<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[mollygogo的博客]]></title><description><![CDATA[Learning coding]]></description><link>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; MOOLLLLLLLLLLL]]></copyright><item><title><![CDATA[password_hash/password_verify/(JAVA)]]></title><link>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL/article/details/122567728</link><guid>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL/article/details/122567728</guid><author>MOOLLLLLLLLLLL</author><pubDate>Tue, 18 Jan 2022 20:27:20 +0800</pubDate><description><![CDATA[承接了一个新项目，需要继承旧的用户数据。

账号密码解密使用了\yii\base\Security::generatePasswordHash($password, $cost)方法。


generatePasswordHash($password, $cost = null)
//php的生成密文
//默认的cost是13，是时间复杂度
return password_hash($password, PASSWORD_DEFAULT, ['cost' =&gt; $cost]);



账号密码解密使用]]></description><category></category></item><item><title><![CDATA[AES-128-CBC加解密/openssl_decrypt]]></title><link>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL/article/details/122567405</link><guid>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL/article/details/122567405</guid><author>MOOLLLLLLLLLLL</author><pubDate>Tue, 18 Jan 2022 20:09:52 +0800</pubDate><description><![CDATA[针对php的AES-128-CBC加解密代码，需要使用JAVA解密。



1. php加解密


//加密
$result = openssl_encrypt($data, "AES-128-CBC", $aesKey, 0, $aesIv);
//解密
$result = openssl_decrypt($data, "AES-128-CBC", $aesKey, 0, $aesIv);




需要注意$options变量的不同。
加密过程中的$options=1, 意味着使用OPENSSL_RAW_]]></description><category></category></item><item><title><![CDATA[综述--梯度下降优化算法]]></title><link>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL/article/details/80224045</link><guid>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL/article/details/80224045</guid><author>MOOLLLLLLLLLLL</author><pubDate>Tue, 08 May 2018 22:06:58 +0800</pubDate><description><![CDATA[paper: An overview of gradient descent optimization algorithms梯度下降优化算法，一般作为黑箱优化来使用，对其优缺点缺乏充足的认识。首先，回顾一下一些常用的梯度下降算法。有3种梯度下降法的变种，区别在于使用多少数据来更新梯度，在更新的准确度和计算时间中达到一个平衡。Batch Gradient Descent对整个训练数据集计算参数梯度，...]]></description><category></category></item><item><title><![CDATA[使用上采样加卷积的方法代替反卷积]]></title><link>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL/article/details/80221613</link><guid>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL/article/details/80221613</guid><author>MOOLLLLLLLLLLL</author><pubDate>Mon, 07 May 2018 12:34:46 +0800</pubDate><description><![CDATA[URL: https://distill.pub/2016/deconv-checkerboard/PAPER: Deconvolution and Checkerboard ArtifactsOverlap&amp;amp;Learning神经网络生成的图像，普遍存在checkerboard pattern of artifacts的现象（高频），尤其在色彩比较强烈的图像中。（棋盘状）。因为神经网络有一...]]></description><category></category></item><item><title><![CDATA[ResNet]]></title><link>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL/article/details/80107028</link><guid>https://blog.youkuaiyun.com/MOOLLLLLLLLLLL/article/details/80107028</guid><author>MOOLLLLLLLLLLL</author><pubDate>Fri, 27 Apr 2018 16:23:49 +0800</pubDate><description><![CDATA[Paper: Deep Residual Learning for Image Recognition---Q:Is learning better networks as easy as stacking more layers?A:vanishing/exploding gradients, which is largely addressed by normalization initial...]]></description><category></category></item></channel></rss>