基于TensorFlow使用卷积神经网络以及循环神经网络进行中文文本分类

不念不忘少年蓝@ 2024-03-24 20:39 60阅读 0赞

[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/88248121  
[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/88248121

## Text Classification with CNN and RNN ##

使用卷积神经网络以及循环神经网络进行中文文本分类

CNN 做句子分类的论文可以参看: [Convolutional Neural Networks for Sentence Classification][]

还可以去读 dennybritz 大牛的博客：[Implementing a CNN for Text Classification in TensorFlow][]

以及字符级 CNN 的论文：[Character-level Convolutional Networks for Text Classification][]

本文是基于 TensorFlow 在中文数据集上的简化实现，使用了字符级 CNN 和 RNN 对中文文本进行分类，达到了较好的效果。

文中所使用的 Conv1D 与论文中有些不同，详细参考官方文档：[tf.nn.conv1d][]

### 环境 ###

*  Python 2/3 (感谢 [howie.hu][] 调试 Python2 环境)
 *  TensorFlow 1.3 以上
 *  numpy
 *  scikit-learn
 *  scipy

### 数据集 ###

使用 THUCNews 的一个子集进行训练与测试，数据集请自行到 [THUCTC：一个高效的中文文本分类工具包][THUCTC]下载，请遵循数据提供方的开源协议。

本次训练使用了其中的 10 个分类，每个分类 6500 条数据。

类别如下：

体育, 财经, 房产, 家居, 教育, 科技, 时尚, 时政, 游戏, 娱乐

这个子集可以在此下载：链接: [https://pan.baidu.com/s/1hugrfRu][https_pan.baidu.com_s_1hugrfRu] 密码: qfud

数据集划分如下：

*  训练集: 5000\*10
 *  验证集: 500\*10
 *  测试集: 1000\*10

从原数据集生成子集的过程请参看 `helper` 下的两个脚本。其中，`copy_data.sh` 用于从每个分类拷贝 6500 个文件，`cnews_group.py` 用于将多个文件整合到一个文件中。执行该文件后，得到三个数据文件：

*  cnews.train.txt: 训练集(50000 条)
 *  cnews.val.txt: 验证集(5000 条)
 *  cnews.test.txt: 测试集(10000 条)

### 预处理 ###

`data/cnews_loader.py` 为数据的预处理文件。

*  `read_file()`: 读取文件数据;
 *  `build_vocab()`: 构建词汇表，使用字符级的表示，这一函数会将词汇表存储下来，避免每一次重复处理;
 *  `read_vocab()`: 读取上一步存储的词汇表，转换为 `{词：id}` 表示;
 *  `read_category()`: 将分类目录固定，转换为 `{类别: id}` 表示;
 *  `to_words()`: 将一条由 id 表示的数据重新转换为文字;
 *  `process_file()`: 将数据集从文字转换为固定长度的 id 序列表示;
 *  `batch_iter()`: 为神经网络的训练准备经过 shuffle 的批次的数据。

经过数据预处理，数据的格式如下：

<table> 
 <thead> 
  <tr> 
   <th align="left">Data</th> 
   <th align="left">Shape</th> 
   <th align="left">Data</th> 
   <th align="left">Shape</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td align="left">x_train</td> 
   <td align="left">[50000, 600]</td> 
   <td align="left">y_train</td> 
   <td align="left">[50000, 10]</td> 
  </tr> 
  <tr> 
   <td align="left">x_val</td> 
   <td align="left">[5000, 600]</td> 
   <td align="left">y_val</td> 
   <td align="left">[5000, 10]</td> 
  </tr> 
  <tr> 
   <td align="left">x_test</td> 
   <td align="left">[10000, 600]</td> 
   <td align="left">y_test</td> 
   <td align="left">[10000, 10]</td> 
  </tr> 
 </tbody> 
</table>

### CNN 卷积神经网络 ###

#### 配置项 ####

CNN 可配置的参数如下所示，在 `cnn_model.py` 中。

class TCNNConfig(object):
        """CNN配置参数"""
    
        embedding_dim = 64      # 词向量维度
        seq_length = 600        # 序列长度
        num_classes = 10        # 类别数
        num_filters = 128        # 卷积核数目
        kernel_size = 5         # 卷积核尺寸
        vocab_size = 5000       # 词汇表达小
    
        hidden_dim = 128        # 全连接层神经元
    
        dropout_keep_prob = 0.5 # dropout保留比例
        learning_rate = 1e-3    # 学习率
    
        batch_size = 64         # 每批训练大小
        num_epochs = 10         # 总迭代轮次
    
        print_per_batch = 100    # 每多少轮输出一次结果
        save_per_batch = 10      # 每多少轮存入tensorboard

#### CNN 模型 ####

具体参看 `cnn_model.py` 的实现。

大致结构如下：

![1216357a4ae0552466f2cac6f8936527.png][]

#### 训练与验证 ####

运行 `python run_cnn.py train`，可以开始训练。

> 若之前进行过训练，请把 tensorboard/textcnn 删除，避免 TensorBoard 多次训练结果重叠。

Configuring CNN model...
    Configuring TensorBoard and Saver...
    Loading training and validation data...
    Time usage: 0:00:14
    Training and evaluating...
    Epoch: 1
    Iter:      0, Train Loss:    2.3, Train Acc:  10.94%, Val Loss:    2.3, Val Acc:   8.92%, Time: 0:00:01 *
    Iter:    100, Train Loss:   0.88, Train Acc:  73.44%, Val Loss:    1.2, Val Acc:  68.46%, Time: 0:00:04 *
    Iter:    200, Train Loss:   0.38, Train Acc:  92.19%, Val Loss:   0.75, Val Acc:  77.32%, Time: 0:00:07 *
    Iter:    300, Train Loss:   0.22, Train Acc:  92.19%, Val Loss:   0.46, Val Acc:  87.08%, Time: 0:00:09 *
    Iter:    400, Train Loss:   0.24, Train Acc:  90.62%, Val Loss:    0.4, Val Acc:  88.62%, Time: 0:00:12 *
    Iter:    500, Train Loss:   0.16, Train Acc:  96.88%, Val Loss:   0.36, Val Acc:  90.38%, Time: 0:00:15 *
    Iter:    600, Train Loss:  0.084, Train Acc:  96.88%, Val Loss:   0.35, Val Acc:  91.36%, Time: 0:00:17 *
    Iter:    700, Train Loss:   0.21, Train Acc:  93.75%, Val Loss:   0.26, Val Acc:  92.58%, Time: 0:00:20 *
    Epoch: 2
    Iter:    800, Train Loss:   0.07, Train Acc:  98.44%, Val Loss:   0.24, Val Acc:  94.12%, Time: 0:00:23 *
    Iter:    900, Train Loss:  0.092, Train Acc:  96.88%, Val Loss:   0.27, Val Acc:  92.86%, Time: 0:00:25
    Iter:   1000, Train Loss:   0.17, Train Acc:  95.31%, Val Loss:   0.28, Val Acc:  92.82%, Time: 0:00:28
    Iter:   1100, Train Loss:    0.2, Train Acc:  93.75%, Val Loss:   0.23, Val Acc:  93.26%, Time: 0:00:31
    Iter:   1200, Train Loss:  0.081, Train Acc:  98.44%, Val Loss:   0.25, Val Acc:  92.96%, Time: 0:00:33
    Iter:   1300, Train Loss:  0.052, Train Acc: 100.00%, Val Loss:   0.24, Val Acc:  93.58%, Time: 0:00:36
    Iter:   1400, Train Loss:    0.1, Train Acc:  95.31%, Val Loss:   0.22, Val Acc:  94.12%, Time: 0:00:39
    Iter:   1500, Train Loss:   0.12, Train Acc:  98.44%, Val Loss:   0.23, Val Acc:  93.58%, Time: 0:00:41
    Epoch: 3
    Iter:   1600, Train Loss:    0.1, Train Acc:  96.88%, Val Loss:   0.26, Val Acc:  92.34%, Time: 0:00:44
    Iter:   1700, Train Loss:  0.018, Train Acc: 100.00%, Val Loss:   0.22, Val Acc:  93.46%, Time: 0:00:47
    Iter:   1800, Train Loss:  0.036, Train Acc: 100.00%, Val Loss:   0.28, Val Acc:  92.72%, Time: 0:00:50
    No optimization for a long time, auto-stopping...

在验证集上的最佳效果为 94.12%，且只经过了 3 轮迭代就已经停止。

准确率和误差如图所示：

![c3a03c4f5e8983003faa53ed5ebf3836.png][]

#### 测试 ####

运行 `python run_cnn.py test` 在测试集上进行测试。

Configuring CNN model...
    Loading test data...
    Testing...
    Test Loss:   0.14, Test Acc:  96.04%
    Precision, Recall and F1-Score...
                 precision    recall  f1-score   support
    
             体育       0.99      0.99      0.99      1000
             财经       0.96      0.99      0.97      1000
             房产       1.00      1.00      1.00      1000
             家居       0.95      0.91      0.93      1000
             教育       0.95      0.89      0.92      1000
             科技       0.94      0.97      0.95      1000
             时尚       0.95      0.97      0.96      1000
             时政       0.94      0.94      0.94      1000
             游戏       0.97      0.96      0.97      1000
             娱乐       0.95      0.98      0.97      1000
    
    avg / total       0.96      0.96      0.96     10000
    
    Confusion Matrix...
    [[991   0   0   0   2   1   0   4   1   1]
     [  0 992   0   0   2   1   0   5   0   0]
     [  0   1 996   0   1   1   0   0   0   1]
     [  0  14   0 912   7  15   9  29   3  11]
     [  2   9   0  12 892  22  18  21  10  14]
     [  0   0   0  10   1 968   4   3  12   2]
     [  1   0   0   9   4   4 971   0   2   9]
     [  1  16   0   4  18  12   1 941   1   6]
     [  2   4   1   5   4   5  10   1 962   6]
     [  1   0   1   6   4   3   5   0   1 979]]
    Time usage: 0:00:05

在测试集上的准确率达到了 96.04%，且各类的 precision, recall 和 f1-score 都超过了 0.9。

从混淆矩阵也可以看出分类效果非常优秀。

### RNN 循环神经网络 ###

#### 配置项 ####

RNN 可配置的参数如下所示，在 `rnn_model.py` 中。

class TRNNConfig(object):
        """RNN配置参数"""
    
        # 模型参数
        embedding_dim = 64      # 词向量维度
        seq_length = 600        # 序列长度
        num_classes = 10        # 类别数
        vocab_size = 5000       # 词汇表达小
    
        num_layers= 2           # 隐藏层层数
        hidden_dim = 128        # 隐藏层神经元
        rnn = 'gru'             # lstm 或 gru
    
        dropout_keep_prob = 0.8 # dropout保留比例
        learning_rate = 1e-3    # 学习率
    
        batch_size = 128         # 每批训练大小
        num_epochs = 10          # 总迭代轮次
    
        print_per_batch = 100    # 每多少轮输出一次结果
        save_per_batch = 10      # 每多少轮存入tensorboard

#### RNN 模型 ####

具体参看 `rnn_model.py` 的实现。

大致结构如下：

![3d4e91046e0f8efe2e56bf9f926a0c3d.png][]

#### 训练与验证 ####

> 这部分的代码与 run\_cnn.py 极为相似，只需要将模型和部分目录稍微修改。

运行 `python run_rnn.py train`，可以开始训练。

> 若之前进行过训练，请把 tensorboard/textrnn 删除，避免 TensorBoard 多次训练结果重叠。

Configuring RNN model...
    Configuring TensorBoard and Saver...
    Loading training and validation data...
    Time usage: 0:00:14
    Training and evaluating...
    Epoch: 1
    Iter:      0, Train Loss:    2.3, Train Acc:   8.59%, Val Loss:    2.3, Val Acc:  11.96%, Time: 0:00:08 *
    Iter:    100, Train Loss:   0.95, Train Acc:  64.06%, Val Loss:    1.3, Val Acc:  53.06%, Time: 0:01:15 *
    Iter:    200, Train Loss:   0.61, Train Acc:  79.69%, Val Loss:   0.94, Val Acc:  69.88%, Time: 0:02:22 *
    Iter:    300, Train Loss:   0.49, Train Acc:  85.16%, Val Loss:   0.63, Val Acc:  81.44%, Time: 0:03:29 *
    Epoch: 2
    Iter:    400, Train Loss:   0.23, Train Acc:  92.97%, Val Loss:    0.6, Val Acc:  82.86%, Time: 0:04:36 *
    Iter:    500, Train Loss:   0.27, Train Acc:  92.97%, Val Loss:   0.47, Val Acc:  86.72%, Time: 0:05:43 *
    Iter:    600, Train Loss:   0.13, Train Acc:  98.44%, Val Loss:   0.43, Val Acc:  87.46%, Time: 0:06:50 *
    Iter:    700, Train Loss:   0.24, Train Acc:  91.41%, Val Loss:   0.46, Val Acc:  87.12%, Time: 0:07:57
    Epoch: 3
    Iter:    800, Train Loss:   0.11, Train Acc:  96.09%, Val Loss:   0.49, Val Acc:  87.02%, Time: 0:09:03
    Iter:    900, Train Loss:   0.15, Train Acc:  96.09%, Val Loss:   0.55, Val Acc:  85.86%, Time: 0:10:10
    Iter:   1000, Train Loss:   0.17, Train Acc:  96.09%, Val Loss:   0.43, Val Acc:  89.44%, Time: 0:11:18 *
    Iter:   1100, Train Loss:   0.25, Train Acc:  93.75%, Val Loss:   0.42, Val Acc:  88.98%, Time: 0:12:25
    Epoch: 4
    Iter:   1200, Train Loss:   0.14, Train Acc:  96.09%, Val Loss:   0.39, Val Acc:  89.82%, Time: 0:13:32 *
    Iter:   1300, Train Loss:    0.2, Train Acc:  96.09%, Val Loss:   0.43, Val Acc:  88.68%, Time: 0:14:38
    Iter:   1400, Train Loss:  0.012, Train Acc: 100.00%, Val Loss:   0.37, Val Acc:  90.58%, Time: 0:15:45 *
    Iter:   1500, Train Loss:   0.15, Train Acc:  96.88%, Val Loss:   0.39, Val Acc:  90.58%, Time: 0:16:52
    Epoch: 5
    Iter:   1600, Train Loss:  0.075, Train Acc:  97.66%, Val Loss:   0.41, Val Acc:  89.90%, Time: 0:17:59
    Iter:   1700, Train Loss:  0.042, Train Acc:  98.44%, Val Loss:   0.41, Val Acc:  90.08%, Time: 0:19:06
    Iter:   1800, Train Loss:   0.08, Train Acc:  97.66%, Val Loss:   0.38, Val Acc:  91.36%, Time: 0:20:13 *
    Iter:   1900, Train Loss:  0.089, Train Acc:  98.44%, Val Loss:   0.39, Val Acc:  90.18%, Time: 0:21:20
    Epoch: 6
    Iter:   2000, Train Loss:  0.092, Train Acc:  96.88%, Val Loss:   0.36, Val Acc:  91.42%, Time: 0:22:27 *
    Iter:   2100, Train Loss:  0.062, Train Acc:  98.44%, Val Loss:   0.39, Val Acc:  90.56%, Time: 0:23:34
    Iter:   2200, Train Loss:  0.053, Train Acc:  98.44%, Val Loss:   0.39, Val Acc:  90.02%, Time: 0:24:41
    Iter:   2300, Train Loss:   0.12, Train Acc:  96.09%, Val Loss:   0.37, Val Acc:  90.84%, Time: 0:25:48
    Epoch: 7
    Iter:   2400, Train Loss:  0.014, Train Acc: 100.00%, Val Loss:   0.41, Val Acc:  90.38%, Time: 0:26:55
    Iter:   2500, Train Loss:   0.14, Train Acc:  96.88%, Val Loss:   0.37, Val Acc:  91.22%, Time: 0:28:01
    Iter:   2600, Train Loss:   0.11, Train Acc:  96.88%, Val Loss:   0.43, Val Acc:  89.76%, Time: 0:29:08
    Iter:   2700, Train Loss:  0.089, Train Acc:  97.66%, Val Loss:   0.37, Val Acc:  91.18%, Time: 0:30:15
    Epoch: 8
    Iter:   2800, Train Loss: 0.0081, Train Acc: 100.00%, Val Loss:   0.44, Val Acc:  90.66%, Time: 0:31:22
    Iter:   2900, Train Loss:  0.017, Train Acc: 100.00%, Val Loss:   0.44, Val Acc:  89.62%, Time: 0:32:29
    Iter:   3000, Train Loss:  0.061, Train Acc:  96.88%, Val Loss:   0.43, Val Acc:  90.04%, Time: 0:33:36
    No optimization for a long time, auto-stopping...

在验证集上的最佳效果为 91.42%，经过了 8 轮迭代停止，速度相比 CNN 慢很多。

准确率和误差如图所示：

![387ba93dc85ff1e2fe945bb994adbff8.png][]

#### 测试 ####

运行 `python run_rnn.py test` 在测试集上进行测试。

Testing...
    Test Loss:   0.21, Test Acc:  94.22%
    Precision, Recall and F1-Score...
                 precision    recall  f1-score   support
    
             体育       0.99      0.99      0.99      1000
             财经       0.91      0.99      0.95      1000
             房产       1.00      1.00      1.00      1000
             家居       0.97      0.73      0.83      1000
             教育       0.91      0.92      0.91      1000
             科技       0.93      0.96      0.94      1000
             时尚       0.89      0.97      0.93      1000
             时政       0.93      0.93      0.93      1000
             游戏       0.95      0.97      0.96      1000
             娱乐       0.97      0.96      0.97      1000
    
    avg / total       0.94      0.94      0.94     10000
    
    Confusion Matrix...
    [[988   0   0   0   4   0   2   0   5   1]
     [  0 990   1   1   1   1   0   6   0   0]
     [  0   2 996   1   1   0   0   0   0   0]
     [  2  71   1 731  51  20  88  28   3   5]
     [  1   3   0   7 918  23   4  31   9   4]
     [  1   3   0   3   0 964   3   5  21   0]
     [  1   0   1   7   1   3 972   0   6   9]
     [  0  16   0   0  22  26   0 931   2   3]
     [  2   3   0   0   2   2  12   0 972   7]
     [  0   3   1   1   7   3  11   5   9 960]]
    Time usage: 0:00:33

在测试集上的准确率达到了 94.22%，且各类的 precision, recall 和 f1-score，除了家居这一类别，都超过了 0.9。

从混淆矩阵可以看出分类效果非常优秀。

对比两个模型，可见 RNN 除了在家居分类的表现不是很理想，其他几个类别较 CNN 差别不大。

还可以通过进一步的调节参数，来达到更好的效果。

### 预测 ###

为方便预测，repo 中 `predict.py` 提供了 CNN 模型的预测方法。

[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/88248121  
[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/88248121

[Link 1]: https://download.csdn.net/download/sheziqiong/88248121
[Convolutional Neural Networks for Sentence Classification]: https://arxiv.org/abs/1408.5882
[Implementing a CNN for Text Classification in TensorFlow]: http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
[Character-level Convolutional Networks for Text Classification]: https://arxiv.org/abs/1509.01626
[tf.nn.conv1d]: https://www.tensorflow.org/api_docs/python/tf/nn/conv1d
[howie.hu]: https://github.com/howie6879
[THUCTC]: http://thuctc.thunlp.org/
[https_pan.baidu.com_s_1hugrfRu]: https://pan.baidu.com/s/1hugrfRu
[1216357a4ae0552466f2cac6f8936527.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/0d58b6eba09d4d2ca4df5bb977631479.png
[c3a03c4f5e8983003faa53ed5ebf3836.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/2924e6903edd4b49a8dafcee90930f71.png
[3d4e91046e0f8efe2e56bf9f926a0c3d.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/4ed57e50e82046dea6aa1d7a4da4b336.png
[387ba93dc85ff1e2fe945bb994adbff8.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/bed048c1adfd436da51affff63981a1d.png