org.nd4j.linalg.api.ops.impl.transforms.gradient.LogSoftMaxDerivative Maven / Gradle / Ivy
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.common.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.DynamicCustomOp;
import java.util.Collections;
import java.util.List;
/**
*
*/
public class LogSoftMaxDerivative extends DynamicCustomOp {
public LogSoftMaxDerivative(SameDiff sameDiff, SDVariable in, SDVariable gradO) {
super(sameDiff, new SDVariable[]{in, gradO});
}
public LogSoftMaxDerivative() {
}
public LogSoftMaxDerivative(INDArray in, INDArray gradO, INDArray out) {
super(null, new INDArray[]{in, gradO}, new INDArray[]{out});
}
public LogSoftMaxDerivative(SameDiff sameDiff, SDVariable arg, SDVariable wrt, int dimension) {
this(sameDiff, arg, wrt);
this.addIArgument(dimension);
}
/**
* The opName of this operation
*
* @return the opName of this operation
*/
@Override
public String opName() {
return "log_softmax_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) {
throw new UnsupportedOperationException("Differentation of op not supported: " + getClass().getName());
}
@Override
public List calculateOutputDataTypes(List inTypes){
Preconditions.checkState(inTypes != null && inTypes.size() == 2, "Expected 2 input datatypes for %s, got %s",
getClass(), inTypes);
return Collections.singletonList(inTypes.get(0));
}
}