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):
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