public class LossSparseMCXENT extends LossMCXENT
softmaxClipEps, weights
Constructor and Description |
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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.
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LossSparseMCXENT(INDArray weights)
Multi-Class Cross Entropy loss function where each the output is (optionally) weighted/scaled by a flags 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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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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protected INDArray |
sparseScoreArray(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask) |
String |
toString() |
name, scoreArray
public LossSparseMCXENT()
public LossSparseMCXENT(INDArray weights)
weights
- Weights array (row vector). May be null.public LossSparseMCXENT(double softmaxClipEps, INDArray weights)
weights
- Weights array (row vector). May be null.protected INDArray sparseScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
public double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average)
ILossFunction
computeScore
in interface ILossFunction
computeScore
in class LossMCXENT
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
computeScoreArray
in class LossMCXENT
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
computeGradient
in class LossMCXENT
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 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
computeGradientAndScore
in class LossMCXENT
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 toString()
toString
in class LossMCXENT
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