Package org.nd4j.linalg.lossfunctions.impl

Class LossSparseMCXENT

java.lang.Object

org.nd4j.linalg.lossfunctions.impl.LossMCXENT

org.nd4j.linalg.lossfunctions.impl.LossSparseMCXENT

All Implemented Interfaces:

Serializable, ILossFunction

public class LossSparseMCXENT
extends LossMCXENT

See Also:

Serialized Form

Field Summary

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

softmaxClipEps, weights

Constructor Summary

Constructors

Constructor	Description
LossSparseMCXENT()
LossSparseMCXENT(double softmaxClipEps, INDArray weights)	Multi-Class Cross Entropy loss function where each the output is (optionally) weighted/scaled by a fixed scalar value.
LossSparseMCXENT(INDArray weights)	Multi-Class Cross Entropy loss function where each the output is (optionally) weighted/scaled by a flags scalar value.

Method Summary

Modifier and Type	Method	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
Pair<Double,INDArray>	computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)	Compute both the score (loss function value) and gradient.
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.
protected INDArray	sparseScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
String	toString()

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

name, scoreArray

Methods inherited from class java.lang.Object

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

Constructor Detail

LossSparseMCXENT

public LossSparseMCXENT()

LossSparseMCXENT

public LossSparseMCXENT(INDArray weights)

Multi-Class Cross Entropy 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.

LossSparseMCXENT

public LossSparseMCXENT(double softmaxClipEps,
                        INDArray weights)

Multi-Class Cross Entropy loss function where each the output is (optionally) weighted/scaled by a fixed 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

sparseScoreArray

protected INDArray sparseScoreArray(INDArray labels,
                                    INDArray preOutput,
                                    IActivation activationFn,
                                    INDArray mask)

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 LossMCXENT

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 LossMCXENT

Parameters:

labels - Labels/expected output

preOutput - Output of the model (neural network)

activationFn - Activation function that should be applied to preOutput

Returns:

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 LossMCXENT

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

public Pair<Double,INDArray> computeGradientAndScore(INDArray labels,
                                                           INDArray preOutput,
                                                           IActivation activationFn,
                                                           INDArray mask,
                                                           boolean average)

Description copied from interface: ILossFunction

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

Specified by:

computeGradientAndScore in interface ILossFunction

Overrides:

computeGradientAndScore in class LossMCXENT

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

toString

public String toString()

Overrides:

toString in class LossMCXENT