sample code in DL4J and MNIST dataset

mnist_dl4j.java

String dataset_home = System.getProperty("user.home") + File.separator + "datasets" + File.separator;
String mnist_home = dataset_home + "mnist_png" + File.separator;
String trainPath = mnist_home + "training";
String testPath = mnist_home + "testing";

int numRows = 28; // height
int numColumns = 28; // width
int channels = 1; // depth
int outputNum = 10;
int batchSize = 128;
int seed = 123;
Random rng = new Random(seed);
int numEpochs = 1;
String pathToSaveModel = "D:\\";
boolean saveModel = true;

File trainData = new File(trainPath);
        FileSplit train = new FileSplit(trainData, NativeImageLoader.ALLOWED_FORMATS, rng);

        ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator();

        ImageRecordReader recordReader = new ImageRecordReader(numRows, numColumns, channels, labelMaker);

        recordReader.initialize(train);

        DataSetIterator mnistTrain = new RecordReaderDataSetIterator(recordReader, batchSize, 1, outputNum);

        DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
        scaler.fit(mnistTrain);
        mnistTrain.setPreProcessor(scaler);

        System.out.println("contruyendo el modelo");

        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(new Nesterovs())
//                .updater(new Nesterovs(0.006, 0.9))
                .l2(1e-4)
                .list()
                .layer(0, new DenseLayer.Builder()
                        .nIn(numRows * numColumns)
                        .nOut(100)
                        .activation(Activation.RELU)
                        .weightInit(WeightInit.XAVIER)
                        .build()
                )
                .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nOut(outputNum)
                        .activation(Activation.SOFTMAX)
                        .weightInit(WeightInit.XAVIER)
                        .build()
                )
                .pretrain(false).backprop(true)
                .setInputType(InputType.convolutional(numRows, numColumns, channels))
                .build();

        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();

        model.setListeners(new ScoreIterationListener(100));

        System.out.println("entrenando el modelo");
        for (int i = 0; i < numEpochs; i++) {
            model.fit(mnistTrain);
        }
        if (saveModel)
            model.save(new File(pathToSaveModel + "modelDL4J.zip"));

        System.out.println("evaluando el modelo");
        File testData = new File(testPath);
        FileSplit test = new FileSplit(testData, NativeImageLoader.ALLOWED_FORMATS, rng);
        recordReader.reset();

        recordReader.initialize(test);
        DataSetIterator mnistTest = new RecordReaderDataSetIterator(recordReader, batchSize, 1, outputNum);
        scaler.fit(mnistTest);
        mnistTest.setPreProcessor(scaler);

        Evaluation evaluation = new Evaluation(outputNum);
        while (mnistTest.hasNext()) {
            DataSet next = mnistTest.next();
            INDArray output = model.output(next.getFeatureMatrix());
            evaluation.eval(next.getLabels(), output);
        }

        System.out.println(evaluation.stats());