ai.djl.training.loss.L1WeightDecay Maven / Gradle / Ivy
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* Copyright 2021 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.NDList;
import ai.djl.ndarray.NDManager;
/**
* {@code L1WeightDecay} calculates L1 penalty of a set of parameters. Used for regularization.
*
* L1 loss is defined as \(L1 = \lambda \sum_i \vert W_i\vert\).
*/
public class L1WeightDecay extends Loss {
private float lambda;
private NDList parameters;
/**
* Calculates L1 weight decay for regularization.
*
* @param parameters holds the model weights that will be penalized
*/
public L1WeightDecay(NDList parameters) {
this("L1WeightDecay", parameters);
}
/**
* Calculates L1 weight decay for regularization.
*
* @param name the name of the penalty
* @param parameters holds the model weights that will be penalized
*/
public L1WeightDecay(String name, NDList parameters) {
this(name, parameters, 1);
}
/**
* Calculates L1 weight decay for regularization.
*
* @param name the name of the penalty
* @param parameters holds the model weights that will be penalized
* @param lambda the weight to apply to the penalty value, default 1
*/
public L1WeightDecay(String name, NDList parameters, float lambda) {
super(name);
this.lambda = lambda;
this.parameters = parameters;
}
private NDArray l1(NDArray w) {
return ((w.abs()).sum());
}
/** {@inheritDoc} */
@Override
public NDArray evaluate(NDList label, NDList prediction) {
NDManager manager = parameters.getManager();
NDArray sum = manager.create(0.0f);
for (NDArray wi : parameters) {
sum.addi(l1(wi));
}
return sum.muli(lambda);
}
}