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/*
* ******************************************************************************
* *
* *
* * 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.
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* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.nd4j.linalg.api.ops.impl.loss;
import org.nd4j.autodiff.loss.LossReduce;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.loss.bp.CosineDistanceLossBp;
import java.util.List;
public class CosineDistanceLoss extends BaseLoss {
private int dimension;
public CosineDistanceLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, int dimension){
super(sameDiff, lossReduce, predictions, weights, labels);
this.dimension = dimension;
this.addIArgument(dimension);
}
public CosineDistanceLoss(SameDiff sameDiff, SDVariable labels, SDVariable predictions, SDVariable weights,
LossReduce lossReduce, int dimension) {
this(sameDiff, lossReduce, predictions, weights, labels, dimension);
}
public CosineDistanceLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce, int dimension){
super(lossReduce, predictions, weights, labels);
this.dimension = dimension;
this.addIArgument(dimension);
}
public CosineDistanceLoss(){ }
@Override
public String opName() {
return "cosine_distance_loss";
}
@Override
public List doDiff(List grad){
//No external gradient.
//Args are: predictions, weights, label
return new CosineDistanceLossBp(sameDiff, lossReduce, arg(0), arg(1), arg(2), dimension).outputs();
}
}
