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<!DOCTYPE html>
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<title>python自动化测试人工智能</title>
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<h1><a href="/">python自动化测试人工智能 </a></h1>
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<h1><a href="/python_vision_deep3.html" rel="bookmark"
title="Permalink to python计算机视觉深度学习3图像分类基础">python计算机视觉深度学习3图像分类基础</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-25T07:35:00+08:00">
Published: 二 25 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <p><a href="https://china-testing.github.io/python_vision_deep.html">本文教程目录</a></p>
<h3 id="_1">什么是图像分类？</h3>
<p>图像分类的核心任务是从预定义的一类图像中为图像分配标签。分析输入图像并返回标签对图像进行分类。标签始终来自一组预定义的可能类别。</p>
<p>比如预定义的标签为：</p>
<div class="highlight"><pre><span></span><span class="n">categories</span> <span class="o">=</span> <span class="p">{</span><span class="n">cat</span><span class="p">,</span> <span class="n">dog</span><span class="p">,</span> <span class="n">panda</span><span class="p">}</span>
</pre></div>
<p><img alt="image.png" src="https://upload-images.jianshu.io/upload_images/10819934-f3241e2af4aeb025.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>分类系统还可以通过概率为图像分配多个标签。比如狗：95％;猫：4％;熊猫：1％。更重要的是，W×H像素输入图像有三个通道，Red，Green和Blue，分析W×H×3 = N像素图像，并弄清楚如何正确分类图像的内容。</p>
<p><img alt="image.png" src="https://upload-images.jianshu.io/upload_images/10819934-54931b9b725c4a23.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>在图像分类中，我们的数据集是图像的集合。因此，每个图像都是数据点。</p>
<p><img alt="image.png" src="https://upload-images.jianshu.io/upload_images/10819934-4d820c1ca4b36890.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>左边猫，右边狗，电脑看到的是像素矩阵。实际上，计算机并不知道图像中有动物。</p>
<p><img alt="image.png" src="https://upload-images.jianshu.io/upload_images/10819934-a6ae0fd8aadc7d84.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>上图可描述如下：
•空间：天空位于图像的顶部，沙/海洋位于底部。
•颜色：天空是深蓝色，海水浅蓝色，而沙子则是
棕褐色。
•纹理：天空具有相对均匀的图案，而沙子非常粗糙。</p>
<p>需要应用特征提取来量化图像的内容 …</p>
<a class="readmore" href="/python_vision_deep3.html">read more</a>
</div><!-- /.entry-content -->
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<h1><a href="/python_vision_deep2.html" rel="bookmark"
title="Permalink to python计算机视觉深度学习工具2图像基础">python计算机视觉深度学习工具2图像基础</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-24T21:35:00+08:00">
Published: 一 24 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <p><a href="https://china-testing.github.io/python_vision_deep.html">本文教程目录</a></p>
<p>构建自己的图像分类器之前需要了解图像是什么。</p>
<h3 id="_1">像素：图像的元素</h3>
<p>像素是图像的基本元素。每个图像都由一组像素组成。没有比像素更细的粒度。</p>
<p>通常像素是光的“颜色”或“强度”。</p>
<p>下图的分辨率为1,000×750，这意味着它是1,000像素宽750像素高。我们可以将图像概念化为(多维)矩阵。图片中总共有1,000×750 = 750,000像素。</p>
<p><img alt="image.png" src="https://upload-images.jianshu.io/upload_images/10819934-03baa175f901e0bd.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>大多数像素以两种方式表示：
1.灰度/单通道
2.颜色</p>
<p>在灰度图像中，每个像素是0到255之间的标量值，其中零对应为“黑色”，255为“白色”。
<img alt="image.png" src="https://upload-images.jianshu.io/upload_images/10819934-def6b6370b523d5d.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>彩色像素通常在RGB颜色空间中表示（其他颜色空间通常要转成RGB）。</p>
<p><img alt="image.png" src="https://upload-images.jianshu.io/upload_images/10819934-588ed03546979273.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>黑色：(0, 0, 0)
红色：(255, 0, 0)</p>
<p>RGB色彩空间的主要缺点包括：
•不使用“颜色选择器”工具时表示颜色不直观 …</p>
<a class="readmore" href="/python_vision_deep2.html">read more</a>
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<h1><a href="/python_vision_deep1.html" rel="bookmark"
title="Permalink to python计算机视觉深度学习1简介">python计算机视觉深度学习1简介</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-24T08:35:00+08:00">
Published: 一 24 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li>
<p><a href="https://china-testing.github.io/python_vision_deep.html">本文教程目录</a></p>
</li>
<li>
<p><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></p>
</li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li><a href="https://china-testing.github.io/python3_quick.html">python 3.7极速入门教程 - 目录</a></li>
</ul>
<h3 id="_1">神经网络和深度学习简史</h3>
<p>人工神经网络(ANN Artificial Neural Network)是一类学习的机器学习算法，它专注于模式识别，对数据进行学习，灵感来自大脑的结构和功能深度学习属于ANN算法的家族，在大多数情况下，两者可以互换使用。</p>
<p>事实上，你可能会惊讶地发现深度学习领域已经存在了60多年。自20世纪40年代以来，“深度学习”一直存在着各种各样的名字
变化，包括控制论，连接主义和最熟悉的人工神经网络。</p>
<p><img alt="image.png" src="https://upload-images.jianshu.io/upload_images/10819934-2696e8df787598dc.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>第一个神经网络模型来自McCulloch和Pitts，1943年。这个网络是一个二元分类器，能够根据一些输入识别两个不同的类别。</p>
<p>然后，在20世纪50年代，开创性的Perceptron算法由Rosenblatt发表 - 这个模型可以自动学习输入分类所需的权重（无人为干预需要）。这个自动训练程序构成了随机梯度下降（SGD Stochastic Gradient Descent)的基础，今天仍用于训练非常深的神经网络。</p>
<p><img alt="image.png" src="https://upload-images.jianshu.io/upload_images/10819934-267a55cb8f5b94d4.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>在此期间，基于感知器的技术在神经网络社区中风靡一时。然而，Minsky和Papert 在1969年出版的一本书有效地停滞了神经
网络研究近十年。他们的工作证明了具有线性的感知器激活函数 …</p>
<a class="readmore" href="/python_vision_deep1.html">read more</a>
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<h1><a href="/python_vision_deep.html" rel="bookmark"
title="Permalink to python计算机视觉深度学习">python计算机视觉深度学习</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-24T08:25:00+08:00">
Published: 一 24 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li>
<p><a href="https://china-testing.github.io/python_vision_deep.html">本文教程目录</a></p>
</li>
<li>
<p><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></p>
</li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li>
<p><a href="https://china-testing.github.io/python3_quick.html">python计算机视觉深度学习3图像分类基础</a></p>
</li>
<li>
<p><a href="https://china-testing.github.io/python_vision_deep1.html">python计算机视觉深度学习1简介</a></p>
</li>
<li>
<p><a href="https://china-testing.github.io/python_vision_deep2.html">python计算机视觉深度学习工具2图像基础</a></p>
</li>
<li>
<p><a href="https://china-testing.github.io/python_vision_deep3.html">python计算机视觉深度学习工具2图像基础</a></p>
</li>
</ul>
<h3 id="_1">参考资料</h3>
<ul>
<li>讨论 qq群144081101 567351477</li>
<li><a href="https://china-testing.github.io/python3_quick4.html">本文最新版本地址</a></li>
<li><a href="https://github.com/china-testing/python-api-tesing">本文涉及的python测试开发库</a> 谢谢点赞！</li>
<li><a href="https://github.com/china-testing/python-api-tesing/blob/master/books.md">本文相关海量书籍下载</a> </li>
<li>道家技术-手相手诊看相中医等钉钉群21734177 qq群：391441566 184175668 338228106 看手相、面相、舌相、抽签、体质识别。服务费50元每人次起。请联系钉钉或者微信pythontesting</li>
<li><a href="https://china-testing.github.io/testing_training.html">接口自动化性能测试线上培训大纲</a></li>
<li><a href="https://www.fullstackpython.com/monitoring.html">Monitoring</a></li>
</ul>
<a class="readmore" href="/python_vision_deep.html">read more</a>
</div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
<header>
<h1><a href="/pandas_tips.html" rel="bookmark"
title="Permalink to pandas试题与技巧">pandas试题与技巧</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-20T07:20:00+08:00">
Published: 四 20 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li><a href="https://china-testing.github.io/python3_quick.html">python 3.7极速入门教程 - 目录</a></li>
</ul>
<h3 id="_1">删除包含某个值的行</h3>
<p>现有df = pd.DataFrame([[1,0,2],[1,2,3],[0,1,2],[4,5,6]])，请删除其中包含0的行。</p>
<p>参考答案：</p>
<div class="highlight"><pre><span></span><span class="o">>>></span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="o">>>></span> <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]])</span>
<span class="o">>>></span> <span class="n">df</span>
<span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span>
<span class="mi">0</span> <span class="mi">1</span> <span class="mi">0 …</span></pre></div>
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</div><!-- /.entry-content -->
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<header>
<h1><a href="/nltk1.html" rel="bookmark"
title="Permalink to Python自然语言工具库NLTK快速入门教程1简介">Python自然语言工具库NLTK快速入门教程1简介</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-19T18:25:00+08:00">
Published: 三 19 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li><a href="https://china-testing.github.io/python3_quick.html">python 3.7极速入门教程 - 目录</a></li>
</ul>
<h3 id="_1">什么是自然语言处理？</h3>
<p>自然语言处理是指通过软件或机器理解并操作文本或语音。 人类互动，了解彼此的观点，并用适当的答案作出回应。 在NLP中，这种交互，理解，响应是由计算机而不是人类完成的。</p>
<h3 id="nltk">什么是NLTK？</h3>
<p>NLTK代表Natural Language Toolkit。它包使计算机理解人类语言并使用适当的响应回复它。 本教程中将讨论标记，粉刺，词形还原，标点，字符计数，字数统计等。</p>
<h3 id="_2">自然语言库介绍</h3>
<ul>
<li>NLTK 最有用，且是是所有NLP库中的鼻祖。</li>
<li>spaCy 这是完全优化和高度准确的库，广泛用于深度学习</li>
<li>Stanford CoreNLP Python 基于C-S的体系结构,用JAVA编写的，但它提供了在Python API</li>
<li>TextBlob 处理文本数据，主要以API的形式提供所有类型的操作。</li>
<li>Gensim 强大、非常高效且可扩展。</li>
<li>Pattern 个轻量级NLP模块。 这通常用于Web挖掘，爬虫 …</li></ul>
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<h1><a href="/pandas1.0.html" rel="bookmark"
title="Permalink to 最流行的开源数据分析，处理和可视化工具pandas的未来">最流行的开源数据分析，处理和可视化工具pandas的未来</a></h1>
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<footer class="post-info">
<abbr class="published" title="2018-12-18T09:20:00+08:00">
Published: 二 18 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li><a href="https://china-testing.github.io/python3_quick.html">python 3.7极速入门教程 - 目录</a></li>
</ul>
<p>pandas是一个功能强大的开源Python库，用于数据分析，处理和可视化，当前版本：0.23.4。用户在1000万左右，并成为Python数据科学工具包中的“必须使用”的工具。</p>
<p>许多数据科学家都向我提出过这样的问题：</p>
<p>pandas 可靠吗？</p>
<p>以后还会维护么？</p>
<p>为什么没有发布1.0版本！</p>
<p>版本号可用于表示产品的成熟度。但在开源世界中，版本号并不一定能告诉关于库的成熟度或可靠性的信息。 实际上 pandas既成熟又可靠！不过版本号传达了API的稳定性。</p>
<ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li><a href="https://china-testing.github.io/python3_quick.html">python 3.7极速入门教程 - 目录</a></li>
</ul>
<h3 id="pandas-10">走向pandas 1.0</h3>
<ul>
<li>推荐使用方法链</li>
</ul>
<p>不使用方法链的例子：</p>
<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'data/titanic.csv.gz …</span></pre></div>
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<h1><a href="/nlp_books.html" rel="bookmark"
title="Permalink to 2019 最佳自然语言工具书籍下载">2019 最佳自然语言工具书籍下载</a></h1>
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<footer class="post-info">
<abbr class="published" title="2018-12-17T08:20:00+08:00">
Published: 一 17 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h3 id="natural-language-processing-with-tensorflow-2018pdf"><a href="https://itbooks.pipipan.com/fs/18113597-325450926">Natural Language Processing with TensorFlow - 2018.pdf</a></h3>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-091e808154321809.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>https://github.com/PacktPublishing/Natural-Language-Processing-with-TensorFlow</p>
<h3 id="mastering-natural-language-processing-with-python-2016pdf"><a href="https://itbooks.pipipan.com/fs/18113597-325450863">Mastering Natural Language Processing with Python - 2016.pdf</a></h3>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-13d2970a2d60dc60.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p><a href="https://itbooks.pipipan.com/fs/18113597-325460790">精通Python自然语言处理.pdf</a></p>
<h3 id="speech-and-language-processing-3rd-edition-in-making-dan-jurafsky-james-h-martin">斯坦福权威教程 <a href="https://web.stanford.edu/~jurafsky/slp3/">Speech and Language Processing</a> (3rd edition in making) - Dan Jurafsky, James H. Martin 可在线下载</h3>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-c21fc2c5978a2a72.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"> </p>
<p>适合人群：初级到中级学者</p>
<p>推荐指数：★★★★★</p>
<p>主要内容：本书内容涵盖了自然语言处理的方方面面，从底层的词法分词、语法分析和语义分析，到和应用更为接近的自然语言处理任务，如信息抽取、机器翻译、自动问答、文本摘要、对话系统等 …</p>
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<h1><a href="/python3_lib_boom.html" rel="bookmark"
title="Permalink to python标准模块介绍-性能测试工具boom">python标准模块介绍-性能测试工具boom</a></h1>
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<abbr class="published" title="2018-12-10T07:20:00+08:00">
Published: 一 10 十二月 2018
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<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h3 id="boom">boom简介</h3>
<p>boom是python替代ab的模块，并增加了部分功能。</p>
<h4 id="boom_1">boom安装</h4>
<p>ubuntu 执行“pip install boom“即可，注意可能需要先执行"apt-get install libevent python-dev"。它使用Gevent创建虚拟用户，使用Requests发送请求。</p>
<h4 id="boom_2">boom快速入门</h4>
<div class="highlight"><pre><span></span><span class="c1"># boom -n1000 -c10 http://localhost</span>
/usr/local/anaconda3/lib/python3.6/site-packages/boom/boom.py:28: MonkeyPatchWarning: Monkey-patching ssl after ssl has already been imported may lead to errors, including RecursionError on …</pre></div>
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<h1><a href="/merge_excel_rows2.html" rel="bookmark"
title="Permalink to python数据分析工具pandas作业：把多行合并成一行">python数据分析工具pandas作业：把多行合并成一行</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-06T08:55:00+08:00">
Published: 四 06 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li><a href="https://china-testing.github.io/python3_quick.html">python 3.7极速入门教程 - 目录</a></li>
</ul>
<h3 id="pythonpandasexcel">python数据分析工具pandas作业：合并excel重复行</h3>
<p>数据示例：</p>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-e61ea4bc9abc5a1c.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>转换成：</p>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-012737f08825f15d.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<h3 id="_1">代码</h3>
<div class="highlight"><pre><span></span><span class="ch">#!/usr/bin/python3</span>
<span class="c1"># -*- coding: utf-8 -*-</span>
<span class="c1"># 技术支持：https://www.jianshu.com/u/69f40328d4f0 </span>
<span class="c1"># 技术支持 https://china-testing.github.io/merge_excel_rows.html</span>
<span class="c1"># https://github.com/china-testing/python-api-tesing/blob/master/practices/pandas/merge_excel_rows2.py</span>
<span class="c1"># 项目实战讨论QQ群630011153 144081101</span>
<span class="c1"># CreateDate: 2018-12-06</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="n">df …</span></pre></div>
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