train.append(k)
# Word Count and representation
words = []
wc = Counter()
for sent in src:
for w in sent:
words.append(w)
wc[w] += 1
for sent in tgt:
for w in sent:
words.append(w)
wc[w] += 1
vw = Vocab.from_corpus([words])
S = vw.w2i["<s>"]
nwords = vw.size()
model = dy.Model()
trainer = dy.AdamTrainer(model)
seq2seq = sequence_to_sequence(num_layers, num_input, num_hidden, model,
nwords, vw)
# Have fun
num_tagged = cum_loss = 0
for ITER in xrange(50):
random.shuffle(train)
for i, s in enumerate(train, 1):
if i % 1000 == 0:
