org.nd4j.linalg.api.ops.impl.transforms.custom.ATan2 Maven / Gradle / Ivy
/*******************************************************************************
* 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.custom;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.BaseDynamicTransformOp;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import org.nd4j.linalg.ops.transforms.Transforms;
/**
* Arc Tangent elementwise function
*
* @author Adam Gibson
*/
public class ATan2 extends BaseDynamicTransformOp {
public ATan2(SameDiff sameDiff, SDVariable y, SDVariable x) {
super(sameDiff, new SDVariable[] {y, x} ,false);
}
/**
* Note that the order of x and y match {@link java.lang.Math#atan2(double, double)},
* and are reversed when compared to OldATan2.
* See {@link Transforms#atan2(org.nd4j.linalg.api.ndarray.INDArray, org.nd4j.linalg.api.ndarray.INDArray)}
*/
public ATan2(INDArray x, INDArray y, INDArray z) {
super(new INDArray[]{x, y}, new INDArray[]{ z });
}
public ATan2() {}
@Override
public String opName() {
return "tf_atan2";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
return "Atan2";
}
@Override
public List doDiff(List i_v) {
//Let z=atan2(r), with r=y/x
//dz/dr = 1/(r^2+1), dr/dy = 1/x, dr/dx = -y/x^2
SDVariable y = larg();
SDVariable x = rarg();
/* SDVariable r = y.div(x);
SDVariable dOutdr = f().square(r).add(1.0).rdiv(1.0);
SDVariable drdy = x.rdiv(1.0);
SDVariable drdx = f().neg(y).div(f().square(x));
SDVariable xGrad = dOutdr.mul(drdx).mul(i_v.get(0));
SDVariable yGrad = dOutdr.mul(drdy).mul(i_v.get(0));
*/
val xGrad = f().neg(y.div(x.pow(2).add(y.pow(2)))).mul(i_v.get(0));
val yGrad = x.div(x.pow(2).add(y.pow(2))).mul(i_v.get(0));
return Arrays.asList(yGrad, xGrad);
}
@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));
}
}