public class LossMCXENT extends Object implements ILossFunction
LossNegativeLogLikelihood
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Serialized FormConstructor and Description |
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LossMCXENT() |
LossMCXENT(double softmaxClipEps,
INDArray weights)
Multi-Class Cross Entropy loss function where each the output is (optionally) weighted/scaled by a fixed scalar value.
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LossMCXENT(INDArray weights)
Multi-Class Cross Entropy loss function where each the output is (optionally) weighted/scaled by a fixed scalar value.
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Modifier and Type | Method and Description |
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INDArray |
computeGradient(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask)
Compute the gradient of the loss function with respect to the inputs: dL/dOutput
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org.nd4j.linalg.primitives.Pair<Double,INDArray> |
computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average)
Compute both the score (loss function value) and gradient.
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double |
computeScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average)
Compute the score (loss function value) for the given inputs.
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INDArray |
computeScoreArray(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask)
Compute the score (loss function value) for each example individually.
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String |
name()
The name of this function
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String |
toString() |
public LossMCXENT()
public LossMCXENT(INDArray weights)
weights
- Weights array (row vector). May be null.public LossMCXENT(double softmaxClipEps, INDArray weights)
weights
- Weights array (row vector). May be null.public double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
ILossFunction
computeScore
in interface ILossFunction
labels
- Label/expected preOutputpreOutput
- Output of the model (neural network)activationFn
- Activation function that should be applied to preOutputmask
- Mask array; may be nullaverage
- Whether the score should be averaged (divided by number of rows in labels/preOutput) or not @return Loss function valuepublic INDArray computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
ILossFunction
computeScoreArray
in interface ILossFunction
labels
- Labels/expected outputpreOutput
- Output of the model (neural network)activationFn
- Activation function that should be applied to preOutputmask
- @return Loss function value for each example; column vectorpublic INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
ILossFunction
computeGradient
in interface ILossFunction
labels
- Label/expected outputpreOutput
- Output of the model (neural network), before the activation function is appliedactivationFn
- Activation function that should be applied to preOutputmask
- Mask array; may be nullpublic org.nd4j.linalg.primitives.Pair<Double,INDArray> computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
ILossFunction
ILossFunction.computeScore(INDArray, INDArray, IActivation, INDArray, boolean)
and ILossFunction.computeGradient(INDArray, INDArray, IActivation, INDArray)
individuallycomputeGradientAndScore
in interface ILossFunction
labels
- Label/expected outputpreOutput
- Output of the model (neural network)activationFn
- Activation function that should be applied to preOutputmask
- Mask array; may be nullaverage
- Whether the score should be averaged (divided by number of rows in labels/output) or notpublic String name()
name
in interface ILossFunction
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