org.nd4j.linalg.api.ops.impl.transforms.gradient.SoftmaxBp 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.transforms.gradient;
import lombok.NonNull;
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 java.util.Collections;
import java.util.List;
public class SoftmaxBp extends DynamicCustomOp {
public SoftmaxBp(){ }
public SoftmaxBp(SameDiff sd, SDVariable input, SDVariable grad, Integer dimension){
super(null, sd, new SDVariable[]{input, grad});
if(dimension != null)
addIArgument(dimension);
}
public SoftmaxBp(@NonNull INDArray input, @NonNull INDArray grad, Integer dimension){
this(input, grad, null, dimension);
}
public SoftmaxBp(@NonNull INDArray input, @NonNull INDArray grad, INDArray output, Integer dimension){
super(new INDArray[]{input, grad}, wrapOrNull(output));
if(dimension != null)
addIArgument(dimension);
}
@Override
public String opName() {
return "softmax_bp";
}
@Override
public List doDiff(List grad){
throw new UnsupportedOperationException("Differentiating op softmax_bp not supported");
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 inputs datatype for %s, got %s", getClass(), dataTypes);
Preconditions.checkState(dataTypes.get(0).isFPType(), "Input 0 must be a floating point type, got %s", dataTypes.get(0));
Preconditions.checkState(dataTypes.get(1).isFPType(), "Input 1 must be a floating point type, got %s", dataTypes.get(1));
Preconditions.checkState(dataTypes.get(0) == dataTypes.get(1), "Both input must be same type: got %s", dataTypes);
return Collections.singletonList(dataTypes.get(0));
}
}