"""
n, e = model.test(codified_sentences[minibatch_index],\
codified_tags[minibatch_index])
"""
if e == 0 and n < eps:
ignored.add(minibatch_index)
"""
nll += n
total_errors += e
parsed += 1
max_e = max(e, max_e)
if e >= 1 and False:
current_sentence = [reader.uncodify_word(t[args.window//2]) for t in codified_sentences[minibatch_index]]
current_tags = codified_tags[minibatch_index]
y_pred, probs = model.classify(codified_sentences[minibatch_index])
print('---------------------------')
print('sent', current_sentence)
print('gold', [reader.reverse_tag_dict[t] for t in current_tags])
print('gues', [reader.reverse_tag_dict[t] for t in y_pred])
if args.validation_filename:
nllv = 0
total_errorsv = 0
parsedv = 0
misclass = {}
for minibatch_index in range(len(valid_reader.sentences)):
nv, ev = model.test(codified_sentences_valid[minibatch_index],\
codified_tags_valid[minibatch_index])
