Examples with IMax org.nd4j.linalg.api.ops.impl.indexaccum.IMax used on opensource projects

Example 1 with IMax

use of org.nd4j.linalg.api.ops.impl.indexaccum.IMax in project nd4j by deeplearning4j.

the class CrashTest method op.

protected void op(INDArray x, INDArray y, int i) {
    // broadcast along row & column
    INDArray row = Nd4j.ones(64);
    INDArray column = Nd4j.ones(1024, 1);
    x.addiRowVector(row);
    x.addiColumnVector(column);
    // casual scalar
    x.addi(i * 2);
    // reduction along all dimensions
    float sum = x.sumNumber().floatValue();
    // index reduction
    Nd4j.getExecutioner().exec(new IMax(x), Integer.MAX_VALUE);
    // casual transform
    Nd4j.getExecutioner().exec(new Sqrt(x, x));
    // dup
    INDArray x1 = x.dup(x.ordering());
    INDArray x2 = x.dup(x.ordering());
    INDArray x3 = x.dup('c');
    INDArray x4 = x.dup('f');
    // vstack && hstack
    INDArray vstack = Nd4j.vstack(x, x1, x2, x3, x4);
    INDArray hstack = Nd4j.hstack(x, x1, x2, x3, x4);
    // reduce3 call
    Nd4j.getExecutioner().exec(new ManhattanDistance(x, x2));
    // flatten call
    INDArray flat = Nd4j.toFlattened(x, x1, x2, x3, x4);
    // reduction along dimension: row & column
    INDArray max_0 = x.max(0);
    INDArray max_1 = x.max(1);
    // index reduction along dimension: row & column
    INDArray imax_0 = Nd4j.argMax(x, 0);
    INDArray imax_1 = Nd4j.argMax(x, 1);
    // logisoftmax, softmax & softmax derivative
    Nd4j.getExecutioner().exec(new OldSoftMax(x));
    Nd4j.getExecutioner().exec(new SoftMaxDerivative(x));
    Nd4j.getExecutioner().exec(new LogSoftMax(x));
    // BooleanIndexing
    BooleanIndexing.replaceWhere(x, 5f, Conditions.lessThan(8f));
    // assing on view
    BooleanIndexing.assignIf(x, x1, Conditions.greaterThan(-1000000000f));
    // std var along all dimensions
    float std = x.stdNumber().floatValue();
    // std var along row & col
    INDArray xStd_0 = x.std(0);
    INDArray xStd_1 = x.std(1);
    // blas call
    float dot = (float) Nd4j.getBlasWrapper().dot(x, x1);
    // mmul
    for (boolean tA : paramsA) {
        for (boolean tB : paramsB) {
            INDArray xT = tA ? x.dup() : x.dup().transpose();
            INDArray yT = tB ? y.dup() : y.dup().transpose();
            Nd4j.gemm(xT, yT, tA, tB);
        }
    }
    // specially for views, checking here without dup and rollover
    Nd4j.gemm(x, y, false, false);
    System.out.println("Iteration passed: " + i);
}

Example 2 with IMax

use of org.nd4j.linalg.api.ops.impl.indexaccum.IMax in project nd4j by deeplearning4j.

the class CudaIndexReduceTests method testPinnedIMax.

@Test
public void testPinnedIMax() throws Exception {
    // simple way to stop test if we're not on CUDA backend here
    assertEquals("JcublasLevel1", Nd4j.getBlasWrapper().level1().getClass().getSimpleName());
    INDArray array1 = Nd4j.create(new float[] { 1.0f, 0.1f, 2.0f, 3.0f, 4.0f, 5.0f });
    int idx = ((IndexAccumulation) Nd4j.getExecutioner().exec(new IMax(array1))).getFinalResult();
    System.out.println("Array1: " + array1);
    assertEquals(5, idx);
}

Example 3 with IMax

use of org.nd4j.linalg.api.ops.impl.indexaccum.IMax in project nd4j by deeplearning4j.

the class CudaIndexReduceTests method testPinnedIMax3.

@Test
public void testPinnedIMax3() throws Exception {
    // simple way to stop test if we're not on CUDA backend here
    assertEquals("JcublasLevel1", Nd4j.getBlasWrapper().level1().getClass().getSimpleName());
    INDArray array1 = Nd4j.create(new float[] { 6.0f, 0.1f, 2.0f, 3.0f, 7.0f, 9.0f });
    int idx = ((IndexAccumulation) Nd4j.getExecutioner().exec(new IMax(array1))).getFinalResult();
    System.out.println("Array1: " + array1);
    assertEquals(5, idx);
}

Example 4 with IMax

use of org.nd4j.linalg.api.ops.impl.indexaccum.IMax in project nd4j by deeplearning4j.

the class Nd4jTestsC method testIMax2of4d.

@Test
public void testIMax2of4d() {
    Nd4j.getRandom().setSeed(12345);
    int[] s = new int[] { 2, 3, 4, 5 };
    INDArray arr = Nd4j.rand(s);
    // Test 0,1
    INDArray exp = Nd4j.create(new int[] { 4, 5 });
    for (int i = 0; i < 4; i++) {
        for (int j = 0; j < 5; j++) {
            INDArray subset = arr.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(i), NDArrayIndex.point(j));
            assertArrayEquals(new int[] { 2, 3 }, subset.shape());
            NdIndexIterator iter = new NdIndexIterator('c', 2, 3);
            double max = -Double.MAX_VALUE;
            int maxIdxPos = -1;
            int count = 0;
            while (iter.hasNext()) {
                int[] next = iter.next();
                double d = subset.getDouble(next);
                if (d > max) {
                    max = d;
                    maxIdxPos = count;
                }
                count++;
            }
            exp.putScalar(i, j, maxIdxPos);
        }
    }
    INDArray actC = Nd4j.getExecutioner().exec(new IMax(arr.dup('c')), 0, 1);
    INDArray actF = Nd4j.getExecutioner().exec(new IMax(arr.dup('f')), 0, 1);
    // 
    assertEquals(exp, actC);
    assertEquals(exp, actF);
    // Test 2,3
    exp = Nd4j.create(new int[] { 2, 3 });
    for (int i = 0; i < 2; i++) {
        for (int j = 0; j < 3; j++) {
            INDArray subset = arr.get(NDArrayIndex.point(i), NDArrayIndex.point(j), NDArrayIndex.all(), NDArrayIndex.all());
            assertArrayEquals(new int[] { 4, 5 }, subset.shape());
            NdIndexIterator iter = new NdIndexIterator('c', 4, 5);
            int maxIdxPos = -1;
            double max = -Double.MAX_VALUE;
            int count = 0;
            while (iter.hasNext()) {
                int[] next = iter.next();
                double d = subset.getDouble(next);
                if (d > max) {
                    max = d;
                    maxIdxPos = count;
                }
                count++;
            }
            exp.putScalar(i, j, maxIdxPos);
        }
    }
    actC = Nd4j.getExecutioner().exec(new IMax(arr.dup('c')), 2, 3);
    actF = Nd4j.getExecutioner().exec(new IMax(arr.dup('f')), 2, 3);
    assertEquals(exp, actC);
    assertEquals(exp, actF);
}

Example 5 with IMax

use of org.nd4j.linalg.api.ops.impl.indexaccum.IMax in project deeplearning4j by deeplearning4j.

the class BagOfWordsVectorizerTest method testBagOfWordsVectorizer.

@Test
public void testBagOfWordsVectorizer() throws Exception {
    File rootDir = new ClassPathResource("rootdir").getFile();
    LabelAwareSentenceIterator iter = new LabelAwareFileSentenceIterator(rootDir);
    List<String> labels = Arrays.asList("label1", "label2");
    TokenizerFactory tokenizerFactory = new DefaultTokenizerFactory();
    BagOfWordsVectorizer vectorizer = new BagOfWordsVectorizer.Builder().setMinWordFrequency(1).setStopWords(new ArrayList<String>()).setTokenizerFactory(tokenizerFactory).setIterator(iter).allowParallelTokenization(false).build();
    vectorizer.fit();
    VocabWord word = vectorizer.getVocabCache().wordFor("file.");
    assumeNotNull(word);
    assertEquals(word, vectorizer.getVocabCache().tokenFor("file."));
    assertEquals(2, vectorizer.getVocabCache().totalNumberOfDocs());
    assertEquals(2, word.getSequencesCount());
    assertEquals(2, word.getElementFrequency(), 0.1);
    VocabWord word1 = vectorizer.getVocabCache().wordFor("1");
    assertEquals(1, word1.getSequencesCount());
    assertEquals(1, word1.getElementFrequency(), 0.1);
    log.info("Labels used: " + vectorizer.getLabelsSource().getLabels());
    assertEquals(2, vectorizer.getLabelsSource().getNumberOfLabelsUsed());
    ///////////////////
    INDArray array = vectorizer.transform("This is 2 file.");
    log.info("Transformed array: " + array);
    assertEquals(5, array.columns());
    VocabCache<VocabWord> vocabCache = vectorizer.getVocabCache();
    assertEquals(2, array.getDouble(vocabCache.tokenFor("This").getIndex()), 0.1);
    assertEquals(2, array.getDouble(vocabCache.tokenFor("is").getIndex()), 0.1);
    assertEquals(2, array.getDouble(vocabCache.tokenFor("file.").getIndex()), 0.1);
    assertEquals(0, array.getDouble(vocabCache.tokenFor("1").getIndex()), 0.1);
    assertEquals(1, array.getDouble(vocabCache.tokenFor("2").getIndex()), 0.1);
    DataSet dataSet = vectorizer.vectorize("This is 2 file.", "label2");
    assertEquals(array, dataSet.getFeatureMatrix());
    INDArray labelz = dataSet.getLabels();
    log.info("Labels array: " + labelz);
    int idx2 = ((IndexAccumulation) Nd4j.getExecutioner().exec(new IMax(labelz))).getFinalResult();
    //        assertEquals(1.0, dataSet.getLabels().getDouble(0), 0.1);
    //        assertEquals(0.0, dataSet.getLabels().getDouble(1), 0.1);
    dataSet = vectorizer.vectorize("This is 1 file.", "label1");
    assertEquals(2, dataSet.getFeatureMatrix().getDouble(vocabCache.tokenFor("This").getIndex()), 0.1);
    assertEquals(2, dataSet.getFeatureMatrix().getDouble(vocabCache.tokenFor("is").getIndex()), 0.1);
    assertEquals(2, dataSet.getFeatureMatrix().getDouble(vocabCache.tokenFor("file.").getIndex()), 0.1);
    assertEquals(1, dataSet.getFeatureMatrix().getDouble(vocabCache.tokenFor("1").getIndex()), 0.1);
    assertEquals(0, dataSet.getFeatureMatrix().getDouble(vocabCache.tokenFor("2").getIndex()), 0.1);
    int idx1 = ((IndexAccumulation) Nd4j.getExecutioner().exec(new IMax(dataSet.getLabels()))).getFinalResult();
    //assertEquals(0.0, dataSet.getLabels().getDouble(0), 0.1);
    //assertEquals(1.0, dataSet.getLabels().getDouble(1), 0.1);
    assertNotEquals(idx2, idx1);
    // Serialization check
    File tempFile = File.createTempFile("fdsf", "fdfsdf");
    tempFile.deleteOnExit();
    SerializationUtils.saveObject(vectorizer, tempFile);
    BagOfWordsVectorizer vectorizer2 = SerializationUtils.readObject(tempFile);
    vectorizer2.setTokenizerFactory(tokenizerFactory);
    dataSet = vectorizer2.vectorize("This is 2 file.", "label2");
    assertEquals(array, dataSet.getFeatureMatrix());
}