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

Example 16 with DeepLearningParameters

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

the class DeepLearningTest method testInitialWeightsAndBiasesPartial.

@Test
public void testInitialWeightsAndBiasesPartial() {
    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._initial_weights[1].remove();
            parms._initial_weights[1] = null;
            parms._initial_biases[0].remove();
            parms._initial_biases[0] = null;
            parms._epochs = 10;
            dl2 = new DeepLearning(parms).trainModel().get();
        }
        Log.info("dl1  : MSE=" + dl1._output._training_metrics.mse());
        Log.info("dl2  : MSE=" + dl2._output._training_metrics.mse());
        // the second model is better since it got warm-started at least partially
        Assert.assertTrue(dl1._output._training_metrics.mse() > dl2._output._training_metrics.mse());
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl1 != null)
            dl1.delete();
        if (dl2 != null)
            dl2.delete();
        for (Key f : dl1._output.weights) if (f != null)
            f.remove();
        for (Key f : dl1._output.biases) if (f != null)
            f.remove();
    }
}

Example 17 with DeepLearningParameters

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

the class DeepLearningTest method testNoRowWeights.

@Test
public void testNoRowWeights() {
    Frame tfr = null, vfr = null, pred = null, fr2 = null;
    Scope.enter();
    try {
        tfr = parse_test_file("smalldata/junit/no_weights.csv");
        DKV.put(tfr);
        DeepLearningParameters parms = new DeepLearningParameters();
        parms._train = tfr._key;
        parms._response_column = "response";
        parms._reproducible = true;
        parms._seed = 0xdecaf;
        parms._l1 = 0.1;
        parms._epochs = 1;
        parms._hidden = new int[] { 1 };
        parms._classification_stop = -1;
        // Build a first model; all remaining models should be equal
        DeepLearningModel dl = new DeepLearning(parms).trainModel().get();
        pred = dl.score(parms.train());
        hex.ModelMetricsBinomial mm = hex.ModelMetricsBinomial.getFromDKV(dl, parms.train());
        assertEquals(0.7592592592592592, mm.auc_obj()._auc, 1e-8);
        double mse = dl._output._training_metrics.mse();
        assertEquals(0.314813341867078, mse, 1e-8);
        assertTrue(dl.testJavaScoring(tfr, fr2 = dl.score(tfr), 1e-5));
        dl.delete();
    } finally {
        if (tfr != null)
            tfr.remove();
        if (vfr != null)
            vfr.remove();
        if (pred != null)
            pred.remove();
        if (fr2 != null)
            fr2.remove();
    }
    Scope.exit();
}

Example 18 with DeepLearningParameters

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

the class DeepLearningTest method testMiniBatch1.

@Test
public void testMiniBatch1() {
    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 = 1;
        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 19 with DeepLearningParameters

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

the class DeepLearningTest method testHuber.

@Test
public void testHuber() {
    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 = huber;
        dl = new DeepLearning(parms).trainModel().get();
        Assert.assertEquals(6.4964976811, ((ModelMetricsRegression) dl._output._training_metrics)._mean_residual_deviance, 1e-5);
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl != null)
            dl.deleteCrossValidationModels();
        if (dl != null)
            dl.delete();
    }
}

Example 20 with DeepLearningParameters

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

the class DeepLearningTest method testCheckpointOverwriteWithBestModel2.

// Check that the restarted model honors the previous model as a best model so far
@Test
public void testCheckpointOverwriteWithBestModel2() {
    Frame tfr = null;
    DeepLearningModel dl = null;
    DeepLearningModel dl2 = null;
    Frame train = null, valid = null;
    try {
        tfr = parse_test_file("./smalldata/iris/iris.csv");
        FrameSplitter fs = new FrameSplitter(tfr, new double[] { 0.8 }, new Key[] { Key.make("train"), Key.make("valid") }, null);
        fs.compute2();
        train = fs.getResult()[0];
        valid = fs.getResult()[1];
        DeepLearningParameters parms = new DeepLearningParameters();
        parms._train = train._key;
        parms._valid = valid._key;
        parms._epochs = 10;
        parms._response_column = "C5";
        parms._reproducible = true;
        parms._hidden = new int[] { 50, 50 };
        parms._seed = 0xdecaf;
        parms._train_samples_per_iteration = 0;
        parms._score_duty_cycle = 1;
        parms._score_interval = 0;
        parms._stopping_rounds = 0;
        parms._overwrite_with_best_model = true;
        dl = new DeepLearning(parms).trainModel().get();
        double ll1 = ((ModelMetricsMultinomial) dl._output._validation_metrics).logloss();
        DeepLearningParameters parms2 = (DeepLearningParameters) parms.clone();
        parms2._epochs = 20;
        parms2._checkpoint = dl._key;
        dl2 = new DeepLearning(parms2).trainModel().get();
        double ll2 = ((ModelMetricsMultinomial) dl2._output._validation_metrics).logloss();
        Assert.assertTrue(ll2 <= ll1);
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl != null)
            dl.delete();
        if (dl2 != null)
            dl2.delete();
        if (train != null)
            train.delete();
        if (valid != null)
            valid.delete();
    }
}