verbose=1,
need_segmented=True,
full_mode=full_mode,
remove_stopword=True,
replace_number=True,
lowercase=True,
zhs2zht=True,
remove_url=True,
padding_mode='center',
add_unkown_word=True,
feature_type=feature_type,
)
train_X_feature = feature_encoder.fit_transform(train_data=train_data['SENTENCE'].as_matrix())
feature_encoder.print_model_descibe()
feature_encoder.print_sentence_length_detail()
# train_y = train_data['LABEL_INDEX'].as_matrix()
test_all_X_feature = feature_encoder.transform(test_data['SENTENCE'].as_matrix())
test_all_y = test_data['LABEL_INDEX'].as_matrix()
print(train_X_feature.shape)
print(test_all_X_feature.shape)
logging.debug('=' * 20)
# ****************************************************************
# ------------- region end : 2. 转换数据的格式并特征编码 -------------
# ****************************************************************
remove_stopword=True,
replace_number=True,
lowercase=True,
zhs2zht=True,
remove_url=True,
padding_mode='center',
add_unkown_word=True,
mask_zero=True)
train_X_features = feature_encoder.fit_transform(train_data=train_X)
test_X_features = feature_encoder.transform(test_X)
feature_encoder.print_sentence_length_detail
print(feature_encoder.vocabulary_size)
# print ','.join(sorted(feature_encoder.vocabulary))
# quit()
feature_encoder.print_model_descibe()
# -------------- code start : 结束 -------------
if verbose > 2:
logging.debug('-' * 20)
print('-' * 20)
# -------------- region end : 2. 转换数据格式，以可以进行分类 ---------------
for seed in config['rand_seed']:
# -------------- region start : 3. 初始化CNN模型并训练 -------------
if verbose > 2:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('3. 初始化CNN模型并训练')
print('3. 初始化CNN模型并训练')
# -------------- code start : 开始 -------------
