Interface Loss

All Known Implementing Classes:

BinaryCrossentropy, CategoricalCrossentropy, CategoricalHinge, CosineSimilarity, Hinge, Huber, KLDivergence, LogCosh, MeanAbsoluteError, MeanAbsolutePercentageError, MeanSquaredError, MeanSquaredLogarithmicError, Poisson, SparseCategoricalCrossentropy, SquaredHinge

Functional Interface:

This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.

@FunctionalInterface
public interface Loss

Interface for loss calc ulation

Method Summary

Modifier and Type	Method	Description
<T extends TNumber> Operand<T>	call(Ops tf, Operand<? extends TNumber> labels, Operand<T> predictions, Operand<T> sampleWeights)	Generates an Operand that calculates the loss.

Method Details

call

<T extends TNumber> Operand<T> call(Ops tf,
 Operand<? extends TNumber> labels,
 Operand<T> predictions,
 Operand<T> sampleWeights)

Generates an Operand that calculates the loss.

Type Parameters:

T - The data type of the predictions, sampleWeights and loss.

Parameters:

tf - the TensorFlow Ops

labels - the truth values or labels

predictions - the predictions

sampleWeights - Optional sampleWeights acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If SampleWeights is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the SampleWeights vector. If the shape of SampleWeights is [batch_size, d0, .. dN-1] (or can be broadcast to this shape), then each loss element of predictions is scaled by the corresponding value of SampleWeights. (Note on dN-1: all loss functions reduce by 1 dimension, usually axis=-1.)

Returns:

the loss