def __call__(self, inputs):
if not isinstance(inputs, (list, tuple)):
raise TypeError('`inputs` should be a list or tuple.')
feed_dict = {}
for tensor, value in zip(self.inputs, inputs):
if is_sparse(tensor):
sparse_coo = value.tocoo()
indices = np.concatenate((np.expand_dims(
sparse_coo.row, 1), np.expand_dims(sparse_coo.col, 1)), 1)
value = (indices, sparse_coo.data, sparse_coo.shape)
feed_dict[tensor] = value
session = get_session()
enqueue_ops = self._enqueue_ops
neops = len(enqueue_ops)
updated = session.run(enqueue_ops + self.outputs + [self.updates_op],
feed_dict=feed_dict)
nouts = len(self.outputs)
# return updated[:len(self.outputs)]
return updated[neops:nouts + neops]
def __call__(self, inputs):
if not isinstance(inputs, (list, tuple)):
raise TypeError('`inputs` should be a list or tuple.')
feed_dict = {}
for tensor, value in zip(self.inputs, inputs):
if is_sparse(tensor):
sparse_coo = value.tocoo()
indices = np.concatenate((np.expand_dims(sparse_coo.row, 1),
np.expand_dims(sparse_coo.col, 1)),
1)
value = (indices, sparse_coo.data, sparse_coo.shape)
feed_dict[tensor] = value
session = get_session()
enqueue_ops = self._enqueue_ops
neops = len(enqueue_ops)
updated = session.run(enqueue_ops + self.outputs + [self.updates_op],
feed_dict=feed_dict,
**self.session_kwargs)
nouts = len(self.outputs)
# return updated[:len(self.outputs)]
return updated[neops:nouts + neops]