lstm = LSTM(emb.output, args.l2, args.hidden, num_words + 1, num_tags, args.num_features)
emb.load(directory_model, varlist)
lstm.load(directory_model, varlist)
classify = th.function(
inputs = [x],
outputs = [lstm.y_pred, lstm.p_y_given_x])
print('#words: {}, #tags : {}, #hidden : {}, embedding size: {} '.format(\
len(reader.word_dict), len(reader.tag_dict), args.hidden, args.num_features))
print('>>> READY')
while True:
sent = input()
coded = reader.codify_string(sent)
coded_tags, probilities = classify(coded)
tags = [reader.reverse_tag_dict[t] for t in coded_tags]
sent = sent.split(' ')
p = lambda s: ' '.join(["{:>14}".format(x) for x in s])
c = lambda x: x if ' O' in x else colored(x, 'green')
cp = lambda s: ' '.join([c("{:>14}".format(x)) for x in s])
print('[INPUT] ' + p(sent))
print('[CODED] ' + p(coded))
print('[ TAG ] ' + p(coded_tags))
print('[UNTAG] ' + cp(tags))
print('[PROBS]')
probs = [sorted([(p, reader.reverse_tag_dict[i]) for i,p in enumerate(w)])[-5:][::-1] for w in probilities]
for w, p in zip(sent, probs):
