org.nd4j.linalg.lossfunctions.impl.LossMultiLabel Maven / Gradle / Ivy
package org.nd4j.linalg.lossfunctions.impl;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import onnx.OnnxProto3;
import org.nd4j.autodiff.functions.DifferentialFunction;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.Op;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossUtil;
import org.nd4j.linalg.ops.transforms.Transforms;
import org.nd4j.linalg.primitives.Pair;
import org.nd4j.shade.jackson.annotation.JsonInclude;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import javax.naming.OperationNotSupportedException;
import java.util.List;
import java.util.Map;
/**
* Multi-Label-Loss Function, maybe more commonly known as BPMLL
*
* This Loss function requires that the Labels are given as a multi-hot encoded vector. It doesn't require any special
* Activation method, i.e. the network output doesn't have to be in any specific range.
*
* The loss is calculated based on the classification difference on labels that the examples has, and those that it
* doesn't have. Assume that each example has a set of labels, these labels are the positive set, the labels that do not
* belong to the example are in the negative set. This loss function trains the network to produce a higher value for
* labels that are in the positive set than those that are in the negative set.
*
* For a more detailed explanation and the actual formulas, read the original paper by Zhang and Zhou. The
* implementation on scoreArray is based on equation 3, while computeGradient is based on equation 11. The main
* difference being that -(c_k - c_l) = (c_l - c_k) was used to simplify the calculations.
*
* Min-Ling Zhang and Zhi-Hua Zhou, "Multilabel Neural Networks with Applications to Functional Genomics and Text
* Categorization," in IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 10, pp. 1338-1351, Oct. 2006.
* doi: 10.1109/TKDE.2006.162
* *
*
* @author Paul Dubs
*/
@EqualsAndHashCode
@JsonInclude(JsonInclude.Include.NON_NULL)
@Getter
public class LossMultiLabel extends DifferentialFunction implements ILossFunction {
public LossMultiLabel() {
}
private void calculate(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, INDArray scoreOutput, INDArray gradientOutput) {
if (scoreOutput == null && gradientOutput == null) {
throw new IllegalArgumentException("You have to provide at least one of scoreOutput or gradientOutput!");
}
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) + ") ");
}
final INDArray postOutput = activationFn.getActivation(preOutput.dup(), true);
final INDArray positive = labels;
final INDArray negative = labels.eq(0.0);
final INDArray normFactor = negative.sum(1).muli(positive.sum(1));
int examples = positive.size(0);
for (int i = 0; i < examples; i++) {
final INDArray locCfn = postOutput.getRow(i);
final int[] shape = locCfn.shape();
final INDArray locPositive = positive.getRow(i);
final INDArray locNegative = negative.getRow(i);
final Double locNormFactor = normFactor.getDouble(i);
final INDArray operandA = Nd4j.ones(shape[1], shape[0]).mmul(locCfn);
final INDArray operandB = operandA.transpose();
final INDArray pairwiseSub = Transforms.exp(operandA.sub(operandB));
final INDArray selection = locPositive.transpose().mmul(locNegative);
final INDArray classificationDifferences = pairwiseSub.muli(selection).divi(locNormFactor);
if (scoreOutput != null) {
if (mask != null) {
final INDArray perLabel = classificationDifferences.sum(0);
LossUtil.applyMask(perLabel, mask.getRow(i));
perLabel.sum(scoreOutput.getRow(i), 0);
} else {
classificationDifferences.sum(scoreOutput.getRow(i), 0, 1);
}
}
if (gradientOutput != null) {
gradientOutput.getRow(i).assign(classificationDifferences.sum(0).addi(classificationDifferences.sum(1).transposei().negi()));
}
}
if (gradientOutput != null) {
gradientOutput.assign(activationFn.backprop(preOutput.dup(), gradientOutput).getFirst());
//multiply with masks, always
if (mask != null) {
LossUtil.applyMask(gradientOutput, mask);
}
}
}
public INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
final INDArray scoreArr = Nd4j.create(labels.size(0), 1);
calculate(labels, preOutput, activationFn, mask, scoreArr, null);
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(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) + ") ");
}
final INDArray grad = Nd4j.ones(labels.shape());
calculate(labels, preOutput, activationFn, mask, null, grad);
return grad;
}
@Override
public Pair computeGradientAndScore(INDArray labels,
INDArray preOutput, IActivation activationFn, INDArray mask, boolean average) {
final INDArray scoreArr = Nd4j.create(labels.size(0), 1);
final INDArray grad = Nd4j.ones(labels.shape());
calculate(labels, preOutput, activationFn, mask, scoreArr, grad);
double score = scoreArr.sumNumber().doubleValue();
if (average)
score /= scoreArr.size(0);
return new Pair<>(score, grad);
}
@Override
public String name() {
return toString();
}
@Override
public String toString() {
return "LossMultiLabel";
}
@Override
public SDVariable[] outputVariables() {
throw new UnsupportedOperationException();
}
@Override
public SDVariable[] outputVariables(String baseName) {
throw new UnsupportedOperationException();
}
@Override
public List doDiff(List f1) {
throw new UnsupportedOperationException();
}
@Override
public String opName() {
return name();
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op name found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op name found for " + opName());
}
}