Examples with IrisDataSetIterator org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator used on opensource projects

Example 26 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class DataSetIteratorTest method testBatchSizeOfOneIris.

@Test
public void testBatchSizeOfOneIris() throws Exception {
    //Test for (a) iterators returning correct number of examples, and
    //(b) Labels are a proper one-hot vector (i.e., sum is 1.0)
    //Iris:
    DataSetIterator iris = new IrisDataSetIterator(1, 5);
    int irisC = 0;
    while (iris.hasNext()) {
        irisC++;
        DataSet ds = iris.next();
        assertTrue(ds.getLabels().sum(Integer.MAX_VALUE).getDouble(0) == 1.0);
    }
    assertEquals(5, irisC);
}

Example 27 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class ROCTest method RocEvalSanityCheck.

@Test
public void RocEvalSanityCheck() {
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1, new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    NormalizerStandardize ns = new NormalizerStandardize();
    DataSet ds = iter.next();
    ns.fit(ds);
    ns.transform(ds);
    iter.setPreProcessor(ns);
    for (int i = 0; i < 30; i++) {
        net.fit(ds);
    }
    ROCMultiClass roc = net.evaluateROCMultiClass(iter, 32);
    INDArray f = ds.getFeatures();
    INDArray l = ds.getLabels();
    INDArray out = net.output(f);
    ROCMultiClass manual = new ROCMultiClass(32);
    manual.eval(l, out);
    for (int i = 0; i < 3; i++) {
        assertEquals(manual.calculateAUC(i), roc.calculateAUC(i), 1e-6);
        double[][] rocCurve = roc.getResultsAsArray(i);
        double[][] rocManual = manual.getResultsAsArray(i);
        assertArrayEquals(rocCurve[0], rocManual[0], 1e-6);
        assertArrayEquals(rocCurve[1], rocManual[1], 1e-6);
    }
}

Example 28 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class EvaluationToolsTests method testRocHtml.

@Test
public void testRocHtml() throws Exception {
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1, new OutputLayer.Builder().nIn(4).nOut(2).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    NormalizerStandardize ns = new NormalizerStandardize();
    DataSet ds = iter.next();
    ns.fit(ds);
    ns.transform(ds);
    INDArray newLabels = Nd4j.create(150, 2);
    newLabels.getColumn(0).assign(ds.getLabels().getColumn(0));
    newLabels.getColumn(0).addi(ds.getLabels().getColumn(1));
    newLabels.getColumn(1).assign(ds.getLabels().getColumn(2));
    ds.setLabels(newLabels);
    for (int i = 0; i < 30; i++) {
        net.fit(ds);
    }
    ROC roc = new ROC(20);
    iter.reset();
    INDArray f = ds.getFeatures();
    INDArray l = ds.getLabels();
    INDArray out = net.output(f);
    roc.eval(l, out);
    String str = EvaluationTools.rocChartToHtml(roc);
//        System.out.println(str);
}

Example 29 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class TestEarlyStoppingSpark method testEarlyStoppingIris.

@Test
public void testEarlyStoppingIris() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).weightInit(WeightInit.XAVIER).list().layer(0, new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.setListeners(new ScoreIterationListener(1));
    JavaRDD<DataSet> irisData = getIris();
    EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>().epochTerminationConditions(new MaxEpochsTerminationCondition(5)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES)).scoreCalculator(new SparkDataSetLossCalculator(irisData, true, sc.sc())).modelSaver(saver).build();
    IEarlyStoppingTrainer<MultiLayerNetwork> trainer = new SparkEarlyStoppingTrainer(getContext().sc(), new ParameterAveragingTrainingMaster.Builder(irisBatchSize()).saveUpdater(true).averagingFrequency(1).build(), esConf, net, irisData);
    EarlyStoppingResult<MultiLayerNetwork> result = trainer.fit();
    System.out.println(result);
    assertEquals(5, result.getTotalEpochs());
    assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
    Map<Integer, Double> scoreVsIter = result.getScoreVsEpoch();
    assertEquals(5, scoreVsIter.size());
    String expDetails = esConf.getEpochTerminationConditions().get(0).toString();
    assertEquals(expDetails, result.getTerminationDetails());
    MultiLayerNetwork out = result.getBestModel();
    assertNotNull(out);
    //Check that best score actually matches (returned model vs. manually calculated score)
    MultiLayerNetwork bestNetwork = result.getBestModel();
    double score = bestNetwork.score(new IrisDataSetIterator(150, 150).next());
    double bestModelScore = result.getBestModelScore();
    assertEquals(bestModelScore, score, 1e-3);
}

Example 30 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class TestEarlyStoppingSpark method getIris.

private JavaRDD<DataSet> getIris() {
    JavaSparkContext sc = getContext();
    IrisDataSetIterator iter = new IrisDataSetIterator(irisBatchSize(), 150);
    List<DataSet> list = new ArrayList<>(150);
    while (iter.hasNext()) list.add(iter.next());
    return sc.parallelize(list);
}