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

Example 56 with DataSet

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

the class TestDataSetConsumer method consumeWhileHasNext.

/**
     * This method cycles through iterator, whie iterator.hasNext() returns true. After each cycle execution time is simulated either using Thread.sleep() or empty cycle
     *
     * @param consumeWithSleep
     * @return
     */
public long consumeWhileHasNext(boolean consumeWithSleep) {
    count.set(0);
    if (iterator == null)
        throw new RuntimeException("Can't use consumeWhileHasNext() if iterator isn't set");
    while (iterator.hasNext()) {
        DataSet ds = iterator.next();
        this.consumeOnce(ds, consumeWithSleep);
    }
    return count.get();
}

Example 57 with DataSet

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

the class TestCompGraphCNN method testCNNComputationGraphKernelTooLarge.

@Test(expected = InvalidInputTypeException.class)
public void testCNNComputationGraphKernelTooLarge() {
    int imageWidth = 23;
    int imageHeight = 19;
    int nChannels = 1;
    int classes = 2;
    int numSamples = 200;
    int kernelHeight = 3;
    int kernelWidth = imageWidth;
    DataSet trainInput;
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123).graphBuilder().addInputs("input").setInputTypes(InputType.convolutional(nChannels, imageWidth, imageHeight)).addLayer("conv1", new ConvolutionLayer.Builder().kernelSize(kernelHeight, kernelWidth).stride(1, 1).nIn(nChannels).nOut(2).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build(), "input").addLayer("pool1", new SubsamplingLayer.Builder().poolingType(SubsamplingLayer.PoolingType.MAX).kernelSize(imageHeight - kernelHeight + 1, 1).stride(1, 1).build(), "conv1").addLayer("output", new OutputLayer.Builder().nOut(classes).build(), "pool1").setOutputs("output").backprop(true).pretrain(false).build();
    ComputationGraph model = new ComputationGraph(conf);
    model.init();
    INDArray emptyFeatures = Nd4j.zeros(numSamples, imageWidth * imageHeight * nChannels);
    INDArray emptyLables = Nd4j.zeros(numSamples, classes);
    trainInput = new DataSet(emptyFeatures, emptyLables);
    model.fit(trainInput);
}

Example 58 with DataSet

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

the class TestCompGraphCNN method getDS.

protected static DataSetIterator getDS() {
    List<DataSet> list = new ArrayList<>(5);
    for (int i = 0; i < 5; i++) {
        INDArray f = Nd4j.create(1, 32 * 32 * 3);
        INDArray l = Nd4j.create(1, 10);
        l.putScalar(i, 1.0);
        list.add(new DataSet(f, l));
    }
    return new ListDataSetIterator(list, 5);
}

Example 59 with DataSet

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

the class TestComputationGraphNetwork method testForwardBasicIris.

@Test
public void testForwardBasicIris() {
    ComputationGraphConfiguration configuration = getIrisGraphConfiguration();
    ComputationGraph graph = new ComputationGraph(configuration);
    graph.init();
    MultiLayerConfiguration mlc = getIrisMLNConfiguration();
    MultiLayerNetwork net = new MultiLayerNetwork(mlc);
    net.init();
    DataSetIterator iris = new IrisDataSetIterator(150, 150);
    DataSet ds = iris.next();
    graph.setInput(0, ds.getFeatureMatrix());
    Map<String, INDArray> activations = graph.feedForward(false);
    //2 layers + 1 input node
    assertEquals(3, activations.size());
    assertTrue(activations.containsKey("input"));
    assertTrue(activations.containsKey("firstLayer"));
    assertTrue(activations.containsKey("outputLayer"));
    //Now: set parameters of both networks to be identical. Then feedforward, and check we get the same outputs
    Nd4j.getRandom().setSeed(12345);
    int nParams = getNumParams();
    INDArray params = Nd4j.rand(1, nParams);
    graph.setParams(params.dup());
    net.setParams(params.dup());
    List<INDArray> mlnAct = net.feedForward(ds.getFeatureMatrix(), false);
    activations = graph.feedForward(ds.getFeatureMatrix(), false);
    assertEquals(mlnAct.get(0), activations.get("input"));
    assertEquals(mlnAct.get(1), activations.get("firstLayer"));
    assertEquals(mlnAct.get(2), activations.get("outputLayer"));
}

Example 60 with DataSet

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

the class TestComputationGraphNetwork method testScoringDataSet.

@Test
public void testScoringDataSet() {
    ComputationGraphConfiguration configuration = getIrisGraphConfiguration();
    ComputationGraph graph = new ComputationGraph(configuration);
    graph.init();
    MultiLayerConfiguration mlc = getIrisMLNConfiguration();
    MultiLayerNetwork net = new MultiLayerNetwork(mlc);
    net.init();
    DataSetIterator iris = new IrisDataSetIterator(150, 150);
    DataSet ds = iris.next();
    //Now: set parameters of both networks to be identical. Then feedforward, and check we get the same score
    Nd4j.getRandom().setSeed(12345);
    int nParams = getNumParams();
    INDArray params = Nd4j.rand(1, nParams);
    graph.setParams(params.dup());
    net.setParams(params.dup());
    double scoreMLN = net.score(ds, false);
    double scoreCG = graph.score(ds, false);
    assertEquals(scoreMLN, scoreCG, 1e-4);
}