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Java example source code file (LBFGS.java)

This example Java source code file (LBFGS.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, gradient_key, gradients, illegalstateexception, indarray, iterator, lbfgs, linkedlist, model, most, override, stepfunction, still, util

The LBFGS.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.berkeley.Pair;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.optimize.api.StepFunction;
import org.deeplearning4j.optimize.api.TerminationCondition;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;



import java.util.Collection;
import java.util.Iterator;
import java.util.LinkedList;

/**
 * LBFGS
 * @author Adam Gibson
 */
public class LBFGS extends BaseOptimizer {
    private static final long serialVersionUID = 9148732140255034888L;
    private int m = 4;

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

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

    @Override
    public void setupSearchState(Pair<Gradient, Double> pair) {
        super.setupSearchState(pair);
        INDArray params = (INDArray) searchState.get(PARAMS_KEY);
        searchState.put("s", new LinkedList<INDArray>()); // holds parameters differences
        searchState.put("y", new LinkedList<INDArray>()); // holds gradients differences
        searchState.put("rho", new LinkedList<Double>());
        searchState.put("oldparams", params.dup());

    }

    @Override
    public void preProcessLine() {
    	if(!searchState.containsKey(SEARCH_DIR))
    		searchState.put(SEARCH_DIR, ((INDArray)searchState.get(GRADIENT_KEY)).dup());
    }

    // Numerical Optimization (Nocedal & Wright) section 7.2
    // s = parameters differences (old & current)
    // y = gradient differences (old & current)
    // gamma = initial Hessian approximation (i.e., equiv. to gamma*IdentityMatrix for Hessian)
    // rho = scalar. rho_i = 1/(y_i \dot s_i)
    @Override
    public void postStep(INDArray gradient) {
        INDArray previousParameters = (INDArray) searchState.get("oldparams");
        INDArray parameters = model.params();
        INDArray previousGradient = (INDArray) searchState.get(GRADIENT_KEY);

        LinkedList<Double> rho = (LinkedList) searchState.get("rho");
        LinkedList<INDArray> s = (LinkedList) searchState.get("s");
        LinkedList<INDArray> y = (LinkedList) searchState.get("y");

        double sy = Nd4j.getBlasWrapper().dot(previousParameters, previousGradient) + Nd4j.EPS_THRESHOLD;
        double yy = Nd4j.getBlasWrapper().dot(previousGradient, previousGradient) + Nd4j.EPS_THRESHOLD;

        INDArray sCurrent;
        INDArray yCurrent;
        if( s.size() >= m ){
            //Optimization: Remove old (no longer needed) INDArrays, and use assign for re-use.
            //Better to do this: fewer objects created -> less memory overall + less garbage collection
            sCurrent = s.removeLast();
            yCurrent = y.removeLast();
            rho.removeLast();
            sCurrent.assign(parameters).subi(previousParameters);
            yCurrent.assign(gradient).subi(previousGradient);
        } else {
            //First few iterations. Need to allocate new INDArrays for storage (via copy operation sub)
            sCurrent = parameters.sub(previousParameters);
            yCurrent = gradient.sub(previousGradient);
        }

        rho.addFirst(1.0 / sy);	//Most recent first
        s.addFirst(sCurrent);	//Most recent first. si = currParams - oldParams
        y.addFirst(yCurrent);	//Most recent first. yi = currGradient - oldGradient

        //assert (s.size()==y.size()) : "Gradient and parameter sizes are not equal";
        if(s.size() != y.size())
            throw new IllegalStateException("Gradient and parameter sizes are not equal");

        //In general: have m elements in s,y,rho.
        //But for first few iterations, have less.
        int numVectors = Math.min(m,s.size());

        double[] alpha = new double[numVectors];

        // First work backwards, from the most recent difference vectors
        Iterator<INDArray> sIter = s.iterator();
        Iterator<INDArray> yIter = y.iterator();
        Iterator<Double> rhoIter = rho.iterator();

        //searchDir: first used as equivalent to q as per N&W, then later used as r as per N&W.
        //Re-using existing array for performance reasons
        INDArray searchDir = (INDArray) searchState.get(SEARCH_DIR);
        searchDir.assign(gradient);

        for( int i=0; i<numVectors; i++ ){
            INDArray si = sIter.next();
            INDArray yi = yIter.next();
            double rhoi = rhoIter.next();

            if(si.length() != searchDir.length())
                throw new IllegalStateException("Gradients and parameters length not equal");

            alpha[i] = rhoi * Nd4j.getBlasWrapper().dot(si,searchDir);
            Nd4j.getBlasWrapper().level1().axpy(searchDir.length(),-alpha[i],yi,searchDir);	//q = q-alpha[i]*yi
        }

        //Use Hessian approximation initialization scheme
        //searchDir = H0*q = (gamma*IdentityMatrix)*q = gamma*q
        double gamma = sy / yy;
        searchDir.muli(gamma);

        //Reverse iterators: end to start. Java LinkedLists are doubly-linked,
        // so still O(1) for reverse iteration operations.
        sIter = s.descendingIterator();
        yIter = y.descendingIterator();
        rhoIter = rho.descendingIterator();
        for( int i=0; i<numVectors; i++ ){
            INDArray si = sIter.next();
            INDArray yi = yIter.next();
            double rhoi = rhoIter.next();

            double beta = rhoi * Nd4j.getBlasWrapper().dot(yi, searchDir);		//beta = rho_i * y_i^T * r
            //r = r + s_i * (alpha_i - beta)
            Nd4j.getBlasWrapper().level1().axpy(gradient.length(), alpha[i] - beta, si, searchDir);
        }

        previousParameters.assign(parameters);
        previousGradient.assign(gradient);	//Update gradient. Still in searchState map keyed by GRADIENT_KEY
    }
}

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