Examples with NormalizerStandardize org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize used on opensource projects

Example 1 with NormalizerStandardize

use of org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize 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 2 with NormalizerStandardize

use of org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize 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 3 with NormalizerStandardize

use of org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize in project tutorials by eugenp.

the class IrisClassifier method main.

public static void main(String[] args) throws IOException, InterruptedException {
    DataSet allData;
    try (RecordReader recordReader = new CSVRecordReader(0, ',')) {
        recordReader.initialize(new FileSplit(new ClassPathResource("iris.txt").getFile()));
        DataSetIterator iterator = new RecordReaderDataSetIterator(recordReader, 150, FEATURES_COUNT, CLASSES_COUNT);
        allData = iterator.next();
    }
    allData.shuffle(42);
    DataNormalization normalizer = new NormalizerStandardize();
    normalizer.fit(allData);
    normalizer.transform(allData);
    SplitTestAndTrain testAndTrain = allData.splitTestAndTrain(0.65);
    DataSet trainingData = testAndTrain.getTrain();
    DataSet testData = testAndTrain.getTest();
    MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder().iterations(1000).activation(Activation.TANH).weightInit(WeightInit.XAVIER).learningRate(0.1).regularization(true).l2(0.0001).list().layer(0, new DenseLayer.Builder().nIn(FEATURES_COUNT).nOut(3).build()).layer(1, new DenseLayer.Builder().nIn(3).nOut(3).build()).layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).activation(Activation.SOFTMAX).nIn(3).nOut(CLASSES_COUNT).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork model = new MultiLayerNetwork(configuration);
    model.init();
    model.fit(trainingData);
    INDArray output = model.output(testData.getFeatureMatrix());
    Evaluation eval = new Evaluation(CLASSES_COUNT);
    eval.eval(testData.getLabels(), output);
    System.out.println(eval.stats());
}

Example 4 with NormalizerStandardize

use of org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize in project nd4j by deeplearning4j.

the class DataSet method normalize.

@Override
public void normalize() {
    // FeatureUtil.normalizeMatrix(getFeatures());
    NormalizerStandardize inClassPreProcessor = new NormalizerStandardize();
    inClassPreProcessor.fit(this);
    inClassPreProcessor.transform(this);
}

Example 5 with NormalizerStandardize

use of org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize in project nd4j by deeplearning4j.

the class StandardizeSerializerStrategy method restore.

@Override
public NormalizerStandardize restore(@NonNull InputStream stream) throws IOException {
    DataInputStream dis = new DataInputStream(stream);
    boolean fitLabels = dis.readBoolean();
    NormalizerStandardize result = new NormalizerStandardize(Nd4j.read(dis), Nd4j.read(dis));
    result.fitLabel(fitLabels);
    if (fitLabels) {
        result.setLabelStats(Nd4j.read(dis), Nd4j.read(dis));
    }
    return result;
}