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Java example source code file (ConjugateGradient.java)

This example Java source code file (ConjugateGradient.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

baseoptimizer, collection, conjugategradient, indarray, logger, model, override, polak-ribiere, previous, sgd, stepfunction, util

The ConjugateGradient.java Java example source code

 *  * Copyright 2015 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.deeplearning4j.optimize.solvers;

import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.optimize.api.*;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.Collection;

/**Originally based on cc.mallet.optimize.ConjugateGradient
 * Rewritten based on Conjugate Gradient algorithm in Bengio et al.,
 * Deep Learning (in preparation) Ch8.
 * See also Nocedal & Wright, Numerical optimization, Ch5
public class ConjugateGradient extends BaseOptimizer {
	private static final long serialVersionUID = -1269296013474864091L;
	private static final Logger logger = LoggerFactory.getLogger(ConjugateGradient.class);

    public ConjugateGradient(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Model model) {
        super(conf, stepFunction, iterationListeners, model);

    public ConjugateGradient(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Collection terminationConditions, Model model) {
        super(conf, stepFunction, iterationListeners, terminationConditions, model);

    public void preProcessLine() {
        INDArray gradient = (INDArray) searchState.get(GRADIENT_KEY);
        INDArray searchDir = (INDArray) searchState.get(SEARCH_DIR);
        if( searchDir == null )
            searchState.put(SEARCH_DIR, gradient);
        else searchDir.assign(gradient);

    public void postStep(INDArray gradient) {
        //line is current gradient
        //Last gradient is stored in searchState map
        INDArray gLast = (INDArray) searchState.get(GRADIENT_KEY);		//Previous iteration gradient
        INDArray searchDirLast = (INDArray) searchState.get(SEARCH_DIR);//Previous iteration search dir

        //Calculate gamma (or beta, by Bengio et al. notation). Polak and Ribiere method.
        // = ((grad(current)-grad(last)) \dot (grad(current))) / (grad(last) \dot grad(last))
        double dgg = Nd4j.getBlasWrapper().dot(gradient.sub(gLast), gradient);
        double gg = Nd4j.getBlasWrapper().dot(gLast, gLast);
        double gamma = Math.max(dgg / gg, 0.0);
        if( dgg <= 0.0 ) logger.debug("Polak-Ribiere gamma <= 0.0; using gamma=0.0 -> SGD line search. dgg={}, gg={}",dgg,gg);

        //Standard Polak-Ribiere does not guarantee that the search direction is a descent direction
        //But using max(gamma_Polak-Ribiere,0) does guarantee a descent direction. Hence the max above.
        //See Nocedal & Wright, Numerical Optimization, Ch5
        //If gamma==0.0, this is equivalent to SGD line search (i.e., search direction == negative gradient)

        //Compute search direction:
        //searchDir = gradient + gamma * searchDirLast
        INDArray searchDir = searchDirLast.muli(gamma).addi(gradient);

        //Store current gradient and search direction for
        //(a) use in BaseOptimizer.optimize(), and (b) next iteration
        searchState.put(GRADIENT_KEY, gradient);
        searchState.put(SEARCH_DIR, searchDir);


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