BaseMultiLayerNetwork encoder
BaseMultiLayerNetwork decoder
Object[] trainingParams
int nIns
int[] hiddenLayerSizes
int nOuts
int nLayers
LogisticRegression logLayer
org.apache.commons.math3.random.RandomGenerator rng
org.apache.commons.math3.distribution.RealDistribution dist
double momentum
MultiLayerNetworkOptimizer optimizer
ActivationFunction activation
boolean toDecode
double l2
boolean shouldInit
double fanIn
int renderWeightsEveryNEpochs
boolean useRegularization
Map<K,V> weightTransforms
boolean shouldBackProp
boolean forceNumEpochs
int cursor
int numOutcomes
int inputColumns
DataSet curr
int totalExamples
LFWLoader loader
int batch
int numExamples
DataSetFetcher fetcher
DataSet sampleFrom
int batchSize
int totalNumberSamples
int numTimesSampled
int nIns
int[] hiddenLayerSizes
int nOuts
int nLayers
HiddenLayer[] sigmoidLayers
LogisticRegression logLayer
org.apache.commons.math3.random.RandomGenerator rng
org.apache.commons.math3.distribution.RealDistribution dist
double momentum
org.jblas.DoubleMatrix input
org.jblas.DoubleMatrix labels
MultiLayerNetworkOptimizer optimizer
ActivationFunction activation
boolean toDecode
double l2
boolean shouldInit
double fanIn
int renderWeightsEveryNEpochs
boolean useRegularization
Map<K,V> weightTransforms
Map<K,V> hiddenBiasTransforms
Map<K,V> visibleBiasTransforms
boolean shouldBackProp
boolean forceNumEpochs
double sparsity
org.jblas.DoubleMatrix columnSums
org.jblas.DoubleMatrix columnMeans
org.jblas.DoubleMatrix columnStds
boolean initCalled
boolean useHiddenActivationsForwardProp
boolean useAdaGrad
double learningRateUpdate
NeuralNetwork[] layers
double errorTolerance
Map<K,V> gradientListeners
List<E> multiLayerGradientListeners
double dropOut
boolean normalizeByInputRows
NeuralNetwork.OptimizationAlgorithm optimizationAlgorithm
NeuralNetwork.LossFunction lossFunction
int nVisible
int nHidden
org.jblas.DoubleMatrix W
org.jblas.DoubleMatrix hBias
org.jblas.DoubleMatrix vBias
org.apache.commons.math3.random.RandomGenerator rng
org.jblas.DoubleMatrix input
double sparsity
double momentum
double l2
int renderWeightsEveryNumEpochs
double fanIn
boolean useRegularization
boolean useAdaGrad
boolean firstTimeThrough
boolean normalizeByInputRows
boolean applySparsity
List<E> gradientListeners
double dropOut
org.jblas.DoubleMatrix doMask
NeuralNetwork.OptimizationAlgorithm optimizationAlgo
NeuralNetwork.LossFunction lossFunction
AdaGrad wAdaGrad
AdaGrad hBiasAdaGrad
AdaGrad vBiasAdaGrad
int nIn
int nOut
org.jblas.DoubleMatrix W
org.jblas.DoubleMatrix b
org.apache.commons.math3.random.RandomGenerator rng
org.jblas.DoubleMatrix input
ActivationFunction activationFunction
org.apache.commons.math3.distribution.RealDistribution dist
int nIn
int nOut
org.jblas.DoubleMatrix input
org.jblas.DoubleMatrix labels
org.jblas.DoubleMatrix W
org.jblas.DoubleMatrix b
double l2
boolean useRegularization
boolean useAdaGrad
AdaGrad adaGrad
boolean firstTimeThrough
boolean normalizeByInputRows
NeuralNetwork.OptimizationAlgorithm optimizationAlgorithm
List<E> gradients
LogisticRegressionGradient logRegGradient
double masterStepSize
org.jblas.DoubleMatrix historicalGradient
org.jblas.DoubleMatrix adjustedGradient
double fudgeFactor
org.jblas.DoubleMatrix gradient
int rows
int cols
int numIterations
double lrDecay
boolean decayLr
double minLearningRate
BaseMultiLayerNetwork network
double lr
NeuralNetwork network
double lr
Object[] extraParams
double tolerance
List<E> errors
double minLearningRate
NeuralNetwork.OptimizationAlgorithm optimizationAlgorithm
NeuralNetwork.LossFunction lossFunction
BufferedImage image
NeuralNetworkOptimizer optimizer
DataSetIterator wrapped
int numDataSets
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