Package org.nd4j.linalg.api.ops.impl.loss

Class Summary

Class	Description
AbsoluteDifferenceLoss	Absolute difference loss
BaseLoss
CosineDistanceLoss	Cosine distance loss
HingeLoss	Hinge loss
HuberLoss	Huber loss
L2Loss	L2 loss op wrapper
LogLoss	Binary log loss, or cross entropy loss: -1/numExamples * sum_i (labels[i] * log(predictions[i] + epsilon) + (1-labels[i]) * log(1-predictions[i] + epsilon))
LogPoissonLoss	Log Poisson loss Note: This expects that the input/predictions are log(x) not x!
MeanPairwiseSquaredErrorLoss	Mean Pairwise Squared Error Loss
MeanSquaredErrorLoss	Mean squared error loss
SigmoidCrossEntropyLoss	Sigmoid cross entropy loss with logits
SoftmaxCrossEntropyLoss	Softmax cross entropy loss
SoftmaxCrossEntropyWithLogitsLoss	Softmax cross entropy loss with Logits
SparseSoftmaxCrossEntropyLossWithLogits	Sparse softmax cross entropy loss with logits.
WeightedCrossEntropyLoss	Weighted cross entropy loss with logits