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/*
* ******************************************************************************
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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
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package org.nd4j.linalg.api.ops.impl.loss;
import lombok.NonNull;
import org.nd4j.autodiff.loss.LossReduce;
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.Op;
import java.util.Collections;
import java.util.List;
public class WeightedCrossEntropyLoss extends BaseLoss {
public WeightedCrossEntropyLoss(@NonNull SameDiff sameDiff, @NonNull LossReduce lossReduce, @NonNull SDVariable predictions, SDVariable weights, @NonNull SDVariable labels) {
super(sameDiff, lossReduce, predictions, weights, labels);
}
public WeightedCrossEntropyLoss(@NonNull LossReduce lossReduce, @NonNull INDArray predictions, INDArray weights, @NonNull INDArray labels) {
super(lossReduce, predictions, weights, labels);
}
public WeightedCrossEntropyLoss() {
}
public WeightedCrossEntropyLoss(SameDiff sd, SDVariable targets, SDVariable inputs, SDVariable weights) {
this(sd,LossReduce.SUM,inputs,weights,targets);
}
public WeightedCrossEntropyLoss(INDArray targets, INDArray inputs, INDArray weights) {
this(LossReduce.SUM,inputs,targets,weights);
}
@Override
public String opName() {
return "weighted_cross_entropy_with_logits";
}
@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 Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 3, "Expected exactly 3 input datatypes for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0)); //Same as predictions
}
}