org.nd4j.linalg.learning.regularization.WeightDecay Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
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* * information regarding copyright ownership.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.learning.regularization;
import lombok.Data;
import lombok.NonNull;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.Axpy;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.schedule.FixedSchedule;
import org.nd4j.linalg.schedule.ISchedule;
import org.nd4j.shade.jackson.annotation.JsonProperty;
@Data
public class WeightDecay implements Regularization {
protected final ISchedule coeff;
protected final boolean applyLR;
/**
* @param coeff Weight decay regularization coefficient
* @param applyLR If true, multiply the regularization coefficient by the current learning rate. If false, do not multiply by LR.
*/
public WeightDecay(double coeff, boolean applyLR) {
this(new FixedSchedule(coeff), applyLR);
}
/**
* @param coeff Weight decay regularization coefficient (schedule)
* @param applyLR If true, multiply the regularization coefficient by the current learning rate. If false, do not multiply by LR.
*/
public WeightDecay(@JsonProperty("coeff") @NonNull ISchedule coeff, @JsonProperty("applyLR") boolean applyLR){
this.coeff = coeff;
this.applyLR = applyLR;
}
@Override
public ApplyStep applyStep() {
return ApplyStep.POST_UPDATER;
}
@Override
public void apply(INDArray param, INDArray gradView, double lr, int iteration, int epoch) {
//L = loss + coeff * 0.5 * sum_i x[i]^2
//dL/dx[i] = coeff * x[i]
//update(x[i]) = coeff * x[i] * ( applyLR ? lr : )
double scale = coeff.valueAt(iteration, epoch);
if(applyLR){
scale *= lr;
}
Nd4j.exec(new Axpy(param, gradView, gradView, scale)); //update = scale * param + update
}
@Override
public double score(INDArray param, int iteration, int epoch) {
//Score: L = 0.5 * sum_i x[i]^2
double norm2 = param.norm2Number().doubleValue(); //Norm2 is sqrt(sum_i x[i]^2)
return coeff.valueAt(iteration, epoch) * 0.5 * norm2 * norm2;
}
@Override
public Regularization clone() {
return new WeightDecay(coeff.clone(), applyLR);
}
}