<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Alpha_P的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/Alpha_P</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Alpha_P]]></copyright><item><title><![CDATA[CCS导入工程时报错“overlaps the location of another project”解决办法]]></title><link>https://blog.youkuaiyun.com/Alpha_P/article/details/125065759</link><guid>https://blog.youkuaiyun.com/Alpha_P/article/details/125065759</guid><author>Alpha_P</author><pubDate>Tue, 31 May 2022 15:06:15 +0800</pubDate><description><![CDATA[想调试mcsdk自带的demo工程时，将mcsdk_2_01_02_06/demos/image_processing/serial里的工程复制到wordspace

在ccs7.0里选择project-&gt;Import CCS Projects，导入工程时会报错“overlaps the location of another project”，原因可能是工程名重复

解决办法：

打开工程目录里的.project文件，修改下图红框处的工程名即可。




...]]></description><category></category></item><item><title><![CDATA[CCS7.0 com/ti/dvt/energytrace/views/Dashboard错误解决办法]]></title><link>https://blog.youkuaiyun.com/Alpha_P/article/details/125042643</link><guid>https://blog.youkuaiyun.com/Alpha_P/article/details/125042643</guid><author>Alpha_P</author><pubDate>Mon, 30 May 2022 11:02:14 +0800</pubDate><description><![CDATA[CCS 7.0 Debug的时候出现了如下错误：

An error has occurred. See error log for more details.
com/ti/dvt/energytrace/views/Dashboard



解决方法：https://software-dl.ti.com/ccs/esd/documents/users_guide/ccs_troubleshooting.html



删除 workspace目录下的.metadata\.plugins\com.ti..]]></description><category></category></item><item><title><![CDATA[centos7卸载cuda8.0，安装cuda9.0 cudnn7.3 tensorflow-gpu1.8.0]]></title><link>https://blog.youkuaiyun.com/Alpha_P/article/details/107957908</link><guid>https://blog.youkuaiyun.com/Alpha_P/article/details/107957908</guid><author>Alpha_P</author><pubDate>Wed, 12 Aug 2020 16:27:11 +0800</pubDate><description><![CDATA[最近在看《21个项目玩转深度学习》，在使用Object Detection API训练自己的数据集时，出现了错误：

AttributeError: 'module' object has no attribute 'parallel_interleave'

需要安装tensorflow-gpu1.8.0，同时需要卸载cuda8.0，安装cuda9.0和cudnn7.3

系统配置：CentOS7.3系统64bit |python版本2.7.15


	卸载cuda8.0
	

cd /usr/loca.]]></description><category></category></item><item><title><![CDATA[CentOS删除PATH路径的方法]]></title><link>https://blog.youkuaiyun.com/Alpha_P/article/details/107902316</link><guid>https://blog.youkuaiyun.com/Alpha_P/article/details/107902316</guid><author>Alpha_P</author><pubDate>Sun, 09 Aug 2020 23:20:08 +0800</pubDate><description><![CDATA[系统配置：阿里云GPU服务器 | CentOS7.3系统64bit

查看所有PATH路径：


echo $PATH

把想保留的路径复制下来


/root/bin:/usr/local/cuda-8.0/bin:/usr/bin:/usr/local/bin:/usr/local/sbin:/usr/sbin:


清空PATH变量：


unset PATH

这时，vim等命令都无法使用了，使用xftp将/etc/profile文件传到本地电脑，用notepad打开，在文件的开头插入：


exp]]></description><category></category></item><item><title><![CDATA[阿里云服务器CentOS7安装anaconda并配置jupyter远程登录]]></title><link>https://blog.youkuaiyun.com/Alpha_P/article/details/107894813</link><guid>https://blog.youkuaiyun.com/Alpha_P/article/details/107894813</guid><author>Alpha_P</author><pubDate>Sun, 09 Aug 2020 21:20:07 +0800</pubDate><description><![CDATA[最近在看《21个项目玩转深度学习》，在阿里云申请了GPU服务器，申请时在镜像市场选择“CentOS7.3(预装NVIDIA GPU驱动和深度学习框架)”

系统配置：CentOS7.3系统64bit | python版本2.7.5 |


	安装anaconda2
	
anaconda中集成了jupyter

创建anaconda文件夹存放安装包，并进入文件夹：


mkdir anaconda
cd anaconda

下载地址：https://mirrors.tuna.tsinghua.edu.cn/a]]></description><category></category></item><item><title><![CDATA[CentOS7.3安装TensorFlow Object Detection API]]></title><link>https://blog.youkuaiyun.com/Alpha_P/article/details/107881479</link><guid>https://blog.youkuaiyun.com/Alpha_P/article/details/107881479</guid><author>Alpha_P</author><pubDate>Sat, 08 Aug 2020 17:45:19 +0800</pubDate><description><![CDATA[最近在看《21个项目玩转深度学习》，在阿里云申请了GPU服务器，申请时在镜像市场选择“CentOS7.3(预装NVIDIA GPU驱动和深度学习框架)”，

系统配置：CentOS7.3系统64bit | python版本2.7.5 | pip版本9.0.1 | numpy版本1.13.3 | scipy版本1.2.3 | tensorflow-gpu1.3.0 | tensorflow-tensorboard0.1.8

下载TensorFlow Object Detection API
在百度网盘下载旧]]></description><category></category></item><item><title><![CDATA[新版的scipy.misc库中不再包含imread、imresize、toimage等的解决办法]]></title><link>https://blog.youkuaiyun.com/Alpha_P/article/details/107689968</link><guid>https://blog.youkuaiyun.com/Alpha_P/article/details/107689968</guid><author>Alpha_P</author><pubDate>Thu, 30 Jul 2020 15:35:59 +0800</pubDate><description><![CDATA[最近在看《21个项目玩转深度学习》，书里提供的源代码用到了scipy.misc.imresize()等函数，而1.3.0版本以后的scipy库中已经不再包含imread、imresize、toimage等函数。本文提供了基于python2.7的windows和CentOS系统下的两种解决办法，除了安装1.2.3版本的scipy库，还需要安装PIL。




	windows系统python2.7的PIL库安装
	
系统配置：windows XP系统 | python版本2.7.17 | pip版本19.2.]]></description><category></category></item><item><title><![CDATA[pip版本升级]]></title><link>https://blog.youkuaiyun.com/Alpha_P/article/details/107687829</link><guid>https://blog.youkuaiyun.com/Alpha_P/article/details/107687829</guid><author>Alpha_P</author><pubDate>Thu, 30 Jul 2020 13:20:22 +0800</pubDate><description><![CDATA[window XP系统，只支持python3.4，安装了python3.4.4，自带的pip版本为7.1.2，版本太低

https://pypi.org/project/pip/19.1.1/#files下载pip-19.1.1.tar.gz

解压缩后，将命令行地址切换到/pip-19.1.1下，输入python setup.py install，即可安装完成



参考：

https://blog.youkuaiyun.com/qq_36196621/article/details/89892470
...]]></description><category></category></item><item><title><![CDATA[pip安装错误：is not a supported wheel on this platform解决办法]]></title><link>https://blog.youkuaiyun.com/Alpha_P/article/details/107686742</link><guid>https://blog.youkuaiyun.com/Alpha_P/article/details/107686742</guid><author>Alpha_P</author><pubDate>Thu, 30 Jul 2020 12:36:06 +0800</pubDate><description><![CDATA[python安装库可以使用：pip install 库名称==版本号

也可以下载.whl文件进行安装，命令行下将地址切换到.whl所在的位置：pip install XXX.whl

python库下载地址：http://www.lfd.uci.edu/~gohlke/pythonlibs

                 https://pypi.org/

下载的库文件名中的cp27代表python2.7



pip安装.whl文件可能会报错：is not a...]]></description><category></category></item><item><title><![CDATA[CRC校验原理与计算]]></title><link>https://blog.youkuaiyun.com/Alpha_P/article/details/78419877</link><guid>https://blog.youkuaiyun.com/Alpha_P/article/details/78419877</guid><author>Alpha_P</author><pubDate>Wed, 01 Nov 2017 22:58:08 +0800</pubDate><description><![CDATA[CRC，(Cyclic Redundancy Check)，循环冗余校验。
1，CRC的原理
CRC校验的原理在很多地方写的都比较详细，也比较复杂，但其本质就是一种校验码的计算方法。收、发双方在通信之前要约定好一个多项式(介绍原理的地方也叫做生成多项式，其实就是一串二进制码，关于多项式跟二进制码的对应关系可以参考附录1)，通过信息码跟生成多项式做模2除法就能计算出校验码，当然，校验码的位数也是]]></description><category></category></item></channel></rss>