Examples with DataSet org.nd4j.linalg.dataset.DataSet used on opensource projects

Example 1 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class CNNGradientCheckTest method testGradientCNNL1L2MLN.

@Test
public void testGradientCNNL1L2MLN() {
    //Parameterized test, testing combinations of:
    // (a) activation function
    // (b) Whether to test at random initialization, or after some learning (i.e., 'characteristic mode of operation')
    // (c) Loss function (with specified output activations)
    String[] activFns = { "sigmoid", "tanh" };
    //If true: run some backprop steps first
    boolean[] characteristic = { false, true };
    LossFunctions.LossFunction[] lossFunctions = { LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD, LossFunctions.LossFunction.MSE };
    //i.e., lossFunctions[i] used with outputActivations[i] here
    String[] outputActivations = { "softmax", "tanh" };
    DataSet ds = new IrisDataSetIterator(150, 150).next();
    ds.normalizeZeroMeanZeroUnitVariance();
    INDArray input = ds.getFeatureMatrix();
    INDArray labels = ds.getLabels();
    //use l2vals[i] with l1vals[i]
    double[] l2vals = { 0.4, 0.0, 0.4, 0.4 };
    double[] l1vals = { 0.0, 0.0, 0.5, 0.0 };
    double[] biasL2 = { 0.0, 0.0, 0.0, 0.2 };
    double[] biasL1 = { 0.0, 0.0, 0.6, 0.0 };
    for (String afn : activFns) {
        for (boolean doLearningFirst : characteristic) {
            for (int i = 0; i < lossFunctions.length; i++) {
                for (int k = 0; k < l2vals.length; k++) {
                    LossFunctions.LossFunction lf = lossFunctions[i];
                    String outputActivation = outputActivations[i];
                    double l2 = l2vals[k];
                    double l1 = l1vals[k];
                    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().regularization(true).l2(l2).l1(l1).l2Bias(biasL2[k]).l1Bias(biasL1[k]).optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).seed(12345L).list().layer(0, new ConvolutionLayer.Builder(new int[] { 1, 1 }).nIn(1).nOut(6).weightInit(WeightInit.XAVIER).activation(afn).updater(Updater.NONE).build()).layer(1, new OutputLayer.Builder(lf).activation(outputActivation).nOut(3).weightInit(WeightInit.XAVIER).updater(Updater.NONE).build()).pretrain(false).backprop(true).setInputType(InputType.convolutionalFlat(1, 4, 1));
                    MultiLayerConfiguration conf = builder.build();
                    MultiLayerNetwork mln = new MultiLayerNetwork(conf);
                    mln.init();
                    String testName = new Object() {
                    }.getClass().getEnclosingMethod().getName();
                    if (doLearningFirst) {
                        //Run a number of iterations of learning
                        mln.setInput(ds.getFeatures());
                        mln.setLabels(ds.getLabels());
                        mln.computeGradientAndScore();
                        double scoreBefore = mln.score();
                        for (int j = 0; j < 10; j++) mln.fit(ds);
                        mln.computeGradientAndScore();
                        double scoreAfter = mln.score();
                        //Can't test in 'characteristic mode of operation' if not learning
                        String msg = testName + "- score did not (sufficiently) decrease during learning - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst=" + doLearningFirst + " (before=" + scoreBefore + ", scoreAfter=" + scoreAfter + ")";
                        assertTrue(msg, scoreAfter < 0.8 * scoreBefore);
                    }
                    if (PRINT_RESULTS) {
                        System.out.println(testName + "- activationFn=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst=" + doLearningFirst);
                        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);
                    assertTrue(gradOK);
                }
            }
        }
    }
}

Example 2 with DataSet

use of org.nd4j.linalg.dataset.DataSet in project deeplearning4j by deeplearning4j.

the class CNNGradientCheckTest method testGradientCNNMLN.

@Test
public void testGradientCNNMLN() {
    //Parameterized test, testing combinations of:
    // (a) activation function
    // (b) Whether to test at random initialization, or after some learning (i.e., 'characteristic mode of operation')
    // (c) Loss function (with specified output activations)
    String[] activFns = { "sigmoid", "tanh" };
    //If true: run some backprop steps first
    boolean[] characteristic = { false, true };
    LossFunctions.LossFunction[] lossFunctions = { LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD, LossFunctions.LossFunction.MSE };
    //i.e., lossFunctions[i] used with outputActivations[i] here
    String[] outputActivations = { "softmax", "tanh" };
    DataSet ds = new IrisDataSetIterator(150, 150).next();
    ds.normalizeZeroMeanZeroUnitVariance();
    INDArray input = ds.getFeatureMatrix();
    INDArray labels = ds.getLabels();
    for (String afn : activFns) {
        for (boolean doLearningFirst : characteristic) {
            for (int i = 0; i < lossFunctions.length; i++) {
                LossFunctions.LossFunction lf = lossFunctions[i];
                String outputActivation = outputActivations[i];
                MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().regularization(false).optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).updater(Updater.NONE).weightInit(WeightInit.XAVIER).seed(12345L).list().layer(0, new ConvolutionLayer.Builder(1, 1).nOut(6).activation(afn).build()).layer(1, new OutputLayer.Builder(lf).activation(outputActivation).nOut(3).build()).setInputType(InputType.convolutionalFlat(1, 4, 1)).pretrain(false).backprop(true);
                MultiLayerConfiguration conf = builder.build();
                MultiLayerNetwork mln = new MultiLayerNetwork(conf);
                mln.init();
                String name = new Object() {
                }.getClass().getEnclosingMethod().getName();
                if (doLearningFirst) {
                    //Run a number of iterations of learning
                    mln.setInput(ds.getFeatures());
                    mln.setLabels(ds.getLabels());
                    mln.computeGradientAndScore();
                    double scoreBefore = mln.score();
                    for (int j = 0; j < 10; j++) mln.fit(ds);
                    mln.computeGradientAndScore();
                    double scoreAfter = mln.score();
                    //Can't test in 'characteristic mode of operation' if not learning
                    String msg = name + " - score did not (sufficiently) decrease during learning - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst= " + doLearningFirst + " (before=" + scoreBefore + ", scoreAfter=" + scoreAfter + ")";
                    assertTrue(msg, scoreAfter < 0.8 * scoreBefore);
                }
                if (PRINT_RESULTS) {
                    System.out.println(name + " - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst=" + doLearningFirst);
                    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);
                assertTrue(gradOK);
            }
        }
    }
}

Example 3 with DataSet

use of org.nd4j.linalg.dataset.DataSet 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 4 with DataSet

use of org.nd4j.linalg.dataset.DataSet 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 5 with DataSet

use of org.nd4j.linalg.dataset.DataSet 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());
}