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Java example source code file (Solver.java)

This example Java source code file (Solver.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

builder, collection, conjugategradient, convexoptimizer, illegalstateexception, lbfgs, line_gradient_descent, linegradientdescent, list, model, neuralnetconfiguration, solver, stochastic_gradient_descent, stochasticgradientdescent, util

The Solver.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;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.List;

import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.optimize.api.ConvexOptimizer;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.optimize.api.StepFunction;
import org.deeplearning4j.optimize.solvers.ConjugateGradient;
import org.deeplearning4j.optimize.solvers.LineGradientDescent;
import org.deeplearning4j.optimize.solvers.StochasticGradientDescent;
import org.deeplearning4j.optimize.solvers.LBFGS;
import org.deeplearning4j.optimize.stepfunctions.StepFunctions;

 * Generic purpose solver
 * @author Adam Gibson
public class Solver {
    private NeuralNetConfiguration conf;
    private Collection<IterationListener> listeners;
    private Model model;
    private ConvexOptimizer optimizer;
    private StepFunction stepFunction;

    public void optimize() {
        if(optimizer == null)
            optimizer = getOptimizer();


    public ConvexOptimizer getOptimizer() {
        if(optimizer != null) return optimizer;
        switch(conf.getOptimizationAlgo()) {
            case LBFGS:
                optimizer = new LBFGS(conf,stepFunction,listeners,model);
            case LINE_GRADIENT_DESCENT:
                optimizer = new LineGradientDescent(conf,stepFunction,listeners,model);
            case CONJUGATE_GRADIENT:
                optimizer = new ConjugateGradient(conf,stepFunction,listeners,model);
                optimizer = new StochasticGradientDescent(conf,stepFunction,listeners,model);
                throw new IllegalStateException("No optimizer found");
        return optimizer;

    public void setListeners(Collection<IterationListener> listeners){
        this.listeners = listeners;
        if(optimizer != null ) optimizer.setListeners(listeners);

    public static class Builder {
        private NeuralNetConfiguration conf;
        private Model model;
        private List<IterationListener> listeners = new ArrayList<>();

        public Builder configure(NeuralNetConfiguration conf) {
            this.conf = conf;
            return this;
        public Builder listener(IterationListener... listeners) {
            return this;

        public Builder listeners(Collection<IterationListener> listeners) {
            return this;
        public Builder model(Model model) {
            this.model = model;
            return this;

        public Solver build() {
            Solver solver = new Solver();
            solver.conf = conf;
            solver.stepFunction = StepFunctions.createStepFunction(conf.getStepFunction());
            solver.model = model;
            solver.listeners = listeners;
            return solver;


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