Merge pull request #122 from rbharath/cleanup

    else:

      # Reload should automatically find required files

      shard_files = None

      #shard_files = []

      #feature_dir_files = os.listdir(feature_dir)

      #for feature_dir_file in feature_dir_files:

      #  basename, extension = os.path.splitext(feature_dir_file)

      #  if extension == ".joblib" and "shard" in basename:

      #    shard_files.append(os.path.join(feature_dir, feature_dir_file))

    featurizers = self.compound_featurizers + self.complex_featurizers

    samples = FeaturizedSamples(samples_dir=samples_dir, featurizers=featurizers, 

        model_params[hyperparam] = hyperparam_val

      model_dir = tempfile.mkdtemp()

      #model = self.model_class(self.task_types, model_params, verbosity=verbosity)

      model = self.model_class(self.task_types, model_params, logdir=logdir, train=True)

      model = self.model_class(self.task_types, model_params, logdir=logdir,

                               train=True)

      model.fit(train_dataset)

      model.save(model_dir)

      self.restore()

    with self.graph.as_default():

      assert not model_ops.is_training()

      #assert self._restored_model

      self.require_attributes(['output', 'labels', 'weights'])

      # run eval data through the model