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Deep Learning - 雪地
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<h1>原始模型优化笔记</h1>
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<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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<a href="14883590547961.html">Read more</a>
<span class="date">2017/3/1 17:4 下午</span>
<span>posted in </span>
<span class="posted-in"><a href='Deep%20Learning.html'>Deep Learning</a></span>
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<h1>低素质弹幕分类器的CNN实现</h1>
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<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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<a href="14863637393852.html">Read more</a>
<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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<h1>低素质弹幕分类器 CNN 训练笔记</h1>
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<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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