/**
* Helper to create the DataInfo object from training/validation frames and the DL parameters
*
* @param train Training frame
* @param valid Validation frame
* @param parms Model parameters
* @param nClasses Number of response levels (1: regression, >=2: classification)
* @return DataInfo
*/
static DataInfo makeDataInfo(
Frame train, Frame valid, DeepLearningParameters parms, int nClasses) {
double x = 0.782347234;
boolean identityLink = new Distribution(parms._distribution, parms._tweedie_power).link(x) == x;
DataInfo dinfo =
new DataInfo(
train,
valid,
parms._autoencoder ? 0 : 1, // nResponses
parms._autoencoder
|| parms._use_all_factor_levels, // use all FactorLevels for auto-encoder
parms._standardize
? (parms._autoencoder
? DataInfo.TransformType.NORMALIZE
: parms._sparse
? DataInfo.TransformType.DESCALE
: DataInfo.TransformType.STANDARDIZE)
: DataInfo.TransformType.NONE, // transform predictors
!parms._standardize || train.lastVec().isCategorical()
? DataInfo.TransformType.NONE
: identityLink
? DataInfo.TransformType.STANDARDIZE
: DataInfo.TransformType
.NONE, // transform response for regression with identity link
parms._missing_values_handling
== DeepLearningParameters.MissingValuesHandling.Skip, // whether to skip missing
false, // do not replace NAs in numeric cols with mean
true, // always add a bucket for missing values
parms._weights_column != null, // observation weights
parms._offset_column != null,
parms._fold_column != null);
// Checks and adjustments:
// 1) observation weights (adjust mean/sigmas for predictors and response)
// 2) NAs (check that there's enough rows left)
GLMTask.YMUTask ymt =
new GLMTask.YMUTask(
dinfo,
nClasses,
true,
!parms._autoencoder && nClasses == 1,
false,
!parms._autoencoder)
.doAll(dinfo._adaptedFrame);
if (ymt._wsum == 0
&& parms._missing_values_handling == DeepLearningParameters.MissingValuesHandling.Skip)
throw new H2OIllegalArgumentException(
"No rows left in the dataset after filtering out rows with missing values. Ignore columns with many NAs or set missing_values_handling to 'MeanImputation'.");
if (parms._weights_column != null && parms._offset_column != null) {
Log.warn(
"Combination of offset and weights can lead to slight differences because Rollupstats aren't weighted - need to re-calculate weighted mean/sigma of the response including offset terms.");
}
if (parms._weights_column != null
&& parms._offset_column == null /*FIXME: offset not yet implemented*/) {
dinfo.updateWeightedSigmaAndMean(ymt._basicStats.sigma(), ymt._basicStats.mean());
if (nClasses == 1)
dinfo.updateWeightedSigmaAndMeanForResponse(
ymt._basicStatsResponse.sigma(), ymt._basicStatsResponse.mean());
}
return dinfo;
}
/**
* Train a Deep Learning neural net model
*
* @param model Input model (e.g., from initModel(), or from a previous training run)
* @return Trained model
*/
public final DeepLearningModel trainModel(DeepLearningModel model) {
Frame validScoreFrame = null;
Frame train, trainScoreFrame;
try {
// if (checkpoint == null && !quiet_mode) logStart(); //if checkpoint is given, some
// Job's params might be uninitialized (but the restarted model's parameters are correct)
if (model == null) {
model = DKV.get(dest()).get();
}
Log.info(
"Model category: "
+ (_parms._autoencoder
? "Auto-Encoder"
: isClassifier() ? "Classification" : "Regression"));
final long model_size = model.model_info().size();
Log.info(
"Number of model parameters (weights/biases): " + String.format("%,d", model_size));
model.write_lock(_job);
_job.update(0, "Setting up training data...");
final DeepLearningParameters mp = model.model_info().get_params();
// temporary frames of the same "name" as the orig _train/_valid (asking the parameter's
// Key, not the actual frame)
// Note: don't put into DKV or they would overwrite the _train/_valid frames!
Frame tra_fr = new Frame(mp._train, _train.names(), _train.vecs());
Frame val_fr = _valid != null ? new Frame(mp._valid, _valid.names(), _valid.vecs()) : null;
train = tra_fr;
if (model._output.isClassifier() && mp._balance_classes) {
_job.update(0, "Balancing class distribution of training data...");
float[] trainSamplingFactors =
new float
[train
.lastVec()
.domain()
.length]; // leave initialized to 0 -> will be filled up below
if (mp._class_sampling_factors != null) {
if (mp._class_sampling_factors.length != train.lastVec().domain().length)
throw new IllegalArgumentException(
"class_sampling_factors must have "
+ train.lastVec().domain().length
+ " elements");
trainSamplingFactors =
mp._class_sampling_factors.clone(); // clone: don't modify the original
}
train =
sampleFrameStratified(
train,
train.lastVec(),
train.vec(model._output.weightsName()),
trainSamplingFactors,
(long) (mp._max_after_balance_size * train.numRows()),
mp._seed,
true,
false);
Vec l = train.lastVec();
Vec w = train.vec(model._output.weightsName());
MRUtils.ClassDist cd = new MRUtils.ClassDist(l);
model._output._modelClassDist =
_weights != null ? cd.doAll(l, w).rel_dist() : cd.doAll(l).rel_dist();
}
model.training_rows = train.numRows();
if (_weights != null && _weights.min() == 0 && _weights.max() == 1 && _weights.isInt()) {
model.training_rows = Math.round(train.numRows() * _weights.mean());
Log.warn(
"Not counting "
+ (train.numRows() - model.training_rows)
+ " rows with weight=0 towards an epoch.");
}
Log.info("One epoch corresponds to " + model.training_rows + " training data rows.");
trainScoreFrame =
sampleFrame(
train,
mp._score_training_samples,
mp._seed); // training scoring dataset is always sampled uniformly from the training
// dataset
if (trainScoreFrame != train) Scope.track(trainScoreFrame);
if (!_parms._quiet_mode)
Log.info("Number of chunks of the training data: " + train.anyVec().nChunks());
if (val_fr != null) {
model.validation_rows = val_fr.numRows();
// validation scoring dataset can be sampled in multiple ways from the given validation
// dataset
if (model._output.isClassifier()
&& mp._balance_classes
&& mp._score_validation_sampling
== DeepLearningParameters.ClassSamplingMethod.Stratified) {
_job.update(0, "Sampling validation data (stratified)...");
validScoreFrame =
sampleFrameStratified(
val_fr,
val_fr.lastVec(),
val_fr.vec(model._output.weightsName()),
null,
mp._score_validation_samples > 0
? mp._score_validation_samples
: val_fr.numRows(),
mp._seed + 1,
false /* no oversampling */,
false);
} else {
_job.update(0, "Sampling validation data...");
validScoreFrame = sampleFrame(val_fr, mp._score_validation_samples, mp._seed + 1);
if (validScoreFrame != val_fr) Scope.track(validScoreFrame);
}
if (!_parms._quiet_mode)
Log.info(
"Number of chunks of the validation data: " + validScoreFrame.anyVec().nChunks());
}
// Set train_samples_per_iteration size (cannot be done earlier since this depends on
// whether stratified sampling is done)
model.actual_train_samples_per_iteration =
computeTrainSamplesPerIteration(mp, model.training_rows, model);
// Determine whether shuffling is enforced
if (mp._replicate_training_data
&& (model.actual_train_samples_per_iteration
== model.training_rows * (mp._single_node_mode ? 1 : H2O.CLOUD.size()))
&& !mp._shuffle_training_data
&& H2O.CLOUD.size() > 1
&& !mp._reproducible) {
if (!mp._quiet_mode)
Log.info(
"Enabling training data shuffling, because all nodes train on the full dataset (replicated training data).");
mp._shuffle_training_data = true;
}
if (!mp._shuffle_training_data
&& model.actual_train_samples_per_iteration == model.training_rows
&& train.anyVec().nChunks() == 1) {
if (!mp._quiet_mode)
Log.info(
"Enabling training data shuffling to avoid training rows in the same order over and over (no Hogwild since there's only 1 chunk).");
mp._shuffle_training_data = true;
}
// if (!mp._quiet_mode) Log.info("Initial model:\n" + model.model_info());
long now = System.currentTimeMillis();
model._timeLastIterationEnter = now;
if (_parms._autoencoder) {
_job.update(0, "Scoring null model of autoencoder...");
if (!mp._quiet_mode) Log.info("Scoring the null model of the autoencoder.");
model.doScoring(
trainScoreFrame,
validScoreFrame,
_job._key,
0,
false); // get the null model reconstruction error
}
// put the initial version of the model into DKV
model.update(_job);
model.total_setup_time_ms += now - _job.start_time();
Log.info("Total setup time: " + PrettyPrint.msecs(model.total_setup_time_ms, true));
Log.info("Starting to train the Deep Learning model.");
_job.update(0, "Training...");
// main loop
for (; ; ) {
model.iterations++;
model.set_model_info(
mp._epochs == 0
? model.model_info()
: H2O.CLOUD.size() > 1 && mp._replicate_training_data
? (mp._single_node_mode
? new DeepLearningTask2(
_job._key,
train,
model.model_info(),
rowFraction(train, mp, model),
model.iterations)
.doAll(Key.make(H2O.SELF))
.model_info()
: // replicated data + single node mode
new DeepLearningTask2(
_job._key,
train,
model.model_info(),
rowFraction(train, mp, model),
model.iterations)
.doAllNodes()
.model_info())
: // replicated data + multi-node mode
new DeepLearningTask(
_job._key,
model.model_info(),
rowFraction(train, mp, model),
model.iterations)
.doAll(train)
.model_info()); // distributed data (always in multi-node mode)
if (stop_requested() && !timeout()) break; // cancellation
if (!model.doScoring(
trainScoreFrame, validScoreFrame, _job._key, model.iterations, false))
break; // finished training (or early stopping or convergence)
if (timeout()) break; // stop after scoring
}
// replace the model with the best model so far (if it's better)
if (!stop_requested()
&& _parms._overwrite_with_best_model
&& model.actual_best_model_key != null
&& _parms._nfolds == 0) {
DeepLearningModel best_model = DKV.getGet(model.actual_best_model_key);
if (best_model != null
&& best_model.loss() < model.loss()
&& Arrays.equals(best_model.model_info().units, model.model_info().units)) {
if (!_parms._quiet_mode)
Log.info("Setting the model to be the best model so far (based on scoring history).");
DeepLearningModelInfo mi = best_model.model_info().deep_clone();
// Don't cheat - count full amount of training samples, since that's the amount of
// training it took to train (without finding anything better)
mi.set_processed_global(model.model_info().get_processed_global());
mi.set_processed_local(model.model_info().get_processed_local());
model.set_model_info(mi);
model.update(_job);
model.doScoring(trainScoreFrame, validScoreFrame, _job._key, model.iterations, true);
assert (best_model.loss() == model.loss());
}
}
// store coefficient names for future use
// possibly change
model.model_info().data_info().coefNames();
if (!_parms._quiet_mode) {
Log.info(
"==============================================================================================================================================================================");
if (stop_requested()) {
Log.info("Deep Learning model training was interrupted.");
} else {
Log.info("Finished training the Deep Learning model.");
Log.info(model);
}
Log.info(
"==============================================================================================================================================================================");
}
} finally {
if (model != null) {
model.deleteElasticAverageModels();
model.unlock(_job);
if (model.actual_best_model_key != null) {
assert (model.actual_best_model_key != model._key);
DKV.remove(model.actual_best_model_key);
}
}
}
return model;
}
/**
* Main constructor
*
* @param params Model parameters
* @param dinfo Data Info
* @param nClasses number of classes (1 for regression, 0 for autoencoder)
* @param train User-given training data frame, prepared by AdaptTestTrain
* @param valid User-specified validation data frame, prepared by AdaptTestTrain
*/
public DeepLearningModelInfo(
final DeepLearningParameters params,
final DataInfo dinfo,
int nClasses,
Frame train,
Frame valid) {
_classification = nClasses > 1;
_train = train;
_valid = valid;
data_info = dinfo;
parameters =
(DeepLearningParameters) params.clone(); // make a copy, don't change model's parameters
DeepLearningParameters.Sanity.modifyParms(
parameters, parameters, nClasses); // sanitize the model_info's parameters
final int num_input = dinfo.fullN();
final int num_output =
get_params()._autoencoder
? num_input
: (_classification ? train.lastVec().cardinality() : 1);
if (!get_params()._autoencoder) assert (num_output == nClasses);
_saw_missing_cats = dinfo._cats > 0 ? new boolean[data_info._cats] : null;
assert (num_input > 0);
assert (num_output > 0);
if (has_momenta() && adaDelta())
throw new IllegalArgumentException(
"Cannot have non-zero momentum and adaptive rate at the same time.");
final int layers = get_params()._hidden.length;
// units (# neurons for each layer)
units = new int[layers + 2];
if (get_params()._max_categorical_features <= Integer.MAX_VALUE - dinfo._nums)
units[0] = Math.min(dinfo._nums + get_params()._max_categorical_features, num_input);
else units[0] = num_input;
System.arraycopy(get_params()._hidden, 0, units, 1, layers);
units[layers + 1] = num_output;
boolean printLevels = units[0] > 1000L;
boolean warn = units[0] > 100000L;
if (printLevels) {
final String[][] domains = dinfo._adaptedFrame.domains();
int[] levels = new int[domains.length];
for (int i = 0; i < levels.length; ++i) {
levels[i] = domains[i] != null ? domains[i].length : 0;
}
Arrays.sort(levels);
if (warn) {
Log.warn(
"===================================================================================================================================");
Log.warn(
num_input
+ " input features"
+ (dinfo._cats > 0 ? " (after categorical one-hot encoding)" : "")
+ ". Can be slow and require a lot of memory.");
}
if (levels[levels.length - 1] > 0) {
int levelcutoff = levels[levels.length - 1 - Math.min(10, levels.length - 1)];
int count = 0;
for (int i = 0;
i < dinfo._adaptedFrame.numCols() - (get_params()._autoencoder ? 0 : 1) && count < 10;
++i) {
if (dinfo._adaptedFrame.domains()[i] != null
&& dinfo._adaptedFrame.domains()[i].length >= levelcutoff) {
if (warn) {
Log.warn(
"Categorical feature '"
+ dinfo._adaptedFrame._names[i]
+ "' has cardinality "
+ dinfo._adaptedFrame.domains()[i].length
+ ".");
} else {
Log.info(
"Categorical feature '"
+ dinfo._adaptedFrame._names[i]
+ "' has cardinality "
+ dinfo._adaptedFrame.domains()[i].length
+ ".");
}
}
count++;
}
}
if (warn) {
Log.warn("Suggestions:");
Log.warn(" *) Limit the size of the first hidden layer");
if (dinfo._cats > 0) {
Log.warn(
" *) Limit the total number of one-hot encoded features with the parameter 'max_categorical_features'");
Log.warn(
" *) Run h2o.interaction(...,pairwise=F) on high-cardinality categorical columns to limit the factor count, see http://learn.h2o.ai");
}
Log.warn(
"===================================================================================================================================");
}
}
// weights (to connect layers)
dense_row_weights = new Storage.DenseRowMatrix[layers + 1];
dense_col_weights = new Storage.DenseColMatrix[layers + 1];
// decide format of weight matrices row-major or col-major
if (get_params()._col_major)
dense_col_weights[0] = new Storage.DenseColMatrix(units[1], units[0]);
else dense_row_weights[0] = new Storage.DenseRowMatrix(units[1], units[0]);
for (int i = 1; i <= layers; ++i)
dense_row_weights[i] = new Storage.DenseRowMatrix(units[i + 1] /*rows*/, units[i] /*cols*/);
// biases (only for hidden layers and output layer)
biases = new Storage.DenseVector[layers + 1];
for (int i = 0; i <= layers; ++i) biases[i] = new Storage.DenseVector(units[i + 1]);
// average activation (only for hidden layers)
if (get_params()._autoencoder && get_params()._sparsity_beta > 0) {
avg_activations = new Storage.DenseVector[layers];
mean_a = new float[layers];
for (int i = 0; i < layers; ++i) avg_activations[i] = new Storage.DenseVector(units[i + 1]);
}
allocateHelperArrays();
// for diagnostics
mean_rate = new float[units.length];
rms_rate = new float[units.length];
mean_bias = new float[units.length];
rms_bias = new float[units.length];
mean_weight = new float[units.length];
rms_weight = new float[units.length];
}