org.nd4j.linalg.learning.Adam Maven / Gradle / Ivy
package org.nd4j.linalg.learning;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.apache.commons.math3.util.FastMath;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.ops.transforms.Transforms;
import java.io.Serializable;
/**
* The Adam updater.
* http://arxiv.org/abs/1412.6980
*
* @author Adam Gibson
*/
@Data
@NoArgsConstructor
public class Adam implements Serializable, GradientUpdater {
public static final double DEFAULT_ADAM_EPSILON = 1e-8;
public static final double DEFAULT_ADAM_BETA1_MEAN_DECAY = 0.9;
public static final double DEFAULT_ADAM_BETA2_VAR_DECAY = 0.999;
private double learningRate = 1e-3; // learning rate
private double beta1 = DEFAULT_ADAM_BETA1_MEAN_DECAY; // gradient moving avg decay rate
private double beta2 = DEFAULT_ADAM_BETA2_VAR_DECAY; // gradient sqrd decay rate
private double epsilon = DEFAULT_ADAM_EPSILON;
private INDArray m, v; // moving avg & sqrd gradients
@Override
public int stateSizeForInputSize(int inputSize) {
return 2 * inputSize;
}
@Override
public void setStateViewArray(INDArray viewArray, int[] gradientShape, char gradientOrder, boolean initialize) {
if (!viewArray.isRowVector())
throw new IllegalArgumentException("Invalid input: expect row vector input");
if (initialize)
viewArray.assign(0);
int length = viewArray.length();
this.m = viewArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, length / 2));
this.v = viewArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(length / 2, length));
//Reshape to match the expected shape of the input gradient arrays
this.m = Shape.newShapeNoCopy(this.m, gradientShape, gradientOrder == 'f');
this.v = Shape.newShapeNoCopy(this.v, gradientShape, gradientOrder == 'f');
if (m == null || v == null)
throw new IllegalStateException("Could not correctly reshape gradient view arrays");
}
public Adam(double alpha, double beta1, double beta2, double epsilon) {
this.learningRate = alpha;
this.beta1 = beta1;
this.beta2 = beta2;
this.epsilon = epsilon; // fudge factor to avoid zeros
}
public Adam(double alpha, double beta1, double beta2) {
this.learningRate = alpha;
this.beta1 = beta1;
this.beta2 = beta2;
}
public Adam(double alpha) {
this.learningRate = alpha;
}
@Override
public void update(Object... args) {
if (args.length > 0) {
learningRate = (Double) args[0];
}
}
/**
* Calculate the update based on the given gradient
*
* @param gradient the gradient to get the update for
* @param iteration
* @return the gradient
*/
@Override
public INDArray getGradient(INDArray gradient, int iteration) {
if (m == null || v == null)
throw new IllegalStateException("Updater has not been initialized with view state");
INDArray oneMinusBeta1Grad = gradient.mul(1.0 - beta1);
m.muli(beta1).addi(oneMinusBeta1Grad);
INDArray oneMinusBeta2GradSquared = gradient.mul(gradient).muli(1 - beta2);
v.muli(beta2).addi(oneMinusBeta2GradSquared);
double beta1t = FastMath.pow(beta1, iteration + 1);
double beta2t = FastMath.pow(beta2, iteration + 1);
double alphat = learningRate * FastMath.sqrt(1 - beta2t) / (1 - beta1t);
if (Double.isNaN(alphat) || alphat == 0.0)
alphat = epsilon;
INDArray sqrtV = Transforms.sqrt(v, true).addi(epsilon);
INDArray ret = m.mul(alphat).divi(sqrtV);
gradient.assign(ret);
return gradient;
}
@Override
public GradientUpdaterAggregator getAggregator(boolean addThis) {
AdamAggregator ag = new AdamAggregator();
if (addThis)
ag.aggregate(this);
return ag;
}
public static class AdamAggregator implements GradientUpdaterAggregator {
private INDArray mSum;
private INDArray vSum;
private double lrSum;
private double beta1Sum;
private double beta2Sum;
private double epsilonSum;
private int count = 0;
@Override
public GradientUpdater getUpdater() {
Adam adam = new Adam(lrSum / count, beta1Sum / count, beta2Sum / count, epsilonSum / count);
adam.setM(mSum.div(count));
adam.setV(vSum.div(count));
return adam;
}
@Override
public void aggregate(GradientUpdater updater) {
if (!(updater instanceof Adam))
throw new UnsupportedOperationException("Cannot aggregate Adam with updater: " + updater);
Adam adam = (Adam) updater;
if (mSum == null) {
mSum = adam.m.dup();
vSum = adam.v.dup();
lrSum = adam.learningRate;
beta1Sum = adam.beta1;
beta2Sum = adam.beta2;
epsilonSum = adam.epsilon;
} else {
mSum.addi(adam.m);
vSum.addi(adam.v);
lrSum += adam.learningRate;
beta1Sum += adam.beta1;
beta2Sum += adam.beta2;
epsilonSum += adam.epsilon;
}
count++;
}
@Override
public GradientUpdaterAggregator combine(GradientUpdaterAggregator other) {
if (!(other instanceof AdamAggregator))
throw new IllegalArgumentException("Cannot combine AdamAggregator with aggregator: " + other);
AdamAggregator aggregator = (AdamAggregator) other;
mSum.addi(aggregator.mSum);
vSum.addi(aggregator.vSum);
lrSum += aggregator.lrSum;
beta1Sum += aggregator.beta1Sum;
beta2Sum += aggregator.beta2Sum;
epsilonSum += aggregator.epsilonSum;
count += aggregator.count;
return this;
}
}
}