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

Example 21 with DeepLearningParameters

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

the class DeepLearningTest method testConvergenceAUC_ModifiedHuber.

@Ignore
public void testConvergenceAUC_ModifiedHuber() {
    Frame tfr = null;
    DeepLearningModel dl = null;
    DeepLearningModel dl2 = null;
    try {
        tfr = parse_test_file("./smalldata/logreg/prostate.csv");
        for (String s : new String[] { "CAPSULE" }) {
            Vec resp = tfr.vec(s).toCategoricalVec();
            tfr.remove(s).remove();
            tfr.add(s, resp);
            DKV.put(tfr);
        }
        DeepLearningParameters parms = new DeepLearningParameters();
        parms._train = tfr._key;
        parms._epochs = 1000000;
        parms._response_column = "CAPSULE";
        parms._reproducible = true;
        parms._hidden = new int[] { 2, 2 };
        parms._seed = 0xdecaf;
        parms._variable_importances = true;
        parms._distribution = modified_huber;
        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.AUC;
        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();
    }
}

Example 22 with DeepLearningParameters

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

the class DeepLearningTest method testInitialWeightsAndBiases.

@Test
public void testInitialWeightsAndBiases() {
    Frame tfr = null;
    DeepLearningModel dl1 = null;
    DeepLearningModel dl2 = null;
    try {
        tfr = parse_test_file("./smalldata/gbm_test/BostonHousing.csv");
        // train DL model from scratch
        {
            DeepLearningParameters parms = new DeepLearningParameters();
            parms._train = tfr._key;
            parms._response_column = tfr.lastVecName();
            parms._activation = DeepLearningParameters.Activation.Tanh;
            parms._reproducible = true;
            parms._hidden = new int[] { 20, 20 };
            parms._seed = 0xdecad;
            parms._export_weights_and_biases = true;
            dl1 = new DeepLearning(parms).trainModel().get();
        }
        // train DL model starting from weights/biases from first model
        {
            DeepLearningParameters parms = new DeepLearningParameters();
            parms._train = tfr._key;
            parms._response_column = tfr.lastVecName();
            parms._activation = DeepLearningParameters.Activation.Tanh;
            parms._reproducible = true;
            parms._hidden = new int[] { 20, 20 };
            parms._seed = 0xdecad;
            parms._initial_weights = dl1._output.weights;
            parms._initial_biases = dl1._output.biases;
            parms._epochs = 0;
            dl2 = new DeepLearning(parms).trainModel().get();
        }
        Log.info("dl1  : MSE=" + dl1._output._training_metrics.mse());
        Log.info("dl2  : MSE=" + dl2._output._training_metrics.mse());
        Assert.assertTrue(Math.abs(dl1._output._training_metrics.mse() - dl2._output._training_metrics.mse()) < 1e-6);
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl1 != null)
            dl1.delete();
        if (dl2 != null)
            dl2.delete();
        for (Key f : dl1._output.weights) f.remove();
        for (Key f : dl1._output.biases) f.remove();
    }
}

Example 23 with DeepLearningParameters

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

the class DeepLearningTest method testGaussian.

@Test
public void testGaussian() {
    Frame tfr = null;
    DeepLearningModel dl = null;
    try {
        tfr = parse_test_file("./smalldata/gbm_test/BostonHousing.csv");
        DeepLearningParameters parms = new DeepLearningParameters();
        parms._train = tfr._key;
        parms._response_column = tfr.lastVecName();
        parms._reproducible = true;
        parms._hidden = new int[] { 20, 20 };
        parms._seed = 0xdecaf;
        parms._distribution = gaussian;
        dl = new DeepLearning(parms).trainModel().get();
        Assert.assertEquals(12.93808, /*MSE*/
        ((ModelMetricsRegression) dl._output._training_metrics)._mean_residual_deviance, 1e-5);
        Assert.assertEquals(12.93808, /*MSE*/
        ((ModelMetricsRegression) dl._output._training_metrics)._MSE, 1e-5);
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl != null)
            dl.deleteCrossValidationModels();
        if (dl != null)
            dl.delete();
    }
}

Example 24 with DeepLearningParameters

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

the class DeepLearningTest method testCheckpointBackwards.

@Test
public void testCheckpointBackwards() {
    Frame tfr = null;
    DeepLearningModel dl = null;
    DeepLearningModel dl2 = null;
    try {
        tfr = parse_test_file("./smalldata/iris/iris.csv");
        DeepLearningParameters parms = new DeepLearningParameters();
        parms._train = tfr._key;
        parms._epochs = 10;
        parms._response_column = "C5";
        parms._reproducible = true;
        parms._hidden = new int[] { 2, 2 };
        parms._seed = 0xdecaf;
        parms._variable_importances = true;
        dl = new DeepLearning(parms).trainModel().get();
        DeepLearningParameters parms2 = (DeepLearningParameters) parms.clone();
        parms2._epochs = 9;
        parms2._checkpoint = dl._key;
        try {
            dl2 = new DeepLearning(parms2).trainModel().get();
            Assert.fail("Should toss exception instead of reaching here");
        } catch (H2OIllegalArgumentException ex) {
        }
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl != null)
            dl.delete();
        if (dl2 != null)
            dl2.delete();
    }
}

Example 25 with DeepLearningParameters

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

the class DeepLearningTest method testRegression.

@Test
public void testRegression() {
    Frame train = null;
    Frame preds = null;
    DeepLearningModel model = null;
    Scope.enter();
    try {
        train = parse_test_file("./smalldata/junit/titanic_alt.csv");
        DeepLearningParameters parms = new DeepLearningParameters();
        parms._train = train._key;
        parms._response_column = "age";
        parms._reproducible = true;
        parms._hidden = new int[] { 20, 20 };
        parms._distribution = laplace;
        parms._seed = 0xdecaf;
        model = new DeepLearning(parms).trainModel().get();
        preds = model.score(train);
        //actual
        Vec targets = train.vec("age");
        ModelMetricsRegression mm = ModelMetricsRegression.make(preds.vec(0), targets, parms._distribution);
        Log.info(mm.toString());
        mm = ModelMetricsRegression.make(preds.vec(0), targets, gaussian);
        Log.info(mm.toString());
        mm = ModelMetricsRegression.make(preds.vec(0), targets, poisson);
        Log.info(mm.toString());
    } catch (Throwable t) {
        t.printStackTrace();
        throw t;
    } finally {
        if (model != null)
            model.delete();
        if (preds != null)
            preds.remove();
        if (train != null)
            train.remove();
        Scope.exit();
    }
}