org.nd4j.linalg.lossfunctions.impl

Class LossMAE

java.lang.Object

org.nd4j.linalg.lossfunctions.impl.LossL1

org.nd4j.linalg.lossfunctions.impl.LossMAE

All Implemented Interfaces:

Serializable, ILossFunction

public class LossMAE
extends LossL1

See Also:

Serialized Form

Field Summary

Fields inherited from class org.nd4j.linalg.lossfunctions.impl.LossL1

weights

Constructor Summary

Constructors

Constructor and Description
LossMAE()
LossMAE(INDArray weights) Mean Absolute Error loss function where each the output is (optionally) weighted/scaled by a flags scalar value.

Method Summary

Modifier and Type	Method and Description
INDArray	computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) Compute the gradient of the loss function with respect to the inputs: dL/dOutput
double	computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average) Compute the score (loss function value) for the given inputs.
INDArray	computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) Compute the score (loss function value) for each example individually.
String	name() The opName of this function
String	toString()

Methods inherited from class org.nd4j.linalg.lossfunctions.impl.LossL1

computeGradientAndScore, scoreArray

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

LossMAE

public LossMAE()

LossMAE

public LossMAE(INDArray weights)

Mean Absolute Error loss function where each the output is (optionally) weighted/scaled by a flags scalar value. Note that the weights array must be a row vector, of length equal to the labels/output dimension 1 size. A weight vector of 1s should give identical results to no weight vector.

Parameters:

weights - Weights array (row vector). May be null.

Method Detail

computeScore

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

Description copied from interface: ILossFunction

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

Specified by:

computeScore in interface ILossFunction

Overrides:

computeScore in class LossL1

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

public INDArray computeScoreArray(INDArray labels,
                                  INDArray preOutput,
                                  IActivation activationFn,
                                  INDArray mask)

Description copied from interface: ILossFunction

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

Specified by:

computeScoreArray in interface ILossFunction

Overrides:

computeScoreArray in class LossL1

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

public INDArray computeGradient(INDArray labels,
                                INDArray preOutput,
                                IActivation activationFn,
                                INDArray mask)

Description copied from interface: ILossFunction

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

Specified by:

computeGradient in interface ILossFunction

Overrides:

computeGradient in class LossL1

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

name

public String name()

The opName of this function

Specified by:

name in interface ILossFunction

Overrides:

name in class LossL1

Returns:

toString

public String toString()

Overrides:

toString in class LossL1