基于TensorFlowCNNRNN神经网络实现中文文本分类
完整代码：https://download.csdn.net/download/qq_38735017/87416893
环境
Python 2/3
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 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：
Data |
Shape |
Data |
Shape |
x_train |
[50000, 600] |
y_train |
[50000, 10] |
x_val |
[5000, 600] |
y_val |
[5000, 10] |
x_test |
[10000, 600] |
y_test |
[10000, 10] |
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。
classTCNNConfig(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tensorboardCNN 模型
具体参看 cnn_model.py 的实现。
大致结构如下：
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：
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。
classTRNNConfig(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 grudropout_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tensorboardRNN 模型
具体参看 rnn_model.py 的实现。
大致结构如下：
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：
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。