TestDataSetIterator.java

TestDataSetIterator.java

// TestDataSetIterator.java
// ver 1.0.0-beta
import org.datavec.api.records.reader.SequenceRecordReader;
import org.datavec.api.records.reader.impl.csv.CSVSequenceRecordReader;
import org.datavec.api.split.FileSplit;
import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;

import java.io.File;
import java.io.IOException;

public interface TestDataSetIterator {
    static DataSetIterator createDataSetIterator () throws IOException, InterruptedException {
        SequenceRecordReader testFeatures = new CSVSequenceRecordReader(0,",");
        testFeatures.initialize(new FileSplit(new File("resources/test_train.csv")));
        SequenceRecordReader testLabels = new CSVSequenceRecordReader(0,",");
        testLabels.initialize(new FileSplit(new File("resources/test_label.csv")));
        return new SequenceRecordReaderDataSetIterator(
                testFeatures,
                testLabels,
                3,
                6,
                false,
                SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END
        );
    }
    static void main (String... args) throws IOException, InterruptedException {
        DataSetIterator dsi = createDataSetIterator();
        System.out.println(dsi.next());
    }
}
// --- test_train.csv ---
// 1,2,3,4,5
// 1,2,3,4,5,6
// 1,2,3,4,5,6,7
// 1,2,3
// 1,2,3,4,5,6
// -----------------------
// --- test_label.csv ---
// 1
// 2
// 3
// 4
// 5
// -----------------------
//
//    Exception in thread "main" java.lang.IndexOutOfBoundsException: 25
//        at org.bytedeco.javacpp.indexer.Indexer.checkIndex(Indexer.java:90)
//        at org.bytedeco.javacpp.indexer.FloatRawIndexer.put(FloatRawIndexer.java:90)
//        at org.nd4j.linalg.api.buffer.BaseDataBuffer.put(BaseDataBuffer.java:1116)
//        at org.nd4j.linalg.jcublas.buffer.BaseCudaDataBuffer.put(BaseCudaDataBuffer.java:684)
//        at org.nd4j.linalg.api.ndarray.BaseNDArray.putScalar(BaseNDArray.java:1414)
//        at org.deeplearning4j.datasets.datavec.RecordReaderMultiDataSetIterator.convertWritablesSequence(RecordReaderMultiDataSetIterator.java:661)
//        at org.deeplearning4j.datasets.datavec.RecordReaderMultiDataSetIterator.convertFeaturesOrLabels(RecordReaderMultiDataSetIterator.java:367)
//        at org.deeplearning4j.datasets.datavec.RecordReaderMultiDataSetIterator.nextMultiDataSet(RecordReaderMultiDataSetIterator.java:325)
//        at org.deeplearning4j.datasets.datavec.RecordReaderMultiDataSetIterator.next(RecordReaderMultiDataSetIterator.java:213)
//        at org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator.next(SequenceRecordReaderDataSetIterator.java:345)
//        at org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator.next(SequenceRecordReaderDataSetIterator.java:324)
//        at org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator.next(SequenceRecordReaderDataSetIterator.java:32)
//        at TestDataSetIterator.main(TestDataSetIterator.java:28)
//
//        Process finished with exit code 1

// But
//
// --- test_train.csv ---
// 1,2,3,4,5,6,7
// 1,2,3,4,5,6
// 1,2,3,4,5
// 1,2,3
// 1,2,3,4,5,6
// -----------------------
// --- test_label.csv ---
// 1
// 2
// 3
// 4
// 5
// -----------------------
//
// ===========INPUT===================
// [[[    1.0000,    1.0000,    1.0000,    1.0000,    1.0000],
//   [    2.0000,    2.0000,    2.0000,    2.0000,    2.0000],
//   [    3.0000,    3.0000,    3.0000,    3.0000,    3.0000],
//   [    4.0000,    4.0000,    4.0000,         0,    4.0000],
//   [    5.0000,    5.0000,    5.0000,         0,    5.0000],
//   [    6.0000,         0,    6.0000,         0,    6.0000],
//   [    7.0000,         0,         0,         0,         0]]]
// =================OUTPUT==================
//   [[[         0,         0,         0,         0,         0],
//     [    1.0000,         0,         0,         0,         0],
//     [         0,    1.0000,         0,         0,         0],
//     [         0,         0,    1.0000,         0,         0],
//     [         0,         0,         0,    1.0000,         0],
//     [         0,         0,         0,         0,    1.0000]]]