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add models

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  1. DPCNN-THUCNews/Classification/README.md +110 -0
  2. DPCNN-THUCNews/Classification/dataset/THUCNews/data/class.txt +10 -0
  3. DPCNN-THUCNews/Classification/dataset/THUCNews/data/dev.txt +0 -0
  4. DPCNN-THUCNews/Classification/dataset/THUCNews/data/embedding_SougouNews.npz +3 -0
  5. DPCNN-THUCNews/Classification/dataset/THUCNews/data/embedding_Tencent.npz +3 -0
  6. DPCNN-THUCNews/Classification/dataset/THUCNews/data/test.txt +0 -0
  7. DPCNN-THUCNews/Classification/dataset/THUCNews/data/train.txt +0 -0
  8. DPCNN-THUCNews/Classification/dataset/THUCNews/data/vocab.pkl +3 -0
  9. DPCNN-THUCNews/Classification/dataset/THUCNews/saved_dict/model.ckpt +0 -0
  10. DPCNN-THUCNews/Classification/dataset/index.json +0 -0
  11. DPCNN-THUCNews/Classification/dataset/info.json +15 -0
  12. DPCNN-THUCNews/Classification/dataset/labels.npy +3 -0
  13. DPCNN-THUCNews/Classification/epochs/epoch_1/embeddings.npy +3 -0
  14. DPCNN-THUCNews/Classification/epochs/epoch_1/model.pt +3 -0
  15. DPCNN-THUCNews/Classification/epochs/epoch_1/predictions.npy +3 -0
  16. DPCNN-THUCNews/Classification/epochs/epoch_10/embeddings.npy +3 -0
  17. DPCNN-THUCNews/Classification/epochs/epoch_10/model.pt +3 -0
  18. DPCNN-THUCNews/Classification/epochs/epoch_10/predictions.npy +3 -0
  19. DPCNN-THUCNews/Classification/epochs/epoch_11/embeddings.npy +3 -0
  20. DPCNN-THUCNews/Classification/epochs/epoch_11/model.pt +3 -0
  21. DPCNN-THUCNews/Classification/epochs/epoch_11/predictions.npy +3 -0
  22. DPCNN-THUCNews/Classification/epochs/epoch_12/embeddings.npy +3 -0
  23. DPCNN-THUCNews/Classification/epochs/epoch_12/model.pt +3 -0
  24. DPCNN-THUCNews/Classification/epochs/epoch_12/predictions.npy +3 -0
  25. DPCNN-THUCNews/Classification/epochs/epoch_13/embeddings.npy +3 -0
  26. DPCNN-THUCNews/Classification/epochs/epoch_13/model.pt +3 -0
  27. DPCNN-THUCNews/Classification/epochs/epoch_13/predictions.npy +3 -0
  28. DPCNN-THUCNews/Classification/epochs/epoch_14/embeddings.npy +3 -0
  29. DPCNN-THUCNews/Classification/epochs/epoch_14/model.pt +3 -0
  30. DPCNN-THUCNews/Classification/epochs/epoch_14/predictions.npy +3 -0
  31. DPCNN-THUCNews/Classification/epochs/epoch_15/embeddings.npy +3 -0
  32. DPCNN-THUCNews/Classification/epochs/epoch_15/model.pt +3 -0
  33. DPCNN-THUCNews/Classification/epochs/epoch_15/predictions.npy +3 -0
  34. DPCNN-THUCNews/Classification/epochs/epoch_16/embeddings.npy +3 -0
  35. DPCNN-THUCNews/Classification/epochs/epoch_16/model.pt +3 -0
  36. DPCNN-THUCNews/Classification/epochs/epoch_16/predictions.npy +3 -0
  37. DPCNN-THUCNews/Classification/epochs/epoch_17/embeddings.npy +3 -0
  38. DPCNN-THUCNews/Classification/epochs/epoch_17/model.pt +3 -0
  39. DPCNN-THUCNews/Classification/epochs/epoch_17/predictions.npy +3 -0
  40. DPCNN-THUCNews/Classification/epochs/epoch_18/embeddings.npy +3 -0
  41. DPCNN-THUCNews/Classification/epochs/epoch_18/model.pt +3 -0
  42. DPCNN-THUCNews/Classification/epochs/epoch_18/predictions.npy +3 -0
  43. DPCNN-THUCNews/Classification/epochs/epoch_19/embeddings.npy +3 -0
  44. DPCNN-THUCNews/Classification/epochs/epoch_19/model.pt +3 -0
  45. DPCNN-THUCNews/Classification/epochs/epoch_19/predictions.npy +3 -0
  46. DPCNN-THUCNews/Classification/epochs/epoch_2/embeddings.npy +3 -0
  47. DPCNN-THUCNews/Classification/epochs/epoch_2/model.pt +3 -0
  48. DPCNN-THUCNews/Classification/epochs/epoch_2/predictions.npy +3 -0
  49. DPCNN-THUCNews/Classification/epochs/epoch_20/embeddings.npy +3 -0
  50. DPCNN-THUCNews/Classification/epochs/epoch_20/model.pt +3 -0
DPCNN-THUCNews/Classification/README.md ADDED
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+ # TextCNN 可视化实验脚本
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+
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+ 基于原始 Chinese-Text-Classification-Pytorch 仓库重构的 TextCNN 训练脚本，专门用于可视化实验。
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+
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+ ## 目录结构
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+
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+ ```
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+ Classification/
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+ ├── scripts/ # 脚本文件
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+ │ ├── model.py # 模型定义，包含feature、get_prediction、prediction函数
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+ │ ├── train.py # 训练脚本，支持多卡训练
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+ │ ├── train.yaml # 训练配置文件
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+ │ ├── dataset_utils.py # 数据集处理工具
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+ │ └── get_label.py # 标签提取脚本
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+ ├── dataset/ # 数据集文件
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+ │ ├── train.txt # 训练数据
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+ │ ├── dev.txt # 验证数据
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+ │ ├── test.txt # 测试数据
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+ │ ├── class.txt # 类别列表
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+ │ ├── vocab.pkl # 词汇表
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+ │ └── labels.npy # 提取的标签
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+ └── epochs/ # 按epoch存放模型文件和特征向量
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+ ├── epoch_1/
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+ │ ├── model.pt # 模型权重
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+ │ ├── embeddings.npy # 特征向量
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+ │ └── predictions.npy # 预测值
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+ └── epoch_2/
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+ └── ...
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+ ```
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+
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+ ## 功能说明
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+
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+ ### 1. model.py
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+ - **Model类**: TextCNN模型实现
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+ - **feature()**: 提取中间层特征向量（dropout层输出），用于可视化
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+ - **get_prediction()**: 获取模型最终层输出向量（logits）
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+ - **prediction()**: 根据中间特征向量预测结果
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+
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+ ### 2. train.py
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+ - 支持多GPU训练
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+ - 每个epoch自动保存模型、特征向量、预测值到 `epochs/epoch_N/`
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+ - 支持配置文件驱动训练
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+ - 实时显示训练进度和验证结果
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+
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+ ### 3. dataset_utils.py
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+ - 数据集加载和预处理
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+ - 词汇表构建
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+ - 数据迭代器实现
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+
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+ ### 4. get_label.py
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+ - 提取数据集标签并保存为 `labels.npy`
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+ - 生成类别名称映射文件
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+
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+ ## 使用方法
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+
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+ ### 1. 准备数据集
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+ 将THUCNews数据集放入 `dataset/` 目录：
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+ ```bash
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+ # 数据格式：每行一个样本，用tab分隔文本和标签
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+ text1\t0
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+ text2\t1
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+ ...
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+ ```
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+
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+ ### 2. 提取标签
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+ ```bash
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+ cd scripts
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+ python get_label.py --config train.yaml --output ../dataset
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+ ```
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+
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+ ### 3. 训练模型
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+ ```bash
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+ cd scripts
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+ python train.py --config train.yaml
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+ ```
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+
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+ ### 4. 配置文件说明
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+ 编辑 `scripts/train.yaml` 来调整训练参数：
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+ ```yaml
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+ dataset_path: "../dataset" # 数据集路径
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+ num_epochs: 20 # 训练轮数
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+ batch_size: 128 # 批次大小
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+ learning_rate: 0.001 # 学习率
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+ use_word: false # false=字符级，true=词级
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+ epochs_dir: "../epochs" # 模型保存路径
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+ ```
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+
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+ ## 可视化数据
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+
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+ 训练完成后，每个epoch的数据保存在 `epochs/epoch_N/` 中：
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+ - `model.pt`: 模型权重文件
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+ - `embeddings.npy`: 特征向量矩阵 (N_samples, feature_dim)
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+ - `predictions.npy`: 预测值矩阵 (N_samples, num_classes)
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+
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+ 这些数据可以直接用于可视化分析，如t-SNE降维、特征分布分析等。
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+
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+ ## 多GPU训练
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+
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+ 脚本自动检测可用GPU数量并启用多GPU训练：
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+ ```python
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+ # 自动使用所有可用GPU
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+ if torch.cuda.device_count() > 1:
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+ model = nn.DataParallel(model)
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+ ```
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+
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+ ## 依赖要求
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+
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+ ```bash
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+ pip install torch numpy scikit-learn tqdm pyyaml
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+ ```
DPCNN-THUCNews/Classification/dataset/THUCNews/data/class.txt ADDED
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+ finance
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+ realty
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+ stocks
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+ education
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+ science
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+ society
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+ politics
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+ sports
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+ game
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+ entertainment
DPCNN-THUCNews/Classification/dataset/THUCNews/data/dev.txt ADDED
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DPCNN-THUCNews/Classification/dataset/index.json ADDED
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DPCNN-THUCNews/Classification/dataset/info.json ADDED
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+ {
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+ "model": "DPCNN",
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+ "classes": [
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+ "finance",
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+ "realty",
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+ "stocks",
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+ "education",
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+ "science",
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+ "society",
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+ "politics",
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+ "sports",
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+ "game",
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+ "entertainment"
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+ ]
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+ }
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