Java Code Examples for org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator#reset()

public static double evaluate(Model model, int outputNum, MultiDataSetIterator testData, int topN, int batchSize) {
	log.info("Evaluate model....");
    Evaluation clsEval = new Evaluation(createLabels(outputNum), topN);
    RegressionEvaluation valueRegEval1 = new RegressionEvaluation(1);
    int count = 0;
    
    long begin = 0;
    long consume = 0;
    while(testData.hasNext()){
    	MultiDataSet ds = testData.next();
    	begin = System.nanoTime();
    	INDArray[] output = ((ComputationGraph) model).output(false, ds.getFeatures());
    	consume += System.nanoTime() - begin;
        clsEval.eval(ds.getLabels(0), output[0]);
        valueRegEval1.eval(ds.getLabels(1), output[1]);
        count++;
    }
    String stats = clsEval.stats();
    int pos = stats.indexOf("===");
    stats = "\n" + stats.substring(pos);
    log.info(stats);
    log.info(valueRegEval1.stats());
    testData.reset();
    log.info("Evaluate time: " + consume + " count: " + (count * batchSize) + " average: " + ((float) consume/(count*batchSize)/1000));
	return clsEval.accuracy();
}
 

/**
 * Fit an iterator
 *
 * @param iterator for the data to iterate over
 */
public void fit(@NonNull MultiDataSetIterator iterator) {
    List<S.Builder> featureNormBuilders = new ArrayList<>();
    List<S.Builder> labelNormBuilders = new ArrayList<>();

    iterator.reset();
    while (iterator.hasNext()) {
        MultiDataSet next = iterator.next();
        fitPartial(next, featureNormBuilders, labelNormBuilders);
    }

    featureStats = buildList(featureNormBuilders);
    if (isFitLabel()) {
        labelStats = buildList(labelNormBuilders);
    }
}
 

/**
 * Fit an iterator
 *
 * @param iterator for the data to iterate over
 */
public void fit(@NonNull MultiDataSetIterator iterator) {
    List<S.Builder> featureNormBuilders = new ArrayList<>();
    List<S.Builder> labelNormBuilders = new ArrayList<>();

    iterator.reset();
    while (iterator.hasNext()) {
        MultiDataSet next = iterator.next();
        fitPartial(next, featureNormBuilders, labelNormBuilders);
    }

    featureStats = buildList(featureNormBuilders);
    if (isFitLabel()) {
        labelStats = buildList(labelNormBuilders);
    }
}
 

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);
}
 

/**
 * Iterates over a dataset
 * accumulating statistics for normalization
 *
 * @param iterator the iterator to use for collecting statistics
 */
@Override
public void fit(@NonNull MultiDataSetIterator iterator) {
    Map<Integer, NormalizerStats.Builder> inputStatsBuilders = new HashMap<>();
    Map<Integer, NormalizerStats.Builder> outputStatsBuilders = new HashMap<>();

    iterator.reset();
    while (iterator.hasNext()) {
        fitPartial(iterator.next(), inputStatsBuilders, outputStatsBuilders);
    }

    inputStats = buildAllStats(inputStatsBuilders);
    outputStats = buildAllStats(outputStatsBuilders);
}
 

/**
 * Iterates over a dataset
 * accumulating statistics for normalization
 *
 * @param iterator the iterator to use for collecting statistics
 */
@Override
public void fit(@NonNull MultiDataSetIterator iterator) {
    Map<Integer, NormalizerStats.Builder> inputStatsBuilders = new HashMap<>();
    Map<Integer, NormalizerStats.Builder> outputStatsBuilders = new HashMap<>();

    iterator.reset();
    while (iterator.hasNext()) {
        fitPartial(iterator.next(), inputStatsBuilders, outputStatsBuilders);
    }

    inputStats = buildAllStats(inputStatsBuilders);
    outputStats = buildAllStats(outputStatsBuilders);
}
 

@Test
public void testCallstoNextNotAllowed() throws IOException {
    int terminateAfter = 1;

    MultiDataSetIterator iter =
                    new MultiDataSetIteratorAdapter(new MnistDataSetIterator(minibatchSize, numExamples));
    EarlyTerminationMultiDataSetIterator earlyEndIter =
                    new EarlyTerminationMultiDataSetIterator(iter, terminateAfter);

    earlyEndIter.next(10);
    iter.reset();
    exception.expect(RuntimeException.class);
    earlyEndIter.next(10);
}
 

@Test
public void testMDSLoaderIter(){

    for(boolean r : new boolean[]{false, true}) {
        List<String> l = Arrays.asList("3", "0", "1");
        Random rng = r ? new Random(12345) : null;
        MultiDataSetIterator iter = new MultiDataSetLoaderIterator(l, null, new Loader<MultiDataSet>() {
            @Override
            public MultiDataSet load(Source source) throws IOException {
                INDArray i = Nd4j.scalar(Integer.valueOf(source.getPath()));
                return new org.nd4j.linalg.dataset.MultiDataSet(i, i);
            }
        }, new LocalFileSourceFactory());

        int count = 0;
        int[] exp = {3, 0, 1};
        while (iter.hasNext()) {
            MultiDataSet ds = iter.next();
            if(!r) {
                assertEquals(exp[count], ds.getFeatures()[0].getInt(0));
            }
            count++;
        }
        assertEquals(3, count);

        iter.reset();
        assertTrue(iter.hasNext());
    }
}