Comments (2)
보고해주신 에러를 해결하여 업로드해두었습니다. cifar와 mnist에 대해서 training이 돌아가는 것을 확인하였습니다.
- 해당 keyword argument들을 지웠습니다.
- OmegaConf의 최신 버젼(2.1.0)에서 key가 없는 경우의 정책이 바뀌어서 발생하는 에러였습니다. 기존에 제가 사용하던OmegaConf 버젼(2.0.6)에서는 key가 없으면 그냥 None을 반환했었습니다.
학습하는 부분을 제가 꼼꼼히 보지 못해서 죄송합니다. 다른 문제점이나 질문 있으면 언제든지 알려주세요.
=======
I have fixed the reported problems. The training code now works for MNIST and CIFAR-10 without errors.
- I have deleted the keyword arguments.
- The error is due to the changed behavior of OmegaConf for missing keys in the newest version (2,1.0). In the version I used, OmegaConf simply returns None when there was no matched key.
Thank you for reporting the issue. Please let me know if there is another problem or question.
Have a nice day!
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보고해주신 에러를 해결하여 업로드해두었습니다. cifar와 mnist에 대해서 training이 돌아가는 것을 확인하였습니다.
- 해당 keyword argument들을 지웠습니다.
- OmegaConf의 최신 버젼(2.1.0)에서 key가 없는 경우의 정책이 바뀌어서 발생하는 에러였습니다. 기존에 제가 사용하던OmegaConf 버젼(2.0.6)에서는 key가 없으면 그냥 None을 반환했었습니다.
학습하는 부분을 제가 꼼꼼히 보지 못해서 죄송합니다. 다른 문제점이나 질문 있으면 언제든지 알려주세요.
=======
I have fixed the reported problems. The training code now works for MNIST and CIFAR-10 without errors.
- I have deleted the keyword arguments.
- The error is due to the changed behavior of OmegaConf for missing keys in the newest version (2,1.0). In the version I used, OmegaConf simply returns None when there was no matched key.
Thank you for reporting the issue. Please let me know if there is another problem or question.
Have a nice day!
안녕하세요. 저번에 말씀드린 부분 중 일부가 수정이 되지 않은 것 같습니다.
train.py 57 line의 clip_grad와 nae.py 39 line의 small_lr_D 부분은 아직 남아있는데, yml파일에는 두 keyword가 존재하지 않아 run이 안되고 있습니다.
from normalized-autoencoders.
Related Issues (12)
- where is cifar10_trainval_idx.npy? HOT 1
- VAE Comparison HOT 1
- Divergence of CD/PCD HOT 2
- Noise Annealing with the Step Size HOT 1
- some key word missing HOT 2
- 안녕하세요. 최근에 딥러닝을 공부하고있는 직장인입니다. 로스함수 관련 궁금한게 있어 문의드립니다. HOT 2
- Some unexpected keyword augments when constructing the model HOT 1
- Not Good Quality of the Generated Images on CIFAR-10 HOT 3
- model_best VS. Training from Scratch HOT 5
- pos_out > neg_out HOT 1
- Availability of 2D Experiments? HOT 1
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