ai.djl.training.loss.HingeLoss Maven / Gradle / Ivy
/*
* Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
* with the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0/
*
* or in the "license" file accompanying this file. This file 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.
*/
package ai.djl.training.loss;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDArrays;
import ai.djl.ndarray.NDList;
import ai.djl.nn.Activation;
/**
* {@code HingeLoss} is a type of {@link Loss}.
*
* Hinge loss is defined by: \(L = \sum_i max(0, margin - pred_i \cdot label_i)\)
*/
public class HingeLoss extends Loss {
private int margin;
private float weight;
/** Calculates Hinge loss. */
public HingeLoss() {
this("HingeLoss");
}
/**
* Calculates Hinge loss.
*
* @param name the name of the loss
*/
public HingeLoss(String name) {
this(name, 1, 1);
}
/**
* Calculates Hinge loss.
*
* @param name the name of the loss
* @param margin the margin in hinge loss. Defaults to 1.0
* @param weight the weight to apply on loss value, default 1
*/
public HingeLoss(String name, int margin, float weight) {
super(name);
this.margin = margin;
this.weight = weight;
}
/** {@inheritDoc} */
@Override
public NDArray evaluate(NDList label, NDList prediction) {
NDArray pred = prediction.singletonOrThrow();
NDArray labelReshaped = label.singletonOrThrow().reshape(pred.getShape());
NDArray loss = Activation.relu(NDArrays.sub(margin, labelReshaped.mul(pred)));
if (weight != 1) {
loss = loss.mul(weight);
}
return loss.mean();
}
}