Beispiel #1
0
 /**
  * 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;
 }
Beispiel #2
0
    /**
     * 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];
  }