Examples with FileSplit org.datavec.api.split.FileSplit used on opensource projects

Example 1 with FileSplit

use of org.datavec.api.split.FileSplit in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetiteratorTest method testSequenceRecordReaderTwoReadersWithEmptyFeatureSequenceThrows.

@Test(expected = ZeroLengthSequenceException.class)
public void testSequenceRecordReaderTwoReadersWithEmptyFeatureSequenceThrows() throws Exception {
    SequenceRecordReader featureReader = new CSVSequenceRecordReader(1, ",");
    SequenceRecordReader labelReader = new CSVSequenceRecordReader(1, ",");
    featureReader.initialize(new FileSplit(new ClassPathResource("empty.txt").getTempFileFromArchive()));
    labelReader.initialize(new FileSplit(new ClassPathResource("csvsequencelabels_0.txt").getTempFileFromArchive()));
    new SequenceRecordReaderDataSetIterator(featureReader, labelReader, 1, -1, true).next();
}

Example 2 with FileSplit

use of org.datavec.api.split.FileSplit in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetiteratorTest method testRecordReaderMaxBatchLimit.

@Test
public void testRecordReaderMaxBatchLimit() throws Exception {
    RecordReader recordReader = new CSVRecordReader();
    FileSplit csv = new FileSplit(new ClassPathResource("csv-example.csv").getTempFileFromArchive());
    recordReader.initialize(csv);
    DataSetIterator iter = new RecordReaderDataSetIterator(recordReader, 10, -1, -1, 2);
    iter.next();
    iter.next();
    assertEquals(false, iter.hasNext());
}

Example 3 with FileSplit

use of org.datavec.api.split.FileSplit in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetiteratorTest method testSequenceRecordReaderTwoReadersWithEmptyLabelSequenceThrows.

@Test(expected = ZeroLengthSequenceException.class)
public void testSequenceRecordReaderTwoReadersWithEmptyLabelSequenceThrows() throws Exception {
    SequenceRecordReader featureReader = new CSVSequenceRecordReader(1, ",");
    SequenceRecordReader labelReader = new CSVSequenceRecordReader(1, ",");
    featureReader.initialize(new FileSplit(new ClassPathResource("csvsequence_0.txt").getTempFileFromArchive()));
    labelReader.initialize(new FileSplit(new ClassPathResource("empty.txt").getTempFileFromArchive()));
    new SequenceRecordReaderDataSetIterator(featureReader, labelReader, 1, -1, true).next();
}

Example 4 with FileSplit

use of org.datavec.api.split.FileSplit in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetiteratorTest method testRecordReaderMultiRegression.

@Test
public void testRecordReaderMultiRegression() throws Exception {
    RecordReader csv = new CSVRecordReader();
    csv.initialize(new FileSplit(new ClassPathResource("iris.txt").getTempFileFromArchive()));
    int batchSize = 3;
    int labelIdxFrom = 3;
    int labelIdxTo = 4;
    DataSetIterator iter = new RecordReaderDataSetIterator(csv, batchSize, labelIdxFrom, labelIdxTo, true);
    DataSet ds = iter.next();
    INDArray f = ds.getFeatureMatrix();
    INDArray l = ds.getLabels();
    assertArrayEquals(new int[] { 3, 3 }, f.shape());
    assertArrayEquals(new int[] { 3, 2 }, l.shape());
    //Check values:
    double[][] fExpD = new double[][] { { 5.1, 3.5, 1.4 }, { 4.9, 3.0, 1.4 }, { 4.7, 3.2, 1.3 } };
    double[][] lExpD = new double[][] { { 0.2, 0 }, { 0.2, 0 }, { 0.2, 0 } };
    INDArray fExp = Nd4j.create(fExpD);
    INDArray lExp = Nd4j.create(lExpD);
    assertEquals(fExp, f);
    assertEquals(lExp, l);
}

Example 5 with FileSplit

use of org.datavec.api.split.FileSplit in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetiteratorTest method testRecordReader.

@Test
public void testRecordReader() throws Exception {
    RecordReader recordReader = new CSVRecordReader();
    FileSplit csv = new FileSplit(new ClassPathResource("csv-example.csv").getTempFileFromArchive());
    recordReader.initialize(csv);
    DataSetIterator iter = new RecordReaderDataSetIterator(recordReader, 34);
    DataSet next = iter.next();
    assertEquals(34, next.numExamples());
}