<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Ninomi_的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/Ninomi_</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Ninomi_]]></copyright><item><title><![CDATA[Netty学习笔记-Netty入门]]></title><link>https://blog.youkuaiyun.com/Ninomi_/article/details/137759373</link><guid>https://blog.youkuaiyun.com/Ninomi_/article/details/137759373</guid><author>Ninomi_</author><pubDate>Mon, 15 Apr 2024 00:45:55 +0800</pubDate><description><![CDATA[EventLoopGroup 翻译：事件循环组，channel一般会调用EventLoopGroup的register的方法来绑定其中一个EventLoop，后续这个channel的io事件都由这个EventLoop 来处理（保证了io事件处理时的线程安全）负责根据一定的策略选择下一个可用的EventExecutor，通常是一个NioEventLoop。底层实现是两个不同的。的执行器，里面有run方法处理channel上源源不断的io事件。next()方法的实现逻辑主要是通过对内部维护的。]]></description><category></category></item><item><title><![CDATA[Netty学习笔记-Nio基础]]></title><link>https://blog.youkuaiyun.com/Ninomi_/article/details/137481978</link><guid>https://blog.youkuaiyun.com/Ninomi_/article/details/137481978</guid><author>Ninomi_</author><pubDate>Sun, 07 Apr 2024 23:09:16 +0800</pubDate><description><![CDATA[Netty学习笔记]]></description><category></category></item><item><title><![CDATA[Java线程join方法源码详解]]></title><link>https://blog.youkuaiyun.com/Ninomi_/article/details/133759036</link><guid>https://blog.youkuaiyun.com/Ninomi_/article/details/133759036</guid><author>Ninomi_</author><pubDate>Wed, 11 Oct 2023 01:20:46 +0800</pubDate><description><![CDATA[详细解释了Java中Thread类join方法的底层实现]]></description><category></category></item><item><title><![CDATA[macbook macOS 上 fashion-mnist数据集加载的问题以及解决方法]]></title><link>https://blog.youkuaiyun.com/Ninomi_/article/details/103435824</link><guid>https://blog.youkuaiyun.com/Ninomi_/article/details/103435824</guid><author>Ninomi_</author><pubDate>Sat, 07 Dec 2019 16:05:06 +0800</pubDate><description><![CDATA[、
Keras分类问题数据集fashion_minist
在学习使用tensorflow的过程中，必不可少的额就是
import tensorflow as tf
from tensorflow import keras 

#导入fashion_minist数据集
fashion_mnist = keras.datasets.fashion_mnist

新的改变
我们对Markdown编辑器进...]]></description><category></category></item></channel></rss>