[], Map) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- arrayToString(String[]) - Static method in class org.deeplearning4j.util.StringUtils
-
Given an array of strings, return a comma-separated list of its elements.
- asciify(String) - Method in class org.deeplearning4j.util.FingerPrintKeyer
-
- asCounter() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
Returns a counter whose keys are the elements in this priority queue, and
whose counts are the priorities in this queue.
- asMinPriorityQueue() - Method in class org.deeplearning4j.berkeley.Counter
-
Warning: all priorities are the negative of their counts in the counter
here
- asPriorityQueue() - Method in class org.deeplearning4j.berkeley.Counter
-
Builds a priority queue whose elements are the counter's elements, and
whose priorities are those elements' counts in the counter.
- assertNInNOutSet(String, String, int, int, int) - Static method in class org.deeplearning4j.util.LayerValidation
-
Asserts that the layer nIn and nOut values are set for the layer
- async - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- AsyncDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
AsyncDataSetIterator takes an existing DataSetIterator and loads one or more DataSet objects
from it using a separate thread.
- AsyncDataSetIterator(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
Create an AsyncDataSetIterator with a queue size of 1 (i.e., only load a
single additional DataSet)
- AsyncDataSetIterator(DataSetIterator, int) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
Create an AsyncDataSetIterator with a specified queue size.
- AsyncMultiDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
Async prefetching iterator wrapper for MultiDataSetIterator implementations
- AsyncMultiDataSetIterator(MultiDataSetIterator, int) - Constructor for class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
- asyncSupported() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- AutoEncoder - Class in org.deeplearning4j.nn.conf.layers
-
Autoencoder.
- AutoEncoder - Class in org.deeplearning4j.nn.layers.feedforward.autoencoder
-
Autoencoder.
- AutoEncoder(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- AutoEncoder(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- AutoEncoder.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- calcBackpropGradients(boolean, INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Do backprop (gradient calculation)
- calcBackpropGradients(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Calculate gradients and errors.
- calcGradient(Gradient, INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
Calculate the gradient
- calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
-
- calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- calcL1() - Method in interface org.deeplearning4j.nn.api.Layer
-
Calculate the l1 regularization term
0.0 if regularization is not used.
- calcL1() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Calculate the L1 regularization term for all layers in the entire network.
- calcL1() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
-
- calcL1() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- calcL1() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- calcL1() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- calcL1() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- calcL1() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- calcL1() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- calcL1() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- calcL1() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- calcL1() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- calcL2() - Method in interface org.deeplearning4j.nn.api.Layer
-
Calculate the l2 regularization term
0.0 if regularization is not used.
- calcL2() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Calculate the L2 regularization term for all layers in the entire network.
- calcL2() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
-
- calcL2() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- calcL2() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- calcL2() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- calcL2() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- calcL2() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- calcL2() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- calcL2() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- calcL2() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- calcL2() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- calculateAUC() - Method in class org.deeplearning4j.eval.ROC
-
Calculate the AUC - Area Under Curve
Utilizes trapezoidal integration internally
- calculateScore(MultiLayerNetwork) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
-
- calculateScore(ComputationGraph) - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG
-
- calculateScore(T) - Method in interface org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator
-
Calculate the score for the given MultiLayerNetwork
- call(I) - Method in interface org.deeplearning4j.berkeley.MyMethod
-
- canDoBackward() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- canDoBackward() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Whether the GraphVertex can do backward pass.
- canDoBackward() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
-
- canDoForward() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- canDoForward() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Whether the GraphVertex can do forward pass.
- capitalize(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Uppercases the first character of a string.
- checkGradients(MultiLayerNetwork, double, double, double, boolean, boolean, INDArray, INDArray) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
-
Check backprop gradients for a MultiLayerNetwork.
- checkGradients(ComputationGraph, double, double, double, boolean, boolean, INDArray[], INDArray[]) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil
-
Check backprop gradients for a ComputationGraph
- checkTerminalConditions(INDArray, double, double, int) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
Check termination conditions
setup a search state
- checkTerminalConditions(INDArray, double, double, int) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- children() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- chiSquare2by2(int, int, int, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Find a 2x2 chi-square value.
- choleskyFromMatrix(RealMatrix) - Method in class org.deeplearning4j.util.MathUtils
-
This will return the cholesky decomposition of
the given matrix
- clamp(int, int, int) - Static method in class org.deeplearning4j.util.MathUtils
-
Clamps the value to a discrete value
- classCount(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the number of times the given label
has actually occurred
- Classifier - Interface in org.deeplearning4j.nn.api
-
A classifier (this is for supervised learning)
- classifier() - Method in interface org.deeplearning4j.nn.api.SequenceClassifier
-
Deprecated.
- clear() - Method in class org.deeplearning4j.berkeley.Counter
-
- clear() - Method in interface org.deeplearning4j.nn.api.Model
-
Clear input
- clear() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
-
- clear() - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
Clear residual parameters (useful for returning a gradient and then clearing old objects)
- clear() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- clear() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- clear() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Clear the internal state (if any) of the GraphVertex.
- clear() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- clear() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- clear() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- clear() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Clear the inputs.
- clear() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- clear() - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Removes all of the mappings from this map (optional operation).
- clear() - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Removes all of the elements from this applyTransformToDestination (optional operation).
- clearLayerMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- clearLayerMaskArrays() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- clearVariables() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- clone() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
Returns a clone of this priority queue.
- clone() - Method in interface org.deeplearning4j.nn.api.Layer
-
Clone the layer
- clone() - Method in interface org.deeplearning4j.nn.api.Updater
-
- clone() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
- clone() - Method in class org.deeplearning4j.nn.conf.distribution.Distribution
-
- clone() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
-
- clone() - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
-
- clone() - Method in class org.deeplearning4j.nn.conf.graph.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.PreprocessorVertex
-
- 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.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.BatchNormalization
-
- clone() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- 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.SubsamplingLayer
-
- clone() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
-
- clone() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- clone() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
Creates and returns a deep copy of the configuration.
- clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor
-
- clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
-
- clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
-
- clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
-
- clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
-
- clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
-
- clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.ReshapePreProcessor
-
Deprecated.
- clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
-
- clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
-
- clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.UnitVarianceProcessor
-
- clone() - Method in class org.deeplearning4j.nn.conf.stepfunctions.StepFunction
-
- clone() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- clone() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- clone() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- clone() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- clone() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- clone() - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
-
- clone() - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
- clone() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
-
- clusterColumn(int) - Method in class org.deeplearning4j.util.StringGrid
-
- cnnInputSize - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
Deprecated.
- cnnInputSize(int, int, int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- cnnInputSize(int[]) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- CnnToFeedForwardPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
-
A preprocessor to allow CNN and standard feed-forward network layers to be used together.
For example, CNN -> Denselayer
This does two things:
(b) Reshapes 4d activations out of CNN layer, with shape
[numExamples, numChannels, inputHeight, inputWidth]) into 2d activations (with shape
[numExamples, inputHeight*inputWidth*numChannels]) for use in feed forward layer
(a) Reshapes epsilons (weights*deltas) out of FeedFoward layer (which is 2D or 3D with shape
[numExamples, inputHeight*inputWidth*numChannels]) into 4d epsilons (with shape
[numExamples, numChannels, inputHeight, inputWidth]) suitable to feed into CNN layers.
Note: numChannels is equivalent to depth or featureMaps referenced in different literature
- CnnToFeedForwardPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
-
- CnnToFeedForwardPreProcessor(int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
-
- CnnToFeedForwardPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
-
- CnnToRnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
-
A preprocessor to allow CNN and RNN layers to be used together.
For example, ConvolutionLayer -> GravesLSTM
Functionally equivalent to combining CnnToFeedForwardPreProcessor + FeedForwardToRnnPreProcessor
Specifically, this does two things:
(a) Reshape 4d activations out of CNN layer, with shape [timeSeriesLength*miniBatchSize, numChannels, inputHeight, inputWidth])
into 3d (time series) activations (with shape [numExamples, inputHeight*inputWidth*numChannels, timeSeriesLength])
for use in RNN layers
(b) Reshapes 3d epsilons (weights.*deltas) out of RNN layer (with shape
[miniBatchSize,inputHeight*inputWidth*numChannels,timeSeriesLength]) into 4d epsilons with shape
[miniBatchSize*timeSeriesLength, numChannels, inputHeight, inputWidth] suitable to feed into CNN layers.
- CnnToRnnPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
-
- CollectScoresIterationListener - Class in org.deeplearning4j.optimize.listeners
-
CollectScoresIterationListener simply stores the model scores internally (along with the iteration) every 1 or N
iterations (this is configurable).
- CollectScoresIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
-
Constructor for collecting scores with default saving frequency of 1
- CollectScoresIterationListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
-
Constructor for collecting scores with the specified frequency.
- combination(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the combination of n choose r
- combineColumns(int, Integer[]) - Method in class org.deeplearning4j.util.StringGrid
-
Combine the column based on a template and a number of template variable
columns.
- combineColumns(int, int[]) - Method in class org.deeplearning4j.util.StringGrid
-
Combine the column based on a template and a number of template variable
columns.
- CombinedPreProcessor - Class in org.deeplearning4j.datasets.iterator
-
This is special preProcessor, that allows to combine multiple prerpocessors, and apply them to data sequentially.
- CombinedPreProcessor.Builder - Class in org.deeplearning4j.datasets.iterator
-
- COMMA - Static variable in class org.deeplearning4j.util.StringUtils
-
- compare(Pair<F, S>, Pair<F, S>) - Method in class org.deeplearning4j.berkeley.Pair.DefaultLexicographicPairComparator
-
- compare(Pair<S, T>, Pair<S, T>) - Method in class org.deeplearning4j.berkeley.Pair.FirstComparator
-
- compare(Pair<F, S>, Pair<F, S>) - Method in class org.deeplearning4j.berkeley.Pair.LexicographicPairComparator
-
- compare(Pair<S, T>, Pair<S, T>) - Method in class org.deeplearning4j.berkeley.Pair.ReverseFirstComparator
-
- compare(Pair<S, T>, Pair<S, T>) - Method in class org.deeplearning4j.berkeley.Pair.ReverseSecondComparator
-
- compare(Pair<S, T>, Pair<S, T>) - Method in class org.deeplearning4j.berkeley.Pair.SecondComparator
-
- compare(Map<String, Integer>, Map<String, Integer>) - Method in class org.deeplearning4j.util.StringCluster.SizeComparator
-
- compareTo(Pair<F, S>) - Method in class org.deeplearning4j.berkeley.Pair
-
Compares this object with the specified object for order.
- ComposableInputPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
-
Composable input pre processor
- ComposableInputPreProcessor(InputPreProcessor...) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
-
- ComposableIterationListener - Class in org.deeplearning4j.optimize.listeners
-
A group of listeners
- ComposableIterationListener(IterationListener...) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener
-
- ComposableIterationListener(Collection<IterationListener>) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener
-
- ComputationGraph - Class in org.deeplearning4j.nn.graph
-
A ComputationGraph network is a neural network with arbitrary (directed acyclic graph) connection structure.
- ComputationGraph(ComputationGraphConfiguration) - Constructor for class org.deeplearning4j.nn.graph.ComputationGraph
-
- ComputationGraphConfiguration - Class in org.deeplearning4j.nn.conf
-
ComputationGraphConfiguration is a configuration object for neural networks with arbitrary connection structure.
- ComputationGraphConfiguration() - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
- ComputationGraphConfiguration.GraphBuilder - Class in org.deeplearning4j.nn.conf
-
- ComputationGraphUpdater - Class in org.deeplearning4j.nn.updater.graph
-
Gradient updater for ComputationGraph.
Note: ComputationGraph does not implement the Layer interface (due to multiple in/out etc), hence ComputationGraphUpdater
can't be defined as an
Updater.
- ComputationGraphUpdater(ComputationGraph) - Constructor for class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
-
- ComputationGraphUpdater(ComputationGraph, INDArray) - Constructor for class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
-
- computationGraphUpdater - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- ComputationGraphUtil - Class in org.deeplearning4j.nn.graph.util
-
- computeDeltas2() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- computeDeltasR(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- computeGradientAndScore() - Method in interface org.deeplearning4j.nn.api.Model
-
Update the score
- computeGradientAndScore() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
- computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- computeGradientAndScore() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- computeScore(double, double, boolean) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer
-
Compute score after labels and input have been set.
- computeScore(double, double, boolean) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Compute score after labels and input have been set.
- computeScore(double, double, boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Compute score after labels and input have been set.
- computeScoreForExamples(double, double) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer
-
Compute the score for each example individually, after labels and input have been set.
- computeScoreForExamples(double, double) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Compute the score for each example individually, after labels and input have been set.
- computeScoreForExamples(double, double) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Compute the score for each example individually, after labels and input have been set.
- computeZ(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
* Compute input linear transformation (z) of the output layer
- computeZ(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Compute activations from input to output of the output layer
- concat(Iterable<Iterator<? extends T>>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- concat(Iterator<? extends T>...) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- concat(Iterable<T>, Iterable<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- concurrentSkipListSet() - Static method in class org.deeplearning4j.util.MultiDimensionalSet
-
- conf() - Method in interface org.deeplearning4j.nn.api.Model
-
The configuration for the neural network
- conf - Variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- conf() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- conf - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- conf() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- conf() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- conf - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- configuration - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
-
- configure(NeuralNetConfiguration) - Method in class org.deeplearning4j.optimize.Solver.Builder
-
- confs - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- confs(List<NeuralNetConfiguration>) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- confs - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
-
- confusion - Variable in class org.deeplearning4j.eval.Evaluation
-
- ConfusionMatrix<T extends Comparable<? super T>> - Class in org.deeplearning4j.eval
-
- ConfusionMatrix(List<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix
-
Creates an empty confusion Matrix
- ConfusionMatrix() - Constructor for class org.deeplearning4j.eval.ConfusionMatrix
-
- ConfusionMatrix(ConfusionMatrix<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix
-
Creates a new ConfusionMatrix initialized with the contents of another ConfusionMatrix.
- confusionMatrixMetaData - Variable in class org.deeplearning4j.eval.Evaluation
-
- confusionToString() - Method in class org.deeplearning4j.eval.Evaluation
-
Get a String representation of the confusion matrix
- ConjugateGradient - Class in org.deeplearning4j.optimize.solvers
-
Originally based on cc.mallet.optimize.ConjugateGradient
Rewritten based on Conjugate Gradient algorithm in Bengio et al.,
Deep Learning (in preparation) Ch8.
- ConjugateGradient(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.ConjugateGradient
-
- ConjugateGradient(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Collection<TerminationCondition>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.ConjugateGradient
-
- connect(List<Tree>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Connects the given trees
and sets the parents of the children
- consumeOnce(DataSet, boolean) - Method in class org.deeplearning4j.util.TestDataSetConsumer
-
This method consumes single DataSet, and spends delay time simulating execution of this dataset
- consumeWhileHasNext(boolean) - Method in class org.deeplearning4j.util.TestDataSetConsumer
-
This method cycles through iterator, whie iterator.hasNext() returns true.
- contains(Object) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- contains(K, T) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
- contains(Object) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns true if this applyTransformToDestination contains the specified element.
- contains(K, V) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
- containsAll(Collection<?>) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- containsAll(Collection<?>) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns true if this applyTransformToDestination contains all of the elements of the
specified collection.
- containsKey(E) - Method in class org.deeplearning4j.berkeley.Counter
-
Returns whether the counter contains the given key.
- containsKey(K) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- containsKey(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns true if this map contains a mapping for the specified
key.
- containsValue(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns true if this map maps one or more keys to the
specified value.
- contrastiveDivergence() - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Deprecated.
- ConvexOptimizer - Interface in org.deeplearning4j.optimize.api
-
Convex optimizer.
- CONVOLUTION_LAYER - Static variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- convolutional(int, int, int) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
-
Input type for convolutional (CNN) data, that is 4d with shape [miniBatchSize, depth, height, width].
- convolutionalFlat(int, int, int) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
-
Input type for convolutional (CNN) data, where the data is in flattened (row vector) format.
- ConvolutionHelper - Interface in org.deeplearning4j.nn.layers.convolution
-
Helper for the convolution layer.
- ConvolutionLayer - Class in org.deeplearning4j.nn.conf.layers
-
- ConvolutionLayer - Class in org.deeplearning4j.nn.layers.convolution
-
Convolution layer
- ConvolutionLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- ConvolutionLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- ConvolutionLayer.AlgoMode - Enum in org.deeplearning4j.nn.conf.layers
-
- ConvolutionLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- ConvolutionLayerSetup - Class in org.deeplearning4j.nn.conf.layers.setup
-
- ConvolutionLayerSetup(MultiLayerConfiguration.Builder, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
- ConvolutionMode - Enum in org.deeplearning4j.nn.conf
-
ConvolutionMode defines how convolution operations should be executed for Convolutional and Subsampling layers,
for a given input size and network configuration (specifically stride/padding/kernel sizes).
Currently, 3 modes are provided:
Strict: Output size for Convolutional and Subsampling layers are calculated as follows, in each dimension:
outputSize = (inputSize - kernelSize + 2*padding) / stride + 1.
- convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
-
Set the convolution mode for the Convolution layer.
- convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
-
Set the convolution mode for the Convolution layer.
- convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- convolutionMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- ConvolutionParamInitializer - Class in org.deeplearning4j.nn.params
-
Initialize convolution params.
- ConvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.ConvolutionParamInitializer
-
- convolutionType(Convolution.Type) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
-
- ConvolutionUtils - Class in org.deeplearning4j.util
-
Convolutional shape utilities
- coordSplit(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the coordinate split in a list of coordinates
such that the values for ret[0] are the x values
and ret[1] are the y values
- coordSplit(List<Double>) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the coordinate split in a list of coordinates
such that the values for ret[0] are the x values
and ret[1] are the y values
- correlation(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Returns the correlation coefficient of two double vectors.
- correlationR2(int) - Method in class org.deeplearning4j.eval.RegressionEvaluation
-
- corruptionLevel(double) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder
-
- corruptionLevel - Variable in class org.deeplearning4j.nn.conf.layers.AutoEncoder
-
- count(V, V) - Method in interface org.deeplearning4j.berkeley.CounterMap.CountFunction
-
- Counter<E> - Class in org.deeplearning4j.berkeley
-
A map from objects to doubles.
- Counter() - Constructor for class org.deeplearning4j.berkeley.Counter
-
- Counter(boolean) - Constructor for class org.deeplearning4j.berkeley.Counter
-
- Counter(MapFactory<E, Double>) - Constructor for class org.deeplearning4j.berkeley.Counter
-
- Counter(Map<? extends E, Double>) - Constructor for class org.deeplearning4j.berkeley.Counter
-
- Counter(Counter<? extends E>) - Constructor for class org.deeplearning4j.berkeley.Counter
-
- Counter(Collection<? extends E>) - Constructor for class org.deeplearning4j.berkeley.Counter
-
- CounterMap<K,V> - Class in org.deeplearning4j.berkeley
-
Maintains counts of (key, value) pairs.
- CounterMap(CounterMap<K, V>) - Constructor for class org.deeplearning4j.berkeley.CounterMap
-
- CounterMap() - Constructor for class org.deeplearning4j.berkeley.CounterMap
-
- CounterMap(MapFactory<K, Counter<V>>, MapFactory<V, Double>) - Constructor for class org.deeplearning4j.berkeley.CounterMap
-
- CounterMap(boolean) - Constructor for class org.deeplearning4j.berkeley.CounterMap
-
- CounterMap.CountFunction<V> - Interface in org.deeplearning4j.berkeley
-
- createAppendingOutputStream(File) - Static method in class org.deeplearning4j.util.FileOperations
-
- createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
-
- createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
-
- createDistribution(Distribution) - Static method in class org.deeplearning4j.nn.conf.distribution.Distributions
-
- createGradient(INDArray...) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
Create a gradient list based on the passed in parameters.
- createGradient(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
-
- createInputMatrix(int) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
Creates a feature vector
- createOutputMatrix(int) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- createOutputVector(int) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
Creates an output label matrix
- createStepFunction(StepFunction) - Static method in class org.deeplearning4j.optimize.stepfunctions.StepFunctions
-
- createVisibleBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer
-
- createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
-
- createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
-
- cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
-
Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
- cudnnAlgoMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
- curr - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- cursor - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- cursor() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- cursor() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
The current cursor if applicable
- cursor() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- cursor() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- cursor() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Deprecated.
Direct access to a number represenative of iterating through a dataset
- cursor() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- cursor() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- cursor() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- cursor() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
The current cursor if applicable
- cursor() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
The current cursor if applicable
- cursor() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- CUSTOM_FUNCTIONALITY - Static variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
System property for custom layers, preprocessors, graph vertices etc.
- customLossFunction - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
-
- customLossFunction(String) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
-
- customLossFunction - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
-
- dampingFactor - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- DataSetFetcher - Interface in org.deeplearning4j.datasets.iterator
-
Deprecated.
- DataSetIterator - Interface in org.deeplearning4j.datasets.iterator
-
- DataSetLossCalculator - Class in org.deeplearning4j.earlystopping.scorecalc
-
Given a DataSetIterator: calculate the total loss for the model on that data set.
- DataSetLossCalculator(DataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
-
Calculate the score (loss function value) on a given data set (usually a test set)
- DataSetLossCalculatorCG - Class in org.deeplearning4j.earlystopping.scorecalc
-
Given a DataSetIterator: calculate the total loss for the model on that data set.
- DataSetLossCalculatorCG(DataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG
-
Calculate the score (loss function value) on a given data set (usually a test set)
- DataSetLossCalculatorCG(MultiDataSetIterator, boolean) - Constructor for class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG
-
Calculate the score (loss function value) on a given data set (usually a test set)
- DataSetPreProcessor - Interface in org.deeplearning4j.datasets.iterator
-
- decay - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
- decay(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
At test time: we can use a global estimate of the mean and variance, calculated using a moving average
of the batch means/variances.
- decay - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- decode(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- decode(INDArray) - Method in class org.deeplearning4j.util.Viterbi
-
Decodes the given labels, assuming its a binary label matrix
- decode(INDArray, boolean) - Method in class org.deeplearning4j.util.Viterbi
-
Decodes a series of labels
- dedupeByCluster(int) - Method in class org.deeplearning4j.util.StringGrid
-
Deduplicate based on the column clustering signature
- dedupeByClusterAll() - Method in class org.deeplearning4j.util.StringGrid
-
- DeepLearningException - Exception in org.deeplearning4j.exception
-
- DeepLearningException() - Constructor for exception org.deeplearning4j.exception.DeepLearningException
-
- DeepLearningException(String, Throwable, boolean, boolean) - Constructor for exception org.deeplearning4j.exception.DeepLearningException
-
- DeepLearningException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DeepLearningException
-
- DeepLearningException(String) - Constructor for exception org.deeplearning4j.exception.DeepLearningException
-
- DeepLearningException(Throwable) - Constructor for exception org.deeplearning4j.exception.DeepLearningException
-
- DeepLearningIOUtil - Class in org.deeplearning4j.util
-
- DEFAULT_EDGE_VALUE - Static variable in class org.deeplearning4j.eval.Evaluation
-
- DEFAULT_FLATTENING_ORDER - Static variable in class org.deeplearning4j.nn.gradient.DefaultGradient
-
- DEFAULT_PRECISION - Static variable in class org.deeplearning4j.eval.RegressionEvaluation
-
- 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
-
- DefaultFactory(Class) - Constructor for class org.deeplearning4j.berkeley.Factory.DefaultFactory
-
- DefaultGradient - Class in org.deeplearning4j.nn.gradient
-
Default gradient implementation.
- DefaultGradient() - Constructor for class org.deeplearning4j.nn.gradient.DefaultGradient
-
- DefaultGradient(INDArray) - Constructor for class org.deeplearning4j.nn.gradient.DefaultGradient
-
- DefaultLexicographicPairComparator() - Constructor for class org.deeplearning4j.berkeley.Pair.DefaultLexicographicPairComparator
-
- DefaultParamInitializer - Class in org.deeplearning4j.nn.params
-
Static weight initializer with just a weight matrix and a bias
- DefaultParamInitializer() - Constructor for class org.deeplearning4j.nn.params.DefaultParamInitializer
-
- DefaultStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions
-
Default step function
- DefaultStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction
-
- DefaultStepFunction - Class in org.deeplearning4j.optimize.stepfunctions
-
Default step function
- DefaultStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
-
- defineOutputDir(String) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Define output directory based on network type
- DENSE_LAYER - Static variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- DenseLayer - Class in org.deeplearning4j.nn.conf.layers
-
Dense layer: fully connected feed forward layer trainable by backprop.
- DenseLayer - Class in org.deeplearning4j.nn.layers.feedforward.dense
-
- DenseLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer
-
- DenseLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer
-
- DenseLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- depth() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Finds the depth of the tree.
- depth(Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Returns the distance between this node
and the specified subnode
- derivativeActivation(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
Take the derivative of the given input
based on the activation
- derivativeActivation(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- derivativeActivation(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- determinationCoefficient(double[], double[], int) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the determination coefficient of two vectors given a length
- difference(Counter<E>) - Method in class org.deeplearning4j.berkeley.Counter
-
- difference(Collection<? extends T>, Collection<? extends T>) - Static method in class org.deeplearning4j.util.SetUtils
-
Return is s1 \ s2
- discretize(double, double, double, int) - Static method in class org.deeplearning4j.util.MathUtils
-
Discretize the given value
- DiskBasedQueue<E> - Class in org.deeplearning4j.util
-
Naive disk based queue for storing items on disk.
- DiskBasedQueue() - Constructor for class org.deeplearning4j.util.DiskBasedQueue
-
- DiskBasedQueue(String) - Constructor for class org.deeplearning4j.util.DiskBasedQueue
-
- DiskBasedQueue(File) - Constructor for class org.deeplearning4j.util.DiskBasedQueue
-
- dist - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- dist(Distribution) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Distribution to sample initial weights from.
- dist - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- dist - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- dist(Distribution) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Distribution to sample initial weights from.
- distanceFinderZValue(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This will translate a vector in to an equivalent integer
- Distribution - Class in org.deeplearning4j.nn.conf.distribution
-
An abstract distribution.
- Distribution() - Constructor for class org.deeplearning4j.nn.conf.distribution.Distribution
-
- Distributions - Class in org.deeplearning4j.nn.conf.distribution
-
Static method for instantiating an nd4j distribution from a configuration object.
- DL4JException - Exception in org.deeplearning4j.exception
-
Base exception for DL4J
- DL4JException() - Constructor for exception org.deeplearning4j.exception.DL4JException
-
- DL4JException(String) - Constructor for exception org.deeplearning4j.exception.DL4JException
-
- DL4JException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JException
-
- DL4JException(Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JException
-
- DL4JInvalidConfigException - Exception in org.deeplearning4j.exception
-
Exception signifying that the specified configuration is invalid
- DL4JInvalidConfigException() - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException
-
- DL4JInvalidConfigException(String) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException
-
- DL4JInvalidConfigException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException
-
- DL4JInvalidConfigException(Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidConfigException
-
- DL4JInvalidInputException - Exception in org.deeplearning4j.exception
-
DL4J Exception thrown when invalid input is provided (wrong rank, wrong size, etc)
- DL4JInvalidInputException() - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException
-
- DL4JInvalidInputException(String) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException
-
- DL4JInvalidInputException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException
-
- DL4JInvalidInputException(Throwable) - Constructor for exception org.deeplearning4j.exception.DL4JInvalidInputException
-
- Dl4jReflection - Class in org.deeplearning4j.util
-
- DL4JSubTypesScanner - Class in org.deeplearning4j.util.reflections
-
Custom Reflections library scanner for finding DL4J subtypes (custom layers, graph vertices, etc)
- DL4JSubTypesScanner(List<Class<?>>, List<Class<?>>) - Constructor for class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
-
- doBackward(boolean) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Do backward pass
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
-
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
-
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
-
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
-
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
-
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
-
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
-
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
-
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
-
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
-
- doBackward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
-
- doForward(boolean) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Do forward pass using the stored inputs
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
-
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
-
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
-
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
-
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
-
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
-
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
-
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
-
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
-
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
-
- doForward(boolean) - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
-
- dotProduct(Counter<E>) - Method in class org.deeplearning4j.berkeley.Counter
-
- doTruncatedBPTT(INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Fit the network using truncated BPTT
- doTruncatedBPTT(INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- DoublesDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
- DoublesDataSetIterator(Iterable<Pair<double[], double[]>>, int) - Constructor for class org.deeplearning4j.datasets.iterator.DoublesDataSetIterator
-
- dropOut - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- dropOut(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- dropOut - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- dropOut - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- dropOut(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Dropout probability.
- Dropout - Class in org.deeplearning4j.util
-
- dropoutApplied - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- dropoutMask - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- ds - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- DummyPreProcessor - Class in org.deeplearning4j.datasets.iterator
-
This is special dummy preProcessor, that does nothing.
- DummyPreProcessor() - Constructor for class org.deeplearning4j.datasets.iterator.DummyPreProcessor
-
- DuplicateToTimeSeriesVertex - Class in org.deeplearning4j.nn.conf.graph.rnn
-
DuplicateToTimeSeriesVertex is a vertex that goes from 2d activations to a 3d time series activations, by means of
duplication.
- DuplicateToTimeSeriesVertex(String) - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
-
- DuplicateToTimeSeriesVertex - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn
-
DuplicateToTimeSeriesVertex is a vertex that goes from 2d activations to a 3d time series activations, by means of
duplication.
- DuplicateToTimeSeriesVertex(ComputationGraph, String, int, String) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
-
- DuplicateToTimeSeriesVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], String) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
-
- EarlyStoppingConfiguration<T extends Model> - Class in org.deeplearning4j.earlystopping
-
Early stopping configuration: Specifies the various configuration options for running training with early stopping.
Users need to specify the following:
(a) EarlyStoppingModelSaver: How models will be saved (to disk, to memory, etc) (Default: in memory)
(b) Termination conditions: at least one termination condition must be specified
(i) Iteration termination conditions: calculated once for each minibatch.
- EarlyStoppingConfiguration.Builder<T extends Model> - Class in org.deeplearning4j.earlystopping
-
- EarlyStoppingGraphTrainer - Class in org.deeplearning4j.earlystopping.trainer
-
Class for conducting early stopping training locally (single machine).
Can be used to train a
ComputationGraph
- EarlyStoppingGraphTrainer(EarlyStoppingConfiguration<ComputationGraph>, ComputationGraph, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
-
- EarlyStoppingGraphTrainer(EarlyStoppingConfiguration<ComputationGraph>, ComputationGraph, DataSetIterator, EarlyStoppingListener<ComputationGraph>) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
-
Constructor for training using a DataSetIterator
- EarlyStoppingGraphTrainer(EarlyStoppingConfiguration<ComputationGraph>, ComputationGraph, MultiDataSetIterator, EarlyStoppingListener<ComputationGraph>) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
-
Constructor for training using a MultiDataSetIterator
- EarlyStoppingListener<T extends Model> - Interface in org.deeplearning4j.earlystopping.listener
-
EarlyStoppingListener is a listener interface for conducting early stopping training.
- EarlyStoppingModelSaver<T extends Model> - Interface in org.deeplearning4j.earlystopping
-
Interface for saving MultiLayerNetworks learned during early stopping, and retrieving them again later
- EarlyStoppingResult<T extends Model> - Class in org.deeplearning4j.earlystopping
-
EarlyStoppingResult: contains the results of the early stopping training, such as:
- Why the training was terminated
- Score vs.
- EarlyStoppingResult(EarlyStoppingResult.TerminationReason, String, Map<Integer, Double>, int, double, int, T) - Constructor for class org.deeplearning4j.earlystopping.EarlyStoppingResult
-
- EarlyStoppingResult.TerminationReason - Enum in org.deeplearning4j.earlystopping
-
- EarlyStoppingTrainer - Class in org.deeplearning4j.earlystopping.trainer
-
Class for conducting early stopping training locally (single machine), for training a
MultiLayerNetwork.
- EarlyStoppingTrainer(EarlyStoppingConfiguration<MultiLayerNetwork>, MultiLayerConfiguration, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
-
- EarlyStoppingTrainer(EarlyStoppingConfiguration<MultiLayerNetwork>, MultiLayerNetwork, DataSetIterator) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
-
- EarlyStoppingTrainer(EarlyStoppingConfiguration<MultiLayerNetwork>, MultiLayerNetwork, DataSetIterator, EarlyStoppingListener<MultiLayerNetwork>) - Constructor for class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
-
- editDistance(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Computes the Levenshtein (edit) distance of the two given Strings.
- element() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- ElementWiseVertex - Class in org.deeplearning4j.nn.conf.graph
-
An ElementWiseVertex is used to combine the activations of two or more layer in an element-wise manner
For example, the activations may be combined by addition, subtraction or multiplication.
- ElementWiseVertex(ElementWiseVertex.Op) - Constructor for class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
-
- ElementWiseVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
-
An ElementWiseVertex is used to combine the activations of two or more layer in an element-wise manner
For example, the activations may be combined by addition, subtraction or multiplication.
- ElementWiseVertex(ComputationGraph, String, int, ElementWiseVertex.Op) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
-
- ElementWiseVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], ElementWiseVertex.Op) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
-
- ElementWiseVertex.Op - Enum in org.deeplearning4j.nn.conf.graph
-
- ElementWiseVertex.Op - Enum in org.deeplearning4j.nn.graph.vertex.impl
-
- EmbeddingLayer - Class in org.deeplearning4j.nn.conf.layers
-
Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to numClass-1)
as input.
- EmbeddingLayer - Class in org.deeplearning4j.nn.layers.feedforward.embedding
-
Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to numClass-1)
as input.
- EmbeddingLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
-
- EmbeddingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- emptyIterator() - Static method in class org.deeplearning4j.berkeley.Iterators
-
- EmptyParamInitializer - Class in org.deeplearning4j.nn.params
-
- EmptyParamInitializer() - Constructor for class org.deeplearning4j.nn.params.EmptyParamInitializer
-
- emptyStringArray - Static variable in class org.deeplearning4j.util.StringUtils
-
- encode(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- ensureCounter(K) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- entropy(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the entropy (information gain, or uncertainty of a random variable).
- Entry(K, T, V) - Constructor for class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- entrySet() - Method in class org.deeplearning4j.berkeley.Counter
-
- entrySet() - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns a
Set view of the mappings contained in this map.
- EnumUtil - Class in org.deeplearning4j.util
-
Created by agibsonccc on 9/3/14.
- epochs - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- EpochTerminationCondition - Interface in org.deeplearning4j.earlystopping.termination
-
Interface for termination conditions to be evaluated once per epoch (i.e., once per pass of the full data set),
based on a score and epoch number
- epochTerminationConditions(EpochTerminationCondition...) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder
-
Termination conditions to be evaluated every N epochs, with N set by evaluateEveryNEpochs option
- epochTerminationConditions(List<EpochTerminationCondition>) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder
-
Termination conditions to be evaluated every N epochs, with N set by evaluateEveryNEpochs option
- eps - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
- eps(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
Epsilon value for batch normalization; small floating point value added to variance
(algorithm 1 in http://arxiv.org/pdf/1502.03167v3.pdf) to reduce/avoid underflow issues.
Default: 1e-5
- eps - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- epsilon - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- epsilon(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Epsilon value for updaters: Adagrad and Adadelta.
- epsilon - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- epsilon - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- epsilon(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Epsilon value for updaters: Adagrad and Adadelta.
- epsilon - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- epsilon - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- epsilon() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- EpsTermination - Class in org.deeplearning4j.optimize.terminations
-
Epsilon termination (absolute change based on tolerance)
- EpsTermination(double, double) - Constructor for class org.deeplearning4j.optimize.terminations.EpsTermination
-
- EpsTermination() - Constructor for class org.deeplearning4j.optimize.terminations.EpsTermination
-
- equals(Object) - Method in class org.deeplearning4j.berkeley.Counter
-
- equals(Object) - Method in class org.deeplearning4j.berkeley.Pair
-
- equals(Object) - Method in class org.deeplearning4j.berkeley.Triple
-
- equals(Object) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.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.PreprocessorVertex
-
- 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.StackVertex
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction
-
- equals(Object) - Method in class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction
-
- equals(Object) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- equals(Object) - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
-
- equals(Object) - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
- equals(Object) - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
-
- equals(Object) - Method in class org.deeplearning4j.util.Index
-
- equals(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
- error(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
Calculate error with respect to the
current layer.
- error(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- error(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- error() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Returns the prediction error for this node
- error(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- error(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- errorFor(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
- errorSum() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Returns the total prediction error for this
tree and its children
- ESCAPE_CHAR - Static variable in class org.deeplearning4j.util.StringUtils
-
- escapeHTML(String) - Static method in class org.deeplearning4j.util.StringUtils
-
Escapes HTML Special characters present in the string.
- escapeString(String, char[], char) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- escapeString(String) - Static method in class org.deeplearning4j.util.StringUtils
-
Escape commas in the string using the default escape char
- escapeString(String, char, char) - Static method in class org.deeplearning4j.util.StringUtils
-
Escape charToEscape in the string
with the escape char escapeChar
- escapeString(String, char, char[]) - Static method in class org.deeplearning4j.util.StringUtils
-
- esConfig - Variable in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
-
- euclideanDistance(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the distance of two vectors
sum(i=1,n) (q_i - p_i)^2
- euclideanDistance(float[], float[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the distance of two vectors
sum(i=1,n) (q_i - p_i)^2
- eval(INDArray, INDArray, ComputationGraph) - Method in class org.deeplearning4j.eval.Evaluation
-
Evaluate the output
using the given true labels,
the input to the multi layer network
and the multi layer network to
use for evaluation
- eval(INDArray, INDArray, MultiLayerNetwork) - Method in class org.deeplearning4j.eval.Evaluation
-
Evaluate the output
using the given true labels,
the input to the multi layer network
and the multi layer network to
use for evaluation
- eval(INDArray, INDArray) - Method in class org.deeplearning4j.eval.Evaluation
-
Collects statistics on the real outcomes vs the
guesses.
- eval(INDArray, INDArray, List<?>) - Method in class org.deeplearning4j.eval.Evaluation
-
Evaluate the network, with optional metadata
- eval(int, int) - Method in class org.deeplearning4j.eval.Evaluation
-
Evaluate a single prediction (one prediction at a time)
- eval(INDArray, INDArray) - Method in class org.deeplearning4j.eval.RegressionEvaluation
-
- eval(INDArray, INDArray) - Method in class org.deeplearning4j.eval.ROC
-
Evaluate (collect statistics for) the given minibatch of data.
- evalTimeSeries(INDArray, INDArray) - Method in class org.deeplearning4j.eval.Evaluation
-
Convenience method for evaluation of time series.
- evalTimeSeries(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.eval.Evaluation
-
Evaluate a time series, whether the output is masked usind a masking array.
- evalTimeSeries(INDArray, INDArray) - Method in class org.deeplearning4j.eval.RegressionEvaluation
-
Convenience method for evaluation of time series.
- evalTimeSeries(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.eval.RegressionEvaluation
-
Evaluate a time series, whether the output is masked usind a masking array.
- evalTimeSeries(INDArray, INDArray) - Method in class org.deeplearning4j.eval.ROC
-
Evaluate (collect statistics for) the given minibatch of data time series (3d) data, with no mask array
- evalTimeSeries(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.eval.ROC
-
Evaluate (collect statistics for) the given minibatch of data time series (3d) data, with optional (nullable)
output mask array.
- evaluate(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Evaluate the network (classification performance)
- evaluate(DataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
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) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Evaluate the network (classification performance)
- evaluate(DataSetIterator, List<String>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Evaluate the network on the provided data set.
- evaluate(DataSetIterator, List<String>, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Evaluate the network (for classification) on the provided data set, with top N accuracy in addition to standard accuracy.
- evaluateEveryNEpochs(int) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder
-
How frequently should evaluations be conducted (in terms of epochs)? Defaults to every (1) epochs.
- Evaluation - Class in org.deeplearning4j.eval
-
Evaluation metrics:
precision, recall, f1
- Evaluation() - Constructor for class org.deeplearning4j.eval.Evaluation
-
- Evaluation(int) - Constructor for class org.deeplearning4j.eval.Evaluation
-
The number of classes to account
for in the evaluation
- Evaluation(List<String>) - Constructor for class org.deeplearning4j.eval.Evaluation
-
The labels to include with the evaluation.
- Evaluation(Map<Integer, String>) - Constructor for class org.deeplearning4j.eval.Evaluation
-
Use a map to generate labels
Pass in a label index with the actual label
you want to use for output
- Evaluation(List<String>, int) - Constructor for class org.deeplearning4j.eval.Evaluation
-
Constructor to use for top N accuracy
- exactBinomial(int, int, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Find a one tailed exact binomial test probability.
- ExistingDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
This wrapper provides DataSetIterator interface to existing java Iterable and Iterator
- ExistingDataSetIterator(Iterator<DataSet>) - Constructor for class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- ExistingDataSetIterator(Iterator<DataSet>, List<String>) - Constructor for class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- ExistingDataSetIterator(Iterable<DataSet>) - Constructor for class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- ExistingDataSetIterator(Iterable<DataSet>, List<String>) - Constructor for class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- ExistingDataSetIterator(Iterable<DataSet>, int, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- exp(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- exportScores(OutputStream) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
-
Export the scores in tab-delimited (one per line) UTF-8 format.
- exportScores(OutputStream, String) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
-
Export the scores in delimited (one per line) UTF-8 format with the specified delimiter
- exportScores(File) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
-
Export the scores to the specified file in delimited (one per line) UTF-8 format, tab delimited
- exportScores(File, String) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
-
Export the scores to the specified file in delimited (one per line) UTF-8 format, using the specified delimiter
- f1(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
Calculate f1 score for a given class
- f1() - Method in class org.deeplearning4j.eval.Evaluation
-
TP: true positive
FP: False Positive
FN: False Negative
F1 score: 2 * TP / (2TP + FP + FN)
- f1Score(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- f1Score(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Returns the f1 score for the given examples.
- f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Returns the f1 score for the given examples.
- f1Score(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Returns the f1 score for the given examples.
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
-
Returns the f1 score for the given examples.
- f1Score(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- f1Score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- fa - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- factorial(double) - Static method in class org.deeplearning4j.util.MathUtils
-
This will return the factorial of the given number n.
- Factory<T> - Interface in org.deeplearning4j.berkeley
-
- Factory.DefaultFactory<T> - Class in org.deeplearning4j.berkeley
-
- falseAlarmRate() - Method in class org.deeplearning4j.eval.Evaluation
-
False Alarm Rate (FAR) reflects rate of misclassified to classified records
http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1058&context=isw
- falseNegativeRate(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the false negative rate for a given label
- falseNegativeRate(Integer, double) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the false negative rate for a given label
- falseNegativeRate() - Method in class org.deeplearning4j.eval.Evaluation
-
False negative rate based on guesses so far
Takes into account all known classes and outputs average fnr across all of them
- falseNegatives - Variable in class org.deeplearning4j.eval.Evaluation
-
- falseNegatives() - Method in class org.deeplearning4j.eval.Evaluation
-
False negatives: correctly rejected
- falsePositiveRate(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the false positive rate for a given label
- falsePositiveRate(Integer, double) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the false positive rate for a given label
- falsePositiveRate() - Method in class org.deeplearning4j.eval.Evaluation
-
False positive rate based on guesses so far
Takes into account all known classes and outputs average fpr across all of them
- falsePositives - Variable in class org.deeplearning4j.eval.Evaluation
-
- falsePositives() - Method in class org.deeplearning4j.eval.Evaluation
-
False positive: wrong guess
- feedForward(int) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
-
InputType for feed forward network data
- feedForward(INDArray, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Conduct forward pass using a single input array.
- feedForward(INDArray[], boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Conduct forward pass using an array of inputs
- feedForward() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Conduct forward pass using the stored inputs, at test time
- feedForward(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Conduct forward pass using the stored inputs
- feedForward(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Compute activations from input to output of the output layer
- feedForward(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Compute activations from input to output of the output layer
- feedForward() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Compute activations from input to output of the output layer
- feedForward(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Compute activations from input to output of the output layer
- feedForward(INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Compute the activations from the input to the output layer, given mask arrays (that may be null)
The masking arrays are used in situations such an one-to-many and many-to-one rucerrent neural network (RNN)
designs, as well as for supporting time series of varying lengths within the same minibatch for RNNs.
- FeedForwardLayer - Class in org.deeplearning4j.nn.conf.layers
-
Created by jeffreytang on 7/21/15.
- FeedForwardLayer(FeedForwardLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
-
- FeedForwardLayer.Builder<T extends FeedForwardLayer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers
-
- feedForwardR(List<INDArray>, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Feed forward with the r operator
- FeedForwardToCnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
-
A preprocessor to allow CNN and standard feed-forward network layers to be used together.
For example, DenseLayer -> CNN
This does two things:
(a) Reshapes activations out of FeedFoward layer (which is 2D or 3D with shape
[numExamples, inputHeight*inputWidth*numChannels]) into 4d activations (with shape
[numExamples, numChannels, inputHeight, inputWidth]) suitable to feed into CNN layers.
(b) Reshapes 4d epsilons (weights*deltas) from CNN layer, with shape
[numExamples, numChannels, inputHeight, inputWidth]) into 2d epsilons (with shape
[numExamples, inputHeight*inputWidth*numChannels]) for use in feed forward layer
Note: numChannels is equivalent to depth or featureMaps referenced in different literature
- FeedForwardToCnnPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
-
Reshape to a channels x rows x columns tensor
- FeedForwardToCnnPreProcessor(int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
-
- feedForwardToLayer(int, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Compute the activations from the input to the specified layer.
To compute activations for all layers, use feedForward(...) methods
Note: output list includes the original input.
- feedForwardToLayer(int, INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Compute the activations from the input to the specified layer.
To compute activations for all layers, use feedForward(...) methods
Note: output list includes the original input.
- feedForwardToLayer(int, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Compute the activations from the input to the specified layer, using the currently set input for the network.
To compute activations for all layers, use feedForward(...) methods
Note: output list includes the original input.
- FeedForwardToRnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
-
A preprocessor to allow RNN and feed-forward network layers to be used together.
For example, DenseLayer -> GravesLSTM
This does two things:
(a) Reshapes activations out of FeedFoward layer (which is 2D with shape
[miniBatchSize*timeSeriesLength,layerSize]) into 3d activations (with shape
[miniBatchSize,layerSize,timeSeriesLength]) suitable to feed into RNN layers.
(b) Reshapes 3d epsilons (weights*deltas from RNN layer, with shape
[miniBatchSize,layerSize,timeSeriesLength]) into 2d epsilons (with shape
[miniBatchSize*timeSeriesLength,layerSize]) for use in feed forward layer
- FeedForwardToRnnPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
-
- fetch(int) - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Deprecated.
Fetches the next dataset.
- fetch(int) - Method in class org.deeplearning4j.datasets.iterator.impl.MovingWindowDataSetFetcher
-
Fetches the next dataset.
- fetcher - Variable in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- fileNameClean(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns a "clean" version of the given filename in which spaces have
been converted to dashes and all non-alphaneumeric chars are underscores.
- FileOperations - Class in org.deeplearning4j.util
-
- fillDown(String, int) - Method in class org.deeplearning4j.util.StringGrid
-
- fillList(Iterator<? extends T>, List<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- fillList(Iterator<? extends T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- fillQueue() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
- Filter<T> - Interface in org.deeplearning4j.berkeley
-
Filters are boolean cooccurrences which accept or reject items.
- filter(Iterator<T>, Filter<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- filterBySimilarity(double, int, int) - Method in class org.deeplearning4j.util.StringGrid
-
- FilteredIterator(Filter<T>, Iterator<T>) - Constructor for class org.deeplearning4j.berkeley.Iterators.FilteredIterator
-
- FilteredIterator(Filter<T>, Iterable<T>) - Constructor for class org.deeplearning4j.berkeley.Iterators.FilteredIterator
-
- filterResultsBy(Predicate<String>) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
-
- filterRowsByColumn(int, Collection<String>) - Method in class org.deeplearning4j.util.StringGrid
-
- find(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Say whether this regular expression can be found inside
this String.
- findNext(String, char, char, int, StringBuilder) - Static method in class org.deeplearning4j.util.StringUtils
-
Finds the first occurrence of the separator character ignoring the escaped
separators starting from the index.
- finetune() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Deprecated.
- FingerPrintKeyer - Class in org.deeplearning4j.util
-
Copied from google refine:
takes the key and gets rid of all punctuation, transforms to lower case
and alphabetic sorts the words
- FingerPrintKeyer() - Constructor for class org.deeplearning4j.util.FingerPrintKeyer
-
- firstChild() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- FirstComparator() - Constructor for class org.deeplearning4j.berkeley.Pair.FirstComparator
-
- fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
-
- fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
-
- fit() - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
-
- fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
-
- fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer
-
- fit(DataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
-
- fit(MultiDataSet) - Method in class org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer
-
- fit() - Method in interface org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer
-
Conduct early stopping training
- fit(DataSetIterator) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Train the model based on the datasetiterator
- fit(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(INDArray, int[]) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit() - Method in interface org.deeplearning4j.nn.api.Model
-
All models have a fit method
- fit(INDArray) - Method in interface org.deeplearning4j.nn.api.Model
-
Fit the model to the given data
- fit(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.SequenceClassifier
-
Deprecated.
- fit(DataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Fit the ComputationGraph using a DataSet.
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Fit the ComputationGraph using a DataSetIterator.
- fit(MultiDataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Fit the ComputationGraph using a MultiDataSet
- fit(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Fit the ComputationGraph using a MultiDataSetIterator
- fit(INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Fit the ComputationGraph given arrays of inputs and labels.
- fit(INDArray[], INDArray[], INDArray[], INDArray[]) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Fit the ComputationGraph using the specified inputs and labels (and mask arrays)
- fit() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- fit(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- fit(INDArray) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
-
- fit() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- fit(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Fit the model
- fit(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Fit the model
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Fit the model
- fit(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Fit the model to the given data
- fit(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- fit() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- fit(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- fit(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer
-
- fit(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Fit the model
- fit(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Fit the model
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Fit the model
- fit(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- fit(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- fit(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Fit the model
- fit(INDArray, INDArray, INDArray, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Fit the model
- fit(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Fit the unsupervised model
- fit(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Fit the model
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Fit the model
- fit() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- flattenedGradients - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
-
- flattenedGradients - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- flattenedParams - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
-
- flattenedParams - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- flatteningOrderForVariable(String) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
-
- flatteningOrderForVariable(String) - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
Return the gradient flattening order for the specified variable, or null if it is not explicitly set
- FloatsDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
float[] wrapper for DataSetIterator impementation.
- FloatsDataSetIterator(Iterable<Pair<float[], float[]>>, int) - Constructor for class org.deeplearning4j.datasets.iterator.FloatsDataSetIterator
-
- forgetGateBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder
-
Set forget gate bias initalizations.
- forgetGateBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder
-
Set forget gate bias initalizations.
- formatPercent(double, int) - Static method in class org.deeplearning4j.util.StringUtils
-
Format a percentage for presentation to the user.
- formatTime(long) - Static method in class org.deeplearning4j.util.StringUtils
-
Given the time in long milliseconds, returns a
String in the format Xhrs, Ymins, Z sec.
- formatTimeDiff(long, long) - Static method in class org.deeplearning4j.util.StringUtils
-
Given a finish and start time in long milliseconds, returns a
String in the format Xhrs, Ymins, Z sec, for the time difference between two times.
- from - Variable in class org.deeplearning4j.nn.conf.graph.UnstackVertex
-
- fromFile(String, String) - Static method in class org.deeplearning4j.util.StringGrid
-
- fromInput(InputStream, String) - Static method in class org.deeplearning4j.util.StringGrid
-
- fromJson(String) - 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.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
- fromString(String, String) - Static method in class org.deeplearning4j.util.MathUtils
-
This will take a given string and separator and convert it to an equivalent
double array.
- fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
Create a neural net configuration from json
- fromYaml(String) - Static method in class org.deeplearning4j.nn.conf.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
- fwdPassOutput - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- fwdPassOutputAsArrays - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- FwdPassReturn - Class in org.deeplearning4j.nn.layers.recurrent
-
Created by benny on 12/31/15.
- FwdPassReturn() - Constructor for class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- ga - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- gamma - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
- gamma(double) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
- gamma - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- GAMMA - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
-
- GaussianDistribution - Class in org.deeplearning4j.nn.conf.distribution
-
A normal distribution.
- GaussianDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.GaussianDistribution
-
Create a gaussian distribution (equivalent to normal)
with the given mean and std
- generateUniform(int) - Static method in class org.deeplearning4j.util.MathUtils
-
This will generate a series of uniformally distributed
numbers between l times
- get(int) - Method in class org.deeplearning4j.util.Index
-
- get(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns the value to which the specified key is mapped,
or null if this map contains no mapping for the key.
- get(K, T) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
- getActualTotal(T) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Computes the total number of times the class actually appeared in the data.
- getAllFields(Class<?>) - Static method in class org.deeplearning4j.util.Dl4jReflection
-
- getAllWithSimilarity(double, int, int) - Method in class org.deeplearning4j.util.StringGrid
-
- 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
-
- getChildren() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- getClass(T) - Static method in class org.deeplearning4j.util.ReflectionUtils
-
Return the correctly-typed
Class of the given object.
- getClasses() - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Gives the applyTransformToDestination of all classes in the confusion matrix.
- getClassLabel(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
- getClusters() - Method in class org.deeplearning4j.util.StringCluster
-
- getColumn(int) - Method in class org.deeplearning4j.util.StringGrid
-
- getComputationGraphUpdater() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
- getComputationGraphUpdater() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- getConf(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
-
- getConf() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
- getConf() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- getConfiguration() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- getConfusionMatrix() - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the confusion matrix variable
- getCorruptedInput(INDArray, double) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
-
Corrupts the given input by doing a binomial sampling
given the corruption level
- getCount(E) - Method in class org.deeplearning4j.berkeley.Counter
-
Get the count of the element, or zero if the element is not in the
counter.
- getCount(K, V) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Gets the count of the given (key, value) entry, or zero if that entry is
not present.
- getCount(K) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Gets the total count of the given key, or zero if that key is
not present.
- getCount(T, T) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Gives the count of the number of times the "predicted" class was predicted for the "actual"
class.
- getCount() - Method in class org.deeplearning4j.util.TestDataSetConsumer
-
- getCounter(K) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Gets the sub-counter for the given key.
- getCounters() - Method in class org.deeplearning4j.berkeley.CounterMap
-
- getDefaultConfiguration() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getDefaultStepFunctionForOptimizer(Class<? extends ConvexOptimizer>) - Static method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- getDeflt() - Method in class org.deeplearning4j.berkeley.Counter
-
- getEmptyConstructor(Class<?>) - Static method in class org.deeplearning4j.util.Dl4jReflection
-
Gets the empty constructor from a class
- getEnd() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- getEntrySet() - Method in class org.deeplearning4j.berkeley.Counter
-
- getEntrySet() - Method in class org.deeplearning4j.berkeley.CounterMap
-
- getEpsilon() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- getEpsilon() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Get the epsilon/error (i.e., dL/dOutput) array previously set for this GraphVertex
- getErrors() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- getErrors() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
- getExtraArgs() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getFieldsAsProperties(Object, Class<?>[]) - Static method in class org.deeplearning4j.util.Dl4jReflection
-
Get fields as properties
- getFirst() - Method in class org.deeplearning4j.berkeley.Pair
-
- getFirst() - Method in class org.deeplearning4j.berkeley.Triple
-
- getFirstKey() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- getFlattenedSize() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
-
- getFormattedTimeWithDiff(DateFormat, long, long) - Static method in class org.deeplearning4j.util.StringUtils
-
Formats time in ms and appends difference (finishTime - startTime)
as returned by formatTimeDiff().
- 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
- getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in interface org.deeplearning4j.nn.api.ParamInitializer
-
Return a map of gradients (in their standard non-flattened representation), taken from the flattened (row vector) gradientView array.
- getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
-
- getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
-
- getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
-
- getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
-
- getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
-
- getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
-
- getGradientsFromFlattened(NeuralNetConfiguration, INDArray) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer
-
- getHardwareUID() - Static method in class org.deeplearning4j.util.UIDProvider
-
- getHeadWord() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- getHeightAndWidth(NeuralNetConfiguration) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get the height and width
from the configuration
- getHeightAndWidth(int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get the height and width
for an image
- getHostname() - Static method in class org.deeplearning4j.util.StringUtils
-
Return hostname without throwing exception.
- getIdParamPaths(String, int[]) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Create map of *int* layerIds to path
- getIndex() - Method in interface org.deeplearning4j.nn.api.Layer
-
Get the layer index.
- getIndex() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- getIndex() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- getIndex() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getInput(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the previously set input for the ComputationGraph
- getInput() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- getInput() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
-
- getInput() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getInputMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the previously set feature/input mask arrays for the ComputationGraph
- getInputMiniBatchSize() - Method in interface org.deeplearning4j.nn.api.Layer
-
Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)
- getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- getInputMiniBatchSize() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getInputPreProcess(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
-
- getInputs() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the previously set inputs for the ComputationGraph
- getInputs() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Get the array of inputs previously set for this GraphVertex
- getInputVertices() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
A representation of the vertices that are inputs to this vertex (inputs duing forward pass)
Specifically, if inputVertices[X].getVertexIndex() = Y, and inputVertices[X].getVertexEdgeNumber() = Z
then the Zth output of vertex Y is the Xth input to this vertex
- getInputVertices() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
A representation of the vertices that are inputs to this vertex (inputs duing forward pass)
Specifically, if inputVertices[X].getVertexIndex() = Y, and inputVertices[X].getVertexEdgeNumber() = Z
then the Zth output connection (see
GraphVertex.getNumOutputConnections() of vertex Y is the Xth input to this vertex
- getInstance() - Static method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
-
- getInstance() - Static method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
-
- getInstance() - Static method in class org.deeplearning4j.nn.params.DefaultParamInitializer
-
- getInstance() - Static method in class org.deeplearning4j.nn.params.EmptyParamInitializer
-
- getInstance() - Static method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
-
- getInstance() - Static method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
-
- getInstance() - Static method in class org.deeplearning4j.nn.params.PretrainParamInitializer
-
- getJVMUID() - Static method in class org.deeplearning4j.util.UIDProvider
-
- getKey() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
Returns the key corresponding to this entry.
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
-
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
-
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
-
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM
-
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM
-
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer
-
Get the L1 coefficient for the given parameter.
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
-
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- getL1ByParam(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
-
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
-
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
-
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM
-
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM
-
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer
-
Get the L2 coefficient for the given parameter.
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
-
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- getL2ByParam(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getLabelMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the previously set label/output mask arrays for the ComputationGraph
- getLabelName(int) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- getLabels() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
Get dataset iterator record reader labels
- getLabels() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- getLabels() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- getLabels() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- getLabels() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- getLabels() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- getLabels() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- getLabels() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
- getLabels() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- getLabels() - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer
-
- getLabels() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- getLabels() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- getLabels() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getLabels2d() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- getLabels2d() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- getLabels2d() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
-
- getLastHeight() - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- getLastOutChannels() - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- getLastWidth() - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- getLatestModel() - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver
-
Retrieve the most recent model that was previously saved
- getLatestModel() - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
-
- getLatestModel() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
-
- getLatestModel() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
-
- getLayer(int, MultiLayerConfiguration.Builder) - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- getLayer(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the layer by the number of that layer, in range 0 to getNumLayers()-1
NOTE: This is different from the internal GraphVertex index for the layer
- getLayer(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get a given layer by name.
- getLayer() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Get the Layer (if any).
- getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
-
- getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
-
- getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.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.PreprocessorVertex
-
- 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.StackVertex
-
- getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
-
- getLayer() - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
-
- getLayer(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getLayer(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getLayerNames() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getLayers() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get all layers in the ComputationGraph
- getLayers() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getLayerwise() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
-
- getLayerWiseConfigurations() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
-
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
-
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
-
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM
-
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM
-
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer
-
Get the (initial) learning rate coefficient for the given parameter.
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
-
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- getLearningRateByParam(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getLeaves() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Gets the leaves of the tree.
- getLeaves(List<T>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Gets the leaves of the tree.
- getListeners() - Method in interface org.deeplearning4j.nn.api.Layer
-
Get the iteration listeners for this layer.
- getListeners() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the IterationListeners for the ComputationGraph
- getListeners() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- getListeners() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- getListeners() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getLogMetaInstability() - Method in class org.deeplearning4j.util.Viterbi
-
- getLogOfDiangnalTProb() - Method in class org.deeplearning4j.util.Viterbi
-
- getLogPCorrect() - Method in class org.deeplearning4j.util.Viterbi
-
- getLogPIncorrect() - Method in class org.deeplearning4j.util.Viterbi
-
- getLogStates() - Method in class org.deeplearning4j.util.Viterbi
-
- getLossFunction() - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
-
- getLower() - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
-
- getMask() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getMaskArray() - Method in interface org.deeplearning4j.nn.api.Layer
-
- getMaskArray() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- getMaskArray() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getMax() - Method in class org.deeplearning4j.util.SummaryStatistics
-
- getMaxIterations() - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
-
- getMean() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
-
- getMean() - Method in class org.deeplearning4j.util.SummaryStatistics
-
- getMetaStability() - Method in class org.deeplearning4j.util.Viterbi
-
- getMin() - Method in class org.deeplearning4j.util.SummaryStatistics
-
- getModuleName() - Method in interface org.deeplearning4j.nn.conf.module.GraphBuilderModule
-
A module should return its name.
- getnInForLayer() - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- getnLayers() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Get the number of layers in the network
- getNumberOfTrials() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
-
- getNumColumns() - Method in class org.deeplearning4j.util.StringGrid
-
- getNumInputArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
The number of inputs to this network
- getNumInputArrays() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- getNumInputArrays() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Get the number of input arrays.
- getNumLayers() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Returns the number of layers in the ComputationGraph
- getNumOutputArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
The number of output (arrays) for this network
- getNumOutputConnections() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- getNumOutputConnections() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Get the number of outgoing connections from this GraphVertex.
- getNumRowCounter() - Method in class org.deeplearning4j.eval.Evaluation
-
- getOptimizer() - Method in interface org.deeplearning4j.nn.api.Model
-
Returns this models optimizer
- getOptimizer() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- getOptimizer() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- getOptimizer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getOptimizer() - Method in class org.deeplearning4j.optimize.Solver
-
- getOutputLayer(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the specified output layer, by index.
- getOutputLayer() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Get the output layer
- getOutputSize(INDArray, int[], int[], int[], ConvolutionMode) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get the output size (height/width) for the given inpud data and CNN configuration
- getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
-
- getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
-
Determine the type of output for this GraphVertex, given the specified inputs.
- getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.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.PreprocessorVertex
-
- 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.StackVertex
-
- getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
-
- getOutputType(int, InputType...) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
-
- getOutputType(InputType) - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor
-
For a given type of input to this preprocessor, what is the type of the output?
- getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
-
- getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer
-
- getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
-
- getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.Layer
-
For a given type of input to this layer, what is the type of the output?
- getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
-
- getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
-
- getOutputType(int, InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.BinomialSamplingPreProcessor
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.ReshapePreProcessor
-
Deprecated.
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.UnitVarianceProcessor
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanAndUnitVariancePreProcessor
-
- getOutputType(InputType) - Method in class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanPrePreProcessor
-
- getOutputTypeCnnLayers(InputType, int[], int[], int[], ConvolutionMode, int, int, String, Class<?>) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
-
- getOutputVertices() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
A representation of the vertices that this vertex is connected to (outputs duing forward pass)
Specifically, if outputVertices[X].getVertexIndex() = Y, and outputVertices[X].getVertexEdgeNumber() = Z
then the Xth output of this vertex is connected to the Zth input of vertex Y
- getOutputVertices() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
A representation of the vertices that this vertex is connected to (outputs duing forward pass)
Specifically, if outputVertices[X].getVertexIndex() = Y, and outputVertices[X].getVertexEdgeNumber() = Z
then the Xth output of this vertex is connected to the Zth input of vertex Y
- getOutSizesEachLayer() - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- getPairIterator() - Method in class org.deeplearning4j.berkeley.CounterMap
-
- 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.BaseLayer
-
- getParam(String) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- getParam(String) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- getParam(String) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- getpCorrect() - Method in class org.deeplearning4j.util.Viterbi
-
- getPossibleLabels() - Method in class org.deeplearning4j.util.Viterbi
-
- getPredictedTotal(T) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Computes the total number of times the class was predicted by the classifier.
- getPredictionByPredictedClass(int) - Method in class org.deeplearning4j.eval.Evaluation
-
Get a list of predictions, for all data with the specified predicted class, regardless of the actual data
class.
- getPredictionErrors() - Method in class org.deeplearning4j.eval.Evaluation
-
Get a list of prediction errors, on a per-record basis
- getPredictions(int, int) - Method in class org.deeplearning4j.eval.Evaluation
-
Get a list of predictions in the specified confusion matrix entry (i.e., for the given actua/predicted class pair)
- getPredictionsByActualClass(int) - Method in class org.deeplearning4j.eval.Evaluation
-
Get a list of predictions, for all data with the specified actual class, regardless of the predicted
class.
- getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
- getPreProcessor() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
-
- getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BaseRecurrentLayer
-
- getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
-
- getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.Layer
-
For the given type of input to this layer, what preprocessor (if any) is required?
Returns null if no preprocessor is required, otherwise returns an appropriate
InputPreProcessor
for this layer, such as a
CnnToFeedForwardPreProcessor
- getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
-
- getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
-
- getPreProcessorForInputType(InputType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- getPreProcessorForInputTypeCnnLayers(InputType, String) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
-
- getPreprocessorForInputTypeRnnLayers(InputType, String) - Static method in class org.deeplearning4j.nn.conf.layers.InputTypeUtil
-
- getPriority() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- getPriority() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Gets the priority of the highest-priority element of the queue.
- getProbability(E) - Method in class org.deeplearning4j.berkeley.Counter
-
I know, I know, this should be wrapped in a Distribution class, but it's
such a common use...why not.
- getProbabilityOfSuccess() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
-
- getRecordMetaData(Class<T>) - Method in class org.deeplearning4j.eval.meta.Prediction
-
Convenience method for getting the record meta data as a particular class (as an alternative to casting it manually).
- getResults() - Method in class org.deeplearning4j.eval.ROC
-
Get the ROC curve, as a set of points
- getResultsAsArray() - Method in class org.deeplearning4j.eval.ROC
-
Get the ROC curve, as a set of (falsePositive, truePositive) points
- getRow(int) - Method in class org.deeplearning4j.util.StringGrid
-
- getRowsWithColumnValues(Collection<String>, int) - Method in class org.deeplearning4j.util.StringGrid
-
- getRowsWithDuplicateValuesInColumn(int) - Method in class org.deeplearning4j.util.StringGrid
-
- getRowWithOnlyOneOccurrence(int) - Method in class org.deeplearning4j.util.StringGrid
-
- getSameModeTopLeftPadding(int[], int[], int[], int[]) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
Get top and left padding for same mode only.
- getScoreVsIter() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
-
- getSecond() - Method in class org.deeplearning4j.berkeley.Pair
-
- getSecond() - Method in class org.deeplearning4j.berkeley.Triple
-
- getSecondKey() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- getShape(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- getSortedKeys() - Method in class org.deeplearning4j.berkeley.Counter
-
- getStates() - Method in class org.deeplearning4j.util.Viterbi
-
- getStateViewArray() - Method in interface org.deeplearning4j.nn.api.Updater
-
- getStateViewArray() - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
-
- getStateViewArray() - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
- getStateViewArray() - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
-
- getStd() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
-
- getStepMax() - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
-
- getStore() - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
-
- getStringCollection(String) - Static method in class org.deeplearning4j.util.StringUtils
-
Returns a collection of strings.
- getStringParamPaths(String, String[]) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Create map of *string* layerIds to path
- getStrings(String) - Static method in class org.deeplearning4j.util.StringUtils
-
Returns an arraylist of strings.
- getSum() - Method in class org.deeplearning4j.util.SummaryStatistics
-
- getThird() - Method in class org.deeplearning4j.berkeley.Triple
-
- getTokens() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- getTopNCorrectCount() - Method in class org.deeplearning4j.eval.Evaluation
-
Return the number of correct predictions according to top N value.
- getTopNTotalCount() - Method in class org.deeplearning4j.eval.Evaluation
-
Return the total number of top N evaluations.
- getTrimmedStringCollection(String) - Static method in class org.deeplearning4j.util.StringUtils
-
Splits a comma separated value String, trimming leading and trailing whitespace on each value.
- getTrimmedStrings(String) - Static method in class org.deeplearning4j.util.StringUtils
-
Splits a comma separated value String, trimming leading and trailing whitespace on each value.
- getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType
-
- getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional
-
- getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
-
- getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward
-
- getType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
-
- getType() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
The type of node; mainly extra meta data
- getUnflattenedType() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
-
- getUniqueRows() - Method in class org.deeplearning4j.util.StringGrid
-
- getUpdater() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the ComputationGraphUpdater for the network
- getUpdater() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Get the updater for this MultiLayerNetwork
- getUpdater(Model) - Static method in class org.deeplearning4j.nn.updater.UpdaterCreator
-
- getUpdater() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
- getUpdater() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- getUpdaterByParam(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer
-
Get the updater for the given parameter.
- getUpdaterForVariable() - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
- getUpper() - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
-
- getValue() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- getVertex(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Return a given GraphVertex by name, or null if no vertex with that name exists
- getVertexEdgeNumber() - Method in class org.deeplearning4j.nn.graph.vertex.VertexIndices
-
The edge number.
- getVertexIndex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- getVertexIndex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Get the index of the GraphVertex
- getVertexIndex() - Method in class org.deeplearning4j.nn.graph.vertex.VertexIndices
-
Index of the vertex
- getVertexName() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- getVertexName() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Get the name/label of the GraphVertex
- getVertices() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Returns an array of all GraphVertex objects.
- gibbhVh(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Gibbs sampling step: hidden ---> visible ---> hidden
- GLOBAL_MEAN - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
-
- GLOBAL_VAR - Static variable in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
-
- globalConfiguration - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- goldLabel() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- gr(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Tests if a is greater than b.
- gradient() - Method in interface org.deeplearning4j.nn.api.Model
-
Calculate a gradient
- gradient(List<String>) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
-
- gradient() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
-
- Gradient - Interface in org.deeplearning4j.nn.gradient
-
Generic gradient
- gradient(List<String>) - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
The full gradient as one flat vector
- gradient() - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
The full gradient as one flat vector
- gradient - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
-
- gradient() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- gradient - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- gradient() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- gradient() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Gets the gradient from one training iteration
- gradient() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Gets the gradient from one training iteration
- gradient() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- gradient() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- gradient - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- gradient() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- 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.BaseLayer
-
- gradientAndScore() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- gradientAndScore() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- gradientAndScore() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- gradientAndScore() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
The gradient and score for this optimizer
- gradientAndScore() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- GradientCheckUtil - Class in org.deeplearning4j.gradientcheck
-
A utility for numerically checking gradients.
- gradientForVariable() - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
-
- gradientForVariable() - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
Gradient look up table
- GradientNormalization - Enum in org.deeplearning4j.nn.conf
-
Gradient normalization strategies.
- gradientNormalization - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- gradientNormalization(GradientNormalization) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Gradient normalization strategy.
- gradientNormalization - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- gradientNormalization - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- gradientNormalization(GradientNormalization) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Gradient normalization strategy.
- gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- gradientNormalizationThreshold(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.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.Layer
-
- gradientNormalizationThreshold - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- gradientNormalizationThreshold(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer,
GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue
Not used otherwise.
L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping.
- gradientsFlattened - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- GradientStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions
-
Normal gradient step function
- GradientStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.GradientStepFunction
-
- GradientStepFunction - Class in org.deeplearning4j.optimize.stepfunctions
-
Normal gradient step function
- GradientStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
-
- gradientViews - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- graph - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- GraphBuilder(NeuralNetConfiguration.Builder) - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- graphBuilder() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Create a GraphBuilder (for creating a ComputationGraphConfiguration).
- GraphBuilderModule - Interface in org.deeplearning4j.nn.conf.module
-
GraphBuilderModule for nn layers.
- GraphVertex - Class in org.deeplearning4j.nn.conf.graph
-
A GraphVertex is a vertex in the computation graph.
- GraphVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.GraphVertex
-
- GraphVertex - Interface in org.deeplearning4j.nn.graph.vertex
-
A GraphVertex is a vertex in the computation graph.
- GRAVES_BIDIRECTIONAL_LSTM - Static variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- GRAVES_LSTM - Static variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- GravesBidirectionalLSTM - Class in org.deeplearning4j.nn.conf.layers
-
LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks
http://www.cs.toronto.edu/~graves/phd.pdf
- GravesBidirectionalLSTM - Class in org.deeplearning4j.nn.layers.recurrent
-
RNN tutorial: http://deeplearning4j.org/usingrnns.html
READ THIS FIRST
Bdirectional LSTM layer implementation.
- GravesBidirectionalLSTM(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- GravesBidirectionalLSTM(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- GravesBidirectionalLSTM.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- GravesBidirectionalLSTMParamInitializer - Class in org.deeplearning4j.nn.params
-
LSTM Parameter initializer, for LSTM based on
Graves: Supervised Sequence Labelling with Recurrent Neural Networks
http://www.cs.toronto.edu/~graves/phd.pdf
- GravesBidirectionalLSTMParamInitializer() - Constructor for class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
-
- GravesLSTM - Class in org.deeplearning4j.nn.conf.layers
-
LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks
http://www.cs.toronto.edu/~graves/phd.pdf
- GravesLSTM - Class in org.deeplearning4j.nn.layers.recurrent
-
LSTM layer implementation.
- GravesLSTM(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- GravesLSTM(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- GravesLSTM.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- GravesLSTMParamInitializer - Class in org.deeplearning4j.nn.params
-
LSTM Parameter initializer, for LSTM based on
Graves: Supervised Sequence Labelling with Recurrent Neural Networks
http://www.cs.toronto.edu/~graves/phd.pdf
- GravesLSTMParamInitializer() - Constructor for class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
-
- grow(int) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- ia - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- IdentityHashMapFactory() - Constructor for class org.deeplearning4j.berkeley.MapFactory.IdentityHashMapFactory
-
- idf(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Inverse document frequency: the total docs divided by the number of times the word
appeared in a document
- IEarlyStoppingTrainer<T extends Model> - Interface in org.deeplearning4j.earlystopping.trainer
-
Interface for early stopping trainers
- incrementAll(Collection<? extends E>, double) - Method in class org.deeplearning4j.berkeley.Counter
-
Increment each element in a given collection by a given amount.
- incrementAll(Counter<T>) - Method in class org.deeplearning4j.berkeley.Counter
-
- incrementAll(Map<K, V>, double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- incrementAll(CounterMap<K, V>) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- incrementCount(E, double) - Method in class org.deeplearning4j.berkeley.Counter
-
Increment a key's count by the given amount.
- incrementCount(K, V, double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Increments the count for a particular (key, value) pair.
- incrementFalseNegatives(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
- incrementFalsePositives(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
- incrementTrueNegatives(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
- incrementTruePositives(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
- INDArrayDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
- INDArrayDataSetIterator(Iterable<Pair<INDArray, INDArray>>, int) - Constructor for class org.deeplearning4j.datasets.iterator.INDArrayDataSetIterator
-
- index - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- index - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- Index - Class in org.deeplearning4j.util
-
An index is a transform of objects augmented with a list and a reverse lookup table
for fast lookups.
- Index() - Constructor for class org.deeplearning4j.util.Index
-
- indexOf(Object) - Method in class org.deeplearning4j.util.Index
-
- information(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the entropy for a given vector of probabilities.
- init(NeuralNetConfiguration, INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.ParamInitializer
-
Initialize the parameters
- init() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Initialize the ComputationGraph network
- init(INDArray, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Initialize the ComputationGraph, optionally with an existing parameters array.
- init() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Initialize the MultiLayerNetwork.
- init(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Initialize the MultiLayerNetwork, optionally with an existing parameters array.
- init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
-
- init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
-
- init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
-
- init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
-
- init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
-
- init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
-
- init(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer
-
- init() - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
- init(String, Layer) - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
- initCalled - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
-
- initCalled - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- initDone - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- initGradientsView() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
This method: initializes the flattened gradients array (used in backprop) and sets the appropriate subset in all layers.
- initGradientsView() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
This method: initializes the flattened gradients array (used in backprop) and sets the appropriate subset in all layers.
- initialize() - Method in class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition
-
- initialize() - Method in interface org.deeplearning4j.earlystopping.termination.EpochTerminationCondition
-
Initialize the epoch termination condition (often a no-op)
- initialize() - Method in class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition
-
- initialize() - Method in interface org.deeplearning4j.earlystopping.termination.IterationTerminationCondition
-
Initialize the iteration termination condition (sometimes a no-op)
- initialize() - Method in class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition
-
- initialize() - Method in class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition
-
- initialize() - Method in class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition
-
- initialize() - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
-
- initialize(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Sets the input and labels from this dataset
- initializeCurrFromList(List<DataSet>) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
Initializes this data transform fetcher from the passed in datasets
- initializeLayers(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Base class for initializing the neuralNets based on the input.
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.Layer
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.LossLayer
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.RBM
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
-
- initializer() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- initParams() - Method in interface org.deeplearning4j.nn.api.Model
-
Initialize the parameters
- initParams() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- initParams() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- initParams() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- initWeights(int[], float, float) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil
-
- initWeights(double, double, int[], WeightInit, Distribution, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil
-
Initializes a matrix with the given weight initialization scheme.
- initWeights(double, double, int[], WeightInit, Distribution, char, INDArray) - Static method in class org.deeplearning4j.nn.weights.WeightInitUtil
-
- inLayerName - Variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- InMemoryModelSaver<T extends Model> - Class in org.deeplearning4j.earlystopping.saver
-
Save the best (and latest) models for early stopping training to memory for later retrieval
Note: Assumes that network is cloneable via .clone() method
- InMemoryModelSaver() - Constructor for class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
-
- input() - Method in interface org.deeplearning4j.nn.api.Model
-
The input/feature matrix for the model
- input() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- input - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- input() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- input - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- input() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- INPUT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
-
- INPUT_WEIGHT_KEY_BACKWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
-
- INPUT_WEIGHT_KEY_FORWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
-
- inputColumns - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- inputColumns() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
Input columns for the dataset
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- inputColumns() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Deprecated.
The length of a feature vector for an individual example
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Input columns for the dataset
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Input columns for the dataset
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- inputPreProcessor(String, InputPreProcessor) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- InputPreProcessor - Interface in org.deeplearning4j.nn.conf
-
Input pre processor used
for pre processing input before passing it
to the neural network.
- inputPreProcessor(Integer, InputPreProcessor) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
Specify the processors.
- inputPreProcessors - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- inputPreProcessors - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- inputPreProcessors(Map<Integer, InputPreProcessor>) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- inputPreProcessors - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
-
- inputs - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- InputSplit - Class in org.deeplearning4j.util
-
- inputStreamFromPath(String) - Static method in class org.deeplearning4j.util.DeepLearningIOUtil
-
- InputType - Class in org.deeplearning4j.nn.conf.inputs
-
The InputType class is used to track and define the types of activations etc used in a ComputationGraph.
- InputType() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType
-
- inputType - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- InputType.InputTypeConvolutional - Class in org.deeplearning4j.nn.conf.inputs
-
- InputType.InputTypeConvolutionalFlat - Class in org.deeplearning4j.nn.conf.inputs
-
- InputType.InputTypeFeedForward - Class in org.deeplearning4j.nn.conf.inputs
-
- InputType.InputTypeRecurrent - Class in org.deeplearning4j.nn.conf.inputs
-
- InputType.Type - Enum in org.deeplearning4j.nn.conf.inputs
-
The type of activations in/out of a given GraphVertex
FF: Standard feed-foward (2d minibatch, 1d per example) data
RNN: Recurrent neural network (3d minibatch) time series data
CNN: Convolutional neural n
- InputTypeConvolutional() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional
-
- InputTypeConvolutionalFlat() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
-
- InputTypeFeedForward() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward
-
- InputTypeRecurrent() - Constructor for class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
-
- InputTypeUtil - Class in org.deeplearning4j.nn.conf.layers
-
Utilities for calculating input types
- InputTypeUtil() - Constructor for class org.deeplearning4j.nn.conf.layers.InputTypeUtil
-
- InputVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
-
An InputVertex simply defines the location (and connection structure) of inputs to the ComputationGraph.
- InputVertex(ComputationGraph, String, int, VertexIndices[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
-
- inputVertices - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
A representation of the vertices that are inputs to this vertex (inputs during forward pass)
Specifically, if inputVertices[X].getVertexIndex() = Y, and inputVertices[X].getVertexEdgeNumber() = Z
then the Zth output of vertex Y is the Xth input to this vertex
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
-
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
-
Create a
GraphVertex instance, for the given computation graph,
given the configuration instance.
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
-
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
-
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
-
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
-
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.DuplicateToTimeSeriesVertex
-
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
-
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.StackVertex
-
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
-
- instantiate(ComputationGraph, String, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ActivationLayer
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.DenseLayer
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.EmbeddingLayer
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.GravesLSTM
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Layer
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.OutputLayer
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RBM
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
-
- instantiate(NeuralNetConfiguration, Collection<IterationListener>, int, INDArray, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- intersection(Collection<T>, Collection<T>) - Static method in class org.deeplearning4j.util.SetUtils
-
- intersectionP(Set<? extends T>, Set<? extends T>) - Static method in class org.deeplearning4j.util.SetUtils
-
- intializeConfigurations() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- intPow(int, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
exponentiation like we learned in grade school: multiply b by itself e
times.
- intPow(float, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
exponentiation like we learned in grade school: multiply b by itself e
times.
- intPow(double, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
exponentiation like we learned in grade school: multiply b by itself e
times.
- InvalidInputTypeException - Exception in org.deeplearning4j.nn.conf.inputs
-
InvalidInputTypeException: Thrown if the GraphVertex cannot handle the type of input provided.
- InvalidInputTypeException(String) - Constructor for exception org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException
-
- InvalidInputTypeException(String, Throwable) - Constructor for exception org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException
-
- InvalidInputTypeException(Throwable) - Constructor for exception org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException
-
- InvalidScoreIterationTerminationCondition - Class in org.deeplearning4j.earlystopping.termination
-
Terminate training at this iteration if score is NaN or Infinite for the last minibatch
- InvalidScoreIterationTerminationCondition() - Constructor for class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition
-
- InvalidStepException - Exception in org.deeplearning4j.exception
-
Created by agibsonccc on 8/20/14.
- InvalidStepException(String) - Constructor for exception org.deeplearning4j.exception.InvalidStepException
-
Constructs a new exception with the specified detail message.
- InvalidStepException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.InvalidStepException
-
Constructs a new exception with the specified detail message and
cause.
- InvalidStepException(Throwable) - Constructor for exception org.deeplearning4j.exception.InvalidStepException
-
Constructs a new exception with the specified cause and a detail
message of (cause==null ? null : cause.toString()) (which
typically contains the class and detail message of cause).
- InvalidStepException(String, Throwable, boolean, boolean) - Constructor for exception org.deeplearning4j.exception.InvalidStepException
-
Constructs a new exception with the specified detail message,
cause, suppression enabled or disabled, and writable stack
trace enabled or disabled.
- inverse(RBM.HiddenUnit) - Static method in class org.deeplearning4j.util.RBMUtil
-
- inverse(RBM.VisibleUnit) - Static method in class org.deeplearning4j.util.RBMUtil
-
- invert() - Method in class org.deeplearning4j.berkeley.CounterMap
-
Constructs reverse CounterMap where the count of a pair (k,v)
is the count of (v,k) in the current CounterMap
- invoke() - Method in interface org.deeplearning4j.optimize.api.IterationListener
-
Change invoke to true
- invoke() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
-
- invoke() - Method in class org.deeplearning4j.optimize.listeners.ComposableIterationListener
-
- invoke() - Method in class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener
-
- invoke() - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener
-
- invoke() - Method in class org.deeplearning4j.optimize.listeners.ScoreIterationListener
-
- invoked() - Method in interface org.deeplearning4j.optimize.api.IterationListener
-
Get if listener invoked
- invoked() - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
-
- invoked() - Method in class org.deeplearning4j.optimize.listeners.ComposableIterationListener
-
- invoked() - Method in class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener
-
- invoked() - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener
-
- invoked() - Method in class org.deeplearning4j.optimize.listeners.ScoreIterationListener
-
- IOutputLayer - Interface in org.deeplearning4j.nn.api.layers
-
Interface for output layers (those that calculate gradients with respect to a labels array)
- isDangerous(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns true if the argument is a "dangerous" double to have around,
namely one that is infinite, NaN or zero.
- isDangerous(float) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- isDiscreteProb(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- isEmpty() - Method in class org.deeplearning4j.berkeley.Counter
-
True if there are no entries in the counter (false does not mean
totalCount > 0)
- isEmpty() - Method in class org.deeplearning4j.berkeley.CounterMap
-
True if there are no entries in the CounterMap (false does not mean
totalCount > 0)
- isEmpty() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- isEmpty() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
True if the queue is empty (size == 0).
- isEmpty() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- isEmpty() - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns true if this map contains no key-value mappings.
- isEmpty() - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns true if this applyTransformToDestination contains no elements.
- isEqualTo(Counter<E>) - Method in class org.deeplearning4j.berkeley.Counter
-
- isEqualTo(CounterMap<K, V>) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- isGreater(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- isInitCalled() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- isInputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- isInputVertex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Whether the GraphVertex is an input vertex
- isInputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
-
- isLeaf() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Returns whether the node has any children or not
- isMinibatch - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
- isMinibatch - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- isOutputVertex() - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Whether the GraphVertex is an output vertex
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex
-
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.InputVertex
-
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
-
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
-
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
-
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
-
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.DuplicateToTimeSeriesVertex
-
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
-
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
-
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
-
- isOutputVertex() - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
-
- isPreTerminal() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Node has one child that is a leaf
- isVeryDangerous(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns true if the argument is a "very dangerous" double to have around,
namely one that is infinite or NaN.
- iter - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- iterate(INDArray) - Method in interface org.deeplearning4j.nn.api.Model
-
Run one iteration
- iterate(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
iterate one iteration of the network
- iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Deprecated.
- iterate(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- iterate(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- iteration - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- iterationDone(Model, int) - Method in interface org.deeplearning4j.optimize.api.IterationListener
-
Event listener for each iteration
- iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.CollectScoresIterationListener
-
- iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.ComposableIterationListener
-
- iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener
-
- iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener
-
- iterationDone(Model, int) - Method in class org.deeplearning4j.optimize.listeners.ScoreIterationListener
-
- IterationListener - Interface in org.deeplearning4j.optimize.api
-
Each iteration the listener is called, mainly used for debugging or visualizations
- iterationListeners - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- iterationListeners - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- iterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Number of optimization iterations.
- iterationsCounter - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- IterationTerminationCondition - Interface in org.deeplearning4j.earlystopping.termination
-
Interface for termination conditions to be evaluated once per iteration (i.e., once per minibatch).
- iterationTerminationConditions(IterationTerminationCondition...) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder
-
Termination conditions to be evaluated every iteration (minibatch)
- iterator() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- iterator() - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns an iterator over the elements in this applyTransformToDestination.
- IteratorDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
A DataSetIterator that works on an Iterator, combining and splitting the input DataSet objects as
required to get a consistent batch size.
- IteratorDataSetIterator(Iterator<DataSet>, int) - Constructor for class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- IteratorIterator(Iterator, Factory<Iterator<T>>) - Constructor for class org.deeplearning4j.berkeley.Iterators.IteratorIterator
-
- IteratorMultiDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
A DataSetIterator that works on an Iterator, combining and splitting the input DataSet objects as
required to get a consistent batch size.
- IteratorMultiDataSetIterator(Iterator<MultiDataSet>, int) - Constructor for class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
-
- Iterators - Class in org.deeplearning4j.berkeley
-
- Iterators.FilteredIterator<T> - Class in org.deeplearning4j.berkeley
-
Creates an iterator that only returns items of a base iterator that pass
a filter.
- Iterators.IteratorIterator<T> - Class in org.deeplearning4j.berkeley
-
Wraps a two-level iteration scenario in an iterator.
- Iterators.Transform<S,T> - Class in org.deeplearning4j.berkeley
-
WraTps a base iterator with a transformation function.
- Iterators.TransformingIterator<I,O> - Class in org.deeplearning4j.berkeley
-
- iz - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- l1 - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- l1(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
L1 regularization coefficient.
- l1 - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- l1 - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- l1(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
L1 regularization coefficient.
- l1ByParam - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- l2 - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- l2(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
L2 regularization coefficient.
- l2 - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- l2 - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- l2(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
L2 regularization coefficient
Use with .regularization(true)
- l2ByParam - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- L2Vertex - Class in org.deeplearning4j.nn.conf.graph
-
L2Vertex calculates the L2 least squares error of two inputs.
- L2Vertex() - Constructor for class org.deeplearning4j.nn.conf.graph.L2Vertex
-
- L2Vertex - Class in org.deeplearning4j.nn.graph.vertex.impl
-
L2Vertex calculates the L2 least squares error of two inputs.
- L2Vertex(ComputationGraph, String, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
-
- L2Vertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.L2Vertex
-
- label() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- labelProbabilities(INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Returns the probabilities for each label
for each example row wise
- labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Returns the probabilities for each label
for each example row wise
- labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Returns the probabilities for each label
for each example row wise
- labelProbabilities(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Returns the probabilities for each label
for each example row wise
- labels - Variable in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- labels - Variable in class org.deeplearning4j.nn.layers.LossLayer
-
- labels - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- labelsList - Variable in class org.deeplearning4j.eval.Evaluation
-
- lambert(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- lastAct - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- lastBatch - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- lastChild() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- lastHeight - Variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- lastMemCell - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- lastnOut - Variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- lastOutChannels - Variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- LastTimeStepVertex - Class in org.deeplearning4j.nn.conf.graph.rnn
-
LastTimeStepVertex is used in the context of recurrent neural network activations, to go from 3d (time series)
activations to 2d activations, by extracting out the last time step of activations for each example.
This can be used for example in sequence to sequence architectures, and potentially for sequence classification.
- LastTimeStepVertex(String) - Constructor for class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
-
- LastTimeStepVertex - Class in org.deeplearning4j.nn.graph.vertex.impl.rnn
-
LastTimeStepVertex is used in the context of recurrent neural network activations, to go from 3d (time series)
activations to 2d activations, by extracting out the last time step of activations for each example.
This can be used for example in sequence to sequence architectures, and potentially for sequence classification.
- LastTimeStepVertex(ComputationGraph, String, int, String) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
-
- LastTimeStepVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], String) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.rnn.LastTimeStepVertex
-
- lastWidth - Variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- Layer - Interface in org.deeplearning4j.nn.api
-
Interface for a layer of a neural network.
- Layer - Class in org.deeplearning4j.nn.conf.layers
-
A neural network layer.
- Layer(Layer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.Layer
-
- layer - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- layer(Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Layer class.
- layer - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- layer(int, Layer) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
-
- Layer.Builder<T extends Layer.Builder<T>> - Class in org.deeplearning4j.nn.conf.layers
-
- Layer.TrainingMode - Enum in org.deeplearning4j.nn.api
-
- Layer.Type - Enum in org.deeplearning4j.nn.api
-
- layerConf() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- layerIndex - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- layerMap - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- 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
-
- LayerUpdater - Class in org.deeplearning4j.nn.updater
-
- LayerUpdater() - Constructor for class org.deeplearning4j.nn.updater.LayerUpdater
-
- LayerValidation - Class in org.deeplearning4j.util
-
Created by Alex on 12/11/2016.
- LayerValidation() - Constructor for class org.deeplearning4j.util.LayerValidation
-
- LayerVertex - Class in org.deeplearning4j.nn.conf.graph
-
LayerVertex is a GraphVertex with a neural network Layer (and, optionally an
InputPreProcessor) in it
- LayerVertex(NeuralNetConfiguration, InputPreProcessor) - Constructor for class org.deeplearning4j.nn.conf.graph.LayerVertex
-
- LayerVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
-
LayerVertex is a GraphVertex with a neural network Layer (and, optionally an
InputPreProcessor) in it
- LayerVertex(ComputationGraph, String, int, Layer, InputPreProcessor, boolean) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
-
Create a network input vertex:
- LayerVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], Layer, InputPreProcessor, boolean) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
-
- layerWiseConfigurations - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- LBFGS - Class in org.deeplearning4j.optimize.solvers
-
LBFGS
- LBFGS(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LBFGS
-
- LBFGS(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Collection<TerminationCondition>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LBFGS
-
- leakyreluAlpha - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- leakyreluAlpha(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- leakyreluAlpha - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- learningRate - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- learningRate(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Learning rate.
- learningRate - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- learningRate - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- learningRate(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Learning rate.
- learningRateByParam - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- learningRateDecayPolicy(LearningRatePolicy) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Learning rate decay policy.
- learningRateDecayPolicy(LearningRatePolicy) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Learning rate decay policy.
- learningRatePolicy - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- LearningRatePolicy - Enum in org.deeplearning4j.nn.conf
-
Learning Rate Policy
How to decay learning rate during training.
- learningRatePolicy - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- learningRatePolicy - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- learningRateSchedule - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- learningRateSchedule(Map<Integer, Double>) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Learning rate schedule.
- learningRateSchedule - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- learningRateSchedule - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- learningRateSchedule(Map<Integer, Double>) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Learning rate schedule.
- learningRateScoreBasedDecayRate(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Rate to decrease learningRate by when the score stops improving.
- leftChild(int) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- LexicographicPairComparator(Comparator<F>, Comparator<S>) - Constructor for class org.deeplearning4j.berkeley.Pair.LexicographicPairComparator
-
- limitDecimalTo2(double) - Static method in class org.deeplearning4j.util.StringUtils
-
- LineGradientDescent - Class in org.deeplearning4j.optimize.solvers
-
Stochastic Gradient Descent with Line Search
- LineGradientDescent(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LineGradientDescent
-
- LineGradientDescent(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Collection<TerminationCondition>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.LineGradientDescent
-
- lineMaximizer - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- LineOptimizer - Interface in org.deeplearning4j.optimize.api
-
Line optimizer interface adapted from mallet
- list() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Create a ListBuilder (for creating a MultiLayerConfiguration)
Usage:
- list(Layer...) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Create a ListBuilder (for creating a MultiLayerConfiguration) with the specified layers
Usage:
- ListBuilder(NeuralNetConfiguration.Builder, Map<Integer, NeuralNetConfiguration.Builder>) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
-
- ListBuilder(NeuralNetConfiguration.Builder) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
-
- ListDataSetIterator - Class in org.deeplearning4j.datasets.iterator.impl
-
Wraps a data applyTransformToDestination collection
- ListDataSetIterator(Collection<DataSet>, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- ListDataSetIterator(Collection<DataSet>) - Constructor for class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
Initializes with a batch of 5
- listener(IterationListener...) - Method in class org.deeplearning4j.optimize.Solver.Builder
-
- listeners - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- listeners(Collection<IterationListener>) - Method in class org.deeplearning4j.optimize.Solver.Builder
-
- loadLayerParameters(Layer, String) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Load existing parameters to the layer
- loadNetworkAndParameters(String, String) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Load existing model configuration and parameters
- loadParameters(MultiLayerNetwork, int[], Map<Integer, String>) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Load existing parameters for the network
- loadParameters(MultiLayerNetwork, String[], Map<String, String>) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Load existing parameters for the network
- loadUpdators(String) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Load model updators
- LOCAL_RESPONSE_NORMALIZATION - Static variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- LocalFileGraphSaver - Class in org.deeplearning4j.earlystopping.saver
-
Save the best (and latest/most recent)
ComputationGraphs learned during early stopping training to the local file system.
Instances of this class will save 3 files for best (and optionally, latest) models:
(a) The network configuration: bestGraphConf.json
(b) The network parameters: bestGraphParams.bin
(c) The network updater: bestGraphUpdater.bin
NOTE: The model updater is an object that contains the internal state for training features such as AdaGrad, Momentum
and RMSProp.
The updater is
not required to use the network at test time; it is saved in case further training is required.
- LocalFileGraphSaver(String) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
-
Constructor that uses default character set for configuration (json) encoding
- LocalFileGraphSaver(String, Charset) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
-
- LocalFileModelSaver - Class in org.deeplearning4j.earlystopping.saver
-
Save the best (and latest/most recent) models learned during early stopping training to the local file system.
Instances of this class will save 3 files for best (and optionally, latest) models:
(a) The network configuration: bestModelConf.json
(b) The network parameters: bestModelParams.bin
(c) The network updater: bestModelUpdater.bin
NOTE: The model updater is an object that contains the internal state for training features such as AdaGrad, Momentum
and RMSProp.
The updater is not required to use the network at test time; it is saved in case further training is required.
- LocalFileModelSaver(String) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
-
Constructor that uses default character set for configuration (json) encoding
- LocalFileModelSaver(String, Charset) - Constructor for class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
-
- LocalResponseNormalization - Class in org.deeplearning4j.nn.conf.layers
-
Created by nyghtowl on 10/29/15.
- LocalResponseNormalization - Class in org.deeplearning4j.nn.layers.normalization
-
Deep neural net normalization approach normalizes activations between layers
"brightness normalization"
Used for nets like AlexNet
- LocalResponseNormalization(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- LocalResponseNormalization(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- LocalResponseNormalization.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- LocalResponseNormalizationHelper - Interface in org.deeplearning4j.nn.layers.normalization
-
Helper for the local response normalization layer.
- lockGammaBeta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
- lockGammaBeta(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
- lockGammaBeta - Variable in class org.deeplearning4j.nn.conf.layers.BatchNormalization
-
- log - Static variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- log - Static variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- log - Static variable in class org.deeplearning4j.eval.Evaluation
-
- log - Static variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- log - Static variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- log - Static variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- log - Static variable in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- log - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- log2 - Static variable in class org.deeplearning4j.util.MathUtils
-
The natural logarithm of 2.
- log2(double) - Static method in class org.deeplearning4j.util.MathUtils
-
Returns the logarithm of a for base 2.
- logAdd(float, float) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the log of the sum of two numbers, which are themselves input in
log form.
- logAdd(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the log of the sum of two numbers, which are themselves input in
log form.
- logAdd(float[]) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- logAdd(double[]) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- logAdd(List<Double>) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- logAdd(float[], int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- logAdd(double[], int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- logAdd(Counter<T>) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- logAdd_Old(float[]) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- logger - Static variable in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- logger - Static variable in class org.deeplearning4j.util.TestDataSetConsumer
-
- logNormalize(double[]) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- logs2probs(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Converts an array containing the natural logarithms of
probabilities stored in a vector back into probabilities.
- logSubtract(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- LOGTOLERANCE - Static variable in class org.deeplearning4j.berkeley.SloppyMath
-
If a difference is bigger than this in log terms, then the sum or
difference of them will just be the larger (to 12 or so decimal places
for double, and 7 or 8 for float).
- longestCommonSubstring(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Computes the longest common substring of s and t.
- lookingAt(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Say whether this regular expression can be found at the beginning of
this String.
- lossFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
-
- lossFn - Variable in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
-
- lossFn - Variable in class org.deeplearning4j.nn.conf.layers.LossLayer
-
- lossFunction(LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
-
- lossFunction(ILossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
-
- lossFunction - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
-
- lossFunction(LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
-
- lossFunction - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
-
- LossLayer - Class in org.deeplearning4j.nn.conf.layers
-
LossLayer is a flexible output "layer" that performs a loss function on
an input without MLP logic.
- LossLayer(LossLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.LossLayer
-
- LossLayer - Class in org.deeplearning4j.nn.layers
-
LossLayer is a flexible output "layer" that performs a loss function on
an input without MLP logic.
- LossLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.LossLayer
-
- LossLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.LossLayer
-
- LossLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- lrPolicyDecayRate - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- lrPolicyDecayRate(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Set the decay rate for the learning rate decay policy.
- lrPolicyDecayRate - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- lrPolicyPower - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- lrPolicyPower(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Set the power used for learning rate inverse policy.
- lrPolicyPower - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- lrPolicySteps - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- lrPolicySteps(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Set the number of steps used for learning decay rate steps policy.
- lrPolicySteps - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- lrScoreBasedDecay - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- LSTMHelpers - Class in org.deeplearning4j.nn.layers.recurrent
-
RNN tutorial: http://deeplearning4j.org/usingrnns.html
READ THIS FIRST if you want to understand what the heck is happening here.
- main(String[]) - Static method in class org.deeplearning4j.berkeley.Counter
-
- main(String[]) - Static method in class org.deeplearning4j.berkeley.CounterMap
-
- main(String[]) - Static method in class org.deeplearning4j.berkeley.PriorityQueue
-
- main(String[]) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Tests the hypergeometric distribution code, or other cooccurrences provided
in this module.
- main(String[]) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- main(String[]) - Static method in class org.deeplearning4j.eval.ConfusionMatrix
-
- makePair(S, T) - Static method in class org.deeplearning4j.berkeley.Pair
-
- makeTriple(S, T, U) - Static method in class org.deeplearning4j.berkeley.Triple
-
- manhattanDistance(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This will calculate the Manhattan distance between two sets of points.
- mapByPrimaryKey(int) - Method in class org.deeplearning4j.util.StringGrid
-
- MapFactory<K,V> - Class in org.deeplearning4j.berkeley
-
The MapFactory is a mechanism for specifying what kind of map is to be used
by some object.
- MapFactory() - Constructor for class org.deeplearning4j.berkeley.MapFactory
-
- MapFactory.HashMapFactory<K,V> - Class in org.deeplearning4j.berkeley
-
- MapFactory.IdentityHashMapFactory<K,V> - Class in org.deeplearning4j.berkeley
-
- MapFactory.TreeMapFactory<K,V> - Class in org.deeplearning4j.berkeley
-
- MapFactory.WeakHashMapFactory<K,V> - Class in org.deeplearning4j.berkeley
-
- mapper() - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
Object mapper for serialization of configurations
- mapperYaml() - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
Object mapper for serialization of configurations
- mask - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- maskArray - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- matches(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Say whether this regular expression matches
this String.
- MathUtils - Class in org.deeplearning4j.util
-
This is a math utils class.
- MathUtils() - Constructor for class org.deeplearning4j.util.MathUtils
-
- max() - Method in class org.deeplearning4j.berkeley.Counter
-
- max(int, int, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the minimum of three int values.
- max(float, float) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the greater of two float values.
- max(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the greater of two double values.
- max(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
- MaxEpochsTerminationCondition - Class in org.deeplearning4j.earlystopping.termination
-
Terminate training if the number of epochs exceeds the maximum number of epochs
- MaxEpochsTerminationCondition(int) - Constructor for class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition
-
- maxIndex(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Returns index of maximum element in a given
array of doubles.
- maxLengthIterator(Iterator<T>, int) - Static method in class org.deeplearning4j.berkeley.Iterators
-
Provides a max number of elements for an underlying base iterator.
- maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- maxNumLineSearchIterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Maximum number of line search iterations.
- maxNumLineSearchIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- MaxScoreIterationTerminationCondition - Class in org.deeplearning4j.earlystopping.termination
-
Iteration termination condition for terminating training if the minibatch score exceeds a certain value.
- MaxScoreIterationTerminationCondition(double) - Constructor for class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition
-
- MaxTimeIterationTerminationCondition - Class in org.deeplearning4j.earlystopping.termination
-
Terminate training based on max time.
- MaxTimeIterationTerminationCondition(long, TimeUnit) - Constructor for class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition
-
- mean(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Computes the mean for an array of doubles.
- meanAbsoluteError(int) - Method in class org.deeplearning4j.eval.RegressionEvaluation
-
- meanSquaredError(int) - Method in class org.deeplearning4j.eval.RegressionEvaluation
-
- memCellActivations - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- memCellState - Variable in class org.deeplearning4j.nn.layers.recurrent.FwdPassReturn
-
- merge(Evaluation) - Method in class org.deeplearning4j.eval.Evaluation
-
Merge the other evaluation object into this one.
- merge(Layer, int) - Method in interface org.deeplearning4j.nn.api.Layer
-
Parameter averaging
- merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
-
- merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
Averages the given logistic regression from a mini batch into this layer
- merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- merge(Layer, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Averages the given logistic regression
from a mini batch in to this one
- merge(MultiLayerNetwork, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Merges this network with the other one.
- merge(int, int) - Method in class org.deeplearning4j.util.StringGrid
-
- mergeCoords(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This will merge the coordinates of the given coordinate system.
- mergeCoords(List<Double>, List<Double>) - Static method in class org.deeplearning4j.util.MathUtils
-
This will merge the coordinates of the given coordinate system.
- MergeVertex - Class in org.deeplearning4j.nn.conf.graph
-
A MergeVertex is used to combine the activations of two or more layers/GraphVertex by means of concatenation/merging.
Exactly how this is done depends on the type of input.
For 2d (feed forward layer) inputs: MergeVertex([numExamples,layerSize1],[numExamples,layerSize2]) -> [numExamples,layerSize1 + layerSize2]
For 3d (time series) inputs: MergeVertex([numExamples,layerSize1,timeSeriesLength],[numExamples,layerSize2,timeSeriesLength])
-> [numExamples,layerSize1 + layerSize2,timeSeriesLength]
For 4d (convolutional) inputs: MergeVertex([numExamples,depth1,width,height],[numExamples,depth2,width,height])
-> [numExamples,depth1 + depth2,width,height]
- MergeVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.MergeVertex
-
- MergeVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
-
A MergeVertex is used to combine the activations of two or more layers/GraphVertex by means of concatenation/merging.
Exactly how this is done depends on the type of input.
For 2d (feed forward layer) inputs: MergeVertex([numExamples,layerSize1],[numExamples,layerSize2]) -> [numExamples,layerSize1 + layerSize2]
For 3d (time series) inputs: MergeVertex([numExamples,layerSize1,timeSeriesLength],[numExamples,layerSize2,timeSeriesLength])
-> [numExamples,layerSize1 + layerSize2,timeSeriesLength]
For 4d (convolutional) inputs: MergeVertex([numExamples,depth1,width,height],[numExamples,depth2,width,height])
-> [numExamples,depth1 + depth2,width,height]
- MergeVertex(ComputationGraph, String, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
-
- MergeVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.MergeVertex
-
- min() - Method in class org.deeplearning4j.berkeley.Counter
-
- min(int, int, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the minimum of three int values.
- min(float, float) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the smaller of two float values.
- min(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the smaller of two double values.
- min(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
- minibatch(boolean) - Method in class org.deeplearning4j.nn.conf.layers.BatchNormalization.Builder
-
If doing minibatch training or not.
- miniBatch - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- miniBatch(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Process input as minibatch vs full dataset.
- miniBatch - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- minimize - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- minimize(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Objective function to minimize or maximize cost function
Default set to minimize true.
- minimize - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- model - Variable in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
-
- Model - Interface in org.deeplearning4j.nn.api
-
A Model is meant for predicting something from data.
- model(Model) - Method in class org.deeplearning4j.optimize.Solver.Builder
-
- model - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- modelSaver(EarlyStoppingModelSaver<T>) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder
-
How should models be saved? (Default: in memory)
- ModelSerializer - Class in org.deeplearning4j.util
-
Utility class suited to save/restore neural net models
- momentum - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- momentum(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Momentum rate.
- momentum - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- momentum - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- momentum(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- momentumAfter - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- momentumAfter(Map<Integer, Double>) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Momentum schedule.
- momentumAfter(Map<Integer, Double>) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Momentum schedule.
- momentumSchedule - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- momentumSchedule - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- mostLikelyInSequence(INDArray) - Method in interface org.deeplearning4j.nn.api.SequenceClassifier
-
Deprecated.
- movingAverage(INDArray, int) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
-
Calculate a moving average given the length
- MovingWindowBaseDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
DataSetIterator for moving window (rotating matrices)
- MovingWindowBaseDataSetIterator(int, int, DataSet, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.MovingWindowBaseDataSetIterator
-
- MovingWindowDataSetFetcher - Class in org.deeplearning4j.datasets.iterator.impl
-
Moving window data fetcher.
- MovingWindowDataSetFetcher(DataSet, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.MovingWindowDataSetFetcher
-
- MovingWindowMatrix - Class in org.deeplearning4j.util
-
Moving window on a matrix (usually used for images)
Given a: This is a list of flattened arrays:
1 1 1 1 1 1 2 2
2 2 2 2 ----> 1 1 2 2
3 3 3 3 3 3 4 4
4 4 4 4 3 3 4 4
- MovingWindowMatrix(INDArray, int, int, boolean) - Constructor for class org.deeplearning4j.util.MovingWindowMatrix
-
- MovingWindowMatrix(INDArray, int, int) - Constructor for class org.deeplearning4j.util.MovingWindowMatrix
-
Same as calling new MovingWindowMatrix(toSlice,windowRowSize,windowColumnSize,false)
- MultiDataSetIteratorAdapter - Class in org.deeplearning4j.datasets.iterator.impl
-
Iterator that adapts a DataSetIterator to a MultiDataSetIterator
- MultiDataSetIteratorAdapter(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
-
- MultiDimensionalMap<K,T,V> - Class in org.deeplearning4j.util
-
Multiple key map
- MultiDimensionalMap(Map<Pair<K, T>, V>) - Constructor for class org.deeplearning4j.util.MultiDimensionalMap
-
- MultiDimensionalMap.Entry<K,T,V> - Class in org.deeplearning4j.util
-
- MultiDimensionalSet<K,V> - Class in org.deeplearning4j.util
-
Created by agibsonccc on 4/29/14.
- MultiDimensionalSet(Set<Pair<K, V>>) - Constructor for class org.deeplearning4j.util.MultiDimensionalSet
-
- MultiLayerConfiguration - Class in org.deeplearning4j.nn.conf
-
Configuration for a multi layer network
- MultiLayerConfiguration() - Constructor for class org.deeplearning4j.nn.conf.MultiLayerConfiguration
-
- MultiLayerConfiguration.Builder - Class in org.deeplearning4j.nn.conf
-
- MultiLayerNetwork - Class in org.deeplearning4j.nn.multilayer
-
MultiLayerNetwork is a neural network with multiple layers in a stack, and usually an output layer.
- MultiLayerNetwork(MultiLayerConfiguration) - Constructor for class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- MultiLayerNetwork(String, INDArray) - Constructor for class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Initialize the network based on the configuration
- MultiLayerNetwork(MultiLayerConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Initialize the network based on the configuraiton
- MultiLayerUpdater - Class in org.deeplearning4j.nn.updater
-
MultiLayerUpdater: Gradient updater for MultiLayerNetworks.
- MultiLayerUpdater(MultiLayerNetwork) - Constructor for class org.deeplearning4j.nn.updater.MultiLayerUpdater
-
- MultiLayerUpdater(MultiLayerNetwork, INDArray) - Constructor for class org.deeplearning4j.nn.updater.MultiLayerUpdater
-
- MultiLayerUtil - Class in org.deeplearning4j.util
-
Various cooccurrences for manipulating a multi layer network
- MultipleEpochsIterator - Class in org.deeplearning4j.datasets.iterator
-
A dataset iterator for doing multiple passes over a dataset
- MultipleEpochsIterator(int, DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- MultipleEpochsIterator(int, DataSetIterator, int) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- MultipleEpochsIterator(DataSetIterator, int, long) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- MultipleEpochsIterator(DataSetIterator, long) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- MultipleEpochsIterator(int, DataSet) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- MultiThreadUtils - Class in org.deeplearning4j.util
-
- MyMethod<I,O> - Interface in org.deeplearning4j.berkeley
-
A function wrapping interface.
- n(double) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization.Builder
-
Number of adjacent kernel maps to use when doing LRN.
- n - Variable in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
-
- name(String) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Layer name assigns layer string name.
- nChooseK(int, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Computes n choose k in an efficient way.
- negative() - Method in class org.deeplearning4j.eval.Evaluation
-
Total negatives true negatives + false negatives
- NegativeDefaultStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions
-
Inverse step function
- NegativeDefaultStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.NegativeDefaultStepFunction
-
- NegativeDefaultStepFunction - Class in org.deeplearning4j.optimize.stepfunctions
-
Inverse step function
- NegativeDefaultStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
-
- NegativeGradientStepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions
-
Subtract the line
- NegativeGradientStepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.NegativeGradientStepFunction
-
- NegativeGradientStepFunction - Class in org.deeplearning4j.optimize.stepfunctions
-
Subtract the line
- NegativeGradientStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
-
- NetSaverLoaderUtils - Class in org.deeplearning4j.util
-
Deprecated.
- 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
-
- networkOutputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- networkOutputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
List of network outputs, by name
- NeuralNetConfiguration - Class in org.deeplearning4j.nn.conf
-
A Serializable configuration
for neural nets that covers per layer parameters
- NeuralNetConfiguration() - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- NeuralNetConfiguration.Builder - Class in org.deeplearning4j.nn.conf
-
- NeuralNetConfiguration.ListBuilder - Class in org.deeplearning4j.nn.conf
-
Fluent interface for building a list of configurations
- newEpoch - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- newExecutorService() - Static method in class org.deeplearning4j.util.MultiThreadUtils
-
- newHashBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap
-
Thread safe hash map impl
- newInstance(Object...) - Method in class org.deeplearning4j.berkeley.Factory.DefaultFactory
-
- newInstance(Object...) - Method in interface org.deeplearning4j.berkeley.Factory
-
- newIterable(Iterator<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
Wraps an iterator as an iterable
- newPair(S, T) - Static method in class org.deeplearning4j.berkeley.Pair
-
- newThreadSafeHashBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap
-
Thread safe hash map implementation
- newThreadSafeTreeBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap
-
Thread safe sorted map implementation
- newTreeBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap
-
Tree map implementation
- next() - Method in class org.deeplearning4j.berkeley.Iterators.FilteredIterator
-
- next() - Method in class org.deeplearning4j.berkeley.Iterators.IteratorIterator
-
- next() - Method in class org.deeplearning4j.berkeley.Iterators.Transform
-
- next() - Method in class org.deeplearning4j.berkeley.Iterators.TransformingIterator
-
- next() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- next() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Returns the element in the queue with highest priority, and pops it from
the queue.
- next() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
Like the standard next method but allows a
customizable number of examples returned
- next() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
Returns the next element in the iteration.
- next(int) - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- next() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
-
- next() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
-
- next() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- next() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Deprecated.
Returns the next data applyTransformToDestination
- next(int) - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- next() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- next() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
-
- next() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
-
- next() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- next() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Like the standard next method but allows a
customizable number of examples returned
- next() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Like the standard next method but allows a
customizable number of examples returned
- next() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Returns the next element in the iteration.
- next() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- nextList(List<Iterator<T>>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- nextPowOf2(long) - Static method in class org.deeplearning4j.util.MathUtils
-
See: http://stackoverflow.com/questions/466204/rounding-off-to-nearest-power-of-2
- nIn - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
-
- nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
-
- nIn - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
-
- nIn(int) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
-
- nInsPerLayer - Variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- noNaNDivide(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- NONE - Static variable in class org.deeplearning4j.util.StringGrid
-
- Norm2Termination - Class in org.deeplearning4j.optimize.terminations
-
Terminate if the norm2 of the gradient is < a certain tolerance
- Norm2Termination(double) - Constructor for class org.deeplearning4j.optimize.terminations.Norm2Termination
-
- NormalDistribution - Class in org.deeplearning4j.nn.conf.distribution
-
A normal distribution.
- NormalDistribution(double, double) - Constructor for class org.deeplearning4j.nn.conf.distribution.NormalDistribution
-
Create a normal distribution
with the given mean and std
- normalize() - Method in class org.deeplearning4j.berkeley.Counter
-
Destructively normalize this Counter in place.
- normalize() - Method in class org.deeplearning4j.berkeley.CounterMap
-
- normalize(double, double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Normalize a value
(val - min) / (max - min)
- normalize(double[], double) - Static method in class org.deeplearning4j.util.MathUtils
-
Normalizes the doubles in the array using the given value.
- normalizeToOne(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
- normalizeWithDiscount(double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- nOut - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
-
- nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
-
- nOut - Variable in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
-
- nOut(int) - Method in class org.deeplearning4j.nn.conf.layers.LossLayer.Builder
-
- nOutsPerLayer - Variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- nthIndex(String, char, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns the index of the nth occurrence of ch in s, or -1
if there are less than n occurrences of ch.
- 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
- numColumns() - Method in class org.deeplearning4j.eval.RegressionEvaluation
-
- numEpochs - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
Total number of examples in the dataset
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- numExamples - Variable in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Total number of examples in the dataset
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Total number of examples in the dataset
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- numFeatureMap(NeuralNetConfiguration) - Static method in class org.deeplearning4j.util.ConvolutionUtils
-
- numIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- numIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- numLabels() - Method in interface org.deeplearning4j.nn.api.Classifier
-
Returns the number of possible labels
- numLabels() - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Returns the number of possible labels
- numLabels() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Returns the number of possible labels
- numLabels() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Returns the number of possible labels
- numLayers - Variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- numOutcomes - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- numParams() - Method in interface org.deeplearning4j.nn.api.Model
-
the number of parameters for the model
- numParams(boolean) - Method in interface org.deeplearning4j.nn.api.Model
-
the number of parameters for the model
- numParams(NeuralNetConfiguration) - Method in interface org.deeplearning4j.nn.api.ParamInitializer
-
- numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
-
- numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
-
- numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.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.PreprocessorVertex
-
- 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.StackVertex
-
- numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.SubsetVertex
-
- numParams(boolean) - Method in class org.deeplearning4j.nn.conf.graph.UnstackVertex
-
- numParams() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- numParams(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- numParams() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
The number of parameters for the model
- numParams(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- numParams(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
-
- numParams() - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
-
The number of parameters for the model, for backprop (i.e., excluding visible bias)
- numParams() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- numParams() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Returns a 1 x m vector where the vector is composed of
a flattened vector of all of the weights for the
various neuralNets and output layer
- numParams(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.BatchNormalizationParamInitializer
-
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
-
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
-
- numParams(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.params.EmptyParamInitializer
-
- 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.PretrainParamInitializer
-
- numRowCounter - Variable in class org.deeplearning4j.eval.Evaluation
-
- pack() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- pack(List<Pair<INDArray, INDArray>>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- pad(String, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Return a String of length a minimum of totalChars characters by
padding the input String str with spaces.
- pad(Object, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Pads the toString value of the given Object.
- padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
-
- padding - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- padding(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
-
Padding
- padding - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- padLeft(String, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Pads the given String to the left with spaces to ensure that it's
at least totalChars long.
- padLeft(Object, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- padLeft(int, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- padLeft(double, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- padOrTrim(String, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Pad or trim so as to produce a string of exactly a certain length.
- padOrTrim(Object, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Pad or trim the toString value of the given Object.
- Pair<F,S> - Class in org.deeplearning4j.berkeley
-
A generic-typed pair of objects.
- Pair(F, S) - Constructor for class org.deeplearning4j.berkeley.Pair
-
- Pair.DefaultLexicographicPairComparator<F extends Comparable<F>,S extends Comparable<S>> - Class in org.deeplearning4j.berkeley
-
- Pair.FirstComparator<S extends Comparable<? super S>,T> - Class in org.deeplearning4j.berkeley
-
- Pair.LexicographicPairComparator<F,S> - Class in org.deeplearning4j.berkeley
-
- Pair.ReverseFirstComparator<S extends Comparable<? super S>,T> - Class in org.deeplearning4j.berkeley
-
- Pair.ReverseSecondComparator<S,T extends Comparable<? super T>> - Class in org.deeplearning4j.berkeley
-
- Pair.SecondComparator<S,T extends Comparable<? super T>> - Class in org.deeplearning4j.berkeley
-
- parallelCounterMap() - Static method in class org.deeplearning4j.berkeley.CounterMap
-
Returns a thread safe counter map
- parallelTasks(List<Runnable>, ExecutorService) - Static method in class org.deeplearning4j.util.MultiThreadUtils
-
- ParamAndGradientIterationListener - Class in org.deeplearning4j.optimize.listeners
-
An iteration listener that provides details on parameters and gradients at each iteration during traning.
- ParamAndGradientIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener
-
Default constructor for output to console only every iteration, tab delimited
- ParamAndGradientIterationListener(int, boolean, boolean, boolean, boolean, boolean, boolean, boolean, File, String) - Constructor for class org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener
-
Full constructor with all options.
- ParamInitializer - Interface in org.deeplearning4j.nn.api
-
Param initializer for a layer
- params() - Method in interface org.deeplearning4j.nn.api.Model
-
Parameters of the model (if any)
- params(boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Get the parameters for the ComputationGraph
- params() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- params() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
-
- params - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- params() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
Returns the parameters of the neural network as a flattened row vector
- params() - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
-
- params() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- params() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- params() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- params() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- params(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Returns a 1 x m vector where the vector is composed of
a flattened vector of all of the weights for the
various neuralNets(w,hbias NOT VBIAS) and output layer
- params() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Returns a 1 x m vector where the vector is composed of
a flattened vector of all of the weights for the
various neuralNets(w,hbias NOT VBIAS) and output layer
- PARAMS_KEY - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- paramsFlattened - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- 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.BaseLayer
-
- paramTable() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- parent(int) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- parent(Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
Returns the parent of the passed in tree via traversal
- parent() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- parse(String, Class<E>) - Static method in class org.deeplearning4j.util.EnumUtil
-
- parseCommandLineArguments(String[]) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
A simpler form of command line argument parsing.
- partitionVariable(List<Double>, int) - Static method in class org.deeplearning4j.util.MathUtils
-
This will partition the given whole variable data applyTransformToDestination in to the specified chunk number.
- peek() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- peek() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Returns the highest-priority element in the queue, but does not pop it.
- peek() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- pennPOSToWordnetPOS(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Computes the WordNet 2.0 POS tag corresponding to the PTB POS tag s.
- PerformanceListener - Class in org.deeplearning4j.optimize.listeners
-
Simple IterationListener that tracks time spend on training per iteration.
- PerformanceListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.PerformanceListener
-
- PerformanceListener(int, boolean) - Constructor for class org.deeplearning4j.optimize.listeners.PerformanceListener
-
- PerformanceListener.Builder - Class in org.deeplearning4j.optimize.listeners
-
- permutation(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the permutation of n choose r.
- poll() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- poolingType(SubsamplingLayer.PoolingType) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
-
- poolingType - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- positive() - Method in class org.deeplearning4j.eval.Evaluation
-
Returns all of the positive guesses:
true positive + false negative
- postApply(Layer, INDArray, String, int) - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
Apply the regularization
- postFirstStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- postStep(INDArray) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
After the step has been made, do an action
- postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
Post step to update searchDirection with new gradient and parameter information
- postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.ConjugateGradient
-
- postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.LBFGS
-
- postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.LineGradientDescent
-
- postStep(INDArray) - Method in class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
-
- preApply(Layer, Gradient, int) - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
Apply gradient normalization: scale based on L2, clipping etc.
- precision(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the precision for a given label
- precision(Integer, double) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the precision for a given label
- precision() - Method in class org.deeplearning4j.eval.Evaluation
-
Precision based on guesses so far
Takes into account all known classes and outputs average precision across all of them
- predict(INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Takes in a list of examples
For each row, returns a label
- predict(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Takes in a DataSet of examples
For each row, returns a label
- predict(INDArray) - Method in interface org.deeplearning4j.nn.api.SequenceClassifier
-
Deprecated.
- predict(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Returns the predictions for each example in the dataset
- predict(DataSet) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
Return predicted label names
- predict(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Returns the predictions for each example in the dataset
- predict(DataSet) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
Return predicted label names
- predict(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Returns the predictions for each example in the dataset
- predict(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Return predicted label names
- Prediction - Class in org.deeplearning4j.eval.meta
-
Prediction: a prediction for classification, used with the
Evaluation class.
- Prediction() - Constructor for class org.deeplearning4j.eval.meta.Prediction
-
- prediction() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- preOutput(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
Raw activations
- preOutput(INDArray, Layer.TrainingMode) - Method in interface org.deeplearning4j.nn.api.Layer
-
Raw activations
- preOutput(INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.Layer
-
Raw activations
- preOutput(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
Classify input
- preOutput(INDArray, INDArray, INDArray, int[], int[], int[], ConvolutionLayer.AlgoMode, ConvolutionMode) - Method in interface org.deeplearning4j.nn.layers.convolution.ConvolutionHelper
-
- preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- preOutput(boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer
-
- preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
- preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- preOutput(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- preOutput(INDArray, boolean, int[], INDArray, INDArray, INDArray, INDArray, double, double) - Method in interface org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper
-
- preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
-
- preOutput(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- preOutput(INDArray, Layer.TrainingMode) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- preOutput(INDArray, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- preOutput2d(boolean) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- preOutput2d(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
-
- prependToEach(String, int) - Method in class org.deeplearning4j.util.StringGrid
-
- preProcess(DataSet) - Method in class org.deeplearning4j.datasets.iterator.CombinedPreProcessor
-
Pre process a dataset sequentially
- preProcess(DataSet) - Method in class org.deeplearning4j.datasets.iterator.DummyPreProcessor
-
Pre process a dataset
- preProcess(INDArray, int) - Method in interface org.deeplearning4j.nn.conf.InputPreProcessor
-
Pre preProcess input/activations for a multi layer network
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.BinomialSamplingPreProcessor
-
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor
-
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor
-
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
-
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
-
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
-
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ReshapePreProcessor
-
Deprecated.
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
-
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
-
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.UnitVarianceProcessor
-
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanAndUnitVariancePreProcessor
-
- preProcess(INDArray, int) - Method in class org.deeplearning4j.nn.conf.preprocessor.ZeroMeanPrePreProcessor
-
- preProcessLine() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
Pre preProcess a line before an iteration
- preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
Pre preProcess to setup initial searchDirection approximation
- preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.ConjugateGradient
-
- preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.LBFGS
-
- preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.LineGradientDescent
-
- preProcessLine() - Method in class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
-
- preProcessor - Variable in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- preProcessor - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- PreprocessorVertex - Class in org.deeplearning4j.nn.conf.graph
-
PreprocessorVertex is a simple adaptor class that allows a
InputPreProcessor to be used in a ComputationGraph
GraphVertex, without it being associated with a layer.
- PreprocessorVertex(InputPreProcessor) - Constructor for class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
-
- PreprocessorVertex(InputPreProcessor, InputType) - Constructor for class org.deeplearning4j.nn.conf.graph.PreprocessorVertex
-
- PreprocessorVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
-
PreprocessorVertex is a simple adaptor class that allows a
InputPreProcessor to be used in a ComputationGraph
GraphVertex, without it being associated with a layer.
- PreprocessorVertex(ComputationGraph, String, int, InputPreProcessor) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
-
- PreprocessorVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], InputPreProcessor) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.PreprocessorVertex
-
- pretrain - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- pretrain(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
Whether to do layerwise pre training or not
- pretrain - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
- pretrain - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- pretrain(boolean) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
Whether to do pre train or not
- pretrain - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
-
- pretrain - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- pretrain(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder
-
- pretrain - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- pretrain(DataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Pretrain network with a single input and single output.
- pretrain(MultiDataSetIterator) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Pretrain network with multiple inputs and/or outputs
- pretrain(DataSetIterator) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
This unsupervised learning method runs
contrastive divergence on each RBM layer in the network.
- pretrain(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
This unsupervised learning method runs
contrastive divergence on each RBM layer in the network.
- preTrainIterations - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
-
- preTrainIterations(int) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
-
- PretrainParamInitializer - Class in org.deeplearning4j.nn.params
-
Pretrain weight initializer.
- PretrainParamInitializer() - Constructor for class org.deeplearning4j.nn.params.PretrainParamInitializer
-
- printConfiguration() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Prints the configuration
- printStringOneCharPerLine(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- printThreadInfo(PrintWriter, String) - Static method in class org.deeplearning4j.util.ReflectionUtils
-
Print all of the thread's information and stack traces.
- printToFile(File, String, boolean) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Prints to a file.
- printToFile(File, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Prints to a file.
- printToFile(String, String, boolean) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Prints to a file.
- printToFile(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Prints to a file.
- PriorityQueue<E> - Class in org.deeplearning4j.berkeley
-
A priority queue based on a binary heap.
- PriorityQueue() - Constructor for class org.deeplearning4j.berkeley.PriorityQueue
-
- PriorityQueue(int) - Constructor for class org.deeplearning4j.berkeley.PriorityQueue
-
- PriorityQueueInterface<E> - Interface in org.deeplearning4j.berkeley
-
- probRound(double, Random) - Static method in class org.deeplearning4j.util.MathUtils
-
Rounds a double to the next nearest integer value in a probabilistic
fashion (e.g.
- probToLogOdds(double) - Static method in class org.deeplearning4j.util.MathUtils
-
Returns the log-odds for a given probability.
- propDown(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Calculates the activation of the hidden:
activation(h * W + vbias)
- propUp(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Calculates the activation of the visible :
sigmoid(v * W + hbias)
- propUp(INDArray, boolean) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Calculates the activation of the visible :
sigmoid(v * W + hbias)
- propUpDerivative(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
- pruneKeysBelowThreshold(double) - Method in class org.deeplearning4j.berkeley.Counter
-
- put(E, double, boolean) - Method in class org.deeplearning4j.berkeley.Counter
-
Set the count for the given key if it is larger than the previous one;
- put(E, double) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- put(E, double) - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Adds a key to the queue with the given priority.
- put(Pair<K, T>, V) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Associates the specified value with the specified key in this map
(optional operation).
- put(K, T, V) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
- putAll(Map<? extends Pair<K, T>, ? extends V>) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Copies all of the mappings from the specified map to this map
(optional operation).
- randomDoubleBetween(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
- randomFloatBetween(float, float) - Static method in class org.deeplearning4j.util.MathUtils
-
- randomNumberBetween(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Generates a random integer between the specified numbers
- randomNumberBetween(double, double, RandomGenerator) - Static method in class org.deeplearning4j.util.MathUtils
-
Generates a random integer between the specified numbers
- RBM - Class in org.deeplearning4j.nn.conf.layers
-
Restricted Boltzmann Machine.
- RBM - Static variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- RBM - Class in org.deeplearning4j.nn.layers.feedforward.rbm
-
Restricted Boltzmann Machine.
- RBM(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
- RBM(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
- RBM.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- RBM.HiddenUnit - Enum in org.deeplearning4j.nn.conf.layers
-
- RBM.VisibleUnit - Enum in org.deeplearning4j.nn.conf.layers
-
- RBMUtil - Class in org.deeplearning4j.util
-
Handles various cooccurrences for RBM specific cooccurrences
- readObject(File) - Static method in class org.deeplearning4j.util.SerializationUtils
-
- readObject(InputStream) - Static method in class org.deeplearning4j.util.SerializationUtils
-
Reads an object from the given input stream
- readString(DataInputStream, int) - Static method in class org.deeplearning4j.util.ByteUtil
-
- recall(Integer) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the recall for a given label
- recall(Integer, double) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the recall for a given label
- recall() - Method in class org.deeplearning4j.eval.Evaluation
-
Recall based on guesses so far
Takes into account all known classes and outputs average recall across all of them
- reconstruct(INDArray, int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Reconstructs the input.
- ReconstructionDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
Wraps a data applyTransformToDestination iterator setting the first (feature matrix) as
the labels.
- ReconstructionDataSetIterator(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
- recurrent(int) - Static method in class org.deeplearning4j.nn.conf.inputs.InputType
-
InputType for recurrent neural network (time series) data
- RECURRENT_WEIGHT_KEY - Static variable in class org.deeplearning4j.nn.params.GravesLSTMParamInitializer
-
Weights for previous time step -> current time step connections
- RECURRENT_WEIGHT_KEY_BACKWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
-
- RECURRENT_WEIGHT_KEY_FORWARDS - Static variable in class org.deeplearning4j.nn.params.GravesBidirectionalLSTMParamInitializer
-
Weights for previous time step -> current time step connections
- RecurrentLayer - Interface in org.deeplearning4j.nn.api.layers
-
Created by Alex on 28/08/2016.
- RECURSIVE_AUTO_ENCODER - Static variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- redistributeParams(boolean) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- ReflectionUtils - Class in org.deeplearning4j.util
-
General reflection utils
- RegressionEvaluation - Class in org.deeplearning4j.eval
-
Evaluation method for the evaluation of regression algorithms.
Provides the following metrics, for each column:
- MSE: mean squared error
- MAE: mean absolute error
- RMSE: root mean squared error
- RSE: relative squared error
- correlation coefficient
See for example: http://www.saedsayad.com/model_evaluation_r.htm
For classification, see
Evaluation
- RegressionEvaluation(int) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation
-
Create a regression evaluation object with the specified number of columns, and default precision
for the stats() method.
- RegressionEvaluation(int, int) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation
-
Create a regression evaluation object with the specified number of columns, and specified precision
for the stats() method.
- RegressionEvaluation(String...) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation
-
Create a regression evaluation object with default precision for the stats() method
- RegressionEvaluation(List<String>) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation
-
Create a regression evaluation object with default precision for the stats() method
- RegressionEvaluation(List<String>, int) - Constructor for class org.deeplearning4j.eval.RegressionEvaluation
-
Create a regression evaluation object with specified precision for the stats() method
- regularization(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Whether to use regularization (l1, l2, dropout, etc
- reinitMapperWithSubtypes(Collection<NamedType>) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
Reinitialize and return the Jackson/json ObjectMapper with additional named types.
- relativeDifferance(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- relativeSquaredError(int) - Method in class org.deeplearning4j.eval.RegressionEvaluation
-
- remove() - Method in class org.deeplearning4j.berkeley.Iterators.FilteredIterator
-
- remove() - Method in class org.deeplearning4j.berkeley.Iterators.IteratorIterator
-
- remove() - Method in class org.deeplearning4j.berkeley.Iterators.Transform
-
- remove() - Method in class org.deeplearning4j.berkeley.Iterators.TransformingIterator
-
- remove() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- remove() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Not supported -- next() already removes the head of the queue.
- remove() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
Removes from the underlying collection the last element returned
by this iterator (optional operation).
- remove() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- remove() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
-
- remove() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- remove() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- remove() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- remove() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
-
- remove() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- remove() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
-
- remove() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Removes from the underlying collection the last element returned
by this iterator (optional operation).
- remove() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Removes from the underlying collection the last element returned
by this iterator (optional operation).
- remove() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- remove(Object) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- remove() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- remove(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Removes the mapping for a key from this map if it is present
(optional operation).
- remove(Object) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Removes the specified element from this applyTransformToDestination if it is present
(optional operation).
- removeAll(Collection<?>) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- removeAll(Collection<?>) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Removes from this applyTransformToDestination all of its elements that are contained in the
specified collection (optional operation).
- removeColumns(Integer...) - Method in class org.deeplearning4j.util.StringGrid
-
Removes the specified columns from the grid
- removeFirst() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- removeKey(E) - Method in class org.deeplearning4j.berkeley.Counter
-
- removeKey(K) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- removeKeyFromEntries(E) - Method in class org.deeplearning4j.berkeley.Counter
-
- removeRowsWithEmptyColumn(int) - Method in class org.deeplearning4j.util.StringGrid
-
Removes all rows with a column of NONE
- removeRowsWithEmptyColumn(int, String) - Method in class org.deeplearning4j.util.StringGrid
-
Removes all rows with a column of missingValue
- reportBatch(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder
-
This method defines, if batches/sec should be reported together with other data
- reportIteration(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder
-
This method defines, if iteration number should be reported together with other data
- reportSample(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder
-
This method defines, if samples/sec should be reported together with other data
- reportScore(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder
-
This method defines, if score should be reported together with other data
- reportTime(boolean) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder
-
This method defines, if time per iteration should be reported together with other data
- reset() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- reset() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
Resets the iterator back to the beginning
- reset() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- reset() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
-
- reset() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- reset() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Deprecated.
Returns the fetcher back to the beginning of the dataset
- reset() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- reset() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- reset() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
-
- reset() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- reset() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
-
- reset() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Resets the iterator back to the beginning
- reset() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Resets the iterator back to the beginning
- reset() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- reset() - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
- resetSupported() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- ReshapePreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
-
Deprecated.
- ReshapePreProcessor(int[], int[], boolean) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.ReshapePreProcessor
-
Deprecated.
- ReshapePreProcessor(int...) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.ReshapePreProcessor
-
Deprecated.
- ReshapePreProcessor(int[], int[]) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.ReshapePreProcessor
-
Deprecated.
- reshapeTimeSeriesMaskToVector(INDArray) - Static method in class org.deeplearning4j.util.TimeSeriesUtils
-
Reshape time series mask arrays.
- 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.
- restoreComputationGraph(String) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a computation graph from a file
- restoreComputationGraph(String, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a computation graph from a file
- restoreComputationGraph(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a computation graph from a InputStream
- restoreComputationGraph(InputStream) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a computation graph from a InputStream
- restoreComputationGraph(File) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a computation graph from a file
- restoreComputationGraph(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a computation graph from a file
- restoreMultiLayerNetwork(File) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a multi layer network from a file
- restoreMultiLayerNetwork(File, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a multi layer network from a file
- restoreMultiLayerNetwork(InputStream, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a MultiLayerNetwork from InputStream from a file
- restoreMultiLayerNetwork(InputStream) - Static method in class org.deeplearning4j.util.ModelSerializer
-
- restoreMultiLayerNetwork(String) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a MultilayerNetwork model from a file
- restoreMultiLayerNetwork(String, boolean) - Static method in class org.deeplearning4j.util.ModelSerializer
-
Load a MultilayerNetwork model from a file
- retainAll(Collection<?>) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- retainAll(Collection<?>) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Retains only the elements in this applyTransformToDestination that are contained in the
specified collection (optional operation).
- reverse() - Method in class org.deeplearning4j.berkeley.Pair
-
- ReverseFirstComparator() - Constructor for class org.deeplearning4j.berkeley.Pair.ReverseFirstComparator
-
- ReverseSecondComparator() - Constructor for class org.deeplearning4j.berkeley.Pair.ReverseSecondComparator
-
- rho - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- rho(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Ada delta coefficient, rho.
- rho - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- rho - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- rho(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Ada delta coefficient
- rightChild(int) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- rmsDecay - Variable in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
- rmsDecay(double) - Method in class org.deeplearning4j.nn.conf.layers.Layer.Builder
-
Decay rate for RMSProp.
- rmsDecay - Variable in class org.deeplearning4j.nn.conf.layers.Layer
-
- rmsDecay - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- rmsDecay(double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Decay rate for RMSProp.
- RNN_OUTPUT_LAYER - Static variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- rnnActivateUsingStoredState(INDArray, boolean, boolean) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer
-
Similar to rnnTimeStep, this method is used for activations using the state
stored in the stateMap as the initialization.
- rnnActivateUsingStoredState(INDArray[], boolean, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Similar to rnnTimeStep and feedForward() methods.
- rnnActivateUsingStoredState(INDArray, boolean, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- rnnActivateUsingStoredState(INDArray, boolean, boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- rnnActivateUsingStoredState(INDArray, boolean, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Similar to rnnTimeStep and feedForward() methods.
- rnnClearPreviousState() - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer
-
Reset/clear the stateMap for rnnTimeStep() and tBpttStateMap for rnnActivateUsingStoredState()
- rnnClearPreviousState() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- rnnClearPreviousState() - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
-
Reset/clear the stateMap for rnnTimeStep() and tBpttStateMap for rnnActivateUsingStoredState()
- rnnClearPreviousState() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Clear the previous state of the RNN layers (if any).
- rnnGetPreviousState() - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer
-
Returns a shallow copy of the RNN stateMap (that contains the stored history for use in methods such
as rnnTimeStep
- rnnGetPreviousState(int) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- rnnGetPreviousState(String) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- rnnGetPreviousState() - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
-
Returns a shallow copy of the stateMap
- rnnGetPreviousState(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Get the state of the RNN layer, as used in rnnTimeStep().
- rnnGetPreviousStates() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- rnnGetTBPTTState() - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer
-
Get the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer.
- rnnGetTBPTTState() - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
-
- RnnOutputLayer - Class in org.deeplearning4j.nn.conf.layers
-
- RnnOutputLayer - Class in org.deeplearning4j.nn.layers.recurrent
-
Recurrent Neural Network Output Layer.
Handles calculation of gradients etc for various objective functions.
Functionally the same as OutputLayer, but handles output and label reshaping
automatically.
Input and output activations are same as other RNN layers: 3 dimensions with shape
[miniBatchSize,nIn,timeSeriesLength] and [miniBatchSize,nOut,timeSeriesLength] respectively.
- RnnOutputLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
-
- RnnOutputLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
-
- RnnOutputLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- rnnSetPreviousState(Map<String, INDArray>) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer
-
Set the stateMap (stored history).
- rnnSetPreviousState(int, Map<String, INDArray>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- rnnSetPreviousState(String, Map<String, INDArray>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- rnnSetPreviousState(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
-
Set the state map.
- rnnSetPreviousState(int, Map<String, INDArray>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Set the state of the RNN layer.
- rnnSetPreviousStates(Map<String, Map<String, INDArray>>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- rnnSetTBPTTState(Map<String, INDArray>) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer
-
Set the RNN truncated backpropagations through time (TBPTT) state for the recurrent layer.
- rnnSetTBPTTState(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
-
- rnnTimeStep(INDArray) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer
-
Do one or more time steps using the previous time step state stored in stateMap.
Can be used to efficiently do forward pass one or n-steps at a time (instead of doing
forward pass always from t=0)
If stateMap is empty, default initialization (usually zeros) is used
Implementations also update stateMap at the end of this method
- rnnTimeStep(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
If this ComputationGraph contains one or more RNN layers: conduct forward pass (prediction)
but using previous stored state for any RNN layers.
- rnnTimeStep(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- rnnTimeStep(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- rnnTimeStep(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
If this MultiLayerNetwork contains one or more RNN layers: conduct forward pass (prediction)
but using previous stored state for any RNN layers.
- RnnToCnnPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
-
A preprocessor to allow RNN and CNN layers to be used together
For example, time series (video) input -> ConvolutionLayer, or conceivable GravesLSTM -> ConvolutionLayer
Functionally equivalent to combining RnnToFeedForwardPreProcessor + FeedForwardToCnnPreProcessor
Specifically, this does two things:
(a) Reshape 3d activations out of RNN layer, with shape [miniBatchSize, numChannels*inputHeight*inputWidth, timeSeriesLength])
into 4d (CNN) activations (with shape [numExamples*timeSeriesLength, numChannels, inputWidth, inputHeight])
(b) Reshapes 4d epsilons (weights.*deltas) out of CNN layer (with shape
[numExamples*timeSeriesLength, numChannels, inputHeight, inputWidth]) into 3d epsilons with shape
[miniBatchSize, numChannels*inputHeight*inputWidth, timeSeriesLength] suitable to feed into CNN layers.
- RnnToCnnPreProcessor(int, int, int) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
-
- RnnToFeedForwardPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor
-
A preprocessor to allow RNN and feed-forward network layers to be used together.
For example, GravesLSTM -> OutputLayer or GravesLSTM -> DenseLayer
This does two things:
(a) Reshapes activations out of RNN layer (which is 3D with shape
[miniBatchSize,layerSize,timeSeriesLength]) into 2d activations (with shape
[miniBatchSize*timeSeriesLength,layerSize]) suitable for use in feed-forward layers.
(b) Reshapes 2d epsilons (weights*deltas from feed forward layer, with shape
[miniBatchSize*timeSeriesLength,layerSize]) into 3d epsilons (with shape
[miniBatchSize,layerSize,timeSeriesLength]) for use in RNN layer
- RnnToFeedForwardPreProcessor() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
-
- rnnUpdateStateWithTBPTTState() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Update the internal state of RNN layers after a truncated BPTT fit call
- ROC - Class in org.deeplearning4j.eval
-
ROC (Receiver Operating Characteristic) for binary classifiers, using the specified number of threshold steps.
- ROC(int) - Constructor for class org.deeplearning4j.eval.ROC
-
- ROC.ROCValue - Class in org.deeplearning4j.eval
-
- ROCValue() - Constructor for class org.deeplearning4j.eval.ROC.ROCValue
-
- rootMeanSquaredError(int) - Method in class org.deeplearning4j.eval.RegressionEvaluation
-
- rootMeansSquaredError(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the root mean squared error of two data sets
- round(double) - Static method in class org.deeplearning4j.util.MathUtils
-
Rounds a double to the next nearest integer value.
- roundDouble(double, int) - Static method in class org.deeplearning4j.util.MathUtils
-
Rounds a double to the given number of decimal places.
- roundFloat(float, int) - Static method in class org.deeplearning4j.util.MathUtils
-
Rounds a double to the given number of decimal places.
- runPairWise(List<V>, CounterMap.CountFunction<V>) - Static method in class org.deeplearning4j.berkeley.CounterMap
-
Build a counter map by iterating pairwise over the list.
- sample(Random) - Method in class org.deeplearning4j.berkeley.Counter
-
Will return a sample from the counter, will throw exception if any of the
counts are < 0.0 or if the totalCount() <= 0.0
- sample() - Method in class org.deeplearning4j.berkeley.Counter
-
Will return a sample from the counter, will throw exception if any of the
counts are < 0.0 or if the totalCount() <= 0.0
- sampleDoublesInInterval(double[][], int) - Static method in class org.deeplearning4j.util.MathUtils
-
- sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
-
Sample the hidden distribution given the visible
- sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Binomial sampling of the hidden values given visible
- sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
-
Sample the visible distribution given the hidden
- sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder
-
- sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Guess the visible values given the hidden
- SamplingDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
A wrapper for a dataset to sample from.
- SamplingDataSetIterator(DataSet, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- saveBestModel(T, double) - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver
-
Save the best model (so far) learned during early stopping training
- saveBestModel(T, double) - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
-
- saveBestModel(ComputationGraph, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
-
- saveBestModel(MultiLayerNetwork, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
-
- saveLastModel(boolean) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder
-
Save the last model? If true: save the most recent model at each epoch, in addition to the best
model (whenever the best model improves).
- saveLatestModel(T, double) - Method in interface org.deeplearning4j.earlystopping.EarlyStoppingModelSaver
-
Save the latest (most recent) model learned during early stopping
- saveLatestModel(T, double) - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
-
- saveLatestModel(ComputationGraph, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
-
- saveLatestModel(MultiLayerNetwork, double) - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
-
- saveLayerParameters(INDArray, String) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Save existing parameters for the layer
- saveNetworkAndParameters(MultiLayerNetwork, String) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Save model configuration and parameters
- saveObject(Object, File) - Static method in class org.deeplearning4j.util.SerializationUtils
-
- saveParameters(MultiLayerNetwork, int[], Map<Integer, String>) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Save existing parameters for the network
- saveParameters(MultiLayerNetwork, String[], Map<String, String>) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Save existing parameters for the network
- saveUpdators(MultiLayerNetwork, String) - Static method in class org.deeplearning4j.util.NetSaverLoaderUtils
-
Deprecated.
Save model updators
- scale(double) - Method in class org.deeplearning4j.berkeley.Counter
-
- scale(double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Scale all entries in CounterMap
by scaleFactor
- scaledClone(double) - Method in class org.deeplearning4j.berkeley.Counter
-
- scan(Object) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
-
- scan(Vfs.File, Object) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
-
- score() - Method in interface org.deeplearning4j.nn.api.Model
-
The score for the model
- score - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
-
- score(DataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Sets the input and labels and returns a score for the prediction with respect to the true labels
This is equivalent to
ComputationGraph.score(DataSet, boolean) with training==true.
NOTE: this version of the score function can only be used with ComputationGraph networks that have
a single input and a single output.
- score(DataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Sets the input and labels and returns a score for the prediction with respect to the true labels
NOTE: this version of the score function can only be used with ComputationGraph networks that have
a single input and a single output.
- score(MultiDataSet) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Score the network given the MultiDataSet, at test time
- score(MultiDataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Sets the input and labels and returns a score for the prediction with respect to the true labels
- score() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- score - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- score() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
Objective function: the specified objective
- score() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- score - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- score(DataSet) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- score(DataSet, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Calculate the score (loss function) of the prediction with respect to the true labels
- score() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Score of the model (relative to the objective function)
- score() - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
The score for the optimizer so far
- score - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- score() - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- SCORE_KEY - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- scoreCalculator(ScoreCalculator<T>) - Method in class org.deeplearning4j.earlystopping.EarlyStoppingConfiguration.Builder
-
Score calculator.
- ScoreCalculator<T extends Model> - Interface in org.deeplearning4j.earlystopping.scorecalc
-
ScoreCalculator interface is used to calculate a score for a neural network.
- scoreExamples(DataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Calculate the score for each example in a DataSet individually.
- scoreExamples(MultiDataSet, boolean) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Calculate the score for each example in a DataSet individually.
- scoreExamples(DataSetIterator, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- scoreExamples(DataSet, boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Calculate the score for each example in a DataSet individually.
- ScoreImprovementEpochTerminationCondition - Class in org.deeplearning4j.earlystopping.termination
-
Terminate training if best model score does not improve for N epochs
- ScoreImprovementEpochTerminationCondition(int) - Constructor for class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
-
- ScoreImprovementEpochTerminationCondition(int, double) - Constructor for class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
-
- ScoreIterationListener - Class in org.deeplearning4j.optimize.listeners
-
Score iteration listener
- ScoreIterationListener(int) - Constructor for class org.deeplearning4j.optimize.listeners.ScoreIterationListener
-
- ScoreIterationListener() - Constructor for class org.deeplearning4j.optimize.listeners.ScoreIterationListener
-
Default constructor printing every 10 iterations
- SEARCH_DIR - Static variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- searchState - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- SecondComparator() - Constructor for class org.deeplearning4j.berkeley.Pair.SecondComparator
-
- seed - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- seed(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Random number generator seed.
- seed(long) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Random number generator seed.
- seed - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- select(int, String) - Method in class org.deeplearning4j.util.StringGrid
-
- SequenceClassifier - Interface in org.deeplearning4j.nn.api
-
Deprecated.
- SerializationUtils - Class in org.deeplearning4j.util
-
Serialization utils for saving and reading serializable objects
- setAbsTolx(double) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
-
Sets the tolerance of absolute diff in function value.
- setAllCounts(double) - Method in class org.deeplearning4j.berkeley.Counter
-
Sets all counts to the given value, but does not remove any keys
- setBackpropGradientsViewArray(INDArray) - Method in interface org.deeplearning4j.nn.api.Model
-
Set the gradients array as a view of the full (backprop) network parameters
NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
- setBackpropGradientsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- setBackpropGradientsViewArray(INDArray) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
- 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.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.PreprocessorVertex
-
- 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.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.BaseLayer
-
- 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
-
- 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.BaseLayer
-
- setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setConfiguration(Configuration) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
-
- setContentionTracing(boolean) - Static method in class org.deeplearning4j.util.ReflectionUtils
-
- setCount(E, double) - Method in class org.deeplearning4j.berkeley.Counter
-
Set the count for the given key, clobbering any previous count.
- setCount(K, V, double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Sets the count for a particular (key, value) pair.
- setCounter(K, Counter<V>) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- setDefault(double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- setDeflt(double) - Method in class org.deeplearning4j.berkeley.Counter
-
- setDirty(boolean) - Method in class org.deeplearning4j.berkeley.Counter
-
- setEnd(int) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setEpsilon(INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- setEpsilon(INDArray) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Set the errors (epsilon - aka dL/dActivation) for this GraphVertex
- setError(int, INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- setError(int, INDArray) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
- setError(double) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setErrors(INDArray...) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- setErrors(INDArray...) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
- setFirst(F) - Method in class org.deeplearning4j.berkeley.Pair
-
- setFirst(S) - Method in class org.deeplearning4j.berkeley.Triple
-
- setFirstKey(K) - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- setFrequency(int) - Method in class org.deeplearning4j.optimize.listeners.PerformanceListener.Builder
-
Desired IterationListener activation frequency
- setGoldLabel(int) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setGradientFor(String, INDArray) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
-
- setGradientFor(String, INDArray, Character) - Method in class org.deeplearning4j.nn.gradient.DefaultGradient
-
- setGradientFor(String, INDArray) - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
Update gradient for the given variable
- setGradientFor(String, INDArray, Character) - Method in interface org.deeplearning4j.nn.gradient.Gradient
-
Update gradient for the given variable; also (optionally) specify the order in which the array should be flattened
to a row vector
- setHeadWord(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setIndex(int) - Method in interface org.deeplearning4j.nn.api.Layer
-
Set the layer index.
- setIndex(int) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setIndex(int) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- setIndex(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setInput(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
Get the layer input.
- setInput(int, INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Set the specified input for the ComputationGraph
- setInput(int, INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- setInput(int, INDArray) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Set the input activations.
- setInput(int, INDArray) - Method in class org.deeplearning4j.nn.graph.vertex.impl.LayerVertex
-
- setInput(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setInput(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Note that if input isn't null
and the neuralNets are null, this is a way
of initializing the neural network
- setInputMiniBatchSize(int) - Method in interface org.deeplearning4j.nn.api.Layer
-
Set current/last input mini-batch size.
Used for score and gradient calculations.
- setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setInputMiniBatchSize(int) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setInputs(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Set all inputs for the ComputationGraph network
- setInputs(INDArray...) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Set all inputs for this GraphVertex
- setInputType(InputType) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- setInputTypes(InputType...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
Specify the types of inputs to the network, so that:
(a) preprocessors can be automatically added, and
(b) the nIns (input size) for each layer can be automatically calculated and set
The order here is the same order as .addInputs().
- setInputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- setInputVertices(VertexIndices[]) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
Sets the input vertices.
- setLabel(int, INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Set the specified label for the ComputationGraph
- setLabel(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setLabelNames(List<String>) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
Sets a list of label names to the curr dataset
- setLabels(INDArray) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer
-
Set the labels array for this output layer
- setLabels(INDArray...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Set all labels for the ComputationGraph network
- setLabels(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- setLabels(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- setLabels(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setLastHeight(int) - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- setLastOutChannels(int) - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- setLastWidth(int) - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- 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.
- setLayerParamLR(String) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setLayers(Layer[]) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setLayerWiseConfigurations(MultiLayerConfiguration) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setLearningRateByParam(String, double) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setListener(EarlyStoppingListener<T>) - Method in class org.deeplearning4j.earlystopping.trainer.BaseEarlyStoppingTrainer
-
- setListener(EarlyStoppingListener<T>) - Method in interface org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer
-
Set the early stopping listener
- setListeners(IterationListener...) - Method in interface org.deeplearning4j.nn.api.Layer
-
Set the iteration listeners for this layer.
- setListeners(Collection<IterationListener>) - Method in interface org.deeplearning4j.nn.api.Layer
-
Set the iteration listeners for this layer.
- setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Set the IterationListeners for the ComputationGraph (and all layers in the network)
- setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Set the IterationListeners for the ComputationGraph (and all layers in the network)
- setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setListeners(IterationListener...) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setListeners(Collection<IterationListener>) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
- setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.optimize.Solver
-
- setListeners(Collection<IterationListener>) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- setLogMetaInstability(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setLogOfDiangnalTProb(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setLogPCorrect(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setLogPIncorrect(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setLogStates(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setLower(double) - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
-
- setMask(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setMaskArray(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
- setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
-
- setMaskArray(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setMax(double) - Method in class org.deeplearning4j.util.SummaryStatistics
-
- setMaxCount(E, double) - Method in class org.deeplearning4j.berkeley.Counter
-
Set's the key's count to the maximum of the current count and val.
- setMaxIterations(int) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
-
- setMean(double) - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
-
- setMean(double) - Method in class org.deeplearning4j.util.SummaryStatistics
-
- setMetaStability(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setMin(double) - Method in class org.deeplearning4j.util.SummaryStatistics
-
- setMinCount(E, double) - Method in class org.deeplearning4j.berkeley.Counter
-
Set's the key's count to the minimum of the current count and val.
- 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.ConvolutionLayer
-
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
-
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.Layer
-
Set the nIn value (number of inputs, or input depth for CNNs) based on the given input type
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
-
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.RnnOutputLayer
-
- setNIn(InputType, boolean) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- setnInForLayer(Map<String, Integer>) - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- setOutputs(String...) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
Set the network output labels.
- setOutputVertices(VertexIndices[]) - Method in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- setOutputVertices(VertexIndices[]) - Method in interface org.deeplearning4j.nn.graph.vertex.GraphVertex
-
set the output vertices.
- setOutSizesEachLayer(Map<String, int[]>) - Method in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- 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.BaseLayer
-
- setParam(String, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setParameters(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Sets parameters for the model.
- setParams(INDArray) - Method in interface org.deeplearning4j.nn.api.Model
-
Set the parameters for this model.
- setParams(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setParams(INDArray, char) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
-
- setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- setParams(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Set the parameters for this model.
- setParamsViewArray(INDArray) - Method in interface org.deeplearning4j.nn.api.Model
-
Set the initial parameters array as a view of the full (backprop) network parameters
NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
- setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setParamsViewArray(INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setParamTable(Map<String, INDArray>) - Method in interface org.deeplearning4j.nn.api.Model
-
Setter for the param table
- setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setParamTable(Map<String, INDArray>) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setParent(Tree) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setParse(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setpCorrect(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setPossibleLabels(INDArray) - Method in class org.deeplearning4j.util.Viterbi
-
- setPrediction(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
Set a pre processor
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
-
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter
-
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- setPreProcessor(MultiDataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.IteratorMultiDataSetIterator
-
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- setProbabilityOfSuccess(double) - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
-
- setProperties(Object, Properties) - Static method in class org.deeplearning4j.util.Dl4jReflection
-
Sets the properties of the given object
- setRelTolx(double) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
-
Sets the tolerance of relative diff in function value.
- setScore(double) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
- setScore(double) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- setScoreFor(INDArray) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
-
- setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer
-
- setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
-
- setScoreWithZ(INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- setSecond(S) - Method in class org.deeplearning4j.berkeley.Pair
-
- setSecond(T) - Method in class org.deeplearning4j.berkeley.Triple
-
- setSecondKey(T) - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- setStates(int) - Method in class org.deeplearning4j.util.Viterbi
-
- setStateViewArray(Layer, INDArray, boolean) - Method in interface org.deeplearning4j.nn.api.Updater
-
Set the internal (historical) state view array for this updater
- setStateViewArray(INDArray) - Method in class org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater
-
- setStateViewArray(Layer, INDArray, boolean) - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
- setStateViewArray(Layer, INDArray, boolean) - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
-
- setStd(double) - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
-
- setStepMax(double) - Method in class org.deeplearning4j.optimize.solvers.BackTrackLineSearch
-
- setStore(Multimap<String, String>) - Method in class org.deeplearning4j.util.reflections.DL4JSubTypesScanner
-
- setSum(double) - Method in class org.deeplearning4j.util.SummaryStatistics
-
- setTags(List<String>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setThird(U) - Method in class org.deeplearning4j.berkeley.Triple
-
- setTokens(List<String>) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setType(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setUpdater(ComputationGraphUpdater) - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Set the computationGraphUpdater for the network
- setUpdater(Updater) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
Set the updater for the MultiLayerNetwork
- setUpdater(Updater) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
- setUpdater(Updater) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- setUpdaterComputationGraph(ComputationGraphUpdater) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
- setUpdaterComputationGraph(ComputationGraphUpdater) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- setUpper(double) - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
-
- setupSearchState(Pair<Gradient, Double>) - Method in interface org.deeplearning4j.optimize.api.ConvexOptimizer
-
Based on the gradient and score
setup a search state
- setupSearchState(Pair<Gradient, Double>) - Method in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
Setup the initial search state
- setupSearchState(Pair<Gradient, Double>) - Method in class org.deeplearning4j.optimize.solvers.LBFGS
-
- SetUtils - Class in org.deeplearning4j.util
-
- setValue(String) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- setValue(V) - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
Replaces the value corresponding to this entry with the specified
value (optional operation).
- setVector(INDArray) - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- shuffleArray(int[], long) - Static method in class org.deeplearning4j.util.MathUtils
-
- shuffleArray(int[], Random) - Static method in class org.deeplearning4j.util.MathUtils
-
- shutdown() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
Shut down the async data set iterator thread
This is not typically necessary if using a single AsyncDataSetIterator
(thread is a daemon thread and so shouldn't block the JVM from exiting)
Behaviour of next(), hasNext() etc methods after shutdown of async iterator is undefined
- shutdown() - Method in class org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator
-
Shut down the async data set iterator thread
This is not typically necessary if using a single AsyncDataSetIterator
(thread is a daemon thread and so shouldn't block the JVM from exiting)
Behaviour of next(), hasNext() etc methods after shutdown of async iterator is undefined
- sigma - Variable in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Deprecated.
- SIGMOID - Static variable in class org.deeplearning4j.nn.layers.recurrent.LSTMHelpers
-
- sigmoid(double) - Static method in class org.deeplearning4j.util.MathUtils
-
1 / 1 + exp(-x)
- simpleHostname(String) - Static method in class org.deeplearning4j.util.StringUtils
-
Given a full hostname, return the word upto the first dot.
- size() - Method in class org.deeplearning4j.berkeley.Counter
-
The number of entries in the counter (not the total count -- use
totalCount() instead).
- size() - Method in class org.deeplearning4j.berkeley.CounterMap
-
The number of keys in this CounterMap (not the number of key-value entries
-- use totalSize() for that)
- size() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- size() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Number of elements in the queue.
- size() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- size() - Method in class org.deeplearning4j.util.Index
-
- size() - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns the number of key-value mappings in this map.
- size() - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns the number of elements in this applyTransformToDestination (its cardinality).
- SizeComparator() - Constructor for class org.deeplearning4j.util.StringCluster.SizeComparator
-
- slope(double, double, double, double) - Method in class org.deeplearning4j.util.MathUtils
-
This returns the slope of the given points.
- SloppyMath - Class in org.deeplearning4j.berkeley
-
The class SloppyMath contains methods for performing basic
numeric operations.
- slurpFile(File) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given File.
- slurpFile(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given file with the given encoding.
- slurpFile(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given file
- slurpFileNoExceptions(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given file with the given encoding.
- slurpFileNoExceptions(File) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given File.
- slurpFileNoExceptions(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given File.
- slurpGBFile(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- slurpGBFileNoExceptions(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- slurpGBURL(URL) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpGBURLNoExceptions(URL) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpReader(Reader) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text from the given Reader.
- slurpURL(URL, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpURL(URL) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpURL(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpURLNoExceptions(URL, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpURLNoExceptions(URL) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpURLNoExceptions(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- sm(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Tests if a is smaller than b.
- SMALL - Static variable in class org.deeplearning4j.util.MathUtils
-
The small deviation allowed in double comparisons.
- solver - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
-
- solver - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- solver - Variable in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- Solver - Class in org.deeplearning4j.optimize
-
Generic purpose solver
- Solver() - Constructor for class org.deeplearning4j.optimize.Solver
-
- Solver.Builder - Class in org.deeplearning4j.optimize
-
- sort() - Method in class org.deeplearning4j.util.StringCluster
-
- sortBy(int) - Method in class org.deeplearning4j.util.StringGrid
-
- sortColumnsByWordLikelihoodIncluded(int) - Method in class org.deeplearning4j.util.StringGrid
-
- sparsity(double) - Method in class org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder
-
- sparsity - Variable in class org.deeplearning4j.nn.conf.layers.AutoEncoder
-
- sparsity(double) - Method in class org.deeplearning4j.nn.conf.layers.RBM.Builder
-
- sparsity - Variable in class org.deeplearning4j.nn.conf.layers.RBM
-
- split(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Splits on whitespace (\\s+).
- split(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Splits the given string using the given regex as delimiters.
- split(int, String) - Method in class org.deeplearning4j.util.StringGrid
-
- split(String) - Static method in class org.deeplearning4j.util.StringUtils
-
Split a string using the default separator
- split(String, char, char) - Static method in class org.deeplearning4j.util.StringUtils
-
Split a string using the given separator
- splitInputs(INDArray, INDArray, List<Pair<INDArray, INDArray>>, List<Pair<INDArray, INDArray>>, double) - Static method in class org.deeplearning4j.util.InputSplit
-
- splitInputs(List<Pair<INDArray, INDArray>>, List<Pair<INDArray, INDArray>>, List<Pair<INDArray, INDArray>>, double) - Static method in class org.deeplearning4j.util.InputSplit
-
- splitOnCharWithQuoting(String, char, char, char) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
This function splits the String s into multiple Strings using the
splitChar.
- squaredLoss(double[], double[], double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
This will return the squared loss of the given
points
- ssError(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
How much of the variance is NOT explained by the regression
- ssReg(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
How much of the variance is explained by the regression
- ssTotal(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Total variance in target attribute
- stackSize - Variable in class org.deeplearning4j.nn.conf.graph.UnstackVertex
-
- StackVertex - Class in org.deeplearning4j.nn.conf.graph
-
StackVertex allows for stacking of inputs so that they may be forwarded through
a network.
- StackVertex() - Constructor for class org.deeplearning4j.nn.conf.graph.StackVertex
-
- StackVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
-
StackVertex allows for stacking of inputs so that they may be forwarded through
a network.
- StackVertex(ComputationGraph, String, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
-
- StackVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[]) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.StackVertex
-
- STATE_KEY_PREV_ACTIVATION - Static variable in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- STATE_KEY_PREV_MEMCELL - Static variable in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- stateMap - Variable in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
-
stateMap stores the INDArrays needed to do rnnTimeStep() forward pass.
- stateSizeForLayer(Layer) - Method in interface org.deeplearning4j.nn.api.Updater
-
Calculate and return the state size for this updater (for the given layer).
- stateSizeForLayer(Layer) - Method in class org.deeplearning4j.nn.updater.LayerUpdater
-
- stateSizeForLayer(Layer) - Method in class org.deeplearning4j.nn.updater.MultiLayerUpdater
-
- stats() - Method in class org.deeplearning4j.eval.Evaluation
-
- stats(boolean) - Method in class org.deeplearning4j.eval.Evaluation
-
Method to obtain the classification report as a String
- stats() - Method in class org.deeplearning4j.eval.RegressionEvaluation
-
- std - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- step(INDArray, INDArray, double) - Method in interface org.deeplearning4j.optimize.api.StepFunction
-
Step with the given parameters
- step(INDArray, INDArray) - Method in interface org.deeplearning4j.optimize.api.StepFunction
-
Step with no parameters
- step() - Method in interface org.deeplearning4j.optimize.api.StepFunction
-
- step - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
-
Does x = x + stepSize * line
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
-
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
-
- step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
-
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
-
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.GradientStepFunction
-
- step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
-
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
-
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
-
- step(INDArray, INDArray, double) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
-
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
-
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeGradientStepFunction
-
- stepFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- stepFunction(StepFunction) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
Step function to apply for back track line search.
- stepFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- StepFunction - Class in org.deeplearning4j.nn.conf.stepfunctions
-
Custom step function for line search.
- StepFunction() - Constructor for class org.deeplearning4j.nn.conf.stepfunctions.StepFunction
-
- StepFunction - Interface in org.deeplearning4j.optimize.api
-
Custom step function for line search
- stepFunction - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- StepFunctions - Class in org.deeplearning4j.optimize.stepfunctions
-
- stepMax - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- StochasticGradientDescent - Class in org.deeplearning4j.optimize.solvers
-
Stochastic Gradient Descent
Standard fix step size
No line search
- StochasticGradientDescent(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
-
- StochasticGradientDescent(NeuralNetConfiguration, StepFunction, Collection<IterationListener>, Collection<TerminationCondition>, Model) - Constructor for class org.deeplearning4j.optimize.solvers.StochasticGradientDescent
-
- stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder
-
- stride - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
-
- stride(int...) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.Builder
-
Stride
- stride - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
-
- string2long(String) - Static method in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix
-
Convert a string to long.
- StringCluster - Class in org.deeplearning4j.util
-
Clusters strings based on fingerprint: for example
Two words and TWO words or WORDS TWO would be put together
- StringCluster(List<String>) - Constructor for class org.deeplearning4j.util.StringCluster
-
- StringCluster.SizeComparator - Class in org.deeplearning4j.util
-
- StringGrid - Class in org.deeplearning4j.util
-
String matrix
- StringGrid(StringGrid) - Constructor for class org.deeplearning4j.util.StringGrid
-
- StringGrid(String, int) - Constructor for class org.deeplearning4j.util.StringGrid
-
- StringGrid(String, Collection<String>) - Constructor for class org.deeplearning4j.util.StringGrid
-
- stringifyException(Throwable) - Static method in class org.deeplearning4j.util.StringUtils
-
Make a string representation of the exception.
- stringSimilarity(String...) - Static method in class org.deeplearning4j.util.MathUtils
-
Calculate string similarity with tfidf weights relative to each character
frequency and how many times a character appears in a given string
- stringToProperties(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
This method converts a comma-separated String (with whitespace
optionally allowed after the comma) representing properties
to a Properties object.
- stringToURI(String[]) - Static method in class org.deeplearning4j.util.StringUtils
-
- StringUtils - Class in org.deeplearning4j.berkeley
-
StringUtils is a class for random String things.
- StringUtils - Class in org.deeplearning4j.util
-
General string utils
- StringUtils.TraditionalBinaryPrefix - Enum in org.deeplearning4j.util
-
The traditional binary prefixes, kilo, mega, ..., exa,
which can be represented by a 64-bit integer.
- stripDuplicateRows() - Method in class org.deeplearning4j.util.StringGrid
-
- stripNonAlphaNumerics(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- SUBSAMPLING_LAYER - Static variable in class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup
-
Deprecated.
- SubsamplingHelper - Interface in org.deeplearning4j.nn.layers.convolution.subsampling
-
Helper for the subsampling layer.
- SubsamplingLayer - Class in org.deeplearning4j.nn.conf.layers
-
Subsampling layer also referred to as pooling in convolution neural nets
Supports the following pooling types:
MAX
AVG
NON
- SubsamplingLayer - Class in org.deeplearning4j.nn.layers.convolution.subsampling
-
Subsampling layer.
- SubsamplingLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- SubsamplingLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- SubsamplingLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
-
- SubsamplingLayer.PoolingType - Enum in org.deeplearning4j.nn.conf.layers
-
- SubsetVertex - Class in org.deeplearning4j.nn.conf.graph
-
SubsetVertex is used to select a subset of the activations out of another GraphVertex.
For example, a subset of the activations out of a layer.
Note that this subset is specifying by means of an interval of the original activations.
- SubsetVertex(int, int) - Constructor for class org.deeplearning4j.nn.conf.graph.SubsetVertex
-
- SubsetVertex - Class in org.deeplearning4j.nn.graph.vertex.impl
-
SubsetVertex is used to select a subset of the activations out of another GraphVertex.
For example, a subset of the activations out of a layer.
Note that this subset is specifying by means of an interval of the original activations.
- SubsetVertex(ComputationGraph, String, int, int, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
-
- SubsetVertex(ComputationGraph, String, int, VertexIndices[], VertexIndices[], int, int) - Constructor for class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
-
- sum(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the sum of the given array.
- SummaryStatistics - Class in org.deeplearning4j.util
-
- summaryStats(INDArray) - Static method in class org.deeplearning4j.util.SummaryStatistics
-
- summaryStatsString(INDArray) - Static method in class org.deeplearning4j.util.SummaryStatistics
-
- sumOfMeanDifferences(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Used for calculating top part of simple regression for
beta 1
- sumOfMeanDifferencesOnePoint(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Used for calculating top part of simple regression for
beta 1
- sumOfProducts(double[]...) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the sum of products for the given
numbers.
- sumOfSquares(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the sum of squares for the given vector.
- swap(int, int) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- swap(int, int) - Method in class org.deeplearning4j.util.StringGrid
-
- symbol - Variable in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix
-
- tags() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- 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
-
- tbpttBackpropGradient(INDArray, int) - Method in interface org.deeplearning4j.nn.api.layers.RecurrentLayer
-
Truncated BPTT equivalent of Layer.backpropGradient().
- tbpttBackpropGradient(INDArray, int) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- tbpttBackpropGradient(INDArray, int) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- tBPTTBackwardLength(int) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
When doing truncated BPTT: how many steps of backward should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
This is the k2 parameter on pg23 of
http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
- tBPTTBackwardLength(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
When doing truncated BPTT: how many steps of backward should we do?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
This is the k2 parameter on pg23 of
http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
- tBPTTForwardLength(int) - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
When doing truncated BPTT: how many steps of forward pass should we do
before doing (truncated) backprop?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter,
but may be larger than it in some circumstances (but never smaller)
Ideally your training data time series length should be divisible by this
This is the k1 parameter on pg23 of
http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
- tBPTTForwardLength(int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
When doing truncated BPTT: how many steps of forward pass should we do
before doing (truncated) backprop?
Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter,
but may be larger than it in some circumstances (but never smaller)
Ideally your training data time series length should be divisible by this
This is the k1 parameter on pg23 of
http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
- tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
- tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
-
- tbpttFwdLength - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
-
- tBpttStateMap - Variable in class org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer
-
State map for use specifically in truncated BPTT training.
- terminate(int, double) - Method in class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition
-
- terminate(int, double) - Method in interface org.deeplearning4j.earlystopping.termination.EpochTerminationCondition
-
Should the early stopping training terminate at this epoch, based on the calculated score and the epoch number?
Returns true if training should terminated, or false otherwise
- terminate(double) - Method in class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition
-
- terminate(double) - Method in interface org.deeplearning4j.earlystopping.termination.IterationTerminationCondition
-
Should early stopping training terminate at this iteration, based on the score for the last iteration?
return true if training should be terminated immediately, or false otherwise
- terminate(int, double) - Method in class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition
-
- terminate(double) - Method in class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition
-
- terminate(double) - Method in class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition
-
- terminate(int, double) - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
-
- terminate(double, double, Object[]) - Method in interface org.deeplearning4j.optimize.api.TerminationCondition
-
Whether to terminate based on the given metadata
- terminate(double, double, Object[]) - Method in class org.deeplearning4j.optimize.terminations.EpsTermination
-
- terminate(double, double, Object[]) - Method in class org.deeplearning4j.optimize.terminations.Norm2Termination
-
- terminate(double, double, Object[]) - Method in class org.deeplearning4j.optimize.terminations.ZeroDirection
-
- TerminationCondition - Interface in org.deeplearning4j.optimize.api
-
Created by agibsonccc on 12/24/14.
- terminationConditions - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
-
- TerminationConditions - Class in org.deeplearning4j.optimize.terminations
-
Created by agibsonccc on 12/24/14.
- TerminationConditions() - Constructor for class org.deeplearning4j.optimize.terminations.TerminationConditions
-
- TestDataSetConsumer - Class in org.deeplearning4j.util
-
Class that consumes DataSets with specified delays, suitable for testing
- TestDataSetConsumer(long) - Constructor for class org.deeplearning4j.util.TestDataSetConsumer
-
- TestDataSetConsumer(DataSetIterator, long) - Constructor for class org.deeplearning4j.util.TestDataSetConsumer
-
- tf(int, int) - Static method in class org.deeplearning4j.util.MathUtils
-
Term frequency: 1+ log10(count)
- tfidf(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Return td * idf
- thread(Iterator<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
Executes calls to next() in a different thread
- times(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the product of all numbers in the given array.
- TimeSeriesUtils - Class in org.deeplearning4j.util
-
Basic time series utils
- toArray() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- toArray(T[]) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- toArray() - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns an array containing all of the elements in this applyTransformToDestination.
- toArray(T[]) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns an array containing all of the elements in this applyTransformToDestination; the
runtime type of the returned array is that of the specified array.
- toByteArray(Serializable) - Static method in class org.deeplearning4j.util.SerializationUtils
-
Converts the given object to a byte array
- toCSV() - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Outputs the ConfusionMatrix as comma-separated values for easy import into spreadsheets
- toDecimal(String) - Static method in class org.deeplearning4j.util.MathUtils
-
This will convert the given binary string to a decimal based
integer
- toHTML() - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Outputs Confusion Matrix in an HTML table.
- toJson() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
- toJson() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
-
- toJson() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
Return this configuration as json
- toLines() - Method in class org.deeplearning4j.util.StringGrid
-
- toLogSpace() - Method in class org.deeplearning4j.berkeley.Counter
-
- toMultiDataSet(DataSet) - Static method in class org.deeplearning4j.nn.graph.util.ComputationGraphUtil
-
Convert a DataSet to the equivalent MultiDataSet
- toMultiDataSetIterator(DataSetIterator) - Static method in class org.deeplearning4j.nn.graph.util.ComputationGraphUtil
-
Convert a DataSetIterator to a MultiDataSetIterator, via an adaptor class
- topN - Variable in class org.deeplearning4j.eval.Evaluation
-
- topNAccuracy() - Method in class org.deeplearning4j.eval.Evaluation
-
Top N accuracy of the predictions so far.
- topNCorrectCount - Variable in class org.deeplearning4j.eval.Evaluation
-
- topNTotalCount - Variable in class org.deeplearning4j.eval.Evaluation
-
- topologicalOrder - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
-
Indexes of graph vertices, in topological order.
- topologicalSortOrder() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
-
Calculate a topological sort order for the vertices in the graph.
- toString() - Method in class org.deeplearning4j.berkeley.Counter
-
Returns a string representation with the keys ordered by decreasing
counts.
- toString(int) - Method in class org.deeplearning4j.berkeley.Counter
-
Returns a string representation which includes no more than the
maxKeysToPrint elements with largest counts.
- toString(int, boolean) - Method in class org.deeplearning4j.berkeley.Counter
-
Returns a string representation which includes no more than the
maxKeysToPrint elements with largest counts and optionally prints
one element per line.
- toString(int) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- toString() - Method in class org.deeplearning4j.berkeley.CounterMap
-
- toString(Collection<String>) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- toString() - Method in class org.deeplearning4j.berkeley.Pair
-
- toString() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
Returns a representation of the queue in decreasing priority order.
- toString(int, boolean) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
Returns a representation of the queue in decreasing priority order,
displaying at most maxKeysToPrint elements and optionally printing
one element per line.
- toString() - Method in class org.deeplearning4j.berkeley.Triple
-
- toString() - Method in class org.deeplearning4j.earlystopping.EarlyStoppingResult
-
- toString() - Method in class org.deeplearning4j.earlystopping.saver.InMemoryModelSaver
-
- toString() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver
-
- toString() - Method in class org.deeplearning4j.earlystopping.saver.LocalFileModelSaver
-
- toString() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator
-
- toString() - Method in class org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG
-
- toString() - Method in class org.deeplearning4j.earlystopping.termination.BestScoreEpochTerminationCondition
-
- toString() - Method in class org.deeplearning4j.earlystopping.termination.InvalidScoreIterationTerminationCondition
-
- toString() - Method in class org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition
-
- toString() - Method in class org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition
-
- toString() - Method in class org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition
-
- toString() - Method in class org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition
-
- toString() - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
- toString() - Method in class org.deeplearning4j.eval.meta.Prediction
-
- toString() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
- toString() - Method in class org.deeplearning4j.nn.conf.distribution.BinomialDistribution
-
- toString() - Method in class org.deeplearning4j.nn.conf.distribution.NormalDistribution
-
- toString() - Method in class org.deeplearning4j.nn.conf.distribution.UniformDistribution
-
- toString() - Method in class org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex
-
- toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional
-
- toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutionalFlat
-
- toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeFeedForward
-
- toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType.InputTypeRecurrent
-
- toString() - Method in class org.deeplearning4j.nn.conf.inputs.InputType
-
- toString() - Method in class org.deeplearning4j.nn.conf.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.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.PreprocessorVertex
-
- 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.StackVertex
-
- toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex
-
- toString() - Method in class org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex
-
- toString() - Method in class org.deeplearning4j.nn.graph.vertex.VertexIndices
-
- toString() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- toString() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- toString() - Method in class org.deeplearning4j.util.Index
-
- toString() - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
- toString() - Method in class org.deeplearning4j.util.SummaryStatistics
-
- toStringSortedByKeys() - Method in class org.deeplearning4j.berkeley.Counter
-
- toStringTabSeparated() - Method in class org.deeplearning4j.berkeley.Counter
-
- totalCount() - Method in class org.deeplearning4j.berkeley.Counter
-
Finds the total of all counts in the counter.
- totalCount() - Method in class org.deeplearning4j.berkeley.CounterMap
-
Returns the total of all counts in sub-counters.
- totalExamples - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- totalExamples() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
Total examples in the iterator
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- totalExamples() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Deprecated.
The total number of examples
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Total examples in the iterator
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Total examples in the iterator
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- totalIterations - Variable in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- totalOutcomes() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.AbstractDataSetIterator
-
The number of labels for the dataset
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.AsyncDataSetIterator
-
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- totalOutcomes() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Deprecated.
The number of labels for a dataset
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.ExistingDataSetIterator
-
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.IteratorDataSetIterator
-
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
The number of labels for the dataset
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
The number of labels for the dataset
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- totalSize() - Method in class org.deeplearning4j.berkeley.CounterMap
-
Returns the total number of (key, value) entries in the CounterMap (not
their total counts).
- toYaml() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
- toYaml() - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration
-
- toYaml() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
Return this configuration as json
- trackEpochs() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- TrainingListener - Interface in org.deeplearning4j.optimize.api
-
TrainingListener: an extension of
IterationListener that adds onEpochStart, onEpochEnd, onForwardPass and
onBackwardPass methods
- Transform(Iterator<S>) - Constructor for class org.deeplearning4j.berkeley.Iterators.Transform
-
- transform(S) - Method in class org.deeplearning4j.berkeley.Iterators.Transform
-
- TransformingIterator(Iterator<I>, MyMethod<I, O>) - Constructor for class org.deeplearning4j.berkeley.Iterators.TransformingIterator
-
- transpose() - Method in interface org.deeplearning4j.nn.api.Layer
-
Return a transposed copy of the weights/bias
(this means reverse the number of inputs and outputs on the weights)
- transpose() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
-
- transpose() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- transpose() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
-
- transpose() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
-
- transpose() - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM
-
Deprecated.
- transpose() - Method in class org.deeplearning4j.nn.layers.LossLayer
-
- transpose() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
-
- transpose() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
-
- transpose() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
-
- transpose() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
-
- transpose() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- Tree - Class in org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive
-
Tree for a recursive neural tensor network
based on Socher et al's work.
- Tree(Tree) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
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Clone constructor (all but the children)
- Tree(Tree, List<String>) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
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- Tree(List<String>) - Constructor for class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
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- TreeMapFactory() - Constructor for class org.deeplearning4j.berkeley.MapFactory.TreeMapFactory
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- treeSet() - Static method in class org.deeplearning4j.util.MultiDimensionalSet
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- trim(String, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
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Returns s if it's at most maxWidth chars, otherwise chops right side to fit.
- trim(Object, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
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- Triple<S,T,U> - Class in org.deeplearning4j.berkeley
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- Triple(S, T, U) - Constructor for class org.deeplearning4j.berkeley.Triple
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- trueNegatives - Variable in class org.deeplearning4j.eval.Evaluation
-
- trueNegatives() - Method in class org.deeplearning4j.eval.Evaluation
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True negatives: correctly rejected
- truePositives - Variable in class org.deeplearning4j.eval.Evaluation
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- truePositives() - Method in class org.deeplearning4j.eval.Evaluation
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True positives: correctly rejected
- truncate(int, int, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
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This returns a string from decimal digit smallestDigit to decimal digit
biggest digit.
- truncatedBPTTGradient() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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Equivalent to backprop(), but calculates gradient for truncated BPTT instead.
- type() - Method in interface org.deeplearning4j.nn.api.Layer
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Returns the layer type
- type() - Method in class org.deeplearning4j.nn.layers.ActivationLayer
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- type() - Method in class org.deeplearning4j.nn.layers.BaseLayer
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- type() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
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- type() - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
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- type() - Method in class org.deeplearning4j.nn.layers.LossLayer
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- type() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
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- type() - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
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- type() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
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- type() - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
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- type() - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer
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- type() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
-
- validate() - Method in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
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Check the configuration, make sure it is valid
- validateInput() - Method in interface org.deeplearning4j.nn.api.Model
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Validate the input
- validateInput() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
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- validateInput() - Method in class org.deeplearning4j.nn.layers.BaseLayer
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- validateInput() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
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- validateShapes(int, int, int, int, int, int, int, int) - Static method in class org.deeplearning4j.nn.layers.convolution.KernelValidationUtil
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- value() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
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- value - Variable in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix
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- valueOf(String) - Static method in enum org.deeplearning4j.earlystopping.EarlyStoppingResult.TerminationReason
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.api.Layer.TrainingMode
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.api.Layer.Type
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.api.OptimizationAlgorithm
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.BackpropType
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.ConvolutionMode
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.GradientNormalization
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.graph.ElementWiseVertex.Op
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.inputs.InputType.Type
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.RBM.HiddenUnit
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.RBM.VisibleUnit
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.layers.SubsamplingLayer.PoolingType
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.LearningRatePolicy
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.conf.Updater
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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
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.nn.weights.WeightInit
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Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix
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Returns the enum constant of this type with the specified name.
- valueOf(char) - Static method in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix
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- values() - Static method in enum org.deeplearning4j.earlystopping.EarlyStoppingResult.TerminationReason
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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
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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
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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.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
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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
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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.RBM.HiddenUnit
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Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum org.deeplearning4j.nn.conf.layers.RBM.VisibleUnit
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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
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Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum org.deeplearning4j.nn.conf.LearningRatePolicy
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum org.deeplearning4j.nn.conf.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.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() - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns a
Collection view of the values contained in this map.
- values() - Static method in enum org.deeplearning4j.util.StringUtils.TraditionalBinaryPrefix
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- variables - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- variables() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- variables(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- variance(double[]) - Static method in class org.deeplearning4j.util.MathUtils
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- vector() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
-
- vectorLength(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Returns the vector length (sqrt(sum(x_i))
- vertexIndex - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
The index of this vertex
- VertexIndices - Class in org.deeplearning4j.nn.graph.vertex
-
VertexIndices defines a pair of integers: the index of a vertex, and the edge number of that vertex.
- VertexIndices() - Constructor for class org.deeplearning4j.nn.graph.vertex.VertexIndices
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- vertexInputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
Key: graph node.
- vertexInputs - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
- vertexName - Variable in class org.deeplearning4j.nn.graph.vertex.BaseGraphVertex
-
- vertices - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder
-
- vertices - Variable in class org.deeplearning4j.nn.conf.ComputationGraphConfiguration
-
- vertices - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
-
All GraphVertex objects in the network.
- verticesMap - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
-
Map of vertices by name
- viewArray - Variable in class org.deeplearning4j.nn.updater.LayerUpdater
-
- VISIBLE_BIAS_KEY - Static variable in class org.deeplearning4j.nn.params.PretrainParamInitializer
-
- visibleBiasInit - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
-
- visibleBiasInit(double) - Method in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork.Builder
-
- visibleBiasInit - Variable in class org.deeplearning4j.nn.conf.layers.BasePretrainNetwork
-
- visibleUnit(RBM.VisibleUnit) - Method in class org.deeplearning4j.nn.conf.layers.RBM.Builder
-
- visibleUnit - Variable in class org.deeplearning4j.nn.conf.layers.RBM
-
- Viterbi - Class in org.deeplearning4j.util
-
Based on the impl from:
https://gist.github.com/rmcgibbo/3915977
- Viterbi(INDArray) - Constructor for class org.deeplearning4j.util.Viterbi
-
The possible outcomes for the chain.