/* * Encog(tm) Core v3.4 - Java Version * http://www.heatonresearch.com/encog/ * https://github.com/encog/encog-java-core * Copyright 2008-2016 Heaton Research, 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. * * For more information on Heaton Research copyrights, licenses * and trademarks visit: * http://www.heatonresearch.com/copyright */ package org.encog.neural.networks.training.propagation.sgd.update; import org.encog.neural.networks.training.propagation.sgd.StochasticGradientDescent; /** * Created by jeffh on 7/15/2016. */ public class AdamUpdate implements UpdateRule { private StochasticGradientDescent training; private double[] m; private double[] v; private double beta1 = 0.9; private double beta2 = 0.999; private double eps = 1e-8; @Override public void init(StochasticGradientDescent theTraining) { this.training = theTraining; this.m = new double[theTraining.getFlat().getWeights().length]; this.v = new double[theTraining.getFlat().getWeights().length]; } @Override public void update(double[] gradients, double[] weights) { for(int i=0;i<weights.length;i++) { m[i] = (this.beta1*m[i])+(1-this.beta1)*gradients[i]; v[i] = (this.beta2*v[i])+(1-this.beta2)*gradients[i]*gradients[i]; double mCorrect = m[i]/(1-Math.pow(this.beta1,this.training.getIteration())); double vCorrect = v[i]/(1-Math.pow(this.beta2,this.training.getIteration())); final double delta = (training.getLearningRate()*mCorrect)/(Math.sqrt(vCorrect)+this.eps); weights[i] += delta; } } }