# 文本分类 以下是本例的简要目录结构及说明： ```text . ├── nets.py # 模型定义 ├── README.md # 文档 ├── train.py # 训练脚本 ├── infer.py # 预测脚本 └── utils.py # 定义通用函数，从外部获取 ``` ## 简介，模型详解 在PaddlePaddle v2版本[文本分类](https://github.com/PaddlePaddle/models/blob/develop/legacy/text_classification/README.md)中对于文本分类任务有较详细的介绍，在本例中不再重复介绍。 在模型上，我们采用了bow, cnn, lstm, gru四种常见的文本分类模型。 ## 训练 1. 运行命令 `python train.py bow` 开始训练模型。 ```python python train.py bow # bow指定网络结构，可替换成cnn, lstm, gru ``` 2. (可选）想自定义网络结构，需在[nets.py](./nets.py)中自行添加，并设置[train.py](./train.py)中的相应参数。 ```python def train(train_reader, # 训练数据 word_dict, # 数据字典 network, # 模型配置 use_cuda, # 是否用GPU parallel, # 是否并行 save_dirname, # 保存模型路径 lr=0.2, # 学习率大小 batch_size=128, # 每个batch的样本数 pass_num=30): # 训练的轮数 ``` ## 训练结果示例 ```text pass_id: 0, avg_acc: 0.848040, avg_cost: 0.354073 pass_id: 1, avg_acc: 0.914200, avg_cost: 0.217945 pass_id: 2, avg_acc: 0.929800, avg_cost: 0.184302 pass_id: 3, avg_acc: 0.938680, avg_cost: 0.164240 pass_id: 4, avg_acc: 0.945120, avg_cost: 0.149150 pass_id: 5, avg_acc: 0.951280, avg_cost: 0.137117 pass_id: 6, avg_acc: 0.955360, avg_cost: 0.126434 pass_id: 7, avg_acc: 0.961400, avg_cost: 0.117405 pass_id: 8, avg_acc: 0.963560, avg_cost: 0.110070 pass_id: 9, avg_acc: 0.965840, avg_cost: 0.103273 pass_id: 10, avg_acc: 0.969800, avg_cost: 0.096314 pass_id: 11, avg_acc: 0.971720, avg_cost: 0.090206 pass_id: 12, avg_acc: 0.974800, avg_cost: 0.084970 pass_id: 13, avg_acc: 0.977400, avg_cost: 0.078981 pass_id: 14, avg_acc: 0.980000, avg_cost: 0.073685 pass_id: 15, avg_acc: 0.981080, avg_cost: 0.069898 pass_id: 16, avg_acc: 0.982080, avg_cost: 0.064923 pass_id: 17, avg_acc: 0.984680, avg_cost: 0.060861 pass_id: 18, avg_acc: 0.985840, avg_cost: 0.057095 pass_id: 19, avg_acc: 0.988080, avg_cost: 0.052424 pass_id: 20, avg_acc: 0.989160, avg_cost: 0.049059 pass_id: 21, avg_acc: 0.990120, avg_cost: 0.045882 pass_id: 22, avg_acc: 0.992080, avg_cost: 0.042140 pass_id: 23, avg_acc: 0.992280, avg_cost: 0.039722 pass_id: 24, avg_acc: 0.992840, avg_cost: 0.036607 pass_id: 25, avg_acc: 0.994440, avg_cost: 0.034040 pass_id: 26, avg_acc: 0.995000, avg_cost: 0.031501 pass_id: 27, avg_acc: 0.995440, avg_cost: 0.028988 pass_id: 28, avg_acc: 0.996240, avg_cost: 0.026639 pass_id: 29, avg_acc: 0.996960, avg_cost: 0.024186 ``` ## 预测 1. 运行命令 `python infer.py bow_model`, 开始预测。 ```python python infer.py bow_model # bow_model指定需要导入的模型 ## 预测结果示例 ```text model_path: bow_model/epoch0, avg_acc: 0.882800 model_path: bow_model/epoch1, avg_acc: 0.882360 model_path: bow_model/epoch2, avg_acc: 0.881400 model_path: bow_model/epoch3, avg_acc: 0.877800 model_path: bow_model/epoch4, avg_acc: 0.872920 model_path: bow_model/epoch5, avg_acc: 0.872640 model_path: bow_model/epoch6, avg_acc: 0.869960 model_path: bow_model/epoch7, avg_acc: 0.865160 model_path: bow_model/epoch8, avg_acc: 0.863680 model_path: bow_model/epoch9, avg_acc: 0.861200 model_path: bow_model/epoch10, avg_acc: 0.853520 model_path: bow_model/epoch11, avg_acc: 0.850400 model_path: bow_model/epoch12, avg_acc: 0.855960 model_path: bow_model/epoch13, avg_acc: 0.853480 model_path: bow_model/epoch14, avg_acc: 0.855960 model_path: bow_model/epoch15, avg_acc: 0.854120 model_path: bow_model/epoch16, avg_acc: 0.854160 model_path: bow_model/epoch17, avg_acc: 0.852240 model_path: bow_model/epoch18, avg_acc: 0.852320 model_path: bow_model/epoch19, avg_acc: 0.850280 model_path: bow_model/epoch20, avg_acc: 0.849760 model_path: bow_model/epoch21, avg_acc: 0.850160 model_path: bow_model/epoch22, avg_acc: 0.846800 model_path: bow_model/epoch23, avg_acc: 0.845440 model_path: bow_model/epoch24, avg_acc: 0.845640 model_path: bow_model/epoch25, avg_acc: 0.846200 model_path: bow_model/epoch26, avg_acc: 0.845880 model_path: bow_model/epoch27, avg_acc: 0.844880 model_path: bow_model/epoch28, avg_acc: 0.844680 model_path: bow_model/epoch29, avg_acc: 0.844960 ``` 注：过拟合导致acc持续下降，请忽略