Use torch like keras
Project description
Documentation | Bert4torch | Examples | Source code
1. 下载安装
安装稳定版
pip install torch4keras
安装最新版
pip install git+https://github.com/Tongjilibo/torch4keras.git
2. 功能
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简述:抽象出来的Trainer,适用于一般神经网络的训练,仅需关注网络结构代码
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特色:进度条展示训练过程,自定义metric,自带Evaluator, Checkpoint, Tensorboard, Logger等Callback,也可自定义Callback
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初衷:前期功能是作为bert4torch和rec4torch的Trainer
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训练:
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bert4torch的训练过程
- 简单示例: turorials_mnist
4. 版本历史
更新日期 | 版本 | 版本说明 |
---|---|---|
20231208 | v0.1.6.post2 | 监控fit过程,有报错则发送邮件提醒; 解决torch2.0的compile冲突问题; 修复clip_grad_norm的bug |
20230928 | v0.1.5 | 进度条中显示已经训练的时间 |
20230912 | v0.1.4.post2 | History增加plot()方法, 增加add_module()方法,修复0.1.4的_argparse_forward的bug, 增加loss2metrics方法 |
5. 更新历史:
- 20231208: 监控fit过程,有报错则发送邮件提醒; 解决torch2.0的compile冲突问题; 修复clip_grad_norm的bug
- 20230928: 进度条中显示已经训练的时间
- 20230912: History增加plot()方法, 增加add_module()方法,修复0.1.4的_argparse_forward的bug, 增加loss2metrics方法
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