org.nd4j.linalg.api.ops.impl.loss.CosineDistanceLoss Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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package org.nd4j.linalg.api.ops.impl.loss;
import lombok.Getter;
import lombok.NonNull;
import lombok.Setter;
import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.loss.bp.CosineDistanceLossBp;
import java.util.List;
public class CosineDistanceLoss extends BaseLoss {
@Getter
@Setter
protected int dimension;
public CosineDistanceLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights,
SDVariable labels, int dimension) {
super(sameDiff, lossReduce, predictions, weights, labels);
this.dimension = dimension;
this.addIArgument(dimension);
}
public CosineDistanceLoss(SameDiff sameDiff, SDVariable labels, SDVariable predictions, SDVariable weights,
LossReduce lossReduce, int dimension) {
this(sameDiff, lossReduce, predictions, weights, labels, dimension);
}
public CosineDistanceLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce,
int dimension) {
super(lossReduce, predictions, weights, labels);
this.dimension = dimension;
this.addIArgument(dimension);
}
public CosineDistanceLoss() {
}
@Override
public String opName() {
return "cosine_distance_loss";
}
@Override
public List doDiff(List grad) {
// No external gradient.
// Args are: predictions, weights, label
if (iArguments.size() > 1) {
this.dimension = iArguments.get(iArguments.size() - 1).intValue();
}
return new CosineDistanceLossBp(sameDiff, lossReduce, arg(0), arg(1), arg(2), dimension).outputs();
}
}