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Java example source code file (IteratorMultiDataSetIterator.java)

This example Java source code file (IteratorMultiDataSetIterator.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

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Java - Java tags/keywords

arraylist, indarray, invalid, iteratormultidatasetiterator, linkedlist, multidataset, multidatasetpreprocessor, nosuchelementexception, not, override, reset, runtimeexception, unsupportedoperationexception, used, util

The IteratorMultiDataSetIterator.java Java example source code

package org.deeplearning4j.datasets.iterator;

import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.MultiDataSet;
import org.nd4j.linalg.dataset.api.MultiDataSetPreProcessor;
import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
import org.nd4j.linalg.indexing.NDArrayIndex;

import java.util.*;

 * A DataSetIterator that works on an Iterator<DataSet>, combining and splitting the input DataSet objects as
 * required to get a consistent batch size.
 * Typically used in Spark training, but may be used elsewhere.
 * NOTE: some methods are not supported here.
public class IteratorMultiDataSetIterator implements MultiDataSetIterator {

    private final Iterator<MultiDataSet> iterator;
    private final int batchSize;
    private final LinkedList<MultiDataSet> queued; //Used when splitting larger examples than we want to return in a batch
    private MultiDataSetPreProcessor preProcessor;

    public IteratorMultiDataSetIterator(Iterator<MultiDataSet> iterator, int batchSize){
        this.iterator = iterator;
        this.batchSize = batchSize;
        this.queued = new LinkedList<>();

    public boolean hasNext() {
        return !queued.isEmpty() || iterator.hasNext();

    public MultiDataSet next() {
        return next(batchSize);

    public MultiDataSet next(int num) {
        if(!hasNext()) throw new NoSuchElementException();

        List<MultiDataSet> list = new ArrayList<>();
        int countSoFar = 0;
        while((!queued.isEmpty() || iterator.hasNext()) && countSoFar < batchSize){
            MultiDataSet next;
                next = queued.removeFirst();
            } else {
                next = iterator.next();
            int nExamples = next.getFeatures(0).size(0);
            if( countSoFar + nExamples <= batchSize ){
                //Add the entire MultiDataSet as-is
            } else {
                //Split the MultiDataSet

                int nFeatures = next.numFeatureArrays();
                int nLabels = next.numLabelsArrays();

                INDArray[] fToKeep = new INDArray[nFeatures];
                INDArray[] lToKeep = new INDArray[nLabels];
                INDArray[] fToCache = new INDArray[nFeatures];
                INDArray[] lToCache = new INDArray[nLabels];
                INDArray[] fMaskToKeep = (next.getFeaturesMaskArrays() != null ? new INDArray[nFeatures] : null);
                INDArray[] lMaskToKeep = (next.getLabelsMaskArrays() != null ? new INDArray[nLabels] : null);
                INDArray[] fMaskToCache = (next.getFeaturesMaskArrays() != null ? new INDArray[nFeatures] : null);
                INDArray[] lMaskToCache = (next.getLabelsMaskArrays() != null ? new INDArray[nLabels] : null);

                for( int i=0; i<nFeatures; i++ ){
                    INDArray fi = next.getFeatures(i);
                    INDArray li = next.getFeatures(i);
                    fToKeep[i] = getRange(fi,0,batchSize-countSoFar);
                    fToCache[i] = getRange(fi,batchSize-countSoFar, nExamples);
                    lToKeep[i] = getRange(li,0,batchSize-countSoFar);
                    lToCache[i] = getRange(li,batchSize-countSoFar, nExamples);

                    if(fMaskToKeep != null){
                        INDArray fmi = next.getFeaturesMaskArray(i);
                        fMaskToKeep[i] = getRange(fmi,0,batchSize-countSoFar);
                        fMaskToCache[i] = getRange(fmi,batchSize-countSoFar, nExamples);
                    if(lMaskToKeep != null){
                        INDArray lmi = next.getLabelsMaskArray(i);
                        lMaskToKeep[i] = getRange(lmi,0,batchSize-countSoFar);
                        lMaskToCache[i] = getRange(lmi,batchSize-countSoFar, nExamples);

                MultiDataSet toKeep = new org.nd4j.linalg.dataset.MultiDataSet(fToKeep,lToKeep, fMaskToKeep, lMaskToKeep);
                MultiDataSet toCache = new org.nd4j.linalg.dataset.MultiDataSet(fToCache,lToCache, fMaskToCache, lMaskToCache);

            countSoFar += nExamples;

        MultiDataSet out;
        if(list.size() == 1){
            out = list.get(0);
        } else {
            out = org.nd4j.linalg.dataset.MultiDataSet.merge(list);

        if(preProcessor != null) preProcessor.preProcess(out);
        return out;

    private static INDArray getRange(INDArray arr, int exampleFrom, int exampleToExclusive){
        if(arr == null) return null;

        int rank = arr.rank();
            case 2:
                return arr.get(NDArrayIndex.interval(exampleFrom, exampleToExclusive), NDArrayIndex.all());
            case 3:
                return arr.get(NDArrayIndex.interval(exampleFrom, exampleToExclusive), NDArrayIndex.all(), NDArrayIndex.all());
            case 4:
                return arr.get(NDArrayIndex.interval(exampleFrom, exampleToExclusive), NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.all());
                throw new RuntimeException("Invalid rank: " + rank);

    public void reset() {
        throw new UnsupportedOperationException("Reset not supported");

    public void setPreProcessor(MultiDataSetPreProcessor preProcessor) {
        this.preProcessor = preProcessor;

    public void remove() {
        throw new UnsupportedOperationException("Not supported");

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