CNN, RNN等模型实现文本分类

科技2026-09-30 11

布置了一个NLP的大作业，用神经网络模型对中文文本进行分类，使用THUCNews(百度网盘地址提取码:tkvz)作为数据集。是个入门级别的作业，但是由于对pytorch以及python的语法不熟悉，导致写得很慢，下面大概总结一下学习过程。

数据集

文章分为十个类别，分别是体育，娱乐，家居，房产，教育，时尚，时政，游戏，科技，财经。训练集有五万条数据，测试集有一万条数据，验证集有五千条数据，数据长下面这个样子

图一数据集样本示例

知识预备

分词：我们认为词是一篇文章的最基本单元，词构成句子，句子构成篇，所以要对文章进行分类处理，首先要将文章分割成若干个词语，由词语再对文章类别进行判断。中文可以用jieba库对文本进行分词词向量：NLP的很重要的概念，单词是无法直接送入模型中进行训练的，必须要先要将它量化，用向量去表示一个词语，比如有3个单词，我们可以用[1, 0 0], [0, 1, 0], [0, 0 ,1]去表示每个词语，这种编码方式叫做独热（one-hot）编码。当然这种编码方式过于冗余了，如果词有几十万，就需要几十万长度的向量，耗费空间太大了。google 提出了word2vec模型，能够将词语用比较小的向量表示出来（几百维）。不清楚的同学可以自行百度word2vec词向量矩阵：由所有词向量所构成的矩阵，比如由1000个词，每个词向量为300维，那么矩阵的大小为[1000, 300]嵌入层(embedding layer)：嵌入层实质上就是一个词向量矩阵，它包含着所有有可能出现的词语的词向量，这个词向量矩阵可以随机生成，也可以由自己使用word2vec训练得到，也可以使用别人训练好的词向量。需要注意的一点是，嵌入层的输入是词语的序号，也就是说每个词语对应着一个独一无二的序号，通过这个序号就可以在词向量矩阵当中选出对应的那一个词向量。

pipeline

网上很多教程都是上来就开始讲原理，讲原理的博客有很多，我在这里不详细的说明，而我在这里主要着重说明比较宏观的流程。知乎上有一篇写得很好的专栏中文文本分类的pytorch实现, 他的github地址, 大佬的代码写得很漂亮，自己的代码基本上是模仿他写的。OK，现在进入我们的正题。

预处理

网上的教程很多都是用的自带的数据集，可以直接从库中直接加载，不需要预处理。而自己进行预处理就麻烦很多，以图一的数据样本为例，这些都是文本信息，我们要将他们全部数字化

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.edge-pattern-dashed{stroke-dasharray:3}#mermaid-svg-tF4QaKqN5PU8XkiZ .edge-pattern-dotted{stroke-dasharray:2}#mermaid-svg-tF4QaKqN5PU8XkiZ .marker{fill:#333}#mermaid-svg-tF4QaKqN5PU8XkiZ .marker.cross{stroke:#333} :root { --mermaid-font-family: "trebuchet ms", verdana, arial;} #mermaid-svg-tF4QaKqN5PU8XkiZ { color: rgba(0, 0, 0, 0.75); font: ; } 分割类别与正文类别数字化正文分词正文词语数字化截断与填充图二预处理流程图分割类别与正文：很简单用split函数就可以很好地分割出来, 记住读取的格式必须要为utf-8。 with open(path, 'r', enconding = 'utf-8' ) as f: for line in f.readlines() label, content = line.strip().split('\t') 类别数字化：定义一个类别的字典，就可以把类别映射为数字了正文分词：可以先过滤掉非中文字符，然后使用jieba进行分词 pattern = re.compile(r'[^\u4e00-\u9fa5]') #取出中文字符 content = re.sub(pattern, '', content) words = jieba.lcut(content) 正文词语数字化：我们首先需要定义一个包含所有词的字典，给每个词一个序号，这样就可以将词语映射为数字了。在这个任务当中，我使用的是预训练的词向量，所以词典要包含预训练中的词向量中的所有单词。 for word in words: res.append(word_to_id.get(word, 0)) 截断与填充：经过之前处理之后，我们得到了长度不一的文章词语数字序列，由于输入模型的数据需要具有相同长度所构成的tensor，首先我们自定义一个pad_size，在该任务中，我取的是300，对于大于pad_size的数字序列进行截断，对于小于pad_size的数字序列进行填充，填充的值可以自己指定，这里我用0进行填充 if(len(data[i]) > pad_size): #单词量比所给长度大，截断 data[i] = data[i][:pad_size] else: data[i].extend([0] *(pad_size - len(data[i]))) #单词量比所给的小，填充0

输入模型

经过预处理后，我们得到一个矩阵，矩阵的列数是pad_size，即文章的词数，矩阵的行数是文章的数目，这就是我们输入模型的结果。除embedding层外，后面的接的模型可以随意改动。下面约定一下变量的含义：

batch_size: 一个batch里面含有的文章数pad_size：文章的词数embedding_size：词向量的维度conv_width：卷积核宽度conv_num：卷积核数目conv_type：卷积核种类num_class：类别数

模型

此部分基本复述上述提及的知乎专栏，自己写一遍加深理解

CNN模型

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.edge-pattern-dashed{stroke-dasharray:3}#mermaid-svg-r5ErQbJID2cEWJxY .edge-pattern-dotted{stroke-dasharray:2}#mermaid-svg-r5ErQbJID2cEWJxY .marker{fill:#333}#mermaid-svg-r5ErQbJID2cEWJxY .marker.cross{stroke:#333} :root { --mermaid-font-family: "trebuchet ms", verdana, arial;} #mermaid-svg-r5ErQbJID2cEWJxY { color: rgba(0, 0, 0, 0.75); font: ; } 输入层 embedding层卷积层池化层全连接层输出层图二 CNN流程图输入层：每次随机抽取batch_size数量的文章。tensor的size为 [batch_size, pad_size]embedding层：经过embedding之后，每个词序号对应一个词向量。tensor的size变为[batch_size, pad_size, embedding_size]卷积层：卷积核的size为[conv_num, conv_width, embedding_size], 卷积完后的输出为[batch_size, conv_num, pad_size - conv_width + , 1], 需要对第3维度（从0开始计数）进行squeeze，变成[batch_size, conv_num, pad_size - conv_width + 1]。在这个任务中，使用了conv_type种卷积核，会生成conv_type个上述的tensor池化层：使用max pooling，经过池化后，[batch_size, conv_num, pad_size - conv_width + 1]变为[batch_size, conv_num], 然后将多个卷积核得到的tensor在第1维上拼接起来，得到[batch_size, conv_num * conv_type]的tensor全连接层：输入为[batch_size, conv_num * conv_type]，输出为[batch_size, hidden_size]输出层：输入为[batch_size, hidden_size], 输出为[batch_size, num_class]

RNN模型

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图四 LSTM的结构输入层：每次随机抽取batch_size数量的文章。tensor的size为 [batch_size, pad_size]embedding层：经过embedding之后，每个词序号对应一个词向量。tensor的size变为[batch_size, pad_size, embedding_size]双向LSTM层：隐层大小为hidden_size，得到所有时刻的隐层状态(前向隐层和后向隐层拼接)，尺寸为[batch_size, pad_size, hidden_size * 2], 取出最后一个状态的tensor，size变为[batch_size, hidden_size * 2]全连接层：输出为[batch_size, num_class]

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