org.nd4j.linalg.api.ops.impl.loss.BaseLoss Maven / Gradle / Ivy
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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.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Collections;
import java.util.List;
public abstract class BaseLoss extends DynamicCustomOp {
@Getter
@Setter
protected LossReduce lossReduce;
public BaseLoss(@NonNull SameDiff sameDiff, @NonNull LossReduce lossReduce, @NonNull SDVariable predictions, SDVariable weights,
@NonNull SDVariable labels){
super(null, sameDiff, new SDVariable[]{predictions, getWeights(sameDiff, weights, predictions), labels});
this.lossReduce = lossReduce;
addArgs();
}
public BaseLoss(@NonNull LossReduce lossReduce, @NonNull INDArray predictions, INDArray weights, @NonNull INDArray labels){
super(new INDArray[]{predictions, getWeights(weights, predictions), labels}, null);
this.lossReduce = lossReduce;
addArgs();
}
protected static INDArray getWeights(INDArray weights, INDArray predictions) {
return (weights != null) ? weights : Nd4j.scalar(predictions.dataType(), 1.0);
}
protected static SDVariable getWeights(SameDiff sd, SDVariable weights, SDVariable predictions){
return weights != null ? weights : sd.constant(Nd4j.scalar(predictions.dataType(), 1.0));
}
protected BaseLoss(){ }
protected void addArgs(){
iArguments.clear();
tArguments.clear();
addIArgument(lossReduce.ordinal()); //Ops: 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
}
public abstract String opName();
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() >= 2, "Expected exactly 2 or more input datatypes for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0)); //Same as predictions
}
}