WINDOWS下的启动BERT-SERVING-SERVER报错 TypeError: ‘NoneType‘ object is not iterable

原因分析

python3.7.3版本下安装tensorflow默认2.0.0，不支持tensorflow1.10.0，而暂时bert-serving-server不支持tensorflow2.0.0，建议tensorflow1.10.0版本，python3.5。
不然会报错：
TypeError: ‘NoneType’ object is not iterable #377
fail to optimize the graph!
附github链接
启动出错其他问题可查询链接

一、ANACONDA环境配置

创建虚拟环境（建议PYTHON3.5版本）

conda create --name py3.5 python=3.5

安装指定版本的TENSORFLOW（建议1.10.0版本）

conda install --channel https://conda.anaconda.org/anaconda tensorflow=1.10.0

二、安装并启动BERT-SERVING-SERVER

1、使用 PIP 命令进行安装

pip install bert-serving-server # 服务端
pip install bert-serving-client # 客户端

2、下载bert预训练模型
模型下载链接

3、启动bert服务

bert-serving-start -model_dir /tmp/english_L-12_H-768_A-12/ -num_worker=2  //模型路径自改

或
建一个服务端的代码

# -*- coding: utf-8 -*-
from bert_serving.server import BertServer
from bert_serving.server.helper import get_args_parser
def main():
    args = get_args_parser().parse_args(['-model_dir', r'C:\project\bert-as-service\chinese_L-12_H-768_A-12',
                                         '-port', '86500',
                                         '-port_out', '86501',
                                         '-max_seq_len', '512',
                                         '-mask_cls_sep',
                                         '-cpu'])
 
    bs = BertServer(args)
    bs.start()
if __name__ == "__main__":

客户端代码

# 导入bert客户端
from bert_serving.client import BertClient
import numpy as np


class SimilarModel:
    def __init__(self):
        # ip默认为本地模式，如果bert服务部署在其他服务器上，修改为对应ip
        self.bert_client = BertClient(ip='192.168.x.x')

    def close_bert(self):
        self.bert_client .close()

    def get_sentence_vec(self,sentence):
        '''
        根据bert获取句子向量
        :param sentence:
        :return:
        '''
        return self.bert_client .encode([sentence])[0]

    def cos_similar(self,sen_a_vec, sen_b_vec):
        '''
        计算两个句子的余弦相似度
        :param sen_a_vec:
        :param sen_b_vec:
        :return:
        '''
        vector_a = np.mat(sen_a_vec)
        vector_b = np.mat(sen_b_vec)
        num = float(vector_a * vector_b.T)
        denom = np.linalg.norm(vector_a) * np.linalg.norm(vector_b)
        cos = num / denom
        return cos

if __name__=='__main__':
    # 从候选集condinates 中选出与sentence_a 最相近的句子
    condinates = ['machine ','computer science','dicision tree']
    sentence_a = 'machine learning'
    bert_client = SimilarModel()
    max_cos_similar = 0
    most_similar_sentence = ''
    for sentence_b in condinates:
        sentence_a_vec = bert_client .get_sentence_vec(sentence_a)
        sentence_b_vec = bert_client .get_sentence_vec(sentence_b)
        cos_similar = bert_client .cos_similar(sentence_a_vec,sentence_b_vec)
        if cos_similar > max_cos_similar:
            max_cos_similar = cos_similar
            most_similar_sentence = sentence_b

    print('最相似的句子：',most_similar_sentence)
    bert_client .close_bert()