def preprocess_test(self, sentence):
splits = self.parse_sentence(sentence)
mapped_vectors = self.map_vector(splits)
pad_len = max([len(mapped_vectors[0]), len(mapped_vectors[1])])
padded_vectors = [
pad_sequence(mapped_vectors[0], pad_len, word2vec_mapped_size),
pad_sequence(mapped_vectors[1], pad_len, word2vec_mapped_size)
]
return padded_vectors
def do_shell(model, dev, input_model=None):
""" Interactive shell
Type a question, write next for the next paragraph or enter a blank for another human's question.
Args:
model: QA model that has an instance variable 'answer' that returns answer span and takes placeholders
question, question_length, paragraph, paragraph_length
dev: Development set
"""
# what is is_training if import_meta_graph
checkpoint_dir = os.path.join(FLAGS.train_dir, FLAGS.model_name)
vocab_path = FLAGS.vocab_path or pjoin(FLAGS.data_dir, "vocab.dat")
vocab, rev_vocab = initialize_vocab(vocab_path)
# TODO no logs
saver = tf.train.Saver()
with tf.Session() as session:
if False: # load_meta
last_meta = next(
reversed(
[f for f in os.listdir(checkpoint_dir) if '.meta' in f]))
saver = tf.train.import_meta_graph(os.path.join(last_meta))
saver.restore(session, tf.train.latest_checkpoint(checkpoint_dir))
print('HINT: Input as question "next" for next paragraph')
while True:
original_question, paragraphs, question_lengths, paragraph_lengths, answers = dev.get_batch(
1)
for i in itertools.count():
paragraph = reverse_indices(paragraphs[0], rev_vocab)
if not i:
print('\n')
print(paragraph, end='\n\n')
question_input = input('QUESTION: ')
if question_input == 'next':
break
elif question_input:
question = [
vocab.get(word, UNK_ID)
for word in tokenize(question_input)
]
question, question_length = pad_sequence(
question, FLAGS.max_question_length)
questions, question_lengths = [question], [question_length]
else:
question_words = reverse_indices(original_question[0],
rev_vocab)
questions = original_question
print(question_words)
if input_model:
#feed into siamese model instead
question = feed_dict_inputs[0]
question = input_model.run(question)
feed_dict = model.fill_feed_dict(questions, paragraphs,
question_lengths,
paragraph_lengths)
if False: #load_meta
start, end = session.run([
'prediction/answer_start:0', 'prediction/answer_end:0'
], feed_dict)
start, end = start[0], end[0]
else:
start, end = session.run(model.answer, feed_dict)
start, end = start[0], end[0]
answer_idxs = paragraphs[0][start:end + 1]
answer_words = ''.join(reverse_indices(answer_idxs, rev_vocab))
print(f'COMPUTER: {answer_words}')
if not question_input:
start, end = answers[0]
correct_answer_idxs = paragraphs[0][start:end + 1]
correct_answer = ''.join(
reverse_indices(correct_answer_idxs, rev_vocab))
print(f'HUMAN: {correct_answer}')
print()