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org.nd4j.linalg.lossfunctions.impl.LossCosineProximity Maven / Gradle / Ivy
package org.nd4j.linalg.lossfunctions.impl;
import lombok.EqualsAndHashCode;
import org.apache.commons.math3.util.Pair;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.ops.transforms.Transforms;
import java.util.Arrays;
/**
* Created by susaneraly on 9/9/16.
*/
@EqualsAndHashCode
public class LossCosineProximity implements ILossFunction {
/**
*
* @param labels
* @param preOutput
* @param activationFn
* @param mask
* @return
*/
public INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if (labels.size(1) != preOutput.size(1)) {
throw new IllegalArgumentException("Labels array numColumns (size(1) = " + labels.size(1)
+ ") does not match output layer" + " number of outputs (nOut = " + preOutput.size(1)
+ ") ");
}
/*
mean of -(y.dot(yhat)/||y||*||yhat||)
*/
//INDArray postOutput = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(activationFn, preOutput.dup()));
INDArray postOutput = activationFn.getActivation(preOutput.dup(), true);
INDArray yhatmag = postOutput.norm2(1);
INDArray ymag = labels.norm2(1);
yhatmag = Transforms.max(yhatmag, Nd4j.EPS_THRESHOLD, false);
ymag = Transforms.max(ymag, Nd4j.EPS_THRESHOLD, false);
INDArray scoreArr = postOutput.mul(labels);
scoreArr.diviColumnVector(yhatmag);
scoreArr.diviColumnVector(ymag);
if (mask != null) {
if(!mask.isColumnVector()){
//Per-output masking doesn't really make sense for cosine proximity
throw new UnsupportedOperationException("Expected column vector mask array for LossCosineProximity." +
" Got mask array with shape " + Arrays.toString(mask.shape()) + "; per-output masking is not " +
"supported for LossCosineProximity");
}
scoreArr.muliColumnVector(mask);
}
return scoreArr.muli(-1);
}
@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(1);
}
@Override
public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if (labels.size(1) != preOutput.size(1)) {
throw new IllegalArgumentException("Labels array numColumns (size(1) = " + labels.size(1)
+ ") does not match output layer" + " number of outputs (nOut = " + preOutput.size(1)
+ ") ");
}
INDArray yhat = activationFn.getActivation(preOutput.dup(), true);
INDArray yL2norm = labels.norm2(1);
INDArray yhatL2norm = yhat.norm2(1);
INDArray yhatL2normSq = yhatL2norm.mul(yhatL2norm);
//Note: This is not really the L1 norm since I am not taking abs values
INDArray yhatDotyL1norm = labels.mul(yhat).sum(1);
INDArray dLda = labels.mulColumnVector(yhatL2normSq);
dLda.subi(yhat.mulColumnVector(yhatDotyL1norm));
// transform vals to avoid nans before div
yL2norm = Transforms.max(yL2norm, Nd4j.EPS_THRESHOLD, false);
yhatL2norm = Transforms.max(yhatL2norm, Nd4j.EPS_THRESHOLD, false);
yhatL2normSq = Transforms.max(yhatL2normSq, Nd4j.EPS_THRESHOLD, false);
dLda.diviColumnVector(yL2norm);
dLda.diviColumnVector(yhatL2norm.mul(yhatL2normSq));
dLda.muli(-1);
//dL/dz
INDArray gradients = activationFn.backprop(preOutput, dLda).getFirst(); //TODO loss functions with params
if (mask != null) {
gradients.muliColumnVector(mask);
}
return gradients;
}
@Override
public org.apache.commons.math3.util.Pair computeGradientAndScore(INDArray labels,
INDArray preOutput, IActivation activationFn, INDArray mask, boolean average) {
//TODO: probably a more efficient way to do this...
return new Pair<>(computeScore(labels, preOutput, activationFn, mask, average),
computeGradient(labels, preOutput, activationFn, mask));
}
/**
* The name of this function
*
* @return
*/
@Override
public String name() {
return toString();
}
@Override
public String toString() {
return "LossCosineProximity()";
}
}