log.info('')
log.info('model loaded... %s' % model_file)
saver = tf.train.Saver(max_to_keep=None)
saver.restore(sess, model_file)
log.info('model loaded OK. %s' % model_file)
coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coordinator)
try:
watch = WatchUtil()
watch.start()
_test_x_batch, _test_y_batch = sess.run([test_x_batch, test_y_batch])
_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, x: _test_x_batch, y: _test_y_batch})
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.
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, remove_markdown=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)))