- able(Iterator<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
Wraps an iterator as an iterable
- abs(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- accept(T) - Method in interface org.deeplearning4j.berkeley.Filter
-
- accuracy() - Method in class org.deeplearning4j.eval.Evaluation
-
Accuracy:
TP + TN / (P + N)
- activate() - Method in interface org.deeplearning4j.nn.api.Layer
-
Trigger an activation with the last specified input
- activate(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
Initialize the layer with the given input
and return the activation for this layer
given this input
- activate() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Triggers the activation of the last hidden layer ie: not logistic regression
- activate(int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Triggers the activation for a given layer
- activate(int, INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Triggers the activation of the given layer
- activate() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- activate(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- activate(INDArray) - Method in class org.deeplearning4j.nn.layers.ConvolutionDownSampleLayer
-
- activate(INDArray) - Method in class org.deeplearning4j.nn.layers.SubsamplingLayer
-
- activationFromPrevLayer(int, INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- activationFunction - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- activationFunction(ActivationFunction) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- activationMean() - Method in interface org.deeplearning4j.nn.api.Layer
-
- activationMean() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- adagradResetIterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- add(E, double) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- add(T, T) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Increments the entry specified by actual and predicted by one.
- add(T, T, int) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Increments the entry specified by actual and predicted by count.
- add(ConfusionMatrix<T>) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Adds the entries from another confusion matrix to this one.
- add(NeuralNetworkGradient) - Method in class org.deeplearning4j.nn.gradient.NeuralNetworkGradient
-
Adds the given gradient and this one together
- add(OutputLayerGradient) - Method in class org.deeplearning4j.nn.gradient.OutputLayerGradient
-
Sums this gradient with the given one
- add(E) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- add(Object, int) - Method in class org.deeplearning4j.util.Index
-
- add(Object) - Method in class org.deeplearning4j.util.Index
-
- add(Pair<K, V>) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Adds the specified element to this applyTransformToDestination if it is not already present
(optional operation).
- add(K, V) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
- addAll(Collection<? extends E>) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- addAll(Collection<? extends Pair<K, V>>) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Adds all of the elements in the specified collection to this applyTransformToDestination if
they're not already present (optional operation).
- addColumn(List<String>) - Method in class org.deeplearning4j.util.StringGrid
-
- addExp(float[], int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- addExp_Old(float[]) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Similar to logAdd, but without the final log.
- addGradient(MultiLayerGradient) - Method in class org.deeplearning4j.nn.gradient.MultiLayerGradient
-
- addRow(List<String>) - Method in class org.deeplearning4j.util.StringGrid
-
- addToConfusion(int, int) - Method in class org.deeplearning4j.eval.Evaluation
-
Adds to the confusion matrix
- adjustedrSquared(double, int, int) - Static method in class org.deeplearning4j.util.MathUtils
-
This calculates the adjusted r^2 including degrees of freedom.
- allMatches(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- appendTo(String, File) - Static method in class org.deeplearning4j.util.FileOperations
-
- appendToEach(String, int) - Method in class org.deeplearning4j.util.StringGrid
-
- applyDropConnectIfNecessary(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Applies drop connect relative to connections.
- applyDropOutIfNecessary(INDArray) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- applyDropOutIfNecessary(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- applySparsity(INDArray) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
Applies sparsity to the passed in hbias gradient
- applySparsity - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- applySparsity(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- applyTransforms() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- approxEquals(Counter<E>, double) - Method in class org.deeplearning4j.berkeley.Counter
-
- approxExp(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- approxLog(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- approxPow(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- ArchiveUtils - Class in org.deeplearning4j.util
-
- ArchiveUtils() - Constructor for class org.deeplearning4j.util.ArchiveUtils
-
- argMax() - Method in class org.deeplearning4j.berkeley.Counter
-
Finds the key with maximum count.
- argMax() - Method in class org.deeplearning4j.berkeley.CounterMap
-
Finds the key with maximum count.
- argsToMap(String[]) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Parses command line arguments into a Map.
- argsToMap(String[], Map<String, Integer>) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Parses command line arguments into a Map.
- argsToProperties(String[]) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- argsToProperties(String[], Map) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- asciify(String) - Method in class org.deeplearning4j.util.FingerPrintKeyer
-
- asCounter() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
Returns a counter whose keys are the elements in this priority queue, and
whose counts are the priorities in this queue.
- asImageMiniBatches(File, int, int) - Method in class org.deeplearning4j.util.ImageLoader
-
Slices up an image in to a mini batch.
- asMatrix(File) - Method in class org.deeplearning4j.util.ImageLoader
-
- asMinPriorityQueue() - Method in class org.deeplearning4j.berkeley.Counter
-
Warning: all priorities are the negative of their counts in the counter
here
- asPriorityQueue() - Method in class org.deeplearning4j.berkeley.Counter
-
Builds a priority queue whose elements are the counter's elements, and
whose priorities are those elements' counts in the counter.
- asRowVector(File) - Method in class org.deeplearning4j.util.ImageLoader
-
- asType(Class<RBM>) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM.Builder
-
- asType(Class<E>) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork.Builder
-
- AutoEncoder - Class in org.deeplearning4j.models.featuredetectors.autoencoder
-
Normal 2 layer back propagation network
- AutoEncoder(INDArray, INDArray, INDArray, INDArray, NeuralNetConfiguration) - Constructor for class org.deeplearning4j.models.featuredetectors.autoencoder.AutoEncoder
-
- AutoEncoder.Builder - Class in org.deeplearning4j.models.featuredetectors.autoencoder
-
- AutoEncoderOptimizer - Class in org.deeplearning4j.optimize.optimizers.autoencoder
-
Auto Encoder Optimizer
- AutoEncoderOptimizer(NeuralNetwork, float, Object[], NeuralNetwork.OptimizationAlgorithm, LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.optimize.optimizers.autoencoder.AutoEncoderOptimizer
-
- calculate(INDArray, int, double) - Method in class org.deeplearning4j.plot.Tsne
-
- call(I) - Method in interface org.deeplearning4j.berkeley.MyMethod
-
- capitalize(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Uppercases the first character of a string.
- cgBackTrack(List<INDArray>, INDArray) - Method in class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
Iterate through the current applyTransformToDestination of gradients
and backtrack upon an optimal step
that improves the current score
- chiSquare2by2(int, int, int, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Find a 2x2 chi-square value.
- choleskyFromMatrix(RealMatrix) - Method in class org.deeplearning4j.util.MathUtils
-
This will return the cholesky decomposition of
the given matrix
- clamp(int, int, int) - Static method in class org.deeplearning4j.util.MathUtils
-
Clamps the value to a discrete value
- classCount(int) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the number of times the given label
has actually occurred
- Classifier - Interface in org.deeplearning4j.nn.api
-
A classifier (this is for supervised learning)
- classifier() - Method in interface org.deeplearning4j.nn.api.SequenceClassifier
-
- classify(INDArray) - Method in class org.deeplearning4j.clustering.KMeansClustering
-
- clazz - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork.Builder
-
- clazz - Variable in class org.deeplearning4j.nn.BaseNeuralNetwork.Builder
-
- clear() - Method in class org.deeplearning4j.berkeley.Counter
-
- 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).
- clearInput() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
Clears the input from the neural net
- clearInput() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Clears the input from all of the neuralNets
- clearInput() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
Clears the input from the neural net
- clone() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
Returns a clone of this priority queue.
- clone() - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
- clone() - Method in interface org.deeplearning4j.nn.api.Layer
-
- clone() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- clone() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- clone() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- clone() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- clone() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
Creates and returns a copy of this object.
- clone() - Method in class org.deeplearning4j.nn.gradient.MultiLayerGradient
-
- clone() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- clone() - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
- close() - Method in class org.deeplearning4j.datasets.mnist.draw.DrawReconstruction
-
- close() - Method in class org.deeplearning4j.plot.FilterRenderer
-
- clusterColumn(int) - Method in class org.deeplearning4j.util.StringGrid
-
- 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.
- compare(Pair<F, S>, Pair<F, S>) - Method in class org.deeplearning4j.berkeley.Pair.DefaultLexicographicPairComparator
-
- compare(Pair<S, T>, Pair<S, T>) - Method in class org.deeplearning4j.berkeley.Pair.FirstComparator
-
- compare(Pair<F, S>, Pair<F, S>) - Method in class org.deeplearning4j.berkeley.Pair.LexicographicPairComparator
-
- compare(Pair<S, T>, Pair<S, T>) - Method in class org.deeplearning4j.berkeley.Pair.ReverseFirstComparator
-
- compare(Pair<S, T>, Pair<S, T>) - Method in class org.deeplearning4j.berkeley.Pair.ReverseSecondComparator
-
- compare(Pair<S, T>, Pair<S, T>) - Method in class org.deeplearning4j.berkeley.Pair.SecondComparator
-
- compare(Map<String, Integer>, Map<String, Integer>) - Method in class org.deeplearning4j.util.StringCluster.SizeComparator
-
- compareTo(Pair<F, S>) - Method in class org.deeplearning4j.berkeley.Pair
-
Compares this object with the specified object for order.
- computeDeltas() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- computeDeltas2() - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
- computeDeltas2() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- computeDeltasR(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
- computeDeltasR(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- computeDxs() - Method in class org.deeplearning4j.clustering.KMeansClustering
-
- computeHistogramBucketIndex(double, double, double, int) - Method in class org.deeplearning4j.plot.FilterRenderer
-
- computeHistogramBucketIndexAlt(double, double, double, int) - Method in class org.deeplearning4j.plot.FilterRenderer
-
This is faster but produces rounding errors
- 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
-
- concatBiases(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- concatBiases - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- concurrentSkipListSet() - Static method in class org.deeplearning4j.util.MultiDimensionalSet
-
- conf() - Method in interface org.deeplearning4j.nn.api.Layer
-
- conf() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- conf - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork.Builder
-
- conf - Variable in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- conf() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- conf - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- conf() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- conf - Variable in class org.deeplearning4j.nn.layers.Layer.Builder
-
- conf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.Layer.Builder
-
- configure(NeuralNetConfiguration) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN.Builder
-
- configure(NeuralNetConfiguration) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder.Builder
-
- configure(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.Builder
-
- configure(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork.Builder
-
- configure(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.OutputLayer.Builder
-
- ConfusionMatrix<T extends Comparable<? super T>> - Class in org.deeplearning4j.eval
-
This data structure provides an easy way to build and output a confusion matrix.
- ConfusionMatrix() - Constructor for class org.deeplearning4j.eval.ConfusionMatrix
-
Creates an empty confusion Matrix
- ConfusionMatrix(ConfusionMatrix<T>) - Constructor for class org.deeplearning4j.eval.ConfusionMatrix
-
Creates a new ConfusionMatrix initialized with the contents of another ConfusionMatrix.
- conjGradient(INDArray, INDArray, INDArray, int) - Method in class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
- constrainGradientToUnitNorm(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- constrainGradientToUnitNorm - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- contains(Object) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- contains(K, T) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
- contains(Object) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns true if this applyTransformToDestination contains the specified element.
- contains(K, V) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
- containsAll(Collection<?>) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- containsAll(Collection<?>) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns true if this applyTransformToDestination contains all of the elements of the
specified collection.
- containsKey(E) - Method in class org.deeplearning4j.berkeley.Counter
-
Returns whether the counter contains the given key.
- containsKey(K) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- containsKey(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns true if this map contains a mapping for the specified
key.
- containsValue(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns true if this map maps one or more keys to the
specified value.
- contrastiveDivergence(double, int, INDArray) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Contrastive divergence revolves around the idea
of approximating the log likelihood around x1(input) with repeated sampling.
- contrastiveDivergence(double, int, INDArray, int) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Contrastive divergence revolves around the idea
of approximating the log likelihood around x1(input) with repeated sampling.
- convertListPairs(List<DataSet>) - Method in class org.deeplearning4j.base.LFWLoader
-
- ConvolutionDownSampleLayer - Class in org.deeplearning4j.nn.layers
-
Convolution layer
- ConvolutionDownSampleLayer(NeuralNetConfiguration, INDArray, INDArray, INDArray) - Constructor for class org.deeplearning4j.nn.layers.ConvolutionDownSampleLayer
-
- ConvolutionDownSampleLayer(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.layers.ConvolutionDownSampleLayer
-
- coordSplit(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the coordinate split in a list of coordinates
such that the values for ret[0] are the x values
and ret[1] are the y values
- coordSplit(List<Double>) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the coordinate split in a list of coordinates
such that the values for ret[0] are the x values
and ret[1] are the y values
- correlation(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Returns the correlation coefficient of two double vectors.
- corruptionLevel - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- Counter<E> - Class in org.deeplearning4j.berkeley
-
A map from objects to doubles.
- Counter() - Constructor for class org.deeplearning4j.berkeley.Counter
-
- Counter(boolean) - Constructor for class org.deeplearning4j.berkeley.Counter
-
- Counter(MapFactory<E, Double>) - Constructor for class org.deeplearning4j.berkeley.Counter
-
- Counter(Map<? extends E, Double>) - Constructor for class org.deeplearning4j.berkeley.Counter
-
- Counter(Counter<? extends E>) - Constructor for class org.deeplearning4j.berkeley.Counter
-
- Counter(Collection<? extends E>) - Constructor for class org.deeplearning4j.berkeley.Counter
-
- CounterMap<K,V> - Class in org.deeplearning4j.berkeley
-
Maintains counts of (key, value) pairs.
- CounterMap(CounterMap<K, V>) - Constructor for class org.deeplearning4j.berkeley.CounterMap
-
- CounterMap() - Constructor for class org.deeplearning4j.berkeley.CounterMap
-
- CounterMap(MapFactory<K, Counter<V>>, MapFactory<V, Double>) - Constructor for class org.deeplearning4j.berkeley.CounterMap
-
- CounterMap(boolean) - Constructor for class org.deeplearning4j.berkeley.CounterMap
-
- createAppendingOutputStream(File) - Static method in class org.deeplearning4j.util.FileOperations
-
- createBias() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- createBias() - Method in class org.deeplearning4j.nn.layers.ConvolutionDownSampleLayer
-
- createBias() - Method in class org.deeplearning4j.nn.layers.SubsamplingLayer
-
- createHiddenLayer(int, INDArray) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN
-
s a hidden layer with the given parameters.
- createHiddenLayer(int, INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Creates a hidden layer with the given parameters.
- createInputMatrix(int) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
Creates a feature vector
- createLayer(INDArray, INDArray, INDArray, INDArray, int) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN
-
- createLayer(INDArray, INDArray, INDArray, INDArray, int) - Method in class org.deeplearning4j.models.classifiers.sae.StackedAutoEncoder
-
- createLayer(INDArray, INDArray, INDArray, INDArray, int) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder
-
- createLayer(INDArray, INDArray, INDArray, INDArray, int) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Creates a layer depending on the index.
- createLayer(INDArray, INDArray, INDArray, INDArray, int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Creates a layer depending on the index.
- createNetworkLayers(int) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN
-
- createNetworkLayers(int) - Method in class org.deeplearning4j.models.classifiers.sae.StackedAutoEncoder
-
- createNetworkLayers(int) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder
-
- createNetworkLayers(int) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
- createNetworkLayers(int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- 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
- createWeightMatrix() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- createWeightMatrix() - Method in class org.deeplearning4j.nn.layers.ConvolutionDownSampleLayer
-
- createWeightMatrix() - Method in class org.deeplearning4j.nn.layers.SubsamplingLayer
-
- CSVDataFetcher - Class in org.deeplearning4j.datasets.fetchers
-
CSV record based data fetcher
- CSVDataFetcher(InputStream, int) - Constructor for class org.deeplearning4j.datasets.fetchers.CSVDataFetcher
-
Constructs a csv data fetcher with the specified label column
skipping no lines
- CSVDataFetcher(File, int) - Constructor for class org.deeplearning4j.datasets.fetchers.CSVDataFetcher
-
Constructs a csv data fetcher with the specified
label column skipping no lines
- CSVDataFetcher(InputStream, int, int) - Constructor for class org.deeplearning4j.datasets.fetchers.CSVDataFetcher
-
Constructs a csv data fetcher with the specified number of lines to skip
- CSVDataFetcher(File, int, int) - Constructor for class org.deeplearning4j.datasets.fetchers.CSVDataFetcher
-
Constructs a csv data fetcher with the specified number of lines to skip
- CSVDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
CSVDataSetIterator
CSV reader for a dataset file
- CSVDataSetIterator(int, int, InputStream, int) - Constructor for class org.deeplearning4j.datasets.iterator.CSVDataSetIterator
-
- CSVDataSetIterator(int, int, File, int) - Constructor for class org.deeplearning4j.datasets.iterator.CSVDataSetIterator
-
- CSVDataSetIterator(int, int, InputStream, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.CSVDataSetIterator
-
- CSVDataSetIterator(int, int, File, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.CSVDataSetIterator
-
- curr - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- currIteration - Variable in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- 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.BaseDatasetIterator
-
- cursor() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Direct access to a number represenative of iterating through a dataset
- cursor() - Method in interface org.deeplearning4j.datasets.iterator.DataSetIterator
-
The current cursor if applicable
- cursor() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- 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
-
- 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
-
- f1() - Method in class org.deeplearning4j.eval.Evaluation
-
TP: true positive
FP: False Positive
FN: False Negative
F1 score: 2 * TP / (2TP + FP + FN)
- f1(int) - Method in class org.deeplearning4j.eval.Evaluation
-
Calculate f1 score for a given class
- factorial(double) - Static method in class org.deeplearning4j.util.MathUtils
-
This will return the factorial of the given number n.
- Factory<T> - Interface in org.deeplearning4j.berkeley
-
- Factory.DefaultFactory<T> - Class in org.deeplearning4j.berkeley
-
- falsePositive() - Method in class org.deeplearning4j.eval.Evaluation
-
False positive: wrong guess
- featureMapSize(int[]) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- feedForward() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Compute activations from input to output of the output layer
- feedForward(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Compute activations from input to output of the output layer
- feedForwardR(List<INDArray>, INDArray) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Feed forward with the r operator
- feedForwardR(List<INDArray>, INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Feed forward with the r operator
- feedForwardR(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Feed forward with the r operator
- fetch(int) - Method in class org.deeplearning4j.datasets.fetchers.CSVDataFetcher
-
Fetches the next dataset.
- fetch(int) - Method in class org.deeplearning4j.datasets.fetchers.CurvesDataFetcher
-
Fetches the next dataset.
- fetch(int) - Method in class org.deeplearning4j.datasets.fetchers.IrisDataFetcher
-
- fetch(int) - Method in class org.deeplearning4j.datasets.fetchers.LFWDataFetcher
-
- fetch(int) - Method in class org.deeplearning4j.datasets.fetchers.MnistDataFetcher
-
- fetch(int) - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Fetches the next dataset.
- fetch(int) - Method in class org.deeplearning4j.datasets.iterator.impl.MovingWindowDataSetFetcher
-
Fetches the next dataset.
- fetcher - Variable in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- fileNameClean(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns a "clean" version of the given filename in which spaces have
been converted to dashes and all non-alphaneumeric chars are underscores.
- FileOperations - Class in org.deeplearning4j.util
-
- fillDown(String, int) - Method in class org.deeplearning4j.util.StringGrid
-
- fillList(Iterator<? extends T>, List<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- fillList(Iterator<? extends T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- Filter<T> - Interface in org.deeplearning4j.berkeley
-
Filters are boolean functions which accept or reject items.
- filter(Iterator<T>, Filter<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- filterBySimilarity(double, int, int) - Method in class org.deeplearning4j.util.StringGrid
-
- FilteredIterator(Filter<T>, Iterator<T>) - Constructor for class org.deeplearning4j.berkeley.Iterators.FilteredIterator
-
- FilteredIterator(Filter<T>, Iterable<T>) - Constructor for class org.deeplearning4j.berkeley.Iterators.FilteredIterator
-
- FilterPanel - Class in org.deeplearning4j.plot
-
- FilterPanel(BufferedImage) - Constructor for class org.deeplearning4j.plot.FilterPanel
-
- FilterRenderer - Class in org.deeplearning4j.plot
-
Adapted from:
https://github.com/jpatanooga/Metronome/blob/master/src/main/java/tv/floe/metronome/deeplearning/rbm/visualization/RBMRenderer.java
- FilterRenderer() - Constructor for class org.deeplearning4j.plot.FilterRenderer
-
- filterRowsByColumn(int, Collection<String>) - Method in class org.deeplearning4j.util.StringGrid
-
- filterSize(int[]) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- find(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Say whether this regular expression can be found inside
this String.
- finetune(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Trains the decoder on the given input
- finetune() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Finetunes with the current cached labels
- finetune(DataSetIterator, double) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Run SGD based on the given labels
- finetune(DataSetIterator, double, int, TrainingEvaluator) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Run training algorithm based on the datastet iterator
- finetune(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Run SGD based on the given labels
- finetune(INDArray, TrainingEvaluator) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Run training algorithm based on the given labels
- FingerPrintKeyer - Class in org.deeplearning4j.util
-
Copied from google refine:
takes the key and gets rid of all punctuation, transforms to lower case
and alphabetic sorts the words
- FingerPrintKeyer() - Constructor for class org.deeplearning4j.util.FingerPrintKeyer
-
- FirstComparator() - Constructor for class org.deeplearning4j.berkeley.Pair.FirstComparator
-
- fit(INDArray, Object[]) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN
-
Fit the model to the given data
- fit(INDArray, Object[]) - Method in class org.deeplearning4j.models.classifiers.sae.StackedAutoEncoder
-
- fit(INDArray, Object[]) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder
-
Fit the model to the given data
- fit(INDArray, Object[]) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.AutoEncoder
-
Trains via an optimization algorithm such as SGD or Conjugate Gradient
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Fit the model
- fit(DataSet) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Fit the model
- fit(INDArray, int[]) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Fit the model
- fit(INDArray, Object[]) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Fit the model to the given data
- fit(INDArray, Object[]) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
- fit(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
Fit the model to the given data
- fit(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Fit the model to the given data
- fit(INDArray, Object[]) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Note: k is the first input iken params.
- fit(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(INDArray, INDArray, Object[]) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(DataSet, Object[]) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(INDArray, int[]) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(INDArray, int[], Object[]) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Fit the model
- fit(INDArray, Object[]) - Method in interface org.deeplearning4j.nn.api.Model
-
Fit the model to the given data
- fit(INDArray) - Method in interface org.deeplearning4j.nn.api.Model
-
Fit the model to the given data
- fit(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.SequenceClassifier
-
- fit(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Fit the model to the given data
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Fit the model
- fit(DataSet) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Fit the model
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Fit the model
- fit(INDArray, INDArray, Object[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Fit the model
- fit(DataSet, Object[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Fit the model
- fit(INDArray, int[], Object[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Fit the model
- fit(INDArray) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
Fit the model to the given data
- fit(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Fit the model
- fit(DataSet) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Fit the model
- fit(INDArray, INDArray, Object[]) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Fit the model
- fit(DataSet, Object[]) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Fit the model
- fit(INDArray, int[]) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Fit the model
- fit(INDArray, int[], Object[]) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Fit the model
- fit(INDArray, Object[]) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Fit the model to the given data
- fit(INDArray) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Fit the model to the given data
- flattenedImageFromFile(File) - Method in class org.deeplearning4j.util.ImageLoader
-
- forceIterations() - Method in class org.deeplearning4j.models.classifiers.dbn.DBN.Builder
-
Forces use of number of epochs for training
SGD style rather than conjugate gradient
- forceIterations() - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder.Builder
-
Forces use of number of epochs for training
SGD style rather than conjugate gradient
- forceIterations() - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing.Builder
-
Forces use of number of epochs for training
SGD style rather than conjugate gradient
- forceIterations() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.Builder
-
Forces use of number of epochs for training
SGD style rather than conjugate gradient
- forceNumEpochs - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- forceNumIterations() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- frame - Variable in class org.deeplearning4j.datasets.mnist.draw.DrawReconstruction
-
- frame - Variable in class org.deeplearning4j.plot.FilterRenderer
-
- freeEnergy(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Free energy for an RBM
Lower energy models have higher probability
of activations
- fromFile(File) - Method in class org.deeplearning4j.util.ImageLoader
-
- fromFile(String, String) - Static method in class org.deeplearning4j.util.StringGrid
-
- fromImageFile(int, File) - Method in class org.deeplearning4j.base.LFWLoader
-
- fromInput(InputStream, String) - Static method in class org.deeplearning4j.util.StringGrid
-
- fromString(String, String) - Static method in class org.deeplearning4j.util.MathUtils
-
This will take a given string and separator and convert it to an equivalent
double array.
- generateHistogramBuckets(INDArray, int) - Method in class org.deeplearning4j.plot.FilterRenderer
-
Take some matrix input data and a bucket count and compute:
- a list of N buckets, each with:
1.
- generateUniform(int) - Static method in class org.deeplearning4j.util.MathUtils
-
This will generate a series of uniformally distributed
numbers between l times
- get(int) - Method in class org.deeplearning4j.util.Index
-
- get(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns the value to which the specified key is mapped,
or null if this map contains no mapping for the key.
- get(K, T) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
- getActivationFunction() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getActivationType() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getActualTotal(T) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Computes the total number of times the class actually appeared in the data.
- getAdaGrad() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- getAdaGrad() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- getAdaGrad() - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
- getAllImagesAsMatrix() - Method in class org.deeplearning4j.base.LFWLoader
-
- getAllImagesAsMatrix(int) - Method in class org.deeplearning4j.base.LFWLoader
-
- getAllWithSimilarity(double, int, int) - Method in class org.deeplearning4j.util.StringGrid
-
- getB() - Method in interface org.deeplearning4j.nn.api.Layer
-
- getB() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- getBackPropGradient2() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Gets the back prop gradient with the r operator (gauss vector)
and the associated precon matrix
This is also called computeGV
- getBackPropRGradient(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Gets the back prop gradient with the r operator (gauss vector)
This is also called computeGV
- getbGradient() - Method in class org.deeplearning4j.nn.gradient.OutputLayerGradient
-
- getBiasAdaGrad() - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
- getBiasEnd() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.ParamRange
-
- getBiasStart() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.ParamRange
-
- getCentroids() - Method in class org.deeplearning4j.clustering.KMeansClustering
-
- getClasses() - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Gives the applyTransformToDestination of all classes in the confusion matrix.
- getClusters() - Method in class org.deeplearning4j.util.StringCluster
-
- getCols() - Method in class org.deeplearning4j.datasets.mnist.MnistImageFile
-
Number of columns per image.
- getColumn(int) - Method in class org.deeplearning4j.util.StringGrid
-
- getCorruptedInput(INDArray, float) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
Corrupts the given input by doing a binomial sampling
given the corruption level
- getCorruptionLevel() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getCount(E) - Method in class org.deeplearning4j.berkeley.Counter
-
Get the count of the element, or zero if the element is not in the
counter.
- getCount(K, V) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Gets the count of the given (key, value) entry, or zero if that entry is
not present.
- getCount(K) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Gets the total count of the given key, or zero if that key is
not present.
- getCount() - Method in class org.deeplearning4j.datasets.mnist.MnistDbFile
-
- getCount(T, T) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Gives the count of the number of times the "predicted" class was predicted for the "actual"
class.
- getCounter(K) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Gets the sub-counter for the given key.
- getCounters() - Method in class org.deeplearning4j.berkeley.CounterMap
-
- getCurrentIndex() - Method in class org.deeplearning4j.datasets.mnist.MnistDbFile
-
The current entry index.
- getDataFor(int) - Method in class org.deeplearning4j.base.LFWLoader
-
- getDefaultConfiguration() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getDeflt() - Method in class org.deeplearning4j.berkeley.Counter
-
- getDgg() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getDist() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getDropOut() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getEmptyConstructor(Class<?>) - Static method in class org.deeplearning4j.util.Dl4jReflection
-
Gets the empty constructor from a class
- getEncoder() - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
- getEntryLength() - Method in class org.deeplearning4j.datasets.mnist.MnistDbFile
-
Number of bytes for each entry.
- getEntryLength() - Method in class org.deeplearning4j.datasets.mnist.MnistImageFile
-
- getEntrySet() - Method in class org.deeplearning4j.berkeley.Counter
-
- getEntrySet() - Method in class org.deeplearning4j.berkeley.CounterMap
-
- getFeatureMapSize() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getFeatureMatrix(int) - Method in class org.deeplearning4j.base.LFWLoader
-
Get the first num found images
- getFilterSize() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getFinetuneEpochs() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getFinetuneLearningRate() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getFirst() - Method in class org.deeplearning4j.berkeley.Pair
-
- getFirst() - Method in class org.deeplearning4j.berkeley.Triple
-
- getFirstKey() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- getFp() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getFret() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getG() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getGam() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getGg() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getGradient(Object[]) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.AutoEncoder
-
- getGradient(Object[]) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
- getGradient(Object[]) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
- getGradient(Object[]) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- getGradient(double) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Gets the gradient from one training iteration
- getGradients() - Method in class org.deeplearning4j.nn.gradient.MultiLayerGradient
-
- getH() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- gethBias() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- gethBias() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- gethBiasAdaGrad() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- gethBiasAdaGrad() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- gethBiasGradient() - Method in class org.deeplearning4j.nn.gradient.NeuralNetworkGradient
-
- getHeaderSize() - Method in class org.deeplearning4j.datasets.mnist.MnistDbFile
-
- getHeaderSize() - Method in class org.deeplearning4j.datasets.mnist.MnistImageFile
-
- getHiddenBiasTransforms() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getHiddenLayerSizes() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getHiddenUnit() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getHiddenValues(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.AutoEncoder
-
- getHiddenValues(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
- getIfNotExists() - Method in class org.deeplearning4j.base.LFWLoader
-
- getImage() - Method in class org.deeplearning4j.datasets.mnist.draw.DrawReconstruction
-
- getImage() - Method in class org.deeplearning4j.plot.FilterRenderer
-
- getImages(int, File) - Method in class org.deeplearning4j.base.LFWLoader
-
- getImages() - Method in class org.deeplearning4j.datasets.mnist.MnistManager
-
- getImagesAsList() - Method in class org.deeplearning4j.base.LFWLoader
-
- getInitialStepSize() - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
- getInitialStepSize() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getInput() - Method in interface org.deeplearning4j.nn.api.Layer
-
- getInput() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- getInput() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getInput() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- getInput() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- getInputLayer() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getIterationListener() - Method in class org.deeplearning4j.plot.Tsne
-
- getK() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getKey() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
Returns the key corresponding to this entry.
- getL2() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getLabels() - Method in class org.deeplearning4j.datasets.mnist.MnistManager
-
- getLabels() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getLabels() - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
- getLayers() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getLayerWiseConfigurations() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getLineMaximizer() - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
- getLogMetaInstability() - Method in class org.deeplearning4j.util.Viterbi
-
- getLogOfDiangnalTProb() - Method in class org.deeplearning4j.util.Viterbi
-
- getLogPCorrect() - Method in class org.deeplearning4j.util.Viterbi
-
- getLogPIncorrect() - Method in class org.deeplearning4j.util.Viterbi
-
- getLogRegGradient() - Method in class org.deeplearning4j.nn.gradient.MultiLayerGradient
-
- getLogStates() - Method in class org.deeplearning4j.util.Viterbi
-
- getLossFunction() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getLr() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getMagicNumber() - Method in class org.deeplearning4j.datasets.mnist.MnistDbFile
-
MNIST DB files start with unique integer number.
- getMagicNumber() - Method in class org.deeplearning4j.datasets.mnist.MnistImageFile
-
- getMagicNumber() - Method in class org.deeplearning4j.datasets.mnist.MnistLabelFile
-
- getMask() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getMax() - Method in class org.deeplearning4j.util.SummaryStatistics
-
- getMaxIterations() - Method in class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
- getMaxIterations() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getMean() - Method in class org.deeplearning4j.util.SummaryStatistics
-
- getMetaStability() - Method in class org.deeplearning4j.util.Viterbi
-
- getMin() - Method in class org.deeplearning4j.util.SummaryStatistics
-
- getMomentum() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getMomentumAfter() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getNeuralNets() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getnIn() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getnLayers() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getnOut() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getNumColumns() - Method in class org.deeplearning4j.util.StringGrid
-
- getNumDataSets() - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
-
- getNumFeatureMaps() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getNumInFeatureMaps() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getNumIterations() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getNumNames() - Method in class org.deeplearning4j.base.LFWLoader
-
- getNumParameters() - Method in interface org.deeplearning4j.optimize.api.OptimizableByGradientValueMatrix
-
- getNumParameters() - Method in class org.deeplearning4j.optimize.optimizers.BackPropOptimizer
-
- getNumParameters() - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- getNumParameters() - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- getNumParameters() - Method in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- getNumParameters() - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- getNumPixelColumns() - Method in class org.deeplearning4j.base.LFWLoader
-
- getOptimizable() - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
- getOptimizationAlgo() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getOutputLayer() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getPairIterator() - Method in class org.deeplearning4j.berkeley.CounterMap
-
- getParameter(int) - Method in interface org.deeplearning4j.optimize.api.OptimizableByGradientValueMatrix
-
- getParameter(int) - Method in class org.deeplearning4j.optimize.optimizers.BackPropOptimizer
-
- getParameter(int) - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- getParameter(int) - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- getParameter(int) - Method in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- getParameter(int) - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- getParameters() - Method in interface org.deeplearning4j.optimize.api.OptimizableByGradientValueMatrix
-
- getParameters() - Method in class org.deeplearning4j.optimize.optimizers.BackPropOptimizer
-
- getParameters(double[]) - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- getParameters() - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- getParameters() - Method in class org.deeplearning4j.optimize.optimizers.da.DenoisingAutoEncoderOptimizer
-
- getParameters(double[]) - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- getParameters() - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- getParameters() - Method in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- getParameters(double[]) - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- getParameters() - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- getpCorrect() - Method in class org.deeplearning4j.util.Viterbi
-
- getPicDraw() - Method in class org.deeplearning4j.plot.DeepAutoEncoderDataSetReconstructionRender
-
- getPossibleLabels() - Method in class org.deeplearning4j.util.Viterbi
-
- getPredictedTotal(T) - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Computes the total number of times the class was predicted by the classifier.
- getPretrainEpochs() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getPriority() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- getPriority() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Gets the priority of the highest-priority element of the queue.
- getProbability(E) - Method in class org.deeplearning4j.berkeley.Counter
-
I know, I know, this should be wrapped in a Distribution class, but it's
such a common use...why not.
- getReconDraw() - Method in class org.deeplearning4j.plot.DeepAutoEncoderDataSetReconstructionRender
-
- getReconstructedInput(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.AutoEncoder
-
- getReconstructedInput(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
- getRenderWeightIterations() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getResetAdaGradIterations() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getRng() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getRow(int) - Method in class org.deeplearning4j.util.StringGrid
-
- getRows() - Method in class org.deeplearning4j.datasets.mnist.MnistImageFile
-
Number of rows per image.
- getRowsWithColumnValues(Collection<String>, int) - Method in class org.deeplearning4j.util.StringGrid
-
- getRowsWithDuplicateValuesInColumn(int) - Method in class org.deeplearning4j.util.StringGrid
-
- getRowWithOnlyOneOccurrence(int) - Method in class org.deeplearning4j.util.StringGrid
-
- getSecond() - Method in class org.deeplearning4j.berkeley.Pair
-
- getSecond() - Method in class org.deeplearning4j.berkeley.Triple
-
- getSecondKey() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- getSeed() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getSortedKeys() - Method in class org.deeplearning4j.berkeley.Counter
-
- getSparsity() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getStates() - Method in class org.deeplearning4j.util.Viterbi
-
- getStep() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getStepSize() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getStpmax() - Method in class org.deeplearning4j.optimize.solvers.VectorizedBackTrackLineSearch
-
- getStpmax() - Method in class org.deeplearning4j.optimize.solvers.VectorizedBackTrackLineSearchMinimum
-
- getStpmax() - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
- getStride() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getSum() - Method in class org.deeplearning4j.util.SummaryStatistics
-
- getThird() - Method in class org.deeplearning4j.berkeley.Triple
-
- getUniqueRows() - Method in class org.deeplearning4j.util.StringGrid
-
- getValue() - Method in interface org.deeplearning4j.optimize.api.OptimizableByGradientValueMatrix
-
- getValue() - Method in class org.deeplearning4j.optimize.optimizers.BackPropOptimizer
-
- getValue() - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- getValue() - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- getValue() - Method in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- getValue() - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- getValue() - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- getValueGradient(int) - Method in interface org.deeplearning4j.optimize.api.OptimizableByGradientValueMatrix
-
- getValueGradient(int) - Method in class org.deeplearning4j.optimize.optimizers.BackPropOptimizer
-
- getValueGradient(double[]) - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- getValueGradient(int) - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- getValueGradient(double[]) - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- getValueGradient(int) - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- getValueGradient(int) - Method in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- getValueGradient(double[]) - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- getValueGradient(int) - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- getValueGradient(int) - Method in class org.deeplearning4j.optimize.optimizers.rbm.RBMOptimizer
-
- getvBias() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- getvBias() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- getVBiasAdaGrad() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- getVBiasAdaGrad() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- getvBiasGradient() - Method in class org.deeplearning4j.nn.gradient.NeuralNetworkGradient
-
- getVisibleBiasTransforms() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getVisibleUnit() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getW() - Method in interface org.deeplearning4j.nn.api.Layer
-
- getW() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- getW() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- getW() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- getWeightInit() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getWeightShape() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- getWeightTransforms() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- getwEnd() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.ParamRange
-
- getwGradient() - Method in class org.deeplearning4j.nn.gradient.NeuralNetworkGradient
-
- getwGradient() - Method in class org.deeplearning4j.nn.gradient.OutputLayerGradient
-
- getwStart() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.ParamRange
-
- getXi() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- getY() - Method in class org.deeplearning4j.plot.Tsne
-
- gibbhVh(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Gibbs sampling step: hidden ---> visible ---> hidden
- gr(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Tests if a is greater than b.
- grow(int) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- gunzipFile(File, File) - Static method in class org.deeplearning4j.base.MnistFetcher
-
- IdentityHashMapFactory() - Constructor for class org.deeplearning4j.berkeley.MapFactory.IdentityHashMapFactory
-
- idf(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Inverse document frequency: the total docs divided by the number of times the word
appeared in a document
- ImageLoader - Class in org.deeplearning4j.util
-
Image loader for taking images and converting them to matrices
- ImageLoader() - Constructor for class org.deeplearning4j.util.ImageLoader
-
- ImageLoader(int, int) - Constructor for class org.deeplearning4j.util.ImageLoader
-
- ImageVectorizer - Class in org.deeplearning4j.datasets.vectorizer
-
An image vectorizer takes an input image (RGB) and
transforms it in to a data applyTransformToDestination
- ImageVectorizer(File, int, int) - Constructor for class org.deeplearning4j.datasets.vectorizer.ImageVectorizer
-
Baseline knowledge needed for the vectorizer
- improvementThreshold() - Method in interface org.deeplearning4j.optimize.api.TrainingEvaluator
-
- improvementThreshold(double) - Method in class org.deeplearning4j.optimize.OutputLayerTrainingEvaluator.Builder
-
- improvementThreshold() - Method in class org.deeplearning4j.optimize.OutputLayerTrainingEvaluator
-
- incrementAll(Collection<? extends E>, double) - Method in class org.deeplearning4j.berkeley.Counter
-
Increment each element in a given collection by a given amount.
- incrementAll(Counter<T>) - Method in class org.deeplearning4j.berkeley.Counter
-
- incrementAll(Map<K, V>, double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- incrementAll(CounterMap<K, V>) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- incrementCount(E, double) - Method in class org.deeplearning4j.berkeley.Counter
-
Increment a key's count by the given amount.
- incrementCount(K, V, double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Increments the count for a particular (key, value) pair.
- incrementFalseNegatives() - Method in class org.deeplearning4j.eval.Evaluation
-
- incrementFalsePositives(int) - Method in class org.deeplearning4j.eval.Evaluation
-
- incrementTruePositives() - Method in class org.deeplearning4j.eval.Evaluation
-
- Index - Class in org.deeplearning4j.util
-
An index is a applyTransformToDestination of objects augmented with a list and a reverse lookup table
for fast lookups.
- Index() - Constructor for class org.deeplearning4j.util.Index
-
- indexOf(Object) - Method in class org.deeplearning4j.util.Index
-
- information(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the entropy for a given vector of probabilities.
- init() - Method in class org.deeplearning4j.nn.BaseConvolutionalMultiLayerNetwork
-
- init() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- initCalled - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- initCentroids() - Method in class org.deeplearning4j.clustering.KMeansClustering
-
Init clusters using the k-means++ algorithm.
- initialize(DataSet) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Sets the input and labels from this dataset
- initializeCurrFromList(List<DataSet>) - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
Initializes this data applyTransformToDestination fetcher from the passed in datasets
- initializeLayers(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Base class for initializing the neuralNets based on the input.
- initIfPossible(INDArray) - Method in class org.deeplearning4j.clustering.KMeansClustering
-
- initWeights() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
Initialize weights.
- initWeights(int[], float, float) - Static method in class org.deeplearning4j.nn.WeightInitUtil
-
- initWeights(int, int, WeightInit, ActivationFunction, RealDistribution) - Static method in class org.deeplearning4j.nn.WeightInitUtil
-
Initializes a matrix with the given weight initialization scheme
- input - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- input - Variable in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- input - Variable in class org.deeplearning4j.nn.layers.BaseLayer
-
- input - Variable in class org.deeplearning4j.nn.layers.Layer.Builder
-
- input(INDArray) - Method in class org.deeplearning4j.nn.layers.OutputLayer.Builder
-
- inputColumns - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- inputColumns() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- inputColumns() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
The length of a feature vector for an individual example
- inputColumns() - Method in interface org.deeplearning4j.datasets.iterator.DataSetIterator
-
Input columns for the dataset
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Input columns for the dataset
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Input columns for the dataset
- inputColumns() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- inputColumns() - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
-
- InputSplit - Class in org.deeplearning4j.util
-
- InputSplit() - Constructor for class org.deeplearning4j.util.InputSplit
-
- inputStreamFromPath(String) - Static method in class org.deeplearning4j.util.DeepLearningIOUtil
-
- intersection(Collection<T>, Collection<T>) - Static method in class org.deeplearning4j.util.SetUtils
-
- intersectionP(Set<? extends T>, Set<? extends T>) - Static method in class org.deeplearning4j.util.SetUtils
-
- intializeConfigurations() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- intPow(int, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
exponentiation like we learned in grade school: multiply b by itself e
times.
- intPow(float, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
exponentiation like we learned in grade school: multiply b by itself e
times.
- intPow(double, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
exponentiation like we learned in grade school: multiply b by itself e
times.
- InvalidStepException - Exception in org.deeplearning4j.exception
-
Created by agibsonccc on 8/20/14.
- InvalidStepException(String) - Constructor for exception org.deeplearning4j.exception.InvalidStepException
-
Constructs a new exception with the specified detail message.
- InvalidStepException(String, Throwable) - Constructor for exception org.deeplearning4j.exception.InvalidStepException
-
Constructs a new exception with the specified detail message and
cause.
- InvalidStepException(Throwable) - Constructor for exception org.deeplearning4j.exception.InvalidStepException
-
Constructs a new exception with the specified cause and a detail
message of (cause==null ? null : cause.toString()) (which
typically contains the class and detail message of cause).
- InvalidStepException(String, Throwable, boolean, boolean) - Constructor for exception org.deeplearning4j.exception.InvalidStepException
-
Constructs a new exception with the specified detail message,
cause, suppression enabled or disabled, and writable stack
trace enabled or disabled.
- inverse(RBM.HiddenUnit) - Static method in class org.deeplearning4j.util.RBMUtil
-
- inverse(RBM.VisibleUnit) - Static method in class org.deeplearning4j.util.RBMUtil
-
- invert() - Method in class org.deeplearning4j.berkeley.CounterMap
-
Constructs reverse CounterMap where the count of a pair (k,v)
is the count of (v,k) in the current CounterMap
- iris() - Static method in class org.deeplearning4j.datasets.DataSets
-
- iris(int) - Static method in class org.deeplearning4j.datasets.DataSets
-
- IrisDataFetcher - Class in org.deeplearning4j.datasets.fetchers
-
- IrisDataFetcher() - Constructor for class org.deeplearning4j.datasets.fetchers.IrisDataFetcher
-
- IrisDataSetIterator - Class in org.deeplearning4j.datasets.iterator.impl
-
- IrisDataSetIterator(int, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator
-
- IrisUtils - Class in org.deeplearning4j.base
-
- IrisUtils() - Constructor for class org.deeplearning4j.base.IrisUtils
-
- isApplySparsity() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- isConcatBiases() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- isConstrainGradientToUnitNorm() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- isConverged() - Method in class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
- isConverged() - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
- isConverged() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- isConverged() - Method in interface org.deeplearning4j.util.OptimizerMatrix
-
Whether the algorithm is converged
- isDangerous(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns true if the argument is a "dangerous" double to have around,
namely one that is infinite, NaN or zero.
- isDangerous(float) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- isDiscreteProb(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- isEmpty() - Method in class org.deeplearning4j.berkeley.Counter
-
True if there are no entries in the counter (false does not mean
totalCount > 0)
- isEmpty() - Method in class org.deeplearning4j.berkeley.CounterMap
-
True if there are no entries in the CounterMap (false does not mean
totalCount > 0)
- isEmpty() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- isEmpty() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
True if the queue is empty (size == 0).
- isEmpty() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- isEmpty() - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns true if this map contains no key-value mappings.
- isEmpty() - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns true if this applyTransformToDestination contains no elements.
- isEqualTo(Counter<E>) - Method in class org.deeplearning4j.berkeley.Counter
-
- isEqualTo(CounterMap<K, V>) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- isGreater(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- isReady() - Method in class org.deeplearning4j.clustering.KMeansClustering
-
- isSampleFromHiddenActivations() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- isUseAdaGrad() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- isUseDropConnect() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- isUseGaussNewtonVectorProductBackProp() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- isUseRegularization() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- isVeryDangerous(double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns true if the argument is a "very dangerous" double to have around,
namely one that is infinite or NaN.
- iterate(INDArray, Object[]) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.AutoEncoder
-
iterate one iteration of the network
- iterate(INDArray, Object[]) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
- iterate(INDArray, Object[]) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
- iterate(INDArray, int[], Object[]) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Iterate once on the model
- iterate(INDArray, Object[]) - Method in interface org.deeplearning4j.nn.api.Model
-
Run one iteration
- iterate(INDArray, Object[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Run one iteration
- iterate(INDArray, int[], Object[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Iterate once on the model
- iterate(INDArray, int[], Object[]) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Iterate once on the model
- iterate(INDArray, Object[]) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Run one iteration
- iterationDone(int) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
- iterationDone(int) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
- iterationDone(int) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- iterationDone(int) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- iterationDone(int) - Method in interface org.deeplearning4j.optimize.api.IterationListener
-
Event listener for each iteration
- iterationDone(int) - Method in class org.deeplearning4j.optimize.optimizers.BackPropOptimizer
-
- iterationDone(int) - Method in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- IterationListener - Interface in org.deeplearning4j.optimize.api
-
Each epoch the listener is called, mainly used for debugging or visualizations
- iterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- iterator() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- iterator() - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns an iterator over the elements in this applyTransformToDestination.
- IteratorIterator(Iterator, Factory<Iterator<T>>) - Constructor for class org.deeplearning4j.berkeley.Iterators.IteratorIterator
-
- Iterators - Class in org.deeplearning4j.berkeley
-
- Iterators.FilteredIterator<T> - Class in org.deeplearning4j.berkeley
-
Creates an iterator that only returns items of a base iterator that pass
a filter.
- Iterators.IteratorIterator<T> - Class in org.deeplearning4j.berkeley
-
Wraps a two-level iteration scenario in an iterator.
- Iterators.Transform<S,T> - Class in org.deeplearning4j.berkeley
-
Wraps a base iterator with a transformation function.
- Iterators.TransformingIterator<I,O> - Class in org.deeplearning4j.berkeley
-
- main(String[]) - Static method in class org.deeplearning4j.berkeley.Counter
-
- main(String[]) - Static method in class org.deeplearning4j.berkeley.CounterMap
-
- main(String[]) - Static method in class org.deeplearning4j.berkeley.PriorityQueue
-
- main(String[]) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Tests the hypergeometric distribution code, or other functions provided
in this module.
- main(String[]) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- main(String[]) - Static method in class org.deeplearning4j.datasets.creator.MnistDataSetCreator
-
- main(String[]) - Static method in class org.deeplearning4j.datasets.mnist.draw.LoadAndDraw
-
- main(String[]) - Static method in class org.deeplearning4j.eval.ConfusionMatrix
-
- makePair(S, T) - Static method in class org.deeplearning4j.berkeley.Pair
-
- makeTriple(S, T, U) - Static method in class org.deeplearning4j.berkeley.Triple
-
- manhattanDistance(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This will calculate the Manhattan distance between two sets of points.
- mapByPrimaryKey(int) - Method in class org.deeplearning4j.util.StringGrid
-
- MapFactory<K,V> - Class in org.deeplearning4j.berkeley
-
The MapFactory is a mechanism for specifying what kind of map is to be used
by some object.
- MapFactory() - Constructor for class org.deeplearning4j.berkeley.MapFactory
-
- MapFactory.HashMapFactory<K,V> - Class in org.deeplearning4j.berkeley
-
- MapFactory.IdentityHashMapFactory<K,V> - Class in org.deeplearning4j.berkeley
-
- MapFactory.TreeMapFactory<K,V> - Class in org.deeplearning4j.berkeley
-
- MapFactory.WeakHashMapFactory<K,V> - Class in org.deeplearning4j.berkeley
-
- mask - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- matches(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Say whether this regular expression matches
this String.
- MathUtils - Class in org.deeplearning4j.util
-
This is a math utils class.
- MathUtils() - Constructor for class org.deeplearning4j.util.MathUtils
-
- max() - Method in class org.deeplearning4j.berkeley.Counter
-
- max(int, int, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the minimum of three int values.
- max(float, float) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the greater of two float values.
- max(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the greater of two double values.
- max(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
- maxIndex(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Returns index of maximum element in a given
array of doubles.
- maxLengthIterator(Iterator<T>, int) - Static method in class org.deeplearning4j.berkeley.Iterators
-
Provides a max number of elements for an underlying base iterator.
- maxStep - Variable in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- mean(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Computes the mean for an array of doubles.
- merge(NeuralNetwork, int) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
Performs a network merge in the form of
a += b - a / n
where a is a matrix here
b is a matrix on the incoming network
and n is the batch size
- merge(BaseMultiLayerNetwork, int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Merges this network with the other one.
- merge(NeuralNetwork, int) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- merge(Layer, int) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
Averages the given logistic regression
from a mini batch in to this one
- merge(int, int) - Method in class org.deeplearning4j.util.StringGrid
-
- mergeCoords(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This will merge the coordinates of the given coordinate system.
- mergeCoords(List<Double>, List<Double>) - Static method in class org.deeplearning4j.util.MathUtils
-
This will merge the coordinates of the given coordinate system.
- min() - Method in class org.deeplearning4j.berkeley.Counter
-
- min(int, int, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the minimum of three int values.
- min(float, float) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the smaller of two float values.
- min(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Returns the smaller of two double values.
- min(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
- minGain(double) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- miniBatchSize() - Method in interface org.deeplearning4j.optimize.api.TrainingEvaluator
-
- miniBatchSize(int) - Method in class org.deeplearning4j.optimize.OutputLayerTrainingEvaluator.Builder
-
- miniBatchSize() - Method in class org.deeplearning4j.optimize.OutputLayerTrainingEvaluator
-
- mnist() - Static method in class org.deeplearning4j.datasets.DataSets
-
- mnist(int) - Static method in class org.deeplearning4j.datasets.DataSets
-
- MnistDataFetcher - Class in org.deeplearning4j.datasets.fetchers
-
Data fetcher for the MNIST dataset
- MnistDataFetcher(boolean) - Constructor for class org.deeplearning4j.datasets.fetchers.MnistDataFetcher
-
Constructor telling whether to binarize the dataset or not
- MnistDataFetcher() - Constructor for class org.deeplearning4j.datasets.fetchers.MnistDataFetcher
-
- MnistDataSetCreator - Class in org.deeplearning4j.datasets.creator
-
- MnistDataSetCreator() - Constructor for class org.deeplearning4j.datasets.creator.MnistDataSetCreator
-
- MnistDataSetIterator - Class in org.deeplearning4j.datasets.iterator.impl
-
Mnist data applyTransformToDestination iterator.
- MnistDataSetIterator(int, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator
-
- MnistDataSetIterator(int, int, boolean) - Constructor for class org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator
-
Whether to binarize the data or not
- MnistDbFile - Class in org.deeplearning4j.datasets.mnist
-
MNIST database file containing entries that can represent image or label
data.
- MnistDbFile(String, String) - Constructor for class org.deeplearning4j.datasets.mnist.MnistDbFile
-
Creates new instance and reads the header information.
- MnistFetcher - Class in org.deeplearning4j.base
-
- MnistFetcher() - Constructor for class org.deeplearning4j.base.MnistFetcher
-
- MnistImageFile - Class in org.deeplearning4j.datasets.mnist
-
MNIST database image file.
- MnistImageFile(String, String) - Constructor for class org.deeplearning4j.datasets.mnist.MnistImageFile
-
Creates new MNIST database image file ready for reading.
- MnistLabelFile - Class in org.deeplearning4j.datasets.mnist
-
MNIST database label file.
- MnistLabelFile(String, String) - Constructor for class org.deeplearning4j.datasets.mnist.MnistLabelFile
-
Creates new MNIST database label file ready for reading.
- MnistManager - Class in org.deeplearning4j.datasets.mnist
-
Utility class for working with the MNIST database.
- MnistManager(String, String) - Constructor for class org.deeplearning4j.datasets.mnist.MnistManager
-
Constructs an instance managing the two given data files.
- Model - Interface in org.deeplearning4j.nn.api
-
A Model is meant for predicting something from data.
- momentum(float) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- momentum - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- momentumAfter(Map<Integer, Float>) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- momentumAfter - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- mostLikelyInSequence(INDArray) - Method in interface org.deeplearning4j.nn.api.SequenceClassifier
-
- MovingWindowBaseDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
DataSetIterator for moving window (rotating matrices)
- MovingWindowBaseDataSetIterator(int, int, DataSet, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.MovingWindowBaseDataSetIterator
-
- MovingWindowDataSetFetcher - Class in org.deeplearning4j.datasets.iterator.impl
-
Moving window data fetcher.
- MovingWindowDataSetFetcher(DataSet, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.MovingWindowDataSetFetcher
-
- MovingWindowMatrix - Class in org.deeplearning4j.util
-
Moving window on a matrix (usually used for images)
Given a: This is a list of flattened arrays:
1 1 1 1 1 1 2 2
2 2 2 2 ----> 1 1 2 2
3 3 3 3 3 3 4 4
4 4 4 4 3 3 4 4
- MovingWindowMatrix(INDArray, int, int, boolean) - Constructor for class org.deeplearning4j.util.MovingWindowMatrix
-
- MovingWindowMatrix(INDArray, int, int) - Constructor for class org.deeplearning4j.util.MovingWindowMatrix
-
Same as calling new MovingWindowMatrix(toSlice,windowRowSize,windowColumnSize,false)
- MultiDimensionalMap<K,T,V> - Class in org.deeplearning4j.util
-
Multiple key map
- MultiDimensionalMap(Map<Pair<K, T>, V>) - Constructor for class org.deeplearning4j.util.MultiDimensionalMap
-
- MultiDimensionalMap.Entry<K,T,V> - Class in org.deeplearning4j.util
-
- MultiDimensionalSet<K,V> - Class in org.deeplearning4j.util
-
Created by agibsonccc on 4/29/14.
- MultiDimensionalSet(Set<Pair<K, V>>) - Constructor for class org.deeplearning4j.util.MultiDimensionalSet
-
- MultiLayerGradient - Class in org.deeplearning4j.nn.gradient
-
Gradient for a whole multi layer network
- MultiLayerGradient(List<NeuralNetworkGradient>, OutputLayerGradient) - Constructor for class org.deeplearning4j.nn.gradient.MultiLayerGradient
-
- MultiLayerNetworkOptimizer - Class in org.deeplearning4j.optimize.optimizers
-
Optimizes the logistic layer for finetuning
a multi layer network.
- MultiLayerNetworkOptimizer(BaseMultiLayerNetwork, double) - Constructor for class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- MultiLayerNetworkReconstructionRender - Class in org.deeplearning4j.plot
-
Reconstruction renders for a multi layer network
- MultiLayerNetworkReconstructionRender(DataSetIterator, BaseMultiLayerNetwork, int) - Constructor for class org.deeplearning4j.plot.MultiLayerNetworkReconstructionRender
-
- MultiLayerNetworkReconstructionRender(DataSetIterator, BaseMultiLayerNetwork) - Constructor for class org.deeplearning4j.plot.MultiLayerNetworkReconstructionRender
-
- MultipleEpochsIterator - Class in org.deeplearning4j.datasets.iterator
-
A dataset iterator for doing multiple passes over a dataset
- MultipleEpochsIterator(int, DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
- MyMethod<I,O> - Interface in org.deeplearning4j.berkeley
-
A function wrapping interface.
- nChooseK(int, int) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
Computes n choose k in an efficient way.
- nearestCentroid(INDArray) - Method in class org.deeplearning4j.clustering.KMeansClustering
-
- negative() - Method in class org.deeplearning4j.eval.Evaluation
-
Total negatives true negatives + falseNegatives
- NegativeDefaultStepFunction - Class in org.deeplearning4j.optimize.stepfunctions
-
Created by agibsonccc on 11/1/14.
- NegativeDefaultStepFunction() - Constructor for class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
-
- network - Variable in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- network - Variable in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- NeuralNetConfiguration - Class in org.deeplearning4j.nn.conf
-
A Serializable configuration
for neural nets that covers per layer parameters
- NeuralNetConfiguration() - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- NeuralNetConfiguration(float, boolean, float, int, float, int, float, float, boolean, Map<Integer, Float>, int, float, boolean, WeightInit, NeuralNetwork.OptimizationAlgorithm, LossFunctions.LossFunction, int, boolean, boolean, RandomGenerator, RealDistribution, long, int, int, ActivationFunction, RBM.VisibleUnit, RBM.HiddenUnit, NeuralNetConfiguration.ActivationType, int[], int[], int, int[], int[], int) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- NeuralNetConfiguration(NeuralNetConfiguration) - Constructor for class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- NeuralNetConfiguration.ActivationType - Enum in org.deeplearning4j.nn.conf
-
- NeuralNetConfiguration.Builder - Class in org.deeplearning4j.nn.conf
-
- NeuralNetConfiguration.ConfOverride - Interface in org.deeplearning4j.nn.conf
-
Interface for a function to override
builder configurations at a particular layer
- NeuralNetConfiguration.ListBuilder - Class in org.deeplearning4j.nn.conf
-
Fluent interface for building a list of configurations
- NeuralNetPlotter - Class in org.deeplearning4j.plot
-
Credit to :
http://yosinski.com/media/papers/Yosinski2012VisuallyDebuggingRestrictedBoltzmannMachine.pdf
for visualizations
- NeuralNetPlotter() - Constructor for class org.deeplearning4j.plot.NeuralNetPlotter
-
- neuralNets - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- NeuralNetwork - Interface in org.deeplearning4j.nn.api
-
Single layer neural network, this is typically one that has
the objective function of reconstruction the input: also called feature detectors
- NeuralNetwork.OptimizationAlgorithm - Enum in org.deeplearning4j.nn.api
-
Optimization algorithm to use
- NeuralNetworkGradient - Class in org.deeplearning4j.nn.gradient
-
Represents the gradient for changing a neural network
- NeuralNetworkGradient(INDArray, INDArray, INDArray) - Constructor for class org.deeplearning4j.nn.gradient.NeuralNetworkGradient
-
- NeuralNetworkOptimizer - Class in org.deeplearning4j.optimize.optimizers
-
Performs basic beam search based on the network's loss function
- NeuralNetworkOptimizer(NeuralNetwork, double, Object[], NeuralNetwork.OptimizationAlgorithm, LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- NeuralNetworkReconstructionRender - Class in org.deeplearning4j.plot
-
Neural Network reconstruction renderer
- NeuralNetworkReconstructionRender(DataSetIterator, NeuralNetwork) - Constructor for class org.deeplearning4j.plot.NeuralNetworkReconstructionRender
-
- newHashBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap
-
Thread safe hash map impl
- newInstance(Object...) - Method in class org.deeplearning4j.berkeley.Factory.DefaultFactory
-
- newInstance(Object...) - Method in interface org.deeplearning4j.berkeley.Factory
-
- newIterable(Iterator<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
Wraps an iterator as an iterable
- newPair(S, T) - Static method in class org.deeplearning4j.berkeley.Pair
-
- newThreadSafeHashBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap
-
Thread safe hash map implementation
- newThreadSafeTreeBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap
-
Thread safe sorted map implementation
- newTreeBackedMap() - Static method in class org.deeplearning4j.util.MultiDimensionalMap
-
Tree map implementation
- next() - Method in class org.deeplearning4j.berkeley.Iterators.FilteredIterator
-
- next() - Method in class org.deeplearning4j.berkeley.Iterators.IteratorIterator
-
- next() - Method in class org.deeplearning4j.berkeley.Iterators.Transform
-
- next() - Method in class org.deeplearning4j.berkeley.Iterators.TransformingIterator
-
- next() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- next() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Returns the element in the queue with highest priority, and pops it from
the queue.
- next() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- next() - Method in class org.deeplearning4j.datasets.fetchers.LFWDataFetcher
-
- next() - Method in class org.deeplearning4j.datasets.fetchers.MnistDataFetcher
-
- next() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- next() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Returns the next data applyTransformToDestination
- next(int) - Method in interface org.deeplearning4j.datasets.iterator.DataSetIterator
-
Like the standard next method but allows a
customizable number of examples returned
- next() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Like the standard next method but allows a
customizable number of examples returned
- next() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Returns the next element in the iteration.
- next(int) - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Like the standard next method but allows a
customizable number of examples returned
- next() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Returns the next element in the iteration.
- next() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- next() - Method in class org.deeplearning4j.datasets.mnist.MnistDbFile
-
Move to the next entry.
- next() - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
-
- next(int) - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
-
- nextBoolean() - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- nextBytes(byte[]) - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- nextDouble() - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- nextFloat() - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- nextGaussian() - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- nextImage() - Method in class org.deeplearning4j.datasets.mnist.MnistImageFile
-
Move the cursor to the next image.
- nextInt() - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- nextInt(int) - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- nextList(List<Iterator<T>>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
- nextLong() - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- nextPowOf2(long) - Static method in class org.deeplearning4j.util.MathUtils
-
See: http://stackoverflow.com/questions/466204/rounding-off-to-nearest-power-of-2
- nIn(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- nIn - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- noNaNDivide(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- NONE - Static variable in class org.deeplearning4j.util.StringGrid
-
- normal(RandomGenerator, double) - Static method in class org.deeplearning4j.distributions.Distributions
-
Returns a normal distribution
with a mean of 0 and a standard deviation of std
- normalize() - Method in class org.deeplearning4j.berkeley.Counter
-
Destructively normalize this Counter in place.
- normalize() - Method in class org.deeplearning4j.berkeley.CounterMap
-
- normalize() - Method in class org.deeplearning4j.datasets.vectorizer.ImageVectorizer
-
Normalize the input image by row sums
- normalize(boolean) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- normalize(double, double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Normalize a value
(val - min) / (max - min)
- normalize(double[], double) - Static method in class org.deeplearning4j.util.MathUtils
-
Normalizes the doubles in the array using the given value.
- normalizeToOne(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
- normalizeWithDiscount(double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- nOut(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- nOut - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- nthIndex(String, char, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns the index of the nth occurrence of ch in s, or -1
if there are less than n occurrences of ch.
- NUM_EXAMPLES - Static variable in class org.deeplearning4j.datasets.fetchers.IrisDataFetcher
-
- NUM_EXAMPLES - Static variable in class org.deeplearning4j.datasets.fetchers.MnistDataFetcher
-
- NUM_IMAGES - Static variable in class org.deeplearning4j.datasets.fetchers.LFWDataFetcher
-
- numExamples - Variable in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- numExamples() - Method in interface org.deeplearning4j.datasets.iterator.DataSetIterator
-
Total number of examples in the dataset
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Total number of examples in the dataset
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Total number of examples in the dataset
- numExamples() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- numExamples() - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
-
- numFeatureMaps(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- numInFeatureMaps(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- numIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- numLabels() - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Returns the number of possible labels
- numLabels() - Method in interface org.deeplearning4j.nn.api.Classifier
-
Returns the number of possible labels
- numLabels() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Returns the number of possible labels
- numLabels() - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Returns the number of possible labels
- numOutcomes - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- numParams() - Method in interface org.deeplearning4j.nn.api.Model
-
The number of parameters for the model
- numParams() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Returns a 1 x m vector where the vector is composed of
a flattened vector of all of the weights for the
various neuralNets and output layer
- numParams() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
The number of parameters for the model
- numParams() - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
The number of parameters for the model
- numTimesIterated - Variable in class org.deeplearning4j.optimize.optimizers.rbm.RBMOptimizer
-
- numtimesPredicted(int) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the number of times a given label was predicted
- numTimesPredicted(int, int) - Method in class org.deeplearning4j.eval.Evaluation
-
Gets the number of times the
given class was predicted for the
given predicted label
- pack() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Packs a set of matrices in to one vector,
where the matrices in this case are the w,hbias at each layer
and the output layer w,bias
- pack(List<Pair<INDArray, INDArray>>) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Packs a set of matrices in to one vector
- pad(String, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Return a String of length a minimum of totalChars characters by
padding the input String str with spaces.
- pad(Object, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Pads the toString value of the given Object.
- padLeft(String, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Pads the given String to the left with spaces to ensure that it's
at least totalChars long.
- padLeft(Object, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- padLeft(int, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- padLeft(double, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- padOrTrim(String, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Pad or trim so as to produce a string of exactly a certain length.
- padOrTrim(Object, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Pad or trim the toString value of the given Object.
- paintComponent(Graphics) - Method in class org.deeplearning4j.plot.FilterPanel
-
- Pair<F,S> - Class in org.deeplearning4j.berkeley
-
A generic-typed pair of objects.
- Pair(F, S) - Constructor for class org.deeplearning4j.berkeley.Pair
-
- Pair.DefaultLexicographicPairComparator<F extends Comparable<F>,S extends Comparable<S>> - Class in org.deeplearning4j.berkeley
-
- Pair.FirstComparator<S extends Comparable<? super S>,T> - Class in org.deeplearning4j.berkeley
-
- Pair.LexicographicPairComparator<F,S> - Class in org.deeplearning4j.berkeley
-
- Pair.ReverseFirstComparator<S extends Comparable<? super S>,T> - Class in org.deeplearning4j.berkeley
-
- Pair.ReverseSecondComparator<S,T extends Comparable<? super T>> - Class in org.deeplearning4j.berkeley
-
- Pair.SecondComparator<S,T extends Comparable<? super T>> - Class in org.deeplearning4j.berkeley
-
- params() - Method in interface org.deeplearning4j.nn.api.Model
-
Parameters of the model (if any)
- params() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Returns a 1 x m vector where the vector is composed of
a flattened vector of all of the weights for the
various neuralNets(w,hbias NOT VBIAS) and output layer
- params() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
Returns the parameters of the neural network
- params() - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Returns the coefficients for this classifier as a raveled vector
- parent(int) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- parse(String, Class<E>) - Static method in class org.deeplearning4j.util.EnumUtil
-
- parseCommandLineArguments(String[]) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
A simpler form of command line argument parsing.
- partitionVariable(List<Double>, int) - Static method in class org.deeplearning4j.util.MathUtils
-
This will partition the given whole variable data applyTransformToDestination in to the specified chunk number.
- patience() - Method in interface org.deeplearning4j.optimize.api.TrainingEvaluator
-
- patience(double) - Method in class org.deeplearning4j.optimize.OutputLayerTrainingEvaluator.Builder
-
- patience() - Method in class org.deeplearning4j.optimize.OutputLayerTrainingEvaluator
-
- patienceIncrease() - Method in interface org.deeplearning4j.optimize.api.TrainingEvaluator
-
Amount patience should be increased when a new best threshold is hit
- patienceIncrease(double) - Method in class org.deeplearning4j.optimize.OutputLayerTrainingEvaluator.Builder
-
- patienceIncrease() - Method in class org.deeplearning4j.optimize.OutputLayerTrainingEvaluator
-
Amount patience should be increased when a new best threshold is hit
- peek() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- peek() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Returns the highest-priority element in the queue, but does not pop it.
- peek() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- pennPOSToWordnetPOS(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Computes the WordNet 2.0 POS tag corresponding to the PTB POS tag s.
- permutation(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the permutation of n choose r.
- perplexity(double) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- Persistable - Interface in org.deeplearning4j.nn.api
-
- plot(INDArray, int, List<String>) - Method in class org.deeplearning4j.plot.Tsne
-
Plot tsne
- plotActivations(Layer) - Method in class org.deeplearning4j.plot.NeuralNetPlotter
-
- plotActivations(NeuralNetwork) - Method in class org.deeplearning4j.plot.NeuralNetPlotter
-
- plotNetworkGradient(Layer, INDArray, int) - Method in class org.deeplearning4j.plot.NeuralNetPlotter
-
- plotNetworkGradient(NeuralNetwork, NeuralNetworkGradient, int) - Method in class org.deeplearning4j.plot.NeuralNetPlotter
-
- plotter - Variable in class org.deeplearning4j.optimize.optimizers.BackPropOptimizer
-
- plotter - Variable in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- poll() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- positive() - Method in class org.deeplearning4j.eval.Evaluation
-
Returns all of the positive guesses:
true positive + false negative
- precision() - Method in class org.deeplearning4j.eval.Evaluation
-
Total precision based on guesses so far
- precision(int) - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the precision for a given label
- predict(INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Takes in a list of examples
For each row, returns a label
- predict(INDArray) - Method in interface org.deeplearning4j.nn.api.SequenceClassifier
-
- predict(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Returns the predictions for each example in the dataset
- predict(INDArray) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Returns the predictions for each example in the dataset
- preOutput(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
- preOutput(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
Classify input
- prependToEach(String, int) - Method in class org.deeplearning4j.util.StringGrid
-
- preProcess(DataSet) - Method in interface org.deeplearning4j.datasets.iterator.DataSetPreProcessor
-
Pre process a dataset
- preProcessInput(INDArray) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- preProcessor - Variable in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- pretrain(boolean) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN.Builder
-
Whether to pretrain or not
- pretrain(DataSetIterator, Object[]) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN
-
- pretrain(INDArray, Object[]) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN
-
- pretrain(DataSetIterator, int, float, int) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN
-
This unsupervised learning method runs
contrastive divergence on each RBM layer in the network.
- pretrain(INDArray, int, float, int) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN
-
This unsupervised learning method runs
contrastive divergence on each RBM layer in the network.
- pretrain(int, float, int) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN
-
- pretrain(DataSetIterator, Object[]) - Method in class org.deeplearning4j.models.classifiers.sae.StackedAutoEncoder
-
- pretrain(INDArray, Object[]) - Method in class org.deeplearning4j.models.classifiers.sae.StackedAutoEncoder
-
- pretrain(boolean) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder.Builder
-
Whether to pretrain or not
- pretrain(float, float, int) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder
-
- pretrain(DataSetIterator, Object[]) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder
-
Pretrain with a data applyTransformToDestination iterator.
- pretrain(DataSetIterator, float, float, int) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder
-
This unsupervised learning method runs
contrastive divergence on each RBM layer in the network.
- pretrain(INDArray, Object[]) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder
-
- pretrain(INDArray, float, float, int) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder
-
Unsupervised pretraining based on reconstructing the input
from a corrupted version
- pretrain(INDArray, Object[]) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Pretrain the network with the given parameters
- pretrain(DataSetIterator, Object[]) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Pretrain with a data applyTransformToDestination iterator.
- pretrain - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork.Builder
-
- pretrain(boolean) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.Builder
-
Whether to pretrain or not
- pretrain - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- pretrain(DataSetIterator, Object[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- pretrain(INDArray, Object[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- prev() - Method in class org.deeplearning4j.datasets.mnist.MnistDbFile
-
Move to the previous entry.
- prevImage() - Method in class org.deeplearning4j.datasets.mnist.MnistImageFile
-
Move the cursor to the previous image.
- printConfiguration() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Prints the configuration
- printStringOneCharPerLine(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- printToFile(File, String, boolean) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Prints to a file.
- printToFile(File, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Prints to a file.
- printToFile(String, String, boolean) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Prints to a file.
- printToFile(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Prints to a file.
- PriorityQueue<E> - Class in org.deeplearning4j.berkeley
-
A priority queue based on a binary heap.
- PriorityQueue() - Constructor for class org.deeplearning4j.berkeley.PriorityQueue
-
- PriorityQueue(int) - Constructor for class org.deeplearning4j.berkeley.PriorityQueue
-
- PriorityQueueInterface<E> - Interface in org.deeplearning4j.berkeley
-
- probRound(double, Random) - Static method in class org.deeplearning4j.util.MathUtils
-
Rounds a double to the next nearest integer value in a probabilistic
fashion (e.g.
- probToLogOdds(double) - Static method in class org.deeplearning4j.util.MathUtils
-
Returns the log-odds for a given probability.
- propDown(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Calculates the activation of the hidden:
sigmoid(h * W + vbias)
- propUp(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Calculates the activation of the visible :
sigmoid(v * W + hbias)
- pruneKeysBelowThreshold(double) - Method in class org.deeplearning4j.berkeley.Counter
-
- put(E, double, boolean) - Method in class org.deeplearning4j.berkeley.Counter
-
Set the count for the given key if it is larger than the previous one;
- put(E, double) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- put(E, double) - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Adds a key to the queue with the given priority.
- put(Pair<K, T>, V) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Associates the specified value with the specified key in this map
(optional operation).
- put(K, T, V) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
- putAll(Map<? extends Pair<K, T>, ? extends V>) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Copies all of the mappings from the specified map to this map
(optional operation).
- randomDoubleBetween(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
- randomFloatBetween(float, float) - Static method in class org.deeplearning4j.util.MathUtils
-
- randomNumberBetween(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Generates a random integer between the specified numbers
- randomNumberBetween(double, double, RandomGenerator) - Static method in class org.deeplearning4j.util.MathUtils
-
Generates a random integer between the specified numbers
- RawMnistDataSetIterator - Class in org.deeplearning4j.datasets.iterator.impl
-
Mnist data with scaled pixels
- RawMnistDataSetIterator(int, int) - Constructor for class org.deeplearning4j.datasets.iterator.impl.RawMnistDataSetIterator
-
- RBM - Class in org.deeplearning4j.models.featuredetectors.rbm
-
Restricted Boltzmann Machine.
- RBM() - Constructor for class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
- RBM(INDArray, INDArray, INDArray, INDArray, NeuralNetConfiguration) - Constructor for class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
- RBM.Builder - Class in org.deeplearning4j.models.featuredetectors.rbm
-
- RBM.HiddenUnit - Enum in org.deeplearning4j.models.featuredetectors.rbm
-
- RBM.VisibleUnit - Enum in org.deeplearning4j.models.featuredetectors.rbm
-
- RBMOptimizer - Class in org.deeplearning4j.optimize.optimizers.rbm
-
Optimizes an RBM.
- RBMOptimizer(BaseNeuralNetwork, float, Object[], NeuralNetwork.OptimizationAlgorithm, LossFunctions.LossFunction) - Constructor for class org.deeplearning4j.optimize.optimizers.rbm.RBMOptimizer
-
- RBMUtil - Class in org.deeplearning4j.util
-
Handles various functions for RBM specific functions
- readImage() - Method in class org.deeplearning4j.datasets.mnist.MnistImageFile
-
Reads the image at the current position.
- readImage() - Method in class org.deeplearning4j.datasets.mnist.MnistManager
-
Reads the current image.
- readjustToData() - Method in class org.deeplearning4j.datasets.mnist.draw.DrawReconstruction
-
- readLabel() - Method in class org.deeplearning4j.datasets.mnist.MnistLabelFile
-
Reads the integer at the current position.
- readLabel() - Method in class org.deeplearning4j.datasets.mnist.MnistManager
-
Reads the current label.
- readObject(File) - Static method in class org.deeplearning4j.util.SerializationUtils
-
- readObject(InputStream) - Static method in class org.deeplearning4j.util.SerializationUtils
-
Reads an object from the given input stream
- readString(DataInputStream, int) - Static method in class org.deeplearning4j.util.ByteUtil
-
- recall() - Method in class org.deeplearning4j.eval.Evaluation
-
Returns the recall for the outcomes
- reconstruct(INDArray, int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Reconstructs the input.
- ReconstructionDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
Wraps a data applyTransformToDestination iterator setting the first (feature matrix) as
the labels.
- ReconstructionDataSetIterator(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
- reductionRatio(INDArray, double, double, INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- regularization(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- relativeDifferance(double, double) - Static method in class org.deeplearning4j.berkeley.SloppyMath
-
- remove() - Method in class org.deeplearning4j.berkeley.Iterators.FilteredIterator
-
- remove() - Method in class org.deeplearning4j.berkeley.Iterators.IteratorIterator
-
- remove() - Method in class org.deeplearning4j.berkeley.Iterators.Transform
-
- remove() - Method in class org.deeplearning4j.berkeley.Iterators.TransformingIterator
-
- remove() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- remove() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Not supported -- next() already removes the head of the queue.
- remove() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- remove() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- remove() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Removes from the underlying collection the last element returned
by this iterator (optional operation).
- remove() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Removes from the underlying collection the last element returned
by this iterator (optional operation).
- remove() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- remove() - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
-
- remove(Object) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- remove() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- remove(Object) - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Removes the mapping for a key from this map if it is present
(optional operation).
- remove(Object) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Removes the specified element from this applyTransformToDestination if it is present
(optional operation).
- removeAll(Collection<?>) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- removeAll(Collection<?>) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Removes from this applyTransformToDestination all of its elements that are contained in the
specified collection (optional operation).
- removeColumns(Integer...) - Method in class org.deeplearning4j.util.StringGrid
-
Removes the specified columns from the grid
- removeFirst() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- removeKey(E) - Method in class org.deeplearning4j.berkeley.Counter
-
- removeKey(K) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- removeKeyFromEntries(E) - Method in class org.deeplearning4j.berkeley.Counter
-
- removeRowsWithEmptyColumn(int) - Method in class org.deeplearning4j.util.StringGrid
-
Removes all rows with a column of NONE
- removeRowsWithEmptyColumn(int, String) - Method in class org.deeplearning4j.util.StringGrid
-
Removes all rows with a column of missingValue
- render(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- renderActivations(int, int, INDArray, String, int) - Method in class org.deeplearning4j.plot.FilterRenderer
-
- renderFilter(INDArray, int, int, long) - Method in class org.deeplearning4j.plot.NeuralNetPlotter
-
- renderFilters(INDArray, String, int, int, int) - Method in class org.deeplearning4j.plot.FilterRenderer
-
Once the probability image and weight histograms are
behaving satisfactorily, we plot the learned filter
for each hidden neuron, one per column of W.
- renderHiddenBiases(int, int, INDArray, String) - Method in class org.deeplearning4j.plot.FilterRenderer
-
- renderHistogram(INDArray, String, int) - Method in class org.deeplearning4j.plot.FilterRenderer
-
Groups values into 1 of 10 bins, sums, and renders
NOTE: this is "render histogram BS code";
- I'm not exactly concerned with how pretty it is.
- renderWeightsEveryNumEpochs - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- reset() - Method in class org.deeplearning4j.clustering.KMeansClustering
-
- reset() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- reset() - Method in class org.deeplearning4j.datasets.fetchers.MnistDataFetcher
-
- reset() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- reset() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
Returns the fetcher back to the beginning of the dataset
- reset() - Method in interface org.deeplearning4j.datasets.iterator.DataSetIterator
-
Resets the iterator back to the beginning
- reset() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- reset() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Resets the iterator back to the beginning
- reset() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Resets the iterator back to the beginning
- reset() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- reset() - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
-
- reset() - Method in class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
- reset() - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- resetAdaGradIterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- resetAdaGradIterations - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- retainAll(Collection<?>) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- retainAll(Collection<?>) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Retains only the elements in this applyTransformToDestination that are contained in the
specified collection (optional operation).
- reverse() - Method in class org.deeplearning4j.berkeley.Pair
-
- ReverseFirstComparator() - Constructor for class org.deeplearning4j.berkeley.Pair.ReverseFirstComparator
-
- ReverseSecondComparator() - Constructor for class org.deeplearning4j.berkeley.Pair.ReverseSecondComparator
-
- rightChild(int) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- rng(RandomGenerator) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- rng - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- rootMeansSquaredError(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the root mean squared error of two data sets
- round(double, int, int) - Static method in class org.deeplearning4j.plot.FilterRenderer
-
- round(double) - Static method in class org.deeplearning4j.util.MathUtils
-
Rounds a double to the next nearest integer value.
- roundDouble(double, int) - Static method in class org.deeplearning4j.util.MathUtils
-
Rounds a double to the given number of decimal places.
- roundFloat(float, int) - Static method in class org.deeplearning4j.util.MathUtils
-
Rounds a double to the given number of decimal places.
- sample(Random) - Method in class org.deeplearning4j.berkeley.Counter
-
Will return a sample from the counter, will throw exception if any of the
counts are < 0.0 or if the totalCount() <= 0.0
- sample() - Method in class org.deeplearning4j.berkeley.Counter
-
Will return a sample from the counter, will throw exception if any of the
counts are < 0.0 or if the totalCount() <= 0.0
- sampleDoublesInInterval(double[][], int) - Static method in class org.deeplearning4j.util.MathUtils
-
- sampleFromHiddenActivations - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.AutoEncoder
-
Sample hidden mean and sample
given visible
- sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
- sampleHiddenGivenVisible(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Binomial sampling of the hidden values given visible
- sampleHiddenGivenVisible(INDArray) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
Sample hidden mean and sample
given visible
- sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.AutoEncoder
-
Sample visible mean and sample
given hidden
- sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
- sampleVisibleGivenHidden(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Guess the visible values given the hidden
- sampleVisibleGivenHidden(INDArray) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
Sample visible mean and sample
given hidden
- SamplingDataSetIterator - Class in org.deeplearning4j.datasets.iterator
-
A wrapper for a dataset to sample from.
- SamplingDataSetIterator(DataSet, int, int) - Constructor for class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- saveImageToDisk(BufferedImage, String) - Static method in class org.deeplearning4j.plot.FilterRenderer
-
- saveObject(Object, File) - Static method in class org.deeplearning4j.util.SerializationUtils
-
- saveToDisk(String) - Method in class org.deeplearning4j.plot.FilterRenderer
-
- scale(double) - Method in class org.deeplearning4j.berkeley.Counter
-
- scale(double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Scale all entries in CounterMap
by scaleFactor
- scaledClone(double) - Method in class org.deeplearning4j.berkeley.Counter
-
- scatter(String[], INDArray[]) - Method in class org.deeplearning4j.plot.NeuralNetPlotter
-
Histograms the given matrices.
- score(DataSet) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- score() - Method in interface org.deeplearning4j.nn.api.Classifier
-
Assuming an input and labels are already set
will score based on what's already set
- score(DataSet) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- score(INDArray, INDArray) - Method in interface org.deeplearning4j.nn.api.Classifier
-
Returns the f1 score for the given examples.
- score() - Method in interface org.deeplearning4j.nn.api.Model
-
- score(DataSet) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- score(DataSet) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- score() - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Score of the model (relative to the objective function)
- score(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Score of the model (relative to the objective function)
- score() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- score() - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Objective function: the specified objective
- score(DataSet) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Sets the input and labels and returns a score for the prediction
wrt true labels
- score(INDArray, INDArray) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Returns the f1 score for the given examples.
- SecondComparator() - Constructor for class org.deeplearning4j.berkeley.Pair.SecondComparator
-
- seed(long) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- seed - Variable in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- select(int, String) - Method in class org.deeplearning4j.util.StringGrid
-
- SemanticHashing - Class in org.deeplearning4j.models.featuredetectors.autoencoder
-
Encapsulates a deep auto encoder and decoder (the transpose of an encoder).
- SemanticHashing() - Constructor for class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
- SemanticHashing.Builder - Class in org.deeplearning4j.models.featuredetectors.autoencoder
-
- SequenceClassifier - Interface in org.deeplearning4j.nn.api
-
Created by agibsonccc on 8/27/14.
- SerializationUtils - Class in org.deeplearning4j.util
-
Serialization utils for saving and reading serializable objects
- SerializationUtils() - Constructor for class org.deeplearning4j.util.SerializationUtils
-
- serialVersionUID - Static variable in class org.deeplearning4j.nn.layers.Layer
-
- setAbsTolx(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedBackTrackLineSearch
-
Sets the tolerance of absolute diff in function value.
- setAbsTolx(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedBackTrackLineSearchMinimum
-
Sets the tolerance of absolute diff in function value.
- setActivationFunction(ActivationFunction) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setActivationType(NeuralNetConfiguration.ActivationType) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setAdaGrad(AdaGrad) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- setAdaGrad(AdaGrad) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- setAdaGrad(AdaGrad) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
- setAllCounts(double) - Method in class org.deeplearning4j.berkeley.Counter
-
Sets all counts to the given value, but does not remove any keys
- setApplySparsity(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setB(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
- setB(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setbGradient(INDArray) - Method in class org.deeplearning4j.nn.gradient.OutputLayerGradient
-
- setBiasAdaGrad(AdaGrad) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
- setBiasEnd(int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.ParamRange
-
- setBiasStart(int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.ParamRange
-
- setClassifier(NeuralNetConfiguration) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
Set the configuration for classification
- setClassifier(NeuralNetConfiguration, boolean) - Static method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
Set the conf for classification
- setConcatBiases(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setConf(NeuralNetConfiguration) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- setConf(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- setConfiguration(NeuralNetConfiguration) - Method in interface org.deeplearning4j.nn.api.Layer
-
- setConfiguration(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setConstrainGradientToUnitNorm(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setCorruptionLevel(float) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setCount(E, double) - Method in class org.deeplearning4j.berkeley.Counter
-
Set the count for the given key, clobbering any previous count.
- setCount(K, V, double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
Sets the count for a particular (key, value) pair.
- setCounter(K, Counter<V>) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- setCurrent(int) - Method in class org.deeplearning4j.datasets.mnist.MnistManager
-
Set the position to be read.
- setCurrentIndex(long) - Method in class org.deeplearning4j.datasets.mnist.MnistDbFile
-
Set the required current entry index.
- setCurrentIteration(int) - Method in interface org.deeplearning4j.optimize.api.OptimizableByGradientValueMatrix
-
- setCurrentIteration(int) - Method in class org.deeplearning4j.optimize.optimizers.BackPropOptimizer
-
- setCurrentIteration(int) - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- setCurrentIteration(int) - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- setCurrentIteration(int) - Method in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- setCurrentIteration(int) - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- setDefault(double) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- setDefaultConfiguration(NeuralNetConfiguration) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setDeflt(double) - Method in class org.deeplearning4j.berkeley.Counter
-
- setDgg(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setDirty(boolean) - Method in class org.deeplearning4j.berkeley.Counter
-
- setDist(RealDistribution) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setDropOut(float) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setEncoder(BaseMultiLayerNetwork) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing
-
- setFeatureMapSize(int[]) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setFilterSize(int[]) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setFinalMomentum(double) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- setFinetuneEpochs(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setFinetuneLearningRate(float) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setFirst(F) - Method in class org.deeplearning4j.berkeley.Pair
-
- setFirst(S) - Method in class org.deeplearning4j.berkeley.Triple
-
- setFirstKey(K) - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- setForceNumEpochs(boolean) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setFp(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setFret(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setG(INDArray) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setGam(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setGg(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setGradients(List<NeuralNetworkGradient>) - Method in class org.deeplearning4j.nn.gradient.MultiLayerGradient
-
- setH(INDArray) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- sethBias(INDArray) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- sethBias(INDArray) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- setHbiasAdaGrad(AdaGrad) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- setHbiasAdaGrad(AdaGrad) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- sethBiasGradient(INDArray) - Method in class org.deeplearning4j.nn.gradient.NeuralNetworkGradient
-
- setHiddenLayerSizes(int[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setHiddenUnit(RBM.HiddenUnit) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setInitialMomentum(double) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- setInitialStepSize(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
- setInitialStepSize(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setInput(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
- setInput(INDArray) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- setInput(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Note that if input isn't null
and the neuralNets are null, this is a way
of initializing the neural network
- setInput(INDArray) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- setInput(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setIterationListener(IterationListener) - Method in class org.deeplearning4j.plot.Tsne
-
- setK(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setL2(float) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setLabels(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setLabels(INDArray) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
- setLayers(Layer[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setLayers(NeuralNetwork[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setLayerWiseConfigurations(List<NeuralNetConfiguration>) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setLineMaximizer(LineOptimizerMatrix) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setLogMetaInstability(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setLogOfDiangnalTProb(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setLogPCorrect(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setLogPIncorrect(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setLogRegGradient(OutputLayerGradient) - Method in class org.deeplearning4j.nn.gradient.MultiLayerGradient
-
- setLogStates(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setLossFunction(LossFunctions.LossFunction) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setLr(float) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setMask(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setMax(double) - Method in class org.deeplearning4j.util.SummaryStatistics
-
- setMaxCount(E, double) - Method in class org.deeplearning4j.berkeley.Counter
-
Set's the key's count to the maximum of the current count and val.
- setMaxIter(int) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- setMaxIterations(int) - Method in class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
- setMaxIterations(int) - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
- setMaxIterations(int) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setMaxIterations(int) - Method in interface org.deeplearning4j.util.OptimizerMatrix
-
The default max number of iterations to run
- setMaxStepSize(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
- setMean(double) - Method in class org.deeplearning4j.util.SummaryStatistics
-
- setMetaStability(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setMin(double) - Method in class org.deeplearning4j.util.SummaryStatistics
-
- setMinCount(E, double) - Method in class org.deeplearning4j.berkeley.Counter
-
Set's the key's count to the minimum of the current count and val.
- setMomentum(float) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setMomentum(double) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- setMomentumAfter(Map<Integer, Float>) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setNeuralNets(NeuralNetwork[]) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setnIn(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setnLayers(int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setnOut(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setNumFeatureMaps(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setNumInFeatureMaps(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setNumIterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setOptimizationAlgo(NeuralNetwork.OptimizationAlgorithm) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setParameter(int, double) - Method in interface org.deeplearning4j.optimize.api.OptimizableByGradientValueMatrix
-
- setParameter(int, double) - Method in class org.deeplearning4j.optimize.optimizers.BackPropOptimizer
-
- setParameter(int, double) - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- setParameter(int, double) - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- setParameter(int, double) - Method in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- setParameter(int, double) - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- setParameters(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Sets parameters for the model.
- setParameters(INDArray) - Method in interface org.deeplearning4j.optimize.api.OptimizableByGradientValueMatrix
-
- setParameters(INDArray) - Method in class org.deeplearning4j.optimize.optimizers.BackPropOptimizer
-
- setParameters(double[]) - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- setParameters(INDArray) - Method in class org.deeplearning4j.optimize.optimizers.BackPropROptimizer
-
- setParameters(double[]) - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- setParameters(INDArray) - Method in class org.deeplearning4j.optimize.optimizers.MultiLayerNetworkOptimizer
-
- setParameters(INDArray) - Method in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- setParameters(double[]) - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- setParameters(INDArray) - Method in class org.deeplearning4j.optimize.optimizers.OutputLayerOptimizer
-
- setParams(INDArray) - Method in interface org.deeplearning4j.nn.api.Model
-
Set the parameters for this model.
- setParams(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Set the parameters for this model.
- setParams(INDArray) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
Set the parameters for this model.
- setParams(INDArray) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Set the parameters for this model.
- setpCorrect(double) - Method in class org.deeplearning4j.util.Viterbi
-
- setPicDraw(MatrixTransform) - Method in class org.deeplearning4j.plot.DeepAutoEncoderDataSetReconstructionRender
-
- setPossibleLabels(INDArray) - Method in class org.deeplearning4j.util.Viterbi
-
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
Set a pre processor
- setPreProcessor(DataSetPreProcessor) - Method in interface org.deeplearning4j.datasets.iterator.DataSetIterator
-
Set a pre processor
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
Set a pre processor
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Set a pre processor
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Set a pre processor
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
Set a pre processor
- setPreProcessor(DataSetPreProcessor) - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
-
Set a pre processor
- setPretrainEpochs(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setPretrainLearningRate(float) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setRealMin(double) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- setReconDraw(MatrixTransform) - Method in class org.deeplearning4j.plot.DeepAutoEncoderDataSetReconstructionRender
-
- setRelTolx(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedBackTrackLineSearch
-
Sets the tolerance of relative diff in function value.
- setRelTolx(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedBackTrackLineSearchMinimum
-
Sets the tolerance of relative diff in function value.
- setRenderWeightIterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setResetAdaGradIterations(int) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setRng(RandomGenerator) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setSampleFromHiddenActivations(boolean) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setSecond(S) - Method in class org.deeplearning4j.berkeley.Pair
-
- setSecond(T) - Method in class org.deeplearning4j.berkeley.Triple
-
- setSecondKey(T) - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
- setSeed(long) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setSeed(int) - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- setSeed(int[]) - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- setSeed(long) - Method in class org.deeplearning4j.rng.SynchronizedRandomGenerator
- setSparsity(float) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setStates(int) - Method in class org.deeplearning4j.util.Viterbi
-
- setStep(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setStpmax(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedBackTrackLineSearch
-
- setStpmax(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedBackTrackLineSearchMinimum
-
- setStpmax(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
- setStride(int[]) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setSum(double) - Method in class org.deeplearning4j.util.SummaryStatistics
-
- setSwitchMomentumIteration(int) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- setThird(U) - Method in class org.deeplearning4j.berkeley.Triple
-
- setTolerance(double) - Method in class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
The tolerance for change when running
- setTolerance(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
Sets the tolerance in the convergence test:
2.0*|value-old_value| <= tolerance*(|value|+|old_value|+eps)
Default value is 0.001.
- setTolerance(double) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setTolerance(double) - Method in interface org.deeplearning4j.util.OptimizerMatrix
-
The tolerance for change when running
- setTrainingEvaluator(TrainingEvaluator) - Method in class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
Sets the training evaluator
- setTrainingEvaluator(TrainingEvaluator) - Method in class org.deeplearning4j.optimize.solvers.VectorizedDeepLearningGradientAscent
-
Sets the training evaluator
- setTrainingEvaluator(TrainingEvaluator) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
Sets the training evaluator
- setTrainingEvaluator(TrainingEvaluator) - Method in interface org.deeplearning4j.util.OptimizerMatrix
-
Sets the training evaluator
- setUseAdaGrad(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setUseDropConnect(boolean) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setUseGaussNewtonVectorProductBackProp(boolean) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
- setUseRegularization(boolean) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- SetUtils - Class in org.deeplearning4j.util
-
- setValue(V) - Method in class org.deeplearning4j.util.MultiDimensionalMap.Entry
-
Replaces the value corresponding to this entry with the specified
value (optional operation).
- setvBias(INDArray) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- setvBias(INDArray) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- setVBiasAdaGrad(AdaGrad) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- setVBiasAdaGrad(AdaGrad) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- setvBiasGradient(INDArray) - Method in class org.deeplearning4j.nn.gradient.NeuralNetworkGradient
-
- setVisibleUnit(RBM.VisibleUnit) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setW(INDArray) - Method in interface org.deeplearning4j.nn.api.Layer
-
- setW(INDArray) - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- setW(INDArray) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- setW(INDArray) - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- setWeightInit(WeightInit) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setWeightShape(int[]) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- setwEnd(int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.ParamRange
-
- setwGradient(INDArray) - Method in class org.deeplearning4j.nn.gradient.NeuralNetworkGradient
-
- setwGradient(INDArray) - Method in class org.deeplearning4j.nn.gradient.OutputLayerGradient
-
- setwStart(int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.ParamRange
-
- setXi(INDArray) - Method in class org.deeplearning4j.optimize.solvers.VectorizedNonZeroStoppingConjugateGradient
-
- setY(INDArray) - Method in class org.deeplearning4j.plot.Tsne
-
- shouldForceEpochs - Variable in class org.deeplearning4j.nn.BaseMultiLayerNetwork.Builder
-
- shouldStop(int) - Method in interface org.deeplearning4j.optimize.api.TrainingEvaluator
-
Whether to terminate or not
- shouldStop(int) - Method in class org.deeplearning4j.optimize.OutputLayerTrainingEvaluator
-
Whether to terminate or not
- sigma - Variable in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
- sigmoid(double) - Static method in class org.deeplearning4j.util.MathUtils
-
1 / 1 + exp(-x)
- size() - Method in class org.deeplearning4j.berkeley.Counter
-
The number of entries in the counter (not the total count -- use
totalCount() instead).
- size() - Method in class org.deeplearning4j.berkeley.CounterMap
-
The number of keys in this CounterMap (not the number of key-value entries
-- use totalSize() for that)
- size() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- size() - Method in interface org.deeplearning4j.berkeley.PriorityQueueInterface
-
Number of elements in the queue.
- size() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- size() - Method in class org.deeplearning4j.util.Index
-
- size() - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
Returns the number of key-value mappings in this map.
- size() - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns the number of elements in this applyTransformToDestination (its cardinality).
- SizeComparator() - Constructor for class org.deeplearning4j.util.StringCluster.SizeComparator
-
- slope(double, double, double, double) - Method in class org.deeplearning4j.util.MathUtils
-
This returns the slope of the given points.
- SloppyMath - Class in org.deeplearning4j.berkeley
-
The class SloppyMath contains methods for performing basic
numeric operations.
- SloppyMath() - Constructor for class org.deeplearning4j.berkeley.SloppyMath
-
- slurpFile(File) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given File.
- slurpFile(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given file with the given encoding.
- slurpFile(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given file
- slurpFileNoExceptions(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given file with the given encoding.
- slurpFileNoExceptions(File) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given File.
- slurpFileNoExceptions(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text in the given File.
- slurpGBFile(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- slurpGBFileNoExceptions(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- slurpGBURL(URL) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpGBURLNoExceptions(URL) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpReader(Reader) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text from the given Reader.
- slurpURL(URL, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpURL(URL) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpURL(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpURLNoExceptions(URL, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpURLNoExceptions(URL) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- slurpURLNoExceptions(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns all the text at the given URL.
- sm(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Tests if a is smaller than b.
- SMALL - Static variable in class org.deeplearning4j.util.MathUtils
-
The small deviation allowed in double comparisons.
- sort() - Method in class org.deeplearning4j.util.StringCluster
-
- sortBy(int) - Method in class org.deeplearning4j.util.StringGrid
-
- sortColumnsByWordLikelihoodIncluded(int) - Method in class org.deeplearning4j.util.StringGrid
-
- sparsity(float) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- split(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Splits on whitespace (\\s+).
- split(String, String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Splits the given string using the given regex as delimiters.
- split(int, String) - Method in class org.deeplearning4j.util.StringGrid
-
- splitInputs(INDArray, INDArray, List<Pair<INDArray, INDArray>>, List<Pair<INDArray, INDArray>>, double) - Static method in class org.deeplearning4j.util.InputSplit
-
- splitInputs(List<Pair<INDArray, INDArray>>, List<Pair<INDArray, INDArray>>, List<Pair<INDArray, INDArray>>, double) - Static method in class org.deeplearning4j.util.InputSplit
-
- splitOnCharWithQuoting(String, char, char, char) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
This function splits the String s into multiple Strings using the
splitChar.
- squaredLoss(double[], double[], double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
This will return the squared loss of the given
points
- ssError(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
How much of the variance is NOT explained by the regression
- ssReg(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
How much of the variance is explained by the regression
- ssTotal(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Total variance in target attribute
- StackedAutoEncoder - Class in org.deeplearning4j.models.classifiers.sae
-
Created by mjk on 9/17/14.
- StackedAutoEncoder() - Constructor for class org.deeplearning4j.models.classifiers.sae.StackedAutoEncoder
-
- StackedDenoisingAutoEncoder - Class in org.deeplearning4j.models.classifiers.sda
-
Stacked Denoising AutoEncoders are merely denoising autoencoders
who's inputs feed in to the next one.
- StackedDenoisingAutoEncoder() - Constructor for class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder
-
- StackedDenoisingAutoEncoder.Builder - Class in org.deeplearning4j.models.classifiers.sda
-
- start() - Method in class org.deeplearning4j.datasets.mnist.draw.DrawReconstruction
-
- start() - Method in class org.deeplearning4j.plot.FilterRenderer
-
- startIndexForLayer(int) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Returns a start index for a given layer (neural net or outputlayer)
- stats() - Method in class org.deeplearning4j.eval.Evaluation
-
- step(INDArray, INDArray, Object[]) - Method in interface org.deeplearning4j.optimize.api.StepFunction
-
Step with the given parameters
- step(INDArray, INDArray) - Method in interface org.deeplearning4j.optimize.api.StepFunction
-
Step with no parameters
- step() - Method in interface org.deeplearning4j.optimize.api.StepFunction
-
- step(INDArray, INDArray, Object[]) - Method in class org.deeplearning4j.optimize.stepfunctions.BackPropStepFunction
-
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.BackPropStepFunction
-
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.BackPropStepFunction
-
- step(INDArray, INDArray, Object[]) - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
-
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
-
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.DefaultStepFunction
-
- step(INDArray, INDArray, Object[]) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
-
- step(INDArray, INDArray) - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
-
- step() - Method in class org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction
-
- step(INDArray, int) - Method in class org.deeplearning4j.plot.Tsne
-
An individual iteration
- StepFunction - Interface in org.deeplearning4j.optimize.api
-
Custom step function for line search
- StochasticHessianFree - Class in org.deeplearning4j.optimize.solvers
-
Hessian Free Optimization
by Ryan Kiros http://www.cs.toronto.edu/~rkiros/papers/shf13.pdf
- StochasticHessianFree(OptimizableByGradientValueMatrix, double, BaseMultiLayerNetwork) - Constructor for class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
- StochasticHessianFree(OptimizableByGradientValueMatrix, IterationListener, BaseMultiLayerNetwork) - Constructor for class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
- StochasticHessianFree(OptimizableByGradientValueMatrix, double, IterationListener, BaseMultiLayerNetwork) - Constructor for class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
- StochasticHessianFree(OptimizableByGradientValueMatrix, BaseMultiLayerNetwork) - Constructor for class org.deeplearning4j.optimize.solvers.StochasticHessianFree
-
- stopLyingIteration(int) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- stride(int[]) - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder
-
- StringCluster - Class in org.deeplearning4j.util
-
Clusters strings based on fingerprint: for example
Two words and TWO words or WORDS TWO would be put together
- StringCluster(List<String>) - Constructor for class org.deeplearning4j.util.StringCluster
-
- StringCluster.SizeComparator - Class in org.deeplearning4j.util
-
- StringGrid - Class in org.deeplearning4j.util
-
String matrix
- StringGrid(StringGrid) - Constructor for class org.deeplearning4j.util.StringGrid
-
- StringGrid(String, int) - Constructor for class org.deeplearning4j.util.StringGrid
-
- StringGrid(String, Collection<String>) - Constructor for class org.deeplearning4j.util.StringGrid
-
- stringSimilarity(String...) - Static method in class org.deeplearning4j.util.MathUtils
-
Calculate string similarity with tfidf weights relative to each character
frequency and how many times a character appears in a given string
- stringToProperties(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
This method converts a comma-separated String (with whitespace
optionally allowed after the comma) representing properties
to a Properties object.
- StringUtils - Class in org.deeplearning4j.berkeley
-
StringUtils is a class for random String things.
- stripDuplicateRows() - Method in class org.deeplearning4j.util.StringGrid
-
- stripNonAlphaNumerics(String) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- SubsamplingLayer - Class in org.deeplearning4j.nn.layers
-
Sub sampling layer
- SubsamplingLayer(NeuralNetConfiguration, INDArray, INDArray, INDArray) - Constructor for class org.deeplearning4j.nn.layers.SubsamplingLayer
-
- sum(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the sum of the given array.
- SummaryStatistics - Class in org.deeplearning4j.util
-
- summaryStats(INDArray) - Static method in class org.deeplearning4j.util.SummaryStatistics
-
- summaryStatsString(INDArray) - Static method in class org.deeplearning4j.util.SummaryStatistics
-
- sumOfMeanDifferences(double[], double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Used for calculating top part of simple regression for
beta 1
- sumOfMeanDifferencesOnePoint(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
Used for calculating top part of simple regression for
beta 1
- sumOfProducts(double[]...) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the sum of products for the given
numbers.
- sumOfSquares(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the sum of squares for the given vector.
- swap(int, int) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
- swap(int, int) - Method in class org.deeplearning4j.util.StringGrid
-
- SynchronizedRandomGenerator - Class in org.deeplearning4j.rng
-
Any RandomGenerator implementation can be thread-safe if it
is used through an instance of this class.
- SynchronizedRandomGenerator(RandomGenerator) - Constructor for class org.deeplearning4j.rng.SynchronizedRandomGenerator
-
Creates a synchronized wrapper for the given RandomGenerator
instance.
- TestDataSetIterator - Class in org.deeplearning4j.datasets.test
-
Track number of times the dataset iterator has been called
- TestDataSetIterator(DataSetIterator) - Constructor for class org.deeplearning4j.datasets.test.TestDataSetIterator
-
- testSet(DataSet) - Method in class org.deeplearning4j.optimize.OutputLayerTrainingEvaluator.Builder
-
- tf(int) - Static method in class org.deeplearning4j.util.MathUtils
-
Term frequency: 1+ log10(count)
- tfidf(double, double) - Static method in class org.deeplearning4j.util.MathUtils
-
Return td * idf
- thread(Iterator<T>) - Static method in class org.deeplearning4j.berkeley.Iterators
-
Executes calls to next() in a different thread
- times(double[]) - Static method in class org.deeplearning4j.util.MathUtils
-
This returns the product of all numbers in the given array.
- title - Variable in class org.deeplearning4j.datasets.mnist.draw.DrawReconstruction
-
- title - Variable in class org.deeplearning4j.plot.FilterRenderer
-
- toArray() - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- toArray(T[]) - Method in class org.deeplearning4j.util.DiskBasedQueue
-
- toArray() - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns an array containing all of the elements in this applyTransformToDestination.
- toArray(T[]) - Method in class org.deeplearning4j.util.MultiDimensionalSet
-
Returns an array containing all of the elements in this applyTransformToDestination; the
runtime type of the returned array is that of the specified array.
- toBufferedImage(Image) - Static method in class org.deeplearning4j.util.ImageLoader
-
Converts a given Image into a BufferedImage
- toByteArray(Serializable) - Static method in class org.deeplearning4j.util.SerializationUtils
-
Converts the given object to a byte array
- toCSV() - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Outputs the ConfusionMatrix as comma-separated values for easy import into spreadsheets
- toDecimal(String) - Static method in class org.deeplearning4j.util.MathUtils
-
This will convert the given binary string to a decimal based
integer
- toHTML() - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
Outputs Confusion Matrix in an HTML table.
- toImage(INDArray) - Static method in class org.deeplearning4j.util.ImageLoader
-
- tolerance - Variable in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- tolerance(double) - Method in class org.deeplearning4j.plot.Tsne.Builder
-
- toLines() - Method in class org.deeplearning4j.util.StringGrid
-
- toLogSpace() - Method in class org.deeplearning4j.berkeley.Counter
-
- toString() - Method in class org.deeplearning4j.berkeley.Counter
-
Returns a string representation with the keys ordered by decreasing
counts.
- toString(int) - Method in class org.deeplearning4j.berkeley.Counter
-
Returns a string representation which includes no more than the
maxKeysToPrint elements with largest counts.
- toString(int, boolean) - Method in class org.deeplearning4j.berkeley.Counter
-
Returns a string representation which includes no more than the
maxKeysToPrint elements with largest counts and optionally prints
one element per line.
- toString(int) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- toString() - Method in class org.deeplearning4j.berkeley.CounterMap
-
- toString(Collection<String>) - Method in class org.deeplearning4j.berkeley.CounterMap
-
- toString() - Method in class org.deeplearning4j.berkeley.Pair
-
- toString() - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
Returns a representation of the queue in decreasing priority order.
- toString(int, boolean) - Method in class org.deeplearning4j.berkeley.PriorityQueue
-
Returns a representation of the queue in decreasing priority order,
displaying at most maxKeysToPrint elements and optionally printing
one element per line.
- toString() - Method in class org.deeplearning4j.berkeley.Triple
-
- toString() - Method in class org.deeplearning4j.eval.ConfusionMatrix
-
- toString() - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
- toString() - Method in class org.deeplearning4j.nn.conf.NeuralNetConfiguration
-
- toString() - Method in class org.deeplearning4j.nn.gradient.NeuralNetworkGradient
-
- toString() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- toString() - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
- toString() - Method in class org.deeplearning4j.util.Index
-
- toString() - Method in class org.deeplearning4j.util.MultiDimensionalMap
-
- toString() - Method in class org.deeplearning4j.util.SummaryStatistics
-
- toStringSortedByKeys() - Method in class org.deeplearning4j.berkeley.Counter
-
- toStringTabSeparated() - Method in class org.deeplearning4j.berkeley.Counter
-
- totalCount() - Method in class org.deeplearning4j.berkeley.Counter
-
Finds the total of all counts in the counter.
- totalCount() - Method in class org.deeplearning4j.berkeley.CounterMap
-
Returns the total of all counts in sub-counters.
- totalExamples - Variable in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- totalExamples() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- totalExamples() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
The total number of examples
- totalExamples() - Method in interface org.deeplearning4j.datasets.iterator.DataSetIterator
-
Total examples in the iterator
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
Total examples in the iterator
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
Total examples in the iterator
- totalExamples() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- totalExamples() - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
-
- totalOutcomes() - Method in class org.deeplearning4j.datasets.fetchers.BaseDataFetcher
-
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.BaseDatasetIterator
-
- totalOutcomes() - Method in interface org.deeplearning4j.datasets.iterator.DataSetFetcher
-
The number of labels for a dataset
- totalOutcomes() - Method in interface org.deeplearning4j.datasets.iterator.DataSetIterator
-
The number of labels for the dataset
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
-
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.MultipleEpochsIterator
-
The number of labels for the dataset
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.ReconstructionDataSetIterator
-
The number of labels for the dataset
- totalOutcomes() - Method in class org.deeplearning4j.datasets.iterator.SamplingDataSetIterator
-
- totalOutcomes() - Method in class org.deeplearning4j.datasets.test.TestDataSetIterator
-
- totalSize() - Method in class org.deeplearning4j.berkeley.CounterMap
-
Returns the total number of (key, value) entries in the CounterMap (not
their total counts).
- train(INDArray, float, float, int) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
Perform one iteration of training
- train(double) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Train with current input and labels
with the given learning rate
- train(INDArray, double) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Train with the given input
and the currently applyTransformToDestination labels
- train(INDArray, INDArray, double) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Train on the given inputs and labels.
- train(INDArray) - Method in class org.deeplearning4j.optimize.optimizers.NeuralNetworkOptimizer
-
- TrainingEvaluator - Interface in org.deeplearning4j.optimize.api
-
Training evaluator, used for determining early stop
- trainingFileLabelsFilename_unzipped - Static variable in class org.deeplearning4j.base.MnistFetcher
-
- trainingFilesFilename_unzipped - Static variable in class org.deeplearning4j.base.MnistFetcher
-
- trainTillConvergence(INDArray, INDArray, double, int) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Run conjugate gradient with the given x and y
- trainTillConvergence(INDArray, double, int, TrainingEvaluator) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Run the optimization algorithm for training
- trainTillConvergence(double, int, TrainingEvaluator) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Run the optimization algorithm for training
- trainTillConvergence(double, int) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Run conjugate gradient
- Transform(Iterator<S>) - Constructor for class org.deeplearning4j.berkeley.Iterators.Transform
-
- transform(S) - Method in class org.deeplearning4j.berkeley.Iterators.Transform
-
- transform(INDArray) - Method in class org.deeplearning4j.models.classifiers.sae.StackedAutoEncoder
-
- transform(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.AutoEncoder
-
All neural networks are based on this idea of
minimizing reconstruction error.
- transform(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.da.DenoisingAutoEncoder
-
- transform(INDArray) - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
Reconstructs the visible input.
- transform(INDArray) - Method in interface org.deeplearning4j.nn.api.Model
-
Transform the data based on the model's output.
- transform(INDArray) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork
-
Transform the data based on the model's output.
- transform(INDArray) - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
All neural networks are based on this idea of
minimizing reconstruction error.
- transform(INDArray) - Method in class org.deeplearning4j.nn.layers.OutputLayer
-
Transform the data based on the model's output.
- TransformingIterator(Iterator<I>, MyMethod<I, O>) - Constructor for class org.deeplearning4j.berkeley.Iterators.TransformingIterator
-
- transformWeightsAt(int, MatrixTransform) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN.Builder
-
Transform the weights at the given layer
- transformWeightsAt(Map<Integer, MatrixTransform>) - Method in class org.deeplearning4j.models.classifiers.dbn.DBN.Builder
-
A map of transformations for transforming
the given neuralNets
- transformWeightsAt(int, MatrixTransform) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder.Builder
-
Transform the weights at the given layer
- transformWeightsAt(Map<Integer, MatrixTransform>) - Method in class org.deeplearning4j.models.classifiers.sda.StackedDenoisingAutoEncoder.Builder
-
A map of transformations for transforming
the given neuralNets
- transformWeightsAt(int, MatrixTransform) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing.Builder
-
Transform the weights at the given layer
- transformWeightsAt(Map<Integer, MatrixTransform>) - Method in class org.deeplearning4j.models.featuredetectors.autoencoder.SemanticHashing.Builder
-
A map of transformations for transforming
the given neuralNets
- transformWeightsAt(int, MatrixTransform) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.Builder
-
Transform the weights at the given layer
- transformWeightsAt(Map<Integer, MatrixTransform>) - Method in class org.deeplearning4j.nn.BaseMultiLayerNetwork.Builder
-
A map of transformations for transforming
the given neuralNets
- transpose() - Method in class org.deeplearning4j.models.featuredetectors.rbm.RBM
-
- transpose() - Method in interface org.deeplearning4j.nn.api.Layer
-
Return a transposed copy of the weights/bias
(this means reverse the number of inputs and outputs on the weights)
- transpose() - Method in interface org.deeplearning4j.nn.api.NeuralNetwork
-
- transpose() - Method in class org.deeplearning4j.nn.BaseNeuralNetwork
-
- transpose() - Method in class org.deeplearning4j.nn.layers.BaseLayer
-
- TreeMapFactory() - Constructor for class org.deeplearning4j.berkeley.MapFactory.TreeMapFactory
-
- treeSet() - Static method in class org.deeplearning4j.util.MultiDimensionalSet
-
- trim(String, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
Returns s if it's at most maxWidth chars, otherwise chops right side to fit.
- trim(Object, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
- Triple<S,T,U> - Class in org.deeplearning4j.berkeley
-
- Triple(S, T, U) - Constructor for class org.deeplearning4j.berkeley.Triple
-
- trueNegatives() - Method in class org.deeplearning4j.eval.Evaluation
-
True negatives: correctly rejected
- truncate(int, int, int) - Static method in class org.deeplearning4j.berkeley.StringUtils
-
This returns a string from decimal digit smallestDigit to decimal digit
biggest digit.
- Tsne - Class in org.deeplearning4j.plot
-
Tsne calculation
- Tsne(int, double, double, double, double, int, boolean, boolean, int, double, double, boolean, double, double) - Constructor for class org.deeplearning4j.plot.Tsne
-
- Tsne.Builder - Class in org.deeplearning4j.plot
-