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org.nd4j.linalg.learning.AMSGradUpdater Maven / Gradle / Ivy
/*-
*
* * Copyright 2017 Skymind,Inc.
* *
* * Licensed under the Apache License, Version 2.0 (the "License");
* * you may not use this file except in compliance with the License.
* * You may obtain a copy of the License at
* *
* * http://www.apache.org/licenses/LICENSE-2.0
* *
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License 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 org.nd4j.linalg.learning;
import lombok.Data;
import org.apache.commons.math3.util.FastMath;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.Sqrt;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.learning.config.AMSGrad;
import org.nd4j.linalg.ops.transforms.Transforms;
/**
* The AMSGrad updater
* Reference: On the Convergence of Adam and Beyond - https://openreview.net/forum?id=ryQu7f-RZ
*
* @author Alex Black
*/
@Data
public class AMSGradUpdater implements GradientUpdater {
private AMSGrad config;
private INDArray m, v, vHat; // moving avg, sqrd gradients, max
private char gradientReshapeOrder;
public AMSGradUpdater(AMSGrad config) {
this.config = config;
}
@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 n = viewArray.length() / 3;
this.m = viewArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, n));
this.v = viewArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(n, 2*n));
this.vHat = viewArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(2*n, 3*n));
//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');
this.vHat = Shape.newShapeNoCopy(this.vHat, gradientShape, gradientOrder == 'f');
if (m == null || v == null || vHat == null)
throw new IllegalStateException("Could not correctly reshape gradient view arrays");
this.gradientReshapeOrder = gradientOrder;
}
@Override
public void applyUpdater(INDArray gradient, int iteration, int epoch) {
if (m == null || v == null || vHat == null)
throw new IllegalStateException("Updater has not been initialized with view state");
double beta1 = config.getBeta1();
double beta2 = config.getBeta2();
double learningRate = config.getLearningRate(iteration, epoch);
double epsilon = config.getEpsilon();
//m_t = b_1 * m_{t-1} + (1-b_1) * g_t eq 1 pg 3
INDArray oneMinusBeta1Grad = gradient.mul(1.0 - beta1);
m.muli(beta1).addi(oneMinusBeta1Grad);
//v_t = b_2 * v_{t-1} + (1-b_2) * (g_t)^2 eq 1 pg 3
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);
//vHat_t = max(vHat_{t-1}, v_t)
Transforms.max(vHat, v, false);
double alphat = learningRate * FastMath.sqrt(1 - beta2t) / (1 - beta1t);
if (Double.isNaN(alphat) || alphat == 0.0)
alphat = epsilon;
//gradient array contains: sqrt(vHat) + eps
Nd4j.getExecutioner().execAndReturn(new Sqrt(vHat, gradient)).addi(epsilon);
//gradient = alphat * m_t / (sqrt(vHat) + eps)
gradient.rdivi(m).muli(alphat);
}
}