/*
* 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.
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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;
}
}
}