@Test
@Ignore
public void run() {
Scope.enter();
try {
File file = find_test_file("bigdata/laptop/mnist/train.csv.gz");
File valid = find_test_file("bigdata/laptop/mnist/test.csv.gz");
if (file != null) {
NFSFileVec trainfv = NFSFileVec.make(file);
Frame frame = ParseDataset.parse(Key.make(), trainfv._key);
NFSFileVec validfv = NFSFileVec.make(valid);
Frame vframe = ParseDataset.parse(Key.make(), validfv._key);
DeepLearningParameters p = new DeepLearningParameters();
// populate model parameters
p._model_id = Key.make("dl_mnist_model");
p._train = frame._key;
// p._valid = vframe._key;
p._response_column = "C785"; // last column is the response
p._activation = DeepLearningParameters.Activation.RectifierWithDropout;
// p._activation = DeepLearningParameters.Activation.MaxoutWithDropout;
p._hidden = new int[] {800, 800};
p._input_dropout_ratio = 0.2;
p._mini_batch_size = 1;
p._train_samples_per_iteration = 50000;
p._score_duty_cycle = 0;
// p._shuffle_training_data = true;
// p._l1= 1e-5;
// p._max_w2= 10;
p._epochs = 10 * 5. / 6;
// Convert response 'C785' to categorical (digits 1 to 10)
int ci = frame.find("C785");
Scope.track(frame.replace(ci, frame.vecs()[ci].toEnum())._key);
Scope.track(vframe.replace(ci, vframe.vecs()[ci].toEnum())._key);
DKV.put(frame);
DKV.put(vframe);
// speed up training
p._adaptive_rate =
true; // disable adaptive per-weight learning rate -> default settings for learning rate
// and momentum are probably not ideal (slow convergence)
p._replicate_training_data =
true; // avoid extra communication cost upfront, got enough data on each node for load
// balancing
p._overwrite_with_best_model = true; // no need to keep the best model around
p._classification_stop = -1;
p._score_interval = 60; // score and print progress report (only) every 20 seconds
p._score_training_samples =
10000; // only score on a small sample of the training set -> don't want to spend too
// much time scoring (note: there will be at least 1 row per chunk)
DeepLearning dl = new DeepLearning(p);
DeepLearningModel model = null;
try {
model = dl.trainModel().get();
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
dl.remove();
if (model != null) {
model.delete();
}
}
} else {
Log.info("Please run ./gradlew syncBigDataLaptop in the top-level directory of h2o-3.");
}
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
Scope.exit();
}
}
