Examples with DeepLearningParameters hex.deeplearning.DeepLearningModel.DeepLearningParameters used on opensource projects

Example 56 with DeepLearningParameters

use of hex.deeplearning.DeepLearningModel.DeepLearningParameters in project h2o-3 by h2oai.

the class DeepLearningTest method elasticAveragingTrivial.

@Test
public void elasticAveragingTrivial() {
    DeepLearningParameters dl;
    Frame frTrain;
    int N = 2;
    DeepLearningModel[] models = new DeepLearningModel[N];
    dl = new DeepLearningParameters();
    Scope.enter();
    try {
        for (int i = 0; i < N; ++i) {
            frTrain = parse_test_file("./smalldata/covtype/covtype.20k.data");
            Vec resp = frTrain.lastVec().toCategoricalVec();
            frTrain.remove(frTrain.vecs().length - 1).remove();
            frTrain.add("Response", resp);
            DKV.put(frTrain);
            dl._train = frTrain._key;
            dl._response_column = ((Frame) DKV.getGet(dl._train)).lastVecName();
            dl._export_weights_and_biases = true;
            dl._hidden = new int[] { 17, 11 };
            dl._quiet_mode = false;
            // make it reproducible
            dl._seed = 1234;
            dl._reproducible = true;
            // only do one M/R iteration, and there's no elastic average yet - so the two paths below should be identical
            dl._epochs = 1;
            dl._train_samples_per_iteration = -1;
            if (i == 0) {
                // no elastic averaging
                dl._elastic_averaging = false;
                //ignored
                dl._elastic_averaging_moving_rate = 0.5;
                //ignored
                dl._elastic_averaging_regularization = 0.9;
            } else {
                // no-op elastic averaging
                //go different path, but don't really do anything because of epochs=1 and train_samples_per_iteration=-1
                dl._elastic_averaging = true;
                //doesn't matter, it's not used since we only do one M/R iteration and there's no time average
                dl._elastic_averaging_moving_rate = 0.5;
                //doesn't matter, since elastic average isn't yet available in first iteration
                dl._elastic_averaging_regularization = 0.1;
            }
            // Invoke DL and block till the end
            DeepLearning job = new DeepLearning(dl);
            // Get the model
            models[i] = job.trainModel().get();
            frTrain.remove();
        }
        for (int i = 0; i < N; ++i) {
            Log.info(models[i]._output._training_metrics.cm().table().toString());
            Assert.assertEquals(models[i]._output._training_metrics._MSE, models[0]._output._training_metrics._MSE, 1e-6);
        }
    } finally {
        for (int i = 0; i < N; ++i) if (models[i] != null)
            models[i].delete();
        Scope.exit();
    }
}

Example 57 with DeepLearningParameters

use of hex.deeplearning.DeepLearningModel.DeepLearningParameters in project h2o-3 by h2oai.

the class DeepLearningTest method testCategoricalEncodingAUTO.

@Test
public void testCategoricalEncodingAUTO() {
    Frame tfr = null;
    DeepLearningModel dl = null;
    try {
        tfr = parse_test_file("./smalldata/junit/titanic_alt.csv");
        Vec v = tfr.remove("survived");
        tfr.add("survived", v.toCategoricalVec());
        v.remove();
        DKV.put(tfr);
        DeepLearningParameters parms = new DeepLearningParameters();
        parms._train = tfr._key;
        parms._valid = tfr._key;
        parms._response_column = "survived";
        parms._reproducible = true;
        parms._hidden = new int[] { 20, 20 };
        parms._seed = 0xdecaf;
        parms._nfolds = 3;
        parms._distribution = bernoulli;
        parms._categorical_encoding = Model.Parameters.CategoricalEncodingScheme.AUTO;
        dl = new DeepLearning(parms).trainModel().get();
        Assert.assertEquals(0.97329, ((ModelMetricsBinomial) dl._output._training_metrics)._auc._auc, 1e-3);
        Assert.assertEquals(0.97329, ((ModelMetricsBinomial) dl._output._validation_metrics)._auc._auc, 1e-3);
        Assert.assertEquals(0.93152165, ((ModelMetricsBinomial) dl._output._cross_validation_metrics)._auc._auc, 1e-5);
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl != null)
            dl.deleteCrossValidationModels();
        if (dl != null)
            dl.delete();
    }
}

Example 58 with DeepLearningParameters

use of hex.deeplearning.DeepLearningModel.DeepLearningParameters in project h2o-3 by h2oai.

the class DeepLearningTest method testConvergenceDeviance.

@Test
public void testConvergenceDeviance() {
    Frame tfr = null;
    DeepLearningModel dl = null;
    DeepLearningModel dl2 = null;
    try {
        tfr = parse_test_file("./smalldata/logreg/prostate.csv");
        DeepLearningParameters parms = new DeepLearningParameters();
        parms._train = tfr._key;
        parms._epochs = 1000000;
        parms._response_column = "AGE";
        parms._reproducible = true;
        parms._hidden = new int[] { 2, 2 };
        parms._seed = 0xdecaf;
        parms._variable_importances = true;
        parms._score_duty_cycle = 1.0;
        parms._score_interval = 0;
        //don't stop based on absolute classification error
        parms._classification_stop = -1;
        //don't stop based on absolute classification error
        parms._stopping_rounds = 2;
        //don't stop based on absolute classification error
        parms._stopping_metric = ScoreKeeper.StoppingMetric.deviance;
        parms._stopping_tolerance = 0.0;
        dl = new DeepLearning(parms).trainModel().get();
        Assert.assertTrue(dl.epoch_counter < parms._epochs);
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl != null)
            dl.delete();
        if (dl2 != null)
            dl2.delete();
    }
}