org.nd4j.linalg.api.ops.impl.transforms.gradient.RationalTanhDerivative 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.gradient;
import org.nd4j.autodiff.functions.DifferentialFunction;
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.BaseTransformOp;
import org.nd4j.linalg.api.ops.BaseTransformStrictOp;
import java.util.List;
/**
* Rational Tanh Derivative, as described at https://github.com/deeplearning4j/libnd4j/issues/351
* Calculates dOut/dIn given input, not dL/dIn given dL/dOut and input
*
* @deprecated Use {@link RationalTanhBp}
*
* @author [email protected]
* @author AlexDBlack
*/
@Deprecated
public class RationalTanhDerivative extends BaseTransformStrictOp {
public RationalTanhDerivative(SameDiff sameDiff, SDVariable in, boolean inPlace) {
super(sameDiff, in, inPlace);
}
public RationalTanhDerivative() {}
public RationalTanhDerivative(INDArray x, INDArray z) {
super(x, z);
}
public RationalTanhDerivative(INDArray x) {
super(x);
}
@Override
public int opNum() {
return 11;
}
@Override
public String opName() {
return "rational_tanh_derivative";
}
@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 f1) {
throw new UnsupportedOperationException("Differentiation not supported: " + getClass().getSimpleName());
}
}