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class Config:seg_dim= 20#切词信息维度
char_dim = 100#字向量模型维度
lstm_dim = 100#lstm 内部维度
dropout= 0.5
learn_rate = 0.001#学习率
max_epoch= 400#最大训练次数
batch_size = 64
steps_check = 300# 检查频率
num_tags= 51
num_chars= 2641
num_segs= 4# 切词信息 四维i b o e
filter_width = 3# 卷积核大小
repeat_times = 4# 膨胀卷积时卷积次数clip = 5
optimizer = 'adam'
model_type = 'idcnn' # 训练模型
tag_schema = 'iobes'
pre_emb = True
lower= False
zeros= True
clean= Trueroot_path = os.getcwd() + os.sep
# ckpt_path = os.path.join(root_path + 'ckpt', "")# 模型路径
cnn_ckpt_path = os.path.join(root_path + 'ckpt\idcnn', '')
lstm_ckpt_path = os.path.join(root_path + 'ckpt\lstm', '')
log_file= os.path.join(root_path + 'log', 'train.log')# 训练日志记录
train_file = os.path.join(root_path + 'data', 'train.txt') # 训练数据集
dev_file= os.path.join(root_path + 'data', 'dev.txt')# 验证数据集
test_file = os.path.join(root_path + 'data', 'test.txt') # 测试数据集
report_file= os.path.join(root_path + 'result', 'predict.txt')# 测试数据集assert0 < dropout< 1, 'dropout must between 0, 1'
assertlearn_rate > 0, 'learn_rate must > 0'
assertoptimizer in ['adam', 'sgd', 'adagrad'] , 'this optimizer not exist'
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