org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator Java Examples

private static DataSetIterator getTrainingData(int batchSize, Random rand) {
    double [] sum = new double[nSamples];
    double [] input1 = new double[nSamples];
    double [] input2 = new double[nSamples];
    for (int i= 0; i< nSamples; i++) {
        input1[i] = MIN_RANGE + (MAX_RANGE - MIN_RANGE) * rand.nextDouble();
        input2[i] =  MIN_RANGE + (MAX_RANGE - MIN_RANGE) * rand.nextDouble();
        sum[i] = input1[i] + input2[i];
    }
    INDArray inputNDArray1 = Nd4j.create(input1, new int[]{nSamples,1});
    INDArray inputNDArray2 = Nd4j.create(input2, new int[]{nSamples,1});
    INDArray inputNDArray = Nd4j.hstack(inputNDArray1,inputNDArray2);
    INDArray outPut = Nd4j.create(sum, new int[]{nSamples, 1});
    DataSet dataSet = new DataSet(inputNDArray, outPut);
    List<DataSet> listDs = dataSet.asList();

    return new ListDataSetIterator(listDs,batchSize);
}
 

private MultiLayerNetwork train(List<Sample> aTrainingData, Object2IntMap<String> aTagset)
    throws IOException
{
    // Configure the neural network
    MultiLayerNetwork model = createConfiguredNetwork(traits, wordVectors.dimensions());

    final int limit = traits.getTrainingSetSizeLimit();
    final int batchSize = traits.getBatchSize();

    // First vectorizing all sentences and then passing them to the model would consume
    // huge amounts of memory. Thus, every sentence is vectorized and then immediately
    // passed on to the model.
    nextEpoch: for (int epoch = 0; epoch < traits.getnEpochs(); epoch++) {
        int sentNum = 0;
        Iterator<Sample> sampleIterator = aTrainingData.iterator();
        while (sampleIterator.hasNext()) {
            List<DataSet> batch = new ArrayList<>();
            while (sampleIterator.hasNext() && batch.size() < batchSize && sentNum < limit) {
                Sample sample = sampleIterator.next();
                DataSet trainingData = vectorize(asList(sample), aTagset, true);
                batch.add(trainingData);
                sentNum++;
            }
            
            model.fit(new ListDataSetIterator<DataSet>(batch, batch.size()));
            log.trace("Epoch {}: processed {} of {} sentences", epoch, sentNum,
                    aTrainingData.size());
            
            if (sentNum >= limit) {
                continue nextEpoch;
            }
        }
    }

    return model;
}
 

@Override
public void train(FederatedDataSet dataSource) {
    DataSet trainingData = (DataSet) dataSource.getNativeDataSet();
    List<DataSet> listDs = trainingData.asList();
    DataSetIterator iterator = new ListDataSetIterator(listDs, BATCH_SIZE);

    //Train the network on the full data set, and evaluate in periodically
    for (int i = 0; i < N_EPOCHS; i++) {
        iterator.reset();
        mNetwork.fit(iterator);
    }
}
 

@Override
public String evaluate(FederatedDataSet federatedDataSet) {
    DataSet testData = (DataSet) federatedDataSet.getNativeDataSet();
    List<DataSet> listDs = testData.asList();
    DataSetIterator iterator = new ListDataSetIterator(listDs, BATCH_SIZE);

    return mNetwork.evaluate(iterator).stats();
}
 

@Override
public String evaluate(FederatedDataSet federatedDataSet) {
    DataSet testData = (DataSet) federatedDataSet.getNativeDataSet();
    List<DataSet> listDs = testData.asList();
    DataSetIterator iterator = new ListDataSetIterator(listDs, BATCH_SIZE);

    Evaluation eval = new Evaluation(OUTPUT_NUM); //create an evaluation object with 10 possible classes
    while (iterator.hasNext()) {
        DataSet next = iterator.next();
        INDArray output = model.output(next.getFeatureMatrix()); //get the networks prediction
        eval.eval(next.getLabels(), output); //check the prediction against the true class
    }

    return eval.stats();
}
 

@Override
public void train(FederatedDataSet federatedDataSet) {
    DataSet trainingData = (DataSet) federatedDataSet.getNativeDataSet();
    List<DataSet> listDs = trainingData.asList();
    DataSetIterator mnistTrain = new ListDataSetIterator(listDs, BATCH_SIZE);
    for (int i = 0; i < N_EPOCHS; i++) {
        model.fit(mnistTrain);
    }
}
 

private static DataSetIterator getTrainingData(int batchSize, Random rand) {
    /**
     * 如何构造我们的训练数据
     * 现有的模型主要是有监督学习
     * 我们的训练集必须有  特征+标签
     * 特征-> x
     * 标签->y
     */
    double [] output = new double[nSamples];
    double [] input = new double[nSamples];
    //随机生成0到3之间的x
    //并且构造 y = 0.5x + 0.1
    //a -> 0.5  b ->0.1
    for (int i= 0; i< nSamples; i++) {
        input[i] = MIN_RANGE + (MAX_RANGE - MIN_RANGE) * rand.nextDouble();

        output[i] = 0.5 * input[i] + 0.1;
    }

    /**
     * 我们nSamples条数据
     * 每条数据只有1个x
     */
    INDArray inputNDArray = Nd4j.create(input, new int[]{nSamples,1});

    INDArray outPut = Nd4j.create(output, new int[]{nSamples, 1});

    /**
     * 构造喂给神经网络的数据集
     * DataSet是将  特征+标签  包装成为一个类
     *
     */
    DataSet dataSet = new DataSet(inputNDArray, outPut);
    List<DataSet> listDs = dataSet.asList();

    return new ListDataSetIterator(listDs,batchSize);
}
 

/** Create a DataSetIterator for training
 * @param x X values
 * @param function Function to evaluate
 * @param batchSize Batch size (number of examples for every call of DataSetIterator.next())
 * @param rng Random number generator (for repeatability)
 */
private static DataSetIterator getTrainingData(final INDArray x, final MathFunction function, final int batchSize, final Random rng) {
    final INDArray y = function.getFunctionValues(x);
    final DataSet allData = new DataSet(x,y);

    final List<DataSet> list = allData.asList();
    Collections.shuffle(list,rng);
    return new ListDataSetIterator(list,batchSize);
}
 

protected static DataSetIterator getDS() {

        List<DataSet> list = new ArrayList<>(5);
        for (int i = 0; i < 5; i++) {
            INDArray f = Nd4j.create(1, 32 * 32 * 3);
            INDArray l = Nd4j.create(1, 10);
            l.putScalar(i, 1.0);
            list.add(new DataSet(f, l));
        }
        return new ListDataSetIterator(list, 5);
    }
 

public void run() throws Exception {
    File trainingOutputFile = new File(trainOutputPath);
    File testOutputFile = new File(testOutputPath);

    if (trainingOutputFile.exists() || testOutputFile.exists()) {
        System.out.println(String.format("Warning: overwriting output files (%s, %s)", trainOutputPath, testOutputPath));

        trainingOutputFile.delete();
        testOutputFile.delete();
    }

    System.out.format("downloading from %s\n", downloadUrl);
    System.out.format("writing training output to %s\n", trainOutputPath);
    System.out.format("writing testing output to %s\n", testOutputPath);

    URL url = new URL(downloadUrl);
    String data = IOUtils.toString(url);
    String[] lines = data.split("\n");
    List<INDArray> arrays = new LinkedList<INDArray>();
    List<Integer> labels = new LinkedList<Integer>();

    for (int i=0; i<lines.length; i++) {
        String line = lines[i];
        String[] cols = line.split("\\s+");

        int label = i / 100;
        INDArray array = Nd4j.zeros(1, 60);

        for (int j=0; j<cols.length; j++) {
            Double d = Double.parseDouble(cols[j]);
            array.putScalar(0, j, d);
        }

        arrays.add(array);
        labels.add(label);
    }

    // Shuffle with **known** seed
    Collections.shuffle(arrays, new Random(12345));
    Collections.shuffle(labels, new Random(12345));

    INDArray trainData = Nd4j.zeros(450, 60);
    INDArray testData = Nd4j.zeros(150, 60);

    for (int i=0; i<arrays.size(); i++) {
        INDArray arr = arrays.get(i);

        if (i < 450) { // Training
            trainData.putRow(i, arr);
        } else { // Test
            testData.putRow(i-450, arr);
        }
    }

    DataSet trainDs = new DataSet(trainData, trainData);
    DataSetIterator trainIt = new ListDataSetIterator(trainDs.asList());

    DataSet testDs = new DataSet(testData, testData);
    DataSetIterator testIt = new ListDataSetIterator(testDs.asList());

    // Fit normalizer on training data only!
    DataNormalization normalizer = dataNormalizer.getNormalizer();
    normalizer.fit(trainIt);

    // Print out basic summary stats
    switch (normalizer.getType()) {
        case STANDARDIZE:
            System.out.format("Normalizer - Standardize:\n  mean=%s\n  std= %s\n",
                    ((NormalizerStandardize)normalizer).getMean(),
                    ((NormalizerStandardize)normalizer).getStd());
    }

    // Use same normalizer for both
    trainIt.setPreProcessor(normalizer);
    testIt.setPreProcessor(normalizer);

    String trainOutput = toCsv(trainIt, labels.subList(0, 450), new int[]{1, 60});
    String testOutput = toCsv(testIt, labels.subList(450, 600), new int[]{1, 60});

    FileUtils.write(trainingOutputFile, trainOutput);
    System.out.format("wrote normalized training file to %s\n", trainingOutputFile);

    FileUtils.write(testOutputFile, testOutput);
    System.out.format("wrote normalized test file to %s\n", testOutputFile);

}
 

@Test
    public void testIris() {

        // Network config
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()

                        .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).seed(42)
                        .updater(new Sgd(1e-6)).list()
                        .layer(0, new DenseLayer.Builder().nIn(4).nOut(2).activation(Activation.TANH)
                                        .weightInit(WeightInit.XAVIER).build())
                        .layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
                                        LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3).weightInit(WeightInit.XAVIER)
                                                        .activation(Activation.SOFTMAX).build())

                        .build();

        // Instantiate model
        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
        model.addListeners(new ScoreIterationListener(1));

        // Train-test split
        DataSetIterator iter = new IrisDataSetIterator(150, 150);
        DataSet next = iter.next();
        next.shuffle();
        SplitTestAndTrain trainTest = next.splitTestAndTrain(5, new Random(42));

        // Train
        DataSet train = trainTest.getTrain();
        train.normalizeZeroMeanZeroUnitVariance();

        // Test
        DataSet test = trainTest.getTest();
        test.normalizeZeroMeanZeroUnitVariance();
        INDArray testFeature = test.getFeatures();
        INDArray testLabel = test.getLabels();

        // Fitting model
        model.fit(train);
        // Get predictions from test feature
        INDArray testPredictedLabel = model.output(testFeature);

        // Eval with class number
        org.nd4j.evaluation.classification.Evaluation eval = new org.nd4j.evaluation.classification.Evaluation(3); //// Specify class num here
        eval.eval(testLabel, testPredictedLabel);
        double eval1F1 = eval.f1();
        double eval1Acc = eval.accuracy();

        // Eval without class number
        org.nd4j.evaluation.classification.Evaluation eval2 = new org.nd4j.evaluation.classification.Evaluation(); //// No class num
        eval2.eval(testLabel, testPredictedLabel);
        double eval2F1 = eval2.f1();
        double eval2Acc = eval2.accuracy();

        //Assert the two implementations give same f1 and accuracy (since one batch)
        assertTrue(eval1F1 == eval2F1 && eval1Acc == eval2Acc);

        org.nd4j.evaluation.classification.Evaluation evalViaMethod = model.evaluate(new ListDataSetIterator<>(Collections.singletonList(test)));
        checkEvaluationEquality(eval, evalViaMethod);

//        System.out.println(eval.getConfusionMatrix().toString());
//        System.out.println(eval.getConfusionMatrix().toCSV());
//        System.out.println(eval.getConfusionMatrix().toHTML());
//        System.out.println(eval.confusionToString());

        eval.getConfusionMatrix().toString();
        eval.getConfusionMatrix().toCSV();
        eval.getConfusionMatrix().toHTML();
        eval.confusionToString();
    }
 

@Test(timeout = 60000L)
public void testEvaluationAndRoc() {
    for( int evalWorkers : new int[]{1, 4, 8}) {
        DataSetIterator iter = new IrisDataSetIterator(5, 150);

        //Make a 2-class version of iris:
        List<DataSet> l = new ArrayList<>();
        iter.reset();
        while (iter.hasNext()) {
            DataSet ds = iter.next();
            INDArray newL = Nd4j.create(ds.getLabels().size(0), 2);
            newL.putColumn(0, ds.getLabels().getColumn(0));
            newL.putColumn(1, ds.getLabels().getColumn(1));
            newL.getColumn(1).addi(ds.getLabels().getColumn(2));
            ds.setLabels(newL);
            l.add(ds);
        }

        iter = new ListDataSetIterator<>(l);

        ComputationGraph cg = getBasicNetIris2Class();

        Evaluation e = cg.evaluate(iter);
        ROC roc = cg.evaluateROC(iter, 32);


        SparkComputationGraph scg = new SparkComputationGraph(sc, cg, null);
        scg.setDefaultEvaluationWorkers(evalWorkers);


        JavaRDD<DataSet> rdd = sc.parallelize(l);
        rdd = rdd.repartition(20);

        Evaluation e2 = scg.evaluate(rdd);
        ROC roc2 = scg.evaluateROC(rdd);


        assertEquals(e2.accuracy(), e.accuracy(), 1e-3);
        assertEquals(e2.f1(), e.f1(), 1e-3);
        assertEquals(e2.getNumRowCounter(), e.getNumRowCounter(), 1e-3);
        assertEquals(e2.falseNegatives(), e.falseNegatives());
        assertEquals(e2.falsePositives(), e.falsePositives());
        assertEquals(e2.trueNegatives(), e.trueNegatives());
        assertEquals(e2.truePositives(), e.truePositives());
        assertEquals(e2.precision(), e.precision(), 1e-3);
        assertEquals(e2.recall(), e.recall(), 1e-3);
        assertEquals(e2.getConfusionMatrix(), e.getConfusionMatrix());

        assertEquals(roc.calculateAUC(), roc2.calculateAUC(), 1e-5);
        assertEquals(roc.calculateAUCPR(), roc2.calculateAUCPR(), 1e-5);
    }
}
 

/**
 * The main method.
 * @param args Not used.
 */
public static void main(String[] args) {
    try {
        int seed = 43;
        double learningRate = 1e-2;
        int nEpochs = 50;
        int batchSize = 500;

        // Setup training data.
        System.out.println("Please wait, reading MNIST training data.");
        String dir = System.getProperty("user.dir");
        MNISTReader trainingReader = MNIST.loadMNIST(dir, true);
        MNISTReader validationReader = MNIST.loadMNIST(dir, false);

        DataSet trainingSet = trainingReader.getData();
        DataSet validationSet = validationReader.getData();

        DataSetIterator trainSetIterator = new ListDataSetIterator(trainingSet.asList(), batchSize);
        DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationReader.getNumRows());

        System.out.println("Training set size: " + trainingReader.getNumImages());
        System.out.println("Validation set size: " + validationReader.getNumImages());

        System.out.println(trainingSet.get(0).getFeatures().size(1));
        System.out.println(validationSet.get(0).getFeatures().size(1));

        int numInputs = trainingReader.getNumCols()*trainingReader.getNumRows();
        int numOutputs = 10;
        int numHiddenNodes = 200;

        // Create neural network.
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .iterations(1)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .learningRate(learningRate)
                .updater(Updater.NESTEROVS).momentum(0.9)
                .regularization(true).dropOut(0.50)
                .list(2)
                .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes)
                        .weightInit(WeightInit.XAVIER)
                        .activation("relu")
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)
                        .weightInit(WeightInit.XAVIER)
                        .activation("softmax")
                        .nIn(numHiddenNodes).nOut(numOutputs).build())
                .pretrain(false).backprop(true).build();


        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
        model.setListeners(new ScoreIterationListener(1));

        // Define when we want to stop training.
        EarlyStoppingModelSaver saver = new InMemoryModelSaver();
        EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder()
                //.epochTerminationConditions(new MaxEpochsTerminationCondition(10))
                .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(5))
                .evaluateEveryNEpochs(1)
                .scoreCalculator(new DataSetLossCalculator(validationSetIterator, true))     //Calculate test set score
                .modelSaver(saver)
                .build();
        EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator);

        // Train and display result.
        EarlyStoppingResult result = trainer.fit();
        System.out.println("Termination reason: " + result.getTerminationReason());
        System.out.println("Termination details: " + result.getTerminationDetails());
        System.out.println("Total epochs: " + result.getTotalEpochs());
        System.out.println("Best epoch number: " + result.getBestModelEpoch());
        System.out.println("Score at best epoch: " + result.getBestModelScore());

        model = saver.getBestModel();

        // Evaluate
        Evaluation eval = new Evaluation(numOutputs);
        validationSetIterator.reset();

        for (int i = 0; i < validationSet.numExamples(); i++) {
            DataSet t = validationSet.get(i);
            INDArray features = t.getFeatureMatrix();
            INDArray labels = t.getLabels();
            INDArray predicted = model.output(features, false);
            eval.eval(labels, predicted);
        }

        //Print the evaluation statistics
        System.out.println(eval.stats());
    } catch(Exception ex) {
        ex.printStackTrace();
    }

}
 

/**
 * The main method.
 * @param args Not used.
 */
public static void main(String[] args) {
    try {
        int seed = 43;
        double learningRate = 1e-2;
        int nEpochs = 50;
        int batchSize = 500;

        // Setup training data.
        System.out.println("Please wait, reading MNIST training data.");
        String dir = System.getProperty("user.dir");
        MNISTReader trainingReader = MNIST.loadMNIST(dir, true);
        MNISTReader validationReader = MNIST.loadMNIST(dir, false);

        DataSet trainingSet = trainingReader.getData();
        DataSet validationSet = validationReader.getData();

        DataSetIterator trainSetIterator = new ListDataSetIterator(trainingSet.asList(), batchSize);
        DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationReader.getNumRows());

        System.out.println("Training set size: " + trainingReader.getNumImages());
        System.out.println("Validation set size: " + validationReader.getNumImages());

        System.out.println(trainingSet.get(0).getFeatures().size(1));
        System.out.println(validationSet.get(0).getFeatures().size(1));

        int numInputs = trainingReader.getNumCols()*trainingReader.getNumRows();
        int numOutputs = 10;
        int numHiddenNodes = 100;

        // Create neural network.
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .iterations(1)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .learningRate(learningRate)
                .updater(Updater.NESTEROVS).momentum(0.9)
                .list(2)
                .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes)
                        .weightInit(WeightInit.XAVIER)
                        .activation("relu")
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)
                        .weightInit(WeightInit.XAVIER)
                        .activation("softmax")
                        .nIn(numHiddenNodes).nOut(numOutputs).build())
                .pretrain(false).backprop(true).build();


        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
        model.setListeners(new ScoreIterationListener(1));

        // Define when we want to stop training.
        EarlyStoppingModelSaver saver = new InMemoryModelSaver();
        EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder()
                //.epochTerminationConditions(new MaxEpochsTerminationCondition(10))
                .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(5))
                .evaluateEveryNEpochs(1)
                .scoreCalculator(new DataSetLossCalculator(validationSetIterator, true))     //Calculate test set score
                .modelSaver(saver)
                .build();
        EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator);

        // Train and display result.
        EarlyStoppingResult result = trainer.fit();
        System.out.println("Termination reason: " + result.getTerminationReason());
        System.out.println("Termination details: " + result.getTerminationDetails());
        System.out.println("Total epochs: " + result.getTotalEpochs());
        System.out.println("Best epoch number: " + result.getBestModelEpoch());
        System.out.println("Score at best epoch: " + result.getBestModelScore());

        model = saver.getBestModel();

        // Evaluate
        Evaluation eval = new Evaluation(numOutputs);
        validationSetIterator.reset();

        for (int i = 0; i < validationSet.numExamples(); i++) {
            DataSet t = validationSet.get(i);
            INDArray features = t.getFeatureMatrix();
            INDArray labels = t.getLabels();
            INDArray predicted = model.output(features, false);
            eval.eval(labels, predicted);
        }

        //Print the evaluation statistics
        System.out.println(eval.stats());
    } catch(Exception ex) {
        ex.printStackTrace();
    }

}
 

/**
 * The main method.
 * @param args Not used.
 */
public static void main(String[] args) {
    try {
        int seed = 43;
        double learningRate = 0.1;
        int splitTrainNum = (int) (150 * .75);

        int numInputs = 4;
        int numOutputs = 3;
        int numHiddenNodes = 50;

        // Setup training data.
        final InputStream istream = LearnIrisBackprop.class.getResourceAsStream("/iris.csv");
        if( istream==null ) {
            System.out.println("Cannot access data set, make sure the resources are available.");
            System.exit(1);
        }
        final NormalizeDataSet ds = NormalizeDataSet.load(istream);
        final CategoryMap species = ds.encodeOneOfN(4); // species is column 4
        istream.close();

        DataSet next = ds.extractSupervised(0, 4, 4, 3);
        next.shuffle();

        // Training and validation data split
        SplitTestAndTrain testAndTrain = next.splitTestAndTrain(splitTrainNum, new Random(seed));
        DataSet trainSet = testAndTrain.getTrain();
        DataSet validationSet = testAndTrain.getTest();

        DataSetIterator trainSetIterator = new ListDataSetIterator(trainSet.asList(), trainSet.numExamples());

        DataSetIterator validationSetIterator = new ListDataSetIterator(validationSet.asList(), validationSet.numExamples());

        // Create neural network.
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .iterations(1)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .learningRate(learningRate)
                .updater(Updater.NESTEROVS).momentum(0.9)
                .list(2)
                .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes)
                        .weightInit(WeightInit.XAVIER)
                        .activation("relu")
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)
                        .weightInit(WeightInit.XAVIER)
                        .activation("softmax")
                        .nIn(numHiddenNodes).nOut(numOutputs).build())
                .pretrain(false).backprop(true).build();


        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
        model.setListeners(new ScoreIterationListener(1));

        // Define when we want to stop training.
        EarlyStoppingModelSaver saver = new InMemoryModelSaver();
        EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder()
                .epochTerminationConditions(new MaxEpochsTerminationCondition(500)) //Max of 50 epochs
                .epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(25))
                .evaluateEveryNEpochs(1)
                .scoreCalculator(new DataSetLossCalculator(validationSetIterator, true))     //Calculate test set score
                .modelSaver(saver)
                .build();
        EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, conf, trainSetIterator);

        // Train and display result.
        EarlyStoppingResult result = trainer.fit();
        System.out.println("Termination reason: " + result.getTerminationReason());
        System.out.println("Termination details: " + result.getTerminationDetails());
        System.out.println("Total epochs: " + result.getTotalEpochs());
        System.out.println("Best epoch number: " + result.getBestModelEpoch());
        System.out.println("Score at best epoch: " + result.getBestModelScore());

        model = saver.getBestModel();

        // Evaluate
        Evaluation eval = new Evaluation(numOutputs);
        validationSetIterator.reset();

        for (int i = 0; i < validationSet.numExamples(); i++) {
            DataSet t = validationSet.get(i);
            INDArray features = t.getFeatureMatrix();
            INDArray labels = t.getLabels();
            INDArray predicted = model.output(features, false);
            System.out.println(features + ":Prediction("+findSpecies(labels,species)
                    +"):Actual("+findSpecies(predicted,species)+")" + predicted );
            eval.eval(labels, predicted);
        }

        //Print the evaluation statistics
        System.out.println(eval.stats());
    } catch(Exception ex) {
        ex.printStackTrace();
    }
}