org.nd4j.linalg.api.ops.impl.transforms.strict.Erf Maven / Gradle / Ivy
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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.strict;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseTransformFloatOp;
import org.nd4j.linalg.api.ops.BaseTransformOp;
import org.nd4j.linalg.api.ops.BaseTransformStrictOp;
import java.util.Collections;
import java.util.List;
/**
* Gaussian error function (erf) function, which is defined as
*
* erf(x) = 1 / sqrt(pi) * integral_(-x, x) exp(-t^2) dt
*
* @author [email protected]
*/
public class Erf extends BaseTransformStrictOp {
public Erf(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
super(sameDiff, i_v, inPlace);
}
public Erf() {
}
public Erf(INDArray x, INDArray z) {
super(x, z);
}
public Erf(INDArray x) {
super(x);
}
@Override
public int opNum() {
return 45;
}
@Override
public String opName() {
return "erf";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
return "Erf";
}
@Override
public List doDiff(List i_v) {
// Derivative of erf(z) is 2 / sqrt(pi) * e^(-z^2)
SDVariable gradient = i_v.get(0);
SDVariable z = arg();
SDVariable constant = sameDiff.onesLike(gradient).mul(2.0 / Math.sqrt(Math.PI));
SDVariable ret = constant.mul(sameDiff.math().exp(z.mul(z).mul(-1))).mul(gradient);
return Collections.singletonList(ret);
}
}