Examples with INDArray org.nd4j.linalg.api.ndarray.INDArray used on opensource projects

Example 11 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class GradientCheckTests method testEmbeddingLayerSimple.

@Test
public void testEmbeddingLayerSimple() {
    Random r = new Random(12345);
    int nExamples = 5;
    INDArray input = Nd4j.zeros(nExamples, 1);
    INDArray labels = Nd4j.zeros(nExamples, 3);
    for (int i = 0; i < nExamples; i++) {
        input.putScalar(i, r.nextInt(4));
        labels.putScalar(new int[] { i, r.nextInt(3) }, 1.0);
    }
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(true).l2(0.2).l1(0.1).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L).list().layer(0, new EmbeddingLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).activation(Activation.TANH).build()).layer(1, new OutputLayer.Builder(LossFunction.MCXENT).nIn(3).nOut(3).weightInit(WeightInit.XAVIER).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).activation(Activation.SOFTMAX).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork mln = new MultiLayerNetwork(conf);
    mln.init();
    if (PRINT_RESULTS) {
        System.out.println("testEmbeddingLayerSimple");
        for (int j = 0; j < mln.getnLayers(); j++) System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
    }
    boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
    String msg = "testEmbeddingLayerSimple";
    assertTrue(msg, gradOK);
}

Example 12 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetiteratorTest method testSeqRRDSIMultipleArrayWritablesOneReader.

@Test
public void testSeqRRDSIMultipleArrayWritablesOneReader() {
    //Input with multiple array writables:
    List<List<Writable>> sequence1 = new ArrayList<>();
    sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 1, 2, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 100, 200, 300 })), new IntWritable(0)));
    sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 4, 5, 6 })), new NDArrayWritable(Nd4j.create(new double[] { 400, 500, 600 })), new IntWritable(1)));
    List<List<Writable>> sequence2 = new ArrayList<>();
    sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 7, 8, 9 })), new NDArrayWritable(Nd4j.create(new double[] { 700, 800, 900 })), new IntWritable(2)));
    sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 10, 11, 12 })), new NDArrayWritable(Nd4j.create(new double[] { 1000, 1100, 1200 })), new IntWritable(3)));
    SequenceRecordReader rr = new CollectionSequenceRecordReader(Arrays.asList(sequence1, sequence2));
    SequenceRecordReaderDataSetIterator iter = new SequenceRecordReaderDataSetIterator(rr, 2, 4, 2, false);
    DataSet ds = iter.next();
    //2 examples, 6 values per time step, 2 time steps
    INDArray expFeatures = Nd4j.create(2, 6, 2);
    expFeatures.tensorAlongDimension(0, 1, 2).assign(Nd4j.create(new double[][] { { 1, 4 }, { 2, 5 }, { 3, 6 }, { 100, 400 }, { 200, 500 }, { 300, 600 } }));
    expFeatures.tensorAlongDimension(1, 1, 2).assign(Nd4j.create(new double[][] { { 7, 10 }, { 8, 11 }, { 9, 12 }, { 700, 1000 }, { 800, 1100 }, { 900, 1200 } }));
    INDArray expLabels = Nd4j.create(2, 4, 2);
    expLabels.tensorAlongDimension(0, 1, 2).assign(Nd4j.create(new double[][] { { 1, 0 }, { 0, 1 }, { 0, 0 }, { 0, 0 } }));
    expLabels.tensorAlongDimension(1, 1, 2).assign(Nd4j.create(new double[][] { { 0, 0 }, { 0, 0 }, { 1, 0 }, { 0, 1 } }));
    assertEquals(expFeatures, ds.getFeatureMatrix());
    assertEquals(expLabels, ds.getLabels());
}

Example 13 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetiteratorTest method testSequenceRecordReaderReset.

@Test
public void testSequenceRecordReaderReset() throws Exception {
    //need to manually extract
    for (int i = 0; i < 3; i++) {
        new ClassPathResource(String.format("csvsequence_%d.txt", i)).getTempFileFromArchive();
        new ClassPathResource(String.format("csvsequencelabels_%d.txt", i)).getTempFileFromArchive();
    }
    ClassPathResource resource = new ClassPathResource("csvsequence_0.txt");
    String featuresPath = resource.getTempFileFromArchive().getAbsolutePath().replaceAll("0", "%d");
    resource = new ClassPathResource("csvsequencelabels_0.txt");
    String labelsPath = resource.getTempFileFromArchive().getAbsolutePath().replaceAll("0", "%d");
    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);
    assertEquals(3, iter.inputColumns());
    assertEquals(4, iter.totalOutcomes());
    int nResets = 5;
    for (int i = 0; i < nResets; i++) {
        iter.reset();
        int count = 0;
        while (iter.hasNext()) {
            DataSet ds = iter.next();
            INDArray features = ds.getFeatureMatrix();
            INDArray labels = ds.getLabels();
            assertArrayEquals(new int[] { 1, 3, 4 }, features.shape());
            assertArrayEquals(new int[] { 1, 4, 4 }, labels.shape());
            count++;
        }
        assertEquals(3, count);
    }
}

Example 14 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetiteratorTest method testRecordReaderDataSetIteratorNDArrayWritableLabels.

@Test
public void testRecordReaderDataSetIteratorNDArrayWritableLabels() {
    Collection<Collection<Writable>> data = new ArrayList<>();
    data.add(Arrays.<Writable>asList(new DoubleWritable(0), new DoubleWritable(1), new NDArrayWritable(Nd4j.create(new double[] { 1.1, 2.1, 3.1 }))));
    data.add(Arrays.<Writable>asList(new DoubleWritable(2), new DoubleWritable(3), new NDArrayWritable(Nd4j.create(new double[] { 4.1, 5.1, 6.1 }))));
    data.add(Arrays.<Writable>asList(new DoubleWritable(4), new DoubleWritable(5), new NDArrayWritable(Nd4j.create(new double[] { 7.1, 8.1, 9.1 }))));
    RecordReader rr = new CollectionRecordReader(data);
    int batchSize = 3;
    int labelIndexFrom = 2;
    int labelIndexTo = 2;
    boolean regression = true;
    DataSetIterator rrdsi = new RecordReaderDataSetIterator(rr, batchSize, labelIndexFrom, labelIndexTo, regression);
    DataSet ds = rrdsi.next();
    INDArray expFeatures = Nd4j.create(new double[][] { { 0, 1 }, { 2, 3 }, { 4, 5 } });
    INDArray expLabels = Nd4j.create(new double[][] { { 1.1, 2.1, 3.1 }, { 4.1, 5.1, 6.1 }, { 7.1, 8.1, 9.1 } });
    assertEquals(expFeatures, ds.getFeatures());
    assertEquals(expLabels, ds.getLabels());
    //ALSO: test if we have NDArrayWritables for BOTH the features and the labels
    data = new ArrayList<>();
    data.add(Arrays.<Writable>asList(new NDArrayWritable(Nd4j.create(new double[] { 0, 1 })), new NDArrayWritable(Nd4j.create(new double[] { 1.1, 2.1, 3.1 }))));
    data.add(Arrays.<Writable>asList(new NDArrayWritable(Nd4j.create(new double[] { 2, 3 })), new NDArrayWritable(Nd4j.create(new double[] { 4.1, 5.1, 6.1 }))));
    data.add(Arrays.<Writable>asList(new NDArrayWritable(Nd4j.create(new double[] { 4, 5 })), new NDArrayWritable(Nd4j.create(new double[] { 7.1, 8.1, 9.1 }))));
    rr = new CollectionRecordReader(data);
    rrdsi = new RecordReaderDataSetIterator(rr, batchSize, labelIndexFrom, labelIndexTo, regression);
    ds = rrdsi.next();
    assertEquals(expFeatures, ds.getFeatures());
    assertEquals(expLabels, ds.getLabels());
}

Example 15 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray 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);
}