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低素质弹幕分类器的CNN实现 - 雪地
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<h1>低素质弹幕分类器的CNN实现</h1>
<div class="read-more clearfix">
<span class="date">2017/2/6 14:48 下午</span>
<span>posted in </span>
<span class="posted-in"><a href='Deep%20Learning.html'>Deep Learning</a></span>
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<div class="article-content">
<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>
<span id="more"></span><!-- more -->
<p>使用游戏区的所有弹幕来训练 word2vec model。这里我是用的是 word2vec 的 Python 实现 gensim</p>
<p>训练脚本来自这篇文章 <a href="http://www.52nlp.cn/%E4%B8%AD%E8%8B%B1%E6%96%87%E7%BB%B4%E5%9F%BA%E7%99%BE%E7%A7%91%E8%AF%AD%E6%96%99%E4%B8%8A%E7%9A%84word2vec%E5%AE%9E%E9%AA%8C">中英文维基百科语料上的Word2Vec实验</a></p>
<pre><code class="language-python"># -*- coding: utf-8 -*-
"""
build a word2vec model by text file, each sentence for a line.
usage: [input file] [gensim model filename] [word2vec model filename]
example: output.txt life_damku.model life_damku.vector
"""
import logging
import os.path
import sys
import multiprocessing
from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence
if __name__ == '__main__':
program = os.path.basename(sys.argv[0])
logger = logging.getLogger(program)
logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s')
logging.root.setLevel(level=logging.INFO)
logger.info("running %s" % ' '.join(sys.argv))
# check and process input arguments
if len(sys.argv) < 4:
print(globals()['__doc__'] % locals())
sys.exit(1)
inp, outp1, outp2 = sys.argv[1:4]
model = Word2Vec(LineSentence(inp), size=400, window=5, min_count=5,
workers=multiprocessing.cpu_count())
# trim unneeded model memory = use(much) less RAM
# model.init_sims(replace=True)
model.save(outp1)
model.save_word2vec_format(outp2, binary=False)
</code></pre>
<p>然后，我从所有弹幕中随机抽取了5000条，进行人工标注分类，其中有63条骂人弹幕。由于骂人弹幕太少，我又通过关键词搜索加人工筛选的方式，增加了4000条骂人弹幕。</p>
<p>以这约9000条弹幕作为训练样本，80%的弹幕作为 train set， 其余的20%作为 validation set</p>
<p>对训练样本进行预处理：</p>
<ol>
<li>分词</li>
<li>转换为词向量</li>
<li>填充至100位长，其中填充的位的词向量全部置零。</li>
</ol>
<p>开始构建卷积神经网络，我选用的框架是使用 TensorFlow 后端的 keras。最终经过调试，得到这样一个结构</p>
<p><a href="https://yinzo.github.io/14863637259966.html">具体的训练过程可以看这里</a></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>需要注意的是，<code>model.fit</code> 指定了 class weight</p>
<pre><code class="language-python">model.fit(X_train, Y_train, nb_epoch=1, batch_size=300, class_weight={
0: len(positive_sample)/len(negative_sample),
1: 1
})
</code></pre>
<p>单次训练迭代时间约25秒，我总共迭代了20次。其中每次迭代我都将 model 保存到一个列表中，并使用当前 model 对验证集进行测试，输出测试结果。</p>
<p>然后根据测试结果，选用了第3次迭代的 model ，尝试使用新的，不在样本中的视频弹幕进行人工检验识别效果。</p>
<p>选用视频 <a href="http://www.bilibili.com/video/av8365806/">av8365806</a> 里面存在大量对骂的弹幕，适合进行测试</p>
<p>这里我花了小半天时间，用 tornado 搭了个服务器，模拟 comment.bilibili.com 的所有请求，其中对于弹幕的请求，插入一层封装好的神经网络模型进行分类，再返回给用户请求，其余请求直接转发。搭建好后通过 Surge 的 URL Rewrite 将请求转到本地服务器上。</p>
<p><img src="media/14856271844324/%E5%B1%8F%E5%B9%95%E5%BF%AB%E7%85%A7%202017-02-06%2013.24.12.png" alt="屏幕快照 2017-02-06 13.24.12"/></p>
<p>然后此时访问弹幕都会先经过本地服务器进行处理了。查看 av8365806 的弹幕分类情况：</p>
<p>首先是比较欣慰的结果，大部分直接用脏字喷人的弹幕都被高确信地识别出来了<br/>
<img src="media/14856271844324/%E5%B1%8F%E5%B9%95%E5%BF%AB%E7%85%A7%202017-02-06%2013.26.37.png" alt="屏幕快照 2017-02-06 13.26.37"/></p>
<p>其次，存在一些短弹幕被误伤的情况，而且确信度还莫名的挺高<br/>
<img src="media/14856271844324/%E5%B1%8F%E5%B9%95%E5%BF%AB%E7%85%A7%202017-02-06%2013.26.51.png" alt="屏幕快照 2017-02-06 13.26.51"/></p>
<p>然后，依然存在一部分弹幕脏字不明显，以及一些反讽的语句没有识别出来<br/>
<img src="media/14856271844324/%E5%B1%8F%E5%B9%95%E5%BF%AB%E7%85%A7%202017-02-06%2013.27.39.png" alt="屏幕快照 2017-02-06 13.27.39"/></p>
<p>另外由于3000弹幕的识别时间在5秒左右，再加上网络延迟以及一些预处理后处理的时间，整体延迟约10秒，所以这个代理服务器可能还没能达到投入大规模使用的效率，比较可惜。</p>
<p>但是其识别效果，对于日常观感绝对是有所提升的。</p>
<p>脑海中对于屏蔽模式可以分为以下三挡：</p>
<ol>
<li>【仁慈模式】被分类器识别为骂人的弹幕，弹幕内容前填充100个空格，颜色变为白色，字号变小，统一为滚动弹幕。其中，填充空格能够使得这条弹幕将会以极快的速度飘过视野。</li>
<li>【常规模式】被分类器识别为骂人的弹幕直接删除，该弹幕的发送者的其他弹幕使用仁慈模式进行修正。</li>
<li>【灭杀模式】被分类器识别为骂人的弹幕，以及其发送者发送的其他弹幕，统一删除。</li>
</ol>
<p>原本对于仁慈模式是想进行高透明度处理的，但是由于B站弹幕不支持分开透明度所以就变成了现在的方案。不过由于可以直接修改弹幕类型，不知道高级弹幕是否支持透明度选项，以及高级弹幕在各个平台上的支持度如何。</p>
<h2 id="toc_2">结论</h2>
<p>这次构建卷积神经网络，是学习深度学习方向以来第一次完全自己设计网络结构，并独立完成所有的过程，包括样本收集、样本处理、搭建网络、训练，并且实现了模型的真实可用。作为深度学习的阶段性成果，也是给了我很大的鼓励。这个假期接下来的时间，准备继续学习 RNN 以及 LSTM 等模型，以及其他优化技巧。</p>
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