Potential fixes

        model = self.model_class(

            self.tasks, self.task_types, model_params, model_dir,

            verbosity=self.verbosity)

      ####################################################### DEBUG

      if "pad_batches" in model_params:

        pad_batches = model_params["pad_batches"]

      else:

        pad_batches = False

      ####################################################### DEBUG

      model.fit(train_dataset, pad_batches=pad_batches)

      model.fit(train_dataset)

      model.save()

      evaluator = Evaluator(model, valid_dataset, output_transformers)

from deepchem.utils.save import load_from_disk

from deepchem.utils.save import save_to_disk

from deepchem.utils.save import log

from deepchem.datasets import pad_batch

class Model(object):

    for epoch in range(self.model_params["nb_epoch"]):

      log("Starting epoch %s" % str(epoch+1), self.verbosity)

      losses = []

      for (X_batch, y_batch, w_batch, _) in dataset.iterbatches(

      for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(

          batch_size, pad_batches=pad_batches):

        if self.fit_transformers:

          X_batch, y_batch, w_batch = self.transform_on_batch(X_batch, y_batch,

                                            w_batch)

        if pad_batches:

          X_batch, y_batch, w_batch, ids_batch = pad_batch(

              batch_size, X_batch, y_batch, w_batch, ids_batch)

        losses.append(self.fit_on_batch(X_batch, y_batch, w_batch))

      log("Avg loss for epoch %d: %f"

          % (epoch+1,np.array(losses).mean()),self.verbosity)