Uses of Class
org.nd4j.autodiff.loss.LossReduce

Packages that use LossReduce

Package	Description
org.nd4j.autodiff.functions
org.nd4j.autodiff.loss
org.nd4j.autodiff.samediff.ops
org.nd4j.linalg.api.ops.impl.loss
org.nd4j.linalg.api.ops.impl.loss.bp

Uses of LossReduce in org.nd4j.autodiff.functions

Methods in org.nd4j.autodiff.functions with parameters of type LossReduce

Modifier and Type	Method and Description
SDVariable	DifferentialFunctionFactory.lossAbsoluteDifference(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable[]	DifferentialFunctionFactory.lossAbsoluteDifferenceBP(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable	DifferentialFunctionFactory.lossCosineDistance(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce, int dimension)
SDVariable[]	DifferentialFunctionFactory.lossCosineDistanceBp(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce, int dimension)
SDVariable	DifferentialFunctionFactory.lossHinge(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable[]	DifferentialFunctionFactory.lossHingeBp(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable	DifferentialFunctionFactory.lossHuber(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce, double delta)
SDVariable[]	DifferentialFunctionFactory.lossHuberBp(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce, double delta)
SDVariable	DifferentialFunctionFactory.lossLog(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce, double epsilon)
SDVariable[]	DifferentialFunctionFactory.lossLogBp(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce, double epsilon)
SDVariable	DifferentialFunctionFactory.lossLogPoisson(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable[]	DifferentialFunctionFactory.lossLogPoissonBp(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable	DifferentialFunctionFactory.lossLogPoissonFull(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable[]	DifferentialFunctionFactory.lossLogPoissonFullBp(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable	DifferentialFunctionFactory.lossMeanPairwiseSquaredError(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable[]	DifferentialFunctionFactory.lossMeanPairwiseSquaredErrorBp(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable	DifferentialFunctionFactory.lossMeanSquaredError(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable[]	DifferentialFunctionFactory.lossMeanSquaredErrorBp(SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce)
SDVariable	DifferentialFunctionFactory.lossSigmoidCrossEntropy(SDVariable labels, SDVariable logits, SDVariable weights, LossReduce lossReduce, double labelSmoothing)
SDVariable[]	DifferentialFunctionFactory.lossSigmoidCrossEntropyBp(SDVariable labels, SDVariable logits, SDVariable weights, LossReduce lossReduce, double labelSmoothing)
SDVariable	DifferentialFunctionFactory.lossSoftmaxCrossEntropy(SDVariable labels, SDVariable logits, SDVariable weights, LossReduce lossReduce, double labelSmoothing)
SDVariable[]	DifferentialFunctionFactory.lossSoftmaxCrossEntropyBp(SDVariable labels, SDVariable logits, SDVariable weights, LossReduce lossReduce, double labelSmoothing)

Uses of LossReduce in org.nd4j.autodiff.loss

Methods in org.nd4j.autodiff.loss that return LossReduce

Modifier and Type	Method and Description
static LossReduce	LossReduce.valueOf(String name) Returns the enum constant of this type with the specified name.
static LossReduce[]	LossReduce.values() Returns an array containing the constants of this enum type, in the order they are declared.

Uses of LossReduce in org.nd4j.autodiff.samediff.ops

Methods in org.nd4j.autodiff.samediff.ops with parameters of type LossReduce

Modifier and Type	Method and Description
SDVariable	SDLoss.absoluteDifference(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce) See SDLoss.absoluteDifference(String, SDVariable, SDVariable, SDVariable, LossReduce).
SDVariable	SDLoss.absoluteDifference(String name, SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce) Absolute difference loss: {@code sum_i abs( label[i] - predictions[i] )
SDVariable	SDLoss.cosineDistance(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce, int dimension) See SDLoss.cosineDistance(String, SDVariable, SDVariable, SDVariable, LossReduce, int).
SDVariable	SDLoss.cosineDistance(String name, SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce, int dimension) Cosine distance loss: 1 - cosineSimilarity(x,y) or 1 - sum_i label[i] * prediction[i], which is equivalent to cosine distance when both the predictions and labels are normalized. Note: This loss function assumes that both the predictions and labels are normalized to have unit l2 norm.
SDVariable	SDLoss.hingeLoss(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce) See SDLoss.hingeLoss(String, SDVariable, SDVariable, SDVariable, LossReduce).
SDVariable	SDLoss.hingeLoss(String name, SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce) Hinge loss: a loss function used for training classifiers.
SDVariable	SDLoss.huberLoss(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce, double delta) See SDLoss.huberLoss(String, SDVariable, SDVariable, SDVariable, LossReduce, double).
SDVariable	SDLoss.huberLoss(String name, SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce, double delta) Huber loss function, used for robust regression.
SDVariable	SDLoss.logLoss(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce) See SDLoss.logLoss(String, SDVariable, SDVariable, SDVariable, LossReduce, double).
SDVariable	SDLoss.logLoss(String name, SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce, double epsilon) Log loss, i.e., binary cross entropy loss, usually used for binary multi-label classification.
SDVariable	SDLoss.logPoisson(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce) See SDLoss.logPoisson(String, SDVariable, SDVariable, SDVariable, LossReduce).
SDVariable	SDLoss.logPoisson(String name, SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce) Log poisson loss: a loss function used for training classifiers.
SDVariable	SDLoss.logPoissonFull(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce) See SDLoss.logPoissonFull(String, SDVariable, SDVariable, SDVariable, LossReduce).
SDVariable	SDLoss.logPoissonFull(String name, SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce) Log poisson loss: a loss function used for training classifiers.
SDVariable	SDLoss.meanPairwiseSquaredError(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce) See SDLoss.meanPairwiseSquaredError(String, SDVariable, SDVariable, SDVariable, LossReduce).
SDVariable	SDLoss.meanPairwiseSquaredError(String name, SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce) Mean pairwise squared error. MPWSE loss calculates the difference between pairs of consecutive elements in the predictions and labels arrays.
SDVariable	SDLoss.meanSquaredError(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce) See SDLoss.meanSquaredError(String, SDVariable, SDVariable, SDVariable, LossReduce).
SDVariable	SDLoss.meanSquaredError(String name, SDVariable label, SDVariable predictions, SDVariable weights, LossReduce lossReduce) Mean squared error loss function.
SDVariable	SDLoss.sigmoidCrossEntropy(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce) See SDLoss.sigmoidCrossEntropy(String, SDVariable, SDVariable, SDVariable, LossReduce, double).
SDVariable	SDLoss.sigmoidCrossEntropy(String name, SDVariable label, SDVariable predictionLogits, SDVariable weights, LossReduce lossReduce, double labelSmoothing) Sigmoid cross entropy: applies the sigmoid activation function on the input logits (input "pre-sigmoid preductions") and implements the binary cross entropy loss function.
SDVariable	SDLoss.softmaxCrossEntropy(String name, SDVariable label, SDVariable predictions, LossReduce lossReduce) See SDLoss.softmaxCrossEntropy(String, SDVariable, SDVariable, SDVariable, LossReduce, double).
SDVariable	SDLoss.softmaxCrossEntropy(String name, SDVariable oneHotLabels, SDVariable logitPredictions, SDVariable weights, LossReduce lossReduce, double labelSmoothing) Applies the softmax activation function to the input, then implement multi-class cross entropy: -sum_classes label[i] * log(p[c]) where p = softmax(logits) If NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels; otherwise, the output is a scalar.

Uses of LossReduce in org.nd4j.linalg.api.ops.impl.loss

Fields in org.nd4j.linalg.api.ops.impl.loss declared as LossReduce

Modifier and Type	Field and Description
protected LossReduce	BaseLoss.lossReduce

Constructors in org.nd4j.linalg.api.ops.impl.loss with parameters of type LossReduce

Constructor and Description
AbsoluteDifferenceLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
BaseLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
CosineDistanceLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, int dimension)
HingeLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
HuberLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, double delta)
LogLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, double epsilon)
LogPoissonLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
LogPoissonLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, boolean full)
MeanPairwiseSquaredErrorLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
MeanSquaredErrorLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
SigmoidCrossEntropyLoss(SameDiff sameDiff, LossReduce reductionMode, SDVariable logits, SDVariable weights, SDVariable labels)
SigmoidCrossEntropyLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable logits, SDVariable weights, SDVariable labels, double labelSmoothing)
SoftmaxCrossEntropyLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable logits, SDVariable weights, SDVariable labels)
SoftmaxCrossEntropyLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable logits, SDVariable weights, SDVariable labels, double labelSmoothing)

Uses of LossReduce in org.nd4j.linalg.api.ops.impl.loss.bp

Fields in org.nd4j.linalg.api.ops.impl.loss.bp declared as LossReduce

Modifier and Type	Field and Description
protected LossReduce	BaseLossBp.lossReduce

Constructors in org.nd4j.linalg.api.ops.impl.loss.bp with parameters of type LossReduce

Constructor and Description
AbsoluteDifferenceLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
BaseLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
CosineDistanceLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, int dimension)
HingeLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
HuberLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, double delta)
LogLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, double epsilon)
LogPoissonLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
LogPoissonLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, boolean full)
MeanPairwiseSquaredErrorLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
MeanSquaredErrorLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels)
SigmoidCrossEntropyLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable logits, SDVariable weights, SDVariable labels, double labelSmoothing)
SoftmaxCrossEntropyLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable logits, SDVariable weights, SDVariable labels, double labelSmoothing)