org.nd4j.linalg.api.ops.impl.loss.LogLoss Maven / Gradle / Ivy
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* Copyright (c) 2015-2019 Skymind, Inc.
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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.
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* Unless required by applicable law or agreed to in writing, software
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* 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 java.util.Arrays;
import java.util.List;
/**
* Binary log loss, or cross entropy loss:
* {@code -1/numExamples * sum_i (labels[i] * log(predictions[i] + epsilon) + (1-labels[i]) * log(1-predictions[i] + epsilon))}
*
* @author Alex Black
*/
public class LogLoss extends BaseLoss {
public static final double DEFAULT_EPSILON = 1e-7;
private double epsilon;
public LogLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels, double epsilon){
super(sameDiff, lossReduce, predictions, weights, labels);
this.epsilon = epsilon;
addTArgument(epsilon);
}
public LogLoss(){ }
@Override
public String opName() {
return "log_loss";
}
@Override
public List doDiff(List grad){
//No external gradient
//Args are: predictions, weights, label
SDVariable[] grads = f().lossLogBp(arg(2), arg(0), arg(1), lossReduce, epsilon);
return Arrays.asList(grads);
}
}