def Predict(max_step, prefix):
edf.components = []
T = max_step
h = edf.Value(np.zeros((1, hidden_dim)))
c = edf.Value(np.zeros((1, hidden_dim)))
prediction = []
for t in range(T):
if t < len(prefix):
pred = edf.Value(prefix[t])
prediction.append(pred)
else:
prediction.append(pred)
wordvec = edf.Embed(pred, C2V)
xt = edf.Reshape(wordvec, [-1, hidden_dim])
h_next, c_next = LSTMCell(xt, h, c)
p = edf.SoftMax(edf.VDot(h_next, V))
pred = edf.ArgMax(p)
h = h_next
c = c_next
edf.Forward()
idx = [pred.value for pred in prediction]
stop_idx = utils.to_index('}')
if stop_idx in idx:
return idx[0:idx.index(stop_idx) + 1]
else:
return idx
def Predict(max_step, prefix, model):
T = max_step
prediction = []
for t in range(T):
if t < len(prefix):
pred = prefix[t]
prediction.append(pred)
else:
prediction.append(pred)
hidden = model.init_hidden(1)
inp = Variable(torch.LongTensor([[pred]]))
if use_cuda:
inp = inp.cuda()
#print(inp)
output, hidden = model(inp, hidden)
output = output.view(n_vocab)
#print(output)
pred = output.data.max(0)[1][0]
#print(pred)
idx = [pred for pred in prediction]
stop_idx = utils.to_index('}')
if stop_idx in idx:
return idx[0:idx.index(stop_idx) + 1]
else:
return idx
def wrapper(*args, **kwargs):
r"""
Run view function if session contains username, otherwise redirect to
index page.
:param \*args: Positional arguments to pass to view function.
:param \*\*kwargs: Keyword arguments to pass to view function.
"""
if not g.username:
return to_index()
return func(*args, **kwargs)
def wrapper(*args, **kwargs):
r"""
Run view function if session contains username, otherwise redirect to
index page.
:param \*args: Positional arguments to pass to view function.
:param \*\*kwargs: Keyword arguments to pass to view function.
"""
if not g.username:
return to_index()
return func(*args, **kwargs)
def Predict(max_step, prefix):
edf.components = []
T = max_step
h = [[None] * layer] * (T + 1)
c = [[None] * layer] * (T + 1)
for i in range(layer):
h[0][i] = edf.Value(np.zeros((1, hidden_dim)))
c[0][i] = edf.Value(np.zeros((1, hidden_dim)))
prediction = []
for t in range(T):
if t < len(prefix):
pred = edf.Value(prefix[t])
prediction.append(pred)
else:
prediction.append(pred)
wordvec = edf.Embed(pred, C2V)
xt = edf.Reshape(wordvec, [-1, hidden_dim])
for i in range(layer):
h[t + 1][i], c[t + 1][i] = LSTMCell(xt, h[t][i], c[t][i], i)
xt = h[t + 1][i]
p = edf.SoftMax(edf.VDot(xt, V))
pred = edf.ArgMax(p)
edf.Forward()
idx = [pred.value for pred in prediction]
stop_idx = utils.to_index('}')
if stop_idx in idx:
return idx[0:idx.index(stop_idx) + 1]
else:
return idx
def quit():
"""Clear username from session and quit from Comments app."""
username_key = USERNAME_KEY.format(current_app.config['KEY_PREFIX'])
session.pop(username_key, None)
return to_index()