Java Code Examples for org.nd4j.linalg.dataset.api.MultiDataSet#getLabels()

@Override
public void call(MultiDataSet multiDataSet) {
    if (multiDataSet != null) {
        if (multiDataSet.getFeatures() != null)
            for (int i = 0; i < multiDataSet.getFeatures().length; i++)
                Nd4j.getAffinityManager().ensureLocation(multiDataSet.getFeatures()[i],
                                AffinityManager.Location.DEVICE);

        if (multiDataSet.getLabels() != null)
            for (int i = 0; i < multiDataSet.getLabels().length; i++)
                Nd4j.getAffinityManager().ensureLocation(multiDataSet.getLabels()[i],
                                AffinityManager.Location.DEVICE);

        if (multiDataSet.getFeaturesMaskArrays() != null)
            for (int i = 0; i < multiDataSet.getFeaturesMaskArrays().length; i++)
                Nd4j.getAffinityManager().ensureLocation(multiDataSet.getFeaturesMaskArrays()[i],
                                AffinityManager.Location.DEVICE);

        if (multiDataSet.getLabelsMaskArrays() != null)
            for (int i = 0; i < multiDataSet.getLabelsMaskArrays().length; i++)
                Nd4j.getAffinityManager().ensureLocation(multiDataSet.getLabelsMaskArrays()[i],
                                AffinityManager.Location.DEVICE);
    }
}
 

@Override
public void fit(MultiDataSet dataSet) {
    if (dataSet.getFeatures().length == 1 && dataSet.getLabels().length == 1) {
        INDArray features = dataSet.getFeatures(0);
        INDArray labels = dataSet.getLabels(0);
        INDArray fMask = null;
        INDArray lMask = null;

        if (dataSet.getFeaturesMaskArrays() != null)
            fMask = dataSet.getFeaturesMaskArrays()[0];

        if (dataSet.getFeaturesMaskArrays() != null)
            lMask = dataSet.getLabelsMaskArrays()[0];

        DataSet ds = new DataSet(features, labels, fMask, lMask);
        fit(ds);
    } else {
        throw new DL4JInvalidInputException(
                "MultiLayerNetwork can't handle MultiDataSet with more than 1 features or labels array." +
                        "Please consider use of ComputationGraph");
    }
}
 

@Override
public DataSet next() {
    MultiDataSet mds = iterator.next();
    if (mds.getFeatures().length > 1 || mds.getLabels().length > 1)
        throw new UnsupportedOperationException(
                        "This iterator is able to convert MultiDataSet with number of inputs/outputs of 1");

    INDArray features = mds.getFeatures()[0];
    INDArray labels = mds.getLabels() != null ? mds.getLabels()[0] : features;
    INDArray fMask = mds.getFeaturesMaskArrays() != null ? mds.getFeaturesMaskArrays()[0] : null;
    INDArray lMask = mds.getLabelsMaskArrays() != null ? mds.getLabelsMaskArrays()[0] : null;

    DataSet ds = new DataSet(features, labels, fMask, lMask);

    if (preProcessor != null)
        preProcessor.preProcess(ds);

    return ds;
}
 

public static void modelPredict(ComputationGraph model, MultiDataSetIterator iterator) {
  int sumCount = 0;
  int correctCount = 0;

  List<String> labelList =
      Arrays.asList(
          "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G",
          "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
          "Y", "Z");

  while (iterator.hasNext()) {
    MultiDataSet mds = iterator.next();
    INDArray[] output = model.output(mds.getFeatures());
    INDArray[] labels = mds.getLabels();
    int dataNum = batchSize > output[0].rows() ? output[0].rows() : batchSize;
    for (int dataIndex = 0; dataIndex < dataNum; dataIndex++) {
      String reLabel = "";
      String peLabel = "";
      INDArray preOutput = null;
      INDArray realLabel = null;
      for (int digit = 0; digit < 5; digit++) {
        preOutput = output[digit].getRow(dataIndex);
        peLabel += labelList.get(Nd4j.argMax(preOutput, 1).getInt(0));

        realLabel = labels[digit].getRow(dataIndex);
 reLabel += labelList.get(Nd4j.argMax(realLabel, 1).getInt(0));
      }
      if (peLabel.equals(reLabel)) {
        correctCount++;
      }
      sumCount++;
      logger.info(
          "real image {}  prediction {} status {}", reLabel, peLabel, peLabel.equals(reLabel));
    }
  }
  iterator.reset();
  System.out.println(
      "validate result : sum count =" + sumCount + " correct count=" + correctCount);
}
 

@Override
public void preProcess(MultiDataSet multiDataSet) {

    for (Integer index : targetMinorityDistMap.keySet()) {
        INDArray label = multiDataSet.getLabels(index);
        INDArray labelMask = multiDataSet.getLabelsMaskArray(index);
        double targetMinorityDist = targetMinorityDistMap.get(index);
        int minorityLabel = minorityLabelMap.get(index);
        multiDataSet.setLabelsMaskArray(index, adjustMasks(label, labelMask, minorityLabel, targetMinorityDist));
    }

}
 

@Override
public void preProcess(MultiDataSet multiDataSet) {

    for (Integer index : targetMinorityDistMap.keySet()) {
        INDArray label = multiDataSet.getLabels(index);
        INDArray labelMask = multiDataSet.getLabelsMaskArray(index);
        double targetMinorityDist = targetMinorityDistMap.get(index);
        int minorityLabel = minorityLabelMap.get(index);
        multiDataSet.setLabelsMaskArray(index, adjustMasks(label, labelMask, minorityLabel, targetMinorityDist));
    }

}
 

@Test
public void testSplittingCSV() throws Exception {
    //Here's the idea: take Iris, and split it up into 2 inputs and 2 output arrays
    //Inputs: columns 0 and 1-2
    //Outputs: columns 3, and 4->OneHot
    //need to manually extract
    RecordReader rr = new CSVRecordReader(0, ',');
    rr.initialize(new FileSplit(Resources.asFile("iris.txt")));
    RecordReaderDataSetIterator rrdsi = new RecordReaderDataSetIterator(rr, 10, 4, 3);

    RecordReader rr2 = new CSVRecordReader(0, ',');
    rr2.initialize(new FileSplit(Resources.asFile("iris.txt")));

    MultiDataSetIterator rrmdsi = new RecordReaderMultiDataSetIterator.Builder(10).addReader("reader", rr2)
                    .addInput("reader", 0, 0).addInput("reader", 1, 2).addOutput("reader", 3, 3)
                    .addOutputOneHot("reader", 4, 3).build();

    while (rrdsi.hasNext()) {
        DataSet ds = rrdsi.next();
        INDArray fds = ds.getFeatures();
        INDArray lds = ds.getLabels();

        MultiDataSet mds = rrmdsi.next();
        assertEquals(2, mds.getFeatures().length);
        assertEquals(2, mds.getLabels().length);
        assertNull(mds.getFeaturesMaskArrays());
        assertNull(mds.getLabelsMaskArrays());
        INDArray[] fmds = mds.getFeatures();
        INDArray[] lmds = mds.getLabels();

        assertNotNull(fmds);
        assertNotNull(lmds);
        for (int i = 0; i < fmds.length; i++)
            assertNotNull(fmds[i]);
        for (int i = 0; i < lmds.length; i++)
            assertNotNull(lmds[i]);

        //Get the subsets of the original iris data
        INDArray expIn1 = fds.get(all(), interval(0,0,true));
        INDArray expIn2 = fds.get(all(), interval(1, 2, true));
        INDArray expOut1 = fds.get(all(), interval(3,3,true));
        INDArray expOut2 = lds;

        assertEquals(expIn1, fmds[0]);
        assertEquals(expIn2, fmds[1]);
        assertEquals(expOut1, lmds[0]);
        assertEquals(expOut2, lmds[1]);
    }
    assertFalse(rrmdsi.hasNext());
}
 

@Test
public void testSplittingCSVSequence() throws Exception {
    //Idea: take CSV sequences, and split "csvsequence_i.txt" into two separate inputs; keep "csvSequencelables_i.txt"
    // as standard one-hot output
    //need to manually extract
    File rootDir = temporaryFolder.newFolder();
    for (int i = 0; i < 3; i++) {
        new ClassPathResource(String.format("csvsequence_%d.txt", i)).getTempFileFromArchive(rootDir);
        new ClassPathResource(String.format("csvsequencelabels_%d.txt", i)).getTempFileFromArchive(rootDir);
        new ClassPathResource(String.format("csvsequencelabelsShort_%d.txt", i)).getTempFileFromArchive(rootDir);
    }

    String featuresPath = FilenameUtils.concat(rootDir.getAbsolutePath(), "csvsequence_%d.txt");
    String labelsPath = FilenameUtils.concat(rootDir.getAbsolutePath(), "csvsequencelabels_%d.txt");

    SequenceRecordReader featureReader = new CSVSequenceRecordReader(1, ",");
    SequenceRecordReader labelReader = new CSVSequenceRecordReader(1, ",");
    featureReader.initialize(new NumberedFileInputSplit(featuresPath, 0, 2));
    labelReader.initialize(new NumberedFileInputSplit(labelsPath, 0, 2));

    SequenceRecordReaderDataSetIterator iter =
                    new SequenceRecordReaderDataSetIterator(featureReader, labelReader, 1, 4, false);

    SequenceRecordReader featureReader2 = new CSVSequenceRecordReader(1, ",");
    SequenceRecordReader labelReader2 = new CSVSequenceRecordReader(1, ",");
    featureReader2.initialize(new NumberedFileInputSplit(featuresPath, 0, 2));
    labelReader2.initialize(new NumberedFileInputSplit(labelsPath, 0, 2));

    MultiDataSetIterator srrmdsi = new RecordReaderMultiDataSetIterator.Builder(1)
                    .addSequenceReader("seq1", featureReader2).addSequenceReader("seq2", labelReader2)
                    .addInput("seq1", 0, 1).addInput("seq1", 2, 2).addOutputOneHot("seq2", 0, 4).build();

    while (iter.hasNext()) {
        DataSet ds = iter.next();
        INDArray fds = ds.getFeatures();
        INDArray lds = ds.getLabels();

        MultiDataSet mds = srrmdsi.next();
        assertEquals(2, mds.getFeatures().length);
        assertEquals(1, mds.getLabels().length);
        assertNull(mds.getFeaturesMaskArrays());
        assertNull(mds.getLabelsMaskArrays());
        INDArray[] fmds = mds.getFeatures();
        INDArray[] lmds = mds.getLabels();

        assertNotNull(fmds);
        assertNotNull(lmds);
        for (int i = 0; i < fmds.length; i++)
            assertNotNull(fmds[i]);
        for (int i = 0; i < lmds.length; i++)
            assertNotNull(lmds[i]);

        INDArray expIn1 = fds.get(all(), NDArrayIndex.interval(0, 1, true), all());
        INDArray expIn2 = fds.get(all(), NDArrayIndex.interval(2, 2, true), all());

        assertEquals(expIn1, fmds[0]);
        assertEquals(expIn2, fmds[1]);
        assertEquals(lds, lmds[0]);
    }
    assertFalse(srrmdsi.hasNext());
}
 

@Test
public void testTimeSeriesRandomOffset() {
    //2 in, 2 out, 3 total sequences of length [1,3,5]

    List<List<Writable>> seq1 =
                    Arrays.asList(Arrays.<Writable>asList(new DoubleWritable(1.0), new DoubleWritable(2.0)));
    List<List<Writable>> seq2 =
                    Arrays.asList(Arrays.<Writable>asList(new DoubleWritable(10.0), new DoubleWritable(11.0)),
                                    Arrays.<Writable>asList(new DoubleWritable(20.0), new DoubleWritable(21.0)),
                                    Arrays.<Writable>asList(new DoubleWritable(30.0), new DoubleWritable(31.0)));
    List<List<Writable>> seq3 =
                    Arrays.asList(Arrays.<Writable>asList(new DoubleWritable(100.0), new DoubleWritable(101.0)),
                                    Arrays.<Writable>asList(new DoubleWritable(200.0), new DoubleWritable(201.0)),
                                    Arrays.<Writable>asList(new DoubleWritable(300.0), new DoubleWritable(301.0)),
                                    Arrays.<Writable>asList(new DoubleWritable(400.0), new DoubleWritable(401.0)),
                                    Arrays.<Writable>asList(new DoubleWritable(500.0), new DoubleWritable(501.0)));

    Collection<List<List<Writable>>> seqs = Arrays.asList(seq1, seq2, seq3);

    SequenceRecordReader rr = new CollectionSequenceRecordReader(seqs);

    RecordReaderMultiDataSetIterator rrmdsi =
                    new RecordReaderMultiDataSetIterator.Builder(3).addSequenceReader("rr", rr).addInput("rr", 0, 0)
                                    .addOutput("rr", 1, 1).timeSeriesRandomOffset(true, 1234L).build();


    Random r = new Random(1234); //Provides seed for each minibatch
    long seed = r.nextLong();
    Random r2 = new Random(seed); //Use same RNG seed in new RNG for each minibatch
    int expOffsetSeq1 = r2.nextInt(5 - 1 + 1); //0 to 4 inclusive
    int expOffsetSeq2 = r2.nextInt(5 - 3 + 1);
    int expOffsetSeq3 = 0; //Longest TS, always 0
    //With current seed: 3, 1, 0
    //        System.out.println(expOffsetSeq1 + "\t" + expOffsetSeq2 + "\t" + expOffsetSeq3);

    MultiDataSet mds = rrmdsi.next();

    INDArray expMask = Nd4j.create(new double[][] {{0, 0, 0, 1, 0}, {0, 1, 1, 1, 0}, {1, 1, 1, 1, 1}});

    assertEquals(expMask, mds.getFeaturesMaskArray(0));
    assertEquals(expMask, mds.getLabelsMaskArray(0));

    INDArray f = mds.getFeatures(0);
    INDArray l = mds.getLabels(0);

    INDArray expF1 = Nd4j.create(new double[] {1.0}, new int[]{1,1});
    INDArray expL1 = Nd4j.create(new double[] {2.0}, new int[]{1,1});

    INDArray expF2 = Nd4j.create(new double[] {10, 20, 30}, new int[]{1,3});
    INDArray expL2 = Nd4j.create(new double[] {11, 21, 31}, new int[]{1,3});

    INDArray expF3 = Nd4j.create(new double[] {100, 200, 300, 400, 500}, new int[]{1,5});
    INDArray expL3 = Nd4j.create(new double[] {101, 201, 301, 401, 501}, new int[]{1,5});

    assertEquals(expF1, f.get(point(0), all(),
                    NDArrayIndex.interval(expOffsetSeq1, expOffsetSeq1 + 1)));
    assertEquals(expL1, l.get(point(0), all(),
                    NDArrayIndex.interval(expOffsetSeq1, expOffsetSeq1 + 1)));

    assertEquals(expF2, f.get(point(1), all(),
                    NDArrayIndex.interval(expOffsetSeq2, expOffsetSeq2 + 3)));
    assertEquals(expL2, l.get(point(1), all(),
                    NDArrayIndex.interval(expOffsetSeq2, expOffsetSeq2 + 3)));

    assertEquals(expF3, f.get(point(2), all(),
                    NDArrayIndex.interval(expOffsetSeq3, expOffsetSeq3 + 5)));
    assertEquals(expL3, l.get(point(2), all(),
                    NDArrayIndex.interval(expOffsetSeq3, expOffsetSeq3 + 5)));
}