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

Example 81 with IrisDataSetIterator

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

the class TestDecayPolicies method testOriginalLearningRateUnchanged.

@Test
public void testOriginalLearningRateUnchanged() {
    // Confirm learning rate is unchanged while hash is updated
    DataSet ds = new IrisDataSetIterator(150, 150).next();
    ds.normalizeZeroMeanZeroUnitVariance();
    Nd4j.getRandom().setSeed(12345);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(false).optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).learningRate(1.0).learningRateDecayPolicy(LearningRatePolicy.Score).lrPolicyDecayRate(0.10).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(org.deeplearning4j.nn.conf.Updater.SGD).seed(12345L).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).activation(Activation.SIGMOID).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.TANH).nIn(3).nOut(3).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork mln = new MultiLayerNetwork(conf);
    mln.init();
    //Run a number of iterations of learning
    mln.setInput(ds.getFeatureMatrix());
    mln.setLabels(ds.getLabels());
    mln.computeGradientAndScore();
    for (int j = 0; j < 1; j++) mln.fit(ds);
    mln.computeGradientAndScore();
    double lr0 = mln.getLayer(0).conf().getLayer().getLearningRate();
    double lr1 = mln.getLayer(1).conf().getLayer().getLearningRate();
    assertEquals(1.0, lr0, 0.0);
    assertEquals(1.0, lr1, 0.0);
}

Example 82 with IrisDataSetIterator

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

the class ModelSerializerTest method testWriteMlnModelInputStream.

@Test
public void testWriteMlnModelInputStream() throws Exception {
    int nIn = 5;
    int nOut = 6;
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).regularization(true).l1(0.01).l2(0.01).learningRate(0.1).activation(Activation.TANH).weightInit(WeightInit.XAVIER).list().layer(0, new DenseLayer.Builder().nIn(nIn).nOut(20).build()).layer(1, new DenseLayer.Builder().nIn(20).nOut(30).build()).layer(2, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(30).nOut(nOut).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    File tempFile = File.createTempFile("tsfs", "fdfsdf");
    tempFile.deleteOnExit();
    FileOutputStream fos = new FileOutputStream(tempFile);
    ModelSerializer.writeModel(net, fos, true);
    // checking adding of DataNormalization to the model file
    NormalizerMinMaxScaler scaler = new NormalizerMinMaxScaler();
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    scaler.fit(iter);
    ModelSerializer.addNormalizerToModel(tempFile, scaler);
    NormalizerMinMaxScaler restoredScaler = ModelSerializer.restoreNormalizerFromFile(tempFile);
    assertNotEquals(null, scaler.getMax());
    assertEquals(scaler.getMax(), restoredScaler.getMax());
    assertEquals(scaler.getMin(), restoredScaler.getMin());
    FileInputStream fis = new FileInputStream(tempFile);
    MultiLayerNetwork network = ModelSerializer.restoreMultiLayerNetwork(fis);
    assertEquals(network.getLayerWiseConfigurations().toJson(), net.getLayerWiseConfigurations().toJson());
    assertEquals(net.params(), network.params());
    assertEquals(net.getUpdater(), network.getUpdater());
}

Example 83 with IrisDataSetIterator

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

the class SerializationUtilsTest method testWriteRead.

@Test
public void testWriteRead() {
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    String irisData = "irisData.dat";
    DataSet freshDataSet = iter.next(150);
    SerializationUtils.saveObject(freshDataSet, new File(irisData));
    DataSet readDataSet = SerializationUtils.readObject(new File(irisData));
    assertEquals(freshDataSet.getFeatureMatrix(), readDataSet.getFeatureMatrix());
    assertEquals(freshDataSet.getLabels(), readDataSet.getLabels());
    try {
        FileUtils.forceDelete(new File(irisData));
    } catch (IOException e) {
        e.printStackTrace();
    }
}

Example 84 with IrisDataSetIterator

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

the class TestParallelEarlyStoppingUI method testParallelStatsListenerCompatibility.

@Test
//To be run manually
@Ignore
public void testParallelStatsListenerCompatibility() throws Exception {
    UIServer uiServer = UIServer.getInstance();
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).weightInit(WeightInit.XAVIER).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).build()).layer(1, new OutputLayer.Builder().nIn(3).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    // it's important that the UI can report results from parallel training
    // there's potential for StatsListener to fail if certain properties aren't set in the model
    StatsStorage statsStorage = new InMemoryStatsStorage();
    net.setListeners(new StatsListener(statsStorage));
    uiServer.attach(statsStorage);
    DataSetIterator irisIter = new IrisDataSetIterator(50, 500);
    EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>().epochTerminationConditions(new MaxEpochsTerminationCondition(500)).scoreCalculator(new DataSetLossCalculator(irisIter, true)).evaluateEveryNEpochs(2).modelSaver(saver).build();
    IEarlyStoppingTrainer<MultiLayerNetwork> trainer = new EarlyStoppingParallelTrainer<>(esConf, net, irisIter, null, 3, 6, 2);
    EarlyStoppingResult<MultiLayerNetwork> result = trainer.fit();
    System.out.println(result);
    assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
}

Example 85 with IrisDataSetIterator

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

the class TestPreProcessedData method testPreprocessedDataCompGraphDataSet.

@Test
public void testPreprocessedDataCompGraphDataSet() {
    //Test _loading_ of preprocessed DataSet data
    int dataSetObjSize = 5;
    int batchSizePerExecutor = 10;
    String path = FilenameUtils.concat(System.getProperty("java.io.tmpdir"), "dl4j_testpreprocdata2");
    File f = new File(path);
    if (f.exists())
        f.delete();
    f.mkdir();
    DataSetIterator iter = new IrisDataSetIterator(5, 150);
    int i = 0;
    while (iter.hasNext()) {
        File f2 = new File(FilenameUtils.concat(path, "data" + (i++) + ".bin"));
        iter.next().save(f2);
    }
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().updater(Updater.RMSPROP).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).graphBuilder().addInputs("in").addLayer("0", new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(4).nOut(3).activation(Activation.TANH).build(), "in").addLayer("1", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3).activation(Activation.SOFTMAX).build(), "0").setOutputs("1").pretrain(false).backprop(true).build();
    SparkComputationGraph sparkNet = new SparkComputationGraph(sc, conf, new ParameterAveragingTrainingMaster.Builder(numExecutors(), dataSetObjSize).batchSizePerWorker(batchSizePerExecutor).averagingFrequency(1).repartionData(Repartition.Always).build());
    sparkNet.setCollectTrainingStats(true);
    sparkNet.fit("file:///" + path.replaceAll("\\\\", "/"));
    SparkTrainingStats sts = sparkNet.getSparkTrainingStats();
    //4 'fits' per averaging (4 executors, 1 averaging freq); 10 examples each -> 40 examples per fit. 150/40 = 3 averagings (round down); 3*4 = 12
    int expNumFits = 12;
    //Unfortunately: perfect partitioning isn't guaranteed by SparkUtils.balancedRandomSplit (esp. if original partitions are all size 1
    // which appears to be occurring at least some of the time), but we should get close to what we expect...
    assertTrue(Math.abs(expNumFits - sts.getValue("ParameterAveragingWorkerFitTimesMs").size()) < 3);
    assertEquals(3, sts.getValue("ParameterAveragingMasterMapPartitionsTimesMs").size());
}