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Java example source code file (LineGradientDescent.java)

This example Java source code file (LineGradientDescent.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, indarray, linegradientdescent, model, override, stepfunction, util

The LineGradientDescent.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;

/**
 * Stochastic Gradient Descent with Line Search
 * @author Adam Gibson
 *
 */
public class LineGradientDescent extends BaseOptimizer {
	private static final long serialVersionUID = 6336124657542062284L;

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

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

    @Override
    public void preProcessLine() {
        INDArray gradient = (INDArray) searchState.get(GRADIENT_KEY);
        searchState.put(SEARCH_DIR, gradient.dup());
    }

    @Override
    public void postStep(INDArray gradient) {
        double norm2 = Nd4j.getBlasWrapper().level1().nrm2(gradient);
        if(norm2 > stepMax)
            searchState.put(SEARCH_DIR,gradient.dup().muli(stepMax / norm2));
        else
            searchState.put(SEARCH_DIR, gradient.dup());
        searchState.put(GRADIENT_KEY,gradient.dup());
    }

}

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