info_file = KO_WIKIPEDIA_ORG_INFO_FILE
urls_file = KO_WIKIPEDIA_ORG_URLS_FILE
sentences_file = KO_WIKIPEDIA_ORG_SENTENCES_FILE
characters_file = KO_WIKIPEDIA_ORG_CHARACTERS_FILE
log.info('info_file: %s' % info_file)
log.info('urls_file: %s' % urls_file)
log.info('sentences_file: %s' % sentences_file)
log.info('characters_file: %s' % characters_file)
if not os.path.exists(characters_file) or not os.path.exists(sentences_file) or not os.path.exists(info_file) or not os.path.exists(urls_file):
try:
log.info('create senences file...')
TextPreprocess.dump_corpus(MONGO_URL, db_name='parsed', collection_name='ko.wikipedia.org', sentences_file=sentences_file,
characters_file=characters_file,
info_file=info_file, urls_file=urls_file,
train_sentences_file=KO_WIKIPEDIA_ORG_TRAIN_SENTENCES_FILE,
valid_sentences_file=KO_WIKIPEDIA_ORG_VALID_SENTENCES_FILE,
test_sentences_file=KO_WIKIPEDIA_ORG_TEST_SENTENCES_FILE,
mongo_query={}) # mongodb -> text file(corpus)
log.info('create senences file OK')
except:
log.error(traceback.format_exc())
if os.path.exists(sentences_file):
os.remove(sentences_file)
if os.path.exists(info_file):
os.remove(info_file)
if os.path.exists(urls_file):
os.remove(urls_file)
if os.path.exists(characters_file):
os.remove(characters_file)
s = line.strip()
sentences.append(s)
log.info('len(sentences): %s' % NumUtil.comma_str(len(sentences)))
watch.stop('read sentences')
watch.start('run tensorflow')
accuracies, costs, sims = [], [], []
with tf.Session() as sess:
X, Y, dropout_keep_prob, train_step, cost, y_hat, accuracy = SpellingErrorCorrection.build_DAE(n_features, window_size, noise_rate, n_hidden1,
learning_rate, watch)
saver = tf.train.Saver()
try:
restored = saver.restore(sess, model_file)
except Exception as e:
log.error('restore failed. model_file: %s' % model_file)
raise e
train_file = os.path.join(KO_WIKIPEDIA_ORG_DIR, 'datasets', 'spelling_error_correction',
'ko.wikipedia.org.dataset.sentences=%s.window_size=%d.train.gz' % (n_train, window_size))
train = DataSet.load(train_file, gzip_format=True, verbose=True)
train_vector = DataSet.load(train_file, gzip_format=True, verbose=True)
train_vector.convert_to_one_hot_vector()
try:
total_test_sampling = 1
for i, sentence in enumerate(sentences):
for nth in range(total_test_sampling):
# log.info('[%s] noise(%.1f) "%s" -> "%s"' % (nth, noise_rate, sentence, noised_sentence))
noised_sentence = SpellingErrorCorrection.encode_noise(sentence, noise_rate=noise_rate)
log.info('')
capacity=capacity,
min_after_dequeue=min_after_dequeue)
else:
features_batch, labels_batch = tf.train.batch([x, y],
batch_size=batch_size,
capacity=capacity)
return features_batch, labels_batch
if __name__ == '__main__':
shuffle = False
batch_size = 5
data_file = os.path.join(DATA_DIR, 'en2kor.tsv')
if not os.path.exists(data_file):
log.error('file not exists. %s' % data_file)
exit()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
filenames = [data_file]
features_batch, labels_batch = input_pipeline(filenames,
batch_size=batch_size,
shuffle=shuffle,
tokens=2)
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coordinator)
if s.count(' ') > 0: # sentence must have one or more space.
sentences.append(s)
log.info('len(sentences): %s' % NumUtil.comma_str(len(sentences)))
watch.stop('read sentences')
watch.start('run tensorflow')
accuracies, sims = [], []
with tf.Session() as sess:
graph = WordSpacing.build_FFNN(n_features, n_classes, n_hidden1, learning_rate)
X, Y, predicted, accuracy = graph['X'], graph['Y'], graph['predicted'], graph['accuracy']
saver = tf.train.Saver()
try:
restored = saver.restore(sess, model_file)
except:
log.error('restore failed. model_file: %s' % model_file)
try:
for i, s in enumerate(sentences):
log.info('')
log.info('[%s] in : "%s"' % (i, s))
_features, _labels = WordSpacing.sentence2features_labels(s, left_gram, right_gram)
dataset = DataSet(features=_features, labels=_labels, features_vector=features_vector, labels_vector=labels_vector)
dataset.convert_to_one_hot_vector()
if len(dataset) > 0:
_predicted, _accuracy = sess.run([predicted, accuracy], feed_dict={X: dataset.features, Y: dataset.labels}) # Accuracy report
sentence_hat = WordSpacing.spacing(s.replace(' ', ''), _predicted)
sim, correct, total = WordSpacing.sim_two_sentence(s, sentence_hat, left_gram=left_gram, right_gram=right_gram)
accuracies.append(_accuracy)
sims.append(sim)
epoch, running = 0, True
while running:
epoch += 1
for _x_batch, _y_batch in next_batch(
[train_file],
data_size=n_train,
batch_size=batch_size,
delim='\t',
splits=3,
shuffle=False):
if stop_timer.is_over():
running = False
break
if len(_x_batch) != batch_size:
log.error('len(_x_batch): %s' %
_x_batch.shape)
nth_batch += 1
_, _train_cost, _summary = sess.run(
[train_step, cost, summary],
feed_dict={
x: _x_batch,
y: _y_batch,
learning_rate: _learning_rate
})
train_writer.add_summary(
_summary, global_step=nth_batch)
if valid_timer.is_over():
for _x_valid_batch, _y_valid_batch in next_batch(
[valid_file],
