C-Index

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

C

cache - Variable in class org.deeplearning4j.bagofwords.vectorizer.Builder: Deprecated.
cache(VocabCache) - Method in class org.deeplearning4j.bagofwords.vectorizer.Builder: Deprecated.
cache(VocabCache<T>) - Method in class org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable.Builder
cache(VocabCache<T>) - Method in class org.deeplearning4j.models.glove.GloveWeightLookupTable.Builder: Deprecated.
cache - Variable in class org.deeplearning4j.text.sentenceiterator.FileSentenceIterator
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.
calcExponentialDecay(double, double, double) - Method in class org.deeplearning4j.nn.updater.TestDecayPolicies
calcGradient(Gradient, INDArray) - Method in interface org.deeplearning4j.nn.api.Layer: Deprecated.
As of 0.7.3 - Feb 2017. No longer used.
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.LossLayer
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
calcGradient(Gradient, INDArray) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
calcInverseDecay(double, double, double, double) - Method in class org.deeplearning4j.nn.updater.TestDecayPolicies
calcL1(boolean) - Method in interface org.deeplearning4j.nn.api.Layer: Calculate the l1 regularization term
0.0 if regularization is not used.
calcL1() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Calculate the L1 regularization term for all layers in the entire network.
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
calcL1(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
calcL1(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
calcL2(boolean) - Method in interface org.deeplearning4j.nn.api.Layer: Calculate the l2 regularization term
0.0 if regularization is not used.
calcL2() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Calculate the L2 regularization term for all layers in the entire network.
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.ActivationLayer
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.BaseLayer
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.BasePretrainNetwork
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.DropoutLayer
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.FrozenLayer
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.pooling.GlobalPoolingLayer
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.recurrent.GravesLSTM
calcL2(boolean) - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
calcL2(boolean) - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
calcPolyDecay(double, double, double, double) - Method in class org.deeplearning4j.nn.updater.TestDecayPolicies
calcSigmoidDecay(double, double, double, double) - Method in class org.deeplearning4j.nn.updater.TestDecayPolicies
calcStepDecay(double, double, double, double) - Method in class org.deeplearning4j.nn.updater.TestDecayPolicies
calcTorchStepDecay(double, double) - Method in class org.deeplearning4j.nn.updater.TestDecayPolicies
calculate(INDArray, int, double) - Method in class org.deeplearning4j.plot.Tsne
calculateAUC() - Method in class org.deeplearning4j.eval.ROC: Calculate the AUC - Area Under Curve
Utilizes trapezoidal integration internally
calculateAUC(int) - Method in class org.deeplearning4j.eval.ROCBinary: Calculate the AUC - Area Under Curve
Utilizes trapezoidal integration internally
calculateAUC(int) - Method in class org.deeplearning4j.eval.ROCMultiClass: Calculate the AUC - Area Under Curve
Utilizes trapezoidal integration internally
calculateAverageAUC() - Method in class org.deeplearning4j.eval.ROCMultiClass: Calculate the average (one-vs-all) AUC for all classes
calculateProb(int, int) - Method in class org.deeplearning4j.graph.models.embeddings.InMemoryGraphLookupTable: Calculate the probability of the second vertex given the first vertex i.e., P(v_second | v_first)
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
calculateScore(int, int) - Method in class org.deeplearning4j.graph.models.embeddings.InMemoryGraphLookupTable: Calculate score.
calculateScore(MultiLayerNetwork) - Method in class org.deeplearning4j.spark.earlystopping.SparkDataSetLossCalculator
calculateScore(ComputationGraph) - Method in class org.deeplearning4j.spark.earlystopping.SparkLossCalculatorComputationGraph
calculateScore(JavaRDD<DataSet>, boolean) - Method in class org.deeplearning4j.spark.impl.graph.SparkComputationGraph: Calculate the score for all examples in the provided JavaRDD<DataSet>, either by summing or averaging over the entire data set.
calculateScore(JavaRDD<DataSet>, boolean, int) - Method in class org.deeplearning4j.spark.impl.graph.SparkComputationGraph: Calculate the score for all examples in the provided JavaRDD<DataSet>, either by summing or averaging over the entire data set.
calculateScore(RDD<DataSet>, boolean) - Method in class org.deeplearning4j.spark.impl.multilayer.SparkDl4jMultiLayer: Overload of SparkDl4jMultiLayer.calculateScore(JavaRDD, boolean) for RDD<DataSet> instead of JavaRDD<DataSet>
calculateScore(JavaRDD<DataSet>, boolean) - Method in class org.deeplearning4j.spark.impl.multilayer.SparkDl4jMultiLayer: Calculate the score for all examples in the provided JavaRDD<DataSet>, either by summing or averaging over the entire data set.
calculateScore(JavaRDD<DataSet>, boolean, int) - Method in class org.deeplearning4j.spark.impl.multilayer.SparkDl4jMultiLayer: Calculate the score for all examples in the provided JavaRDD<DataSet>, either by summing or averaging over the entire data set.
calculateScoreMultiDataSet(JavaRDD<MultiDataSet>, boolean) - Method in class org.deeplearning4j.spark.impl.graph.SparkComputationGraph: Calculate the score for all examples in the provided JavaRDD<MultiDataSet>, either by summing or averaging over the entire data set.
calculateScoreMultiDataSet(JavaRDD<MultiDataSet>, boolean, int) - Method in class org.deeplearning4j.spark.impl.graph.SparkComputationGraph: Calculate the score for all examples in the provided JavaRDD<MultiDataSet>, either by summing or averaging over the entire data set.
call(I) - Method in interface org.deeplearning4j.berkeley.MyMethod
call() - Method in class org.deeplearning4j.models.paragraphvectors.ParagraphVectors.BlindInferenceCallable
call() - Method in class org.deeplearning4j.models.paragraphvectors.ParagraphVectors.InferenceCallable
call(Integer, Iterator<DataSet>) - Method in class org.deeplearning4j.spark.data.BatchAndExportDataSetsFunction
call(Integer, Iterator<MultiDataSet>) - Method in class org.deeplearning4j.spark.data.BatchAndExportMultiDataSetsFunction
call(Iterator<DataSet>) - Method in class org.deeplearning4j.spark.data.DataSetExportFunction
call(Iterator<MultiDataSet>) - Method in class org.deeplearning4j.spark.data.MultiDataSetExportFunction
call(String) - Method in class org.deeplearning4j.spark.data.PathToDataSetFunction
call(String) - Method in class org.deeplearning4j.spark.data.PathToMultiDataSetFunction
call(Tuple2<Text, BytesWritable>) - Method in class org.deeplearning4j.spark.datavec.DataVecByteDataSetFunction
call(List<Writable>) - Method in class org.deeplearning4j.spark.datavec.DataVecDataSetFunction
call(List<List<Writable>>) - Method in class org.deeplearning4j.spark.datavec.DataVecSequenceDataSetFunction
call(Tuple2<List<List<Writable>>, List<List<Writable>>>) - Method in class org.deeplearning4j.spark.datavec.DataVecSequencePairDataSetFunction
call(Iterator<String>) - Method in class org.deeplearning4j.spark.datavec.export.StringToDataSetExportFunction
call(DataSet) - Method in class org.deeplearning4j.spark.datavec.MiniBatchTests.DataSetAssertionFunction
call(String) - Method in class org.deeplearning4j.spark.datavec.RecordReaderFunction
call(INDArray, INDArray) - Method in class org.deeplearning4j.spark.impl.common.Add
call(Integer, Iterator<T>) - Method in class org.deeplearning4j.spark.impl.common.CountPartitionsFunction
call(PortableDataStream) - Method in class org.deeplearning4j.spark.impl.common.LoadSerializedDataSetFunction
call(Tuple2<Integer, Double>, Tuple2<Integer, Double>) - Method in class org.deeplearning4j.spark.impl.common.reduce.IntDoubleReduceFunction
call(Integer, Iterator<T>) - Method in class org.deeplearning4j.spark.impl.common.repartition.AssignIndexFunction
call(Iterator<Tuple2<K, INDArray>>) - Method in class org.deeplearning4j.spark.impl.common.score.BaseVaeScoreWithKeyFunctionAdapter
call(Integer, Iterator<T>) - Method in class org.deeplearning4j.spark.impl.common.SplitPartitionsFunction
call(Integer, Iterator<Tuple2<T, U>>) - Method in class org.deeplearning4j.spark.impl.common.SplitPartitionsFunction2
call(DataSet) - Method in class org.deeplearning4j.spark.impl.graph.dataset.DataSetToMultiDataSetFn
call(Tuple2<K, DataSet>) - Method in class org.deeplearning4j.spark.impl.graph.dataset.PairDataSetToMultiDataSetFn
call(Tuple2<K, INDArray[]>) - Method in class org.deeplearning4j.spark.impl.graph.scoring.ArrayPairToPair
call(Tuple2<K, INDArray>) - Method in class org.deeplearning4j.spark.impl.graph.scoring.PairToArrayPair
call(DataSet) - Method in class org.deeplearning4j.spark.impl.layer.DL4jWorker
call(T, T) - Method in class org.deeplearning4j.spark.impl.multilayer.evaluation.IEvaluationReduceFunction
call(ParameterAveragingAggregationTuple, ParameterAveragingTrainingResult) - Method in class org.deeplearning4j.spark.impl.paramavg.aggregator.ParameterAveragingElementAddFunction
call(ParameterAveragingAggregationTuple, ParameterAveragingAggregationTuple) - Method in class org.deeplearning4j.spark.impl.paramavg.aggregator.ParameterAveragingElementCombineFunction
call(Pair<List<String>, AtomicLong>) - Method in class org.deeplearning4j.spark.models.embeddings.glove.cooccurrences.CoOccurrenceCalculator
call(CounterMap<String, String>, CounterMap<String, String>) - Method in class org.deeplearning4j.spark.models.embeddings.glove.cooccurrences.CoOccurrenceCounts
call(Triple<VocabWord, VocabWord, Double>) - Method in class org.deeplearning4j.spark.models.embeddings.glove.GlovePerformer
call(Triple<String, String, Double>) - Method in class org.deeplearning4j.spark.models.embeddings.glove.VocabWordPairs
call(Iterator<Tuple2<List<VocabWord>, Long>>) - Method in class org.deeplearning4j.spark.models.embeddings.word2vec.FirstIterationFunctionAdapter
call(Map.Entry<VocabWord, INDArray>) - Method in class org.deeplearning4j.spark.models.embeddings.word2vec.MapToPairFunction
call(Word2VecFuncCall) - Method in class org.deeplearning4j.spark.models.embeddings.word2vec.SentenceBatch: Deprecated.
call(Pair<List<VocabWord>, AtomicLong>) - Method in class org.deeplearning4j.spark.models.embeddings.word2vec.Word2VecPerformer: Deprecated.
call(Pair<List<VocabWord>, AtomicLong>) - Method in class org.deeplearning4j.spark.models.embeddings.word2vec.Word2VecPerformerVoid: Deprecated.
call(Tuple2<List<VocabWord>, Long>) - Method in class org.deeplearning4j.spark.models.embeddings.word2vec.Word2VecSetup: Deprecated.
call(LabelledDocument) - Method in class org.deeplearning4j.spark.models.paragraphvectors.functions.DocumentSequenceConvertFunction
call(Tuple2<String, String>) - Method in class org.deeplearning4j.spark.models.paragraphvectors.functions.KeySequenceConvertFunction
call(Sequence<T>) - Method in class org.deeplearning4j.spark.models.sequencevectors.functions.CountFunction
call(T) - Method in class org.deeplearning4j.spark.models.sequencevectors.functions.DistributedFunction
call(T) - Method in class org.deeplearning4j.spark.models.sequencevectors.functions.ExportFunction
call(Sequence<T>) - Method in class org.deeplearning4j.spark.models.sequencevectors.functions.ExtraCountFunction
call(List<T>) - Method in class org.deeplearning4j.spark.models.sequencevectors.functions.ListSequenceConvertFunction
call(Iterator<Sequence<T>>) - Method in class org.deeplearning4j.spark.models.sequencevectors.functions.PartitionTrainingFunction
call(String) - Method in class org.deeplearning4j.spark.models.sequencevectors.functions.TokenizerFunction
call(Sequence<T>) - Method in class org.deeplearning4j.spark.models.sequencevectors.functions.TrainingFunction
call(Integer, Iterator<AtomicLong>) - Method in class org.deeplearning4j.spark.text.functions.FoldBetweenPartitionFunction
call(Integer, Iterator<AtomicLong>) - Method in class org.deeplearning4j.spark.text.functions.FoldWithinPartitionFunction
call(Pair<List<String>, AtomicLong>) - Method in class org.deeplearning4j.spark.text.functions.GetSentenceCountFunction
call(Iterator<?>) - Method in class org.deeplearning4j.spark.text.functions.MapPerPartitionVoidFunction
call(AtomicLong, AtomicLong) - Method in class org.deeplearning4j.spark.text.functions.ReduceSentenceCount
call(String) - Method in class org.deeplearning4j.spark.text.functions.TokenizerFunction
call(List<String>) - Method in class org.deeplearning4j.spark.text.functions.UpdateWordFreqAccumulatorFunction
call(Pair<List<String>, AtomicLong>) - Method in class org.deeplearning4j.spark.text.functions.WordsListToVocabWordsFunction
call(Integer) - Method in class org.deeplearning4j.spark.text.TestFunction
call(T) - Method in class org.deeplearning4j.spark.util.BaseDoubleFlatMapFunctionAdaptee
call(T) - Method in class org.deeplearning4j.spark.util.BasePairFlatMapFunctionAdaptee
call(JavaRDD<DataSet>) - Method in class org.deeplearning4j.streaming.pipeline.spark.PrintDataSet
camelContext(CamelContext) - Method in class org.deeplearning4j.streaming.routes.CamelKafkaRouteBuilder.Builder
CamelKafkaRouteBuilder - Class in org.deeplearning4j.streaming.routes: A Camel Java DSL Router
CamelKafkaRouteBuilder() - Constructor for class org.deeplearning4j.streaming.routes.CamelKafkaRouteBuilder
CamelKafkaRouteBuilder.Builder - Class in org.deeplearning4j.streaming.routes
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.
canEqual(Object) - Method in class org.deeplearning4j.streaming.embedded.StringOption
capacity - Variable in class org.deeplearning4j.parallelism.MagicQueue
capitalize(String) - Static method in class org.deeplearning4j.berkeley.StringUtils: Uppercases the first character of a string.
categoryMap - Variable in class com.atilika.kuromoji.compile.UnknownDictionaryCompiler
CBOW<T extends SequenceElement> - Class in org.deeplearning4j.models.embeddings.learning.impl.elements: CBOW implementation for DeepLearning4j
CBOW() - Constructor for class org.deeplearning4j.models.embeddings.learning.impl.elements.CBOW
cbow(int, List<T>, int, AtomicLong, double, int) - Method in class org.deeplearning4j.models.embeddings.learning.impl.elements.CBOW
Cell - Class in org.deeplearning4j.clustering.quadtree: A cell representing a bounding box forthe quad tree
Cell(double, double, double, double) - Constructor for class org.deeplearning4j.clustering.quadtree.Cell
Cell - Class in org.deeplearning4j.clustering.sptree
Cell(int) - Constructor for class org.deeplearning4j.clustering.sptree.Cell
CENTER_KEY - Static variable in class org.deeplearning4j.nn.params.CenterLossParamInitializer
CenterLossOutputLayer - Class in org.deeplearning4j.nn.conf.layers: Center loss is similar to triplet loss except that it enforces intraclass consistency and doesn't require feed forward of multiple examples.
CenterLossOutputLayer(CenterLossOutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
CenterLossOutputLayer - Class in org.deeplearning4j.nn.layers.training: Center loss is similar to triplet loss except that it enforces intraclass consistency and doesn't require feed forward of multiple examples.
CenterLossOutputLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer
CenterLossOutputLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer
CenterLossOutputLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
CenterLossOutputLayerTest - Class in org.deeplearning4j.nn.layers: Test CenterLossOutputLayer.
CenterLossOutputLayerTest() - Constructor for class org.deeplearning4j.nn.layers.CenterLossOutputLayerTest
CenterLossParamInitializer - Class in org.deeplearning4j.nn.params: Initialize Center Loss params.
CenterLossParamInitializer() - Constructor for class org.deeplearning4j.nn.params.CenterLossParamInitializer
CGVaeReconstructionErrorWithKeyFunction<K> - Class in org.deeplearning4j.spark.impl.graph.scoring: Function to calculate the reconstruction error for a variational autoencoder, that is the first layer in a ComputationGraph.
Note that the VAE must be using a loss function, not a ReconstructionDistribution
Also note that scoring is batched for computational efficiency.
CGVaeReconstructionErrorWithKeyFunction(Broadcast<INDArray>, Broadcast<String>, int) - Constructor for class org.deeplearning4j.spark.impl.graph.scoring.CGVaeReconstructionErrorWithKeyFunction
CGVaeReconstructionProbWithKeyFunction<K> - Class in org.deeplearning4j.spark.impl.graph.scoring: Function to calculate the reconstruction probability for a variational autoencoder, that is the first layer in a ComputationGraph.
Note that scoring is batched for computational efficiency.
CGVaeReconstructionProbWithKeyFunction(Broadcast<INDArray>, Broadcast<String>, boolean, int, int) - Constructor for class org.deeplearning4j.spark.impl.graph.scoring.CGVaeReconstructionProbWithKeyFunction
channels - Static variable in class org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator
CHARACTER_DEFINITIONS_FILENAME - Static variable in class com.atilika.kuromoji.dict.CharacterDefinitions
CharacterDefinitions - Class in com.atilika.kuromoji.dict
CharacterDefinitions(int[][], int[][], String[]) - Constructor for class com.atilika.kuromoji.dict.CharacterDefinitions
characterDefinitions - Variable in class com.atilika.kuromoji.TokenizerBase.Builder
CharacterDefinitionsCompiler - Class in com.atilika.kuromoji.compile
CharacterDefinitionsCompiler(OutputStream) - Constructor for class com.atilika.kuromoji.compile.CharacterDefinitionsCompiler
CharacterDefinitionsCompilerTest - Class in com.atilika.kuromoji.compile
CharacterDefinitionsCompilerTest() - Constructor for class com.atilika.kuromoji.compile.CharacterDefinitionsCompilerTest
Chart - Class in org.deeplearning4j.ui.components.chart: Abstract class for charts
Chart(String) - Constructor for class org.deeplearning4j.ui.components.chart.Chart
Chart(String, Chart.Builder) - Constructor for class org.deeplearning4j.ui.components.chart.Chart
Chart.Builder<T extends Chart.Builder<T>> - Class in org.deeplearning4j.ui.components.chart
CHART_MAX_POINTS_PROPERTY - Static variable in class org.deeplearning4j.ui.module.train.TrainModule
ChartHistogram - Class in org.deeplearning4j.ui.components.chart: Histogram chart, with pre-binned values.
ChartHistogram(ChartHistogram.Builder) - Constructor for class org.deeplearning4j.ui.components.chart.ChartHistogram
ChartHistogram() - Constructor for class org.deeplearning4j.ui.components.chart.ChartHistogram
ChartHistogram.Builder - Class in org.deeplearning4j.ui.components.chart
ChartHorizontalBar - Class in org.deeplearning4j.ui.components.chart
ChartHorizontalBar() - Constructor for class org.deeplearning4j.ui.components.chart.ChartHorizontalBar
ChartHorizontalBar.Builder - Class in org.deeplearning4j.ui.components.chart
ChartLine - Class in org.deeplearning4j.ui.components.chart: Line chart with multiple independent series
ChartLine() - Constructor for class org.deeplearning4j.ui.components.chart.ChartLine
ChartLine.Builder - Class in org.deeplearning4j.ui.components.chart
ChartScatter - Class in org.deeplearning4j.ui.components.chart: Scatter chart
ChartScatter() - Constructor for class org.deeplearning4j.ui.components.chart.ChartScatter
ChartScatter.Builder - Class in org.deeplearning4j.ui.components.chart
ChartStackedArea - Class in org.deeplearning4j.ui.components.chart: Stacked area chart (no normalization), with multiple series.
ChartStackedArea() - Constructor for class org.deeplearning4j.ui.components.chart.ChartStackedArea
ChartStackedArea(ChartStackedArea.Builder) - Constructor for class org.deeplearning4j.ui.components.chart.ChartStackedArea
ChartStackedArea.Builder - Class in org.deeplearning4j.ui.components.chart
ChartTimeline - Class in org.deeplearning4j.ui.components.chart: A timeline/swimlane chart with zoom/scroll functionality.
ChartTimeline() - Constructor for class org.deeplearning4j.ui.components.chart.ChartTimeline
ChartTimeline.Builder - Class in org.deeplearning4j.ui.components.chart
ChartTimeline.TimelineEntry - Class in org.deeplearning4j.ui.components.chart
checkForUnsupportedConfigurations(Map<String, Object>, boolean) - Static method in class org.deeplearning4j.nn.modelimport.keras.KerasLayer: Checks whether layer config contains unsupported options.
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
checkGradients() - Method in class org.deeplearning4j.graph.models.deepwalk.DeepWalkGradientCheck
checkGradients2() - Method in class org.deeplearning4j.graph.models.deepwalk.DeepWalkGradientCheck
checkGradientsPretrainLayer(Layer, double, double, double, boolean, boolean, INDArray, int) - Static method in class org.deeplearning4j.gradientcheck.GradientCheckUtil: Check backprop gradients for a pretrain layer NOTE: gradient checking pretrain layers can be difficult...
checkInitializationFF() - Method in class org.deeplearning4j.nn.layers.custom.TestCustomLayers
checkKryoConfiguration(JavaSparkContext, Logger) - Static method in class org.deeplearning4j.spark.util.SparkUtils: Check the spark configuration for incorrect Kryo configuration, logging a warning message if necessary
checkMaskArrayClearance() - Method in class org.deeplearning4j.nn.graph.ComputationGraphTestRNN
checkMaskArrayClearance() - Method in class org.deeplearning4j.nn.multilayer.TestMasking
checkMeanVarianceEstimate() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalizationTest
checkMeanVarianceEstimateCNN() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalizationTest
checkSerialization() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalizationTest
checkStorageEvents(Persistable) - Method in class org.deeplearning4j.ui.storage.BaseCollectionStatsStorage
checkStorageEvents(Persistable) - Method in class org.deeplearning4j.ui.storage.sqlite.J7FileStatsStorage
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
checkTree() - Method in class org.deeplearning4j.models.embeddings.reader.impl.TreeModelUtils
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
CifarDataSetIterator - Class in org.deeplearning4j.datasets.iterator.impl: CifarDataSetIterator is an iterator for Cifar10 dataset explicitly There is a special preProcessor used to normalize the dataset based on Sergey Zagoruyko example https://github.com/szagoruyko/cifar.torch
CifarDataSetIterator(int, int, boolean) - Constructor for class org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator: Loads images with given batchSize, numExamples, & version returned by the generator.
CifarDataSetIterator(int, int, int[]) - Constructor for class org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator: Loads images with given batchSize, numExamples, & imgDim returned by the generator.
CifarDataSetIterator(int, int, int[], boolean) - Constructor for class org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator: Loads images with given batchSize, numExamples, imgDim & version returned by the generator.
CifarDataSetIterator(int, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator: Loads images with given batchSize & numExamples returned by the generator.
CifarDataSetIterator(int, int[]) - Constructor for class org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator: Loads images with given batchSize & imgDim returned by the generator.
CifarDataSetIterator(int, int, int[], boolean, boolean) - Constructor for class org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator: Loads images with given batchSize, numExamples, imgDim & version returned by the generator.
CifarDataSetIterator(int, int, int[], int, ImageTransform, boolean, boolean) - Constructor for class org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator: Create Cifar data specific iterator
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
classForScore(Double) - Method in class org.deeplearning4j.text.corpora.sentiwordnet.SWN3
Classifier - Interface in org.deeplearning4j.nn.api: A classifier (this is for supervised learning)
classify(String) - Method in class org.deeplearning4j.text.corpora.sentiwordnet.SWN3: Classifies the given text
classify(CAS) - Method in class org.deeplearning4j.text.corpora.sentiwordnet.SWN3
classify(Sentence) - Method in class org.deeplearning4j.text.corpora.sentiwordnet.SWN3
classifyPoint(Point) - Method in class org.deeplearning4j.clustering.cluster.ClusterSet
classifyPoint(Point, boolean) - Method in class org.deeplearning4j.clustering.cluster.ClusterSet
classifyPoint(ClusterSet, Point) - Static method in class org.deeplearning4j.clustering.cluster.ClusterUtils
classifyPoints() - Method in class org.deeplearning4j.clustering.algorithm.BaseClusteringAlgorithm
classifyPoints(List<Point>) - Method in class org.deeplearning4j.clustering.cluster.ClusterSet
classifyPoints(List<Point>, boolean) - Method in class org.deeplearning4j.clustering.cluster.ClusterSet
classifyPoints(ClusterSet, List<Point>, ExecutorService) - Static method in class org.deeplearning4j.clustering.cluster.ClusterUtils: Classify the set of points base on cluster centers.
className - Variable in class org.deeplearning4j.nn.modelimport.keras.KerasLayer
className - Variable in class org.deeplearning4j.nn.modelimport.keras.KerasModel
ClassPathResource - Class in org.deeplearning4j.ui.standalone: Simple utility class used to get access to files at the classpath, or packed into jar.
ClassPathResource(String) - Constructor for class org.deeplearning4j.ui.standalone.ClassPathResource: Builds new ClassPathResource object
cleanup - Variable in class org.deeplearning4j.bagofwords.vectorizer.Builder: Deprecated.
cleanup(boolean) - Method in class org.deeplearning4j.bagofwords.vectorizer.Builder: Deprecated.
cleanup() - Method in interface org.deeplearning4j.text.invertedindex.InvertedIndex: Cleanup any resources used
clear() - Method in class com.atilika.kuromoji.trie.PatriciaTrie: Clears this trie by removing all its key-value pairs
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.FrozenLayer
clear() - Method in class org.deeplearning4j.nn.layers.LossLayer
clear() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
clear() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Clear the inputs.
clear() - Method in class org.deeplearning4j.parallelism.ConcurrentHashSet
clear() - Method in class org.deeplearning4j.parallelism.MagicQueue
clear() - Method in class org.deeplearning4j.plot.BarnesHutTsne
clear() - Method in class org.deeplearning4j.ui.stats.sbe.InitFieldsPresentEncoder
clear() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateFieldsPresentEncoder
clear() - Method in class org.deeplearning4j.util.DiskBasedQueue
clear() - Method in class org.deeplearning4j.util.MultiDimensionalMap: Removes all of the mappings from this map (optional operation).
clear() - Method in class org.deeplearning4j.util.MultiDimensionalSet: Removes all of the elements from this applyTransformToDestination (optional operation).
clearLayerMaskArrays() - Method in class org.deeplearning4j.nn.graph.ComputationGraph: Remove the mask arrays from all layers.
See ComputationGraph.setLayerMaskArrays(INDArray[], INDArray[]) for details on mask arrays.
clearLayerMaskArrays() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork: Remove the mask arrays from all layers.
See MultiLayerNetwork.setLayerMaskArrays(INDArray, INDArray) for details on mask arrays.
clearVariables() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
clone() - Method in interface org.deeplearning4j.api.storage.listener.RoutingIterationListener
clone() - Method in class org.deeplearning4j.berkeley.PriorityQueue: Returns a clone of this priority queue.
clone() - Method in class org.deeplearning4j.eval.ROC.CountsForThreshold
clone() - Method in class org.deeplearning4j.eval.ROCBinary.CountsForThreshold
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.ComputationGraphConfigurationTest.StaticInnerGraphVertex
clone() - Method in class org.deeplearning4j.nn.conf.distribution.Distribution
clone() - Method in class org.deeplearning4j.nn.conf.graph.ElementWiseVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.GraphVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.L2NormalizeVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.L2Vertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.LayerVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.MergeVertex
clone() - Method in class org.deeplearning4j.nn.conf.graph.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.ScaleVertex
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.DropoutLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.Layer
clone() - Method in class org.deeplearning4j.nn.conf.layers.LocalResponseNormalization
clone() - Method in class org.deeplearning4j.nn.conf.layers.Subsampling1DLayer
clone() - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
clone() - Method in class org.deeplearning4j.nn.conf.misc.TestGraphVertex
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.custom.MyCustomPreprocessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor
clone() - Method in class org.deeplearning4j.nn.conf.preprocessor.UnitVarianceProcessor
clone() - Method in class org.deeplearning4j.nn.conf.stepfunctions.StepFunction
clone() - Method in class org.deeplearning4j.nn.graph.ComputationGraph
clone() - Method in class org.deeplearning4j.nn.layers.BaseLayer
clone() - Method in class org.deeplearning4j.nn.layers.feedforward.autoencoder.recursive.Tree
clone() - Method in class org.deeplearning4j.nn.layers.FrozenLayer
clone() - Method in class org.deeplearning4j.nn.layers.normalization.BatchNormalization
clone() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
clone() - Method in class org.deeplearning4j.nn.modelimport.keras.preprocessors.TensorFlowCnnToFeedForwardPreProcessor
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
clone() - Method in class org.deeplearning4j.ui.stats.BaseStatsListener
clone() - Method in class org.deeplearning4j.ui.stats.J7StatsListener
clone() - Method in class org.deeplearning4j.ui.stats.StatsListener
cloneCompGraphFrozen() - Method in class org.deeplearning4j.nn.layers.FrozenLayerTest
cloneListener(IterationListener) - Static method in class org.deeplearning4j.parallelism.trainer.DefaultTrainer
cloneMLNFrozen() - Method in class org.deeplearning4j.nn.layers.FrozenLayerTest
close() - Method in interface org.deeplearning4j.api.storage.StatsStorage: Close any open resources (files, etc)
close() - Method in class org.deeplearning4j.datasets.mnist.draw.DrawReconstruction
close() - Method in class org.deeplearning4j.datasets.mnist.MnistManager: Close any resources opened by the manager.
close() - Method in class org.deeplearning4j.models.embeddings.loader.WordVectorSerializer.BinaryReader
close() - Method in class org.deeplearning4j.models.embeddings.loader.WordVectorSerializer.CSVReader
close() - Method in class org.deeplearning4j.parallelism.ParallelWrapper
close() - Method in class org.deeplearning4j.ui.storage.InMemoryStatsStorage
close() - Method in class org.deeplearning4j.ui.storage.mapdb.MapDBStatsStorage
close() - Method in class org.deeplearning4j.ui.storage.sqlite.J7FileStatsStorage
Cluster - Class in org.deeplearning4j.clustering.cluster
Cluster() - Constructor for class org.deeplearning4j.clustering.cluster.Cluster
Cluster(Point, String) - Constructor for class org.deeplearning4j.clustering.cluster.Cluster
clusterColumn(int) - Method in class org.deeplearning4j.util.StringGrid
ClusterInfo - Class in org.deeplearning4j.clustering.cluster.info
ClusterInfo() - Constructor for class org.deeplearning4j.clustering.cluster.info.ClusterInfo
ClusterInfo(boolean) - Constructor for class org.deeplearning4j.clustering.cluster.info.ClusterInfo
ClusteringAlgorithm - Interface in org.deeplearning4j.clustering.algorithm
ClusteringAlgorithmCondition - Interface in org.deeplearning4j.clustering.algorithm.condition
ClusteringOptimization - Class in org.deeplearning4j.clustering.algorithm.optimisation
ClusteringOptimization() - Constructor for class org.deeplearning4j.clustering.algorithm.optimisation.ClusteringOptimization
ClusteringOptimization(ClusteringOptimizationType, double) - Constructor for class org.deeplearning4j.clustering.algorithm.optimisation.ClusteringOptimization
ClusteringOptimizationType - Enum in org.deeplearning4j.clustering.algorithm.optimisation
ClusteringStrategy - Interface in org.deeplearning4j.clustering.algorithm.strategy
ClusteringStrategyType - Enum in org.deeplearning4j.clustering.algorithm.strategy
ClusterSet - Class in org.deeplearning4j.clustering.cluster
ClusterSet() - Constructor for class org.deeplearning4j.clustering.cluster.ClusterSet
ClusterSet(String) - Constructor for class org.deeplearning4j.clustering.cluster.ClusterSet
ClusterSetInfo - Class in org.deeplearning4j.clustering.cluster.info
ClusterSetInfo() - Constructor for class org.deeplearning4j.clustering.cluster.info.ClusterSetInfo
ClusterSetInfo(boolean) - Constructor for class org.deeplearning4j.clustering.cluster.info.ClusterSetInfo
ClusterSetup - Class in org.deeplearning4j.aws.ec2.provision: Sets up a DL4J cluster
ClusterSetup(String[]) - Constructor for class org.deeplearning4j.aws.ec2.provision.ClusterSetup
ClusterUtils - Class in org.deeplearning4j.clustering.cluster: Basic cluster utilities
CNN1DGradientCheckTest - Class in org.deeplearning4j.gradientcheck
CNN1DGradientCheckTest() - Constructor for class org.deeplearning4j.gradientcheck.CNN1DGradientCheckTest
cnnEpsilon - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalizationTest
CNNGradientCheckTest - Class in org.deeplearning4j.gradientcheck: Created by nyghtowl on 9/1/15.
CNNGradientCheckTest() - Constructor for class org.deeplearning4j.gradientcheck.CNNGradientCheckTest
cnnInput - Variable in class org.deeplearning4j.nn.layers.normalization.BatchNormalizationTest
cnnInputSize - Variable in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Deprecated.
cnnInputSize(int, int, int) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Deprecated.
use MultiLayerConfiguration.Builder.setInputType(InputType) with InputType.convolutional(height,width,depth), for CNN data with shape [minibatchSize,depth,height,width]. For image data that has been flattened into a row vector per example (shape [minibatchSize,depth*height*width]) instead use InputType.convolutionalFlat(height,width,depth)
cnnInputSize(int[]) - Method in class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder: Deprecated.
use MultiLayerConfiguration.Builder.setInputType(InputType) with InputType.convolutional(height,width,depth), for CNN data with shape [minibatchSize,depth,height,width]. For image data that has been flattened into a row vector per example (shape [minibatchSize,depth*height*width]) instead use InputType.convolutionalFlat(height,width,depth)
CNNProcessorTest - Class in org.deeplearning4j.nn.conf.preprocessor
CNNProcessorTest() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CNNProcessorTest
CnnSentenceDataSetIterator - Class in org.deeplearning4j.iterator: A DataSetIterator that provides data for training a CNN sentence classification models (though can of course be used for general documents, not just sentences.
CnnSentenceDataSetIterator.Builder - Class in org.deeplearning4j.iterator
CnnSentenceDataSetIterator.UnknownWordHandling - Enum in org.deeplearning4j.iterator
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
cntGet - Variable in class org.deeplearning4j.parallelism.MagicQueue
cntPut - Variable in class org.deeplearning4j.parallelism.MagicQueue
codec - Variable in class org.deeplearning4j.spark.models.sequencevectors.export.impl.HdfsModelExporter
codeLength - Variable in class org.deeplearning4j.models.sequencevectors.sequence.SequenceElement
codes - Variable in class org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable
codes - Variable in class org.deeplearning4j.models.sequencevectors.sequence.SequenceElement
collapseDimensions(boolean) - Method in class org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer.Builder: Whether to collapse dimensions when pooling or not.
CollapseUnaries - Class in org.deeplearning4j.text.corpora.treeparser: Collapse unaries such that the tree is only made of preterminals and leaves.
CollapseUnaries() - Constructor for class org.deeplearning4j.text.corpora.treeparser.CollapseUnaries
collectGarbageCollectionStats() - Method in interface org.deeplearning4j.ui.stats.api.StatsUpdateConfiguration: Should garbage collection stats be collected and reported?
collectGarbageCollectionStats(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectGarbageCollectionStats() - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration
collectHardwareInfo() - Method in interface org.deeplearning4j.ui.stats.api.StatsInitializationConfiguration: Should hardware configuration information be collected? JVM available processors, number of devices, total memory for each device
collectHardwareInfo() - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsInitializationConfiguration
collectHistograms(StatsType) - Method in interface org.deeplearning4j.ui.stats.api.StatsUpdateConfiguration: Should histograms (per parameter type, or per layer for activations) of the given type be collected?
collectHistograms(StatsType) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration
collectHistogramsActivations(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectHistogramsGradients(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectHistogramsParameters(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectHistogramsUpdates(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
CollectionLabeledSentenceProvider - Class in org.deeplearning4j.iterator.provider: Iterate over a set of sentences/documents, where the sentences and labels are provided in lists.
CollectionLabeledSentenceProvider(List<String>, List<String>) - Constructor for class org.deeplearning4j.iterator.provider.CollectionLabeledSentenceProvider
CollectionLabeledSentenceProvider(List<String>, List<String>, Random) - Constructor for class org.deeplearning4j.iterator.provider.CollectionLabeledSentenceProvider
CollectionSentenceIterator - Class in org.deeplearning4j.text.sentenceiterator
CollectionSentenceIterator(SentencePreProcessor, Collection<String>) - Constructor for class org.deeplearning4j.text.sentenceiterator.CollectionSentenceIterator
CollectionSentenceIterator(Collection<String>) - Constructor for class org.deeplearning4j.text.sentenceiterator.CollectionSentenceIterator
CollectionStatsStorageRouter - Class in org.deeplearning4j.api.storage.impl: A simple StatsStorageRouter that simply stores the metadata, static info and updates in the specified collections.
CollectionStatsStorageRouter() - Constructor for class org.deeplearning4j.api.storage.impl.CollectionStatsStorageRouter
collectLearningRates() - Method in interface org.deeplearning4j.ui.stats.api.StatsUpdateConfiguration: Should per-parameter type learning rates be collected and reported?
collectLearningRates(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectLearningRates() - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration
collectMean(StatsType) - Method in interface org.deeplearning4j.ui.stats.api.StatsUpdateConfiguration: Should the mean values (per parameter type, or per layer for activations) be collected?
collectMean(StatsType) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration
collectMeanActivations(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectMeanGradients(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectMeanMagnitudes(StatsType) - Method in interface org.deeplearning4j.ui.stats.api.StatsUpdateConfiguration: Should the mean magnitude values (per parameter type, or per layer for activations) be collected?
collectMeanMagnitudes(StatsType) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration
collectMeanMagnitudesActivations(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectMeanMagnitudesGradients(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectMeanMagnitudesParameters(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectMeanMagnitudesUpdates(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectMeanParameters(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectMeanUpdates(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectMemoryStats() - Method in interface org.deeplearning4j.ui.stats.api.StatsUpdateConfiguration: Should JVM, off-heap and memory stats be collected/reported?
collectMemoryStats(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectMemoryStats() - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration
collectModelInfo() - Method in interface org.deeplearning4j.ui.stats.api.StatsInitializationConfiguration: Should model information be collected? Model class, configuration (JSON), number of layers, number of parameters, etc.
collectModelInfo() - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsInitializationConfiguration
collectPerformanceStats() - Method in interface org.deeplearning4j.ui.stats.api.StatsUpdateConfiguration: Should performance stats be collected/reported? Total time, total examples, total batches, Minibatches/second, examples/second
collectPerformanceStats(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectPerformanceStats() - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration
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.
collectSoftwareInfo() - Method in interface org.deeplearning4j.ui.stats.api.StatsInitializationConfiguration: Should software configuration information be collected? For example, OS, JVM, and ND4J backend details
collectSoftwareInfo() - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsInitializationConfiguration
collectStdev(StatsType) - Method in interface org.deeplearning4j.ui.stats.api.StatsUpdateConfiguration: Should the standard devication values (per parameter type, or per layer for activations) be collected?
collectStdev(StatsType) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration
collectStdevActivations(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectStdevGradients(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectStdevParameters(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectStdevUpdates(boolean) - Method in class org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration.Builder
collectTrainingStats - Variable in class org.deeplearning4j.spark.api.WorkerConfiguration
color(Color) - Method in class org.deeplearning4j.ui.components.text.style.StyleText.Builder: Color for the text
color(String) - Method in class org.deeplearning4j.ui.components.text.style.StyleText.Builder: Color for the text
colorToHex(Color) - Static method in class org.deeplearning4j.ui.api.Utils: Convert an AWT color to a hex color string, such as #000000
columnWidths(LengthUnit, double...) - Method in class org.deeplearning4j.ui.components.table.style.StyleTable.Builder: Specify the widths for the columns
com.atilika.kuromoji - package com.atilika.kuromoji
com.atilika.kuromoji.buffer - package com.atilika.kuromoji.buffer
com.atilika.kuromoji.compile - package com.atilika.kuromoji.compile
com.atilika.kuromoji.dict - package com.atilika.kuromoji.dict
com.atilika.kuromoji.io - package com.atilika.kuromoji.io
com.atilika.kuromoji.ipadic - package com.atilika.kuromoji.ipadic
com.atilika.kuromoji.ipadic.compile - package com.atilika.kuromoji.ipadic.compile
com.atilika.kuromoji.trie - package com.atilika.kuromoji.trie
com.atilika.kuromoji.util - package com.atilika.kuromoji.util
com.atilika.kuromoji.viterbi - package com.atilika.kuromoji.viterbi
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
combinedSequentialFileInputStream(File) - Method in class com.atilika.kuromoji.compile.TokenInfoDictionaryCompilerBase
COMMA - Static variable in class org.deeplearning4j.util.StringUtils
CommonCornerCasesTest - Class in com.atilika.kuromoji
CommonCornerCasesTest() - Constructor for class com.atilika.kuromoji.CommonCornerCasesTest
CommonPreprocessor - Class in org.deeplearning4j.text.tokenization.tokenizer.preprocessor
CommonPreprocessor() - Constructor for class org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor
CommonSparkTrainingStats - Class in org.deeplearning4j.spark.api.stats: A SparkTrainingStats implementation for common stats functionality used by most workers
CommonSparkTrainingStats() - Constructor for class org.deeplearning4j.spark.api.stats.CommonSparkTrainingStats
CommonSparkTrainingStats.Builder - Class in org.deeplearning4j.spark.api.stats
CompactModelAndGradient - Class in org.deeplearning4j.ui.weights.beans: Slightly modified version of ModelAndGradient, with binned params/gradients, suitable for fast network transfers for HistogramIterationListener
CompactModelAndGradient() - Constructor for class org.deeplearning4j.ui.weights.beans.CompactModelAndGradient
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(Double[], Double[]) - Method in class org.deeplearning4j.models.embeddings.reader.impl.BasicModelUtils.ArrayComparator
compare(BasicModelUtils.WordSimilarity, BasicModelUtils.WordSimilarity) - Method in class org.deeplearning4j.models.embeddings.reader.impl.BasicModelUtils.SimilarityComparator
compare(Vertex<V>, Vertex<V>) - Method in class org.deeplearning4j.models.sequencevectors.graph.walkers.impl.NearestVertexWalker.VertexComparator
compare(DataSet, DataSet) - Method in class org.deeplearning4j.spark.ordering.DataSetOrdering
compare(EventStats, EventStats) - Method in class org.deeplearning4j.spark.stats.StatsUtils.StartTimeComparator
compare(Map<String, Integer>, Map<String, Integer>) - Method in class org.deeplearning4j.util.StringCluster.SizeComparator
compareINDArrays(String, INDArray, INDArray, double) - Static method in class org.deeplearning4j.nn.modelimport.keras.KerasModelEndToEndTest
compareMulticlassAUC(String, INDArray, INDArray, INDArray, int, double) - Static method in class org.deeplearning4j.nn.modelimport.keras.KerasModelEndToEndTest
compareTo(Pair<F, S>) - Method in class org.deeplearning4j.berkeley.Pair: Compares this object with the specified object for order.
compareTo(HeapItem) - Method in class org.deeplearning4j.clustering.sptree.HeapItem
compareTo(SequenceElement) - Method in class org.deeplearning4j.models.sequencevectors.sequence.SequenceElement
compareTo(BaseCollectionStatsStorage.SessionTypeId) - Method in class org.deeplearning4j.ui.storage.BaseCollectionStatsStorage.SessionTypeId
compareTo(BaseCollectionStatsStorage.SessionTypeWorkerId) - Method in class org.deeplearning4j.ui.storage.BaseCollectionStatsStorage.SessionTypeWorkerId
compile() - Method in class com.atilika.kuromoji.compile.CharacterDefinitionsCompiler
compile() - Method in interface com.atilika.kuromoji.compile.Compiler
compile() - Method in class com.atilika.kuromoji.compile.ConnectionCostsCompiler
compile() - Method in class com.atilika.kuromoji.compile.TokenInfoBufferCompiler
compile() - Method in class com.atilika.kuromoji.compile.TokenInfoDictionaryCompilerBase
compile() - Method in class com.atilika.kuromoji.compile.UnknownDictionaryCompiler
compile() - Method in class com.atilika.kuromoji.compile.WordIdMapCompiler
Compiler - Interface in com.atilika.kuromoji.compile
complete() - Method in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayerSetupTest
Component - Class in org.deeplearning4j.ui.api: A component is anything that can be rendered, such at charts, text or tables.
Component(String, Style) - Constructor for class org.deeplearning4j.ui.api.Component
COMPONENT_TYPE - Static variable in class org.deeplearning4j.ui.components.chart.ChartHistogram
COMPONENT_TYPE - Static variable in class org.deeplearning4j.ui.components.chart.ChartHorizontalBar
COMPONENT_TYPE - Static variable in class org.deeplearning4j.ui.components.chart.ChartLine
COMPONENT_TYPE - Static variable in class org.deeplearning4j.ui.components.chart.ChartScatter
COMPONENT_TYPE - Static variable in class org.deeplearning4j.ui.components.chart.ChartStackedArea
COMPONENT_TYPE - Static variable in class org.deeplearning4j.ui.components.chart.ChartTimeline
COMPONENT_TYPE - Static variable in class org.deeplearning4j.ui.components.component.ComponentDiv
COMPONENT_TYPE - Static variable in class org.deeplearning4j.ui.components.decorator.DecoratorAccordion
COMPONENT_TYPE - Static variable in class org.deeplearning4j.ui.components.table.ComponentTable
COMPONENT_TYPE - Static variable in class org.deeplearning4j.ui.components.text.ComponentText
ComponentDiv - Class in org.deeplearning4j.ui.components.component: Div component (as in, HTML div)
ComponentDiv() - Constructor for class org.deeplearning4j.ui.components.component.ComponentDiv
ComponentDiv(Style, Component...) - Constructor for class org.deeplearning4j.ui.components.component.ComponentDiv
ComponentDiv(Style, Collection<Component>) - Constructor for class org.deeplearning4j.ui.components.component.ComponentDiv
ComponentObject - Class in org.deeplearning4j.ui.standalone: Created by Alex on 25/03/2016.
ComponentObject() - Constructor for class org.deeplearning4j.ui.standalone.ComponentObject
ComponentTable - Class in org.deeplearning4j.ui.components.table: Simple 2d table for text,
ComponentTable() - Constructor for class org.deeplearning4j.ui.components.table.ComponentTable
ComponentTable(ComponentTable.Builder) - Constructor for class org.deeplearning4j.ui.components.table.ComponentTable
ComponentTable(String[], String[][], StyleTable) - Constructor for class org.deeplearning4j.ui.components.table.ComponentTable
ComponentTable.Builder - Class in org.deeplearning4j.ui.components.table
ComponentText - Class in org.deeplearning4j.ui.components.text: Simple text component with styling
ComponentText() - Constructor for class org.deeplearning4j.ui.components.text.ComponentText
ComponentText(String, StyleText) - Constructor for class org.deeplearning4j.ui.components.text.ComponentText
ComponentText.Builder - Class in org.deeplearning4j.ui.components.text
componentType - Variable in class org.deeplearning4j.ui.api.Component: Component type: used by the Arbiter UI to determine how to decode and render the object which is represented by the JSON representation of this object
ComposableInputPreProcessor - Class in org.deeplearning4j.nn.conf.preprocessor: Composable input pre processor
ComposableInputPreProcessor(InputPreProcessor...) - Constructor for class org.deeplearning4j.nn.conf.preprocessor.ComposableInputPreProcessor
ComposableIterationListener - Class in org.deeplearning4j.optimize.listeners: A group of listeners
ComposableIterationListener(IterationListener...) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener
ComposableIterationListener(Collection<IterationListener>) - Constructor for class org.deeplearning4j.optimize.listeners.ComposableIterationListener
CompositeReconstructionDistribution - Class in org.deeplearning4j.nn.conf.layers.variational: CompositeReconstructionDistribution is a reconstruction distribution built from multiple other ReconstructionDistribution instances.
The typical use is to combine for example continuous and binary data in the same model, or to combine different distributions for continuous variables.
CompositeReconstructionDistribution(int[], ReconstructionDistribution[], int) - Constructor for class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
CompositeReconstructionDistribution.Builder - Class in org.deeplearning4j.nn.conf.layers.variational
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
computationGraph - Variable in class org.deeplearning4j.streaming.routes.DL4jServeRouteBuilder
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
ComputationGraphConfigurationTest - Class in org.deeplearning4j.nn.conf
ComputationGraphConfigurationTest() - Constructor for class org.deeplearning4j.nn.conf.ComputationGraphConfigurationTest
ComputationGraphConfigurationTest.StaticInnerGraphVertex - Class in org.deeplearning4j.nn.conf
ComputationGraphTestRNN - Class in org.deeplearning4j.nn.graph
ComputationGraphTestRNN() - Constructor for class org.deeplearning4j.nn.graph.ComputationGraphTestRNN
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
computeClusterInfos(Cluster, String) - Static method in class org.deeplearning4j.clustering.cluster.ClusterUtils
computeClusterSetInfo(ClusterSet) - Static method in class org.deeplearning4j.clustering.cluster.ClusterUtils
computeClusterSetInfo(ClusterSet, ExecutorService) - Static method in class org.deeplearning4j.clustering.cluster.ClusterUtils
computeEdgeForces(INDArray, INDArray, INDArray, int, INDArray) - Method in class org.deeplearning4j.clustering.quadtree.QuadTree
computeEdgeForces(INDArray, INDArray, INDArray, int, INDArray) - Method in class org.deeplearning4j.clustering.sptree.SpTree: Compute edge forces using barns hut
computeGaussianKernel(INDArray, double, int) - Method in class org.deeplearning4j.plot.BarnesHutTsne: Computes a gaussian kernel given a vector of squared distance distances
computeGaussianPerplexity(INDArray, double) - Method in class org.deeplearning4j.plot.BarnesHutTsne: Convert data to probability co-occurrences (aka calculating the kernel)
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.FrozenLayer
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.LossLayer
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer
computeGradientAndScore() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
computeGradientAndScore() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
computeGradientAndScore() - Method in class org.deeplearning4j.plot.BarnesHutTsne
computeLossFunctionScoreArray(INDArray, INDArray) - Method in class org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution
computeNonEdgeForces(int, double, INDArray, AtomicDouble) - Method in class org.deeplearning4j.clustering.quadtree.QuadTree: Compute non edge forces using barnes hut
computeNonEdgeForces(int, double, INDArray, AtomicDouble) - Method in class org.deeplearning4j.clustering.sptree.SpTree: Compute non edge forces using barnes hut
computeScore(double, double, boolean) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer: Compute score after labels and input have been set.
computeScore(double, double, boolean) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Compute score after labels and input have been set.
computeScore(double, double, boolean) - Method in class org.deeplearning4j.nn.layers.LossLayer: Compute score after labels and input have been set.
computeScore(double, double, boolean) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer: Compute score after labels and input have been set.
computeScore(VariationalAutoencoder, INDArray) - Method in class org.deeplearning4j.spark.impl.common.score.BaseVaeReconstructionProbWithKeyFunctionAdapter
computeScore(VariationalAutoencoder, INDArray) - Method in class org.deeplearning4j.spark.impl.common.score.BaseVaeScoreWithKeyFunctionAdapter
computeScore(VariationalAutoencoder, INDArray) - Method in class org.deeplearning4j.spark.impl.graph.scoring.CGVaeReconstructionErrorWithKeyFunction
computeScoreForExamples(double, double) - Method in interface org.deeplearning4j.nn.api.layers.IOutputLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, double) - Method in class org.deeplearning4j.nn.layers.BaseOutputLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, double) - Method in class org.deeplearning4j.nn.layers.LossLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, double) - Method in class org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer: Compute the score for each example individually, after labels and input have been set.
computeScoreForExamples(double, double) - Method in class org.deeplearning4j.nn.layers.training.CenterLossOutputLayer: Compute the score for each example individually, after labels and input have been set.
computeSquareDistancesFromNearestCluster(ClusterSet, List<Point>, INDArray, ExecutorService) - Static method in class org.deeplearning4j.clustering.cluster.ClusterUtils
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
ConcurrentHashSet<E> - Class in org.deeplearning4j.parallelism: This is simplified ConcurrentHashSet implementation PLEASE NOTE: This class does NOT implement real equals & hashCode
ConcurrentHashSet() - Constructor for class org.deeplearning4j.parallelism.ConcurrentHashSet
ConcurrentHashSet(Collection<E>) - Constructor for class org.deeplearning4j.parallelism.ConcurrentHashSet
concurrentSkipListSet() - Static method in class org.deeplearning4j.util.MultiDimensionalSet
ConcurrentTokenizer - Class in org.deeplearning4j.text.tokenization.tokenizer: OpenNLP Tokenizer annotator.
ConcurrentTokenizer() - Constructor for class org.deeplearning4j.text.tokenization.tokenizer.ConcurrentTokenizer: Initializes a new instance.
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.graph.TestCompGraphCNN
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.layers.FrozenLayer
conf - Variable in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
conf() - Method in class org.deeplearning4j.nn.layers.variational.VariationalAutoencoder
conf() - Method in class org.deeplearning4j.nn.multilayer.MultiLayerNetwork
conf - Variable in class org.deeplearning4j.optimize.solvers.BaseOptimizer
conf() - Method in class org.deeplearning4j.plot.BarnesHutTsne
configuration - Variable in class org.deeplearning4j.models.embeddings.learning.impl.elements.SkipGram
configuration - Variable in class org.deeplearning4j.models.embeddings.learning.impl.sequence.DBOW
configuration - Variable in class org.deeplearning4j.models.sequencevectors.SequenceVectors.Builder
configuration - Variable in class org.deeplearning4j.models.sequencevectors.SequenceVectors
configuration - Variable in class org.deeplearning4j.nn.graph.ComputationGraph
configuration - Variable in class org.deeplearning4j.spark.models.embeddings.word2vec.Word2Vec.Builder
configuration - Variable in class org.deeplearning4j.spark.models.embeddings.word2vec.Word2Vec
configuration - Variable in class org.deeplearning4j.spark.models.sequencevectors.SparkSequenceVectors.Builder
configurationBroadcast - Variable in class org.deeplearning4j.spark.models.sequencevectors.functions.BaseTokenizerFunction
configurationBroadcast - Variable in class org.deeplearning4j.spark.models.sequencevectors.functions.DistributedFunction
configurationBroadcast - Variable in class org.deeplearning4j.spark.models.sequencevectors.functions.PartitionTrainingFunction
configurationBroadcast - Variable in class org.deeplearning4j.spark.models.sequencevectors.functions.TrainingFunction
configurationBroadcast - Variable in class org.deeplearning4j.spark.models.sequencevectors.SparkSequenceVectors
configure(TokenizerBase.Builder) - Method in class com.atilika.kuromoji.TokenizerBase
configure(VocabCache<T>, WeightLookupTable<T>, VectorsConfiguration) - Method in interface org.deeplearning4j.models.embeddings.learning.ElementsLearningAlgorithm
configure(VocabCache<T>, WeightLookupTable<T>, VectorsConfiguration) - Method in class org.deeplearning4j.models.embeddings.learning.impl.elements.CBOW
configure(VocabCache<T>, WeightLookupTable<T>, VectorsConfiguration) - Method in class org.deeplearning4j.models.embeddings.learning.impl.elements.GloVe
configure(VocabCache<T>, WeightLookupTable<T>, VectorsConfiguration) - Method in class org.deeplearning4j.models.embeddings.learning.impl.elements.SkipGram: SkipGram initialization over given vocabulary and WeightLookupTable
configure(VocabCache<T>, WeightLookupTable<T>, VectorsConfiguration) - Method in class org.deeplearning4j.models.embeddings.learning.impl.sequence.DBOW
configure(VocabCache<T>, WeightLookupTable<T>, VectorsConfiguration) - Method in class org.deeplearning4j.models.embeddings.learning.impl.sequence.DM
configure(VocabCache<T>, WeightLookupTable<T>, VectorsConfiguration) - Method in interface org.deeplearning4j.models.embeddings.learning.SequenceLearningAlgorithm
configure(NeuralNetConfiguration) - Method in class org.deeplearning4j.optimize.Solver.Builder
configure(VocabCache<ShallowSequenceElement>, WeightLookupTable<ShallowSequenceElement>, VectorsConfiguration) - Method in class org.deeplearning4j.spark.models.sequencevectors.learning.elements.BaseSparkLearningAlgorithm
configure(VocabCache<ShallowSequenceElement>, WeightLookupTable<ShallowSequenceElement>, VectorsConfiguration) - Method in class org.deeplearning4j.spark.models.sequencevectors.learning.sequence.BaseSparkSequenceLearningAlgorithm
configure() - Method in class org.deeplearning4j.streaming.kafka.NDArrayPubSubRoute: Called on initialization to build the routes using the fluent builder syntax.
This is a central method for RouteBuilder implementations to implement the routes using the Java fluent builder syntax.
configure() - Method in class org.deeplearning4j.streaming.routes.CamelKafkaRouteBuilder: Let's configure the Camel routing rules using Java code...
configure() - Method in class org.deeplearning4j.streaming.routes.DL4jServeRouteBuilder: Called on initialization to build the routes using the fluent builder syntax.
This is a central method for RouteBuilder implementations to implement the routes using the Java fluent builder syntax.
configured - Variable in class org.deeplearning4j.models.sequencevectors.SequenceVectors
configureListeners(String, Collection<IterationListener>, Collection<IterationListener>) - Method in class org.deeplearning4j.parallelism.trainer.DefaultTrainer
configureMultiLayer() - Method in class org.deeplearning4j.nn.layers.BaseLayerTest
configureSingleLayer() - Method in class org.deeplearning4j.nn.layers.BaseLayerTest
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 java.lang.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
CONJUGATION_FORM - Static variable in class com.atilika.kuromoji.ipadic.compile.DictionaryEntry
CONJUGATION_TYPE - Static variable in class com.atilika.kuromoji.ipadic.compile.DictionaryEntry
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
CONNECTION_COSTS_FILENAME - Static variable in class com.atilika.kuromoji.dict.ConnectionCosts
ConnectionCosts - Class in com.atilika.kuromoji.dict
ConnectionCosts(int, ShortBuffer) - Constructor for class com.atilika.kuromoji.dict.ConnectionCosts
connectionCosts - Variable in class com.atilika.kuromoji.TokenizerBase.Builder
ConnectionCostsCompiler - Class in com.atilika.kuromoji.compile
ConnectionCostsCompiler(OutputStream) - Constructor for class com.atilika.kuromoji.compile.ConnectionCostsCompiler
ConnectionCostsCompilerTest - Class in com.atilika.kuromoji.compile
ConnectionCostsCompilerTest() - Constructor for class com.atilika.kuromoji.compile.ConnectionCostsCompilerTest
constructTempDir(String) - Static method in class org.deeplearning4j.streaming.embedded.TestUtils
consume(InMemoryLookupTable<T>) - Method in class org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable: This method consumes weights of a given InMemoryLookupTable PLEASE NOTE: this method explicitly resets current weights
consumeOnce(DataSet, boolean) - Method in class org.deeplearning4j.util.TestDataSetConsumer: This method consumes single DataSet, and spends delay time simulating execution of this dataset
consumeVocabulary(VocabularyHolder) - Method in class org.deeplearning4j.models.word2vec.wordstore.VocabularyHolder
consumeWhileHasNext(boolean) - Method in class org.deeplearning4j.util.TestDataSetConsumer: This method cycles through iterator, whie iterator.hasNext() returns true.
consumingTopic - Variable in class org.deeplearning4j.streaming.routes.DL4jServeRouteBuilder
contains(INDArray) - Method in class org.deeplearning4j.clustering.kdtree.HyperRect
contains(double) - Method in class org.deeplearning4j.clustering.kdtree.HyperRect.Interval
contains(INDArray) - Method in class org.deeplearning4j.clustering.sptree.Cell
contains(Object) - Method in class org.deeplearning4j.parallelism.ConcurrentHashSet
contains(Object) - Method in class org.deeplearning4j.parallelism.MagicQueue: This method isn't supported
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.parallelism.ConcurrentHashSet
containsAll(Collection<?>) - Method in class org.deeplearning4j.parallelism.MagicQueue: This method isn't supported
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.
containsElement(T) - Method in class org.deeplearning4j.models.word2vec.wordstore.inmemory.AbstractCache: Checks, if specified element exists in vocabulary
containsKey(Object) - Method in class com.atilika.kuromoji.trie.PatriciaTrie: Test membership in this trie
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.
containsKeyPrefix(String) - Method in class com.atilika.kuromoji.trie.PatriciaTrie: Test key prefix membership in this trie (prefix search using key)
containsPoint(INDArray) - Method in class org.deeplearning4j.clustering.quadtree.Cell: Whether the given point is contained within this cell
containsValue(Object) - Method in class com.atilika.kuromoji.trie.PatriciaTrie: Predicate to test value membership
containsValue(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap: Returns true if this map maps one or more keys to the specified value.
containsWord(String) - Method in class org.deeplearning4j.models.word2vec.wordstore.inmemory.AbstractCache: Checks, if specified label exists in vocabulary
containsWord(String) - Method in class org.deeplearning4j.models.word2vec.wordstore.inmemory.InMemoryLookupCache: Deprecated.

Returns true if the cache contains the given word
containsWord(String) - Method in interface org.deeplearning4j.models.word2vec.wordstore.VocabCache: Returns true if the cache contains the given word
containsWord(String) - Method in class org.deeplearning4j.models.word2vec.wordstore.VocabularyHolder: Checks vocabulary for the word existance
content(String[][]) - Method in class org.deeplearning4j.ui.components.table.ComponentTable.Builder: Content for the table, as 2d String[]
content - Variable in class org.deeplearning4j.ui.standalone.ComponentObject
ContextLabelRetriever - Class in org.deeplearning4j.text.movingwindow: Context Label Retriever
ContextLabelTest - Class in org.deeplearning4j.util: Basic test case for the context label test
ContextLabelTest() - Constructor for class org.deeplearning4j.util.ContextLabelTest
contrastiveDivergence() - Method in class org.deeplearning4j.nn.layers.feedforward.rbm.RBM: Deprecated.
No longer used; use fit methods in MultiLayerNetwork
ConvergenceCondition - Class in org.deeplearning4j.clustering.algorithm.condition
ConvergenceCondition() - Constructor for class org.deeplearning4j.clustering.algorithm.condition.ConvergenceCondition
ConvergenceCondition(Condition, double) - Constructor for class org.deeplearning4j.clustering.algorithm.condition.ConvergenceCondition
convert(Collection<Collection<Writable>>, int) - Method in class org.deeplearning4j.streaming.conversion.dataset.CSVRecordToDataSet
convert(Collection<Collection<Writable>>, int) - Method in interface org.deeplearning4j.streaming.conversion.dataset.RecordToDataSet: Converts records in to a dataset
convert(Collection<Collection<Writable>>) - Method in class org.deeplearning4j.streaming.conversion.ndarray.CSVRecordToINDArray
convert(Collection<Collection<Writable>>) - Method in class org.deeplearning4j.streaming.conversion.ndarray.NDArrayRecordToNDArray
convert(Collection<Collection<Writable>>) - Method in interface org.deeplearning4j.streaming.conversion.ndarray.RecordToNDArray: Converts a list of records in to 1 ndarray
converter - Variable in class org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator
convertIpToOctets(String) - Static method in class org.deeplearning4j.spark.models.sequencevectors.utils.NetworkOrganizer
ConvexOptimizer - Interface in org.deeplearning4j.optimize.api: Convex optimizer.
Convolution1DLayer - Class in org.deeplearning4j.nn.conf.layers: 1D (temporal) convolutional layer.
Convolution1DLayer - Class in org.deeplearning4j.nn.layers.convolution: 1D (temporal) convolutional layer.
Convolution1DLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
Convolution1DLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.Convolution1DLayer
Convolution1DLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
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.
ConvolutionalIterationListener - Class in org.deeplearning4j.ui.weights
ConvolutionalIterationListener(UiConnectionInfo, int) - Constructor for class org.deeplearning4j.ui.weights.ConvolutionalIterationListener
ConvolutionalIterationListener(int) - Constructor for class org.deeplearning4j.ui.weights.ConvolutionalIterationListener
ConvolutionalIterationListener(int, boolean) - Constructor for class org.deeplearning4j.ui.weights.ConvolutionalIterationListener
ConvolutionalIterationListener(StatsStorageRouter, int, boolean) - Constructor for class org.deeplearning4j.ui.weights.ConvolutionalIterationListener
ConvolutionalIterationListener(StatsStorageRouter, int, boolean, String, String) - Constructor for class org.deeplearning4j.ui.weights.ConvolutionalIterationListener
ConvolutionalListenerModule - Class in org.deeplearning4j.ui.module.convolutional: Used for plotting results from the ConvolutionalIterationListener
ConvolutionalListenerModule() - Constructor for class org.deeplearning4j.ui.module.convolutional.ConvolutionalListenerModule
ConvolutionHelper - Interface in org.deeplearning4j.nn.layers.convolution: Helper for the convolution layer.
ConvolutionLayer - Class in org.deeplearning4j.nn.conf.layers
ConvolutionLayer(ConvolutionLayer.BaseConvBuilder<?>) - Constructor for class org.deeplearning4j.nn.conf.layers.ConvolutionLayer: ConvolutionLayer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the depth The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size
ConvolutionLayer - Class in org.deeplearning4j.nn.layers.convolution: Convolution layer
ConvolutionLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
ConvolutionLayer(NeuralNetConfiguration, INDArray) - Constructor for class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
ConvolutionLayer.AlgoMode - Enum in org.deeplearning4j.nn.conf.layers
ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>> - Class in org.deeplearning4j.nn.conf.layers
ConvolutionLayer.Builder - Class in org.deeplearning4j.nn.conf.layers
ConvolutionLayerSetup - Class in org.deeplearning4j.nn.conf.layers.setup: Deprecated.
Use MultiLayerConfiguration.Builder.setInputType(InputType) to set nIns and add preprocessors as required. This can be done using builder.setInputType(InputType.convolutional(height, width, channels))
ConvolutionLayerSetup(MultiLayerConfiguration.Builder, int, int, int) - Constructor for class org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup: Deprecated.
Use MultiLayerConfiguration.Builder.setInputType(InputType) to set nIns and add preprocessors as required. This can be done using builder.setInputType(InputType.convolutional(height, width, channels)) For image data that has been flattened into a row vector per example (shape [minibatchSize,depth*height*width]) instead use InputType.convolutionalFlat(height,width,depth).
ConvolutionLayerSetupTest - Class in org.deeplearning4j.nn.layers.convolution
ConvolutionLayerSetupTest() - Constructor for class org.deeplearning4j.nn.layers.convolution.ConvolutionLayerSetupTest
ConvolutionLayerTest - Class in org.deeplearning4j.nn.layers.convolution
ConvolutionLayerTest() - Constructor for class org.deeplearning4j.nn.layers.convolution.ConvolutionLayerTest
ConvolutionListenerPersistable - Class in org.deeplearning4j.ui.weights: Created by Alex on 24/10/2016.
ConvolutionListenerPersistable() - Constructor for class org.deeplearning4j.ui.weights.ConvolutionListenerPersistable
ConvolutionMode - Enum in org.deeplearning4j.nn.conf: ConvolutionMode defines how convolution operations should be executed for Convolutional and Subsampling layers, for a given input size and network configuration (specifically stride/padding/kernel sizes).
Currently, 3 modes are provided:

Strict: Output size for Convolutional and Subsampling layers are calculated as follows, in each dimension: outputSize = (inputSize - kernelSize + 2*padding) / stride + 1.
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: Set the convolution mode for the Convolution layer.
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Set the convolution mode for the Convolution layer.
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer.BaseSubsamplingBuilder: Set the convolution mode for the Convolution layer.
convolutionMode - Variable in class org.deeplearning4j.nn.conf.layers.SubsamplingLayer
convolutionMode - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
convolutionMode(ConvolutionMode) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
convolutionMode - Variable in class org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer
convolutionMode - Variable in class org.deeplearning4j.nn.transferlearning.FineTuneConfiguration
ConvolutionParamInitializer - Class in org.deeplearning4j.nn.params: Initialize convolution params.
ConvolutionParamInitializer() - Constructor for class org.deeplearning4j.nn.params.ConvolutionParamInitializer
ConvolutionUtils - Class in org.deeplearning4j.util: Convolutional shape utilities
CoOccurenceReader<T extends SequenceElement> - Interface in org.deeplearning4j.models.glove.count: Created by raver on 24.12.2015.
CoOccurrenceCalculator - Class in org.deeplearning4j.spark.models.embeddings.glove.cooccurrences: Calculate co occurrences based on tokens
CoOccurrenceCalculator(boolean, Broadcast<VocabCache<VocabWord>>, int) - Constructor for class org.deeplearning4j.spark.models.embeddings.glove.cooccurrences.CoOccurrenceCalculator
CoOccurrenceCounts - Class in org.deeplearning4j.spark.models.embeddings.glove.cooccurrences: Co occurrence count reduction
CoOccurrenceCounts() - Constructor for class org.deeplearning4j.spark.models.embeddings.glove.cooccurrences.CoOccurrenceCounts
coOccurrenceCounts(Broadcast<CounterMap<String, String>>) - Method in class org.deeplearning4j.spark.models.embeddings.glove.GloveParam.Builder
CoOccurrenceWeight<T extends SequenceElement> - Class in org.deeplearning4j.models.glove.count: Simple POJO holding pairs of elements and their respective weights, used in GloVe -> CoOccurrence
CoOccurrenceWeight() - Constructor for class org.deeplearning4j.models.glove.count.CoOccurrenceWeight
CoOccurrenceWriter<T extends SequenceElement> - Interface in org.deeplearning4j.models.glove.count: Created by fartovii on 25.12.15.
Coords - Class in org.deeplearning4j.ui.flow.beans
Coords() - Constructor for class org.deeplearning4j.ui.flow.beans.Coords
Coords(int, int) - Constructor for class org.deeplearning4j.ui.flow.beans.Coords
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
copyWeightsToLayer(Layer) - Method in class org.deeplearning4j.nn.modelimport.keras.KerasLayer: Copy Keras layer weights to DL4J Layer.
corner(int) - Method in class org.deeplearning4j.clustering.sptree.Cell
corner() - Method in class org.deeplearning4j.clustering.sptree.Cell
CORRECT - Static variable in class org.deeplearning4j.models.embeddings.reader.impl.BasicModelUtils
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
count - Static variable in class org.deeplearning4j.models.sequencevectors.transformers.impl.iterables.ParallelTransformerIterator
count() - Method in class org.deeplearning4j.ui.stats.sbe.StaticInfoDecoder.HwDeviceInfoGroupDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.StaticInfoDecoder.ModelParamNamesDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.StaticInfoDecoder.SwEnvironmentInfoDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.StorageMetaDataDecoder.ExtraMetaDataBytesDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.DataSetMetaDataBytesDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.DataSetMetaDataBytesDecoder.MetaDataBytesDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.GcStatsDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.LayerNamesDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.MemoryUseDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.ParamNamesDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.PerformanceDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.PerParameterStatsDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.PerParameterStatsDecoder.HistogramsDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.PerParameterStatsDecoder.HistogramsDecoder.HistogramCountsDecoder
count() - Method in class org.deeplearning4j.ui.stats.sbe.UpdateDecoder.PerParameterStatsDecoder.SummaryStatDecoder
CountCumSum - Class in org.deeplearning4j.spark.text.functions
CountCumSum(JavaRDD<AtomicLong>) - Constructor for class org.deeplearning4j.spark.text.functions.CountCumSum
countDown() - Method in class org.deeplearning4j.models.word2vec.StreamWork
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
counter - Variable in class org.deeplearning4j.models.sequencevectors.transformers.impl.GraphTransformer
counter - Variable in class org.deeplearning4j.spark.models.sequencevectors.learning.elements.SparkSkipGram
counter - Variable in class org.deeplearning4j.spark.models.sequencevectors.utils.NetworkOrganizer.VirtualNode
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
countFinished - Variable in class org.deeplearning4j.models.paragraphvectors.ParagraphVectors
CountFunction<T extends SequenceElement> - Class in org.deeplearning4j.spark.models.sequencevectors.functions: This accumulator function does count individual elements, using provided Accumulator
CountFunction(Broadcast<VectorsConfiguration>, Broadcast<VoidConfiguration>, Accumulator<Counter<Long>>, boolean) - Constructor for class org.deeplearning4j.spark.models.sequencevectors.functions.CountFunction
CountMap<T extends SequenceElement> - Class in org.deeplearning4j.models.glove.count: Drop-in replacement for CounterMap WORK IN PROGRESS, PLEASE DO NOT USE
CountMap() - Constructor for class org.deeplearning4j.models.glove.count.CountMap
CountPartitionsFunction<T> - Class in org.deeplearning4j.spark.impl.common: This is a function use to count the number of elements in each partition.
CountPartitionsFunction() - Constructor for class org.deeplearning4j.spark.impl.common.CountPartitionsFunction
CountsForThreshold(double) - Constructor for class org.deeplearning4j.eval.ROC.CountsForThreshold
CountsForThreshold(double, int) - Constructor for class org.deeplearning4j.eval.ROCBinary.CountsForThreshold
countSubmitted - Variable in class org.deeplearning4j.models.paragraphvectors.ParagraphVectors
countTokens() - Method in class org.deeplearning4j.text.tokenization.tokenizer.DefaultStreamTokenizer: Returns number of tokens PLEASE NOTE: this method effectively preloads all tokens.
countTokens() - Method in class org.deeplearning4j.text.tokenization.tokenizer.DefaultTokenizer
countTokens() - Method in class org.deeplearning4j.text.tokenization.tokenizer.JapaneseTokenizer
countTokens() - Method in class org.deeplearning4j.text.tokenization.tokenizer.KoreanTokenizer
countTokens() - Method in class org.deeplearning4j.text.tokenization.tokenizer.NGramTokenizer
countTokens() - Method in class org.deeplearning4j.text.tokenization.tokenizer.PosUimaTokenizer
countTokens() - Method in interface org.deeplearning4j.text.tokenization.tokenizer.Tokenizer: The number of tokens in the tokenizer
countTokens() - Method in class org.deeplearning4j.text.tokenization.tokenizer.UimaTokenizer
create() - Method in class org.deeplearning4j.aws.ec2.Ec2BoxCreator: Create the instances
create() - Method in class org.deeplearning4j.bagofwords.vectorizer.DefaultInputStreamCreator
create(int) - Method in class org.deeplearning4j.graph.vertexfactory.IntegerVertexFactory
create(int) - Method in class org.deeplearning4j.graph.vertexfactory.StringVertexFactory
create(int) - Method in interface org.deeplearning4j.graph.vertexfactory.VertexFactory
create(int) - Method in class org.deeplearning4j.graph.vertexfactory.VoidVertexFactory
create(int) - Method in class org.deeplearning4j.models.sequencevectors.graph.vertex.AbstractVertexFactory
create(int, T) - Method in class org.deeplearning4j.models.sequencevectors.graph.vertex.AbstractVertexFactory
create(int) - Method in interface org.deeplearning4j.models.sequencevectors.graph.vertex.VertexFactory
create(int, T) - Method in interface org.deeplearning4j.models.sequencevectors.graph.vertex.VertexFactory
create() - Method in interface org.deeplearning4j.models.word2vec.InputStreamCreator: Create an input stream
create(int, Model, int, boolean, ParallelWrapper) - Method in class org.deeplearning4j.parallelism.factory.DefaultTrainerContext: Create a Trainer based on the given parameters
create(int, Model, int, boolean, ParallelWrapper) - Method in interface org.deeplearning4j.parallelism.factory.TrainerContext: Create a Trainer based on the given parameters
create() - Method in interface org.deeplearning4j.parallelism.main.DataSetIteratorProviderFactory: Create an DataSetIterator
create() - Method in class org.deeplearning4j.parallelism.main.MnistDataSetIteratorProviderFactory: Create an DataSetIterator
create() - Method in interface org.deeplearning4j.parallelism.main.MultiDataSetProviderFactory: Create an MultiDataSetIterator
create(int, Model, int, boolean, ParallelWrapper) - Method in class org.deeplearning4j.parallelism.parameterserver.ParameterServerTrainerContext: Create a Trainer based on the given parameters
create(String) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory
create(InputStream) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory
create(String) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.JapaneseTokenizerFactory
create(InputStream) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.JapaneseTokenizerFactory
create(String) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.KoreanTokenizerFactory
create(InputStream) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.KoreanTokenizerFactory
create(String) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.NGramTokenizerFactory
create(InputStream) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.NGramTokenizerFactory
create(String) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.PosUimaTokenizerFactory
create(InputStream) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.PosUimaTokenizerFactory
create(String) - Method in interface org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory: The tokenizer to createComplex
create(InputStream) - Method in interface org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory: Create a tokenizer based on an input stream
create(String) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.UimaTokenizerFactory
create(InputStream) - Method in class org.deeplearning4j.text.tokenization.tokenizerfactory.UimaTokenizerFactory
createAppendingOutputStream(File) - Static method in class org.deeplearning4j.util.FileOperations
createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
createBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createBias(int, double, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createCenterLossMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.CenterLossParamInitializer
createConsumer() - Method in class org.deeplearning4j.streaming.kafka.NDArrayKafkaClient
createData() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfigurationTest
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
createPublisher() - Method in class org.deeplearning4j.streaming.kafka.NDArrayKafkaClient
createRouteBuilder() - Method in class org.deeplearning4j.streaming.routes.Dl4jServingRouteTest
createSpot() - Method in class org.deeplearning4j.aws.ec2.Ec2BoxCreator
createStepFunction(StepFunction) - Static method in class org.deeplearning4j.optimize.stepfunctions.StepFunctions
createToken(int, String, ViterbiNode.Type, int, Dictionary) - Method in interface com.atilika.kuromoji.viterbi.TokenFactory
createTokenList(String) - Method in class com.atilika.kuromoji.TokenizerBase: Tokenizes the provided text and returns a list of tokens with various feature information
createTopic(String, int) - Method in class org.deeplearning4j.streaming.embedded.EmbeddedKafkaCluster
createTopics(String...) - Method in class org.deeplearning4j.streaming.embedded.EmbeddedKafkaCluster
createVisibleBias(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.PretrainParamInitializer
createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.ConvolutionParamInitializer
createWeightMatrix(NeuralNetConfiguration, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createWeightMatrix(int, int, WeightInit, Distribution, INDArray, boolean) - Method in class org.deeplearning4j.nn.params.DefaultParamInitializer
createWithPath(String) - Static method in class org.deeplearning4j.text.sentenceiterator.labelaware.LabelAwareUimaSentenceIterator: Creates a uima sentence iterator with the given path
createWithPath(String) - Static method in class org.deeplearning4j.text.sentenceiterator.UimaSentenceIterator: Creates a uima sentence iterator with the given path
creds - Variable in class org.deeplearning4j.aws.s3.BaseS3
CSVReader(File) - Constructor for class org.deeplearning4j.models.embeddings.loader.WordVectorSerializer.CSVReader
CSVRecordToDataSet - Class in org.deeplearning4j.streaming.conversion.dataset: Assumes csv format and converts a batch of records in to a size() x record length matrix.
CSVRecordToDataSet() - Constructor for class org.deeplearning4j.streaming.conversion.dataset.CSVRecordToDataSet
CSVRecordToINDArray - Class in org.deeplearning4j.streaming.conversion.ndarray: Assumes csv format and converts a batch of records in to a size() x record length matrix.
CSVRecordToINDArray() - Constructor for class org.deeplearning4j.streaming.conversion.ndarray.CSVRecordToINDArray
cudnnAlgoMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder
cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder: Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
cudnnAlgoMode(ConvolutionLayer.AlgoMode) - Method in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer.Builder: Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
cudnnAlgoMode - Variable in class org.deeplearning4j.nn.conf.layers.ConvolutionLayer: Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
CudnnBatchNormalizationHelper - Class in org.deeplearning4j.nn.layers.normalization: cuDNN-based helper for the batch normalization layer.
CudnnBatchNormalizationHelper() - Constructor for class org.deeplearning4j.nn.layers.normalization.CudnnBatchNormalizationHelper
CudnnConvolutionHelper - Class in org.deeplearning4j.nn.layers.convolution: cuDNN-based helper for the convolution layer.
CudnnConvolutionHelper() - Constructor for class org.deeplearning4j.nn.layers.convolution.CudnnConvolutionHelper
CudnnLocalResponseNormalizationHelper - Class in org.deeplearning4j.nn.layers.normalization: cuDNN-based helper for the local response normalization layer.
CudnnLocalResponseNormalizationHelper() - Constructor for class org.deeplearning4j.nn.layers.normalization.CudnnLocalResponseNormalizationHelper
CudnnSubsamplingHelper - Class in org.deeplearning4j.nn.layers.convolution.subsampling: cuDNN-based helper for the subsampling layer.
CudnnSubsamplingHelper() - Constructor for class org.deeplearning4j.nn.layers.convolution.subsampling.CudnnSubsamplingHelper
cumSumBetweenPartition() - Method in class org.deeplearning4j.spark.text.functions.CountCumSum
cumSumWithinPartition() - Method in class org.deeplearning4j.spark.text.functions.CountCumSum
curr - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
currBucket() - Method in class org.deeplearning4j.aws.s3.reader.BaseS3DataSetIterator
currentFile - Variable in class org.deeplearning4j.text.sentenceiterator.FileSentenceIterator
currentIterator - Variable in class org.deeplearning4j.models.sequencevectors.transformers.impl.SentenceTransformer
currentIterator - Variable in class org.deeplearning4j.text.documentiterator.SimpleLabelAwareIterator
currentLabel() - Method in interface org.deeplearning4j.text.documentiterator.LabelAwareDocumentIterator: Returns the current label
currentLabel() - Method in class org.deeplearning4j.text.sentenceiterator.labelaware.LabelAwareFileSentenceIterator
currentLabel() - Method in class org.deeplearning4j.text.sentenceiterator.labelaware.LabelAwareListSentenceIterator: Returns the current label for nextSentence()
currentLabel() - Method in interface org.deeplearning4j.text.sentenceiterator.labelaware.LabelAwareSentenceIterator: Returns the current label for nextSentence()
currentLabel() - Method in class org.deeplearning4j.text.sentenceiterator.labelaware.LabelAwareUimaSentenceIterator: Returns the current label for nextSentence()
currentLabels() - Method in class org.deeplearning4j.text.sentenceiterator.labelaware.LabelAwareFileSentenceIterator
currentLabels() - Method in class org.deeplearning4j.text.sentenceiterator.labelaware.LabelAwareListSentenceIterator
currentLabels() - Method in interface org.deeplearning4j.text.sentenceiterator.labelaware.LabelAwareSentenceIterator: For multi label problems
currentLabels() - Method in class org.deeplearning4j.text.sentenceiterator.labelaware.LabelAwareUimaSentenceIterator
currentTimeMillis() - Method in class org.deeplearning4j.spark.time.NTPTimeSource
currentTimeMillis() - Method in class org.deeplearning4j.spark.time.SystemClockTimeSource
currentTimeMillis() - Method in interface org.deeplearning4j.spark.time.TimeSource: Get the current time in milliseconds, according to this TimeSource
currIndex - Variable in class org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable.WeightIterator
currLineIterator - Variable in class org.deeplearning4j.text.sentenceiterator.FileSentenceIterator
cursor() - Method in class org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator
cursor() - Method in class org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator
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
cursor() - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
cursor() - Method in class org.deeplearning4j.iterator.CnnSentenceDataSetIterator
cursor() - Method in class org.deeplearning4j.keras.HDF5MiniBatchDataSetIterator
cursor() - Method in class org.deeplearning4j.models.word2vec.iterator.Word2VecDataFetcher
cursor() - Method in class org.deeplearning4j.models.word2vec.iterator.Word2VecDataSetIterator
cursor - Variable in class org.deeplearning4j.spark.iterator.BaseDataSetIterator
cursor() - Method in class org.deeplearning4j.spark.iterator.BaseDataSetIterator
CURVES_FILE_NAME - Static variable in class org.deeplearning4j.datasets.fetchers.CurvesDataFetcher
CURVES_URL - Static variable in class org.deeplearning4j.datasets.fetchers.CurvesDataFetcher
CurvesDataFetcher - Class in org.deeplearning4j.datasets.fetchers: Curves data fetcher
CurvesDataFetcher() - Constructor for class org.deeplearning4j.datasets.fetchers.CurvesDataFetcher
CurvesDataSetIterator - Class in org.deeplearning4j.datasets.iterator: Curves data applyTransformToDestination iterator
CurvesDataSetIterator(int, int) - Constructor for class org.deeplearning4j.datasets.iterator.CurvesDataSetIterator
CUSTOM_FUNCTIONALITY - Static variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration: System property for custom layers, preprocessors, graph vertices etc.
CustomActivation - Class in org.deeplearning4j.nn.layers.custom.testclasses: Created by Alex on 19/12/2016.
CustomActivation() - Constructor for class org.deeplearning4j.nn.layers.custom.testclasses.CustomActivation
CustomLayer - Class in org.deeplearning4j.nn.layers.custom.testclasses: Created by Alex on 26/08/2016.
CustomLayer(double) - Constructor for class org.deeplearning4j.nn.layers.custom.testclasses.CustomLayer
CustomLayer - Class in org.deeplearning4j.spark.impl.customlayer.layer: Created by Alex on 26/08/2016.
CustomLayer(double) - Constructor for class org.deeplearning4j.spark.impl.customlayer.layer.CustomLayer
CustomLayerImpl - Class in org.deeplearning4j.nn.layers.custom.testclasses: Basically: identical to DenseLayer Created by Alex on 26/08/2016.
CustomLayerImpl(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.custom.testclasses.CustomLayerImpl
CustomLayerImpl - Class in org.deeplearning4j.spark.impl.customlayer.layer: Basically: identical to DenseLayer Created by Alex on 26/08/2016.
CustomLayerImpl(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.spark.impl.customlayer.layer.CustomLayerImpl
CustomOutputLayer - Class in org.deeplearning4j.nn.layers.custom.testclasses: A custom output layer for testing.
CustomOutputLayer(CustomOutputLayer.Builder) - Constructor for class org.deeplearning4j.nn.layers.custom.testclasses.CustomOutputLayer
CustomOutputLayer.Builder - Class in org.deeplearning4j.nn.layers.custom.testclasses
CustomOutputLayerImpl - Class in org.deeplearning4j.nn.layers.custom.testclasses: Created by Alex on 28/08/2016.
CustomOutputLayerImpl(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.custom.testclasses.CustomOutputLayerImpl
CustomPreprocessorTest - Class in org.deeplearning4j.nn.conf.preprocessor: Created by Alex on 09/09/2016.
CustomPreprocessorTest() - Constructor for class org.deeplearning4j.nn.conf.preprocessor.CustomPreprocessorTest
CustomStemmingPreprocessor - Class in org.deeplearning4j.text.tokenization.tokenizer.preprocessor: This is StemmingPreprocessor compatible with different StemmingProcessors defined as lucene/tartarus SnowballProgram Like, but not limited to: RussianStemmer, DutchStemmer, FrenchStemmer etc PLEASE NOTE: This preprocessor is thread-safe by using synchronized method
CustomStemmingPreprocessor(SnowballProgram) - Constructor for class org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CustomStemmingPreprocessor

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