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

Example 51 with DeepLearningParameters

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

the class DeepLearningTest method testBinomial.

@Test
public void testBinomial() {
    Frame train = null;
    Frame preds = null;
    Frame small = null, large = null;
    DeepLearningModel model = null;
    Scope.enter();
    try {
        train = parse_test_file("./smalldata/junit/titanic_alt.csv");
        Vec v = train.remove("survived");
        train.add("survived", v.toCategoricalVec());
        v.remove();
        DKV.put(train);
        DeepLearningParameters parms = new DeepLearningParameters();
        parms._train = train._key;
        parms._response_column = "survived";
        parms._reproducible = true;
        parms._hidden = new int[] { 20, 20 };
        parms._seed = 0xdecaf;
        model = new DeepLearning(parms).trainModel().get();
        FrameSplitter fs = new FrameSplitter(train, new double[] { 0.002 }, new Key[] { Key.make("small"), Key.make("large") }, null);
        fs.compute2();
        small = fs.getResult()[0];
        large = fs.getResult()[1];
        preds = model.score(small);
        //actual
        Vec labels = small.vec("survived");
        //actual
        String[] fullDomain = train.vec("survived").domain();
        ModelMetricsBinomial mm = ModelMetricsBinomial.make(preds.vec(2), labels, fullDomain);
        Log.info(mm.toString());
        mm = ModelMetricsBinomial.make(preds.vec(2), labels, new String[] { "0", "1" });
        Log.info(mm.toString());
        mm = ModelMetricsBinomial.make(preds.vec(2), labels);
        Log.info(mm.toString());
        try {
            mm = ModelMetricsBinomial.make(preds.vec(2), labels, new String[] { "a", "b" });
            Log.info(mm.toString());
            Assert.assertFalse(true);
        } catch (IllegalArgumentException ex) {
            ex.printStackTrace();
        }
    } catch (Throwable t) {
        t.printStackTrace();
        throw t;
    } finally {
        if (model != null)
            model.delete();
        if (preds != null)
            preds.remove();
        if (train != null)
            train.remove();
        if (small != null)
            small.delete();
        if (large != null)
            large.delete();
        Scope.exit();
    }
}

Example 52 with DeepLearningParameters

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

the class DeepLearningTest method testPretrainedAE.

@Test
public void testPretrainedAE() {
    Frame tfr = null;
    DeepLearningModel dl1 = null;
    DeepLearningModel dl2 = null;
    DeepLearningModel ae = null;
    try {
        tfr = parse_test_file("./smalldata/gbm_test/BostonHousing.csv");
        Vec r = tfr.remove("chas");
        tfr.add("chas", r.toCategoricalVec());
        DKV.put(tfr);
        r.remove();
        // train unsupervised AE
        Key<DeepLearningModel> key = Key.make("ae_model");
        {
            DeepLearningParameters parms = new DeepLearningParameters();
            parms._train = tfr._key;
            parms._ignored_columns = new String[] { "chas" };
            parms._activation = DeepLearningParameters.Activation.TanhWithDropout;
            parms._reproducible = true;
            parms._hidden = new int[] { 20, 20 };
            parms._input_dropout_ratio = 0.1;
            parms._hidden_dropout_ratios = new double[] { 0.2, 0.1 };
            parms._autoencoder = true;
            parms._seed = 0xdecaf;
            ae = new DeepLearning(parms, key).trainModel().get();
            // test POJO
            Frame res = ae.score(tfr);
            assertTrue(ae.testJavaScoring(tfr, res, 1e-5));
            res.remove();
        }
        // train supervised DL model
        {
            DeepLearningParameters parms = new DeepLearningParameters();
            parms._train = tfr._key;
            parms._response_column = "chas";
            parms._activation = DeepLearningParameters.Activation.TanhWithDropout;
            parms._reproducible = true;
            parms._hidden = new int[] { 20, 20 };
            parms._input_dropout_ratio = 0.1;
            parms._hidden_dropout_ratios = new double[] { 0.2, 0.1 };
            parms._seed = 0xdecad;
            parms._pretrained_autoencoder = key;
            parms._rate_decay = 1.0;
            parms._adaptive_rate = false;
            parms._rate_annealing = 1e-3;
            parms._loss = DeepLearningParameters.Loss.CrossEntropy;
            dl1 = new DeepLearning(parms).trainModel().get();
            // test POJO
            Frame res = dl1.score(tfr);
            assertTrue(dl1.testJavaScoring(tfr, res, 1e-5));
            res.remove();
        }
        // train DL model from scratch
        {
            DeepLearningParameters parms = new DeepLearningParameters();
            parms._train = tfr._key;
            parms._response_column = "chas";
            parms._activation = DeepLearningParameters.Activation.TanhWithDropout;
            parms._reproducible = true;
            parms._hidden = new int[] { 20, 20 };
            parms._input_dropout_ratio = 0.1;
            parms._hidden_dropout_ratios = new double[] { 0.2, 0.1 };
            parms._seed = 0xdecad;
            parms._rate_decay = 1.0;
            parms._adaptive_rate = false;
            parms._rate_annealing = 1e-3;
            dl2 = new DeepLearning(parms).trainModel().get();
            // test POJO
            Frame res = dl2.score(tfr);
            assertTrue(dl2.testJavaScoring(tfr, res, 1e-5));
            res.remove();
        }
        Log.info("pretrained  : MSE=" + dl1._output._training_metrics.mse());
        Log.info("from scratch: MSE=" + dl2._output._training_metrics.mse());
    //      Assert.assertTrue(dl1._output._training_metrics.mse() < dl2._output._training_metrics.mse());
    } finally {
        if (tfr != null)
            tfr.delete();
        if (ae != null)
            ae.delete();
        if (dl1 != null)
            dl1.delete();
        if (dl2 != null)
            dl2.delete();
    }
}

Example 53 with DeepLearningParameters

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

the class DeepLearningTest method testMiniBatch50.

@Test
public void testMiniBatch50() {
    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._mini_batch_size = 50;
        dl = new DeepLearning(parms).trainModel().get();
        Assert.assertEquals(12.938076268040659, dl._output._training_metrics._MSE, 1e-6);
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl != null)
            dl.deleteCrossValidationModels();
        if (dl != null)
            dl.delete();
    }
}

Example 54 with DeepLearningParameters

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

the class DeepLearningTest method testMultinomial.

@Test
public void testMultinomial() {
    Frame train = null;
    Frame preds = null;
    DeepLearningModel model = null;
    Scope.enter();
    try {
        train = parse_test_file("./smalldata/junit/titanic_alt.csv");
        Vec v = train.remove("pclass");
        train.add("pclass", v.toCategoricalVec());
        v.remove();
        DKV.put(train);
        DeepLearningParameters p = new DeepLearningParameters();
        p._train = train._key;
        // last column is the response
        p._response_column = "pclass";
        p._activation = DeepLearningParameters.Activation.RectifierWithDropout;
        p._hidden = new int[] { 50, 50 };
        p._epochs = 1;
        p._adaptive_rate = false;
        p._rate = 0.005;
        p._sparse = true;
        model = new DeepLearning(p).trainModel().get();
        preds = model.score(train);
        //remove label, keep only probs
        preds.remove(0);
        //actual
        Vec labels = train.vec("pclass");
        //actual
        String[] fullDomain = train.vec("pclass").domain();
        ModelMetricsMultinomial mm = ModelMetricsMultinomial.make(preds, labels, fullDomain);
        Log.info(mm.toString());
    } finally {
        if (model != null)
            model.delete();
        if (preds != null)
            preds.remove();
        if (train != null)
            train.remove();
        Scope.exit();
    }
}

Example 55 with DeepLearningParameters

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

the class DeepLearningTest method testEarlyStopping.

@Test
public void testEarlyStopping() {
    Frame tfr = null;
    DeepLearningModel dl = null;
    try {
        tfr = parse_test_file("./smalldata/junit/two_spiral.csv");
        for (String s : new String[] { "Class" }) {
            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 = 100;
        parms._response_column = "Class";
        parms._reproducible = true;
        parms._classification_stop = 0.7;
        parms._score_duty_cycle = 1;
        parms._score_interval = 0;
        parms._hidden = new int[] { 100, 100 };
        parms._seed = 0xdecaf;
        // Build a first model; all remaining models should be equal
        dl = new DeepLearning(parms).trainModel().get();
        assertTrue(dl.stopped_early);
        assertTrue(dl.epoch_counter < 100);
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl != null)
            dl.delete();
    }
}