Linux(Centos7.8)中conda虚拟环境搭建LSTM神经网络基于django3.1.2的api接口

「爱情、让人受尽委屈。」 2022-12-19 08:47 30阅读 0赞

### 目录 ###

*  1.准备工作
 *  2.项目需求
 *   *  2.1 根据需求下载LSTM依赖包
     *  2.2 代码实现
 *  3.启动服务
 *  4、第三方应用调用webapi服务提供的api接口

# 1.准备工作 #

由上一博客[Linux(Centos7.8)中Anaconda3使用虚拟环境搭建Django3应用][Linux_Centos7.8_Anaconda3_Django3]Django应用的基础环境已搭建完备。

# 2.项目需求 #

第三方外部应用能够通过调用api接口的方式，获取由LSTM（长短期记忆）递归神经网络预测的未来30天的活期存款余额趋势数据。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg_size_16_color_FFFFFF_t_70_pic_center]

## 2.1 根据需求下载LSTM依赖包 ##

进入到由上篇博客搭建的虚拟环境目录/opt/anaconda3/envs/ml\_api并激活环境

cd /opt/anaconda3/envs/ml_api
    # 激活环境
    conda activate ml_api

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg_size_16_color_FFFFFF_t_70_pic_center 1]

使用conda安装lstm神经网络依赖包,考虑到版本兼容性问题指定版本下载

conda install numpy
    conda install matplotlib
    conda install pandas
    conda install pymysql
    python -m pip install django-cors-headers #跨域
    python -m pip install --upgrade --force-reinstall tensorflow==2.2.0 --ignore-installed
    python -m pip install --upgrade --force-reinstall keras==2.3.0 --ignore-installed
    python -m pip install --upgrade --force-reinstall sklearn --ignore-installed

conda list 查看该虚假环境已安装的包

(ml_api) [root@localhost api]# conda list
    # packages in environment at /opt/anaconda3/envs/ml_api:
    #
    # Name                    Version                   Build  Channel
    _libgcc_mutex             0.1                        main  
    _tflow_select             2.3.0                       mkl  
    absl-py                   0.11.0           py38h06a4308_0  
    aiohttp                   3.6.3            py38h7b6447c_0  
    asgiref                   3.2.10                   pypi_0    pypi
    astunparse                1.6.3                      py_0  
    async-timeout             3.0.1                    py38_0  
    attrs                     20.2.0                     py_0  
    blas                      1.0                         mkl  
    blinker                   1.4                      py38_0  
    brotlipy                  0.7.0           py38h7b6447c_1000  
    c-ares                    1.16.1               h7b6447c_0  
    ca-certificates           2020.10.14                    0  
    cached-property           1.5.2                    pypi_0    pypi
    cachetools                4.1.1                      py_0  
    certifi                   2020.6.20          pyhd3eb1b0_3  
    cffi                      1.14.3           py38he30daa8_0  
    chardet                   3.0.4                 py38_1003  
    click                     7.1.2                      py_0  
    cryptography              3.1.1            py38h1ba5d50_0  
    cycler                    0.10.0                   py38_0  
    dbus                      1.13.18              hb2f20db_0  
    django                    3.1.2                      py_0  
    django-cors-headers       3.5.0                    pypi_0    pypi
    expat                     2.2.10               he6710b0_2  
    fontconfig                2.13.0               h9420a91_0  
    freetype                  2.10.4               h5ab3b9f_0  
    gast                      0.3.3                      py_0  
    glib                      2.66.1               h92f7085_0  
    google-auth               1.23.0             pyhd3eb1b0_0  
    google-auth-oauthlib      0.4.2              pyhd3eb1b0_2  
    google-pasta              0.2.0                      py_0  
    grpcio                    1.31.0           py38hf8bcb03_0  
    gst-plugins-base          1.14.0               hbbd80ab_1  
    gstreamer                 1.14.0               hb31296c_0  
    h5py                      3.0.0                    pypi_0    pypi
    hdf5                      1.10.6               hb1b8bf9_0  
    icu                       58.2                 he6710b0_3  
    idna                      2.10                       py_0  
    importlib-metadata        2.0.0                      py_1  
    intel-openmp              2020.2                      254  
    joblib                    0.17.0                   pypi_0    pypi
    jpeg                      9b                   h024ee3a_2  
    keras                     2.3.0                    pypi_0    pypi
    keras-applications        1.0.8                    pypi_0    pypi
    keras-preprocessing       1.1.2                    pypi_0    pypi
    kiwisolver                1.3.0            py38h2531618_0  
    krb5                      1.17.1               h173b8e3_0  
    lcms2                     2.11                 h396b838_0  
    ld_impl_linux-64          2.33.1               h53a641e_7  
    libedit                   3.1.20191231         h14c3975_1  
    libffi                    3.3                  he6710b0_2  
    libgcc-ng                 9.1.0                hdf63c60_0  
    libgfortran-ng            7.3.0                hdf63c60_0  
    libpng                    1.6.37               hbc83047_0  
    libpq                     12.2                 h20c2e04_0  
    libprotobuf               3.13.0.1             hd408876_0  
    libstdcxx-ng              9.1.0                hdf63c60_0  
    libtiff                   4.1.0                h2733197_1  
    libuuid                   1.0.3                h1bed415_2  
    libxcb                    1.14                 h7b6447c_0  
    libxml2                   2.9.10               hb55368b_3  
    lz4-c                     1.9.2                heb0550a_3  
    markdown                  3.3.2                    py38_0  
    matplotlib                3.3.2                         0  
    matplotlib-base           3.3.2            py38h817c723_0  
    mkl                       2020.2                      256  
    mkl-service               2.3.0            py38he904b0f_0  
    mkl_fft                   1.2.0            py38h23d657b_0  
    mkl_random                1.1.1            py38h0573a6f_0  
    multidict                 4.7.6            py38h7b6447c_1  
    ncurses                   6.2                  he6710b0_1  
    numpy                     1.19.4                   pypi_0    pypi
    numpy-base                1.19.2           py38hfa32c7d_0  
    oauthlib                  3.1.0                      py_0  
    olefile                   0.46                       py_0  
    openssl                   1.1.1h               h7b6447c_0  
    opt_einsum                3.1.0                      py_0  
    pandas                    1.1.3            py38he6710b0_0  
    pcre                      8.44                 he6710b0_0  
    pillow                    8.0.1            py38he98fc37_0  
    pip                       20.2.4                   py38_0  
    protobuf                  3.13.0.1         py38he6710b0_1  
    psycopg2                  2.8.5            py38h1ba5d50_0  
    pyasn1                    0.4.8                      py_0  
    pyasn1-modules            0.2.8                      py_0  
    pycparser                 2.20                       py_2  
    pyjwt                     1.7.1                    py38_0  
    pymysql                   0.10.1                   py38_0  
    pyopenssl                 19.1.0                     py_1  
    pyparsing                 2.4.7                      py_0  
    pyqt                      5.9.2            py38h05f1152_4  
    pysocks                   1.7.1                    py38_0  
    python                    3.8.3                hcff3b4d_2  
    python-dateutil           2.8.1                      py_0  
    pytz                      2020.1                     py_0  
    pyyaml                    5.3.1                    pypi_0    pypi
    qt                        5.9.7                h5867ecd_1  
    readline                  8.0                  h7b6447c_0  
    requests                  2.24.0                     py_0  
    requests-oauthlib         1.3.0                      py_0  
    rsa                       4.6                        py_0  
    scikit-learn              0.23.2                   pypi_0    pypi
    scipy                     1.5.3                    pypi_0    pypi
    setuptools                50.3.0           py38hb0f4dca_1  
    sip                       4.19.13          py38he6710b0_0  
    six                       1.15.0                     py_0  
    sklearn                   0.0                      pypi_0    pypi
    sqlite                    3.33.0               h62c20be_0  
    sqlparse                  0.4.1                      py_0  
    tensorboard               2.3.0              pyh4dce500_0  
    tensorboard-plugin-wit    1.6.0                      py_0  
    tensorflow                2.2.0           mkl_py38h6d3daf0_0  
    tensorflow-base           2.2.0           mkl_py38h5059a2d_0  
    tensorflow-estimator      2.2.0              pyh208ff02_0  
    termcolor                 1.1.0                    py38_1  
    threadpoolctl             2.1.0                    pypi_0    pypi
    tk                        8.6.10               hbc83047_0  
    tornado                   6.0.4            py38h7b6447c_1  
    urllib3                   1.25.11                    py_0  
    werkzeug                  1.0.1                      py_0  
    wheel                     0.35.1                     py_0  
    wrapt                     1.12.1           py38h7b6447c_1  
    xz                        5.2.5                h7b6447c_0  
    yarl                      1.6.2            py38h7b6447c_0  
    zipp                      3.4.0              pyhd3eb1b0_0  
    zlib                      1.2.11               h7b6447c_3  
    zstd                      1.4.5                h9ceee32_0

## 2.2 代码实现 ##

settings.py

(ml_api) [root@localhost webapi]# vim settings.py 
    
    
    ALLOWED_HOSTS = ['*']
    
    # Application definition
    
    INSTALLED_APPS = [
        'django.contrib.admin',
        'django.contrib.auth',
        'django.contrib.contenttypes',
        'django.contrib.sessions',
        'django.contrib.messages',
        'django.contrib.staticfiles',
        'corsheaders', #跨域配置
    ]
    
    MIDDLEWARE = [
        'django.middleware.security.SecurityMiddleware',
        'django.contrib.sessions.middleware.SessionMiddleware',
        'django.middleware.common.CommonMiddleware',
        'django.middleware.csrf.CsrfViewMiddleware',
        'django.contrib.auth.middleware.AuthenticationMiddleware',
        'django.contrib.messages.middleware.MessageMiddleware',
        'django.middleware.clickjacking.XFrameOptionsMiddleware',
        'corsheaders.middleware.CorsMiddleware',#跨域配置
        'django.middleware.common.CommonMiddleware',#跨域配置
    ]
    
    CORS_ORIGIN_ALLOW_ALL = True #跨域配置
    
    ROOT_URLCONF = 'webapi.urls'
    
    TEMPLATES = [
        {
        
            'BACKEND': 'django.template.backends.django.DjangoTemplates',
            'DIRS': [os.path.join(BASE_DIR, "templates")],
            'APP_DIRS': True,
            'OPTIONS': {
        
                'context_processors': [
                    'django.template.context_processors.debug',
                    'django.template.context_processors.request',
                    'django.contrib.auth.context_processors.auth',
                    'django.contrib.messages.context_processors.messages',
                ],
            },
        },
    ]
    
    WSGI_APPLICATION = 'webapi.wsgi.application'
    
    
    # Database
    # https://docs.djangoproject.com/en/3.1/ref/settings/#databases
    
    DATABASES = {
        
        'default': {
        
            'ENGINE': 'django.db.backends.sqlite3',
            'NAME': BASE_DIR / 'db.sqlite3',
        }
    }
    
    
    # Password validation
    # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators
    
    AUTH_PASSWORD_VALIDATORS = [
        {
        
            'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
        },
        {
        
            'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
        },
        {
        
            'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
        },
        {
        
            'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
        },
    ]
    
    
    # Internationalization
    # https://docs.djangoproject.com/en/3.1/topics/i18n/
    
    LANGUAGE_CODE = 'en-us'
    
    TIME_ZONE = 'UTC'
    
    USE_I18N = True
    
    USE_L10N = True
    
    USE_TZ = True
    
    
    # Static files (CSS, JavaScript, Images)
    # https://docs.djangoproject.com/en/3.1/howto/static-files/
    
    STATIC_URL = '/static/'

在项目下新建视图文件 views.py

touch view.py
    # -*- coding: utf-8 -*-
    import json
    from django.http import HttpResponse
    from django.views.decorators.csrf import csrf_exempt
    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
    from sklearn.preprocessing import MinMaxScaler
    from tensorflow.keras.models import Sequential  # Sequential 用于初始化神经网络
    from tensorflow.keras.layers import Dense  # Dense 用于添加全连接的神经网络层
    from tensorflow.keras.layers import LSTM  # LSTM 用于添加长短期内存层
    from tensorflow.keras.layers import Dropout  # Dropout 用于添加防止过拟合的dropout层
    import os
    import datetime
    import matplotlib.dates as mdates
    import matplotlib.pyplot as plt
    import tensorflow as tf
    #os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    import pymysql.cursors
    
    # 连接数据库
    connect = pymysql.Connect(
        host='192.168.6.100',
        port=3306,
        user='root',
        passwd='123456',
        db='test',
        charset='utf8'
    )
    
    
    def plot_show(axis_X, real_alive_amt, predicted_stock_price):
        """
        :param axis_X:
        :param real_alive_amt:
        :param predicted_stock_price:
        :return: z展示图
        """
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.rcParams['axes.unicode_minus'] = False
        plt.plot(axis_X, real_alive_amt, color='green', label='真实活期余额')
        # 显示活期余额
        plt.plot(axis_X, predicted_stock_price[:, -1], ls='--', color='red', label='预测活期余额')
        plt.title('活期余额预测')
    
        plt.gca().xaxis.set_major_locator(mdates.DayLocator())
        plt.xlabel('Time')
        plt.ylabel('余额（元）')
        plt.legend()
        plt.tick_params(axis='both', which='major', labelsize=16)
        plt.gcf().autofmt_xdate()  # 自动旋转日期标记
        plt.show()
    
    
    def train_data(dataset_train,step_len):
        """
        构建训练数据
        :param training_set:
        :return: X_train, y_train, sc
        """
        sc = MinMaxScaler(feature_range=(0, 1))
        training_set = dataset_train.values
        training_set_scaled = sc.fit_transform(training_set)
    
        X_train = []
        y_train = []
        for i in range(step_len, len(training_set_scaled)):
            X_train.append(training_set_scaled[i - step_len:i, :])
            y_train.append(training_set_scaled[i, :])
    
        X_train, y_train = np.array(X_train), np.array(y_train)
    
        X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], X_train.shape[2]))
    
        return X_train, y_train, sc
    
    
    def test_data(dataset_train, dataset_test, sc,step_len):
        """
        构建测试数据
        :param dataset_train:
        :param dataset_test:
        :param sc:
        :return: X_test
        """
    
        # 合并训练集和测试集
        dataset_total = pd.concat((dataset_train, dataset_test), axis=0)
        db_all = dataset_total[:].values
        np.random.shuffle(db_all)  # shuffle() 方法将序列的所有元素随机排序。
        inputs = db_all[len(dataset_total) - len(dataset_test) - int(step_len):]
        inputs = inputs.reshape(-1, dataset_test.shape[1])
    
        inputs = sc.transform(inputs)
        X_test = []
        for i in range(int(step_len), int(step_len) + len(dataset_test)):
            curr_frame = inputs[i - int(step_len):i - 1, :]
            curr_frame = np.insert(curr_frame, step_len-1,dataset_test.values, axis=0)
            X_test.append(curr_frame)
        X_test = np.array(X_test)
        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], X_test.shape[2]))
        return X_test
    
    
    def bulid_model(X_train, y_train):
        """
        创建股票预测模型
        :param X_train:
        :param y_train:
        :return: regressor
        """
        regressor = Sequential()
        # LSTM的输入为 [samples, timesteps, features],这里的timesteps为步数，features为维度 这里我们的数据是11维的
        regressor.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 11)))
    
        regressor.add(Dropout(0.2))
    
        regressor.add(LSTM(units=50, return_sequences=True))
        regressor.add(Dropout(0.2))
    
        regressor.add(LSTM(units=50, return_sequences=True))
        regressor.add(Dropout(0.2))
    
        regressor.add(LSTM(units=50))
        regressor.add(Dropout(0.2))
        # 全连接，输出11个
        regressor.add(Dense(units=11))
    
        regressor.compile(optimizer='adam', loss='mean_squared_error')
        regressor.fit(X_train, y_train, epochs=1, batch_size=60)
        #print(regressor.summary())
        return regressor
    
    def predict_test_data_fun(dataset_train,regressor,sc,X_test,real_alive_amt,step_len,predict_custs):
        """
        最新预测数据
        :param dataset_train:
        :param regressor:
        :param sc:
        :param X_test:
        :param real_alive_amt:
        :param step_len:
        :return:
        """
        predicted_alive_amt = regressor.predict(X_test)
        # 反向转换
        trans_predicted_alive_amt = sc.inverse_transform(predicted_alive_amt)
        predict_custs.insert(step_len,trans_predicted_alive_amt[-1])
        # 添加表头
        result_dataframe = pd.DataFrame(predicted_alive_amt, columns=['idcard_first_6','idcard_middle_8','idcard_last_4','gender','age','alive_avg_amt','c_alive_amt', 'd_alive_amt', 'is_arrear','arrear_amt','total_alive_amt'])
        return  test_data(dataset_train,result_dataframe,sc,step_len),trans_predicted_alive_amt[:, -1],predict_custs
    
    
    def recursion_call(dataset_train,regressor,sc,X_test,real_alive_amt,step_len,recursion_day,predict_custs):
        """
        预测指定天数函数
        :param dataset_train:
        :param regressor:
        :param sc:
        :param X_test:
        :param real_alive_amt:
        :param step_len:
        :param recursion_day:
        :return:
        """
        if recursion_day == 0:
            return 0
        else:
           X_test,real_alive_amt,predict_custs = predict_test_data_fun(dataset_train,regressor,sc,X_test,real_alive_amt,step_len,predict_custs)
           return recursion_call(dataset_train,regressor,sc,X_test,real_alive_amt,step_len,recursion_day - 1,predict_custs)
    
    
    #@csrf_exempt
    def alive_api(request,custno):
        print(custno)
        custno=int(custno)
        result = {
        "result": 0, "msg": "执行成功"}
        # 读取文件
        df = pd.read_csv('/opt/anaconda3/envs/ml_api/api/webapi/alives.csv',parse_dates=['dt'])
        df['idcard_last_4'] =df['idcard_last_4'].apply(lambda x:str(x).replace('X','10').zfill(4))
        # 根据客户号去重
        cust_ids = df.drop_duplicates(subset=['customer_no'], keep='first')['customer_no']
        # 步长
        step_len = 60
        # 递归天数
        recursion_day = 30
        #custno=1000340109
        # 训练集
        dataset_train = df[(df['dt'] < '2020-10-18') & (df['customer_no'] == custno)].loc[:,
                           ['idcard_first_6','idcard_middle_8','idcard_last_4','gender','age','alive_avg_amt', 'c_alive_amt','d_alive_amt','is_arrear','arrear_amt','total_alive_amt']]
            # 测试集
        dataset_test = df[(df['dt'] == '2020-10-18') & (df['customer_no'] == custno)].loc[:,
                           ['idcard_first_6','idcard_middle_8','idcard_last_4','gender','age','alive_avg_amt', 'c_alive_amt','d_alive_amt','is_arrear','arrear_amt','total_alive_amt']]
    
            # 获取存款余额真实值
        real_alive_amt = dataset_test['total_alive_amt'].values
            # 训练数据
        X_train, y_train, sc = train_data(dataset_train,step_len)
            # 构建模型
        regressor = bulid_model(X_train, y_train)
            # 构造测试数据
        X_test = test_data(dataset_train, dataset_test, sc,step_len)
            # 插入数据
        predict_custs = list()
        recursion_call(dataset_train,regressor,sc,X_test,real_alive_amt,step_len,recursion_day,predict_custs)
        #print(predict_custs)
        jsonObj ={
        }
        for index in range(len(predict_custs)):
            key = ((datetime.datetime.now() + datetime.timedelta(days=index)).strftime("%Y-%m-%d"))
            jsonObj[str(key)]=str(predict_custs[index][-1])
        #json返回为中文
        #print(jsonObj)
        return HttpResponse(json.dumps(jsonObj,ensure_ascii=False),content_type="application/json,charset=utf-8")

urls.py

from django.contrib import admin
    from django.urls import path
    from django.conf.urls import url
    from . import views
    
    urlpatterns = [
        path('admin/', admin.site.urls),
        url(r'^api/(?P<custno>\d+)/$',views.alive_api,name='alive_api'),
    ]

核心代码到此为止。

# 3.启动服务 #

python manage.py runserver 0.0.0.0:8000

# 4、第三方应用调用webapi服务提供的api接口 #

图标展示采用echart.js 核心代码如下：  
服务接口：http://192.168.2.9:8000/api/1001324295/，其中1001324295为参数

<div id="main" style="width: 1200px;height:400px;"></div>
    <script type="text/javascript">
            $(function(){
        
                xdata=[]
                ydata=[]
                var myChart = echarts.init(document.getElementById('main'),'macarons');
                myChart.showLoading({
        
                  text: '大数据正在预测...',
                  textStyle: {
         fontSize : 30 , color: '#444' },
                  effectOption: {
        backgroundColor: 'rgba(0, 0, 0, 0)'}
                });
                $.get('http://192.168.2.9:8000/api/1001324295/',function(dic) {
        
                    console.log(dic)
                    $.each(dic,function(index,item){
        
                        console.log(index+"|"+item)
                        xdata.push(index)
                        ydata.push(item)
                    })
                    //console.log(xdata)
                    //console.log(ydata)
                    // 基于准备好的dom，初始化echarts实例
    
                // 指定图表的配置项和数据
                var option = {
        
                    title: {
        
                        text: '未来30天活期存款余额预测趋势图',
                        textStyle:{
        
                               color:'#0DB9F2',        //颜色
                               fontStyle:'normal',     //风格
                               fontWeight:'normal',    //粗细
                               fontFamily:'Microsoft yahei',   //字体
                               fontSize:24,     //大小
                               align:'center'   //水平对齐
                         },
                         x:'center'
                    },
                    tooltip: {
        },
                    legend: {
        
                        data:['未来30天活期存款余额预测趋势图']
                    },
                    xAxis: {
        
                        data: xdata,
                        axisLabel: {
        
                           interval:0,
                           rotate:50
                        }
                    },
                    yAxis: {
        
                         type: 'value',
                         //默认以千分位显示，不想用的可以在这加一段
                         axisLabel : {
           //调整左侧Y轴刻度， 直接按对应数据显示
                             show:true,
                             showMinLabel:true,
                             showMaxLabel:true,
                             formatter: function (value) {
        
                                 return value;
                             }
                         }
                    },
                    series: [{
        
                        name: '活期存款余额（元）',
                        type: 'line',
                        data: ydata,
                        markPoint: {
        
                            itemStyle: {
        
                                normal: {
        
                                   borderColor: '#87cefa',
                                   borderWidth: 1, // 标注边线线宽，单位px，默认为1
                                   label: {
        
                                      show: true
                                   }
                                }
                            },
                            data: [
                               {
        
                                 type: 'max',
                                 name: '最大值'
                                },
                               {
        
                                 type: 'min',
                                 name: '最小值'
                                }
                            ]
                        },
                        markLine: {
        
                            data: [
                                {
        type: 'average', name: '平均值'},
                                [{
        
                                    symbol: 'none',
                                    x: '90%',
                                    yAxis: 'max'
                                }, {
        
                                    symbol: 'circle',
                                    label: {
        
                                        normal: {
        
                                            position: 'start',
                                            formatter: '最大值'
                                        }
                                    },
                                    type: 'max',
                                    name: '最高点'
                                }]
                            ]
                        }
                    }]
                };
    
                // 使用刚指定的配置项和数据显示图表。
                myChart.setOption(option);
                myChart.hideLoading();
                });
            });
        </script>

第三方应用调用预测api接口，由预测模型需要递归多次处理时间比较久，加载过程中提示loading为大数据正在预测…  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg_size_16_color_FFFFFF_t_70_pic_center 2]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg_size_16_color_FFFFFF_t_70_pic_center 3]

[Linux_Centos7.8_Anaconda3_Django3]: https://blog.csdn.net/u012637358/article/details/109526336
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg_size_16_color_FFFFFF_t_70_pic_center]: /images/20221120/65a909613c004666ac6b540e2260110f.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg_size_16_color_FFFFFF_t_70_pic_center 1]: https://img-blog.csdnimg.cn/20201106110256627.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg=,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg_size_16_color_FFFFFF_t_70_pic_center 2]: https://img-blog.csdnimg.cn/20201106112402449.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg=,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg_size_16_color_FFFFFF_t_70_pic_center 3]: https://img-blog.csdnimg.cn/20201106112546150.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTI2MzczNTg=,size_16,color_FFFFFF,t_70#pic_center