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/*******************************************************************************
* Copyright (c) 2015-2018 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
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.ops.impl.transforms;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseTransformOp;
import org.nd4j.linalg.factory.Nd4j;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
/**
* Pow function
*
* @author Adam Gibson
*/
public class Pow extends BaseTransformOp {
private double pow;
public Pow() {
}
public Pow(SameDiff sameDiff, SDVariable i_v, boolean inPlace, double pow) {
super(sameDiff, i_v, inPlace);
this.pow = pow;
this.extraArgs = new Object[]{pow};
}
public Pow(SameDiff sameDiff, SDVariable i_v, long[] shape, boolean inPlace, Object[] extraArgs, double pow) {
super(sameDiff, i_v, shape, inPlace, extraArgs);
this.pow = pow;
this.extraArgs = new Object[]{pow};
}
public Pow(SameDiff sameDiff, SDVariable i_v, Object[] extraArgs, double pow) {
super(sameDiff, i_v, extraArgs);
this.pow = pow;
this.extraArgs = new Object[]{pow};
}
public Pow(INDArray x, INDArray z, double pow) {
super(x, z);
this.pow = pow;
init(x, null, z, x.lengthLong());
}
public Pow(INDArray x, INDArray z, long n, double pow) {
super(x, z, n);
this.pow = pow;
init(x, null, z, n);
}
public Pow(INDArray x, INDArray y, INDArray z, long n, double pow) {
super(x, y, z, n);
this.pow = pow;
init(x, y, z, n);
}
public Pow(INDArray x, double pow) {
super(x);
this.pow = pow;
init(x, null, x, x.lengthLong());
}
@Override
public int opNum() {
return 7;
}
@Override
public String opName() {
return "pow";
}
@Override
public void init(INDArray x, INDArray y, INDArray z, long n) {
super.init(x, y, z, n);
this.extraArgs = new Object[]{pow};
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
val weightsName = nodeDef.getInput(1);
val variable = initWith.getVariable(weightsName);
val tmp = initWith.getArrForVarName(weightsName);
// if second argument is scalar - we should provide array of same shape
if (tmp != null) {
if (tmp.isScalar()) {
this.pow = tmp.getDouble(0);
}
}
}
@Override
public String onnxName() {
return "Pow";
}
@Override
public String tensorflowName() {
return "Pow";
}
@Override
public List doDiff(List i_v1) {
SDVariable g = f().powDerivative(arg(), this.pow).mul(i_v1.get(0));
return Arrays.asList(g);
}
}