ai.djl.training.loss.ElasticNetWeightDecay 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 ElasticWeightDecay} calculates L1+L2 penalty of a set of parameters. Used for
* regularization.
*
* L loss is defined as \(L = \lambda_1 \sum_i \vert W_i\vert + \lambda_2 \sum_i {W_i}^2\).
*/
public class ElasticNetWeightDecay extends Loss {
private float lambda1;
private float lambda2;
private NDList parameters;
/**
* Calculates Elastic Net weight decay for regularization.
*
* @param parameters holds the model weights that will be penalized
*/
public ElasticNetWeightDecay(NDList parameters) {
this("ElasticNetWeightDecay", parameters);
}
/**
* Calculates Elastic Net weight decay for regularization.
*
* @param name the name of the penalty
* @param parameters holds the model weights that will be penalized
*/
public ElasticNetWeightDecay(String name, NDList parameters) {
this(name, parameters, 1);
}
/**
* Calculates Elastic Net 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 (both L1 and L2)
*/
public ElasticNetWeightDecay(String name, NDList parameters, float lambda) {
super(name);
this.lambda1 = lambda;
this.lambda2 = lambda;
this.parameters = parameters;
}
/**
* Calculates Elastic Net weight decay for regularization.
*
* @param name the name of the penalty
* @param parameters holds the model weights that will be penalized
* @param lambda1 the weight to apply to the L1 penalty value, default 1
* @param lambda2 the weight to apply to the L2 penalty value, default 1
*/
public ElasticNetWeightDecay(String name, NDList parameters, float lambda1, float lambda2) {
super(name);
this.lambda1 = lambda1;
this.lambda2 = lambda2;
this.parameters = parameters;
}
private NDArray l1(NDArray w) {
return ((w.abs()).sum());
}
private NDArray l2(NDArray w) {
return ((w.square()).sum());
}
/** {@inheritDoc} */
@Override
public NDArray evaluate(NDList label, NDList prediction) {
NDManager manager = parameters.getManager();
NDArray sum1 = manager.create(0.0f);
NDArray sum2 = manager.create(0.0f);
for (NDArray wi : parameters) {
sum1.addi(l1(wi));
sum2.addi(l2(wi));
}
return sum1.muli(lambda1).addi(sum2.muli(lambda2));
}
}