All Downloads are FREE. Search and download functionalities are using the official Maven repository.

org.nd4j.linalg.learning.AMSGradUpdater Maven / Gradle / Ivy

There is a newer version: 1.0.0-M2.1
Show newest version
/*-
 *
 *  * 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); } }




© 2015 - 2024 Weber Informatics LLC | Privacy Policy