Index

A B C D E F G H I L M N O P R S T U V W

A

AbstractDataSetIterator - Class in weka.dl4j.iterators: An abstract iterator wrapper.
AbstractDataSetIterator() - Constructor for class weka.dl4j.iterators.AbstractDataSetIterator
ActivationCube - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationCube that implements WEKA option handling.
ActivationCube() - Constructor for class weka.dl4j.activations.ActivationCube
ActivationELU - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationELU that implements WEKA option handling.
ActivationELU() - Constructor for class weka.dl4j.activations.ActivationELU
ActivationHardSigmoid - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationHardSigmoid that implements WEKA option handling.
ActivationHardSigmoid() - Constructor for class weka.dl4j.activations.ActivationHardSigmoid
ActivationHardTanH - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationHardTanH that implements WEKA option handling.
ActivationHardTanH() - Constructor for class weka.dl4j.activations.ActivationHardTanH
ActivationIdentity - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationIdentity that implements WEKA option handling.
ActivationIdentity() - Constructor for class weka.dl4j.activations.ActivationIdentity
ActivationLReLU - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationLReLU that implements WEKA option handling.
ActivationLReLU() - Constructor for class weka.dl4j.activations.ActivationLReLU
ActivationRationalTanh - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationRationalTanh that implements WEKA option handling.
ActivationRationalTanh() - Constructor for class weka.dl4j.activations.ActivationRationalTanh
ActivationReLU - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationReLU that implements WEKA option handling.
ActivationReLU() - Constructor for class weka.dl4j.activations.ActivationReLU
ActivationRReLU - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationRReLU that implements WEKA option handling.
ActivationRReLU() - Constructor for class weka.dl4j.activations.ActivationRReLU
ActivationSigmoid - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationSigmoid that implements WEKA option handling.
ActivationSigmoid() - Constructor for class weka.dl4j.activations.ActivationSigmoid
ActivationSoftmax - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationSoftmax that implements WEKA option handling.
ActivationSoftmax() - Constructor for class weka.dl4j.activations.ActivationSoftmax
ActivationSoftPlus - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationSoftPlus that implements WEKA option handling.
ActivationSoftPlus() - Constructor for class weka.dl4j.activations.ActivationSoftPlus
ActivationSoftSign - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationSoftSign that implements WEKA option handling.
ActivationSoftSign() - Constructor for class weka.dl4j.activations.ActivationSoftSign
ActivationTanH - Class in weka.dl4j.activations: A version of DeepLearning4j's ActivationTanH that implements WEKA option handling.
ActivationTanH() - Constructor for class weka.dl4j.activations.ActivationTanH
asyncSupported() - Method in class weka.dl4j.ShufflingDataSetIterator: Whether the iterator can be used asynchronously.

B

batch() - Method in class weka.dl4j.ShufflingDataSetIterator: The size of the mini batches.
BatchNormalization - Class in weka.dl4j.layers: A version of DeepLearning4j's BatchNormalization layer that implements WEKA option handling.
BatchNormalization() - Constructor for class weka.dl4j.layers.BatchNormalization: Constructor for setting some defaults.
BinomialDistribution - Class in weka.dl4j.distribution: A version of DeepLearning4j's BinomialDistribution that implements WEKA option handling.
BinomialDistribution() - Constructor for class weka.dl4j.distribution.BinomialDistribution: Constructs binomial distribution with 1 trial and success probability 0.5.
buildClassifier(Instances) - Method in class weka.classifiers.functions.Dl4jMlpClassifier: The method used to train the classifier.

C

ConvolutionalInstancesIterator - Class in weka.dl4j.iterators: Converts the given Instances object into a DataSet and then constructs and returns a ShufflingDataSetIterator.
ConvolutionalInstancesIterator() - Constructor for class weka.dl4j.iterators.ConvolutionalInstancesIterator
ConvolutionLayer - Class in weka.dl4j.layers: A version of DeepLearning4j's ConvolutionLayer that implements WEKA option handling.
ConvolutionLayer() - Constructor for class weka.dl4j.layers.ConvolutionLayer: Constructor for setting some defaults.
cursor() - Method in class weka.dl4j.ShufflingDataSetIterator: The cursor given the location in the dataset.

D

DefaultInstancesIterator - Class in weka.dl4j.iterators: Converts the given Instances object into a DataSet and then constructs and returns a ShufflingDataSetIterator.
DefaultInstancesIterator() - Constructor for class weka.dl4j.iterators.DefaultInstancesIterator
DefaultStepFunction - Class in weka.dl4j.stepfunctions: A version of DeepLearning4j's DefaultStepFunction that implements WEKA option handling.
DefaultStepFunction() - Constructor for class weka.dl4j.stepfunctions.DefaultStepFunction
DenseLayer - Class in weka.dl4j.layers: A version of DeepLearning4j's DenseLayer that implements WEKA option handling.
DenseLayer() - Constructor for class weka.dl4j.layers.DenseLayer: Constructor for setting some defaults.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.Dl4jMlpClassifier: The method to use when making a prediction for a test instance.
distributionsForInstances(Instances) - Method in class weka.classifiers.functions.Dl4jMlpClassifier: The method to use when making predictions for test instances.
Dl4jMlpClassifier - Class in weka.classifiers.functions: A wrapper for DeepLearning4j that can be used to train a multi-layer perceptron using that library.
Dl4jMlpClassifier() - Constructor for class weka.classifiers.functions.Dl4jMlpClassifier
done() - Method in class weka.classifiers.functions.Dl4jMlpClassifier: Clean up after learning.

E

EasyImageRecordReader - Class in weka.dl4j: ImageRecordReader in DeepLearning4j assumes that your images are separated into different folders, where each folder is a class, e.g.
EasyImageRecordReader(int, int, int, ArrayList<File>, ArrayList<String>, int) - Constructor for class weka.dl4j.EasyImageRecordReader: Constructor for the record reader.

F

FileIterationListener - Class in weka.dl4j: Class for listening to performance stats and writing them to a file.
FileIterationListener(String, int) - Constructor for class weka.dl4j.FileIterationListener: Constructor for this listener.
FILTER_NONE - Static variable in class weka.classifiers.functions.Dl4jMlpClassifier: filter: No normalization/standardization
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.Dl4jMlpClassifier: filter: Normalize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.Dl4jMlpClassifier: filter: Standardize training data

G

getActivationFn() - Method in class weka.dl4j.layers.BatchNormalization
getActivationFn() - Method in class weka.dl4j.layers.ConvolutionLayer
getActivationFn() - Method in class weka.dl4j.layers.DenseLayer
getActivationFn() - Method in class weka.dl4j.layers.OutputLayer
getActivationFn() - Method in class weka.dl4j.layers.SubsamplingLayer
getActivationFunction() - Method in class weka.dl4j.layers.BatchNormalization
getActivationFunction() - Method in class weka.dl4j.layers.ConvolutionLayer
getActivationFunction() - Method in class weka.dl4j.layers.DenseLayer
getActivationFunction() - Method in class weka.dl4j.layers.OutputLayer
getActivationFunction() - Method in class weka.dl4j.layers.SubsamplingLayer
getAdamMeanDecay() - Method in class weka.dl4j.layers.BatchNormalization
getAdamMeanDecay() - Method in class weka.dl4j.layers.ConvolutionLayer
getAdamMeanDecay() - Method in class weka.dl4j.layers.DenseLayer
getAdamMeanDecay() - Method in class weka.dl4j.layers.OutputLayer
getAdamMeanDecay() - Method in class weka.dl4j.layers.SubsamplingLayer
getAdamVarDecay() - Method in class weka.dl4j.layers.BatchNormalization
getAdamVarDecay() - Method in class weka.dl4j.layers.ConvolutionLayer
getAdamVarDecay() - Method in class weka.dl4j.layers.DenseLayer
getAdamVarDecay() - Method in class weka.dl4j.layers.OutputLayer
getAdamVarDecay() - Method in class weka.dl4j.layers.SubsamplingLayer
getBeta() - Method in class weka.dl4j.layers.BatchNormalization
getBiasInit() - Method in class weka.dl4j.layers.BatchNormalization
getBiasInit() - Method in class weka.dl4j.layers.ConvolutionLayer
getBiasInit() - Method in class weka.dl4j.layers.DenseLayer
getBiasInit() - Method in class weka.dl4j.layers.OutputLayer
getBiasInit() - Method in class weka.dl4j.layers.SubsamplingLayer
getBiasL1() - Method in class weka.dl4j.layers.BatchNormalization
getBiasL1() - Method in class weka.dl4j.layers.ConvolutionLayer
getBiasL1() - Method in class weka.dl4j.layers.DenseLayer
getBiasL1() - Method in class weka.dl4j.layers.OutputLayer
getBiasL1() - Method in class weka.dl4j.layers.SubsamplingLayer
getBiasL2() - Method in class weka.dl4j.layers.BatchNormalization
getBiasL2() - Method in class weka.dl4j.layers.ConvolutionLayer
getBiasL2() - Method in class weka.dl4j.layers.DenseLayer
getBiasL2() - Method in class weka.dl4j.layers.OutputLayer
getBiasL2() - Method in class weka.dl4j.layers.SubsamplingLayer
getBiasLearningRate() - Method in class weka.dl4j.layers.BatchNormalization
getBiasLearningRate() - Method in class weka.dl4j.layers.ConvolutionLayer
getBiasLearningRate() - Method in class weka.dl4j.layers.DenseLayer
getBiasLearningRate() - Method in class weka.dl4j.layers.OutputLayer
getBiasLearningRate() - Method in class weka.dl4j.layers.SubsamplingLayer
getCapabilities() - Method in class weka.classifiers.functions.Dl4jMlpClassifier: Returns default capabilities of the classifier.
getConvolutionMode() - Method in class weka.dl4j.layers.ConvolutionLayer
getConvolutionMode() - Method in class weka.dl4j.layers.SubsamplingLayer
getCudnnAlgoMode() - Method in class weka.dl4j.layers.ConvolutionLayer
getDataSetIterator() - Method in class weka.classifiers.functions.Dl4jMlpClassifier
getDecay() - Method in class weka.dl4j.layers.BatchNormalization
getDist() - Method in class weka.dl4j.layers.BatchNormalization
getDist() - Method in class weka.dl4j.layers.ConvolutionLayer
getDist() - Method in class weka.dl4j.layers.DenseLayer
getDist() - Method in class weka.dl4j.layers.OutputLayer
getDist() - Method in class weka.dl4j.layers.SubsamplingLayer
getDropOut() - Method in class weka.dl4j.layers.BatchNormalization
getDropOut() - Method in class weka.dl4j.layers.ConvolutionLayer
getDropOut() - Method in class weka.dl4j.layers.DenseLayer
getDropOut() - Method in class weka.dl4j.layers.OutputLayer
getDropOut() - Method in class weka.dl4j.layers.SubsamplingLayer
getEps() - Method in class weka.dl4j.layers.BatchNormalization
getEps() - Method in class weka.dl4j.layers.SubsamplingLayer
getEpsilon() - Method in class weka.dl4j.layers.BatchNormalization
getEpsilon() - Method in class weka.dl4j.layers.ConvolutionLayer
getEpsilon() - Method in class weka.dl4j.layers.DenseLayer
getEpsilon() - Method in class weka.dl4j.layers.OutputLayer
getEpsilon() - Method in class weka.dl4j.layers.SubsamplingLayer
getFilterType() - Method in class weka.classifiers.functions.Dl4jMlpClassifier
getGamma() - Method in class weka.dl4j.layers.BatchNormalization
getGradientNormalization() - Method in class weka.dl4j.layers.BatchNormalization
getGradientNormalization() - Method in class weka.dl4j.layers.ConvolutionLayer
getGradientNormalization() - Method in class weka.dl4j.layers.DenseLayer
getGradientNormalization() - Method in class weka.dl4j.layers.OutputLayer
getGradientNormalization() - Method in class weka.dl4j.layers.SubsamplingLayer
getGradientNormalizationThreshold() - Method in class weka.dl4j.layers.BatchNormalization
getGradientNormalizationThreshold() - Method in class weka.dl4j.layers.ConvolutionLayer
getGradientNormalizationThreshold() - Method in class weka.dl4j.layers.DenseLayer
getGradientNormalizationThreshold() - Method in class weka.dl4j.layers.OutputLayer
getGradientNormalizationThreshold() - Method in class weka.dl4j.layers.SubsamplingLayer
getHeight() - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator
getHeight() - Method in class weka.dl4j.iterators.ImageDataSetIterator
getImagesLocation() - Method in class weka.dl4j.iterators.ImageDataSetIterator
getIterationCount() - Method in class weka.dl4j.NeuralNetConfiguration
getIterator(Instances, int) - Method in class weka.dl4j.iterators.AbstractDataSetIterator: Returns the actual iterator.
getIterator(Instances, int, int) - Method in class weka.dl4j.iterators.AbstractDataSetIterator: Returns the actual iterator.
getIterator(Instances, int, int) - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator: Returns the actual iterator.
getIterator(Instances, int, int) - Method in class weka.dl4j.iterators.DefaultInstancesIterator: Returns the actual iterator.
getIterator(Instances, int, int) - Method in class weka.dl4j.iterators.ImageDataSetIterator: This method returns the iterator.
getKernelSize() - Method in class weka.dl4j.layers.ConvolutionLayer
getKernelSize() - Method in class weka.dl4j.layers.SubsamplingLayer
getKernelSizeX() - Method in class weka.dl4j.layers.ConvolutionLayer
getKernelSizeX() - Method in class weka.dl4j.layers.SubsamplingLayer
getKernelSizeY() - Method in class weka.dl4j.layers.ConvolutionLayer
getKernelSizeY() - Method in class weka.dl4j.layers.SubsamplingLayer
getL1() - Method in class weka.dl4j.layers.BatchNormalization
getL1() - Method in class weka.dl4j.layers.ConvolutionLayer
getL1() - Method in class weka.dl4j.layers.DenseLayer
getL1() - Method in class weka.dl4j.layers.OutputLayer
getL1() - Method in class weka.dl4j.layers.SubsamplingLayer
getL1Bias() - Method in class weka.dl4j.layers.BatchNormalization
getL1Bias() - Method in class weka.dl4j.layers.ConvolutionLayer
getL1Bias() - Method in class weka.dl4j.layers.DenseLayer
getL1Bias() - Method in class weka.dl4j.layers.OutputLayer
getL1Bias() - Method in class weka.dl4j.layers.SubsamplingLayer
getL2() - Method in class weka.dl4j.layers.BatchNormalization
getL2() - Method in class weka.dl4j.layers.ConvolutionLayer
getL2() - Method in class weka.dl4j.layers.DenseLayer
getL2() - Method in class weka.dl4j.layers.OutputLayer
getL2() - Method in class weka.dl4j.layers.SubsamplingLayer
getL2Bias() - Method in class weka.dl4j.layers.BatchNormalization
getL2Bias() - Method in class weka.dl4j.layers.ConvolutionLayer
getL2Bias() - Method in class weka.dl4j.layers.DenseLayer
getL2Bias() - Method in class weka.dl4j.layers.OutputLayer
getL2Bias() - Method in class weka.dl4j.layers.SubsamplingLayer
getLabels() - Method in class weka.dl4j.ShufflingDataSetIterator: Gets the labels.
getLayerName() - Method in class weka.dl4j.layers.BatchNormalization
getLayerName() - Method in class weka.dl4j.layers.ConvolutionLayer
getLayerName() - Method in class weka.dl4j.layers.DenseLayer
getLayerName() - Method in class weka.dl4j.layers.OutputLayer
getLayerName() - Method in class weka.dl4j.layers.SubsamplingLayer
getLayers() - Method in class weka.classifiers.functions.Dl4jMlpClassifier
getLeakyreluAlpha() - Method in class weka.dl4j.NeuralNetConfiguration
getLearningRate() - Method in class weka.dl4j.layers.BatchNormalization
getLearningRate() - Method in class weka.dl4j.layers.ConvolutionLayer
getLearningRate() - Method in class weka.dl4j.layers.DenseLayer
getLearningRate() - Method in class weka.dl4j.layers.OutputLayer
getLearningRate() - Method in class weka.dl4j.layers.SubsamplingLayer
getLearningRatePolicy() - Method in class weka.dl4j.NeuralNetConfiguration
getLearningRateSchedule() - Method in class weka.dl4j.layers.BatchNormalization
getLearningRateSchedule() - Method in class weka.dl4j.layers.ConvolutionLayer
getLearningRateSchedule() - Method in class weka.dl4j.layers.DenseLayer
getLearningRateSchedule() - Method in class weka.dl4j.layers.OutputLayer
getLearningRateSchedule() - Method in class weka.dl4j.layers.SubsamplingLayer
getLockGammaAndBeta() - Method in class weka.dl4j.layers.BatchNormalization
getLogFile() - Method in class weka.classifiers.functions.Dl4jMlpClassifier: Get the log file
getLossFn() - Method in class weka.dl4j.layers.OutputLayer
getLower() - Method in class weka.dl4j.distribution.UniformDistribution
getLrPolicyDecayRate() - Method in class weka.dl4j.NeuralNetConfiguration
getLrPolicyPower() - Method in class weka.dl4j.NeuralNetConfiguration
getLrPolicySteps() - Method in class weka.dl4j.NeuralNetConfiguration
getMaxNumLineSearchIterations() - Method in class weka.dl4j.NeuralNetConfiguration
getMean() - Method in class weka.dl4j.distribution.NormalDistribution
getMomentum() - Method in class weka.dl4j.layers.BatchNormalization
getMomentum() - Method in class weka.dl4j.layers.ConvolutionLayer
getMomentum() - Method in class weka.dl4j.layers.DenseLayer
getMomentum() - Method in class weka.dl4j.layers.OutputLayer
getMomentum() - Method in class weka.dl4j.layers.SubsamplingLayer
getMomentumSchedule() - Method in class weka.dl4j.layers.BatchNormalization
getMomentumSchedule() - Method in class weka.dl4j.layers.ConvolutionLayer
getMomentumSchedule() - Method in class weka.dl4j.layers.DenseLayer
getMomentumSchedule() - Method in class weka.dl4j.layers.OutputLayer
getMomentumSchedule() - Method in class weka.dl4j.layers.SubsamplingLayer
getNeuralNetConfiguration() - Method in class weka.classifiers.functions.Dl4jMlpClassifier
getNIn() - Method in class weka.dl4j.layers.BatchNormalization
getNIn() - Method in class weka.dl4j.layers.ConvolutionLayer
getNIn() - Method in class weka.dl4j.layers.DenseLayer
getNIn() - Method in class weka.dl4j.layers.OutputLayer
getNoMinibatch() - Method in class weka.dl4j.layers.BatchNormalization
getNOut() - Method in class weka.dl4j.layers.BatchNormalization
getNOut() - Method in class weka.dl4j.layers.ConvolutionLayer
getNOut() - Method in class weka.dl4j.layers.DenseLayer
getNOut() - Method in class weka.dl4j.layers.OutputLayer
getNumAttributes(Instances) - Method in class weka.dl4j.iterators.AbstractDataSetIterator: Get the number of predictor attributes for this iterator.
getNumAttributes(Instances) - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator: Returns the number of predictor attributes for this dataset.
getNumAttributes(Instances) - Method in class weka.dl4j.iterators.DefaultInstancesIterator: Returns the number of predictor attributes for this dataset.
getNumAttributes(Instances) - Method in class weka.dl4j.iterators.ImageDataSetIterator: This just returns the number of channels.
getNumChannels() - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator
getNumChannels() - Method in class weka.dl4j.iterators.ImageDataSetIterator
getNumEpochs() - Method in class weka.classifiers.functions.Dl4jMlpClassifier
getNumIterations() - Method in class weka.dl4j.NeuralNetConfiguration
getOptimizationAlgo() - Method in class weka.dl4j.NeuralNetConfiguration
getOptions() - Method in class weka.dl4j.activations.ActivationCube: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationELU: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationHardSigmoid: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationHardTanH: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationIdentity: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationLReLU: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationRationalTanh: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationReLU: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationRReLU: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationSigmoid: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationSoftmax: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationSoftPlus: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationSoftSign: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.activations.ActivationTanH: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.distribution.BinomialDistribution: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.distribution.NormalDistribution: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.distribution.UniformDistribution: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.iterators.AbstractDataSetIterator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.layers.BatchNormalization: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.layers.ConvolutionLayer: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.layers.DenseLayer: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.layers.OutputLayer: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.layers.SubsamplingLayer: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossBinaryXENT: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossCosineProximity: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossHinge: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossKLD: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossL1: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossL2: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossMAE: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossMAPE: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossMCXENT: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossMSE: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossMSLE: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossNegativeLogLikelihood: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossPoisson: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.lossfunctions.LossSquaredHinge: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.NeuralNetConfiguration: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.stepfunctions.DefaultStepFunction: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.stepfunctions.GradientStepFunction: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.stepfunctions.NegativeDefaultStepFunction: Gets the current settings of the Classifier.
getOptions() - Method in class weka.dl4j.stepfunctions.NegativeGradientStepFunction: Gets the current settings of the Classifier.
getPadding() - Method in class weka.dl4j.layers.ConvolutionLayer
getPadding() - Method in class weka.dl4j.layers.SubsamplingLayer
getPaddingX() - Method in class weka.dl4j.layers.ConvolutionLayer
getPaddingX() - Method in class weka.dl4j.layers.SubsamplingLayer
getPaddingY() - Method in class weka.dl4j.layers.ConvolutionLayer
getPaddingY() - Method in class weka.dl4j.layers.SubsamplingLayer
getPnorm() - Method in class weka.dl4j.layers.SubsamplingLayer
getPoolingType() - Method in class weka.dl4j.layers.SubsamplingLayer
getPreProcessor() - Method in class weka.dl4j.ShufflingDataSetIterator: Gets the preprocessor.
getProbabilityOfSuccess() - Method in class weka.dl4j.distribution.BinomialDistribution
getQueueSize() - Method in class weka.classifiers.functions.Dl4jMlpClassifier
getRho() - Method in class weka.dl4j.layers.BatchNormalization
getRho() - Method in class weka.dl4j.layers.ConvolutionLayer
getRho() - Method in class weka.dl4j.layers.DenseLayer
getRho() - Method in class weka.dl4j.layers.OutputLayer
getRho() - Method in class weka.dl4j.layers.SubsamplingLayer
getRmsDecay() - Method in class weka.dl4j.layers.BatchNormalization
getRmsDecay() - Method in class weka.dl4j.layers.ConvolutionLayer
getRmsDecay() - Method in class weka.dl4j.layers.DenseLayer
getRmsDecay() - Method in class weka.dl4j.layers.OutputLayer
getRmsDecay() - Method in class weka.dl4j.layers.SubsamplingLayer
getSeed() - Method in class weka.dl4j.NeuralNetConfiguration
getStd() - Method in class weka.dl4j.distribution.NormalDistribution
getStepFunction() - Method in class weka.dl4j.NeuralNetConfiguration
getStride() - Method in class weka.dl4j.layers.ConvolutionLayer
getStride() - Method in class weka.dl4j.layers.SubsamplingLayer
getStrideX() - Method in class weka.dl4j.layers.ConvolutionLayer
getStrideX() - Method in class weka.dl4j.layers.SubsamplingLayer
getStrideY() - Method in class weka.dl4j.layers.ConvolutionLayer
getStrideY() - Method in class weka.dl4j.layers.SubsamplingLayer
getTrainBatchSize() - Method in class weka.dl4j.iterators.AbstractDataSetIterator: Getting the training batch size
getTrainBatchSize() - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator
getTrainBatchSize() - Method in class weka.dl4j.iterators.DefaultInstancesIterator
getTrainBatchSize() - Method in class weka.dl4j.iterators.ImageDataSetIterator
getUpdater() - Method in class weka.dl4j.layers.BatchNormalization
getUpdater() - Method in class weka.dl4j.layers.ConvolutionLayer
getUpdater() - Method in class weka.dl4j.layers.DenseLayer
getUpdater() - Method in class weka.dl4j.layers.OutputLayer
getUpdater() - Method in class weka.dl4j.layers.SubsamplingLayer
getUpper() - Method in class weka.dl4j.distribution.UniformDistribution
getWeightInit() - Method in class weka.dl4j.layers.BatchNormalization
getWeightInit() - Method in class weka.dl4j.layers.ConvolutionLayer
getWeightInit() - Method in class weka.dl4j.layers.DenseLayer
getWeightInit() - Method in class weka.dl4j.layers.OutputLayer
getWeightInit() - Method in class weka.dl4j.layers.SubsamplingLayer
getWidth() - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator
getWidth() - Method in class weka.dl4j.iterators.ImageDataSetIterator
globalInfo() - Method in class weka.classifiers.functions.Dl4jMlpClassifier
globalInfo() - Method in class weka.dl4j.layers.BatchNormalization: Global info.
globalInfo() - Method in class weka.dl4j.layers.ConvolutionLayer: Global info.
globalInfo() - Method in class weka.dl4j.layers.DenseLayer: Global info.
globalInfo() - Method in class weka.dl4j.layers.OutputLayer: Global info.
globalInfo() - Method in class weka.dl4j.layers.SubsamplingLayer: Global info.
GradientStepFunction - Class in weka.dl4j.stepfunctions: A version of DeepLearning4j's GradientStepFunction that implements WEKA option handling.
GradientStepFunction() - Constructor for class weka.dl4j.stepfunctions.GradientStepFunction

H

hasNext() - Method in class weka.dl4j.ShufflingDataSetIterator: Whether another batch of data is still available.

I

ImageDataSetIterator - Class in weka.dl4j.iterators: An iterator that loads images.
ImageDataSetIterator() - Constructor for class weka.dl4j.iterators.ImageDataSetIterator
implementsMoreEfficientBatchPrediction() - Method in class weka.classifiers.functions.Dl4jMlpClassifier: Performs efficient batch prediction
initialize(InputSplit) - Method in class weka.dl4j.EasyImageRecordReader: Initializes the iterators.
initializeClassifier(Instances) - Method in class weka.classifiers.functions.Dl4jMlpClassifier: The method used to initialize the classifier.
inputColumns() - Method in class weka.dl4j.ShufflingDataSetIterator: Returns the number of input columns.
instancesToDataSet(Instances) - Static method in class weka.classifiers.functions.dl4j.Utils: Converts a set of training instances to a DataSet.
instanceToINDArray(Instance) - Static method in class weka.classifiers.functions.dl4j.Utils: Converts an instance to an INDArray.
invoke() - Method in class weka.dl4j.FileIterationListener: No-op method.
invoked() - Method in class weka.dl4j.FileIterationListener: Always returns false.
isLockGammaBeta() - Method in class weka.dl4j.layers.BatchNormalization
isMinibatch() - Method in class weka.dl4j.layers.BatchNormalization
isMiniBatch() - Method in class weka.dl4j.NeuralNetConfiguration
isMinimize() - Method in class weka.dl4j.NeuralNetConfiguration
isPretrain() - Method in class weka.dl4j.NeuralNetConfiguration
isUseDropConnect() - Method in class weka.dl4j.NeuralNetConfiguration
isUseRegularization() - Method in class weka.dl4j.NeuralNetConfiguration
iterationDone(Model, int) - Method in class weka.dl4j.FileIterationListener: Method that gets called when an iteration is done.

L

listOptions() - Method in class weka.dl4j.activations.ActivationCube: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationELU: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationHardSigmoid: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationHardTanH: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationIdentity: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationLReLU: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationRationalTanh: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationReLU: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationRReLU: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationSigmoid: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationSoftmax: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationSoftPlus: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationSoftSign: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.activations.ActivationTanH: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.distribution.BinomialDistribution: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.distribution.NormalDistribution: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.distribution.UniformDistribution: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.iterators.AbstractDataSetIterator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.layers.BatchNormalization: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.layers.ConvolutionLayer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.layers.DenseLayer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.layers.OutputLayer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.layers.SubsamplingLayer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossBinaryXENT: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossCosineProximity: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossHinge: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossKLD: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossL1: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossL2: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossMAE: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossMAPE: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossMCXENT: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossMSE: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossMSLE: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossNegativeLogLikelihood: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossPoisson: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.lossfunctions.LossSquaredHinge: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.NeuralNetConfiguration: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.stepfunctions.DefaultStepFunction: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.stepfunctions.GradientStepFunction: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.stepfunctions.NegativeDefaultStepFunction: Returns an enumeration describing the available options.
listOptions() - Method in class weka.dl4j.stepfunctions.NegativeGradientStepFunction: Returns an enumeration describing the available options.
LossBinaryXENT - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossBinaryXENT that implements WEKA option handling.
LossBinaryXENT() - Constructor for class weka.dl4j.lossfunctions.LossBinaryXENT
LossCosineProximity - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossCosineProximity that implements WEKA option handling.
LossCosineProximity() - Constructor for class weka.dl4j.lossfunctions.LossCosineProximity
LossHinge - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossHinge that implements WEKA option handling.
LossHinge() - Constructor for class weka.dl4j.lossfunctions.LossHinge
LossKLD - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossKLD that implements WEKA option handling.
LossKLD() - Constructor for class weka.dl4j.lossfunctions.LossKLD
LossL1 - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossL1 that implements WEKA option handling.
LossL1() - Constructor for class weka.dl4j.lossfunctions.LossL1
LossL2 - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossL2 that implements WEKA option handling.
LossL2() - Constructor for class weka.dl4j.lossfunctions.LossL2
LossMAE - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossMAE that implements WEKA option handling.
LossMAE() - Constructor for class weka.dl4j.lossfunctions.LossMAE
LossMAPE - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossMAPE that implements WEKA option handling.
LossMAPE() - Constructor for class weka.dl4j.lossfunctions.LossMAPE
LossMCXENT - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossMCXENT that implements WEKA option handling.
LossMCXENT() - Constructor for class weka.dl4j.lossfunctions.LossMCXENT
LossMSE - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossMSE that implements WEKA option handling.
LossMSE() - Constructor for class weka.dl4j.lossfunctions.LossMSE
LossMSLE - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossMSLE that implements WEKA option handling.
LossMSLE() - Constructor for class weka.dl4j.lossfunctions.LossMSLE
LossNegativeLogLikelihood - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossNegativeLogLikelihood that implements WEKA option handling.
LossNegativeLogLikelihood() - Constructor for class weka.dl4j.lossfunctions.LossNegativeLogLikelihood
LossPoisson - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossPoisson that implements WEKA option handling.
LossPoisson() - Constructor for class weka.dl4j.lossfunctions.LossPoisson
LossSquaredHinge - Class in weka.dl4j.lossfunctions: A version of DeepLearning4j's LossSquaredHinge that implements WEKA option handling.
LossSquaredHinge() - Constructor for class weka.dl4j.lossfunctions.LossSquaredHinge

M

main(String[]) - Static method in class weka.classifiers.functions.Dl4jMlpClassifier: The main method for running this class.

N

NegativeDefaultStepFunction - Class in weka.dl4j.stepfunctions: A version of DeepLearning4j's NegativeDefaultStepFunction that implements WEKA option handling.
NegativeDefaultStepFunction() - Constructor for class weka.dl4j.stepfunctions.NegativeDefaultStepFunction
NegativeGradientStepFunction - Class in weka.dl4j.stepfunctions: A version of DeepLearning4j's NegativeGradientStepFunction that implements WEKA option handling.
NegativeGradientStepFunction() - Constructor for class weka.dl4j.stepfunctions.NegativeGradientStepFunction
NeuralNetConfiguration - Class in weka.dl4j: A version of DeepLearning4j's NeuralNetConfiguration that implements WEKA option handling.
NeuralNetConfiguration() - Constructor for class weka.dl4j.NeuralNetConfiguration: Constructor that provides default values for the settings.
next() - Method in class weka.classifiers.functions.Dl4jMlpClassifier: Perform another epoch.
next() - Method in class weka.dl4j.EasyImageRecordReader: Gets the next element
next() - Method in class weka.dl4j.ShufflingDataSetIterator: Returns the next mini batch of data.
next(int) - Method in class weka.dl4j.ShufflingDataSetIterator: Returns a batch of the given size
NormalDistribution - Class in weka.dl4j.distribution: A version of DeepLearning4j's NormalDistribution that implements WEKA option handling.
NormalDistribution() - Constructor for class weka.dl4j.distribution.NormalDistribution: Constructions normal distribution with mean 0 and unit variance.
numExamples() - Method in class weka.dl4j.ShufflingDataSetIterator: The number of examples in the dataset.

O

OutputLayer - Class in weka.dl4j.layers: A version of DeepLearning4j's OutputLayer that implements WEKA option handling.
OutputLayer() - Constructor for class weka.dl4j.layers.OutputLayer: Constructor for setting some defaults.

P

preProcess(DataSet) - Method in class weka.dl4j.ScaleImagePixelsPreProcessor: Divides all intensity values by 255.

R

record(URI, DataInputStream) - Method in class weka.dl4j.EasyImageRecordReader: A method that still needs to be implemented.
remove() - Method in class weka.dl4j.ShufflingDataSetIterator: Enables removing of a mini-batch.
reset() - Method in class weka.dl4j.EasyImageRecordReader: Resets the record reader.
reset() - Method in class weka.dl4j.ShufflingDataSetIterator: Resets the cursor.
resetSupported() - Method in class weka.dl4j.ShufflingDataSetIterator: Whether the iterator can be reset.
RNNinstancesToDataSet(Instances) - Static method in class weka.classifiers.functions.dl4j.Utils: Converts a set of training instances corresponding to a time series to a DataSet.

S

ScaleImagePixelsPreProcessor - Class in weka.dl4j: A simple preprocessor that divides all values in the dataset by 255.
ScaleImagePixelsPreProcessor() - Constructor for class weka.dl4j.ScaleImagePixelsPreProcessor
setActivationFn(IActivation) - Method in class weka.dl4j.layers.BatchNormalization
setActivationFn(IActivation) - Method in class weka.dl4j.layers.ConvolutionLayer
setActivationFn(IActivation) - Method in class weka.dl4j.layers.DenseLayer
setActivationFn(IActivation) - Method in class weka.dl4j.layers.OutputLayer
setActivationFn(IActivation) - Method in class weka.dl4j.layers.SubsamplingLayer
setActivationFunction(IActivation) - Method in class weka.dl4j.layers.BatchNormalization
setActivationFunction(IActivation) - Method in class weka.dl4j.layers.ConvolutionLayer
setActivationFunction(IActivation) - Method in class weka.dl4j.layers.DenseLayer
setActivationFunction(IActivation) - Method in class weka.dl4j.layers.OutputLayer
setActivationFunction(IActivation) - Method in class weka.dl4j.layers.SubsamplingLayer
setAdamMeanDecay(double) - Method in class weka.dl4j.layers.BatchNormalization
setAdamMeanDecay(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setAdamMeanDecay(double) - Method in class weka.dl4j.layers.DenseLayer
setAdamMeanDecay(double) - Method in class weka.dl4j.layers.OutputLayer
setAdamMeanDecay(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setAdamVarDecay(double) - Method in class weka.dl4j.layers.BatchNormalization
setAdamVarDecay(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setAdamVarDecay(double) - Method in class weka.dl4j.layers.DenseLayer
setAdamVarDecay(double) - Method in class weka.dl4j.layers.OutputLayer
setAdamVarDecay(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setBeta(double) - Method in class weka.dl4j.layers.BatchNormalization
setBiasInit(double) - Method in class weka.dl4j.layers.BatchNormalization
setBiasInit(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setBiasInit(double) - Method in class weka.dl4j.layers.DenseLayer
setBiasInit(double) - Method in class weka.dl4j.layers.OutputLayer
setBiasInit(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setBiasL1(double) - Method in class weka.dl4j.layers.BatchNormalization
setBiasL1(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setBiasL1(double) - Method in class weka.dl4j.layers.DenseLayer
setBiasL1(double) - Method in class weka.dl4j.layers.OutputLayer
setBiasL1(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setBiasL2(double) - Method in class weka.dl4j.layers.BatchNormalization
setBiasL2(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setBiasL2(double) - Method in class weka.dl4j.layers.DenseLayer
setBiasL2(double) - Method in class weka.dl4j.layers.OutputLayer
setBiasL2(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setBiasLearningRate(double) - Method in class weka.dl4j.layers.BatchNormalization
setBiasLearningRate(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setBiasLearningRate(double) - Method in class weka.dl4j.layers.DenseLayer
setBiasLearningRate(double) - Method in class weka.dl4j.layers.OutputLayer
setBiasLearningRate(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setConvolutionMode(ConvolutionMode) - Method in class weka.dl4j.layers.ConvolutionLayer
setConvolutionMode(ConvolutionMode) - Method in class weka.dl4j.layers.SubsamplingLayer
setCudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class weka.dl4j.layers.ConvolutionLayer
setDataSetIterator(AbstractDataSetIterator) - Method in class weka.classifiers.functions.Dl4jMlpClassifier
setDecay(double) - Method in class weka.dl4j.layers.BatchNormalization
setDist(Distribution) - Method in class weka.dl4j.layers.BatchNormalization
setDist(Distribution) - Method in class weka.dl4j.layers.ConvolutionLayer
setDist(Distribution) - Method in class weka.dl4j.layers.DenseLayer
setDist(Distribution) - Method in class weka.dl4j.layers.OutputLayer
setDist(Distribution) - Method in class weka.dl4j.layers.SubsamplingLayer
setDropOut(double) - Method in class weka.dl4j.layers.BatchNormalization
setDropOut(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setDropOut(double) - Method in class weka.dl4j.layers.DenseLayer
setDropOut(double) - Method in class weka.dl4j.layers.OutputLayer
setDropOut(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setEps(double) - Method in class weka.dl4j.layers.BatchNormalization
setEps(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setEpsilon(double) - Method in class weka.dl4j.layers.BatchNormalization
setEpsilon(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setEpsilon(double) - Method in class weka.dl4j.layers.DenseLayer
setEpsilon(double) - Method in class weka.dl4j.layers.OutputLayer
setEpsilon(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.Dl4jMlpClassifier
setGamma(double) - Method in class weka.dl4j.layers.BatchNormalization
setGradientNormalization(GradientNormalization) - Method in class weka.dl4j.layers.BatchNormalization
setGradientNormalization(GradientNormalization) - Method in class weka.dl4j.layers.ConvolutionLayer
setGradientNormalization(GradientNormalization) - Method in class weka.dl4j.layers.DenseLayer
setGradientNormalization(GradientNormalization) - Method in class weka.dl4j.layers.OutputLayer
setGradientNormalization(GradientNormalization) - Method in class weka.dl4j.layers.SubsamplingLayer
setGradientNormalizationThreshold(double) - Method in class weka.dl4j.layers.BatchNormalization
setGradientNormalizationThreshold(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setGradientNormalizationThreshold(double) - Method in class weka.dl4j.layers.DenseLayer
setGradientNormalizationThreshold(double) - Method in class weka.dl4j.layers.OutputLayer
setGradientNormalizationThreshold(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setHeight(int) - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator
setHeight(int) - Method in class weka.dl4j.iterators.ImageDataSetIterator
setImagesLocation(File) - Method in class weka.dl4j.iterators.ImageDataSetIterator
setIterationCount(int) - Method in class weka.dl4j.NeuralNetConfiguration
setKernelSize(int[]) - Method in class weka.dl4j.layers.ConvolutionLayer
setKernelSize(int[]) - Method in class weka.dl4j.layers.SubsamplingLayer
setKernelSizeX(int) - Method in class weka.dl4j.layers.ConvolutionLayer
setKernelSizeX(int) - Method in class weka.dl4j.layers.SubsamplingLayer
setKernelSizeY(int) - Method in class weka.dl4j.layers.ConvolutionLayer
setKernelSizeY(int) - Method in class weka.dl4j.layers.SubsamplingLayer
setL1(double) - Method in class weka.dl4j.layers.BatchNormalization
setL1(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setL1(double) - Method in class weka.dl4j.layers.DenseLayer
setL1(double) - Method in class weka.dl4j.layers.OutputLayer
setL1(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setL1Bias(int) - Method in class weka.dl4j.layers.BatchNormalization
setL1Bias(int) - Method in class weka.dl4j.layers.ConvolutionLayer
setL1Bias(int) - Method in class weka.dl4j.layers.DenseLayer
setL1Bias(int) - Method in class weka.dl4j.layers.OutputLayer
setL1Bias(int) - Method in class weka.dl4j.layers.SubsamplingLayer
setL2(double) - Method in class weka.dl4j.layers.BatchNormalization
setL2(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setL2(double) - Method in class weka.dl4j.layers.DenseLayer
setL2(double) - Method in class weka.dl4j.layers.OutputLayer
setL2(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setL2Bias(int) - Method in class weka.dl4j.layers.BatchNormalization
setL2Bias(int) - Method in class weka.dl4j.layers.ConvolutionLayer
setL2Bias(int) - Method in class weka.dl4j.layers.DenseLayer
setL2Bias(int) - Method in class weka.dl4j.layers.OutputLayer
setL2Bias(int) - Method in class weka.dl4j.layers.SubsamplingLayer
setLayerName(String) - Method in class weka.dl4j.layers.BatchNormalization
setLayerName(String) - Method in class weka.dl4j.layers.ConvolutionLayer
setLayerName(String) - Method in class weka.dl4j.layers.DenseLayer
setLayerName(String) - Method in class weka.dl4j.layers.OutputLayer
setLayerName(String) - Method in class weka.dl4j.layers.SubsamplingLayer
setLayers(Layer[]) - Method in class weka.classifiers.functions.Dl4jMlpClassifier
setLeakyreluAlpha(double) - Method in class weka.dl4j.NeuralNetConfiguration
setLearningRate(double) - Method in class weka.dl4j.layers.BatchNormalization
setLearningRate(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setLearningRate(double) - Method in class weka.dl4j.layers.DenseLayer
setLearningRate(double) - Method in class weka.dl4j.layers.OutputLayer
setLearningRate(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setLearningRatePolicy(LearningRatePolicy) - Method in class weka.dl4j.NeuralNetConfiguration
setLearningRateSchedule(Map<Integer, Double>) - Method in class weka.dl4j.layers.BatchNormalization
setLearningRateSchedule(Map<Integer, Double>) - Method in class weka.dl4j.layers.ConvolutionLayer
setLearningRateSchedule(Map<Integer, Double>) - Method in class weka.dl4j.layers.DenseLayer
setLearningRateSchedule(Map<Integer, Double>) - Method in class weka.dl4j.layers.OutputLayer
setLearningRateSchedule(Map<Integer, Double>) - Method in class weka.dl4j.layers.SubsamplingLayer
setLockGammaAndBeta(boolean) - Method in class weka.dl4j.layers.BatchNormalization
setLockGammaBeta(boolean) - Method in class weka.dl4j.layers.BatchNormalization
setLogFile(File) - Method in class weka.classifiers.functions.Dl4jMlpClassifier: Set the log file
setLossFn(ILossFunction) - Method in class weka.dl4j.layers.OutputLayer
setLower(double) - Method in class weka.dl4j.distribution.UniformDistribution
setLrPolicyDecayRate(double) - Method in class weka.dl4j.NeuralNetConfiguration
setLrPolicyPower(double) - Method in class weka.dl4j.NeuralNetConfiguration
setLrPolicySteps(double) - Method in class weka.dl4j.NeuralNetConfiguration
setMaxNumLineSearchIterations(int) - Method in class weka.dl4j.NeuralNetConfiguration
setMean(double) - Method in class weka.dl4j.distribution.NormalDistribution
setMinibatch(boolean) - Method in class weka.dl4j.layers.BatchNormalization
setMiniBatch(boolean) - Method in class weka.dl4j.NeuralNetConfiguration
setMinimize(boolean) - Method in class weka.dl4j.NeuralNetConfiguration
setMomentum(double) - Method in class weka.dl4j.layers.BatchNormalization
setMomentum(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setMomentum(double) - Method in class weka.dl4j.layers.DenseLayer
setMomentum(double) - Method in class weka.dl4j.layers.OutputLayer
setMomentum(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setMomentumSchedule(Map<Integer, Double>) - Method in class weka.dl4j.layers.BatchNormalization
setMomentumSchedule(Map<Integer, Double>) - Method in class weka.dl4j.layers.ConvolutionLayer
setMomentumSchedule(Map<Integer, Double>) - Method in class weka.dl4j.layers.DenseLayer
setMomentumSchedule(Map<Integer, Double>) - Method in class weka.dl4j.layers.OutputLayer
setMomentumSchedule(Map<Integer, Double>) - Method in class weka.dl4j.layers.SubsamplingLayer
setNeuralNetConfiguration(NeuralNetConfiguration) - Method in class weka.classifiers.functions.Dl4jMlpClassifier
setNIn(int) - Method in class weka.dl4j.layers.BatchNormalization
setNIn(int) - Method in class weka.dl4j.layers.ConvolutionLayer
setNIn(int) - Method in class weka.dl4j.layers.DenseLayer
setNIn(int) - Method in class weka.dl4j.layers.OutputLayer
setNoMinibatch(boolean) - Method in class weka.dl4j.layers.BatchNormalization
setNOut(int) - Method in class weka.dl4j.layers.BatchNormalization
setNOut(int) - Method in class weka.dl4j.layers.ConvolutionLayer
setNOut(int) - Method in class weka.dl4j.layers.DenseLayer
setNOut(int) - Method in class weka.dl4j.layers.OutputLayer
setNumChannels(int) - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator
setNumChannels(int) - Method in class weka.dl4j.iterators.ImageDataSetIterator
setNumEpochs(int) - Method in class weka.classifiers.functions.Dl4jMlpClassifier
setNumIterations(int) - Method in class weka.dl4j.NeuralNetConfiguration
setOptimizationAlgo(OptimizationAlgorithm) - Method in class weka.dl4j.NeuralNetConfiguration
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationCube: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationELU: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationHardSigmoid: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationHardTanH: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationIdentity: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationLReLU: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationRationalTanh: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationReLU: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationRReLU: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationSigmoid: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationSoftmax: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationSoftPlus: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationSoftSign: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.activations.ActivationTanH: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.distribution.BinomialDistribution: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.distribution.NormalDistribution: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.distribution.UniformDistribution: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.iterators.AbstractDataSetIterator: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.layers.BatchNormalization: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.layers.ConvolutionLayer: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.layers.DenseLayer: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.layers.OutputLayer: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.layers.SubsamplingLayer: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossBinaryXENT: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossCosineProximity: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossHinge: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossKLD: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossL1: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossL2: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossMAE: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossMAPE: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossMCXENT: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossMSE: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossMSLE: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossNegativeLogLikelihood: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossPoisson: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.lossfunctions.LossSquaredHinge: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.NeuralNetConfiguration: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.stepfunctions.DefaultStepFunction: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.stepfunctions.GradientStepFunction: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.stepfunctions.NegativeDefaultStepFunction: Parses a given list of options.
setOptions(String[]) - Method in class weka.dl4j.stepfunctions.NegativeGradientStepFunction: Parses a given list of options.
setPadding(int[]) - Method in class weka.dl4j.layers.ConvolutionLayer
setPadding(int[]) - Method in class weka.dl4j.layers.SubsamplingLayer
setPaddingX(int) - Method in class weka.dl4j.layers.ConvolutionLayer
setPaddingX(int) - Method in class weka.dl4j.layers.SubsamplingLayer
setPaddingY(int) - Method in class weka.dl4j.layers.ConvolutionLayer
setPaddingY(int) - Method in class weka.dl4j.layers.SubsamplingLayer
setPnorm(int) - Method in class weka.dl4j.layers.SubsamplingLayer
setPoolingType(PoolingType) - Method in class weka.dl4j.layers.SubsamplingLayer
setPreProcessor(DataSetPreProcessor) - Method in class weka.dl4j.ShufflingDataSetIterator: Sets the preprocessor.
setPretrain(boolean) - Method in class weka.dl4j.NeuralNetConfiguration
setProbabilityOfSuccess(double) - Method in class weka.dl4j.distribution.BinomialDistribution
setQueueSize(int) - Method in class weka.classifiers.functions.Dl4jMlpClassifier
setRho(double) - Method in class weka.dl4j.layers.BatchNormalization
setRho(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setRho(double) - Method in class weka.dl4j.layers.DenseLayer
setRho(double) - Method in class weka.dl4j.layers.OutputLayer
setRho(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setRmsDecay(double) - Method in class weka.dl4j.layers.BatchNormalization
setRmsDecay(double) - Method in class weka.dl4j.layers.ConvolutionLayer
setRmsDecay(double) - Method in class weka.dl4j.layers.DenseLayer
setRmsDecay(double) - Method in class weka.dl4j.layers.OutputLayer
setRmsDecay(double) - Method in class weka.dl4j.layers.SubsamplingLayer
setSeed(long) - Method in class weka.dl4j.NeuralNetConfiguration
setStd(double) - Method in class weka.dl4j.distribution.NormalDistribution
setStepFunction(StepFunction) - Method in class weka.dl4j.NeuralNetConfiguration
setStride(int[]) - Method in class weka.dl4j.layers.ConvolutionLayer
setStride(int[]) - Method in class weka.dl4j.layers.SubsamplingLayer
setStrideX(int) - Method in class weka.dl4j.layers.ConvolutionLayer
setStrideX(int) - Method in class weka.dl4j.layers.SubsamplingLayer
setStrideY(int) - Method in class weka.dl4j.layers.ConvolutionLayer
setStrideY(int) - Method in class weka.dl4j.layers.SubsamplingLayer
setTrainBatchSize(int) - Method in class weka.dl4j.iterators.AbstractDataSetIterator: Setting the training batch size
setTrainBatchSize(int) - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator
setTrainBatchSize(int) - Method in class weka.dl4j.iterators.DefaultInstancesIterator
setTrainBatchSize(int) - Method in class weka.dl4j.iterators.ImageDataSetIterator
setUpdater(Updater) - Method in class weka.dl4j.layers.BatchNormalization
setUpdater(Updater) - Method in class weka.dl4j.layers.ConvolutionLayer
setUpdater(Updater) - Method in class weka.dl4j.layers.DenseLayer
setUpdater(Updater) - Method in class weka.dl4j.layers.OutputLayer
setUpdater(Updater) - Method in class weka.dl4j.layers.SubsamplingLayer
setUpper(double) - Method in class weka.dl4j.distribution.UniformDistribution
setUseDropConnect(boolean) - Method in class weka.dl4j.NeuralNetConfiguration
setUseRegularization(boolean) - Method in class weka.dl4j.NeuralNetConfiguration
setWeightInit(WeightInit) - Method in class weka.dl4j.layers.BatchNormalization
setWeightInit(WeightInit) - Method in class weka.dl4j.layers.ConvolutionLayer
setWeightInit(WeightInit) - Method in class weka.dl4j.layers.DenseLayer
setWeightInit(WeightInit) - Method in class weka.dl4j.layers.OutputLayer
setWeightInit(WeightInit) - Method in class weka.dl4j.layers.SubsamplingLayer
setWidth(int) - Method in class weka.dl4j.iterators.ConvolutionalInstancesIterator
setWidth(int) - Method in class weka.dl4j.iterators.ImageDataSetIterator
ShufflingDataSetIterator - Class in weka.dl4j: An nd4j mini-batch iterator that shuffles the data whenever it is reset.
ShufflingDataSetIterator(DataSet, int, int) - Constructor for class weka.dl4j.ShufflingDataSetIterator: Constructs a new shuffling iterator.
SubsamplingLayer - Class in weka.dl4j.layers: A version of DeepLearning4j's SubsamplingLayer that implements WEKA option handling.
SubsamplingLayer() - Constructor for class weka.dl4j.layers.SubsamplingLayer: Constructor for setting some defaults.

T

TAGS_FILTER - Static variable in class weka.classifiers.functions.Dl4jMlpClassifier: The filter to apply to the training data
toString() - Method in class weka.classifiers.functions.Dl4jMlpClassifier: Returns a string describing the model.
totalExamples() - Method in class weka.dl4j.ShufflingDataSetIterator: Returns the total number of examples in the dataset.
totalOutcomes() - Method in class weka.dl4j.ShufflingDataSetIterator: Returns the total number of labels.

U

UniformDistribution - Class in weka.dl4j.distribution: A version of DeepLearning4j's UniformDistribution that implements WEKA option handling.
UniformDistribution() - Constructor for class weka.dl4j.distribution.UniformDistribution: Constructions normal distribution with lower limit -1 and upper limit 1.
Utils - Class in weka.classifiers.functions.dl4j: Utility routines for the Dl4jMlpClassifier
Utils() - Constructor for class weka.classifiers.functions.dl4j.Utils

V

validate(Instances) - Method in class weka.dl4j.iterators.ImageDataSetIterator: Validates the input dataset

W

weka.classifiers.functions - package weka.classifiers.functions
weka.classifiers.functions.dl4j - package weka.classifiers.functions.dl4j
weka.dl4j - package weka.dl4j
weka.dl4j.activations - package weka.dl4j.activations
weka.dl4j.distribution - package weka.dl4j.distribution
weka.dl4j.iterators - package weka.dl4j.iterators
weka.dl4j.layers - package weka.dl4j.layers
weka.dl4j.lossfunctions - package weka.dl4j.lossfunctions
weka.dl4j.stepfunctions - package weka.dl4j.stepfunctions

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