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index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=2">
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<link itemprop="mainEntityOfPage" href="https://asswei7.github.io/2021/09/30/%E5%8F%A4%E5%B8%8C%E8%85%8A%E5%BB%BA%E7%AD%91/%E5%8F%A4%E5%B8%8C%E8%85%8A%E5%BB%BA%E7%AD%91/">
<span hidden itemprop="author" itemscope itemtype="http://schema.org/Person">
<meta itemprop="image" content="/uploads/Champion_ka.png">
<meta itemprop="name" content="孙书玮">
<meta itemprop="description" content="篮球，计算机">
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<header class="post-header">
<h2 class="post-title" itemprop="name headline">
<a href="/2021/09/30/%E5%8F%A4%E5%B8%8C%E8%85%8A%E5%BB%BA%E7%AD%91/%E5%8F%A4%E5%B8%8C%E8%85%8A%E5%BB%BA%E7%AD%91/" class="post-title-link" itemprop="url">古希腊建筑</a>
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<span class="post-meta-item-text">Posted on</span>
<time title="Created: 2021-09-30 10:12:37" itemprop="dateCreated datePublished" datetime="2021-09-30T10:12:37+08:00">2021-09-30</time>
</span>
<span class="post-meta-item">
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<span class="post-meta-item-text">Edited on</span>
<time title="Modified: 2021-10-09 09:52:28" itemprop="dateModified" datetime="2021-10-09T09:52:28+08:00">2021-10-09</time>
</span>
<span id="/2021/09/30/%E5%8F%A4%E5%B8%8C%E8%85%8A%E5%BB%BA%E7%AD%91/%E5%8F%A4%E5%B8%8C%E8%85%8A%E5%BB%BA%E7%AD%91/" class="post-meta-item leancloud_visitors" data-flag-title="古希腊建筑" title="Views">
<span class="post-meta-item-icon">
<i class="fa fa-eye"></i>
</span>
<span class="post-meta-item-text">Views: </span>
<span class="leancloud-visitors-count"></span>
</span><br>
<span class="post-meta-item" title="Symbols count in article">
<span class="post-meta-item-icon">
<i class="fa fa-file-word-o"></i>
</span>
<span class="post-meta-item-text">Symbols count in article: </span>
<span>233</span>
</span>
<span class="post-meta-item" title="Reading time">
<span class="post-meta-item-icon">
<i class="fa fa-clock-o"></i>
</span>
<span class="post-meta-item-text">Reading time ≈</span>
<span>1 mins.</span>
</span>
</div>
</header>
<div class="post-body" itemprop="articleBody">
<p>古罗马是古希腊的继承者，从爱琴海文明转移到了地中海文明。<br><img src="/images/古希腊建筑/变迁.png" alt></p>
<p>建筑三原则：坚固、实用、美观</p>
<h2 id="古希腊柱式"><a href="#古希腊柱式" class="headerlink" title="古希腊柱式"></a>古希腊柱式</h2><p><img src="/images/古希腊建筑/三种柱子.png" alt><br>古希腊更多是前两种柱式：<br>经济原因：成本较低<br>思想原因：中庸克制</p>
<p>帕台农神庙：黄金比例</p>
<p>梁柱结构，三角眉式、柱廊</p>
<p>斯多葛学派（廊下学派）：</p>
<h2 id="哥特式建筑"><a href="#哥特式建筑" class="headerlink" title="哥特式建筑"></a>哥特式建筑</h2><p>哥特式建筑：尖锐高耸</p>
<p>肋：复杂的肋<br>窗：大彩玻璃窗<br>拱：哥特尖拱<br>壁：飞扶壁<br>墙：薄墙</p>
<p>中世纪是宗教的时代，希望教堂就是人间的天堂，所以教堂都是封闭的</p>
<p>以前的教堂：低、胖（墙厚）、暗（窗户小）<br>哥特式教堂：高、瘦、亮</p>
<p>以线为主体，视觉引导，显得清瘦、高</p>
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<link itemprop="mainEntityOfPage" href="https://asswei7.github.io/2021/09/29/Linux%E5%AD%A6%E4%B9%A0/Linux%E5%AD%A6%E4%B9%A0/">
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<meta itemprop="image" content="/uploads/Champion_ka.png">
<meta itemprop="name" content="孙书玮">
<meta itemprop="description" content="篮球，计算机">
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<meta itemprop="name" content="Asswei's blog">
</span>
<header class="post-header">
<h2 class="post-title" itemprop="name headline">
<a href="/2021/09/29/Linux%E5%AD%A6%E4%B9%A0/Linux%E5%AD%A6%E4%B9%A0/" class="post-title-link" itemprop="url">Linux学习</a>
</h2>
<div class="post-meta">
<span class="post-meta-item">
<span class="post-meta-item-icon">
<i class="fa fa-calendar-o"></i>
</span>
<span class="post-meta-item-text">Posted on</span>
<time title="Created: 2021-09-29 09:02:57 / Modified: 09:04:18" itemprop="dateCreated datePublished" datetime="2021-09-29T09:02:57+08:00">2021-09-29</time>
</span>
<span id="/2021/09/29/Linux%E5%AD%A6%E4%B9%A0/Linux%E5%AD%A6%E4%B9%A0/" class="post-meta-item leancloud_visitors" data-flag-title="Linux学习" title="Views">
<span class="post-meta-item-icon">
<i class="fa fa-eye"></i>
</span>
<span class="post-meta-item-text">Views: </span>
<span class="leancloud-visitors-count"></span>
</span><br>
<span class="post-meta-item" title="Symbols count in article">
<span class="post-meta-item-icon">
<i class="fa fa-file-word-o"></i>
</span>
<span class="post-meta-item-text">Symbols count in article: </span>
<span>1.3k</span>
</span>
<span class="post-meta-item" title="Reading time">
<span class="post-meta-item-icon">
<i class="fa fa-clock-o"></i>
</span>
<span class="post-meta-item-text">Reading time ≈</span>
<span>1 mins.</span>
</span>
</div>
</header>
<div class="post-body" itemprop="articleBody">
<h2 id="vi的使用"><a href="#vi的使用" class="headerlink" title="vi的使用"></a>vi的使用</h2><p><strong>命令模式：</strong><br>输入i进入编辑模式，输入:进入底线命令模式，输入x类似于[del]删除光标处的字符<br>退出的话，如果没有改动需要:q!退出，改动了:wq退出。<br><strong>输入模式：</strong><br>G：移动到文件的最后一行<br>gg：移动到文件的第一行，相当于1G<br>n<\Enter>: 光标向下移动n行</p>
<p>/word：向光标之下</p>
<p>常用的复制粘贴：<br>复制光标这一行：yy</p>
<p>重复上一个动作：.（小数点）<br>撤销上一个动作：u</p>
<p>ESC切换到命令模式</p>
<p><strong>底线命令模式：</strong><br>q退出程序<br>w保存文件<br>按esc进入命令模式</p>
<p>Swap file “.test927.txt.swp” already exists!<br>使用vim编辑，先复制一份临时文件并映射到内存来编辑，编辑的是临时文件，当执行了:w是保存临时文件到原文件，执行:q后删除临时文件。</p>
<p>每次启动时会检索是否有临时文件。<br>使用ll -a会显示全部文件，包括一些隐藏文件，以及文件的详细信息。例如.test.txt.swp<br>rm-f删除文件</p>
<h2 id="服务器报错"><a href="#服务器报错" class="headerlink" title="服务器报错"></a>服务器报错</h2><p>CUDA Version: 11.2<br>pip install torch==1.5.0</p>
<p>RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED<br>运行报错，原因是cuda和pytorch版本不匹配，服务器上的cuda版本是11.2，而pytorch版本是1.5.0,<br>通过网站<a target="_blank" rel="noopener" href="https://pytorch.org/get-started/previous-versions/">https://pytorch.org/get-started/previous-versions/</a><br>可以看到cuda和pytorch对应的版本，对应cuda 9.2/10.1/10.2三个。</p>
<p>但是import torch, torch.version.cuda，输入版本为10.2</p>
<p>Traceback (most recent call last):<br> File “main.py”, line 365, in <\module><br> train_gat(args)<br> File “main.py”, line 194, in train_gat<br> torch.LongTensor(current_batch_2hop_indices)).cuda()<br>TypeError: expected torch.LongTensor (got torch.FloatTensor)</p>
<p>修改了对应文件的代码<br>current_batch_2hop_indices = torch.tensor<br>current_batch_2hop_indices = torch.LongTensor([])<br>tensor默认是float，改成Long应该可以</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line">Socket error Event: 32 Error: 10053.</span><br><span class="line">Connection closing...Socket close.</span><br><span class="line"></span><br><span class="line">Connection closed by foreign host.</span><br><span class="line"></span><br><span class="line">Disconnected from remote host(3090) at 01:30:17.</span><br><span class="line"></span><br><span class="line">Type help to learn how to use Xshell prompt.</span><br></pre></td></tr></table></figure>
<p>cuda:Compute Unified Device Architecture统一计算架构，是nvidia推出的编程环境，包括</p>
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<h2 id="位置编码"><a href="#位置编码" class="headerlink" title="位置编码"></a>位置编码</h2><p>由于transformer摒弃RNN结构，需要标记各个字之间的时序和位置关系。</p>
<p>首先的想法自然是取[0,1]之间的数分配给每个字，这样的问题是句子长度不同，在短句子中接近的两个字差值为0.1，长句子中相隔很远的两个字差值也是0.1.相同的差值，在不同的句子中含义不同，是不合适的。</p>
<p>另一个想法是线性地给每个时间步分配一个数字，第一个单词为1，第二个为2，这样的问题是1.比字嵌入的值大，会抢了字嵌入的风头。2.最后一个字比第一个字大太多，和字嵌入合并后会出现特征在数值上的倾斜。</p>
<p><strong>理想的情况：</strong></p>
<ul>
<li>为每个字输入唯一的编码，不是一个数字，而是包含句子特定信息的d维向量</li>
<li>不同长度的句子之间，任何两个字的差值保持一致</li>
<li>值应该是有界的</li>
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<p>9月26日，跟着学校一起去参观了中国共产党历史展览馆，一点钟展览馆开门。我们一行是12.45左右到的，但是已经排起了很长的队伍。排的队分成背包的和没背包的两个队伍，没背包的队伍安检速度会快一点。很快一点钟了，安检的速度很快，虽然排队人很多，但是很快就进去了。<br>里面有公益讲解，内容也是涵盖的很广很全面，从1840年开始，一直到最近的脱贫攻坚。这个展馆最大的特点是，里面的东西我们多多少少地都了解过，都学习过。所以看到一些场面时会有所感触，这或许才是展览馆的意义。</p>
<p>我们对于一些历史背景或者说艺术流派有了一定的了解，但是没有那种亲眼见证的体会，这时展览馆才能发挥出它最大的价值。我回想起前几天去的故宫，由于每个宫殿都不让进去，从外面看每个宫殿的造型也是大同小异，很快就失去了游玩的兴趣，只看看这个殿是皇上休息的，那个宫是皇上睡觉的，根本体会不到其中的意义。反而是军机处让我留有印象。因为我很喜欢看《雍正王朝》，而中学历史书对雍正的介绍少之又少，夹在了最有名的康乾盛世中间，历史的考点只有一个，他创办了军机处，皇权达到了顶峰。而在雍正王朝中的军机处特别小，让人很难想象，最高级别的几个人讨论最重要的时期，和珅也是军机大臣，却挤在这么小的一间屋子。</p>
<p>又想到之前去卢浮宫参观，像什么断臂的维纳斯，蒙娜丽莎，一堆人围着，连走近观看都做不到。根本无法理解其中好在哪里。只有课本上的《自由领导人民》是我见过的，而且旁边没啥人，还能看一看，也没看出什么东西来。但看完了西方艺术史，了解了德拉克洛瓦的重要地位，了解了浪漫主义的前因后果，才能理解其的重要意义。</p>
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<h2 id="遇到的问题"><a href="#遇到的问题" class="headerlink" title="遇到的问题"></a>遇到的问题</h2><p>python对GitHub的爬虫特别不稳定，因为自己本地访问GitHub就不稳定，爬虫自然也难以稳定。想到了挂梯子，但是挂梯子后会报错：ValueError: check_hostname requires server_hostname</p>
<p>解决方法：要么取消梯子，要么可以降低requests库版本到2.7.0。<br>用conda命令行，python -m pip install —upgrade requests==2.7.0，但是pip之前还是得取消梯子。<br>成功解决这个问题。</p>
<h2 id="将表格中的元素变成链接"><a href="#将表格中的元素变成链接" class="headerlink" title="将表格中的元素变成链接"></a>将表格中的元素变成链接</h2><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><el-table-column</span><br><span class="line"> prop="url"</span><br><span class="line"> label="Url"></span><br><span class="line"></el-table-column></span><br></pre></td></tr></table></figure>
<p>上面是原来的表格形式，改成如下格式。<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><el-table-column</span><br><span class="line"> prop="url"</span><br><span class="line"> label="Url"></span><br><span class="line"> <template slot-scope="scope"></span><br><span class="line"> <a :href="scope.row.url" target="_blank" >{{scope.row.url}}</a></span><br><span class="line"> </template></span><br><span class="line"></el-table-column></span><br></pre></td></tr></table></figure></p>
<h2 id="子组件无法修改父组件传进来的值"><a href="#子组件无法修改父组件传进来的值" class="headerlink" title="子组件无法修改父组件传进来的值"></a>子组件无法修改父组件传进来的值</h2>
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<h1 id="知识图谱与预训练语言模型"><a href="#知识图谱与预训练语言模型" class="headerlink" title="知识图谱与预训练语言模型"></a>知识图谱与预训练语言模型</h1><h1 id="Focal-Loss"><a href="#Focal-Loss" class="headerlink" title="Focal Loss"></a>Focal Loss</h1><p>做机器学习分类问题，难免遇到Biased-Data-Problem, 例如</p>
<ul>
<li>CV的目标检测问题: 绝大多数检测框里都是 backgroud</li>
<li>NLP的异常文本检测: 绝大多数文本都是 normal</li>
<li>对此，以下套路可以缓解:</li>
<li>升/降采样, 或者调整样本权重</li>
<li>换个更鲁棒的loss函数 ，或者加正则</li>
<li>集成模型: Bagging, RandomForest …</li>
<li>利于外部先验知识: 预训练+微调</li>
<li>多任务联合学习</li>
</ul>
<p><img src="/images/知识图谱思路/CE.png" alt><br>可以看到如果label是1，p越接近1，损失就越小。</p>
<p>考前复习的时候，「他不会划重点，对所有知识点 “一视同仁”」。</p>
<p>如果教科书上有100道例题，包括: 90道加减乘除 + 10道 三角函数。CE同学就会吭哧吭哧的“平均用力”反复练习这100道例题，结果可想而知——他会精通那90道个位数加减乘除题目，然后其他题目基本靠蒙。那10道他不会的题，往往还是分值高的压轴题。</p>
<p>方法一：分科复习</p>
<p>每个【科目】的难度是不同的；你要花 30%的精力在四则运算，70%的精力在三角函数。—- 老师告诉CE同学 第一个技巧<br><img src="/images/知识图谱思路/way1.png" alt></p>
<p>方法二：刷题战术<br><img src="/images/知识图谱思路/way2.png" alt><br>当$p_t$越大，说明准确率越高，观察CE中的$p_t$，它反映了模型对这个样本的识别能力（即 “这个知识点掌握得有多好”）；显然，对于$p_t$越大的样本，我们越要打压它对loss的贡献。</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br></pre></td><td class="code"><pre><span class="line">from keras import backend as K</span><br><span class="line">def focal_loss(alpha=0.75, gamma=2.0):</span><br><span class="line"> """ 参考 https://blog.csdn.net/u011583927/article/details/90716942 """</span><br><span class="line"> def focal_loss_fixed(y_true, y_pred):</span><br><span class="line"> # y_true 是个一阶向量, 下式按照加号分为左右两部分</span><br><span class="line"> # 注意到 y_true的取值只能是 0或者1 (假设二分类问题)，可以视为“掩码”</span><br><span class="line"> # 加号左边的 y_true*alpha 表示将 y_true中等于1的槽位置为标量 alpha</span><br><span class="line"> # 加号右边的 (ones-y_true)*(1-alpha) 则是将等于0的槽位置为 1-alpha</span><br><span class="line"> ones = K.ones_like(y_true)</span><br><span class="line"> alpha_t = y_true*alpha + (ones-y_true)*(1-alpha)</span><br><span class="line"></span><br><span class="line"> # 类似上面，y_true仍然视为 0/1 掩码</span><br><span class="line"> # 第1部分 `y_true*y_pred` 表示 将 y_true中为1的槽位置为 y_pred对应槽位的值</span><br><span class="line"> # 第2部分 `(ones-y_true)*(ones-y_pred)` 表示 将 y_true中为0的槽位置为 (1-y_pred)对应槽位的值</span><br><span class="line"> # 第3部分 K.epsilon() 避免后面 log(0) 溢出</span><br><span class="line"> p_t = y_true*y_pred + (ones-y_true)*(ones-y_pred) + K.epsilon()</span><br><span class="line"></span><br><span class="line"> # 就是公式的字面意思</span><br><span class="line"> focal_loss = -alpha_t * K.pow((ones-p_t),gamma) * K.log(p_t)</span><br><span class="line"> return focal_loss_fixed</span><br><span class="line"></span><br><span class="line">model = ...</span><br><span class="line">model.compile(..., loss=focal_loss(gamma=3, alpha=0.5))</span><br></pre></td></tr></table></figure>
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<h1 id="代理"><a href="#代理" class="headerlink" title="代理"></a>代理</h1><p>设计模式中的代理模式：给某个对象提供一个代理对象，并由代理对象控制原对象的引用。</p>
<p>理解为看房的中介。<br>当使用我们平台的用户越来越多了，并发量慢慢增大了，这时候一台服务器满足不了我们的需求了。于是我们横向扩展，又增加了服务器。这个时候几个项目启动在不同的服务器上，用户要访问，就需要增加一个代理服务器了，通过代理服务器来帮我们转发和处理请求。</p>
<p>我们希望这个代理服务器可以帮助我们接收用户的请求，然后将用户的请求按照规则帮我们转发到不同的服务器节点之上。这个过程用户是无感知的，用户并不知道是哪个服务器返回的结果，我们还希望他可以按照服务器的性能提供不同的权重选择。<br><img src="/images/Nginx/代理.png" alt></p>
<h1 id="正向代理"><a href="#正向代理" class="headerlink" title="正向代理"></a>正向代理</h1><p> 这里我再举一个例子：大家都知道，现在国内是访问不了 Google的，那么怎么才能访问 Google呢？我们又想，美国人不是能访问 Google吗（这不废话，Google就是美国的），如果我们电脑的对外公网 IP 地址能变成美国的 IP 地址，那不就可以访问 Google了。你很聪明，VPN 就是这样产生的。我们在访问 Google 时，先连上 VPN 服务器将我们的 IP 地址变成美国的 IP 地址，然后就可以顺利的访问了。</p>
<p> 这里的 VPN 就是做正向代理的。正向代理服务器位于客户端和服务器之间，为了向服务器获取数据，客户端要向代理服务器发送一个请求，并指定目标服务器，代理服务器将目标服务器返回的数据转交给客户端。这里客户端是要进行一些正向代理的设置的。</p>
<h1 id="反向代理"><a href="#反向代理" class="headerlink" title="反向代理"></a>反向代理</h1><p>正向和反向的区别在于：<strong>正向代理代理的是客户端，反向代理代理的是服务器</strong>。<br>所以，反向代理对客户端是无感知的，因为客户端不需要任何配置就可以访问。只需要将请求发送到反向代理服务器，由反向代理服务器去选择目标服务器获取数据后，再返回客户端。<br><img src="/images/Nginx/正向.png" alt><br><img src="/images/Nginx/反向.png" alt></p>
<p>理解这两种代理的关键在于代理服务器所代理的对象是什么，正向代理代理的是客户端，我们需要在客户端进行一些代理的设置。而反向代理代理的是服务器，作为客户端的我们是无法感知到服务器的真实存在的。</p>
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