org.nd4j.linalg.api.ops.impl.shape.Cross Maven / Gradle / Ivy
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* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.shape;
import lombok.NoArgsConstructor;
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;
@NoArgsConstructor
public class Cross extends DynamicCustomOp {
public Cross(SameDiff sameDiff, SDVariable[] args) {
super(null, sameDiff, args, false);
}
public Cross(SameDiff sameDiff, SDVariable a, SDVariable b) {
this(sameDiff, new SDVariable[]{a,b});
}
public Cross(INDArray a, INDArray b){
this(a,b,null);
}
public Cross(INDArray a, INDArray b, INDArray out){
super(null, new INDArray[]{a,b}, wrapOrNull(out), null, (int[])null);
}
@Override
public String opName() {
return "cross";
}
@Override
public String tensorflowName() {
return "Cross";
}
@Override
public List doDiff(List gradients) {
/**
* dL / dx = dL / dCross * dCross / dx
* dCross(a,b) / da = Cross(1, b)
* dCross(a,b) / db = Cross(a, 1)
*
* return (grad * Cross(1, b), grad * Cross(a, 1)
*/
SDVariable grad = gradients.get(0);
SDVariable a = larg();
SDVariable b = rarg();
SDVariable ones = sameDiff.onesLike(a);
SDVariable gradLeft = grad.mul(sameDiff.math().cross(b, ones));
SDVariable gradRight = grad.mul(sameDiff.math().cross(ones, a));
return Arrays.asList(gradLeft, gradRight);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes.size() == 2, "Expected list with exactly 2 datatype for %s, got %s", getClass(), dataTypes);
//Output type is same as input type
return Collections.singletonList(dataTypes.get(0));
}
}