Awesome
rec4torch
推荐系统的pytorch算法实现
1. 下载安装
安装稳定版
pip install rec4torch
安装最新版
pip install git+https://www.github.com/Tongjilibo/rec4torch.git
2. 功能
- 核心功能:基于pytorch实现各类推荐算法(DeepFM, WideDeep, DCN, DIN, DIEN)
- 主要区别:相对于deep-ctr, 去除对tensorflow和keras的依赖,去除重复过embedding的操作,原生支持multiclass
- 训练过程:
2022-10-28 23:16:10 - Start Training 2022-10-28 23:16:10 - Epoch: 1/5 5000/5000 [==============================] - 13s 3ms/step - loss: 0.1351 - acc: 0.9601 Evaluate: 100%|██████████████████████████████████████████████████| 2500/2500 [00:03<00:00, 798.09it/s] test_acc: 0.98045. best_test_acc: 0.98045 2022-10-28 23:16:27 - Epoch: 2/5 5000/5000 [==============================] - 13s 3ms/step - loss: 0.0465 - acc: 0.9862 Evaluate: 100%|██████████████████████████████████████████████████| 2500/2500 [00:03<00:00, 635.78it/s] test_acc: 0.98280. best_test_acc: 0.98280 2022-10-28 23:16:44 - Epoch: 3/5 5000/5000 [==============================] - 15s 3ms/step - loss: 0.0284 - acc: 0.9915 Evaluate: 100%|██████████████████████████████████████████████████| 2500/2500 [00:03<00:00, 673.60it/s] test_acc: 0.98365. best_test_acc: 0.98365 2022-10-28 23:17:03 - Epoch: 4/5 5000/5000 [==============================] - 15s 3ms/step - loss: 0.0179 - acc: 0.9948 Evaluate: 100%|██████████████████████████████████████████████████| 2500/2500 [00:03<00:00, 692.34it/s] test_acc: 0.98265. best_test_acc: 0.98365 2022-10-28 23:17:21 - Epoch: 5/5 5000/5000 [==============================] - 14s 3ms/step - loss: 0.0129 - acc: 0.9958 Evaluate: 100%|██████████████████████████████████████████████████| 2500/2500 [00:03<00:00, 701.77it/s] test_acc: 0.98585. best_test_acc: 0.98585 2022-10-28 23:17:37 - Finish Training
3. 快速上手
- 参考了deepctr-torch, 使用torch4keras中作为Trainer
- 测试用例
4. 版本说明
- v0.0.2:20240204 更新依赖项torch4keras版本
- v0.0.1:20221027 dcn, deepcrossing, deepfm, din, dien, wide&deep, ncf等模型,训练过程修改为传入dataloader,合并models和layers,合并简化embedding_lookup,去掉一些重复的embedding过程(提速)
5. 更新:
- 20240204:更新依赖项torch4keras版本
- 20221110:增加自定义的TensorDataset和collate_fn_device,支持指定device,防止显存占用多大,用out_dim和loss来替代task参数,兼容多分类
- 20221027:增加deepcrossing, ncf, din, dien算法,使用torch4keras作为trainer
- 20220930:初版提交, 训练过程修改为传入dataloader(参考bert4torch),合并models和layers(模型结构较简单),合并简化embedding_lookup,去掉一些重复的embedding过程(提速)