more cleanup

          feed_dict[initial_state] = zero_state

        yield feed_dict

#  def predict_on_generator(self, generator, transformers=[], outputs=None):

#    """Generates output predictions for the input samples,

#      processing the samples in a batched way.

#

#    Parameters

#    ----------

#    generator: Generator

#      Generator that constructs feed dictionaries for TensorGraph.

#    transformers: list

#      List of dc.trans.Transformers.

#    outputs: object

#      If outputs is None, then will assume outputs = self.outputs[0] (single

#      output). If outputs is a Layer/Tensor, then will evaluate and return as a

#      single ndarray. If outputs is a list of Layers/Tensors, will return a list

#      of ndarrays.

#

#    Returns

#    -------

#    A Numpy array of predictions.

#    """

#    retval = self.predict_proba_on_generator(generator, transformers)

#    if self.mode == 'classification':

#      retval = np.expand_dims(from_one_hot(retval, axis=2), axis=1)

#    return retval

#def predict_proba_on_generator(self, generator, transformers=[]):

  def predict_on_generator(self, generator, transformers=[], outputs=None):

    """

    Parameters