进程以退出代码 -1073740791 (0xC0000409) pycharm 错误完成 [英] Process finished with exit code -1073740791 (0xC0000409) pycharm error

问题描述

我正在尝试将 fastText 与 PyCharm 一起使用.每当我运行以下代码时:

导入fastText模型=fastText.train_unsupervised(data_parsed.txt")model.save_model("模型")

进程退出并出现此错误:

进程以退出代码 -1073740791 (0xC0000409) 结束

是什么导致了这个错误，可以做些什么来避免它?

解决方案

你用的是windows系统吗?0xC0000409 表示堆栈缓冲区溢出，如 此窗口帮助链接.

以下是取自 此链接 的一些建议，用于解决类似类型的问题.

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STATUS_STACK_BUFFER_OVERRUN 是/GS 异常.当 Windows 检测到保护返回地址的安全 cookie 被篡改"时，它们就会被抛出.您可能正在写入超出缓冲区末尾的内容，或者将内容写入指向错误位置的指针.但是，您也可能有一些不可靠的内存或其他有故障的硬件导致验证码跳闸.

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您可以尝试的一件事是禁用/GS 开关(项目属性，查找 C/C++ -> 代码生成 -> 缓冲区安全检查)并重新编译.再次运行代码很可能会导致您可以捕获和跟踪的错误.我认为/GS 旨在出于安全原因不向您提供任何信息.

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您可以做的另一件事是在另一台 PC 上按原样运行代码，看看是否失败，如果没有，这可能表明硬件问题.

其他策略是通过删除一些文本来减小训练文件的大小，并通过运行一些文本规范化来减小词汇表的大小.

I am trying to use fastText with PyCharm. Whenever I run below code:

import fastText

model=fastText.train_unsupervised("data_parsed.txt")

model.save_model("model")

The process exits with this error:

Process finished with exit code -1073740791 (0xC0000409)

What causes this error and what can be done to avoid it?

解决方案

Are you using a windows system? 0xC0000409 means stack buffer overflow as seen in this windows help link.

Below is some advice that is taken from this link to solve similar type of issues.

STATUS_STACK_BUFFER_OVERRUN is a /GS exception. They are thrown when Windows detects 'tampering' of a security cookie protecting a return address. It is probable that you are writing something past the end of a buffer, or writing something to a pointer that is pointing to the wrong place. However it is also possible that you have some dodgy memory or otherwise faulty hardware that is tripping validation code.

One thing that you could try is to disable the /GS switch (project properties, look for C/C++ -> Code Generation -> Buffer Security Check) and recompile. Running the code again may well cause an error that you can trap and trace. I think /GS is designed not to give you any info for security reasons.

Another thing you could do is run the code as is on a different PC and see if that fails, this may point to a hardware problem if it doesn't.

Other strategies are reduce the size of the training file by removing some text and reducing the size of the vocabulary by running some text normalisation.

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