org.nd4j.linalg.learning.regularization.L1Regularization Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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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.ops.transforms.Transforms;
import org.nd4j.linalg.schedule.FixedSchedule;
import org.nd4j.linalg.schedule.ISchedule;
import org.nd4j.shade.jackson.annotation.JsonProperty;
@Data
public class L1Regularization implements Regularization {
protected final ISchedule l1;
/**
* @param l1 l1 regularization coefficient
*/
public L1Regularization(double l1) {
this(new FixedSchedule(l1));
}
/**
* @param l1 L1 regularization coefficient (schedule)
*/
public L1Regularization(@JsonProperty("l1") @NonNull ISchedule l1) {
this.l1 = l1;
}
@Override
public ApplyStep applyStep(){
return ApplyStep.BEFORE_UPDATER;
}
@Override
public void apply(INDArray param, INDArray gradView, double lr, int iteration, int epoch) {
//L = loss + l1 * sum_i abs(x[i])
//dL/dx[i] = dloss/dx[i] + l1 * sign(x[i])
//where sign(x[i]) is -1 or 1
double coeff = l1.valueAt(iteration, epoch);
INDArray sign = Transforms.sign(param, true);
Nd4j.exec(new Axpy(sign, gradView, gradView, coeff)); //Gradient = l1 * sign(param) + gradient
}
@Override
public double score(INDArray param, int iteration, int epoch) {
return l1.valueAt(iteration, epoch) * param.norm1Number().doubleValue();
}
@Override
public Regularization clone() {
return new L1Regularization(l1.clone());
}
}