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<!DOCTYPE HTML>
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好好学习，天天向上.
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<a href="/2019/02/22/react-简单的财务管理系统/" >react/既简单又可以做假账的小账本</a>
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<div class="date">post @ 2019-02-22 </div>
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<h1 id="accounts-app"><a href="#accounts-app" class="headerlink" title="accounts-app"></a>accounts-app</h1><p>simple account app<br>react/一个功能简单的小账本，记录现金的流入流出/可以做假账（随时编辑历史记录）</p>
<p>GitHub源地址：<a href="https://github.com/0rainge/accounts-app" target="_blank" rel="noopener">https://github.com/0rainge/accounts-app</a></p>
<h2 id="0-项目总结"><a href="#0-项目总结" class="headerlink" title="0. 项目总结"></a>0. 项目总结</h2><h3 id="0-1-展示"><a href="#0-1-展示" class="headerlink" title="0-1. 展示"></a>0-1. 展示</h3><p><img src="https://github.com/0rainge/accounts-app/blob/master/imgDoc/demo.png?raw=true" alt="image"></p>
<h3 id="0-2-功能点"><a href="#0-2-功能点" class="headerlink" title="0-2. 功能点"></a>0-2. 功能点</h3><ol>
<li>展示记录（设置表格，展示每条记录）</li>
<li>创建记录（设置表单，输入记录：时间，事项，收支金额）</li>
<li>计算并展示总金额（设置数据展示卡片，计算收入、支出、总金额并展示）</li>
<li>更新/删除记录（在表单中实现数据的更新和删除）</li>
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<a type="button" href="/2019/02/22/react-简单的财务管理系统/#more" class="btn btn-default more">Read More</a>
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<a href="/2019/01/31/nodeJs-Flag清单/" >NodeJs/Flag清单</a>
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<div class="date">post @ 2019-01-31 </div>
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<h1 id="也可以看成一个ToDoList"><a href="#也可以看成一个ToDoList" class="headerlink" title="也可以看成一个ToDoList"></a>也可以看成一个ToDoList</h1><p>a To-Do-List using jQuery, nodejs, Express, mongoDB</p>
<p>实现一个flag清单，添加或删除flag</p>
<p>前端使用模版引擎EJS，引用jQuery库，后端使用nodejs，采用express框架，数据储存在mongoDB上</p>
<p>GitHub源地址：<a href="https://github.com/0rainge/myToDoList" target="_blank" rel="noopener">https://github.com/0rainge/myToDoList</a></p>
<h2 id="界面展示："><a href="#界面展示：" class="headerlink" title="界面展示："></a>界面展示：</h2><p>前端：<br><img src="https://github.com/0rainge/myToDoList/blob/master/img/demo.png?raw=true" alt="image"></p>
<p>后端：<br><img src="https://github.com/0rainge/myToDoList/blob/master/img/db.png?raw=true" alt="image"><br>
</div>
<a type="button" href="/2019/01/31/nodeJs-Flag清单/#more" class="btn btn-default more">Read More</a>
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<h3 class="title">
<a href="/2019/01/29/JavaScript-贪吃蛇/" >JavaScript/两只猫咪</a>
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<div class="col-md-4">
<div class="date">post @ 2019-01-29 </div>
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<h1 id="灵感来源于贪吃蛇retroSnakeGame"><a href="#灵感来源于贪吃蛇retroSnakeGame" class="headerlink" title="灵感来源于贪吃蛇retroSnakeGame"></a>灵感来源于贪吃蛇retroSnakeGame</h1><p>GitHub源地址：<a href="https://github.com/0rainge/retroSnakeGame" target="_blank" rel="noopener">https://github.com/0rainge/retroSnakeGame</a></p>
<h2 id="0-界面展示"><a href="#0-界面展示" class="headerlink" title="0.界面展示"></a>0.界面展示</h2><ul>
<li><h4 id="游戏开始界面"><a href="#游戏开始界面" class="headerlink" title="游戏开始界面"></a>游戏开始界面</h4><img src="https://github.com/0rainge/retroSnakeGame/blob/master/img/docImg/demo1.jpeg?raw=true" alt="image"></li>
<li><h4 id="游戏界面"><a href="#游戏界面" class="headerlink" title="游戏界面"></a>游戏界面</h4><img src="https://github.com/0rainge/retroSnakeGame/blob/master/img/docImg/demo2.jpeg?raw=true" alt="image"></li>
<li><h4 id="游戏结束界面"><a href="#游戏结束界面" class="headerlink" title="游戏结束界面"></a>游戏结束界面</h4><img src="https://github.com/0rainge/retroSnakeGame/blob/master/img/docImg/demo3.png?raw=true" alt="image">
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<a type="button" href="/2019/01/29/JavaScript-贪吃蛇/#more" class="btn btn-default more">Read More</a>
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<h3 class="title">
<a href="/2019/01/24/html:css-淘猫网仿站/" >html/css/淘猫网</a>
</h3>
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<div class="col-md-4">
<div class="date">post @ 2019-01-24 </div>
</div>
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<h1 id="参考淘宝首屏布局"><a href="#参考淘宝首屏布局" class="headerlink" title="参考淘宝首屏布局"></a>参考淘宝首屏布局</h1><p>做网页时的笔记整理，有些意识流。。</p>
<p>GitHub源地址：<a href="https://github.com/0rainge/taobaoHomePageDemo" target="_blank" rel="noopener">https://github.com/0rainge/taobaoHomePageDemo</a></p>
<h2 id="1-demo展示"><a href="#1-demo展示" class="headerlink" title="1. demo展示"></a>1. demo展示</h2><p><img src="https://github.com/0rainge/taobaoHomePageDemo/blob/master/doc_img/demoPage.png?raw=true" alt="image"><br>
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<a type="button" href="/2019/01/24/html:css-淘猫网仿站/#more" class="btn btn-default more">Read More</a>
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<h3 class="title">
<a href="/2018/06/07/fission二次测试以及组件日志分析/" >fission二次测试以及组件日志分析</a>
</h3>
</div>
<div class="col-md-4">
<div class="date">post @ 2018-06-07 </div>
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<p>对fission进行测试分析组件日志：</p>
<h3 id="进行测试如下："><a href="#进行测试如下：" class="headerlink" title="进行测试如下："></a>进行测试如下：</h3><ol>
<li>创建语言环境镜像、给函数创建路由route trigger、上传函数、测试函数、请求函数、查看函数日志</li>
<li>编译多个文件组成的源码函数：对源码进行打包上传，根据压缩包创建函数、创建路由、通过fission进行解释或编译链接、查看bulid日志</li>
<li>编译artifacts（已打包函数），创建函数、创建路由、测试函数、查看全部函数</li>
<li>创建语言环境镜像、创建builder、查看环境</li>
<li>自动扩容（需要未安装工具hey）</li>
<li>创建trigger：包括http trigger，time trigger，message queue trigger</li>
<li>传源码包：通过fission创建由多个文件组成的函数压缩包，创建package，查看package信息，通过package创建函数</li>
<li>创建部署包：通过fission创建由一个文件组成的函数压缩包，创建package，查看package信息，通过package创建函数</li>
<li>测试函数对k8s中secret和ConfigMaps的访问（demo中的secret路径出现问题，还没找到对的路径） </li>
<li>fission的workflow测试</li>
</ol>
<h3 id="组件分析如下："><a href="#组件分析如下：" class="headerlink" title="组件分析如下："></a>组件分析如下：</h3><h4 id="builder："><a href="#builder：" class="headerlink" title="builder："></a>builder：</h4><ul>
<li>作用：编译链接上传的源码package</li>
<li>日志包括：记录创建builder service、builder deployment、builder pod、build程序包（注意给build.dh加权限）、上传部署包、更新部署包、build失败报错信息</li>
</ul>
<h4 id="controller："><a href="#controller：" class="headerlink" title="controller："></a>controller：</h4><ul>
<li>作用：每次http请求函数的信息</li>
<li>日志包括：服务器状态，端口，返回http请求的head信息，每次向fission请求时http信息（包括请求错误url的proxy error）如<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">127.0.0.1 - - [07/Jun/2018:01:50:33 +0000] "GET /v2/packages/demo-src-pkg-zip-8lwt?namespace=default HTTP/1.1" 404 9</span><br><span class="line">以及（应该是）每次请求的函数地址和传入参数的地址如</span><br><span class="line">github.com/fission/fission/controller.(*API).respondWithError(0xc420082ae0, 0x7f5c883e4e60, 0xc4201a4960, 0x178ea60, 0xc420510b40)</span><br><span class="line"> /Volumes/MacintoshHD/Dropbox/Dropbox/goWorkspace/src/github.com/fission/fission/controller/api.go:98 +0x29</span><br></pre></td></tr></table></figure>
</li>
</ul>
<h4 id="executor："><a href="#executor：" class="headerlink" title="executor："></a>executor：</h4><ul>
<li>作用：创建函数</li>
<li>日志包括：检查是缓冲中跑起来的函数，没有找到的话创建一个，为函数分配环境，设置一个数据结构记录函数信息（如函数名，GenerateName:命名空间；SelfLink，Generation；创建时间戳，删除时间戳，存活时间（DeletionGracePeriodSeconds），标签，实例化url），从pool中得到这个函数，选择pod，通过pod ip实例化pod，从pool中找到可以实例化的pod，对函数和pod进行映射，对pod加上标签，选择pod，调用fetcher 拷贝函数到指定url上，实例化pod，用环境镜像实例化pod，为pod加上ip，创建对应语言的pool，拉取fetcher镜像等。</li>
</ul>
<h4 id="kubewatcher："><a href="#kubewatcher：" class="headerlink" title="kubewatcher："></a>kubewatcher：</h4><p>没有查到日志</p>
<h4 id="router："><a href="#router：" class="headerlink" title="router："></a>router：</h4><ul>
<li>作用：对http请求进行转发</li>
<li>日志包括：<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">2018/06/07 05:47:12 Calling getServiceForFunction for function: hello</span><br><span class="line">2018/06/07 05:47:30 Tapped 1 services in batch</span><br><span class="line">2018/06/07 05:47:30 Tapped 1 services in batch</span><br><span class="line">2018/06/07 05:48:00 Tapped 1 services in batch</span><br><span class="line">2018/06/07 05:48:00 Tapped 1 services in batch</span><br><span class="line">2018/06/07 05:48:15 Tapped 1 services in batch</span><br><span class="line">2018/06/07 05:48:15 Tapped 1 services in batch</span><br></pre></td></tr></table></figure>
</li>
</ul>
<h4 id="storagesvc："><a href="#storagesvc：" class="headerlink" title="storagesvc："></a>storagesvc：</h4><ul>
<li>作用：储存服务</li>
<li>日志包括：<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></pre></td><td class="code"><pre><span class="line">time="2018-06-06T07:24:53Z" level=info msg="Storage service started"</span><br><span class="line">time="2018-06-06T07:24:53Z" level=info msg="listening to archiveChannel to prune archives"</span><br><span class="line">time="2018-06-06T08:24:53Z" level=info msg="getting orphan archives"</span><br><span class="line">time="2018-06-06T09:24:53Z" level=info msg="getting orphan archives"</span><br><span class="line">time="2018-06-06T09:32:41Z" level=info msg="Handling upload for /packages/demo-src-pkg-zip-fbza-o3s5sk-icbzii.zip"</span><br><span class="line">172.16.76.86 - - [06/Jun/2018:09:32:41 +0000] "POST /v1/archive HTTP/1.1" 200 72</span><br><span class="line">172.16.76.115 - - [06/Jun/2018:09:40:01 +0000] "GET /v1/archive?id=%2Ffission%2Ffission-functions%2Fde313c06-247e-44a5-83a6-0d257d224212 HTTP/1.1" 200 103852</span><br><span class="line">time="2018-06-06T10:24:53Z" level=info msg="getting orphan archives"</span><br><span class="line">time="2018-06-07T01:50:32Z" level=info msg="Handling upload for /packages/demo-src-pkg-zip-d20x-rnhpna-i8fmoq.zip"</span><br><span class="line">172.16.76.95 - - [07/Jun/2018:01:50:32 +0000] "POST /v1/archive HTTP/1.1" 200 72</span><br><span class="line">time="2018-06-07T02:24:53Z" level=info msg="getting orphan archives"</span><br></pre></td></tr></table></figure>
</li>
</ul>
<h4 id="timer："><a href="#timer：" class="headerlink" title="timer："></a>timer：</h4><ul>
<li>作用：一个基于时间的triggers，定时向请求指定函数。这里是每30s请求一次。</li>
<li>日志包括：<br>2018/06/07 08:17:30 Making HTTP request to <a href="http://router.fission/fission-function/hello" target="_blank" rel="noopener">http://router.fission/fission-function/hello</a><br>主要是记录时间</li>
</ul>
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<a href="/2018/06/01/SqueezeNet介绍/" >SqueezeNet介绍</a>
</h3>
</div>
<div class="col-md-4">
<div class="date">post @ 2018-06-01 </div>
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<div class="entry">
<div class="row">
<h3 id="定义："><a href="#定义：" class="headerlink" title="定义："></a>定义：</h3><p>压缩神经网络参数——轻量化网络<br>SqueezeNet是一个小型化的网络模型结构，在保证不降低检测精度的同时，用了比AlexNet少50倍的参数。</p>
<p>同时采用了deep compression技术，对squeezenet进行了压缩，将原始AlexNet模型压缩至原来的1/500（模型文件< 0.5MB,原始AlexNet模型约为200MB，同时增大了计算量。</p>
<h3 id="措施："><a href="#措施：" class="headerlink" title="措施："></a>措施：</h3><ol>
<li>将一部分3x3的filter（卷集核）替换成1x1的filter；<br>本文替换3x3的卷积kernel为1x1的卷积kernel可以让参数缩小9X。但是为了不影响识别精度，并不是全部替换，而是一部分用3x3，一部分用1x1。</li>
<li>减少输入的channels（3x3卷积核的input feature map数量，输入通道数）；<br>如果是conv1-conv2这样的直连，那么实际上是没有办法减少conv2的input feature map数量的。因此把一层conv分解为两层，封装为一个Fire Module。使用squeeze layers来实现。</li>
<li>在整个网络后期才进行下采样，使得卷积层有比较大的activation maps；<br>分辨率越大的特征图（延迟降采样）可以带来更高的分类精度，因为分辨率越大的输入能够提供的信息就越多。将欠采样操作延后，可以给卷积层提供更大的激活图：更大的激活图保留了更多的信息，可以提供更高的分类准确率。</li>
</ol>
<p>其中：措施1、2可降低参数数量。措施3用来大化网络精度。</p>
<h3 id="fire-module："><a href="#fire-module：" class="headerlink" title="fire module："></a>fire module：</h3><p>一个类似inception的网络单元结构。SqueezeNet的网络结构由若干个 fire module 组成。<br>将原来一层conv层变成两层：squeeze卷集层（有1x1卷集核）+expand卷集层（1x1和3x3卷积核）。</p>
<ol>
<li>策略1：squeeze层用1x1的卷集filter。squeeze层借鉴了inception的思想，利用1x1卷积核来降低输入到expand层中3x3卷积核的输入通道数channels。</li>
<li>策略2: 定义squeeze层中1x1卷积核的数量是s1x1，expand层中1x1卷积核的数量是e1x1， 3x3卷积核的数量是e3x3。令s1x1 < e1x1+ e3x3从而保证输入到3x3的输入通道数减小，这样squeeze layer可以限制输入通道数量。<br>激活函数：为了保证1x1卷积核和3x3卷积核具有相同大小的输出，3x3卷积核采用1像素的zero-padding和步长 squeeze层和expand层均采用RELU作为激活函数 。</li>
</ol>
<p>Fire module输入的feature map为H<em>W</em>M的，输出的feature map为H<em>M</em>(e1+e3)，可以看到feature map的分辨率是不变的，变的仅是维数，也就是通道数，这一点和VGG的思想一致。<br>首先，H<em>W</em>M的feature map经过Squeeze层，得到S1个feature map，这里的S1均是小于M的，以达到“压缩”的目的。<br>其次，H<em>W</em>S1的特征图输入到Expand层，分别经过1<em>1卷积层和3</em>3卷积层进行卷积，再将结果进行concat，得到Fire module的输出，为 H<em>M</em>(e1+e3)的feature map。<br>fire模块有三个可调参数：S1，e1，e3，分别代表卷积核的个数，同时也表示对应输出feature map的维数，在本文提出的SqueezeNet结构中，e1=e3=4s1</p>
<h3 id="模型："><a href="#模型：" class="headerlink" title="模型："></a>模型：</h3><p>SqueezeNet以卷积层（conv1）开始，接着使用8个Fire modules (fire2-9)，最后以卷积层（conv10）结束。每个fire module中的filter数量逐渐增加，并且在conv1, fire4, fire8, 和 conv10这几层之后使用步长为2的max-pooling，即将池化层放在相对靠后的位置，这使用了以上的策略（3）。<br>最后是一个conv10，在fire9后采用50%的dropout 由于全连接层的参数数量巨大，因此借鉴NIN的思想，去除了全连接层FC而改用global average pooling。</p>
<h3 id="减少参数的优点："><a href="#减少参数的优点：" class="headerlink" title="减少参数的优点："></a>减少参数的优点：</h3><p>1、实现更高效的分布式训练；<br>服务器间的通信是分布式CNN训练的重要限制因素。对于分布式 数据并行 训练方式，通信需求和模型参数数量正相关。小模型对通信需求更低。<br>2、训练出轻量级的模型，减小下载模型到客户端的额外开销 ；<br>比如在自动驾驶中，经常需要更新客户端模型。更小的模型可以减少通信的额外开销，使得更新更加容易。<br>3、在FPGA和嵌入式硬件上的部署实现；</p>
<h3 id="GitHub项目："><a href="#GitHub项目：" class="headerlink" title="GitHub项目："></a>GitHub项目：</h3><p>SqueezeNet <a href="https://github.com/DeepScale/SqueezeNet" target="_blank" rel="noopener">https://github.com/DeepScale/SqueezeNet</a> 1.4k star<br>SqueezeNet-Deep-Compression： <a href="https://github.com/songhan/SqueezeNet-Deep-Compression" target="_blank" rel="noopener">https://github.com/songhan/SqueezeNet-Deep-Compression</a> 314 star <a href="https://arxiv.org/abs/1602.07360" target="_blank" rel="noopener">https://arxiv.org/abs/1602.07360</a><br>SqueezeNet-Generator： <a href="https://github.com/songhan/SqueezeNet-Generator" target="_blank" rel="noopener">https://github.com/songhan/SqueezeNet-Generator</a><br>SqueezeNet-DSD-Training： <a href="https://github.com/songhan/SqueezeNet-DSD-Training" target="_blank" rel="noopener">https://github.com/songhan/SqueezeNet-DSD-Training</a><br>SqueezeNet-Residual： <a href="https://github.com/songhan/SqueezeNet-Residual" target="_blank" rel="noopener">https://github.com/songhan/SqueezeNet-Residual</a><br><a href="https://github.com/vonclites/squeezenet" target="_blank" rel="noopener">https://github.com/vonclites/squeezenet</a> 52 star 有原论文 <a href="https://arxiv.org/abs/1602.07360" target="_blank" rel="noopener">https://arxiv.org/abs/1602.07360</a><br><a href="https://github.com/rcmalli/keras-squeezenet" target="_blank" rel="noopener">https://github.com/rcmalli/keras-squeezenet</a> 251 star Keras实现 squeezenet<br><a href="https://github.com/DT42/squeezenet_demo" target="_blank" rel="noopener">https://github.com/DT42/squeezenet_demo</a> 175 star </p>
<h3 id="相关论文："><a href="#相关论文：" class="headerlink" title="相关论文："></a>相关论文：</h3><p><a href="https://arxiv.org/pdf/1602.07360v3.pdf" target="_blank" rel="noopener">https://arxiv.org/pdf/1602.07360v3.pdf</a> squeezenet用了比AlexNet少50倍的参数，达到了AlexNet相同的精度<br><a href="https://arxiv.org/pdf/1506.02626v3.pdf" target="_blank" rel="noopener">https://arxiv.org/pdf/1506.02626v3.pdf</a> Learning both Weights and Connections for Efficient Neural Network (NIPS’15)<br><a href="https://arxiv.org/pdf/1510.00149v5.pdf" target="_blank" rel="noopener">https://arxiv.org/pdf/1510.00149v5.pdf</a> 深度压缩，用剪枝来压缩深度神经网络，训练量化和 Huffman 编码<br><a href="https://arxiv.org/pdf/1602.01528v1.pdf" target="_blank" rel="noopener">https://arxiv.org/pdf/1602.01528v1.pdf</a> 压缩神经网络的Efficient Inference Engine</p>
<h3 id="总结："><a href="#总结：" class="headerlink" title="总结："></a>总结：</h3><p>squeezenet采用“多层小卷积核”策略，通过增加计算量换来更少的参数，把参数读取的代价转移到计算量上。计算耗时还是要远远小于数据存取耗时的，是“多层小卷积核”策略成功的根源。</p>
<h3 id="其他："><a href="#其他：" class="headerlink" title="其他："></a>其他：</h3><p>mobilenet和，squeezenet都是alexnet参数量1/50，mobilenet速度比alexnet快10倍，squeezenet提升3%</p>
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<a href="/2018/06/01/SqueezeNet的ARM和树莓派案例/" >SqueezeNet的ARM和树莓派案例</a>
</h3>
</div>
<div class="col-md-4">
<div class="date">post @ 2018-06-01 </div>
</div>
</div>
<div class="entry">
<div class="row">
<h2 id="ARM"><a href="#ARM" class="headerlink" title="ARM"></a>ARM</h2><ol>
<li>ARM Compute Library案例：</li>
</ol>
<ul>
<li>案例：<br>ARM 计算库（ACL）提供了CNN的基本构造块，比如激活、卷积、全连接和局部连接、规范化、池化和softmax功能. 文章通过这些组件构建SqueezeNet ，并与通过tensorflow构建的SqueezeNet进行比较，</li>
<li>产品化程度：<br>理论性较强，产品化程度不高。重点介绍了如何通过ACL构建SqueezeNet架构的CNN推理机 ，并与通过Zuluko上tensorflow构建的SqueezeNet进行比较。<br>-SqueezeNet 起到的作用：<br>作为用于测试ACL性能的神经网络模型。</li>
<li>链接：<br><a href="https://arxiv.org/pdf/1704.03751.pdf" target="_blank" rel="noopener">https://arxiv.org/pdf/1704.03751.pdf</a> 和<br><a href="https://community.arm.com/iot/embedded/b/embedded-blog/posts/perceptin-enabled-embedded-deep-learning-inference-engine-with-the-arm-compute-library" target="_blank" rel="noopener">https://community.arm.com/iot/embedded/b/embedded-blog/posts/perceptin-enabled-embedded-deep-learning-inference-engine-with-the-arm-compute-library</a></li>
<li>代码：<br>ACL中样例SqueezeNet的C++代码 <a href="https://github.com/ARM-software/ComputeLibrary/blob/master/examples/graph_squeezenet_v1_1.cpp" target="_blank" rel="noopener">https://github.com/ARM-software/ComputeLibrary/blob/master/examples/graph_squeezenet_v1_1.cpp</a></li>
</ul>
<h2 id="Raspberry-Pi-3"><a href="#Raspberry-Pi-3" class="headerlink" title="Raspberry Pi 3"></a>Raspberry Pi 3</h2><ol>
<li>用opencv在树莓派上部署SqueezeNet</li>
</ol>
<ul>
<li>案例：用OpenCV3.3.0在Raspberry Pi 3上部署 pre-trained SqueezeNet Neural Network</li>
<li>产品化程度：<br>比较高，可以输出结果。</li>
</ul>
<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></pre></td><td class="code"><pre><span class="line">（1）在图像上绘出最高的预测分类标签和相应的概率</span><br><span class="line">（2）将前五个结果和概率打印到终端</span><br><span class="line">（3）将图像显示在屏幕上</span><br><span class="line"></span><br><span class="line">[INFO] loading model...</span><br><span class="line">[INFO] classification took 0.4432 seconds</span><br><span class="line">[INFO] 1. label: drake, probability: 0.25705</span><br><span class="line">[INFO] 2. label: goose, probability: 0.18581</span><br><span class="line">[INFO] 3. label: black stork, probability: 0.10414</span><br><span class="line">[INFO] 4. label: hornbill, probability: 0.074497</span><br><span class="line">[INFO] 5. label: quail, probability: 0.051127</span><br></pre></td></tr></table></figure>
<ul>
<li>SqueezeNet 起到的作用：<br>用于图像识别，在案例中起主要作用。</li>
<li>代码：<a href="https://github.com/appinho/Raspberry_Pi_3_Image_Classification/tree/master/SqueezeNet" target="_blank" rel="noopener">https://github.com/appinho/Raspberry_Pi_3_Image_Classification/tree/master/SqueezeNet</a></li>
</ul>
<ol start="2">
<li>使用Raspberry Pi和预先训练的深度学习神经网络对输入图像进行分类</li>
</ol>
<ul>
<li>案例：使用Raspberry Pi 部署预先训练的深度学习网络.不通过树莓派训练模型，只做用来部署预先训练好的模型</li>
<li>产品化程度：<br>比较高，可以输出具体结果，SqueezeNet比GoogLeNet速度更快，但准确率没它高</li>
<li>SqueezeNet 起到的作用：进行图片分类</li>
<li>链接：<a href="https://www.pyimagesearch.com/2017/10/02/deep-learning-on-the-raspberry-pi-with-opencv/" target="_blank" rel="noopener">https://www.pyimagesearch.com/2017/10/02/deep-learning-on-the-raspberry-pi-with-opencv/</a></li>
<li>代码：<a href="https://www.getdrip.com/forms/353154548/submissions" target="_blank" rel="noopener">https://www.getdrip.com/forms/353154548/submissions</a></li>
</ul>
<ol start="3">
<li>在Raspberry Pi 中运行Movidius Neural Compute Stick</li>
</ol>
<ul>
<li>案例： 通过Raspberry Pi 、Raspbian jessie（2017-07-05）、cheep USB camera和SqueezeNet实现。</li>
<li>产品化程度：高，已实现，有demo视频可以进行实时识别。</li>
<li>SqueezeNet 起到的作用：用于图像分类</li>
<li>链接：<a href="https://www.youtube.com/watch?v=41E5hni786Y" target="_blank" rel="noopener">https://www.youtube.com/watch?v=41E5hni786Y</a> 和 <a href="https://www.youtube.com/watch?v=f39NFuZAj6s" target="_blank" rel="noopener">https://www.youtube.com/watch?v=f39NFuZAj6s</a></li>
</ul>
<ol start="4">
<li>使用Raspberry Pi 3进行目标检测</li>
</ol>
<ul>
<li>案例：使用树莓进行对象检测</li>
<li>产品化程度：不高，理论分析</li>
<li>SqueezeNet 起到的作用：建议使用的神经网络之一</li>
<li>链接：<a href="https://medium.com/dt42/run-object-detection-using-deep-learning-on-raspberry-pi-3-1-55027eac26c3" target="_blank" rel="noopener">https://medium.com/dt42/run-object-detection-using-deep-learning-on-raspberry-pi-3-1-55027eac26c3</a></li>
</ul>
<h2 id="其他"><a href="#其他" class="headerlink" title="其他"></a>其他</h2><ol>
<li>通过SqueezeNet进行猫狗识别</li>
</ol>
<ul>
<li>案例：猫狗识别</li>
<li>产品化程度：一般。</li>
<li>SqueezeNet 起到的作用：图像识别</li>
<li>代码：<a href="https://github.com/chasingbob/squeezenet-keras" target="_blank" rel="noopener">https://github.com/chasingbob/squeezenet-keras</a></li>
</ul>
<ol start="2">
<li>MXNet在Raspberry Pi上的实时对象检测</li>
</ol>
<ul>
<li>案例：AWS的loT，MXNet和Raspberry Pi</li>
<li>产品化程度：比较高</li>
<li>SqueezeNet 起到的作用：主要是MXNet</li>
<li>链接：<br><a href="https://mxnet.incubator.apache.org/tutorials/embedded/wine_detector.html" target="_blank" rel="noopener">https://mxnet.incubator.apache.org/tutorials/embedded/wine_detector.html</a></li>
</ul>
<ol start="3">
<li>手机上的Squeezing Deep Learning<br>链接：<a href="https://www.slideshare.net/anirudhkoul/squeezing-deep-learning-into-mobile-phones" target="_blank" rel="noopener">https://www.slideshare.net/anirudhkoul/squeezing-deep-learning-into-mobile-phones</a></li>
</ol>
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<a href="/2018/05/23/交叉编译在树莓派的tensorflowLite /" >交叉编译在树莓派的tensorflow lite</a>
</h3>
</div>
<div class="col-md-4">
<div class="date">post @ 2018-05-23 </div>
</div>
</div>
<div class="entry">
<div class="row">
<p>系统：ubuntu、树莓派（centos系统）、</p>
<ol>
<li><p>调整树莓派时间（默认好像是去年）<br>用ntpdate同步时间<br><a href="https://jingyan.baidu.com/article/f71d6037815a0d1ab741d167.html" target="_blank" rel="noopener">https://jingyan.baidu.com/article/f71d6037815a0d1ab741d167.html</a></p>
</li>
<li><p>失败的尝试，看了之前的博客<br><a href="https://www.wandianshenme.com/play/build-tensorflow-on-raspberry-pi-step-by-step-guide/" target="_blank" rel="noopener">https://www.wandianshenme.com/play/build-tensorflow-on-raspberry-pi-step-by-step-guide/</a><br><a href="http://www.cnblogs.com/jojodru/p/7744630.html" target="_blank" rel="noopener">http://www.cnblogs.com/jojodru/p/7744630.html</a><br>写的比较复杂，而且都没有成功，可能是现在tensorflow lite 发展的比较快，信息更新的比较快</p>
</li>
<li><p>成功的尝试，官网<br><a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/rpi.md" target="_blank" rel="noopener">https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/rpi.md</a><br>很快就build好了</p>
</li>
</ol>
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<a href="/2018/05/20/复现activitynet2016未剪辑视频分类冠军算法模型/" >复现activitynet2016未剪辑视频分类冠军算法模型</a>
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<div class="date">post @ 2018-05-20 </div>
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<p>复现activitynet2016未剪辑视频分类冠军算法模型：</p>
<p>代码链接：<a href="https://github.com/yjxiong/anet2016-cuhk" target="_blank" rel="noopener">https://github.com/yjxiong/anet2016-cuhk</a></p>
<p>系统：ubuntu</p>
<p>装上unzip和cmake</p>
<p>复现activitynet2016未剪辑视频分类冠军算法模型：<a href="https://github.com/yjxiong/anet2016-cuhk" target="_blank" rel="noopener">https://github.com/yjxiong/anet2016-cuhk</a></p>
<p>主要通过脚本安装<a href="https://github.com/yjxiong/anet2016-cuhk/blob/master/build_all.sh" target="_blank" rel="noopener">https://github.com/yjxiong/anet2016-cuhk/blob/master/build_all.sh</a></p>
<p>环境准备如下：</p>
<ol>
<li>安装anaconda</li>
<li>安装caffe</li>
<li>安装tensorflow</li>
<li>安装opencv</li>
</ol>
<p>脚本有一些地方运行不了，整理的之后步骤如下：</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><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br></pre></td><td class="code"><pre><span class="line"></span><br><span class="line"></span><br><span class="line"></span><br><span class="line">#!/usr/bin/env bash</span><br><span class="line"># TODO: add compilation steps</span><br><span class="line"></span><br><span class="line"># update the submodules: Caffe and Dense Flow</span><br><span class="line">git submodule update --remote</span><br><span class="line"></span><br><span class="line"># install Caffe dependencies</span><br><span class="line">sudo apt-get -qq install libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler libatlas-base-dev</span><br><span class="line">不对！！！！！！！！！！sudo apt-get -qq install --no-install-recommends libboost1.55-all-dev</span><br><span class="line"> 改成sudo apt-get -qq install --no-install-recommends libboost-all-dev</span><br><span class="line">sudo apt-get -qq install libgflags-dev libgoogle-glog-dev liblmdb-dev</span><br><span class="line"></span><br><span class="line"># install Dense_Flow dependencies</span><br><span class="line">sudo apt-get -qq install libzip-dev</span><br><span class="line"></span><br><span class="line"># install common dependencies: OpenCV</span><br><span class="line"># adpated from OpenCV.sh</span><br><span class="line">version="2.4.13"</span><br><span class="line"></span><br><span class="line">echo "Building OpenCV" $version</span><br><span class="line">mkdir 3rd-party/</span><br><span class="line">cd 3rd-party/</span><br><span class="line"></span><br><span class="line">echo "Installing Dependenices"</span><br><span class="line">不对！！！！！！！！！sudo apt-get -qq install libopencv-dev build-essential checkinstall cmake pkg-config yasm libjpeg-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev python-dev python-numpy libtbb-dev libqt4-dev libgtk2.0-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils</span><br><span class="line"></span><br><span class="line">改成sudo apt-get -qq install libopencv-dev build-essential checkinstall cmake pkg-config yasm libjpeg-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine2-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev python-dev python-numpy libtbb-dev libqt4-dev libgtk2.0-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils</span><br><span class="line"></span><br><span class="line">echo "Downloading OpenCV" $version</span><br><span class="line"></span><br><span class="line">echo "Installing OpenCV" $version</span><br><span class="line">unzip OpenCV-$version.zip</span><br><span class="line">cd opencv-$version</span><br><span class="line">mkdir build</span><br><span class="line">cd build</span><br><span class="line">cmake -D CMAKE_BUILD_TYPE=RELEASE -D WITH_TBB=ON -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON ..</span><br><span class="line"></span><br><span class="line">!!!!!!!!!!</span><br><span class="line">在这里Makefile.config加入：</span><br><span class="line">LINKFLAGS := -Wl,-rpath,/root/anaconda3/lib</span><br><span class="line">!!!!!!!!!!</span><br><span class="line">？？？？？？？？？？cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D INSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON -D BUILD_TIFF=ON -D WITH_OPENGL=ON..</span><br><span class="line"></span><br><span class="line">cmake -D CMAKE_BUILD_TYPE=RELEASE -D WITH_TBB=ON -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON -D BUILD_TIFF=ON ..</span><br><span class="line"></span><br><span class="line">cp lib/cv2.so ../../../</span><br><span class="line">echo "OpenCV" $version "built"</span><br><span class="line"></span><br><span class="line"># build dense_flow</span><br><span class="line">cd ../../../</span><br><span class="line"></span><br><span class="line">echo "Building Dense Flow"</span><br><span class="line">cd lib/dense_flow</span><br><span class="line">mkdir build</span><br><span class="line">cd build</span><br><span class="line">OpenCV_DIR=../../../3rd-party/opencv-$version/build/ cmake .. -DCUDA_USE_STATIC_CUDA_RUNTIME=OFF</span><br><span class="line">make -j</span><br><span class="line">!!!!!!!!需要下载</span><br><span class="line">echo "Dense Flow built"</span><br><span class="line"></span><br><span class="line">https://github.com/jaejunlee0538/openfabmap/issues/3</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"># build caffe</span><br><span class="line">echo "Building Caffe"</span><br><span class="line">cd ../../caffe-action</span><br><span class="line">mkdir build</span><br><span class="line">cd build</span><br><span class="line">OpenCV_DIR=../../../3rd-party/opencv-$version/build/ cmake .. -DCUDA_USE_STATIC_CUDA_RUNTIME=OFF </span><br><span class="line"></span><br><span class="line">!!!!!!!!!!!!!!!改成</span><br><span class="line">OpenCV_DIR=../../../3rd-party/opencv-$version/build/ cmake .. -DCUDA_USE_STATIC_CUDA_RUNTIME=OFF -D BUILD_TIFF=ON</span><br><span class="line">然后如果你装了anaconda包的话，删除anaconda/lib/下面的 libm https://blog.csdn.net/ccemmawatson/article/details/42004105</span><br><span class="line">sudo rm -rf libm*</span><br><span class="line"></span><br><span class="line">make -j32</span><br><span class="line">echo "Caffe Built"</span><br><span class="line">cd ../../../</span><br><span class="line"></span><br><span class="line"># install python packages</span><br><span class="line">pip install -r py_requirements.txt</span><br><span class="line"></span><br><span class="line"># setup for web demo</span><br><span class="line">mkdir tmp</span><br><span class="line"></span><br><span class="line"># copy website files to the folder</span><br><span class="line">wget -O 3rd-party/bootstrap-fileinput.zip https://github.com/kartik-v/bootstrap-fileinput/zipball/master</span><br><span class="line">cd 3rd-party</span><br><span class="line">unzip bootstrap-fileinput.zip</span><br><span class="line">mv kartik-v-bootstrap-* Bootstrap-fileinput</span><br><span class="line"></span><br><span class="line">!!!!!!!!!!!cannot move 'kartik-v-bootstrap-fileinput-61c9523' to 'Bootstrap-fileinput/kartik-v-bootstrap-fileinput-61c9523': Directory not empty</span><br><span class="line"></span><br><span class="line">cp -r Bootstrap-fileinput/js ../static/js</span><br><span class="line">cp Bootstrap-fileinput/css/* ../static/css/</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">export ANET_HOME=/root/anet2016-cuhk</span><br></pre></td></tr></table></figure>
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<a href="/2018/05/15/CNN_C3D_averagePooling_LSTM_MoE/" >论文阅读笔记之CNN_C3D_averagePooling_LSTM_MoE</a>
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<h4 id="作者"><a href="#作者" class="headerlink" title="作者"></a>作者</h4><p>Feng Mao </p>
<h4 id="思路"><a href="#思路" class="headerlink" title="思路"></a>思路</h4><p>提取CNN和C3D特征作为帧特征，使用average pooling 和 LSTM 将帧特征聚集为视频特征，使用MoE进行分类<br>CNN_C3D_averagePooling_LSTM_MoEcd </p>
<h4 id="特征提取"><a href="#特征提取" class="headerlink" title="特征提取"></a>特征提取</h4><p>CNN：使用数据集ImageNet 21k 训练模型inception-v1 。在分类层POOL5/7X7YS1，选择分类层之前的那一个hidden层作为帧级特征第一部分。<br>C3D：使用PCA降维</p>
<h4 id="特征聚合"><a href="#特征聚合" class="headerlink" title="特征聚合"></a>特征聚合</h4><p>无监督 average pooling<br>有监督 aggregation LSTM</p>
<h4 id="分类"><a href="#分类" class="headerlink" title="分类"></a>分类</h4><p> MoE(Mixture of experts) ，结合不同特征和不同聚合模型：</p>
<ul>
<li>CNN + average pooling </li>
<li>CNN + LSTM </li>
<li>C3D + average pooling</li>
<li>C3D + LSTM </li>
</ul>
<p>需要训练的参数是CNN and C3D 的LSTM参数和 MoE parameters </p>
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