examples.cnn.cifar.Cifar10Classification.java Source code

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Here is the source code for examples.cnn.cifar.Cifar10Classification.java

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/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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 examples.cnn.cifar;

import static examples.cnn.NetworkTrainer.normalize2;

import java.io.Serializable;
import java.util.Arrays;
import java.util.function.Function;

import org.apache.commons.lang.ArrayUtils;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.input.PortableDataStream;
import org.deeplearning4j.spark.impl.multilayer.SparkDl4jMultiLayer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.util.FeatureUtil;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import scala.Tuple2;
import examples.cnn.ModelLibrary;
import examples.cnn.NetworkTrainer;
import examples.utils.CifarReader;

public class Cifar10Classification implements Serializable {

    private static final long serialVersionUID = 1L;

    private static final Logger log = LoggerFactory.getLogger(Cifar10Classification.class);

    private static final int NUM_CORES = 8;

    private static Function<String, String> extractFileName = s -> s.substring(1 + s.lastIndexOf('/'));

    public static void main(String[] args) {

        CifarReader.downloadAndExtract();

        int numLabels = 10;

        SparkConf conf = new SparkConf();
        conf.setMaster(String.format("local[%d]", NUM_CORES));
        conf.setAppName("Cifar-10 CNN Classification");
        conf.set(SparkDl4jMultiLayer.AVERAGE_EACH_ITERATION, String.valueOf(true));

        try (JavaSparkContext sc = new JavaSparkContext(conf)) {

            NetworkTrainer trainer = new NetworkTrainer.Builder().model(ModelLibrary.net2)
                    .networkToSparkNetwork(net -> new SparkDl4jMultiLayer(sc, net)).numLabels(numLabels)
                    .cores(NUM_CORES).build();

            JavaPairRDD<String, PortableDataStream> files = sc.binaryFiles("data/cifar-10-batches-bin");

            JavaRDD<double[]> imagesTrain = files
                    .filter(f -> ArrayUtils.contains(CifarReader.TRAIN_DATA_FILES, extractFileName.apply(f._1)))
                    .flatMap(f -> CifarReader.rawDouble(f._2.open()));

            JavaRDD<double[]> imagesTest = files
                    .filter(f -> CifarReader.TEST_DATA_FILE.equals(extractFileName.apply(f._1)))
                    .flatMap(f -> CifarReader.rawDouble(f._2.open()));

            JavaRDD<DataSet> testDataset = imagesTest.map(i -> {
                INDArray label = FeatureUtil.toOutcomeVector(Double.valueOf(i[0]).intValue(), numLabels);
                double[] arr = Arrays.stream(ArrayUtils.remove(i, 0)).boxed().map(normalize2)
                        .mapToDouble(Double::doubleValue).toArray();
                INDArray features = Nd4j.create(arr, new int[] { 1, arr.length });
                return new DataSet(features, label);
            }).cache();
            log.info("Number of test images {}", testDataset.count());

            JavaPairRDD<INDArray, double[]> labelsWithDataTrain = imagesTrain.mapToPair(i -> {
                INDArray label = FeatureUtil.toOutcomeVector(Double.valueOf(i[0]).intValue(), numLabels);
                double[] arr = Arrays.stream(ArrayUtils.remove(i, 0)).boxed().map(normalize2)
                        .mapToDouble(Double::doubleValue).toArray();
                return new Tuple2<>(label, arr);
            });

            JavaRDD<DataSet> flipped = labelsWithDataTrain.map(t -> {
                double[] arr = t._2;
                int idx = 0;
                double[] farr = new double[arr.length];
                for (int i = 0; i < arr.length; i += trainer.getWidth()) {
                    double[] temp = Arrays.copyOfRange(arr, i, i + trainer.getWidth());
                    ArrayUtils.reverse(temp);
                    for (int j = 0; j < trainer.getHeight(); ++j) {
                        farr[idx++] = temp[j];
                    }
                }
                INDArray features = Nd4j.create(farr, new int[] { 1, farr.length });
                return new DataSet(features, t._1);
            });

            JavaRDD<DataSet> trainDataset = labelsWithDataTrain.map(t -> {
                INDArray features = Nd4j.create(t._2, new int[] { 1, t._2.length });
                return new DataSet(features, t._1);
            }).union(flipped).cache();
            log.info("Number of train images {}", trainDataset.count());

            trainer.train(trainDataset, testDataset);
        }
    }

}