examples.cnn.ImagesClassification.java Source code

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Here is the source code for examples.cnn.ImagesClassification.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;

import static examples.cnn.NetworkTrainer.normalize1;
import static examples.cnn.NetworkTrainer.seed;

import java.io.Serializable;
import java.util.Arrays;
import java.util.Map;

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.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;

public class ImagesClassification implements Serializable {

    private static final long serialVersionUID = 1L;

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

    private static final int NUM_CORES = 8;

    public static void main(String[] args) {

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

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

            JavaRDD<String> raw = sc.textFile("data/images-data-rgb.csv");
            String first = raw.first();

            JavaPairRDD<String, String> labelData = raw.filter(f -> f.equals(first) == false).mapToPair(r -> {
                String[] tab = r.split(";");
                return new Tuple2<>(tab[0], tab[1]);
            });

            Map<String, Long> labels = labelData.map(t -> t._1).distinct().zipWithIndex()
                    .mapToPair(t -> new Tuple2<>(t._1, t._2)).collectAsMap();

            log.info("Number of labels {}", labels.size());
            labels.forEach((a, b) -> log.info("{}: {}", a, b));

            NetworkTrainer trainer = new NetworkTrainer.Builder().model(ModelLibrary.net1)
                    .networkToSparkNetwork(net -> new SparkDl4jMultiLayer(sc, net)).numLabels(labels.size())
                    .cores(NUM_CORES).build();

            JavaRDD<Tuple2<INDArray, double[]>> labelsWithData = labelData.map(t -> {
                INDArray label = FeatureUtil.toOutcomeVector(labels.get(t._1).intValue(), labels.size());
                double[] arr = Arrays.stream(t._2.split(" ")).map(normalize1).mapToDouble(Double::doubleValue)
                        .toArray();
                return new Tuple2<>(label, arr);
            });

            JavaRDD<Tuple2<INDArray, double[]>>[] splited = labelsWithData.randomSplit(new double[] { .8, .2 },
                    seed);

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

            JavaRDD<DataSet> plain = splited[0].map(t -> {
                INDArray features = Nd4j.create(t._2, new int[] { 1, t._2.length });
                return new DataSet(features, t._1);
            });

            /*
             * JavaRDD<DataSet> flipped = splited[0].randomSplit(new double[] { .5, .5 }, seed)[0].
             */
            JavaRDD<DataSet> flipped = splited[0].map(t -> {
                double[] arr = t._2;
                int idx = 0;
                double[] farr = new double[arr.length];
                for (int i = 0; i < arr.length; i += trainer.width) {
                    double[] temp = Arrays.copyOfRange(arr, i, i + trainer.width);
                    ArrayUtils.reverse(temp);
                    for (int j = 0; j < trainer.height; ++j) {
                        farr[idx++] = temp[j];
                    }
                }
                INDArray features = Nd4j.create(farr, new int[] { 1, farr.length });
                return new DataSet(features, t._1);
            });

            JavaRDD<DataSet> trainDataset = plain.union(flipped).cache();
            log.info("Number of train images {}", trainDataset.count());

            trainer.train(trainDataset, testDataset);
        }
    }

}