org.nd4j.linalg.api.ops.impl.loss.WeightedCrossEntropyLoss Maven / Gradle / Ivy

Show more of this group Show more artifacts with this name
Show all versions of nd4j-api Show documentation
The newest version!
/*
 *  ******************************************************************************
 *  *
 *  *
 *  * 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.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
    }
}