val = res_text.xpath("//div[@class='base']//li/text()")解析这句python爬虫代码

这段代码也是使用了lxml库的xpath方法，其中"//"表示匹配任意层级的子元素，"[]"用于筛选元素，"@class"用于选取class属性，"//li"表示选择任意层级下的li元素，"//text()"表示获取该元素下的所有文本内容。因此，这段代码的意思是选取class属性为"base"的div元素下的所有li元素中的文本内容，并存储在val变量中。val变量是一个列表，列表中的每个元素都是一个li元素的文本内容。

相关问题

设置训练和验证集路径 train_vol_path = "data/train/trainvol" train_seg_path = "data/train/trainseg" val_vol_path = "data/val/valvol" val_seg_path = "data/val/valseg" # 定义数据生成器 train_datagen = ImageDataGenerator(rescale=1./255) val_datagen = ImageDataGenerator(rescale=1./255) # 加载训练和验证数据 train_vol = train_datagen.flow_from_directory(train_vol_path, target_size=(256, 256), batch_size=32, class_mode=None) train_seg = train_datagen.flow_from_directory(train_seg_path, target_size=(256, 256), batch_size=32, class_mode=None) val_vol = val_datagen.flow_from_directory(val_vol_path, target_size=(256, 256), batch_size=32, class_mode=None) val_seg = val_datagen.flow_from_directory(val_seg_path, target_size=(256, 256), batch_size=32, class_mode=None) # 合并训练和验证数据 train_generator = zip(train_vol, train_seg) val_generator = zip(val_vol, val_seg) # 定义UNet模型 model = unet() # 编译模型 model.compile(optimizer=Adam(learning_rate=1e-4), loss='binary_crossentropy', metrics=['accuracy'])报错'NoneType' object has no attribute 'compile'怎么改代码

可以尝试将 `class_mode` 参数设置为 `'binary'` 或者其他适合数据集的值，来解决这个问题。修改后的代码如下所示：

# 设置训练和验证集路径
train_vol_path = "data/train/trainvol"
train_seg_path = "data/train/trainseg"
val_vol_path = "data/val/valvol"
val_seg_path = "data/val/valseg"

# 定义数据生成器
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)

# 加载训练和验证数据
train_vol = train_datagen.flow_from_directory(train_vol_path, target_size=(256, 256), batch_size=32, class_mode='binary')
train_seg = train_datagen.flow_from_directory(train_seg_path, target_size=(256, 256), batch_size=32, class_mode='binary')
val_vol = val_datagen.flow_from_directory(val_vol_path, target_size=(256, 256), batch_size=32, class_mode='binary')
val_seg = val_datagen.flow_from_directory(val_seg_path, target_size=(256, 256), batch_size=32, class_mode='binary')

# 合并训练和验证数据
train_generator = zip(train_vol, train_seg)
val_generator = zip(val_vol, val_seg)

# 定义UNet模型
model = unet()

# 编译模型
model.compile(optimizer=Adam(learning_rate=1e-4), loss='binary_crossentropy', metrics=['accuracy'])

怎么将这段python代码采集到的数据插入数据库的表里：for detail_url in t_url2: res = requests.get(url=detail_url, headers=headers) res.encoding = 'utf-8' tree = html.fromstring(res.text) name2 = tree.xpath('/html/body/main/div[1]/div[1]/span/text()') article2 = tree.xpath('/html/body/main/div[1]/div[3]//text()') picture_str2 = html.etree.tostring(article2[0], encoding='utf-8', method='html').decode('utf-8') 如何编写python代码

你可以使用Python的MySQL Connector来将采集到的数据插入到MySQL数据库表中。以下是一个基本的示例代码：

import mysql.connector
import requests
from lxml import html

# 连接到MySQL数据库
mydb = mysql.connector.connect(
  host="localhost",
  user="yourusername",
  password="yourpassword",
  database="mydatabase"
)
mycursor = mydb.cursor()

# 遍历URL列表并采集数据
for detail_url in t_url2:
    res = requests.get(url=detail_url, headers=headers)
    res.encoding = 'utf-8'
    tree = html.fromstring(res.text)
    name2 = tree.xpath('/html/body/main/div[1]/div[1]/span/text()')[0]
    article2 = tree.xpath('/html/body/main/div[1]/div[3]//text()')
    picture_str2 = html.etree.tostring(article2[0], encoding='utf-8', method='html').decode('utf-8')

    # 插入数据到数据库表中
    sql = "INSERT INTO mytable (name, article, picture) VALUES (%s, %s, %s)"
    val = (name2, article2, picture_str2)
    mycursor.execute(sql, val)
    mydb.commit()

print(mycursor.rowcount, "条记录已插入。")

在这个示例中，我们使用了requests和lxml库来采集网页数据，然后将采集到的数据插入到名为“mytable”的MySQL数据库表中。你需要根据自己的需求，修改代码中的数据库连接信息、插入的表名和字段名等，以适应不同的情况。