#BAEL-18260 Restructure ml and deeplearning4j modules
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Alessio Stalla
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package com.baeldung.logreg;
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import java.io.File;
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import java.util.HashMap;
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import java.util.Map;
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import java.util.Random;
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import org.datavec.api.io.labels.ParentPathLabelGenerator;
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import org.datavec.api.split.FileSplit;
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import org.datavec.image.loader.NativeImageLoader;
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import org.datavec.image.recordreader.ImageRecordReader;
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import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.conf.inputs.InputType;
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import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
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import org.deeplearning4j.nn.conf.layers.DenseLayer;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
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import org.deeplearning4j.util.ModelSerializer;
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import org.nd4j.evaluation.classification.Evaluation;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
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import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
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import org.nd4j.linalg.learning.config.Nesterovs;
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import org.nd4j.linalg.lossfunctions.LossFunctions;
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import org.nd4j.linalg.schedule.MapSchedule;
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import org.nd4j.linalg.schedule.ScheduleType;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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/**
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* Handwritten digit image classification based on LeNet-5 architecture by Yann LeCun.
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*
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* This code accompanies the article "Logistic regression in Java" and is heavily based on
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* <a href="https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/mnist/MnistClassifier.java">MnistClassifier</a>.
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* Some minor changes have been made in order to make article's flow smoother.
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*
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*/
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public class MnistClassifier {
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private static final Logger logger = LoggerFactory.getLogger(MnistClassifier.class);
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private static final String basePath = System.getProperty("java.io.tmpdir") + "mnist" + File.separator;
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private static final File modelPath = new File(basePath + "mnist-model.zip");
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private static final String dataUrl = "http://github.com/myleott/mnist_png/raw/master/mnist_png.tar.gz";
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public static void main(String[] args) throws Exception {
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// input image sizes in pixels
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int height = 28;
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int width = 28;
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// input image colour depth (1 for gray scale images)
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int channels = 1;
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// the number of output classes
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int outputClasses = 10;
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// number of samples that will be propagated through the network in each iteration
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int batchSize = 54;
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// total number of training epochs
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int epochs = 1;
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// initialize a pseudorandom number generator
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int seed = 1234;
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Random randNumGen = new Random(seed);
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final String path = basePath + "mnist_png" + File.separator;
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if (!new File(path).exists()) {
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logger.info("Downloading data {}", dataUrl);
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String localFilePath = basePath + "mnist_png.tar.gz";
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File file = new File(localFilePath);
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if (!file.exists()) {
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file.getParentFile()
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.mkdirs();
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Utils.downloadAndSave(dataUrl, file);
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Utils.extractTarArchive(file, basePath);
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}
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} else {
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logger.info("Using the local data from folder {}", path);
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}
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logger.info("Vectorizing the data from folder {}", path);
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// vectorization of train data
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File trainData = new File(path + "training");
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FileSplit trainSplit = new FileSplit(trainData, NativeImageLoader.ALLOWED_FORMATS, randNumGen);
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// use parent directory name as the image label
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ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator();
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ImageRecordReader trainRR = new ImageRecordReader(height, width, channels, labelMaker);
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trainRR.initialize(trainSplit);
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DataSetIterator train = new RecordReaderDataSetIterator(trainRR, batchSize, 1, outputClasses);
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// pixel values from 0-255 to 0-1 (min-max scaling)
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DataNormalization imageScaler = new ImagePreProcessingScaler();
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imageScaler.fit(train);
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train.setPreProcessor(imageScaler);
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// vectorization of test data
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File testData = new File(path + "testing");
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FileSplit testSplit = new FileSplit(testData, NativeImageLoader.ALLOWED_FORMATS, randNumGen);
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ImageRecordReader testRR = new ImageRecordReader(height, width, channels, labelMaker);
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testRR.initialize(testSplit);
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DataSetIterator test = new RecordReaderDataSetIterator(testRR, batchSize, 1, outputClasses);
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// same normalization for better results
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test.setPreProcessor(imageScaler);
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logger.info("Network configuration and training...");
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// reduce the learning rate as the number of training epochs increases
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// iteration #, learning rate
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Map<Integer, Double> learningRateSchedule = new HashMap<>();
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learningRateSchedule.put(0, 0.06);
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learningRateSchedule.put(200, 0.05);
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learningRateSchedule.put(600, 0.028);
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learningRateSchedule.put(800, 0.0060);
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learningRateSchedule.put(1000, 0.001);
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final ConvolutionLayer layer1 = new ConvolutionLayer.Builder(5, 5).nIn(channels)
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.stride(1, 1)
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.nOut(20)
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.activation(Activation.IDENTITY)
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.build();
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final SubsamplingLayer layer2 = new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2)
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.stride(2, 2)
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.build();
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// nIn need not specified in later layers
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final ConvolutionLayer layer3 = new ConvolutionLayer.Builder(5, 5).stride(1, 1)
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.nOut(50)
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.activation(Activation.IDENTITY)
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.build();
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final DenseLayer layer4 = new DenseLayer.Builder().activation(Activation.RELU)
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.nOut(500)
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.build();
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final OutputLayer layer5 = new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(outputClasses)
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.activation(Activation.SOFTMAX)
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.build();
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final MultiLayerConfiguration config = new NeuralNetConfiguration.Builder().seed(seed)
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.l2(0.0005) // ridge regression value
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.updater(new Nesterovs(new MapSchedule(ScheduleType.ITERATION, learningRateSchedule)))
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.weightInit(WeightInit.XAVIER)
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.list()
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.layer(layer1)
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.layer(layer2)
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.layer(layer3)
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.layer(layer2)
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.layer(layer4)
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.layer(layer5)
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.setInputType(InputType.convolutionalFlat(height, width, channels))
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.build();
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final MultiLayerNetwork model = new MultiLayerNetwork(config);
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model.init();
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model.setListeners(new ScoreIterationListener(100));
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logger.info("Total num of params: {}", model.numParams());
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// evaluation while training (the score should go down)
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for (int i = 0; i < epochs; i++) {
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model.fit(train);
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logger.info("Completed epoch {}", i);
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train.reset();
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test.reset();
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}
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Evaluation eval = model.evaluate(test);
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logger.info(eval.stats());
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ModelSerializer.writeModel(model, modelPath, true);
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logger.info("The MINIST model has been saved in {}", modelPath.getPath());
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}
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}
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