def learning(cls, total_epoch, n_train, n_valid, n_test, batch_size, window_size, noise_rate, model_file, features_vector, labels_vector,
n_hidden1,
learning_rate,
dropout_keep_rate, early_stop_cost=0.001):
n_features = len(features_vector) * window_size # number of features = 17,382 * 10
log.info('load characters list...')
log.info('load characters list OK. len: %s' % NumUtil.comma_str(len(features_vector)))
watch = WatchUtil()
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))
valid_file = os.path.join(KO_WIKIPEDIA_ORG_DIR, 'datasets', 'spelling_error_correction',
'ko.wikipedia.org.dataset.sentences=%s.window_size=%d.valid.gz' % (n_valid, window_size))
test_file = os.path.join(KO_WIKIPEDIA_ORG_DIR, 'datasets', 'spelling_error_correction',
'ko.wikipedia.org.dataset.sentences=%s.window_size=%d.test.gz' % (n_test, window_size))
log.info('train_file: %s' % train_file)
log.info('valid_file: %s' % valid_file)
log.info('test_file: %s' % test_file)
if not os.path.exists(train_file) or not os.path.exists(valid_file) or not os.path.exists(test_file):
dataset_dir = os.path.dirname(train_file)
if not os.path.exists(dataset_dir):
os.makedirs(dataset_dir)
watch.start('create dataset') # FIXME: out of memory (1M sentences)
log.info('create dataset...')
data_files = (('train', KO_WIKIPEDIA_ORG_TRAIN_SENTENCES_FILE, n_train, train_file, False),
('valid', KO_WIKIPEDIA_ORG_VALID_SENTENCES_FILE, n_valid, valid_file, False),
('test', KO_WIKIPEDIA_ORG_TEST_SENTENCES_FILE, n_test, test_file, False))
for (name, data_file, total, dataset_file, to_one_hot_vector) in data_files:
check_interval = 10000
log.info('check_interval: %s' % check_interval)
log.info('%s %s total: %s' % (name, os.path.basename(data_file), NumUtil.comma_str(total)))
log.info('noise_rate: %s' % noise_rate)
features, labels = [], []
with gzip.open(data_file, 'rt') as f:
for i, line in enumerate(f, 1):
if total < i:
break
if i % check_interval == 0:
time.sleep(0.01) # prevent cpu overload
percent = i / total * 100
log.info('create dataset... %.1f%% readed. data len: %s. %s' % (percent, NumUtil.comma_str(len(features)), data_file))
sentence = line.strip()
for start in range(0, len(sentence) - window_size + 1): # 문자 단위로 노이즈(공백) 생성
chars = sentence[start: start + window_size]
for idx in range(len(chars)):
noised_chars = StringUtil.replace_with_index(chars, ' ', idx)
features.append(noised_chars)
labels.append(chars)
log.debug('create dataset... %s "%s" -> "%s"' % (name, noised_chars, chars))
# log.info('noise_sampling: %s' % noise_sampling)
# for nth_sample in range(noise_sampling): # 초성, 중성, 종성 단위로 노이즈 생성
# for start in range(0, len(sentence) - window_size + 1):
# chars = sentence[start: start + window_size]
# noised_chars = SpellingErrorCorrection.encode_noise(chars, noise_rate=noise_rate, noise_with_blank=True)
# if chars == noised_chars:
# continue
# if i % check_interval == 0 and nth_sample == 0:
# log.info('create dataset... %s "%s" -> "%s"' % (name, noised_chars, chars))
# features.append(noised_chars)
# labels.append(chars)
# print('dataset features:', features)
# print('dataset labels:', labels)
dataset = DataSet(features=features, labels=labels, features_vector=features_vector, labels_vector=labels_vector, name=name)
log.info('dataset save... %s' % dataset_file)
dataset.save(dataset_file, gzip_format=True, verbose=True)
log.info('dataset save OK. %s' % dataset_file)
log.info('dataset: %s' % dataset)
log.info('create dataset OK.')
log.info('')
watch.stop('create dataset')
watch.start('dataset load')
log.info('dataset load...')
train = DataSet.load(train_file, gzip_format=True, verbose=True)
if n_train >= int('100,000'.replace(',', '')):
valid = DataSet.load(valid_file, gzip_format=True, verbose=True)
else:
valid = DataSet.load(train_file, gzip_format=True, verbose=True)
log.info('valid.convert_to_one_hot_vector()...')
valid = valid.convert_to_one_hot_vector(verbose=True)
log.info('valid.convert_to_one_hot_vector() OK.')
log.info('train dataset: %s' % train)
log.info('valid dataset: %s' % valid)
log.info('dataset load OK.')
log.info('')
watch.stop('dataset load')
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)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
check_interval = max(1, min(1000, n_train // 10))
nth_train, nth_input, total_input = 0, 0, total_epoch * train.size
log.info('')
log.info('learn...')
log.info('total_epoch: %s' % total_epoch)
log.info('train.size (total features): %s' % NumUtil.comma_str(train.size))
log.info('check_interval: %s' % check_interval)
log.info('total_epoch: %s' % total_epoch)
log.info('batch_size: %s' % batch_size)
log.info('total_input: %s (total_epoch * train.size)' % total_input)
log.info('')
watch.start('learn')
valid_cost = sys.float_info.max
for epoch in range(1, total_epoch + 1):
if valid_cost < early_stop_cost:
log.info('valid_cost: %s, early_stop_cost: %s, early stopped.' % (valid_cost, early_stop_cost))
break
for step, (features_batch, labels_batch) in enumerate(train.next_batch(batch_size=batch_size, to_one_hot_vector=True), 1):
if valid_cost < early_stop_cost:
break
nth_train += 1
nth_input += features_batch.shape[0]
sess.run(train_step, feed_dict={X: features_batch, Y: labels_batch, dropout_keep_prob: dropout_keep_rate})
# if nth_train % check_interval == 1:
percent = nth_input / total_input * 100
valid_cost = sess.run(cost, feed_dict={X: valid.features, Y: valid.labels, dropout_keep_prob: 1.0})
log.info('[epoch=%s][%.1f%%] %s cost: %.8f' % (epoch, percent, valid.name, valid_cost))
watch.stop('learn')
log.info('learn OK.')
log.info('')
log.info('model save... %s' % model_file)
watch.start('model save...')
model_dir = os.path.dirname(model_file)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
saver = tf.train.Saver()
saver.save(sess, model_file)
watch.stop('model save...')
log.info('model save OK. %s' % model_file)
log.info('')
log.info('total_epoch: %s' % total_epoch)
log.info('batch_size: %s' % batch_size)
log.info('total_input: %s (total_epoch * train.size)' % total_input)
log.info('')
log.info(watch.summary())
log.info('')
def dump_corpus(mongo_url, db_name, collection_name, sentences_file, characters_file, info_file, urls_file,
train_sentences_file, valid_sentences_file, test_sentences_file,
mongo_query=None, limit=None):
"""
Mongodb에서 문서를 읽어서, 문장 단위로 저장한다. (단 문장안의 단어가 1개 이거나, 한글이 전혀 없는 문장은 추출하지 않는다.)
:param characters_file:
:param urls_file:
:param info_file:
:param mongo_url: mongodb://~~~
:param db_name: database name of mongodb
:param collection_name: collection name of mongodb
:param sentences_file: *.sentence file
:param train_sentences_file:
:param valid_sentences_file:
:param test_sentences_file:
:param mongo_query: default={}
:param limit:
:return:
"""
if mongo_query is None:
mongo_query = {}
corpus_mongo = MongodbUtil(mongo_url, db_name=db_name, collection_name=collection_name)
total_docs = corpus_mongo.count()
log.info('%s total: %s' % (corpus_mongo, NumUtil.comma_str(total_docs)))
output_dir = os.path.basename(sentences_file)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with gzip.open(sentences_file, 'wt') as out_f, \
gzip.open(train_sentences_file, 'wt') as train_f, \
gzip.open(valid_sentences_file, 'wt') as valid_f, \
gzip.open(test_sentences_file, 'wt') as test_f, \
open(info_file, 'wt') as info_f, \
open(urls_file, 'wt') as urls_f:
char_set = set()
n_docs = n_total = n_train = n_valid = n_test = 0
if limit:
cursor = corpus_mongo.find(mongo_query, limit=limit)
else:
cursor = corpus_mongo.find(mongo_query)
for i, row in enumerate(cursor, 1):
if i % 1000 == 0:
log.info('%s %.1f%% writed.' % (os.path.basename(sentences_file), i / total_docs * 100))
sentences = []
for c in row['content']:
sentences.extend(HangulUtil.text2sentences(c['sentences'], remove_only_one_word=True, has_hangul=True))
# sentences = HangulUtil.text2sentences(row['content'], remove_only_one_word=True, has_hangul=True)
log.debug('url: %s, len: %s' % (row['url'], len(sentences)))
if len(sentences) == 0:
# log.error(row['content'])
continue
urls_f.write(row['url'])
urls_f.write('\n')
n_docs += 1
for s in sentences:
_char_set = set([c for c in s])
char_set.update(_char_set)
n_total += 1
out_f.write(s)
out_f.write('\n')
if len(sentences) >= 10: # can split
test_len = valid_len = len(sentences) // 10
# log.info('train: %s, test: %s, valid: %s' % (len(sentences) - test_len - valid_len, test_len, valid_len))
for s in sentences[:test_len]:
n_test += 1
test_f.write(s)
test_f.write('\n')
for s in sentences[test_len:test_len + valid_len]:
n_valid += 1
valid_f.write(s)
valid_f.write('\n')
for s in sentences[test_len + valid_len:]:
n_train += 1
train_f.write(s)
train_f.write('\n')
else: # can't split
for s in sentences:
n_train += 1
train_f.write(s)
train_f.write('\n')
char_list = list(char_set)
char_list.sort()
log.info('writed to %s...' % characters_file)
with open(characters_file, 'w') as f:
for c in char_list:
f.write(c)
f.write('\n')
log.info('writed to %s OK.' % characters_file)
log.info('total docs: %s', NumUtil.comma_str(total_docs))
log.info('total docs: %s (has hangul sentence)', NumUtil.comma_str(n_docs))
log.info('total sentences: %s (has hangul sentence)', NumUtil.comma_str(n_total))
log.info('train: %s', NumUtil.comma_str(n_train))
log.info('valid: %s', NumUtil.comma_str(n_valid))
log.info('test: %s', NumUtil.comma_str(n_test))
log.info('total characters: %s', NumUtil.comma_str(len(char_list)))
info_f.write('total docs: %s\n' % NumUtil.comma_str(total_docs))
info_f.write('total docs: %s (has hangul sentence)\n' % NumUtil.comma_str(n_docs))
info_f.write('total sentences: %s (has hangul sentence)\n' % NumUtil.comma_str(n_total))
info_f.write('train: %s\n' % NumUtil.comma_str(n_train))
info_f.write('valid: %s\n' % NumUtil.comma_str(n_valid))
info_f.write('test: %s\n' % NumUtil.comma_str(n_test))
info_f.write('total characters: %s\n' % NumUtil.comma_str(len(char_list)))
watch = WatchUtil()
watch.start()
_test_cost, _W1, _b1, _x_batch, _y_batch, _y_hat_batch = sess.run(
[cost, W1, b1, x, y, y_hat],
feed_dict={
learning_rate: _learning_rate,
use_first_pipeline: True
})
log.info('')
log.info('W1: %s' % ['%.4f' % i for i in _W1])
log.info('b1: %.4f' % _b1)
for (x1, x2), _y, _y_hat in zip(
_x_batch, _y_batch, _y_hat_batch):
log.debug(
'%3d + %3d = %4d (y_hat: %4.1f)' %
(x1, x2, _y, _y_hat))
log.info('')
log.info(
'"%s" test: test_cost: %.8f, %.2f secs (batch_size: %s)'
% (model_name, _test_cost, watch.elapsed(),
batch_size))
log.info('')
except:
log.info(traceback.format_exc())
finally:
coordinator.request_stop()
coordinator.join(
threads) # Wait for threads to finish.