添加字段到Django多对多模型表的关联表的方法

如何理解Fourier Neural Operator (FNO) 2022年Nvidia发表在arXiv上的的FourcastNet作为几乎是最早的一篇气象大模型的工作(之前Google Research的MetNet主要还是对于降水的预报)。除了Transformer的backbone之外,使用到了2021年发表在ICLR上的Fourier Neural Operator。为了更好的理解模型的理论基础,可以详细学习一下以及他的前期工作GNOFNO作者对这两篇文章的解析blog。 阅读详情

添加字段到Django多对多模型表的关联表的方法

作者:Wally Yu

优快云:http://blog.youkuaiyun.com/quicktest

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最近遇到个麻烦事,想在Django的多对多自动生成的关联表中添加一个字段

原来的模型如下:

class ProjectModule(models.Model):
    name = models.CharField(max_length=100)
    testProject = models.ForeignKey(TestProject)
    needMetaData = models.BooleanField()
    
    def __unicode__(self):
        return self.name
    
    class Meta:
        ordering = ['testProject','name']
        

class Task(models.Model):
    status = models.IntegerField()
    executeTime = models.DateTimeField()
    module = models.ManyToManyField(ProjectModule)
    executor = models.ForeignKey(User)
    automationType = models.ForeignKey(AutomationType)
    browser = model
Python爬虫实战:携程旅游平台数据爬取(酒店+机票+景点),破解加密与反爬(附完整代码) 携程的反爬机制再复杂,核心逻辑都是“识别非真实用户的请求”,而爬虫的核心就是“让请求看起来像真实用户的行为”。本文的方案从登录态处理、参数加密破解、动态数据抓取,到反爬应对,都是围绕“模拟真实”展开:用Playwright模拟真实浏览器登录,用住宅代理模拟真实用户IP,用随机延迟模拟真实用户的操作频率。掌握了这个核心思路,无论平台的反爬如何升级,都能找到应对方法。当然,爬虫技术是一把双刃剑,合理使用才能发挥其价值。如果你的爬虫项目中遇到了携程的反爬问题,不妨按照本文的步骤试试,大概率能解决问题。 阅读详情

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深度学习工具包 DeepLearningToolbox

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