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Java example source code file (BaseSparkTest.java)

This example Java source code file (BaseSparkTest.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

after, arraylist, basesparktest, before, dataset, indarray, javardd, javasparkcontext, multilayerconfiguration, parameteraveragingtrainingmaster, random, serializable, sparkconf, sparkdl4jmultilayer, util

The BaseSparkTest.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.spark;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.spark.impl.multilayer.SparkDl4jMultiLayer;
import org.deeplearning4j.spark.impl.paramavg.ParameterAveragingTrainingMaster;
import org.junit.After;
import org.junit.Before;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;


/**
 * Created by agibsonccc on 1/23/15.
 */
public abstract class BaseSparkTest  implements Serializable
{
    protected transient JavaSparkContext sc;
    protected transient INDArray labels;
    protected transient INDArray input;
    protected transient INDArray rowSums;
    protected transient int nRows = 200;
    protected transient int nIn = 4;
    protected transient int nOut = 3;
    protected transient DataSet data;
    protected transient JavaRDD<DataSet> sparkData;

    @Before
    public void before() {

        sc = getContext();
        Random r = new Random(12345);
        labels = Nd4j.create(nRows, nOut);
        input = Nd4j.rand(nRows,nIn);
        rowSums = input.sum(1);
        input.diviColumnVector(rowSums);

        for( int i=0; i<nRows; i++ ){
            int x1 = r.nextInt(nOut);
            labels.putScalar(new int[]{i, x1}, 1.0);
        }

        sparkData = getBasicSparkDataSet(nRows, input, labels);
    }

    @After
    public void after() {
        sc.close();
        sc = null;
    }

    /**
     *
     * @return
     */
    public JavaSparkContext getContext() {
        if(sc != null)
            return sc;
        // set to test mode
        SparkConf sparkConf = new SparkConf()
                .setMaster("local[" + numExecutors() + "]")
                .setAppName("sparktest");


        sc = new JavaSparkContext(sparkConf);

        return sc;
    }

    protected JavaRDD<DataSet> getBasicSparkDataSet(int nRows, INDArray input, INDArray labels){
        List<DataSet> list = new ArrayList<>();
        for( int i=0; i<nRows; i++ ){
            INDArray inRow = input.getRow(i).dup();
            INDArray outRow = labels.getRow(i).dup();

            DataSet ds = new DataSet(inRow,outRow);
            list.add(ds);
        }
        list.iterator();

        data = new DataSet().merge(list);
        return sc.parallelize(list);
    }


    protected SparkDl4jMultiLayer getBasicNetwork(){
        return new SparkDl4jMultiLayer(sc,getBasicConf(),
                new ParameterAveragingTrainingMaster(true,numExecutors(),10,1,0));
    }

    protected int numExecutors(){
        int numProc = Runtime.getRuntime().availableProcessors();
        return Math.min(4, numProc);
    }

    protected MultiLayerConfiguration getBasicConf(){
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(123)
                .updater(Updater.NESTEROVS)
                .learningRate(0.1)
                .momentum(0.9)
                .list()
                .layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder()
                        .nIn(nIn).nOut(3)
                        .activation("tanh").build())
                .layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                        .nIn(3).nOut(nOut)
                        .activation("softmax")
                        .build())
                .backprop(true)
                .pretrain(false)
                .build();

        return conf;
    }


}

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