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Java example source code file (StochasticGradientDescent.java)

This example Java source code file (StochasticGradientDescent.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.

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Java - Java tags/keywords

baseoptimizer, collection, indarray, model, override, pair, stepfunction, stochasticgradientdescent, util

The StochasticGradientDescent.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.Layer;
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 java.util.Collection;
import java.util.Map;

 * Stochastic Gradient Descent
 * Standard fix step size
 * No line search
 * @author Adam Gibson
public class StochasticGradientDescent extends BaseOptimizer {

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

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

    public boolean optimize() {
        for(int i = 0; i < conf.getNumIterations(); i++) {

            Pair<Gradient,Double> pair = gradientAndScore();
            Gradient gradient = pair.getFirst();

            INDArray params = model.params();
            //Note: model.params() is always in-place for MultiLayerNetwork and ComputationGraph, hence no setParams is necessary there
            //However: for pretrain layers, params are NOT a view. Thus a setParams call is necessary
            //But setParams should be a no-op for MLN and CG

            for(IterationListener listener : iterationListeners)
                listener.iterationDone(model, i);

            checkTerminalConditions(pair.getFirst().gradient(), oldScore, score, i);

        return true;

    public void preProcessLine() {

    public void postStep(INDArray gradient) {

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