org.nd4j.linalg.api.ops.impl.transforms.gradient.TanhDerivative 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.gradient;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
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.DynamicCustomOp;
import java.util.Collections;
import java.util.List;
/**
* TanhDerivative: calculated dL/dIn from dL/dOut and In
*/
public class TanhDerivative extends DynamicCustomOp {
public TanhDerivative(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
super(sameDiff, new SDVariable[]{i_v1, i_v2});
}
/**
*
* @param x Input
* @param y Gradient at output (dL/dOut)
* @param z Output array, gradient at input (dL/dIn - to be calculated)
*/
public TanhDerivative(INDArray x, INDArray y, INDArray z) {
super(null, new INDArray[]{x, y}, new INDArray[]{z});
}
public TanhDerivative() {
}
/**
* @param x Input
* @param y Gradient at output (dL/dOut)
*/
public TanhDerivative(INDArray x, INDArray y) {
this(x, y, null);
}
@Override
public int opNum() {
return 0;
}
/**
* The opName of this operation
*
* @return the opName of this operation
*/
@Override
public String opName() {
return "tanh_bp";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
@Override
public List doDiff(List i_v) {
SDVariable ret = f().div(sameDiff.onesLike(outputVariables()[0]), f().pow(f().cosh(arg()), 2));
return Collections.singletonList(ret);
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
return Collections.singletonList(inputDataTypes.get(0));
}
}