UCISequenceClassificationExample test throws no such element

UCISequenceClassificationExample.java

    public static void main(String[] args) throws Exception {
        downloadUCIData();

        // ----- Load the training data -----
        //Note that we have 450 training files for features: train/features/0.csv through train/features/449.csv
        SequenceRecordReader trainFeatures = new CSVSequenceRecordReader();
        trainFeatures.initialize(new NumberedFileInputSplit(featuresDirTrain.getAbsolutePath() + "/%d.csv", 0, 449));
        SequenceRecordReader trainLabels = new CSVSequenceRecordReader();
        trainLabels.initialize(new NumberedFileInputSplit(labelsDirTrain.getAbsolutePath() + "/%d.csv", 0, 449));

        int miniBatchSize = 10;
        int numLabelClasses = 6;
        DataSetIterator trainData = new SequenceRecordReaderDataSetIterator(trainFeatures, trainLabels, miniBatchSize, numLabelClasses,
            false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);

        //Normalize the training data
        DataNormalization normalizer = new NormalizerStandardize();
        normalizer.fit(trainData);              //Collect training data statistics
        trainData.reset();

        //Use previously collected statistics to normalize on-the-fly. Each DataSet returned by 'trainData' iterator will be normalized
        trainData.setPreProcessor(normalizer);


        // ----- Load the test data -----
        //Same process as for the training data.
        SequenceRecordReader testFeatures = new CSVSequenceRecordReader();
        testFeatures.initialize(new NumberedFileInputSplit(featuresDirTest.getAbsolutePath() + "/%d.csv", 0, 149));
        SequenceRecordReader testLabels = new CSVSequenceRecordReader();
        testLabels.initialize(new NumberedFileInputSplit(labelsDirTest.getAbsolutePath() + "/%d.csv", 0, 149));

        DataSetIterator testData = new SequenceRecordReaderDataSetIterator(testFeatures, testLabels, miniBatchSize, numLabelClasses,
            false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);

        log.info(testData.next().toString());

        testData.setPreProcessor(normalizer);   //Note that we are using the exact same normalization process as the training data


        // ----- Configure the network -----
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(123)    //Random number generator seed for improved repeatability. Optional.
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
                .weightInit(WeightInit.XAVIER)
                .updater(Updater.NESTEROVS).momentum(0.9)
                .learningRate(0.005)
                .gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)  //Not always required, but helps with this data set
                .gradientNormalizationThreshold(0.5)
                .list()
                .layer(0, new GravesLSTM.Builder().activation("tanh").nIn(1).nOut(10).build())
                .layer(1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                        .activation("softmax").nIn(10).nOut(numLabelClasses).build())
                .pretrain(false).backprop(true).build();

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

        net.setListeners(new ScoreIterationListener(20));   //Print the score (loss function value) every 20 iterations


        // ----- Train the network, evaluating the test set performance at each epoch -----
        int nEpochs = 40;
        String str = "Test set evaluation at epoch %d: Accuracy = %.2f, F1 = %.2f";
        for (int i = 0; i < nEpochs; i++) {
            net.fit(trainData);

            //Evaluate on the test set:
            Evaluation evaluation = net.evaluate(testData);
            log.info(String.format(str, i, evaluation.accuracy(), evaluation.f1()));

            DataSet ds = testData.next();
            log.info(ds.toString());

            testData.reset();
            trainData.reset();
        }

        log.info("----- Example Complete -----");
    }