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 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 = [[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
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
f1 = edf.Param(edf.xavier((3, 3, prev_channel, 32)))
b1 = edf.Param(np.zeros(32))
f3 = edf.Param(edf.xavier((3, 3, 32, 64)))
b3 = edf.Param(np.zeros(64))
f5 = edf.Param(edf.xavier((1, 1, 64, 10)))
b5 = edf.Param(np.zeros(10))
layer1 = edf.RELU(edf.Add(Conv(f1, inp, 1, 1), b1))
layer2 = MaxPool(layer1, 4)
layer3 = edf.RELU(edf.Add(Conv(f3, layer2), b3))
layer4 = AvePool(layer3, 6)
layer5 = edf.RELU(edf.Add(Conv(f5, layer4), b5))
pred = edf.Reshape(layer5, (bucket, 10))
# the standard classification layer, which you don't need to modify
pred = edf.SoftMax(pred)
loss = edf.Mean(edf.LogLoss(edf.Aref(pred, lab)))
acc = edf.Accuracy(pred, lab)
##################################################################################################################
def eval_train():
# we only choose 1/5 of the train images for evaluation since evaluation the whole images is time consuming
eval_imgs = t_imgs[::5]
eval_labels = t_labels[::5]
avg_acc = 0