<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[FoolishL的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/FoolishL</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; FoolishL]]></copyright><item><title><![CDATA[AI——注意力机制]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/132623208</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/132623208</guid><author>FoolishL</author><pubDate>Sat, 09 Sep 2023 00:44:33 +0800</pubDate><description><![CDATA[本文是自己学习时候，收集的一些注意力机制的文章和代码，会持续更新。]]></description><category></category></item><item><title><![CDATA[关于pyradiomics库的安装完毕，却显示 No module named ‘pyradiomics‘]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/131069881</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/131069881</guid><author>FoolishL</author><pubDate>Tue, 06 Jun 2023 17:32:20 +0800</pubDate><description><![CDATA[pyradiomics影像组学特征]]></description><category></category></item><item><title><![CDATA[报错： ‘img should be PIL Image. Got ＜class ‘torch.Tensor‘＞’及解决方案]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/126997354</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/126997354</guid><author>FoolishL</author><pubDate>Thu, 22 Sep 2022 18:38:09 +0800</pubDate><description><![CDATA[在定义transforms.Compose()时，把ToTensor()方法写在了resize的方法之前，造成顺序不对。]]></description><category></category></item><item><title><![CDATA[报错：cannot import name ‘PILLOW_VERSION‘ from ‘PIL‘及解决方案]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/126972653</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/126972653</guid><author>FoolishL</author><pubDate>Wed, 21 Sep 2022 15:08:36 +0800</pubDate><description><![CDATA[报错：cannot import name 'PILLOW_VERSION' from 'PIL'及解决方案]]></description><category></category></item><item><title><![CDATA[报错：“images” contains no shape及解决方案]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/125425222</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/125425222</guid><author>FoolishL</author><pubDate>Thu, 23 Jun 2022 13:25:29 +0800</pubDate><description><![CDATA[“images” contains no shape 错误解决
]]></description><category></category></item><item><title><![CDATA[报错：module ‘tensorflow‘ has no attribute ‘optimizers‘及问题解决]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/125424021</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/125424021</guid><author>FoolishL</author><pubDate>Thu, 23 Jun 2022 11:42:14 +0800</pubDate><description><![CDATA[module ‘tensorflow’ has no attribute ‘optimizers’
问题解决]]></description><category></category></item><item><title><![CDATA[报错：pip install image 及问题解决]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/122670955</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/122670955</guid><author>FoolishL</author><pubDate>Mon, 24 Jan 2022 17:04:02 +0800</pubDate><description><![CDATA[错误 pip install skimage


解决方案：

注意：在上面截图报错中，已给出解决方案，安装scikit-image即可
pip install scikit-image


]]></description><category></category></item><item><title><![CDATA[报错：original_keras_version = f.attrs[‘keras_version‘].decode(‘utf8‘)及问题解决]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/122394476</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/122394476</guid><author>FoolishL</author><pubDate>Sun, 09 Jan 2022 15:50:57 +0800</pubDate><description><![CDATA[windows 系统下出现的
original_keras_version = f.attrs[‘keras_version’].decode(‘utf8’)
1. 问题描述
load_weights_from_hdf5_group
    original_keras_version = f.attrs['keras_version'].decode('utf8')

AttributeError: 'str' object has no attribute 'decode'

2. 原因分析
在安装te]]></description><category></category></item><item><title><![CDATA[python基础——推导式]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/122320659</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/122320659</guid><author>FoolishL</author><pubDate>Wed, 05 Jan 2022 15:43:03 +0800</pubDate><description><![CDATA[推导式]]></description><category></category></item><item><title><![CDATA[python基础——列表]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/122308098</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/122308098</guid><author>FoolishL</author><pubDate>Tue, 04 Jan 2022 19:41:52 +0800</pubDate><description><![CDATA[1.列表切片
通过索引查找列表元素
#列表切片   前闭后开   由两个冒号三个数字组成
a = [1,3,5,7,9,0,2,4,6,8,10]

print(a[::2])    #步长为2                             结果[1,5,9,2,6,10]
print(a[3:])     #从索引为3开始                       结果[7,9,0,2,4,6,8,10]
print(a[:2])     #从默认索引为0开始——索引为2结束        结]]></description><category></category></item><item><title><![CDATA[报错Attribute Qt::AA_EnableHighDpiScaling must be set before QCoreApplication is created.]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/114638293</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/114638293</guid><author>FoolishL</author><pubDate>Wed, 10 Mar 2021 16:43:13 +0800</pubDate><description><![CDATA[一、问题描述：
Attribute Qt::AA_EnableHighDpiScaling must be set before QCoreApplication is created.

Attribute Qt::AA_EnableHighDpiScaling must be set before QCoreApplication is created.
二、解决步骤
1. 卸载低版本
卸载低版本Matplotlib

2. 安装最新版本
安装最新版本的Matplotlib

问题解决

...]]></description><category></category></item><item><title><![CDATA[多线程画图]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/114585159</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/114585159</guid><author>FoolishL</author><pubDate>Tue, 09 Mar 2021 15:55:19 +0800</pubDate><description><![CDATA[一 、问题描述
使用多线程处理热成像算法，进行绘制热成像图。出现BUG：消息里的图和文字全部错乱，仅有几组可视化图像正确。
尝试对问题定位。对算法中每一步进行针对性的算时间和修改。最终定位在画图步骤中。于是，发现 import matplotlib.pyplot as plt 是个全局变量。 应该就是它引起的线程不安全。
原始画图代码如下：

图像空白：

图像错乱：

二 、解决方案
加上这个锁操作。
from threading import Lock,RLock

lock = Lock()
rloc]]></description><category></category></item><item><title><![CDATA[python打包exe 踩雷及问题解决（详细流程）]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/113044815</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/113044815</guid><author>FoolishL</author><pubDate>Sat, 23 Jan 2021 14:18:11 +0800</pubDate><description><![CDATA[1.安装pyinstaller
首先安装pyinstaller，pip install pyinstaller，如图所示：（我已经安装过了）

2.打包
我们来将这个.py的文件打包成一个exe，我们直接cmd切换到这个脚本的目录，执行命令：pyinstaller-F post.py，但是出现如下图所示的错误。找了GitHub和bing也没找到对应的解决方案。

然后，我尝试了卸载重装，竟然，发现好了。我猜测估计是旧版本没有这个PackagePath，装了新版本就有了，如图所示。

顺利解决“ImportE]]></description><category></category></item><item><title><![CDATA[算法学习——K近邻算法]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/112272864</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/112272864</guid><author>FoolishL</author><pubDate>Wed, 06 Jan 2021 15:54:26 +0800</pubDate><description><![CDATA[一、K近邻算法


二、K近邻模型
k近邻法使用的模型实际上对应于对特征空间的划分。模型由三个基本要素——距离度量、k值的选择和分类决策规则决定。
2.1 模型

2.2 距离度量


例题：距离度量使用Lp距离进行计算。

下面给出上述计算的做法，便于理解
L1（X1,X3）= 6 = 3+3
L2（X1,X3）= 4.24 = （3的平方+3的平方）开根号
L3（X1,X3）= 3.78 = （3的3次方+3的3次方）开根号3
L4（X1,X3）= 3.57 = （3的4次方+3的4次方）开根号4
2.]]></description><category></category></item><item><title><![CDATA[学习算法——感知机]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/112184293</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/112184293</guid><author>FoolishL</author><pubDate>Mon, 04 Jan 2021 16:43:19 +0800</pubDate><description><![CDATA[一、感知机学习算法的原始形态

例题

二、感知机学习算法的对偶形态

对偶形式中训练实例仅以内积的形式出现。为了方便,可以预先将训练集中实例间的内积计算出来并以矩阵的形式存储，这个矩阵就是所谓的Gram 矩阵(Grammatrix)
	G= [Xi · Xj]N×N


G矩阵计算：
G =  [X1*X1，X1*X2，X1*X3
	  X2*X1，X2*X2，X2*X3
	  X3*X1，X3*X2，X3*X3]

三、感知机的概要


...]]></description><category></category></item><item><title><![CDATA[CCF BDCI竞赛——离散制造过程中典型工件的质量符合率预测（完整代码分享）]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/110131900</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/110131900</guid><author>FoolishL</author><pubDate>Sat, 28 Nov 2020 15:56:26 +0800</pubDate><description><![CDATA[@by:爱吃肉的小吃货
一级目录
二级目录
三级目录

]]></description><category></category></item><item><title><![CDATA[解决 Python 3中，Matplotlib绘图显示方块问题（排雷）]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/110038379</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/110038379</guid><author>FoolishL</author><pubDate>Tue, 24 Nov 2020 09:32:23 +0800</pubDate><description><![CDATA[@[By 爱吃肉的小吃货]
解决Matplotlib绘图出现方块问题
正常情况下，是不会出现无法显示中文的，从而导致原来中文部分由方块代替的。


# 连续性和离散型分析

# 获取列名
# col_name = train_data.drop(['Quality_label'],1).columns
col_name = train_data.columns
Notdlts_count = []
for i in col_name:
    # 计算非重复值的个数   drop_duplicates：去除]]></description><category></category></item><item><title><![CDATA[天池竞赛——工业蒸汽量预测（完整代码分享）]]></title><link>https://blog.youkuaiyun.com/FoolishL/article/details/109678448</link><guid>https://blog.youkuaiyun.com/FoolishL/article/details/109678448</guid><author>FoolishL</author><pubDate>Sat, 14 Nov 2020 16:15:31 +0800</pubDate><description><![CDATA[@[By Boléro_L]
目录
一、赛题描述
1.1  赛题背景
	火力发电的基本原理是：燃料在燃烧时加热水生成蒸汽，蒸汽压力推动汽轮机旋转，然后汽轮机带动发电机旋转，产生电能。在这一系列的能量转化中，影响发电效率的核心是锅炉的燃烧效率，即燃料燃烧加热水产生高温高压蒸汽。锅炉的燃烧效率的影响因素很多，包括锅炉的可调参数，如燃烧给量，一二次风，引风，返料风，给水水量；以及锅炉的工况，比如锅炉床温、床压，炉膛温度、压力，过热器的温度等。

1.2  赛题描述
	经脱敏后的锅炉传感器采集的数据（采集频率是分钟]]></description><category></category></item></channel></rss>