Examples with DataSetIterator org.nd4j.linalg.dataset.api.iterator.DataSetIterator used on opensource projects

Example 56 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class MultiLayerTest method testDbn.

@Test
public void testDbn() throws Exception {
    Nd4j.MAX_SLICES_TO_PRINT = -1;
    Nd4j.MAX_ELEMENTS_PER_SLICE = -1;
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().iterations(100).momentum(0.9).optimizationAlgo(OptimizationAlgorithm.LBFGS).regularization(true).l2(2e-4).list().layer(0, new RBM.Builder(RBM.HiddenUnit.GAUSSIAN, RBM.VisibleUnit.GAUSSIAN).nIn(4).nOut(3).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(0, 1)).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(0, 1)).activation(Activation.SOFTMAX).build()).build();
    MultiLayerNetwork d = new MultiLayerNetwork(conf);
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    DataSet next = iter.next();
    Nd4j.writeTxt(next.getFeatureMatrix(), "iris.txt", "\t");
    next.normalizeZeroMeanZeroUnitVariance();
    SplitTestAndTrain testAndTrain = next.splitTestAndTrain(110);
    DataSet train = testAndTrain.getTrain();
    d.fit(train);
    DataSet test = testAndTrain.getTest();
    Evaluation eval = new Evaluation();
    INDArray output = d.output(test.getFeatureMatrix());
    eval.eval(test.getLabels(), output);
    log.info("Score " + eval.stats());
}

Example 57 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class MultiLayerTest method testPredict.

@Test
public void testPredict() throws Exception {
    Nd4j.getRandom().setSeed(12345);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(false).learningRate(1.0).weightInit(WeightInit.XAVIER).seed(12345L).list().layer(0, new DenseLayer.Builder().nIn(784).nOut(50).activation(Activation.RELU).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(50).nOut(10).build()).pretrain(false).backprop(true).setInputType(InputType.convolutional(28, 28, 1)).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    DataSetIterator ds = new MnistDataSetIterator(10, 10);
    net.fit(ds);
    DataSetIterator testDs = new MnistDataSetIterator(1, 1);
    DataSet testData = testDs.next();
    testData.setLabelNames(Arrays.asList("0", "1", "2", "3", "4", "5", "6", "7", "8", "9"));
    String actualLables = testData.getLabelName(0);
    List<String> prediction = net.predict(testData);
    assertTrue(actualLables != null);
    assertTrue(prediction.get(0) != null);
}

Example 58 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class MultiLayerTest method testBackProp.

@Test
public void testBackProp() {
    Nd4j.getRandom().setSeed(123);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).iterations(5).seed(123).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER).activation(Activation.TANH).build()).layer(1, new DenseLayer.Builder().nIn(3).nOut(2).weightInit(WeightInit.XAVIER).activation(Activation.TANH).build()).layer(2, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nIn(2).nOut(3).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.setListeners(new ScoreIterationListener(1));
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    DataSet next = iter.next();
    next.normalizeZeroMeanZeroUnitVariance();
    SplitTestAndTrain trainTest = next.splitTestAndTrain(110);
    network.setInput(trainTest.getTrain().getFeatureMatrix());
    network.setLabels(trainTest.getTrain().getLabels());
    network.init();
    network.fit(trainTest.getTrain());
    DataSet test = trainTest.getTest();
    Evaluation eval = new Evaluation();
    INDArray output = network.output(test.getFeatureMatrix());
    eval.eval(test.getLabels(), output);
    log.info("Score " + eval.stats());
}

Example 59 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class BackPropMLPTest method testMLPTrivial.

@Test
public void testMLPTrivial() {
    //Simplest possible case: 1 hidden layer, 1 hidden neuron, batch size of 1.
    MultiLayerNetwork network = new MultiLayerNetwork(getIrisMLPSimpleConfig(new int[] { 1 }, Activation.SIGMOID));
    network.setListeners(new ScoreIterationListener(1));
    network.init();
    DataSetIterator iter = new IrisDataSetIterator(1, 10);
    while (iter.hasNext()) network.fit(iter.next());
}

Example 60 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class BackPropMLPTest method testMLP2.

@Test
public void testMLP2() {
    //Simple mini-batch test with multiple hidden layers
    MultiLayerConfiguration conf = getIrisMLPSimpleConfig(new int[] { 5, 15, 3 }, Activation.TANH);
    System.out.println(conf);
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    DataSetIterator iter = new IrisDataSetIterator(12, 120);
    while (iter.hasNext()) {
        network.fit(iter.next());
    }
}