Examples with NNTrainer ml.shifu.shifu.core.alg.NNTrainer used on opensource projects

Example 6 with NNTrainer

use of ml.shifu.shifu.core.alg.NNTrainer in project shifu by ShifuML.

the class NNTrainerTest method testExceptionWhileSetupModel.

@Test(expectedExceptions = RuntimeException.class)
public void testExceptionWhileSetupModel() throws IOException {
    ModelConfig config = ModelConfig.createInitModelConfig(".", ALGORITHM.NN, ".", false);
    config.getTrain().getParams().put("Propagation", "Q");
    config.getTrain().getParams().put("NumHiddenLayers", 2);
    config.getTrain().getParams().put("LearningRate", 0.1);
    List<Integer> nodes = new ArrayList<Integer>();
    nodes.add(3);
    nodes.add(3);
    nodes.add(3);
    List<String> func = new ArrayList<String>();
    func.add("tanh");
    config.getTrain().getParams().put("NumHiddenNodes", nodes);
    config.getTrain().getParams().put("ActivationFunc", func);
    config.getTrain().setNumTrainEpochs(50);
    NNTrainer trainer = new NNTrainer(config, 0, false);
    try {
        trainer.setDataSet(xor_Trainset);
    } catch (IOException e) {
    }
    trainer.buildNetwork();
}

Example 7 with NNTrainer

use of ml.shifu.shifu.core.alg.NNTrainer in project shifu by ShifuML.

the class ScorerTest method setup.

@BeforeClass
public void setup() throws IOException {
    modelConfig = ModelConfig.createInitModelConfig(".", ALGORITHM.NN, ".", false);
    modelConfig.getTrain().getParams().put("Propagation", "B");
    modelConfig.getTrain().getParams().put("NumHiddenLayers", 2);
    modelConfig.getTrain().getParams().put("LearningRate", 0.5);
    List<Integer> nodes = new ArrayList<Integer>();
    nodes.add(3);
    nodes.add(4);
    List<String> func = new ArrayList<String>();
    func.add("linear");
    func.add("tanh");
    modelConfig.getTrain().getParams().put("NumHiddenNodes", nodes);
    modelConfig.getTrain().getParams().put("ActivationFunc", func);
    NNTrainer trainer = new NNTrainer(modelConfig, 0, false);
    double[] input = { 0., 0. };
    double[] ideal = { 1. };
    MLDataPair pair = new BasicMLDataPair(new BasicMLData(input), new BasicMLData(ideal));
    set.add(pair);
    input = new double[] { 0., 1. };
    ideal = new double[] { 0. };
    pair = new BasicMLDataPair(new BasicMLData(input), new BasicMLData(ideal));
    set.add(pair);
    input = new double[] { 1., 0. };
    ideal = new double[] { 0. };
    pair = new BasicMLDataPair(new BasicMLData(input), new BasicMLData(ideal));
    set.add(pair);
    input = new double[] { 1., 1. };
    ideal = new double[] { 1. };
    pair = new BasicMLDataPair(new BasicMLData(input), new BasicMLData(ideal));
    set.add(pair);
    trainer.setTrainSet(set);
    trainer.setValidSet(set);
    trainer.train();
    modelConfig.getTrain().setAlgorithm("SVM");
    modelConfig.getTrain().getParams().put("Kernel", "rbf");
    modelConfig.getTrain().getParams().put("Const", 0.1);
    modelConfig.getTrain().getParams().put("Gamma", 1.0);
    modelConfig.getVarSelect().setFilterNum(2);
    SVMTrainer svm = new SVMTrainer(modelConfig, 1, false);
    svm.setTrainSet(set);
    svm.setValidSet(set);
    svm.train();
    models.add(trainer.getNetwork());
    models.add(svm.getSVM());
}