<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[AIWCZ的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/AIWCZ</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; AIWCZ]]></copyright><item><title><![CDATA[Python 输出图像像素，显示不全的解决]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/121165296</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/121165296</guid><author>AIWCZ</author><pubDate>Fri, 05 Nov 2021 16:09:43 +0800</pubDate><description><![CDATA[在import包后，加上这一行，完事。
np.set_printoptions(threshold=np.inf)


]]></description><category></category></item><item><title><![CDATA[Python 判断列表中元素是否完全相同]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/121157884</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/121157884</guid><author>AIWCZ</author><pubDate>Fri, 05 Nov 2021 10:26:43 +0800</pubDate><description><![CDATA[将列表转换成集合即可
a = [0,0,0,0]
b = set(a)  # {0}
if len(b) == 1:  # 1
	print('True')


]]></description><category></category></item><item><title><![CDATA[Ubuntu下安装rar方式解压缩]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/121148167</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/121148167</guid><author>AIWCZ</author><pubDate>Thu, 04 Nov 2021 18:14:09 +0800</pubDate><description><![CDATA[Ubuntu18.04 默认仅支持以下三种类型文件的解压缩：
①.zip  ②.tar.xz  ③.7z
但rar方式的解压缩也是很常见的
安装时，用apt-get即可
sudo apt-get install rar

解压命令(a-压缩，x-解压。解压时不填DirName，即默认当前路径，但压缩时必须要填)：
rar a/x FileName.rar DirName


...]]></description><category></category></item><item><title><![CDATA[Python 删除文件夹及其中所有文件]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/121144492</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/121144492</guid><author>AIWCZ</author><pubDate>Thu, 04 Nov 2021 15:57:41 +0800</pubDate><description><![CDATA[os只能删除empty的文件夹。
因此：
import shutil
shutil.rmtree(dirpath)


]]></description><category></category></item><item><title><![CDATA[NetworkX中的draw_networkx(画图工具)]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/121138722</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/121138722</guid><author>AIWCZ</author><pubDate>Thu, 04 Nov 2021 11:21:57 +0800</pubDate><description><![CDATA[def draw_networkx(G, pos=None, arrows=True, with_labels=True, **kwds):
    """Draw the graph G using Matplotlib.

    Draw the graph with Matplotlib with options for node positions,
    labeling, titles, and many other drawing features.
    See draw() for ]]></description><category></category></item><item><title><![CDATA[Python中List、numpy.array与torch.Tensor 之间的相互转化]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/121138470</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/121138470</guid><author>AIWCZ</author><pubDate>Thu, 04 Nov 2021 11:17:14 +0800</pubDate><description><![CDATA[list 与 numpy
①list → numpy
array = np.array(list)

②numpy → list
list = array.tolist()

torch.Tensor 与 numpy
①torch.Tensor → numpy
array = tensor.numpy()

②numpy → list
tensor = torch.from_numpy(array)

list 与 torch.Tensor
①list → tensor
tensor = torch.Ten]]></description><category></category></item><item><title><![CDATA[佐久间浩 《乳腺超声入门》 读书笔记]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120967133</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120967133</guid><author>AIWCZ</author><pubDate>Sat, 30 Oct 2021 10:05:51 +0800</pubDate><description><![CDATA[1.超声探头有两种：电子线阵探头、机械扇形扫查探头(环阵探头)


2.使用频率主要在 7.5~12.0 MHz 之间，高频探头虽可增加图像分辨率，但会降低扫查的深度。
3.扫查步骤：只要能够全面检查到整个乳房即可。
4.探头左右倾斜虽容易获得需要的图像，但不易获得良好的图像，因而可依照胸大肌和肋骨等深部结构的清晰程度作为判断图像是否良好的标准。
5.检查时，两侧乳房一般需要3~5min，而如果利用AI这个时间的减少？
6.对肿瘤大小的判断，需要测量立体的三个径线。
7.乳房超声诊断标准。注意，由于诊断水平]]></description><category></category></item><item><title><![CDATA[基于深度学习的Graph Matching中损失函数的理解]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120936813</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120936813</guid><author>AIWCZ</author><pubDate>Tue, 26 Oct 2021 18:52:41 +0800</pubDate><description><![CDATA[挖坑

]]></description><category></category></item><item><title><![CDATA[Ubuntu下 .AppImage文件的使用]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120946681</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120946681</guid><author>AIWCZ</author><pubDate>Mon, 25 Oct 2021 10:01:57 +0800</pubDate><description><![CDATA[该类文件不需要安装，直接打开即可正常使用软件。
右键文件，点开最下面的属性。
转到 权限 选项卡处。
勾选Allow executiong file as program

双击即可打开。


]]></description><category></category></item><item><title><![CDATA[看公式图片生成Latax公式代码的工具]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120946562</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120946562</guid><author>AIWCZ</author><pubDate>Mon, 25 Oct 2021 09:54:23 +0800</pubDate><description><![CDATA[Mathpix,全平台支持。

]]></description><category></category></item><item><title><![CDATA[用Python对.mat文件进行读取]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120940599</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120940599</guid><author>AIWCZ</author><pubDate>Sun, 24 Oct 2021 20:52:07 +0800</pubDate><description><![CDATA[import scipy.io as scio
dataFile='data/WILLOW-ObjectClass/Car/Cars_000a.mat'
data=scio.loadmat(dataFile)
print(data)



]]></description><category></category></item><item><title><![CDATA[python中lower()函数]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120937380</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120937380</guid><author>AIWCZ</author><pubDate>Sun, 24 Oct 2021 17:41:55 +0800</pubDate><description><![CDATA[lower()函数
将字符串中的所有大写字母转换为小写字母。

]]></description><category></category></item><item><title><![CDATA[Ubuntu系统查看隐藏文件和文件夹]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120915249</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120915249</guid><author>AIWCZ</author><pubDate>Fri, 22 Oct 2021 23:43:59 +0800</pubDate><description><![CDATA[CTRL + h


]]></description><category></category></item><item><title><![CDATA[Nvidia-Docker2安装过程中，报错：gpg: 找不到有效的 OpenPGP数据]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120915156</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120915156</guid><author>AIWCZ</author><pubDate>Fri, 22 Oct 2021 23:33:00 +0800</pubDate><description><![CDATA[由于墙的原因，无法访问 https://nvidia.github.io/nvidia-docker/gpgkey，造成安装失败。
打开：
github.io
查询nvidia.github.io的IP地址

将前4个ip 填入
sudo vim /etc/hosts


即


]]></description><category></category></item><item><title><![CDATA[解决Ubuntu系统断网问题]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120893144</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120893144</guid><author>AIWCZ</author><pubDate>Thu, 21 Oct 2021 19:31:27 +0800</pubDate><description><![CDATA[Ubuntu系统会每隔一段时间 lcp-echo-interval=30 （30s）检查是否有echo-reply信号，如果长时间不使用，连续 lcp-echo-failure=4 次都没有信号，Ubuntu就认为网络出现了问题，就会自行断开网络。
sudo vi /etc/ppp/options

找到
lcp-echo-interval 和  lcp-echo-failure
把值改大
再重启网络
sudo service network-manager restart


...]]></description><category></category></item><item><title><![CDATA[/var/cache/apt/archives内存已满的解决方案]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120889619</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120889619</guid><author>AIWCZ</author><pubDate>Thu, 21 Oct 2021 16:37:07 +0800</pubDate><description><![CDATA[改变安装包的存储位置，找一个存储空间充足的TargetPath，建立软连接
sudo ln -s TargetPath /var/cache/apt/archives


]]></description><category></category></item><item><title><![CDATA[无U盘的Ubuntu双系统安装方法]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120887812</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120887812</guid><author>AIWCZ</author><pubDate>Thu, 21 Oct 2021 16:25:04 +0800</pubDate><description><![CDATA[前提
①BIOS模式为： UEFI
②快速启动已关闭
③已禁用安全启动
上述几步在本次安装中暂时没有发现问题，具体的操作步骤后续可能会补上，不是本次的重点。
下载Ubuntu镜像
下载 EasyUEFI
启动项的制作
①准备一个5G左右的分区，存放Ubuntu.iso解压后的文件
特别注意，文件系统一定是格式化为FAT32，否则EasyUEFI中会出现无法选择分区的情况。
②预留Ubuntu安装的磁盘空间
到磁盘管理里去搞个50G以上(几百个G也可以)的 未分配空间 出来。
③添加efi启动项
然后进去了点]]></description><category></category></item><item><title><![CDATA[Pycharm 查看代码修改历史]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120821809</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120821809</guid><author>AIWCZ</author><pubDate>Mon, 18 Oct 2021 10:35:04 +0800</pubDate><description><![CDATA[里面可以看到代码的修改历史，选择accept还可以对代码进行回滚。

]]></description><category></category></item><item><title><![CDATA[Pycharm给整个工程加上 # -*- coding:utf-8 -*- 及其他相关信息]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120821306</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120821306</guid><author>AIWCZ</author><pubDate>Mon, 18 Oct 2021 10:14:25 +0800</pubDate><description><![CDATA[]]></description><category></category></item><item><title><![CDATA[给虚拟环境指定CUDA版本]]></title><link>https://blog.youkuaiyun.com/AIWCZ/article/details/120811245</link><guid>https://blog.youkuaiyun.com/AIWCZ/article/details/120811245</guid><author>AIWCZ</author><pubDate>Sun, 17 Oct 2021 15:22:58 +0800</pubDate><description><![CDATA[①在下列文件夹下建立.sh

②写入内容
ORIGINAL_CUDA_HOME=$CUDA_HOME
ORIGINAL_LD_LIBRARY_PATH=$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda-10.1
export PATH=$PATH:$CUDA_HOME/bin
export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

③再]]></description><category></category></item></channel></rss>