Examples with IrisDataSetIterator org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator used on opensource projects

Example 76 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class MultiLayerTest method testIterationCountAndPersistence.

@Test
public void testIterationCountAndPersistence() throws IOException {
    Nd4j.getRandom().setSeed(123);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER).activation(Activation.TANH).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    DataSetIterator iter = new IrisDataSetIterator(50, 150);
    assertEquals(0, network.getLayerWiseConfigurations().getIterationCount());
    network.fit(iter);
    assertEquals(3, network.getLayerWiseConfigurations().getIterationCount());
    iter.reset();
    network.fit(iter);
    assertEquals(6, network.getLayerWiseConfigurations().getIterationCount());
    iter.reset();
    network.fit(iter.next());
    assertEquals(7, network.getLayerWiseConfigurations().getIterationCount());
    ByteArrayOutputStream baos = new ByteArrayOutputStream();
    ModelSerializer.writeModel(network, baos, true);
    byte[] asBytes = baos.toByteArray();
    ByteArrayInputStream bais = new ByteArrayInputStream(asBytes);
    MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(bais, true);
    assertEquals(7, net.getLayerWiseConfigurations().getIterationCount());
}

Example 77 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class MultiLayerTest method testBatchNorm.

@Test
public void testBatchNorm() {
    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 BatchNormalization.Builder().nOut(2).build()).layer(3, 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.setLabels(trainTest.getTrain().getLabels());
    network.init();
    network.fit(trainTest.getTrain());
}

Example 78 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class BackTrackLineSearchTest method testBackTrackLineGradientDescent.

///////////////////////////////////////////////////////////////////////////
@Test
public void testBackTrackLineGradientDescent() {
    OptimizationAlgorithm optimizer = OptimizationAlgorithm.LINE_GRADIENT_DESCENT;
    DataSetIterator irisIter = new IrisDataSetIterator(1, 1);
    DataSet data = irisIter.next();
    MultiLayerNetwork network = new MultiLayerNetwork(getIrisMultiLayerConfig(Activation.SIGMOID, 100, optimizer));
    network.init();
    IterationListener listener = new ScoreIterationListener(1);
    network.setListeners(Collections.singletonList(listener));
    double oldScore = network.score(data);
    network.fit(data.getFeatureMatrix(), data.getLabels());
    double score = network.score();
    assertTrue(score < oldScore);
}

Example 79 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class TestOptimizers method testOptimizersMLP.

@Test
public void testOptimizersMLP() {
    //Check that the score actually decreases over time
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    OptimizationAlgorithm[] toTest = { OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT, OptimizationAlgorithm.LINE_GRADIENT_DESCENT, OptimizationAlgorithm.CONJUGATE_GRADIENT, OptimizationAlgorithm.LBFGS };
    DataSet ds = iter.next();
    ds.normalizeZeroMeanZeroUnitVariance();
    for (OptimizationAlgorithm oa : toTest) {
        int nIter = 10;
        MultiLayerNetwork network = new MultiLayerNetwork(getMLPConfigIris(oa, nIter));
        network.init();
        double score = network.score(ds);
        assertTrue(score != 0.0 && !Double.isNaN(score));
        if (PRINT_OPT_RESULTS)
            System.out.println("testOptimizersMLP() - " + oa);
        int nCallsToOptimizer = 30;
        double[] scores = new double[nCallsToOptimizer + 1];
        scores[0] = score;
        for (int i = 0; i < nCallsToOptimizer; i++) {
            network.fit(ds);
            double scoreAfter = network.score(ds);
            scores[i + 1] = scoreAfter;
            assertTrue("Score is NaN after optimization", !Double.isNaN(scoreAfter));
            assertTrue("OA= " + oa + ", before= " + score + ", after= " + scoreAfter, scoreAfter <= score);
            score = scoreAfter;
        }
        if (PRINT_OPT_RESULTS)
            System.out.println(oa + " - " + Arrays.toString(scores));
    }
}

Example 80 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class TestParamAndGradientIterationListener method test.

@Test
public void test() {
    IrisDataSetIterator iter = new IrisDataSetIterator(30, 150);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(1e-5).iterations(1).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(20).build()).layer(1, new DenseLayer.Builder().nIn(20).nOut(30).build()).layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(30).nOut(3).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    IterationListener listener = ParamAndGradientIterationListener.builder().outputToFile(true).file(new File(System.getProperty("java.io.tmpdir") + "/paramAndGradTest.txt")).outputToConsole(true).outputToLogger(false).iterations(2).printHeader(true).printMean(false).printMinMax(false).printMeanAbsValue(true).delimiter("\t").build();
    net.setListeners(listener);
    for (int i = 0; i < 2; i++) {
        net.fit(iter);
    }
}