super(WordClassifierBase, self).__init__()
def make_input(self, batch_dict):
x = batch_dict["x"]
xch = batch_dict.get("xch")
y = batch_dict.get("y")
lengths = batch_dict.get("lengths")
if self.gpu:
x = x.cuda()
if xch is not None:
xch = xch.cuda()
if y is not None:
y = y.cuda()
After Change
:param do_dropout: (``bool``) Should we do dropout. Defaults to False
:return:
example_dict = dict({})
for key in self.embeddings.keys():
example_dict[key] = torch.from_numpy(batch_dict[key])
if self.gpu:
example_dict[key] = example_dict[key].cuda()
// Allow us to track a length, which is needed for BLSTMs
if self.lengths_key is not None:
example_dict["lengths"] = torch.from_numpy(batch_dict[self.lengths_key])
if self.gpu:
example_dict["lengths"] = example_dict["lengths"].cuda()
y = batch_dict.get("y")
if y is not None:
y = torch.from_numpy(y)
