org.nd4j.linalg.api.ops.impl.transforms.custom.Pow Maven / Gradle / Ivy
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* Copyright (c) 2015-2019 Skymind, Inc.
*
* 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.
*
* 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
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* under the License.
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* SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.transforms.custom;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
* Broadcastable element-wise power operation: x[i]^y[i]
*
* @author Alex Black
*/
public class Pow extends DynamicCustomOp {
public Pow(SameDiff sameDiff, SDVariable x, SDVariable y){
super(sameDiff, new SDVariable[]{x, y});
}
public Pow(){ }
@Override
public String opName(){
return "Pow";
}
@Override
public String tensorflowName(){
return "Pow";
}
@Override
public List doDiff(List f1) {
//TODO: replace this with discrete op once available: https://github.com/deeplearning4j/deeplearning4j/issues/7461
//If y=a^b, then:
//dL/da = b*a^(b-1) * dL/dy
//dL/db = a^b * log(a) * dL/dy
SDVariable a = arg(0);
SDVariable b = arg(1);
SDVariable dlda = b.mul(sameDiff.math().pow(a,b.sub(1))).mul(f1.get(0));
SDVariable dldb = outputVariable().mul(sameDiff.math().log(a)).mul(f1.get(0));
return Arrays.asList(dlda, dldb);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);
return Collections.singletonList(dataTypes.get(0));
}
}