public class LossL1 extends Object implements ILossFunction
LossMAE
for a mathematically similar loss function (MAE has division by N, where N is output size)Constructor and Description |
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LossL1() |
LossL1(INDArray weights)
L1 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,
String activationFn,
INDArray mask)
Compute the gradient of the loss function with respect to the inputs: dL/dOutput
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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.
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double |
computeScore(INDArray labels,
INDArray preOutput,
String 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,
String activationFn,
INDArray mask)
Compute the score (loss function value) for each example individually.
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INDArray |
scoreArray(INDArray labels,
INDArray preOutput,
String activationFn,
INDArray mask) |
String |
toString() |
protected final INDArray weights
public LossL1()
public LossL1(INDArray weights)
weights
- Weights array (row vector). May be null.public INDArray scoreArray(INDArray labels, INDArray preOutput, String activationFn, INDArray mask)
public double computeScore(INDArray labels, INDArray preOutput, String 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, String 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, String 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.apache.commons.math3.util.Pair<Double,INDArray> computeGradientAndScore(INDArray labels, INDArray preOutput, String activationFn, INDArray mask, boolean average)
ILossFunction
ILossFunction.computeScore(INDArray, INDArray, String, INDArray, boolean)
and ILossFunction.computeGradient(INDArray, INDArray, String, 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 notCopyright © 2016. All Rights Reserved.