def main():
# 加载配置文件
with open('./config.yml') as file_config:
config = yaml.load(file_config)
feature_names = config['model_params']['feature_names']
# 初始化embedding shape, dropouts, 预训练的embedding也在这里初始化)
feature_weight_shape_dict, feature_weight_dropout_dict, \
feature_init_weight_dict = dict(), dict(), dict()
for feature_name in feature_names:
feature_weight_shape_dict[feature_name] = \
config['model_params']['embed_params'][feature_name]['shape']
feature_weight_dropout_dict[feature_name] = \
config['model_params']['embed_params'][feature_name]['dropout_rate']
path_pre_train = config['model_params']['embed_params'][feature_name]['path']
if path_pre_train:
with open(path_pre_train, 'rb') as file_r:
feature_init_weight_dict[feature_name] = pickle.load(file_r)
# 加载数据
# 加载vocs
path_vocs = []
for feature_name in feature_names:
path_vocs.append(config['data_params']['voc_params'][feature_name]['path'])
path_vocs.append(config['data_params']['voc_params']['label']['path'])
vocs = load_vocs(path_vocs)
# 加载训练数据
sep_str = config['data_params']['sep']
assert sep_str in ['table', 'space']
sep = '\t' if sep_str == 'table' else ' '
data_dict = init_data(
path=config['data_params']['path_train'], feature_names=feature_names, sep=sep,
vocs=vocs, max_len=config['model_params']['sequence_length'], model='train')
# 训练模型
model = SequenceLabelingModel(
sequence_length=config['model_params']['sequence_length'],
nb_classes=config['model_params']['nb_classes'],
nb_hidden=config['model_params']['bilstm_params']['num_units'],
feature_weight_shape_dict=feature_weight_shape_dict,
feature_init_weight_dict=feature_init_weight_dict,
feature_weight_dropout_dict=feature_weight_dropout_dict,
dropout_rate=config['model_params']['dropout_rate'],
nb_epoch=config['model_params']['nb_epoch'], feature_names=feature_names,
batch_size=config['model_params']['batch_size'],
train_max_patience=config['model_params']['max_patience'],
use_crf=config['model_params']['use_crf'],
l2_rate=config['model_params']['l2_rate'],
rnn_unit=config['model_params']['rnn_unit'],
learning_rate=config['model_params']['learning_rate'],
clip=config['model_params']['clip'],
path_model=config['model_params']['path_model'])
model.fit(
data_dict=data_dict, dev_size=config['model_params']['dev_size'])
def main():
# 加载配置文件
with open('./config.yml') as file_config:
config = yaml.load(file_config)
feature_names = config['model_params']['feature_names']
logger.info(feature_names)
use_char_feature = config['model_params']['use_char_feature']
logger.info(use_char_feature)
# 初始化embedding shape, dropouts, 预训练的embedding也在这里初始化)
feature_weight_shape_dict = dict()
feature_weight_dropout_dict = dict()
feature_init_weight_dict = dict()
for feature_name in feature_names:
feature_weight_shape_dict[feature_name] = config['model_params'][
'embed_params'][feature_name]['shape']
feature_weight_dropout_dict[feature_name] = config['model_params'][
'embed_params'][feature_name]['dropout_rate']
# embeding mat, 比voc多了两行， 因为voc从2开始编序， 0， 1行用0填充
path_pre_train = config['model_params']['embed_params'][feature_name][
'path'] # 词嵌矩阵位置
# logger.info("%s init mat path: %s" % (feature_name, path_pre_train))
with open(path_pre_train, 'rb') as file_r:
feature_init_weight_dict[feature_name] = pickle.load(file_r)
logger.info(feature_weight_dropout_dict)
logger.info(feature_weight_shape_dict)
logger.info(feature_init_weight_dict)
# char embedding shape
if use_char_feature:
# 暂时不考虑
feature_weight_shape_dict['char'] = config['model_params'][
'embed_params']['char']['shape']
conv_filter_len_list = config['model_params']['conv_filter_len_list']
conv_filter_size_list = config['model_params']['conv_filter_size_list']
else:
# 利用卷集层来提取char的信息
conv_filter_len_list = None
conv_filter_size_list = None
# 加载vocs
path_vocs = []
if use_char_feature:
path_vocs.append(config['data_params']['voc_params']['char']
['path']) # vocs用于将文本数字序列化
for feature_name in feature_names:
path_vocs.append(
config['data_params']['voc_params'][feature_name]['path'])
path_vocs.append(config['data_params']['voc_params']['label']['path'])
vocs = load_vocs(path_vocs)
# 加载训练数据
sep_str = config['data_params']['sep']
assert sep_str in ['table', 'space'] # 数据的分隔方式
sep = '\t' if sep_str == 'table' else ' '
max_len = config['model_params']['sequence_length']
word_len = config['model_params']['word_length']
# 通过voc 将input f1 和输出 label 数字序列化 得到训练的输入和输出
# data_dict = None
data_dict = init_data(path=config['data_params']['path_train'],
feature_names=feature_names,
sep=sep,
vocs=vocs,
max_len=max_len,
model='train',
use_char_feature=use_char_feature,
word_len=word_len)
logger.info(data_dict) # 每个特征序列化后的数据
# 训练模型
model = SequenceLabelingModel(
sequence_length=config['model_params']['sequence_length'], # 句子被固定长度
nb_classes=config['model_params']['nb_classes'],
nb_hidden=config['model_params']['bilstm_params']['num_units'],
num_layers=config['model_params']['bilstm_params']['num_layers'],
rnn_dropout=config['model_params']['bilstm_params']['rnn_dropout'],
feature_weight_shape_dict=feature_weight_shape_dict,
feature_init_weight_dict=feature_init_weight_dict,
feature_weight_dropout_dict=feature_weight_dropout_dict,
dropout_rate=config['model_params']['dropout_rate'],
nb_epoch=config['model_params']['nb_epoch'],
feature_names=feature_names,
batch_size=config['model_params']['batch_size'],
train_max_patience=config['model_params']['max_patience'],
use_crf=config['model_params']['use_crf'],
l2_rate=config['model_params']['l2_rate'],
rnn_unit=config['model_params']['rnn_unit'],
learning_rate=config['model_params']['learning_rate'],
clip=config['model_params']['clip'],
use_char_feature=use_char_feature,
conv_filter_size_list=conv_filter_size_list,
conv_filter_len_list=conv_filter_len_list,
cnn_dropout_rate=config['model_params']['conv_dropout'],
word_length=word_len,
path_model=config['model_params']['path_model'],
last_train_sess_path=None, # 为了加快训练的速度我们继续载入前面训练的参数
transfer=False) # 是否对前面载入的参数进行迁移学习，True的话就重置LSTM的输出层
model.fit(data_dict=data_dict, dev_size=config['model_params']['dev_size'])
"""
def main():
# 加载配置文件
print("config5")
with open('./train_config/config_b2b_tag_5_only_jieba.yml') as file_config:
config = yaml.load(file_config)
feature_names = config['model_params']['feature_names']
use_char_feature = config['model_params']['use_char_feature']
# 初始化embedding shape, dropouts, 预训练的embedding也在这里初始化)
feature_weight_shape_dict, feature_weight_dropout_dict, \
feature_init_weight_dict = dict(), dict(), dict()
for feature_name in feature_names:
feature_weight_shape_dict[feature_name] = \
config['model_params']['embed_params'][feature_name]['shape']
feature_weight_dropout_dict[feature_name] = \
config['model_params']['embed_params'][feature_name]['dropout_rate']
path_pre_train = config['model_params']['embed_params'][feature_name][
'path']
if path_pre_train:
with open(path_pre_train, 'rb') as file_r:
feature_init_weight_dict[feature_name] = pickle.load(file_r)
# char embedding shape
if use_char_feature:
feature_weight_shape_dict['char'] = \
config['model_params']['embed_params']['char']['shape']
conv_filter_len_list = config['model_params']['conv_filter_len_list']
conv_filter_size_list = config['model_params']['conv_filter_size_list']
else:
conv_filter_len_list = None
conv_filter_size_list = None
# 加载数据
# 加载vocs
path_vocs = []
if use_char_feature:
path_vocs.append(config['data_params']['voc_params']['char']['path'])
for feature_name in feature_names:
path_vocs.append(
config['data_params']['voc_params'][feature_name]['path'])
path_vocs.append(config['data_params']['voc_params']['label']['path'])
vocs = load_vocs(path_vocs)
# 加载训练数据
sep_str = config['data_params']['sep']
assert sep_str in ['table', 'space']
sep = '\t' if sep_str == 'table' else ' '
max_len = config['model_params']['sequence_length']
word_len = config['model_params']['word_length']
data_dict = init_data(path=config['data_params']['path_train'],
feature_names=feature_names,
sep=sep,
vocs=vocs,
max_len=max_len,
model='train',
use_char_feature=use_char_feature,
word_len=word_len)
# 训练模型
model = SequenceLabelingModel(
sequence_length=config['model_params']['sequence_length'],
nb_classes=config['model_params']['nb_classes'],
nb_hidden=config['model_params']['bilstm_params']['num_units'],
num_layers=config['model_params']['bilstm_params']['num_layers'],
rnn_dropout=config['model_params']['bilstm_params']['rnn_dropout'],
feature_weight_shape_dict=feature_weight_shape_dict,
feature_init_weight_dict=feature_init_weight_dict,
feature_weight_dropout_dict=feature_weight_dropout_dict,
dropout_rate=config['model_params']['dropout_rate'],
nb_epoch=config['model_params']['nb_epoch'],
feature_names=feature_names,
batch_size=config['model_params']['batch_size'],
train_max_patience=config['model_params']['max_patience'],
use_crf=config['model_params']['use_crf'],
l2_rate=config['model_params']['l2_rate'],
rnn_unit=config['model_params']['rnn_unit'],
learning_rate=config['model_params']['learning_rate'],
clip=config['model_params']['clip'],
use_char_feature=use_char_feature,
conv_filter_size_list=conv_filter_size_list,
conv_filter_len_list=conv_filter_len_list,
cnn_dropout_rate=config['model_params']['conv_dropout'],
word_length=word_len,
path_model=config['model_params']['path_model'])
model.fit(data_dict=data_dict, dev_size=config['model_params']['dev_size'])
feature_names=feature_names,
sep=sep,
vocs=vocs,
max_len=config['model_params']['sequence_length'],
model='train')
# 训练模型
model = SequenceLabelingModel(
sequence_length=config['model_params']['sequence_length'],
nb_classes=config['model_params']['nb_classes'],
nb_hidden=config['model_params']['bilstm_params']['num_units'],
feature_weight_shape_dict=feature_weight_shape_dict,
feature_init_weight_dict=feature_init_weight_dict,
feature_weight_dropout_dict=feature_weight_dropout_dict,
dropout_rate=config['model_params']['dropout_rate'],
nb_epoch=config['model_params']['nb_epoch'],
feature_names=feature_names,
batch_size=config['model_params']['batch_size'],
train_max_patience=config['model_params']['max_patience'],
use_crf=config['model_params']['use_crf'],
l2_rate=config['model_params']['l2_rate'],
rnn_unit=config['model_params']['rnn_unit'],
learning_rate=config['model_params']['learning_rate'],
clip=config['model_params']['clip'],
path_model=config['model_params']['path_model'])
model.fit(data_dict=data_dict, dev_size=config['model_params']['dev_size'])
# if __name__ == '__main__':
# main()