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雪地
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<h1>CS229 学习笔记 Part3</h1>
<div class="a-content">
<div class="a-content-text">
<ul>
<li>
<a href="#toc_0">SVM</a>
<ul>
<li>
<a href="#toc_1">函数间隔和几何间隔 (Functional and geometric margin)</a>
</li>
<li>
<a href="#toc_2">最优间隔分类器 (Optimal margin classifier)</a>
</li>
<li>
<a href="#toc_3">拉格朗日对偶问题</a>
<ul>
<li>
<a href="#toc_4">原始问题</a>
</li>
<li>
<a href="#toc_5">对偶问题</a>
</li>
<li>
<a href="#toc_6">KKT 条件</a>
</li>
</ul>
</li>
<li>
<a href="#toc_7">回到最优间隔分类器问题</a>
</li>
<li>
<a href="#toc_8">核 (Kernels)</a>
</li>
<li>
<a href="#toc_9">正则化</a>
</li>
<li>
<a href="#toc_10">SMO 算法</a>
<ul>
<li>
<a href="#toc_11">坐标上升算法 (Coordinate ascent)</a>
</li>
<li>
<a href="#toc_12">SMO</a>
</li>
</ul>
</li>
</ul>
</li>
</ul>
<h2 id="toc_0">SVM</h2>
<p>CS229 对于 SVM 的理论解释是我学习到的最详细也是最好的一份资料了，对比对象有周志华《机器学习》、《机器学习实战》、Coursera 上的 Machine Learning 等。相当推荐学习 CS229。</p>
<p>分类间隔 (Margin) 和 SVM 的优化目标『最大化分类间隔』这里就不多说了，很好理解，主要还是记录 CS229 中学到的新内容。一个数据点离分类边界 (decision boundary) 越远，则确信度越高。我们的优化目标也相当于寻找一个远离所有数据点的分类边界，当然，前提是这个分类边界得到的分类都正确。</p>
<p>SVM 的一些特殊定义也提及一下，</p>
<ul>
<li>\(y\) 的取值不是 \(\{0,1\}\) 而是 \(\{-1,1\}\)。</li>
<li>假设函数 \(h_{w,b}(x) = g(w^Tx+b)\) 中，我们把截距项单独写出来，便与后续的计算。</li>
<li>我们的分类器输出结果会直接是 1 或 -1，不像 Logistic 回归那样先输出 \(y\) 是某一类的概率。</li>
</ul>
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<span class="date">2017/6/7 14:35 下午</span>
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<h1>CS229 学习笔记 Part2</h1>
<div class="a-content">
<div class="a-content-text">
<ul>
<li>
<a href="#toc_0">判别式和生成式</a>
<ul>
<li>
<a href="#toc_1">判别式</a>
</li>
<li>
<a href="#toc_2">生成式</a>
</li>
</ul>
</li>
<li>
<a href="#toc_3">Gaussian discriminant analysis</a>
</li>
<li>
<a href="#toc_4">讨论：GDA 和 Logistic 回归</a>
</li>
<li>
<a href="#toc_5">朴素贝叶斯</a>
<ul>
<li>
<a href="#toc_6">拉普拉斯平滑</a>
</li>
<li>
<a href="#toc_7">用于文本分类的事件模型</a>
</li>
</ul>
</li>
</ul>
<h2 id="toc_0">判别式和生成式</h2>
<p>对于一个分类任务，判别式和生成式分别代表了两种不同的思路：</p>
<h3 id="toc_1">判别式</h3>
<p>通过直接从输入数据中学习，得到一个『特定输入对应的实际类别』的概率模型，模型的参数为 \(\theta\) 。即学习建模 \(p(y\mid x)\)</p>
<h3 id="toc_2">生成式</h3>
<p>通过对每一个类进行建模，然后就可以通过条件概率算出输入的数据更可能由哪一类生成。即学习建模 \(p(x\mid y)\) 和 \(p(y)\) ，然后计算 \[\arg\max\limits_y\frac{p(x \mid y)p(y)}{p(x)}\]</p>
<p>并且实际计算中，分母 \(p(x)\) 并不会影响各个类别概率的排序，所以最终简化成 \[\arg\max\limits_y p(x \mid y)p(y)\]</p>
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<span class="date">2017/6/5 1:14 上午</span>
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<h1>CS229 学习笔记 Part 1</h1>
<div class="a-content">
<div class="a-content-text">
<p>此笔记为我的 CS229 的学习笔记之一，由 Andrew Ng 的 CS229 Lecture notes 和 课堂录像整理而来。用于记录所学到的内容。记录顺序重新编排过，并非是课程原本的教学顺序，并且省略了课程中的一些推导过程，所以适合学习后整理备忘使用，不适合用于同步辅助学习。</p>
<ul>
<li>
<a href="#toc_0">广义线性模型 GLM (Generalized Linear Models)</a>
<ul>
<li>
<a href="#toc_1">具体步骤</a>
</li>
</ul>
</li>
<li>
<a href="#toc_2">优化方法</a>
<ul>
<li>
<a href="#toc_3">梯度下降法</a>
</li>
<li>
<a href="#toc_4">牛顿法</a>
</li>
</ul>
</li>
<li>
<a href="#toc_5">Linear Regression</a>
<ul>
<li>
<a href="#toc_6">Locally weighted linear regression</a>
</li>
</ul>
</li>
</ul>
<h2 id="toc_0">广义线性模型 GLM (Generalized Linear Models)</h2>
<p>广义线性模型是所学到的 Linear Regression 以及 Logistic Regression 的推广形式（更准确的说，这两种模型都属于 GLM 的特殊情况）。它有三个关键假设(Assumptions)构成:</p>
<ol>
<li>\(y \mid x;\theta\sim ExponentialFamily(\eta)\) ：对于固定的参数 \(\theta\) 以及给定 \(x\)， \(y\) 的分布服从某一指数分布族（如高斯分布、伯努利分布、Softmax分布）</li>
<li>对于给定的 \(x\) ，目标是预测 \(T(y)\) 的值。换一种说法就是，我们定义假设函数 \(h(x) = E[y\mid x]\)</li>
<li>natural parameter \(\eta\) 和 输入 \(x\) 是线性相关的， \(\eta = \theta^ \mathrm{ T } x\) （其中，当输入 \(x\) 和 \(\eta\) 是向量的时候， \(\eta_i = \theta_i^ \mathrm{T}x\)）</li>
</ol>
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<span class="date">2017/5/12 23:14 下午</span>
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<h1>原始模型优化笔记</h1>
<div class="a-content">
<div class="a-content-text">
<p>对于原始弹幕分类CNN模型进行优化。</p>
<h2 id="toc_0">修改 word2vec model 的 vector size</h2>
<ul>
<li>400:
Nice at epoch 38, validation acc 96.56%</li>
<li>200:
Nice at epoch 37, validation acc 95.22%</li>
<li>100:
Nice at epoch 34, validation acc 94.78%
单轮训练时间与50维相近，测试样例测试耗时 0.92secs</li>
<li>50:
Nice at epoch 40, validation acc 94.39%
单轮训练时间在7秒左右，测试样例(av 8365806)测试耗时 0.7secs</li>
</ul>
<h2 id="toc_1">尝试加入dropout</h2>
<p>在两个 conv 层之间和两个 fc 层之间各加入了一个 \(p=0.5\) 的 dropout</p>
<p>40 epoch 时只有 89.1 acc， 和预想的一样，会导致 达到最佳效果的 epoch 数上升。</p>
<p>用了 dropout 后一个很明显的变化是，原本训练过程中通常是train acc 高于 validation acc，现在通常是 validation acc 高于 train acc，训练后期才基本持平或反超</p>
<p>vector在 epoch 90 左右 达到了96.50%上下的 acc，最终在epoch 300 以上能达到 97.10% 左右的 acc</p>
<p><img src="media/14879250453025/%E5%B1%8F%E5%B9%95%E5%BF%AB%E7%85%A7%202017-02-24%2022.15.27.png" alt="屏幕快照 2017-02-24 22.15.27"/></p>
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<span class="date">2017/3/1 17:4 下午</span>
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<h1>低素质弹幕分类器的CNN实现</h1>
<div class="a-content">
<div class="a-content-text">
<h2 id="toc_0">整体架构</h2>
<p>对于一条弹幕，首先进行分词，然后通过 word2vec 转换为词向量，再填充至固定长度，作为卷积神经网络的输入。</p>
<p>卷积神经网络的结构如下：</p>
<pre><code class="language-python">model = Sequential()
model.add(Convolution1D(100, 4, border_mode='valid', input_shape=(100, word_model.vector_size)))
model.add(Activation('relu'))
model.add(Convolution1D(100, 4, border_mode='valid', input_shape=(100, word_model.vector_size)))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
</code></pre>
<p>最终输出为2位的 categorical result，直接使用第一项，即骂人弹幕的概率作为输出。</p>
<p>然后通过代理，在弹幕服务器与播放器之间插入一层，实现弹幕的分类与屏蔽。最终实现了有效的骂人弹幕自动屏蔽，但是误伤的情况依然存在。</p>
<h2 id="toc_1">搭建过程</h2>
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<span class="date">2017/2/6 14:48 下午</span>
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<h1>低素质弹幕分类器 CNN 训练笔记</h1>
<div class="a-content">
<div class="a-content-text">
<p>一开始使用这个结构，迭代10次。</p>
<pre><code class="language-python">model = Sequential()
model.add(Convolution1D(100, 4, border_mode='valid', input_shape=(100, word_model.vector_size)))
model.add(Activation('relu'))
model.add(Convolution1D(5, 4, border_mode='valid'))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
</code></pre>
<p>完成训练后，乍一看准确率很高，结果 print 出来看一下，低素质弹幕完全没有被过滤，完全是将分类全部丢给 positive 达到的高准确率 (0.98) 的确是 meaningless classification<br/>
并且这个结果在loss里看得很清楚，loss一直是处于15+的</p>
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<h1>在 Linode 上编译 hybla 模块</h1>
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<p>考试复习期间，不想复习，搞搞其他东西散散心，发现shadowsocks 有关于 TCP Fast Open 的更新，看说明的过程还发现了官方的速度优化指南，尝试优化自己的 ss 速度，然而报错。</p>
<p>执行到这一步时运行出错</p>
<pre><code class="language-sh">sysctl --system
</code></pre>
<pre><code class="language-sh">...
sysctl: setting key "net.ipv4.tcp_congestion_control": No such file or directory
net.ipv4.tcp_congestion_control = hybla
* Applying /etc/sysctl.conf ...
</code></pre>
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<h1>无需手动缓存长度</h1>
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<p>今天在阅读《机器学习实战》的时候，看到了这样一句描述</p>
<p><img src="media/14696108056583/IMG_0629.png" alt="IMG_0629"/></p>
<p>其中数据集是一个List。看到这里说到为了提高代码效率，特地开了一个变量来保存其长度。</p>
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<h1>使用 Python 装饰器来将普通函数加入任务队列</h1>
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<p>对于操作『函数对象』来说，使用 Python 装饰器是一种非常优雅，非常 Pythonic 的一个方式。而在这篇文章中，对于任何一个普通的函数，只需要在函数定义前加一个装饰器调用，即可使得这一函数被调用时自动加入特定的任务队列，成为异步调用，而不会阻塞主线程。</p>
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<h1>关于Python的logging模块初始化无效的一个小坑</h1>
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<p>众所周知，logging模块是一个非常方便好用的日志输出模块。但是最近的使用发现了一个小坑，记录一下，避免再次踩坑。</p>
<p>一般使用logging模块都会对其进行初始化，使用以下代码：</p>
<pre><code> log_format = '[%(levelname)s] %(asctime)s %(filename)s line %(lineno)d: %(message)s'
date_fmt = '%a, %d %b %Y %H:%M:%S'
logging.basicConfig(
format=log_format,
datefmt=date_fmt,
level=log_level,
)
</code></pre>
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