LOC_Frequency = By.XPATH, '//*[@id="app"]/div/div[2]/section/div/div[2]/div[1]/form/div[4]/div[1]/div[1]/div/div/div/div[1]/input' self.driver.execute_script("arguments[0].removeAttribute('readonly')",Add_regular_quotation.LOC_Frequency) 报错提示;> raise TypeError(f'Object of type {o.__class__.__name__} ' f'is not JSON serializable') E TypeError: Object of type Add_regular_quotation is not JSON serializable 这是一个python自动化的元素定位报错,如何修改,不知道不可以乱说
时间: 2024-02-07 18:03:34 浏览: 27
这个错误是因为 `Add_regular_quotation` 这个对象不能被 JSON 序列化。你可以尝试将其转换为字符串类型,或者使用 `json.dumps()` 函数将其转换为 JSON 格式。例如:
```
import json
# 将对象转换为字符串类型
loc_frequency_str = str(Add_regular_quotation.LOC_Frequency)
# 将对象转换为 JSON 格式
loc_frequency_json = json.dumps(Add_regular_quotation.LOC_Frequency)
```
然后,你可以使用 `loc_frequency_str` 或 `loc_frequency_json` 代替 `Add_regular_quotation.LOC_Frequency`。
相关问题
帮我给每一行代码添加注释 class DeepKalmanFilter(nn.Module): def __init__(self, config): super(DeepKalmanFilter, self).__init__() self.emitter = Emitter(config.z_dim, config.emit_hidden_dim, config.obs_dim) self.transition = Transition(config.z_dim, config.trans_hidden_dim) self.posterior = Posterior( config.z_dim, config.post_hidden_dim, config.obs_dim ) self.z_q_0 = nn.Parameter(torch.zeros(config.z_dim)) self.emit_log_sigma = nn.Parameter(config.emit_log_sigma * torch.ones(config.obs_dim)) self.config = config @staticmethod def reparametrization(mu, sig): return mu + torch.randn_like(sig) * sig @staticmethod def kl_div(mu0, sig0, mu1, sig1): return -0.5 * torch.sum(1 - 2 * sig1.log() + 2 * sig0.log() - (mu1 - mu0).pow(2) / sig1.pow(2) - (sig0 / sig1).pow(2)) def loss(self, obs): time_step = obs.size(1) batch_size = obs.size(0) overshoot_len = self.config.overshooting kl = torch.Tensor([0]).to(self.config.device) reconstruction = torch.Tensor([0]).to(self.config.device) emit_sig = self.emit_log_sigma.exp() for s in range(self.config.sampling_num): z_q_t = self.z_q_0.expand((batch_size, self.config.z_dim)) for t in range(time_step): trans_loc, trans_sig = self.transition(z_q_t) post_loc, post_sig = self.posterior(trans_loc, trans_sig, obs[:, t]) z_q_t = self.reparametrization(post_loc, post_sig) emit_loc = self.emitter(z_q_t) reconstruction += ((emit_loc - obs[:, t]).pow(2).sum(dim=0) / 2 / emit_sig + self.emit_log_sigma * batch_size / 2).sum() if t > 0: over_loc, over_sig = self.transition(overshooting[:overshoot_len - 1]) over_loc = torch.cat([trans_loc.unsqueeze(0), over_loc], dim=0) over_sig = torch.cat([trans_sig.unsqueeze(0), over_sig], dim=0) else: over_loc = trans_loc.unsqueeze(0) over_sig = trans_sig.unsqueeze(0) overshooting = self.reparametrization(over_loc, over_sig) kl = kl + self.kl_div(post_loc.expand_as(over_loc), post_sig.expand_as(over_sig), over_loc, over_sig) / min(t + 1, self.config.overshooting) reconstruction = reconstruction / self.config.sampling_num kl = kl / self.config.sampling_num return reconstruction, kl
答:class DeepKalmanFilter(nn.Module): # 定义深度Kalman滤波器类
def __init__(self, config): # 初始化
super(DeepKalmanFilter, self).__init__() # 调用父类构造函数
self.emitter = Emitter(config.z_dim, config.emit_hidden_dim, config.obs_dim) # 实例化发射器
self.transition = Transition(config.z_dim, config.trans_hidden_dim) # 实例化转换器
self.posterior = Posterior(config.z_dim, config.post_hidden_dim, config.obs_dim) # 实例化后验概率
self.z_q_0 = nn.Parameter(torch.zeros(config.z_dim)) # 设置参数z_q_0
self.emit_log_sigma = nn.Parameter(config.emit_log_sigma * torch.ones(config.obs_dim)) # 发射标准差参数
self.config = config # 配置参数 @staticmethod
def reparametrization(mu, sig): # 重置参数
return mu + torch.randn_like(sig) * sig # 根据均值和标准差重置参数 @staticmethod
def kl_div(mu0, sig0, mu1, sig1): # 计算KL散度
return -0.5 * torch.sum(1 - 2 * sig1.log() + 2 * sig0.log() - (mu1 - mu0).pow(2) / sig1.pow(2) - (sig0 / sig1).pow(2)) # 计算KL散度 def loss(self, obs): # 损失函数
time_step = obs.size(1) # 观测序列的时间步数
batch_size = obs.size(0) # 批量大小
overshoot_len = self.config.overshooting # 超调量
kl = torch.Tensor([0]).to(self.config.device) # kl散度
reconstruction = torch.Tensor([0]).to(self.config.device) # 构建重构误差
emit_sig = self.emit_log_sigma.exp() # 发射标准差
for s in range(self.config.sampling_num): # 采样次数
z_q_t = self.z_q_0.expand((batch_size, self.config.z_dim)) # 估计量初始化
for t in range(time_step): # 遍历每一时刻
trans_loc, trans_sig = self.transition(z_q_t) # 更新转换器
post_loc, post_sig = self.posterior(trans_loc, trans_sig, obs[:, t]) # 更新后验概率
z_q_t = self.reparametrization(post_loc, post_sig) # 重新参数化
emit_loc = self.emitter(z_q_t) # 计算发射器
reconstruction += ((emit_loc - obs[:, t]).pow(2).sum(dim=0) / 2 / emit_sig +
self.emit_log_sigma * batch_size / 2).sum() # 计算重构误差
if t > 0: # 如果不是第一步
over_loc, over_sig = self.transition(overshooting[:overshoot_len - 1]) # 计算超调量
over_loc = torch.cat([trans_loc.unsqueeze(0), over_loc], dim=0) # 转换器的位置
over_sig = torch.cat([trans_sig.unsqueeze(0), over_sig], dim=0) # 转换器的协方差
else: # 如果是第一步
over_loc = trans_loc.unsqueeze(0) # 转换器的位
from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.support.wait import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from time import sleep driver = webdriver.Chrome() driver.get('http://www.baidu.com') # 隐式等待5秒 driver.implicitly_wait(5) loc = (By.XPATH, '//input[@id="kw"]') driver.find_element(*loc).send_keys('selenium') driver.find_element(By.XPATH, '//input[@id="su"]').click() wait = WebDriverWait(driver, 20) loc = (By.XPATH, '//b[text()="selenium"]/parent::span') wait.until(EC.visibility_of_element_located(loc)) driver.find_element(*loc).click() sleep(3) driver.quit()写下注释
这段代码使用了Selenium库来控制Chrome浏览器自动化测试。具体注释如下:
```python
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from time import sleep
# 初始化一个Chrome浏览器实例
driver = webdriver.Chrome()
# 打开百度首页
driver.get('http://www.baidu.com')
# 隐式等待5秒
driver.implicitly_wait(5)
# 定位搜索框并输入关键词'selenium'
loc = (By.XPATH, '//input[@id="kw"]')
driver.find_element(*loc).send_keys('selenium')
# 定位搜索按钮并点击
loc = (By.XPATH, '//input[@id="su"]')
driver.find_element(*loc).click()
# 显式等待20秒,直到页面上的'selenium'元素可见
wait = WebDriverWait(driver, 20)
loc = (By.XPATH, '//b[text()="selenium"]/parent::span')
wait.until(EC.visibility_of_element_located(loc))
# 点击'selenium'元素
driver.find_element(*loc).click()
# 停留3秒后关闭浏览器
sleep(3)
driver.quit()
```
这段代码的功能是:打开百度首页,输入关键词"selenium"进行搜索,然后在搜索结果页面中点击"百度百科"中的"selenium"词条,最后停留3秒后关闭浏览器。其中使用了隐式等待和显式等待来控制程序等待页面加载完成。