org.nd4j.linalg.lossfunctions.impl.LossPoisson Maven / Gradle / Ivy
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package org.nd4j.linalg.lossfunctions.impl;
import lombok.EqualsAndHashCode;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossUtil;
import org.nd4j.linalg.ops.transforms.Transforms;
import org.nd4j.common.primitives.Pair;
@EqualsAndHashCode
public class LossPoisson implements ILossFunction {
public INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if(!labels.equalShapes(preOutput)){
Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
}
labels = labels.castTo(preOutput.dataType()); //No-op if already correct dtype
/*
mean of (yhat - y * log(yhat))
*/
INDArray postOutput = activationFn.getActivation(preOutput.dup(), true);
INDArray scoreArr = Transforms.log(postOutput);
scoreArr.muli(labels);
scoreArr = postOutput.sub(scoreArr);
if (mask != null) {
LossUtil.applyMask(scoreArr, mask);
}
return scoreArr;
}
@Override
public double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask,
boolean average) {
INDArray scoreArr = scoreArray(labels, preOutput, activationFn, mask);
double score = scoreArr.sumNumber().doubleValue();
if (average)
score /= scoreArr.size(0);
return score;
}
@Override
public INDArray computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
INDArray scoreArr = scoreArray(labels, preOutput, activationFn, mask);
return scoreArr.sum(true,1);
}
@Override
public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if(!labels.equalShapes(preOutput)){
Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
}
labels = labels.castTo(preOutput.dataType()); //No-op if already correct dtype
INDArray yHat = activationFn.getActivation(preOutput.dup(), true);
INDArray yDivyhat = labels.div(yHat);
INDArray dLda = yDivyhat.rsubi(1);
if (mask != null && LossUtil.isPerOutputMasking(dLda, mask)) {
//For *most* activation functions: we don't actually need to mask dL/da in addition to masking dL/dz later
//but: some, like softmax, require both (due to dL/dz_i being a function of dL/da_j, for i != j)
//We could add a special case for softmax (activationFn instanceof ActivationSoftmax) but that would be
// error prone - though buy us a tiny bit of performance
LossUtil.applyMask(dLda, mask);
}
INDArray gradients = activationFn.backprop(preOutput, dLda).getFirst(); //TODO activation functions with params
if (mask != null) {
LossUtil.applyMask(gradients, mask);
}
return gradients;
}
@Override
public Pair computeGradientAndScore(INDArray labels,
INDArray preOutput, IActivation activationFn, INDArray mask, boolean average) {
//TODO: probably a more efficient way to do this...
//Yes - will implement in round two. Just want to get done now.
return new Pair<>(computeScore(labels, preOutput, activationFn, mask, average),
computeGradient(labels, preOutput, activationFn, mask));
}
/**
* The opName of this function
*
* @return
*/
@Override
public String name() {
return toString();
}
@Override
public String toString() {
return "LossPoisson()";
}
}