org.nd4j.linalg.lossfunctions

Interface ILossFunction

All Superinterfaces:

Serializable

All Known Implementing Classes:

LossBinaryXENT, LossCosineProximity, LossHinge, LossKLD, LossL1, LossL2, LossMAE, LossMAPE, LossMCXENT, LossMSE, LossMSLE, LossNegativeLogLikelihood, LossPoisson, LossSquaredHinge

public interface ILossFunction
extends Serializable

Interface for loss functions

Method Summary

Modifier and Type	Method and Description
INDArray	computeGradient(INDArray labels, INDArray preOutput, String activationFn, INDArray mask) Compute the gradient of the loss function with respect to the inputs: dL/dOutput
org.apache.commons.math3.util.Pair<Double,INDArray>	computeGradientAndScore(INDArray labels, INDArray preOutput, String activationFn, INDArray mask, boolean average) Compute both the score (loss function value) and gradient.
double	computeScore(INDArray labels, INDArray preOutput, String activationFn, INDArray mask, boolean average) Compute the score (loss function value) for the given inputs.
INDArray	computeScoreArray(INDArray labels, INDArray preOutput, String activationFn, INDArray mask) Compute the score (loss function value) for each example individually.

Method Detail

computeScore

double computeScore(INDArray labels,
                    INDArray preOutput,
                    String activationFn,
                    INDArray mask,
                    boolean average)

Compute the score (loss function value) for the given inputs.

Parameters:

labels - Label/expected preOutput

preOutput - Output of the model (neural network)

activationFn - Activation function that should be applied to preOutput

mask - Mask array; may be null

average - Whether the score should be averaged (divided by number of rows in labels/preOutput) or not @return Loss function value

computeScoreArray

INDArray computeScoreArray(INDArray labels,
                           INDArray preOutput,
                           String activationFn,
                           INDArray mask)

Compute the score (loss function value) for each example individually. For input [numExamples,nOut] returns scores as a column vector: [numExamples,1]

Parameters:

labels - Labels/expected output

preOutput - Output of the model (neural network)

activationFn - Activation function that should be applied to preOutput

mask - @return Loss function value for each example; column vector

computeGradient

INDArray computeGradient(INDArray labels,
                         INDArray preOutput,
                         String activationFn,
                         INDArray mask)

Compute the gradient of the loss function with respect to the inputs: dL/dOutput

Parameters:

labels - Label/expected output

preOutput - Output of the model (neural network), before the activation function is applied

activationFn - Activation function that should be applied to preOutput

mask - Mask array; may be null

Returns:

Gradient dL/dPreOut

computeGradientAndScore

org.apache.commons.math3.util.Pair<Double,INDArray> computeGradientAndScore(INDArray labels,
                                                                            INDArray preOutput,
                                                                            String activationFn,
                                                                            INDArray mask,
                                                                            boolean average)

Compute both the score (loss function value) and gradient. This is equivalent to calling computeScore(INDArray, INDArray, String, INDArray, boolean) and computeGradient(INDArray, INDArray, String, INDArray) individually

Parameters:

labels - Label/expected output

preOutput - Output of the model (neural network)

activationFn - Activation function that should be applied to preOutput

mask - Mask array; may be null

average - Whether the score should be averaged (divided by number of rows in labels/output) or not

Returns:

The score (loss function value) and gradient