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

Example 86 with DataSet

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

the class ActivationLayerTest method testDenseActivationLayer.

@Test
public void testDenseActivationLayer() throws Exception {
    DataSetIterator iter = new MnistDataSetIterator(2, 2);
    DataSet next = iter.next();
    // Run without separate activation layer
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123).list().layer(0, new DenseLayer.Builder().nIn(28 * 28 * 1).nOut(10).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nIn(10).nOut(10).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.fit(next);
    // Run with separate activation layer
    MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123).list().layer(0, new DenseLayer.Builder().nIn(28 * 28 * 1).nOut(10).activation(Activation.IDENTITY).weightInit(WeightInit.XAVIER).build()).layer(1, new org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder().activation(Activation.RELU).build()).layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nIn(10).nOut(10).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork network2 = new MultiLayerNetwork(conf2);
    network2.init();
    network2.fit(next);
    // check parameters
    assertEquals(network.getLayer(0).getParam("W"), network2.getLayer(0).getParam("W"));
    assertEquals(network.getLayer(1).getParam("W"), network2.getLayer(2).getParam("W"));
    assertEquals(network.getLayer(0).getParam("b"), network2.getLayer(0).getParam("b"));
    assertEquals(network.getLayer(1).getParam("b"), network2.getLayer(2).getParam("b"));
    // check activations
    network.init();
    network.setInput(next.getFeatureMatrix());
    List<INDArray> activations = network.feedForward(true);
    network2.init();
    network2.setInput(next.getFeatureMatrix());
    List<INDArray> activations2 = network2.feedForward(true);
    assertEquals(activations.get(1).reshape(activations2.get(2).shape()), activations2.get(2));
    assertEquals(activations.get(2), activations2.get(3));
}

Example 87 with DataSet

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

the class SubsamplingLayerTest method getData.

public INDArray getData() throws Exception {
    DataSetIterator data = new MnistDataSetIterator(5, 5);
    DataSet mnist = data.next();
    nExamples = mnist.numExamples();
    return mnist.getFeatureMatrix().reshape(nExamples, nChannelsIn, inputWidth, inputHeight);
}

Example 88 with DataSet

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

the class RBMTests method testMnist.

@Test
public void testMnist() throws Exception {
    MnistDataFetcher fetcher = new MnistDataFetcher(true);
    Nd4j.ENFORCE_NUMERICAL_STABILITY = true;
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().iterations(30).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(1e-1f).layer(new org.deeplearning4j.nn.conf.layers.RBM.Builder().nIn(784).nOut(600).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(1, 1e-5)).lossFunction(LossFunctions.LossFunction.RECONSTRUCTION_CROSSENTROPY).build()).build();
    conf.setPretrain(true);
    org.deeplearning4j.nn.conf.layers.RBM layerConf = (org.deeplearning4j.nn.conf.layers.RBM) conf.getLayer();
    fetcher.fetch(10);
    DataSet d2 = fetcher.next();
    org.nd4j.linalg.api.rng.distribution.Distribution dist = Nd4j.getDistributions().createNormal(1, 1e-5);
    System.out.println(dist.sample(new int[] { layerConf.getNIn(), layerConf.getNOut() }));
    INDArray input = d2.getFeatureMatrix();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    RBM rbm = (RBM) conf.getLayer().instantiate(conf, null, 0, params, true);
    rbm.fit(input);
}

Example 89 with DataSet

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

the class TestCustomLayers method testCustomOutputLayerMLN.

@Test
public void testCustomOutputLayerMLN() {
    //First: Ensure that the CustomOutputLayer class is registered
    ObjectMapper mapper = NeuralNetConfiguration.mapper();
    AnnotatedClass ac = AnnotatedClass.construct(Layer.class, mapper.getSerializationConfig().getAnnotationIntrospector(), null);
    Collection<NamedType> types = mapper.getSubtypeResolver().collectAndResolveSubtypes(ac, mapper.getSerializationConfig(), mapper.getSerializationConfig().getAnnotationIntrospector());
    Set<Class<?>> registeredSubtypes = new HashSet<>();
    boolean found = false;
    for (NamedType nt : types) {
        System.out.println(nt);
        //            registeredSubtypes.add(nt.getType());
        if (nt.getType() == CustomOutputLayer.class)
            found = true;
    }
    assertTrue("CustomOutputLayer: not registered with NeuralNetConfiguration mapper", found);
    //Second: let's create a MultiLayerCofiguration with one, and check JSON and YAML config actually works...
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).learningRate(0.1).list().layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1, new CustomOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10).build()).pretrain(false).backprop(true).build();
    String json = conf.toJson();
    String yaml = conf.toYaml();
    System.out.println(json);
    MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
    assertEquals(conf, confFromJson);
    MultiLayerConfiguration confFromYaml = MultiLayerConfiguration.fromYaml(yaml);
    assertEquals(conf, confFromYaml);
    //Third: check initialization
    Nd4j.getRandom().setSeed(12345);
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    assertTrue(net.getLayer(1) instanceof CustomOutputLayerImpl);
    //Fourth: compare to an equivalent standard output layer (should be identical)
    MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().seed(12345).learningRate(0.1).weightInit(WeightInit.XAVIER).list().layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10).build()).pretrain(false).backprop(true).build();
    Nd4j.getRandom().setSeed(12345);
    MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
    net2.init();
    assertEquals(net2.params(), net.params());
    INDArray testFeatures = Nd4j.rand(1, 10);
    INDArray testLabels = Nd4j.zeros(1, 10);
    testLabels.putScalar(0, 3, 1.0);
    DataSet ds = new DataSet(testFeatures, testLabels);
    assertEquals(net2.output(testFeatures), net.output(testFeatures));
    assertEquals(net2.score(ds), net.score(ds), 1e-6);
}

Example 90 with DataSet

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

the class TestCustomLayers method checkInitializationFF.

@Test
public void checkInitializationFF() {
    //Actually create a network with a custom layer; check initialization and forward pass
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().learningRate(0.1).list().layer(0, new DenseLayer.Builder().nIn(9).nOut(10).build()).layer(1, //hard-coded nIn/nOut of 10
    new CustomLayer(3.14159)).layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(11).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    assertEquals(9 * 10 + 10, net.getLayer(0).numParams());
    assertEquals(10 * 10 + 10, net.getLayer(1).numParams());
    assertEquals(10 * 11 + 11, net.getLayer(2).numParams());
    //Check for exceptions...
    net.output(Nd4j.rand(1, 9));
    net.fit(new DataSet(Nd4j.rand(1, 9), Nd4j.rand(1, 11)));
}