cannot run, Dont know how to solve the error. Thank you for helping.

MNIST.java

package ai.certifai.farhan.midTerm;

import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.collection.CollectionRecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.datavec.api.transform.TransformProcess;
import org.datavec.api.transform.schema.Schema;
import org.datavec.api.writable.Writable;
import org.datavec.local.transforms.LocalTransformExecutor;
import org.deeplearning4j.core.storage.StatsStorage;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.ui.api.UIServer;
import org.deeplearning4j.ui.model.storage.InMemoryStatsStorage;
import org.nd4j.common.io.ClassPathResource;
import org.nd4j.evaluation.classification.Evaluation;
import org.nd4j.evaluation.regression.RegressionEvaluation;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.SplitTestAndTrain;
import org.nd4j.linalg.dataset.ViewIterator;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.NormalizerMinMaxScaler;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.lossfunctions.LossFunctions;

import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

public class MNIST {

    private static final int seed = 123;
    private static final double learningRate = 0.001;
    private static final int nEpochs = 2000;

    private static final double splitRatio = 0.7;
    static int numInput = 784; // total pixel as input
    static int numClass = 10; // output number as 0 to 9

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

        File dataFile = new ClassPathResource("/mockExam/mnist_784_csv.csv").getFile();

        FileSplit fileSplit = new FileSplit(dataFile);

        RecordReader rr = new CSVRecordReader(1,',');
        rr.initialize(fileSplit);


        Schema sc = new Schema.Builder()
                .addColumnsInteger("pixel1","pixel2","pixel3","pixel4","pixel5","pixel6","pixel7","pixel8","pixel9","pixel10","pixel11","pixel12","pixel13","pixel14","pixel15","pixel16","pixel17","pixel18","pixel19","pixel20","pixel21","pixel22","pixel23","pixel24","pixel25","pixel26","pixel27","pixel28","pixel29","pixel30","pixel31","pixel32","pixel33","pixel34","pixel35","pixel36","pixel37","pixel38","pixel39","pixel40","pixel41","pixel42","pixel43","pixel44","pixel45","pixel46","pixel47","pixel48","pixel49","pixel50","pixel51","pixel52","pixel53","pixel54","pixel55","pixel56","pixel57","pixel58","pixel59","pixel60","pixel61","pixel62","pixel63","pixel64","pixel65","pixel66","pixel67","pixel68","pixel69","pixel70","pixel71","pixel72","pixel73","pixel74","pixel75","pixel76","pixel77","pixel78","pixel79","pixel80","pixel81","pixel82","pixel83","pixel84","pixel85","pixel86","pixel87","pixel88","pixel89","pixel90","pixel91","pixel92","pixel93","pixel94","pixel95","pixel96","pixel97","pixel98","pixel99","pixel100","pixel101","pixel102","pixel103","pixel104","pixel105","pixel106","pixel107","pixel108","pixel109","pixel110","pixel111","pixel112","pixel113","pixel114","pixel115","pixel116","pixel117","pixel118","pixel119","pixel120","pixel121","pixel122","pixel123","pixel124","pixel125","pixel126","pixel127","pixel128","pixel129","pixel130","pixel131","pixel132","pixel133","pixel134","pixel135","pixel136","pixel137","pixel138","pixel139","pixel140","pixel141","pixel142","pixel143","pixel144","pixel145","pixel146","pixel147","pixel148","pixel149","pixel150","pixel151","pixel152","pixel153","pixel154","pixel155","pixel156","pixel157","pixel158","pixel159","pixel160","pixel161","pixel162","pixel163","pixel164","pixel165","pixel166","pixel167","pixel168","pixel169","pixel170","pixel171","pixel172","pixel173","pixel174","pixel175","pixel176","pixel177","pixel178","pixel179","pixel180","pixel181","pixel182","pixel183","pixel184","pixel185","pixel186","pixel187","pixel188","pixel189","pixel190","pixel191","pixel192","pixel193","pixel194","pixel195","pixel196","pixel197","pixel198","pixel199","pixel200","pixel201","pixel202","pixel203","pixel204","pixel205","pixel206","pixel207","pixel208","pixel209","pixel210","pixel211","pixel212","pixel213","pixel214","pixel215","pixel216","pixel217","pixel218","pixel219","pixel220","pixel221","pixel222","pixel223","pixel224","pixel225","pixel226","pixel227","pixel228","pixel229","pixel230","pixel231","pixel232","pixel233","pixel234","pixel235","pixel236","pixel237","pixel238","pixel239","pixel240","pixel241","pixel242","pixel243","pixel244","pixel245","pixel246","pixel247","pixel248","pixel249","pixel250","pixel251","pixel252","pixel253","pixel254","pixel255","pixel256","pixel257","pixel258","pixel259","pixel260","pixel261","pixel262","pixel263","pixel264","pixel265","pixel266","pixel267","pixel268","pixel269","pixel270","pixel271","pixel272","pixel273","pixel274","pixel275","pixel276","pixel277","pixel278","pixel279","pixel280","pixel281","pixel282","pixel283","pixel284","pixel285","pixel286","pixel287","pixel288","pixel289","pixel290","pixel291","pixel292","pixel293","pixel294","pixel295","pixel296","pixel297","pixel298","pixel299","pixel300","pixel301","pixel302","pixel303","pixel304","pixel305","pixel306","pixel307","pixel308","pixel309","pixel310","pixel311","pixel312","pixel313","pixel314","pixel315","pixel316","pixel317","pixel318","pixel319","pixel320","pixel321","pixel322","pixel323","pixel324","pixel325","pixel326","pixel327","pixel328","pixel329","pixel330","pixel331","pixel332","pixel333","pixel334","pixel335","pixel336","pixel337","pixel338","pixel339","pixel340","pixel341","pixel342","pixel343","pixel344","pixel345","pixel346","pixel347","pixel348","pixel349","pixel350","pixel351","pixel352","pixel353","pixel354","pixel355","pixel356","pixel357","pixel358","pixel359","pixel360","pixel361","pixel362","pixel363","pixel364","pixel365","pixel366","pixel367","pixel368","pixel369","pixel370","pixel371","pixel372","pixel373","pixel374","pixel375","pixel376","pixel377","pixel378","pixel379","pixel380","pixel381","pixel382","pixel383","pixel384","pixel385","pixel386","pixel387","pixel388","pixel389","pixel390","pixel391","pixel392","pixel393","pixel394","pixel395","pixel396","pixel397","pixel398","pixel399","pixel400","pixel401","pixel402","pixel403","pixel404","pixel405","pixel406","pixel407","pixel408","pixel409","pixel410","pixel411","pixel412","pixel413","pixel414","pixel415","pixel416","pixel417","pixel418","pixel419","pixel420","pixel421","pixel422","pixel423","pixel424","pixel425","pixel426","pixel427","pixel428","pixel429","pixel430","pixel431","pixel432","pixel433","pixel434","pixel435","pixel436","pixel437","pixel438","pixel439","pixel440","pixel441","pixel442","pixel443","pixel444","pixel445","pixel446","pixel447","pixel448","pixel449","pixel450","pixel451","pixel452","pixel453","pixel454","pixel455","pixel456","pixel457","pixel458","pixel459","pixel460","pixel461","pixel462","pixel4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                .addColumnInteger("class")
                .build();

        TransformProcess tp = new TransformProcess.Builder(sc)
                .build();

        Schema outputSchema = tp.getFinalSchema();
        System.out.println("Output Schema "+outputSchema);


        List<List<Writable>> originalData = new ArrayList<>();

        while(rr.hasNext()){
            List<Writable> data = rr.next();
            originalData.add(data);
        }

        List<List<Writable>> transformedData = LocalTransformExecutor.execute(originalData,tp);

        //Create iterator from process data
        CollectionRecordReader collectionRR = new CollectionRecordReader(transformedData);

        //Input batch size , label index , and number of label
        DataSetIterator dataSetIterator = new RecordReaderDataSetIterator(collectionRR, transformedData.size(),-1,10);

//        //Create Iterator and shuffle the data
        DataSet fullDataset = dataSetIterator.next();
        fullDataset.shuffle(seed);
//
//        //Input split ratio
        SplitTestAndTrain testAndTrain = fullDataset.splitTestAndTrain(splitRatio);
//
        //Get train and test dataset
        DataSet trainData = testAndTrain.getTrain();
        DataSet testData = testAndTrain.getTest();

        //printout size
        System.out.println("Training vector : ");
        System.out.println(Arrays.toString(trainData.getFeatures().shape()));
        System.out.println("Test vector : ");
        System.out.println(Arrays.toString(testData.getFeatures().shape()));

        //Data normalization
        DataNormalization normalizer = new NormalizerMinMaxScaler();
        normalizer.fit(trainData);
        normalizer.transform(trainData);
        normalizer.transform(testData);
        System.out.println("normalize = " + normalizer);

        //  Configuring the structure of the NN
        MultiLayerConfiguration conf= new NeuralNetConfiguration.Builder()
                .seed(seed)
                .updater(new Adam(learningRate))
                .weightInit(WeightInit.XAVIER)
                .l2(0.01)
                .list()
                .layer(0, new DenseLayer.Builder()
                        .nIn(numInput)
                        .nOut(200)
                        .activation(Activation.RELU)
                        .build())
                .layer(1, new DenseLayer.Builder()
                        .nIn(200)
                        .nOut(100)
                        .activation(Activation.RELU)
                        .build())
                .layer(2, new DenseLayer.Builder()
                        .nIn(100)
                        .nOut(50)
                        .activation(Activation.RELU)
                        .build())
                .layer(3, new OutputLayer.Builder()
                        .nIn(50)
                        .nOut(numClass)
                        .activation(Activation.SOFTMAX)
                        .lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .build())
                .build();


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


        //UI-Evaluator
        StatsStorage storage = new InMemoryStatsStorage();
        UIServer server = UIServer.getInstance();
        server.attach(storage);

        //Set model listeners ( uncomment these lines )
        model.setListeners(new ScoreIterationListener(10));

        //Training
        Evaluation eval;
        for(int i=0; i < nEpochs; i++) {
            model.fit(trainData);
            eval = model.evaluate(new ViewIterator(testData, transformedData.size()));
            System.out.println("EPOCH: " + i + " Accuracy: " + eval.accuracy());
        }

        //Confusion matrix
        Evaluation evalTrain = model.evaluate(new ViewIterator(trainData, transformedData.size()));
        Evaluation evalTest = model.evaluate(new ViewIterator(testData,transformedData.size()));

        System.out.print("Train Data");
        System.out.println(evalTrain.stats());

        System.out.print("Test Data");
        System.out.print(evalTest.stats());


    }


}

I got it, thanks, everyone. My schema just got the extra variable "class".