<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[spring_willow的博客]]></title><description><![CDATA[再不学习就死定了]]></description><link>https://blog.youkuaiyun.com/spring_willow</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; spring_willow]]></copyright><item><title><![CDATA[GCC 安装 （Linux + 非root)]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/131448193</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/131448193</guid><author>spring_willow</author><pubDate>Thu, 29 Jun 2023 04:24:15 +0800</pubDate><description><![CDATA[非root用户如何安装与cuda版本对应的GCC]]></description><category></category></item><item><title><![CDATA[Linux-安装显卡驱动NVIDIA，CUDA和cuDNN及所遇问题]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/105564865</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/105564865</guid><author>spring_willow</author><pubDate>Sun, 19 Apr 2020 02:15:02 +0800</pubDate><description><![CDATA[1.安装显卡驱动

删除旧驱动：sudo apt-get purge nvidia*
打开禁用列表： sudo gedit /etc/modprobe.d/blacklist.conf
在禁用列表中添加：

blacklist nouveau
options nouveau modeset=0


更新：sudo update-initramfs -u

|——报错：
W: Possible mi...]]></description><category></category></item><item><title><![CDATA[Linux-更改终端字体大小]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/105564209</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/105564209</guid><author>spring_willow</author><pubDate>Thu, 16 Apr 2020 18:24:31 +0800</pubDate><description><![CDATA[1.终端输入：
sudo apt-get install unity-tweak-tool
unity-tweak-tool

2.更改等宽字体：


]]></description><category></category></item><item><title><![CDATA[Torch-Read error: read 0 blocks instead of 1]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/105549184</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/105549184</guid><author>spring_willow</author><pubDate>Thu, 16 Apr 2020 04:58:33 +0800</pubDate><description><![CDATA[记录一下最近遇到的问题，可能题目与内容不符。但是，知道为什么要这么做才知道要做什么。弄明白之后发现自己很蠢，不求甚解的后果就是重复地踩坑。


在Windows10自带的虚拟机上安装了torch，然后运行th %filename%.lua报错：
Read error: read 0 blocks instead of 1 %file directory% stack traceback：[C]: ...]]></description><category></category></item><item><title><![CDATA[Linux-you need at least 8.6GB disk space to install Ubuntu,this computer has only 8GB]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/105526001</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/105526001</guid><author>spring_willow</author><pubDate>Wed, 15 Apr 2020 08:04:06 +0800</pubDate><description><![CDATA[1.Win+R 输入regedit打开注册表，进入路径：计算机\HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\storahci\StartOverride

]]></description><category></category></item><item><title><![CDATA[Linux-Windows10安装Ubuntu（双系统)]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/102466250</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/102466250</guid><author>spring_willow</author><pubDate>Thu, 10 Oct 2019 22:01:07 +0800</pubDate><description><![CDATA[一、组合范畴语法CCG
(1)概念
CCG全名为Combinatory Categorial Grammar, 其作用是在句法结构(syntacnic)和语义表示(sementic representation)之间提供了一种自然的连接。CCG相比于其他语法更加灵活，不需要导出整个句子的结构就可以得到句子中任意部分的结构
(2)特征：
CCG的显著特征包括一下三个：

以词汇为核心
能够描述一类适...]]></description><category></category></item><item><title><![CDATA[NLP-关于数据集处理的相关代码]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/82852832</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/82852832</guid><author>spring_willow</author><pubDate>Wed, 26 Sep 2018 14:24:04 +0800</pubDate><description><![CDATA[1.将几个文件中的数据合并为一个文件
将要合并的几个文件放入一个文件夹下
import os
#获取目标文件夹的路径
# filedir=os.getcwd()+'/corpus'
#获取当前文件夹中文件名称列表
# filenames=os.listdir(filedir)
#遍历文件名
# for filename in filenames:
    filepath=filedir+'/'+...]]></description><category></category></item><item><title><![CDATA[NLP-神经网络隐藏层节点数设置]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/82833702</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/82833702</guid><author>spring_willow</author><pubDate>Mon, 24 Sep 2018 23:56:26 +0800</pubDate><description><![CDATA[内容来源：http://www.ilovematlab.cn/thread-209458-1-1.html
BP神经网络隐藏层节点数设置—经验公式

mmm：隐藏层节点数
nnn：输入层节点数
lll：输出层节点数
α\alphaα：1–10之间的常数
m=n+l+αm=\sqrt{n+l}+\alpham=n+l​+α
m=log2nm=log_2{n}m=log2​n
m=n∗lm=\sqrt...]]></description><category></category></item><item><title><![CDATA[NLP-准确率、精确率、召回率和F1值]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/82820944</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/82820944</guid><author>spring_willow</author><pubDate>Sun, 23 Sep 2018 11:27:32 +0800</pubDate><description><![CDATA[记录准确率(Accuracy)、精确率(Precision)、召回率(Recall)和F1值(F-Measure)计算公式，和如何使用TensorFlow实现

一、计算公式
二、TensorFlow实现
        # Accuracy
        with tf.name_scope(&amp;quot;accuracy&amp;quot;):
            correct_predictions = tf.e...]]></description><category></category></item><item><title><![CDATA[NLP-Pearson相关系数计算公式及代码]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/82714702</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/82714702</guid><author>spring_willow</author><pubDate>Sat, 15 Sep 2018 15:51:10 +0800</pubDate><description><![CDATA[随便记录一下。。。

计算公式

Pearson=∑ni=1xiyi−∑ni=1xi∑ni=1yin∑ni=1xi2−(∑2i=1xi)2n−−−−−−−−−−−−−−−−√∑ni=1yi2−(∑ni−1yi)2n−−−−−−−−−−−−−−−−√Pearson=∑i=1nxiyi−∑i=1nxi∑i=1nyin∑i=1nxi2−(∑i=12xi)2n∑i=1nyi2−(∑i−1nyi)2nPea...]]></description><category></category></item><item><title><![CDATA[对话系统评测-对话语料]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/82468702</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/82468702</guid><author>spring_willow</author><pubDate>Thu, 06 Sep 2018 21:35:14 +0800</pubDate><description><![CDATA[语料很多，但是找到适合自己的却很难。。。整理一下各种关于对话语料博客的链接，留作备用



内容：中英文对话系统语料

1.csdn 
2.csdn 
3.csdn 
4.github 
5.github 
6.github 
7.知乎...]]></description><category></category></item><item><title><![CDATA[对话系统评测-How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metri]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/81838012</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/81838012</guid><author>spring_willow</author><pubDate>Sun, 19 Aug 2018 14:55:04 +0800</pubDate><description><![CDATA[对话系统评测相关论文——第一篇



论文题目：How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation



论文摘要

对已经存在的评估指标进行定性和定量的分析得出它们具体的不足之处，为未来的对话...]]></description><category></category></item><item><title><![CDATA[NLP-分布表示(distributional representation)与分布式表示(distributed representation)]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/81452162</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/81452162</guid><author>spring_willow</author><pubDate>Mon, 06 Aug 2018 12:16:03 +0800</pubDate><description><![CDATA[写论文好纠结这两个东西的概念。。。对网上查到的资料进行以下整理。。。



distributed representation&amp;amp;amp;amp;distributional representation

分布式表示与分布表示



1.关于Manning 在2015 年深度学习暑期学校（蒙特利尔）的澄清


  Distributed: A concept is represented as cont...]]></description><category></category></item><item><title><![CDATA[Python-制作可视化词云]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/81099937</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/81099937</guid><author>spring_willow</author><pubDate>Wed, 18 Jul 2018 16:07:05 +0800</pubDate><description><![CDATA[目录



目录
一.对于可视化词云的理解
二.python的代码实现
（1）实现步骤
（2）代码说明
1.导入需要的库
2.读取数据
3.去掉停止词
4.统计词频
5.显示词云
6.结果图




三、自定义背景图







一.对于可视化词云的理解


对人、物、事的文本描述中，大量出现的词可以用于这些对象的标签，将这些标签按照词频的大小以图片的方式展示出来，就成为了可视化的个性化词云。
...]]></description><category></category></item><item><title><![CDATA[NLP-UnicodeDecodeError: 'utf8' codec can't decode byte 0xe6 in position 0: invalid continuation byte]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/80947950</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/80947950</guid><author>spring_willow</author><pubDate>Sat, 07 Jul 2018 00:35:11 +0800</pubDate><description><![CDATA[记录使用simhash和 CountVectorizer计算文本相似性时遇到的问题，，主要是我线下的Windows系统使用的是python3.5，线上评测使用的是python2.7。。。





问题：

UnicodeDecodeError: ‘utf8’ codec can’t decode byte 0xe6 in position 0: invalid continuation byte...]]></description><category></category></item><item><title><![CDATA[Python-pandas中的函数(列表拼接，更改行列名称，选取特定数据等）]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/80779749</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/80779749</guid><author>spring_willow</author><pubDate>Fri, 22 Jun 2018 23:38:07 +0800</pubDate><description><![CDATA[记录pandas处理数据时一些函数的使用。。。。





1.列表拼接
2.更改列名
3.更改索引
4.选取指定数据
5.重置索引









1.列表拼接

concat()

In[24]: import pandas as pd
In[25]: a=pd.DataFrame()
In[26]: a['A']=[1,2,3,4,5]
In[27]: a['B']=[6,7,8,9,0]...]]></description><category></category></item><item><title><![CDATA[TensorFlow中的小知识：InteractiveSession()]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/80173927</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/80173927</guid><author>spring_willow</author><pubDate>Wed, 20 Jun 2018 10:07:58 +0800</pubDate><description><![CDATA[为什么有些代码里使用了InteractiveSession()，而有些却没有使用呢？ 
本篇用于记录InteractiveSession()与普通的Session()的使用区别。





InteractiveSession

详见 TensorFlow 官方文档中文版 
在tensorflow中通过session启动构建的计算图来进行计算。在启动session之前需要将整个计算图构建好，启动之...]]></description><category></category></item><item><title><![CDATA[NLP-fuzzywuzzy安装，同simhash]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/80731535</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/80731535</guid><author>spring_willow</author><pubDate>Tue, 19 Jun 2018 11:24:26 +0800</pubDate><description><![CDATA[记录文本相似度计算中fuzzywuzzy的安装使用。。。





安装流程


有git的情况 
git clone git://github.com/seatgeek/fuzzywuzzy.git fuzzywuzzy 
cd fuzzywuzzy 
python setup.py install
无git的情况 
下载zip包解压 
命令行进入文件夹下install 
重启pyCharm 
...]]></description><category></category></item><item><title><![CDATA[NLP-python pyemd安装]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/80729329</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/80729329</guid><author>spring_willow</author><pubDate>Tue, 19 Jun 2018 10:21:24 +0800</pubDate><description><![CDATA[报错内容：

ImportError: Please install pyemd Python package to compute WMD.



解决方案：

使用命令行pip install pyemd



报错内容：

error: Microsoft Visual C++ 14.0 is required. Get it with “Microsoft Visual C++ Build...]]></description><category></category></item><item><title><![CDATA[NLP-英文文本预处理中的文本清洗内容]]></title><link>https://blog.youkuaiyun.com/spring_willow/article/details/80721491</link><guid>https://blog.youkuaiyun.com/spring_willow/article/details/80721491</guid><author>spring_willow</author><pubDate>Sun, 17 Jun 2018 22:30:38 +0800</pubDate><description><![CDATA[记录英文文本中的文本清洗内容：


缩略词更改
拼写校正
标点符号
符号替换
去除空格


    def clean_text(text):
        &quot;&quot;&quot;
        Clean text
        :param text: the string of text
        :return: text string after cleaning
        &quot;&quot;&quot;
 ...]]></description><category></category></item></channel></rss>