'fit_batch.
Train network with a single batch.
Arguments:
feed_dicts: `dict` or list of `dict`. The dictionary to feed
data to the network. It follows Tensorflow feed dict
specifications: \'{placeholder: data}\'. In case of multiple
optimizers, a list of dict is expected, that will
respectively feed optimizers.
dprep_dict: `dict` with `Placeholder` as key and
`DataPreprocessing` as value. Apply realtime data
preprocessing to the given placeholders (Applied at training
and testing time).
daug_dict: `dict` with `Placeholder` as key and
`DataAugmentation` as value. Apply realtime data
augmentation to the given placeholders (Only applied at
training time).'

def fit_batch(self, feed_dicts, dprep_dict=None, daug_dict=None):

    feed_dicts = to_list(feed_dicts)
    for d in feed_dicts:
        standarize_dict(d)
    val_loss = []
    for train_op in self.train_ops:
        if daug_dict:
            for k in daug_dict:
                feed_dicts[k] = daug_dict.apply(feed_dicts[k])
        if dprep_dict:
            for k in dprep_dict:
                feed_dicts[k] = dprep_dict.apply(feed_dicts[k])
    for d in feed_dicts:
        val_loss.append(train_op._train_batch(d))
    if (len(val_loss) == 1):
        val_loss = val_loss[0]
    return val_loss
