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

Example 6 with DeepLearningParameters

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

the class DeepLearningSpiralsTest method run.

@Test
public void run() {
    Scope.enter();
    NFSFileVec nfs = TestUtil.makeNfsFileVec("smalldata/junit/two_spiral.csv");
    Frame frame = ParseDataset.parse(Key.make(), nfs._key);
    Log.info(frame);
    int resp = frame.names().length - 1;
    for (boolean sparse : new boolean[] { true, false }) {
        for (boolean col_major : new boolean[] { false }) {
            if (!sparse && col_major)
                continue;
            Key model_id = Key.make();
            // build the model
            {
                DeepLearningParameters p = new DeepLearningParameters();
                p._epochs = 5000;
                p._hidden = new int[] { 100 };
                p._sparse = sparse;
                p._col_major = col_major;
                p._activation = DeepLearningParameters.Activation.Tanh;
                p._initial_weight_distribution = DeepLearningParameters.InitialWeightDistribution.Normal;
                p._initial_weight_scale = 2.5;
                p._loss = DeepLearningParameters.Loss.CrossEntropy;
                p._train = frame._key;
                p._response_column = frame.names()[resp];
                // Convert response to categorical
                Scope.track(frame.replace(resp, frame.vecs()[resp].toCategoricalVec()));
                DKV.put(frame);
                p._rho = 0.99;
                p._epsilon = 5e-3;
                //stop when reaching 0 classification error on training data
                p._classification_stop = 0;
                p._train_samples_per_iteration = 10000;
                p._stopping_rounds = 5;
                p._stopping_metric = ScoreKeeper.StoppingMetric.misclassification;
                p._score_each_iteration = true;
                p._reproducible = true;
                p._seed = 1234;
                new DeepLearning(p, model_id).trainModel().get();
            }
            // score and check result
            {
                DeepLearningModel mymodel = DKV.getGet(model_id);
                Frame pred = mymodel.score(frame);
                ModelMetricsBinomial mm = ModelMetricsBinomial.getFromDKV(mymodel, frame);
                double error = mm._auc.defaultErr();
                Log.info("Error: " + error);
                if (error > 0.1) {
                    Assert.fail("Test classification error is not <= 0.1, but " + error + ".");
                }
                Assert.assertTrue(mymodel.testJavaScoring(frame, pred, 1e-6));
                pred.delete();
                mymodel.delete();
            }
        }
    }
    frame.delete();
    Scope.exit();
}

Example 7 with DeepLearningParameters

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

the class DeepLearningCheckpointReporting method run.

@Test
public void run() {
    Scope.enter();
    Frame frame = null;
    try {
        NFSFileVec trainfv = TestUtil.makeNfsFileVec("smalldata/logreg/prostate.csv");
        frame = ParseDataset.parse(Key.make(), trainfv._key);
        DeepLearningParameters p = new DeepLearningParameters();
        // populate model parameters
        p._train = frame._key;
        // last column is the response
        p._response_column = "CAPSULE";
        p._activation = DeepLearningParameters.Activation.Rectifier;
        p._epochs = 4;
        p._train_samples_per_iteration = -1;
        p._score_duty_cycle = 1;
        p._score_interval = 0;
        p._overwrite_with_best_model = false;
        p._classification_stop = -1;
        p._seed = 1234;
        p._reproducible = true;
        // Convert response 'C785' to categorical (digits 1 to 10)
        int ci = frame.find("CAPSULE");
        Scope.track(frame.replace(ci, frame.vecs()[ci].toCategoricalVec()));
        DKV.put(frame);
        long start = System.currentTimeMillis();
        //to avoid rounding issues with printed time stamp (1 second resolution)
        try {
            Thread.sleep(1000);
        } catch (InterruptedException ex) {
        }
        DeepLearningModel model = new DeepLearning(p).trainModel().get();
        //seconds
        long sleepTime = 5;
        try {
            Thread.sleep(sleepTime * 1000);
        } catch (InterruptedException ex) {
        }
        // checkpoint restart after sleep
        DeepLearningParameters p2 = (DeepLearningParameters) p.clone();
        p2._checkpoint = model._key;
        p2._epochs *= 2;
        DeepLearningModel model2 = null;
        try {
            model2 = new DeepLearning(p2).trainModel().get();
            long end = System.currentTimeMillis();
            TwoDimTable table = model2._output._scoring_history;
            double priorDurationDouble = 0;
            long priorTimeStampLong = 0;
            DateTimeFormatter fmt = DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss");
            for (int i = 0; i < table.getRowDim(); ++i) {
                // Check that timestamp is correct, and growing monotonically
                String timestamp = (String) table.get(i, 0);
                long timeStampLong = fmt.parseMillis(timestamp);
                Assert.assertTrue("Timestamp must be later than outside timer start", timeStampLong >= start);
                Assert.assertTrue("Timestamp must be earlier than outside timer end", timeStampLong <= end);
                Assert.assertTrue("Timestamp must increase", timeStampLong >= priorTimeStampLong);
                priorTimeStampLong = timeStampLong;
                // Check that duration is growing monotonically
                String duration = (String) table.get(i, 1);
                //"x.xxxx sec"
                duration = duration.substring(0, duration.length() - 4);
                try {
                    double durationDouble = Double.parseDouble(duration);
                    Assert.assertTrue("Duration must be >0: " + durationDouble, durationDouble >= 0);
                    Assert.assertTrue("Duration must increase: " + priorDurationDouble + " -> " + durationDouble, durationDouble >= priorDurationDouble);
                    Assert.assertTrue("Duration cannot be more than outside timer delta", durationDouble <= (end - start) / 1e3);
                    priorDurationDouble = durationDouble;
                } catch (NumberFormatException ex) {
                //skip
                }
                // Check that epoch counting is good
                //1 epoch per step
                Assert.assertTrue("Epoch counter must be contiguous", (Double) table.get(i, 3) == i);
                //1 iteration per step
                Assert.assertTrue("Iteration counter must match epochs", (Integer) table.get(i, 4) == i);
            }
            try {
                // Check that duration doesn't see the sleep
                String durationBefore = (String) table.get((int) (p._epochs), 1);
                durationBefore = durationBefore.substring(0, durationBefore.length() - 4);
                String durationAfter = (String) table.get((int) (p._epochs + 1), 1);
                durationAfter = durationAfter.substring(0, durationAfter.length() - 4);
                double diff = Double.parseDouble(durationAfter) - Double.parseDouble(durationBefore);
                Assert.assertTrue("Duration must be smooth; actual " + diff + ", expected at most " + sleepTime + " (before=" + durationBefore + ", after=" + durationAfter + ")", diff < sleepTime + 1);
                // Check that time stamp does see the sleep
                String timeStampBefore = (String) table.get((int) (p._epochs), 0);
                long timeStampBeforeLong = fmt.parseMillis(timeStampBefore);
                String timeStampAfter = (String) table.get((int) (p._epochs + 1), 0);
                long timeStampAfterLong = fmt.parseMillis(timeStampAfter);
                Assert.assertTrue("Time stamp must experience a delay", timeStampAfterLong - timeStampBeforeLong >= (sleepTime - 1) * 1000);
                // Check that the training speed is similar before and after checkpoint restart
                String speedBefore = (String) table.get((int) (p._epochs), 2);
                speedBefore = speedBefore.substring(0, speedBefore.length() - 9);
                double speedBeforeDouble = Double.parseDouble(speedBefore);
                String speedAfter = (String) table.get((int) (p._epochs + 1), 2);
                speedAfter = speedAfter.substring(0, speedAfter.length() - 9);
                double speedAfterDouble = Double.parseDouble(speedAfter);
                //expect less than 50% change in speed
                Assert.assertTrue("Speed shouldn't change more than 50%", Math.abs(speedAfterDouble - speedBeforeDouble) / speedBeforeDouble < 0.5);
            } catch (NumberFormatException ex) {
            //skip runtimes > 1 minute (too hard to parse into seconds here...).
            }
        } finally {
            if (model != null)
                model.delete();
            if (model2 != null)
                model2.delete();
        }
    } finally {
        if (frame != null)
            frame.remove();
        Scope.exit();
    }
}

Example 8 with DeepLearningParameters

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

the class DeepLearningGradientCheck method gradientCheck.

@Test
public void gradientCheck() {
    Frame tfr = null;
    DeepLearningModel dl = null;
    try {
        tfr = parse_test_file("smalldata/glm_test/cancar_logIn.csv");
        for (String s : new String[] { "Merit", "Class" }) {
            Vec f = tfr.vec(s).toCategoricalVec();
            tfr.remove(s).remove();
            tfr.add(s, f);
        }
        DKV.put(tfr);
        tfr.add("Binary", tfr.anyVec().makeZero());
        new MRTask() {

            public void map(Chunk[] c) {
                for (int i = 0; i < c[0]._len; ++i) if (c[0].at8(i) == 1)
                    c[1].set(i, 1);
            }
        }.doAll(tfr.vecs(new String[] { "Class", "Binary" }));
        Vec cv = tfr.vec("Binary").toCategoricalVec();
        tfr.remove("Binary").remove();
        tfr.add("Binary", cv);
        DKV.put(tfr);
        Random rng = new Random(0xDECAF);
        int count = 0;
        int failedcount = 0;
        double maxRelErr = 0;
        double meanRelErr = 0;
        for (DistributionFamily dist : new DistributionFamily[] { DistributionFamily.gaussian, DistributionFamily.laplace, DistributionFamily.quantile, DistributionFamily.huber, // DistributionFamily.modified_huber,
        DistributionFamily.gamma, DistributionFamily.poisson, DistributionFamily.AUTO, DistributionFamily.tweedie, DistributionFamily.multinomial, DistributionFamily.bernoulli }) {
            for (DeepLearningParameters.Activation act : new DeepLearningParameters.Activation[] { //            DeepLearningParameters.Activation.ExpRectifier,
            DeepLearningParameters.Activation.Tanh, DeepLearningParameters.Activation.Rectifier }) {
                for (String response : new String[] { //binary classification
                "Binary", //multi-class
                "Class", //regression
                "Cost" }) {
                    for (boolean adaptive : new boolean[] { true, false }) {
                        for (int miniBatchSize : new int[] { 1 }) {
                            if (response.equals("Class")) {
                                if (dist != DistributionFamily.multinomial && dist != DistributionFamily.AUTO)
                                    continue;
                            } else if (response.equals("Binary")) {
                                if (dist != DistributionFamily.modified_huber && dist != DistributionFamily.bernoulli && dist != DistributionFamily.AUTO)
                                    continue;
                            } else {
                                if (dist == DistributionFamily.multinomial || dist == DistributionFamily.modified_huber || dist == DistributionFamily.bernoulli)
                                    continue;
                            }
                            DeepLearningParameters parms = new DeepLearningParameters();
                            parms._huber_alpha = rng.nextDouble() + 0.1;
                            parms._tweedie_power = 1.01 + rng.nextDouble() * 0.9;
                            parms._quantile_alpha = 0.05 + rng.nextDouble() * 0.9;
                            parms._train = tfr._key;
                            //converge to a reasonable model to avoid too large gradients
                            parms._epochs = 100;
                            parms._l1 = 1e-3;
                            parms._l2 = 1e-3;
                            parms._force_load_balance = false;
                            parms._hidden = new int[] { 10, 10, 10 };
                            //otherwise we introduce small bprop errors
                            parms._fast_mode = false;
                            parms._response_column = response;
                            parms._distribution = dist;
                            parms._max_w2 = 10;
                            parms._seed = 0xaaabbb;
                            parms._activation = act;
                            parms._adaptive_rate = adaptive;
                            parms._rate = 1e-4;
                            parms._momentum_start = 0.9;
                            parms._momentum_stable = 0.99;
                            parms._mini_batch_size = miniBatchSize;
                            //                DeepLearningModelInfo.gradientCheck = null;
                            //tell it what gradient to collect
                            DeepLearningModelInfo.gradientCheck = new DeepLearningModelInfo.GradientCheck(0, 0, 0);
                            // Build a first model; all remaining models should be equal
                            DeepLearning job = new DeepLearning(parms);
                            try {
                                dl = job.trainModel().get();
                                boolean classification = response.equals("Class") || response.equals("Binary");
                                if (!classification) {
                                    Frame p = dl.score(tfr);
                                    hex.ModelMetrics mm = hex.ModelMetrics.getFromDKV(dl, tfr);
                                    double resdev = ((ModelMetricsRegression) mm)._mean_residual_deviance;
                                    Log.info("Mean residual deviance: " + resdev);
                                    p.delete();
                                }
                                //golden version
                                DeepLearningModelInfo modelInfo = IcedUtils.deepCopy(dl.model_info());
                                //                Log.info(modelInfo.toStringAll());
                                long before = dl.model_info().checksum_impl();
                                float meanLoss = 0;
                                // loop over every row in the dataset and check that the predictions
                                for (int rId = 0; rId < tfr.numRows(); rId += 1) /*miniBatchSize*/
                                {
                                    // start from scratch - with a clean model
                                    dl.set_model_info(IcedUtils.deepCopy(modelInfo));
                                    final DataInfo di = dl.model_info().data_info();
                                    // populate miniBatch (consecutive rows)
                                    final DataInfo.Row[] rowsMiniBatch = new DataInfo.Row[miniBatchSize];
                                    for (int i = 0; i < rowsMiniBatch.length; ++i) {
                                        if (0 <= rId + i && rId + i < tfr.numRows()) {
                                            rowsMiniBatch[i] = new FrameTask.ExtractDenseRow(di, rId + i).doAll(di._adaptedFrame)._row;
                                        }
                                    }
                                    // loss at weight
                                    long cs = dl.model_info().checksum_impl();
                                    double loss = dl.meanLoss(rowsMiniBatch);
                                    assert (cs == before);
                                    assert (before == dl.model_info().checksum_impl());
                                    meanLoss += loss;
                                    for (int layer = 0; layer <= parms._hidden.length; ++layer) {
                                        int rows = dl.model_info().get_weights(layer).rows();
                                        assert (dl.model_info().get_biases(layer).size() == rows);
                                        for (int row = 0; row < rows; ++row) {
                                            //check bias
                                            if (true) {
                                                // start from scratch - with a clean model
                                                dl.set_model_info(IcedUtils.deepCopy(modelInfo));
                                                // do one forward propagation pass (and fill the mini-batch gradients -> set training=true)
                                                Neurons[] neurons = DeepLearningTask.makeNeuronsForTraining(dl.model_info());
                                                double[] responses = new double[miniBatchSize];
                                                double[] offsets = new double[miniBatchSize];
                                                int n = 0;
                                                for (DataInfo.Row myRow : rowsMiniBatch) {
                                                    if (myRow == null)
                                                        continue;
                                                    ((Neurons.Input) neurons[0]).setInput(-1, myRow.numIds, myRow.numVals, myRow.nBins, myRow.binIds, n);
                                                    responses[n] = myRow.response(0);
                                                    offsets[n] = myRow.offset;
                                                    n++;
                                                }
                                                DeepLearningTask.fpropMiniBatch(-1, /*seed doesn't matter*/
                                                neurons, dl.model_info(), null, true, /*training*/
                                                responses, offsets, n);
                                                // check that we didn't change the model's weights/biases
                                                long after = dl.model_info().checksum_impl();
                                                assert (after == before);
                                                // record the gradient since gradientChecking is enabled
                                                //tell it what gradient to collect
                                                DeepLearningModelInfo.gradientCheck = new DeepLearningModelInfo.GradientCheck(layer, row, -1);
                                                //update the weights and biases
                                                DeepLearningTask.bpropMiniBatch(neurons, n);
                                                assert (before != dl.model_info().checksum_impl());
                                                // reset the model back to the trained model
                                                dl.set_model_info(IcedUtils.deepCopy(modelInfo));
                                                assert (before == dl.model_info().checksum_impl());
                                                double bpropGradient = DeepLearningModelInfo.gradientCheck.gradient;
                                                // FIXME: re-enable this once the loss is computed from the de-standardized prediction/response
                                                //                    double actualResponse=myRow.response[0];
                                                //                    double predResponseLinkSpace = neurons[neurons.length-1]._a.get(0);
                                                //                    if (di._normRespMul != null) {
                                                //                      bpropGradient /= di._normRespMul[0]; //no shift for gradient
                                                //                      actualResponse = (actualResponse / di._normRespMul[0] + di._normRespSub[0]);
                                                //                      predResponseLinkSpace = (predResponseLinkSpace / di._normRespMul[0] + di._normRespSub[0]);
                                                //                    }
                                                //                    bpropGradient *= new Distribution(parms._distribution).gradient(actualResponse, predResponseLinkSpace);
                                                final double bias = dl.model_info().get_biases(layer).get(row);
                                                //don't make the weight deltas too small, or the float weights "won't notice"
                                                double eps = 1e-4 * Math.abs(bias);
                                                if (eps == 0)
                                                    eps = 1e-6;
                                                // loss at bias + eps
                                                dl.model_info().get_biases(layer).set(row, bias + eps);
                                                double up = dl.meanLoss(rowsMiniBatch);
                                                // loss at bias - eps
                                                dl.model_info().get_biases(layer).set(row, bias - eps);
                                                double down = dl.meanLoss(rowsMiniBatch);
                                                if (Math.abs(up - down) / Math.abs(up + down) < 1e-8) {
                                                    //relative change in loss function is too small -> skip
                                                    continue;
                                                }
                                                double gradient = ((up - down) / (2. * eps));
                                                double relError = 2 * Math.abs(bpropGradient - gradient) / (Math.abs(gradient) + Math.abs(bpropGradient));
                                                count++;
                                                // if either gradient is tiny, check if both are tiny
                                                if (Math.abs(gradient) < 1e-7 || Math.abs(bpropGradient) < 1e-7) {
                                                    //all good
                                                    if (Math.abs(bpropGradient - gradient) < 1e-7)
                                                        continue;
                                                }
                                                meanRelErr += relError;
                                                if (relError > MAX_TOLERANCE) {
                                                    Log.info("\nDistribution: " + dl._parms._distribution);
                                                    Log.info("\nRow: " + rId);
                                                    Log.info("bias (layer " + layer + ", row " + row + "): " + bias + " +/- " + eps);
                                                    Log.info("loss: " + loss);
                                                    Log.info("losses up/down: " + up + " / " + down);
                                                    Log.info("=> Finite differences gradient: " + gradient);
                                                    Log.info("=> Back-propagation gradient  : " + bpropGradient);
                                                    Log.info("=> Relative error             : " + PrettyPrint.formatPct(relError));
                                                    failedcount++;
                                                }
                                            }
                                            int cols = dl.model_info().get_weights(layer).cols();
                                            for (int col = 0; col < cols; ++col) {
                                                if (rng.nextFloat() >= SAMPLE_RATE)
                                                    continue;
                                                // start from scratch - with a clean model
                                                dl.set_model_info(IcedUtils.deepCopy(modelInfo));
                                                // do one forward propagation pass (and fill the mini-batch gradients -> set training=true)
                                                Neurons[] neurons = DeepLearningTask.makeNeuronsForTraining(dl.model_info());
                                                double[] responses = new double[miniBatchSize];
                                                double[] offsets = new double[miniBatchSize];
                                                int n = 0;
                                                for (DataInfo.Row myRow : rowsMiniBatch) {
                                                    if (myRow == null)
                                                        continue;
                                                    ((Neurons.Input) neurons[0]).setInput(-1, myRow.numIds, myRow.numVals, myRow.nBins, myRow.binIds, n);
                                                    responses[n] = myRow.response(0);
                                                    offsets[n] = myRow.offset;
                                                    n++;
                                                }
                                                DeepLearningTask.fpropMiniBatch(-1, /*seed doesn't matter*/
                                                neurons, dl.model_info(), null, true, /*training*/
                                                responses, offsets, n);
                                                // check that we didn't change the model's weights/biases
                                                long after = dl.model_info().checksum_impl();
                                                assert (after == before);
                                                // record the gradient since gradientChecking is enabled
                                                //tell it what gradient to collect
                                                DeepLearningModelInfo.gradientCheck = new DeepLearningModelInfo.GradientCheck(layer, row, col);
                                                //update the weights
                                                DeepLearningTask.bpropMiniBatch(neurons, n);
                                                assert (before != dl.model_info().checksum_impl());
                                                // reset the model back to the trained model
                                                dl.set_model_info(IcedUtils.deepCopy(modelInfo));
                                                assert (before == dl.model_info().checksum_impl());
                                                double bpropGradient = DeepLearningModelInfo.gradientCheck.gradient;
                                                // FIXME: re-enable this once the loss is computed from the de-standardized prediction/response
                                                //                    double actualResponse=myRow.response[0];
                                                //                    double predResponseLinkSpace = neurons[neurons.length-1]._a.get(0);
                                                //                    if (di._normRespMul != null) {
                                                //                      bpropGradient /= di._normRespMul[0]; //no shift for gradient
                                                //                      actualResponse = (actualResponse / di._normRespMul[0] + di._normRespSub[0]);
                                                //                      predResponseLinkSpace = (predResponseLinkSpace / di._normRespMul[0] + di._normRespSub[0]);
                                                //                    }
                                                //                    bpropGradient *= new Distribution(parms._distribution).gradient(actualResponse, predResponseLinkSpace);
                                                final float weight = dl.model_info().get_weights(layer).get(row, col);
                                                //don't make the weight deltas too small, or the float weights "won't notice"
                                                double eps = 1e-4 * Math.abs(weight);
                                                if (eps == 0)
                                                    eps = 1e-6;
                                                // loss at weight + eps
                                                dl.model_info().get_weights(layer).set(row, col, (float) (weight + eps));
                                                double up = dl.meanLoss(rowsMiniBatch);
                                                // loss at weight - eps
                                                dl.model_info().get_weights(layer).set(row, col, (float) (weight - eps));
                                                double down = dl.meanLoss(rowsMiniBatch);
                                                if (Math.abs(up - down) / Math.abs(up + down) < 1e-8) {
                                                    //relative change in loss function is too small -> skip
                                                    continue;
                                                }
                                                double gradient = ((up - down) / (2. * eps));
                                                double relError = 2 * Math.abs(bpropGradient - gradient) / (Math.abs(gradient) + Math.abs(bpropGradient));
                                                count++;
                                                // if either gradient is tiny, check if both are tiny
                                                if (Math.abs(gradient) < 1e-7 || Math.abs(bpropGradient) < 1e-7) {
                                                    //all good
                                                    if (Math.abs(bpropGradient - gradient) < 1e-7)
                                                        continue;
                                                }
                                                meanRelErr += relError;
                                                if (relError > MAX_TOLERANCE) {
                                                    Log.info("\nDistribution: " + dl._parms._distribution);
                                                    Log.info("\nRow: " + rId);
                                                    Log.info("weight (layer " + layer + ", row " + row + ", col " + col + "): " + weight + " +/- " + eps);
                                                    Log.info("loss: " + loss);
                                                    Log.info("losses up/down: " + up + " / " + down);
                                                    Log.info("=> Finite differences gradient: " + gradient);
                                                    Log.info("=> Back-propagation gradient  : " + bpropGradient);
                                                    Log.info("=> Relative error             : " + PrettyPrint.formatPct(relError));
                                                    failedcount++;
                                                }
                                                //                          Assert.assertTrue(failedcount==0);
                                                maxRelErr = Math.max(maxRelErr, relError);
                                                assert (!Double.isNaN(maxRelErr));
                                            }
                                        }
                                    }
                                }
                                meanLoss /= tfr.numRows();
                                Log.info("Mean loss: " + meanLoss);
                            //                  // FIXME: re-enable this
                            //                  if (parms._l1 == 0 && parms._l2 == 0) {
                            //                    assert(Math.abs(meanLoss-resdev)/Math.abs(resdev) < 1e-5);
                            //                  }
                            } catch (RuntimeException ex) {
                                dl = DKV.getGet(job.dest());
                                if (dl != null)
                                    Assert.assertTrue(dl.model_info().isUnstable());
                                else
                                    Assert.assertTrue(job.isStopped());
                            } finally {
                                if (dl != null)
                                    dl.delete();
                            }
                        }
                    }
                }
            }
        }
        Log.info("Number of tests: " + count);
        Log.info("Number of failed tests: " + failedcount);
        Log.info("Mean. relative error: " + meanRelErr / count);
        Log.info("Max. relative error: " + PrettyPrint.formatPct(maxRelErr));
        Assert.assertTrue("Error too large: " + maxRelErr + " >= " + MAX_TOLERANCE, maxRelErr < MAX_TOLERANCE);
        Assert.assertTrue("Failed count too large: " + failedcount + " > " + MAX_FAILED_COUNT, failedcount <= MAX_FAILED_COUNT);
    } finally {
        if (tfr != null)
            tfr.remove();
    }
}

Example 9 with DeepLearningParameters

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

the class DeepLearningTest method testHuberDeltaTiny.

@Test
public void testHuberDeltaTiny() {
    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;
        parms._huber_alpha = 1e-2;
        // more like Laplace, but different slope and different prefactor -> so can't compare deviance 1:1
        dl = new DeepLearning(parms).trainModel().get();
        double delta = 0.011996;
        // can compute huber loss from MAE since no obs weights
        Assert.assertEquals((2 * 2.31398 - /*MAE*/
        delta) * delta, ((ModelMetricsRegression) dl._output._training_metrics)._mean_residual_deviance, 2e-2);
        Assert.assertEquals(19.856, ((ModelMetricsRegression) dl._output._training_metrics)._MSE, 1e-3);
    } finally {
        if (tfr != null)
            tfr.delete();
        if (dl != null)
            dl.deleteCrossValidationModels();
        if (dl != null)
            dl.delete();
    }
}

Example 10 with DeepLearningParameters

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

the class DeepLearningTest method testCategoricalEncodingBinary.

@Test
public void testCategoricalEncodingBinary() {
    Frame tfr = null;
    DeepLearningModel dl = null;
    try {
        String response = "survived";
        tfr = parse_test_file("./smalldata/junit/titanic_alt.csv");
        if (tfr.vec(response).isBinary()) {
            Vec v = tfr.remove(response);
            tfr.add(response, v.toCategoricalVec());
            v.remove();
        }
        DKV.put(tfr);
        DeepLearningParameters parms = new DeepLearningParameters();
        parms._train = tfr._key;
        parms._valid = tfr._key;
        parms._response_column = response;
        parms._reproducible = true;
        parms._hidden = new int[] { 20, 20 };
        parms._seed = 0xdecaf;
        parms._nfolds = 3;
        parms._categorical_encoding = Model.Parameters.CategoricalEncodingScheme.Binary;
        dl = new DeepLearning(parms).trainModel().get();
        Assert.assertEquals(0.94696, ((ModelMetricsBinomial) dl._output._training_metrics)._auc._auc, 1e-4);
        Assert.assertEquals(0.94696, ((ModelMetricsBinomial) dl._output._validation_metrics)._auc._auc, 1e-4);
        Assert.assertEquals(0.86556613, ((ModelMetricsBinomial) dl._output._cross_validation_metrics)._auc._auc, 1e-4);
        Assert.assertEquals(0.86556613, Double.parseDouble((String) (dl._output._cross_validation_metrics_summary).get(1, 0)), 1e-2);
    } finally {
        if (tfr != null)
            tfr.remove();
        if (dl != null)
            dl.deleteCrossValidationModels();
        if (dl != null)
            dl.delete();
    }
}