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 BuildModel():
edf.components = []
B = inp.value.shape[0]
T = inp.value.shape[1]
h = edf.Value(np.zeros((B, hidden_dim)))
c = edf.Value(np.zeros((B, hidden_dim)))
score = []
for t in range(T - 1):
wordvec = edf.Embed(edf.Value(inp.value[:, t]), C2V)
xt = edf.Reshape(wordvec, [-1, hidden_dim])
h_next, c_next = LSTMCell(xt, h, c)
p = edf.SoftMax(edf.VDot(h_next, V))
logloss = edf.Reshape(
edf.LogLoss(edf.Aref(p, edf.Value(inp.value[:, t + 1]))),
(B, 1))
if t == 0:
loss = logloss
else:
loss = edf.ConCat(loss, logloss)
score.append(p)
h = h_next
c = c_next
masks = np.zeros((B, T - 1), dtype=np.int32)
masks[inp.value[:, 1:] != 0] = 1
loss = edf.MeanwithMask(loss, edf.Value(masks))
return loss, score
def BuildModel():
edf.components = []
B, T = inp.value.shape
score = []
loss = None
# Init h_0 with one-hot
vocab_init = np.ones([B])
vocab_init = edf.Value(vocab_init)
h = edf.Embed(vocab_init, C2V)
# Init C_0 to be zero
c = edf.Value(np.zeros([B, hidden_dim]))
for t in range(T):
x_t = edf.Value(inp.value[:, t])
x_t = edf.Embed(x_t, C2V)
h, c = LSTMCell(x_t, h, c)
# Score and loss
pred = edf.SoftMax(edf.VDot(h, V))
if t != T - 1:
score.append(pred)
x_t1 = edf.Value(inp.value[:, t + 1])
else:
x_t1 = edf.Value(np.zeros(B))
loss_t = edf.LogLoss(edf.Aref(pred, x_t1))
if loss is None:
loss = loss_t
else:
loss = edf.Add(loss, loss_t)
loss = edf.Mul(edf.Mean(loss), edf.Value(np.float64(1) / T))
return loss, score
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 BuildModel():
edf.components = []
B = inp.value.shape[0]
T = inp.value.shape[1]
h = [[None] * layer] * T
c = [[None] * layer] * T
for i in range(layer):
h[0][i] = edf.Value(np.zeros((B, hidden_dim)))
c[0][i] = edf.Value(np.zeros((B, hidden_dim)))
score = []
for t in range(T - 1):
wordvec = edf.Embed(edf.Value(inp.value[:, t]), 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))
logloss = edf.Reshape(edf.LogLoss(edf.Aref(p, edf.Value(inp.value[:, t + 1]))), (B, 1))
if t == 0:
loss = logloss
else:
loss = edf.ConCat(loss, logloss)
score.append(p)
masks = np.zeros((B, T - 1), dtype=np.int32)
masks[inp.value[:, 1:] != 0] = 1
loss = edf.MeanwithMask(loss, edf.Value(masks))
return loss, score
