org.nd4j.linalg.api.ops.impl.transforms.custom.MatrixSetDiag 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.custom;
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.Arrays;
import java.util.Collections;
import java.util.List;
public class MatrixSetDiag extends DynamicCustomOp {
public MatrixSetDiag(SameDiff sameDiff, SDVariable in, SDVariable diag, boolean inPlace) {
super(null, sameDiff, new SDVariable[]{in, diag}, inPlace);
}
public MatrixSetDiag(SameDiff sameDiff, SDVariable in, SDVariable diag) {
this(sameDiff, in, diag, false);
}
public MatrixSetDiag(@NonNull INDArray in, @NonNull INDArray diag){
super(new INDArray[]{in, diag}, null);
}
public MatrixSetDiag(){ }
@Override
public String[] tensorflowNames() {
return new String[]{"MatrixSetDiag", "BatchMatrixSetDiag"};
}
@Override
public String opName() {
return "matrix_set_diag";
}
@Override
public List doDiff(List i_v) {
SDVariable grad = i_v.get(0);
SDVariable in1Grad = sameDiff.math.setDiag(grad, sameDiff.zerosLike(arg(1)));
SDVariable in2Grad = sameDiff.math.diagPart(grad);
return Arrays.asList(in1Grad, in2Grad);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);
Preconditions.checkState(dataTypes.get(0) == dataTypes.get(1), "Input datatypes must be same type, got %s", dataTypes);
return Collections.singletonList(dataTypes.get(0));
}
}