Comments (5)
Hi, @KleinXin , thanks for your interest of our work. As you mentioned that it is a different dataset and task. the hyperparamters need to be consistent with your usual hyper parameters in your task which may have some difference with the training on imagenet. Besides, the accuracy of the search phase is not informative enough to judge the goodness of the searched architectures. You need to train from scratch with the final searched architectures. Besides, the stop epoch is also changeable.
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Hi, @KleinXin , thanks for your interest of our work. As you mentioned that it is a different dataset and task. the hyperparamters need to be consistent with your usual hyper parameters in your task which may have some difference with the training on imagenet. Besides, the accuracy of the search phase is not informative enough to judge the goodness of the searched architectures. You need to train from scratch with the final searched architectures. Besides, the stop epoch is also changeable.
Do you think it is possible to complete searching and training in one-shot?
In fact, now we are trying to finish the recognition job in the searching step instead of searching architecture followed by training model.
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Another question is that in 'train_search_imagenet.py' line 178, the parameter 'architect' is not transferred into train function as in 'train_searcch.py'.
In line 206 of 'train_search_imagenet.py', architect.step is commented.
Does this mean the architecture is not updated in 'train_search_imagenet.py' ?
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@KleinXin, in fact the architecture is updated. I just moved the update codes from architecture.py towards this file. The update code is the corresponding lines: 200~205. Hyperpaprameter args.begin controls from which epoch the arch paras begin to update. Besides, you can double check by see the printed arch para.
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Hi, @KleinXin , thanks for your interest of our work. As you mentioned that it is a different dataset and task. the hyperparamters need to be consistent with your usual hyper parameters in your task which may have some difference with the training on imagenet. Besides, the accuracy of the search phase is not informative enough to judge the goodness of the searched architectures. You need to train from scratch with the final searched architectures. Besides, the stop epoch is also changeable.
Thanks for your kind reply, really helpful.
I came across the same problem when applying to another classification task, and I just wonder do you meet similar problem that accuracy significantly decreases after epoch args.begin
, when searching on CIFAR10 or ImageNet?
And I am interested in your log files when searching on CIFAR10 or ImageNet, just for reference. Would you like to share it ?
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Related Issues (20)
- Is a channel sampling mask fixed? HOT 3
- Is there any plan to release the pretrained imagenet model? HOT 1
- Why modifying architecture after epoch 15
- Data preparation of ImageNet
- How to change the channel proportion K? HOT 2
- Cannot re-implement your claimed result HOT 3
- GPU Utilization is Bad HOT 1
- We cannot obtain your claimed result on ImageNet after trying many configurations HOT 4
- test.py运行报错
- Understanding the two sets of the architecture hyperparameter HOT 2
- how you report the final accuracy in evaluation? Possibly touch the test set for the best acc? HOT 2
- Learning rate schedule
- 你好,结果不一致 HOT 2
- Searched genotype remain / keep unchanged for a great number of epoch HOT 2
- RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cuda:1!
- 您好,想请问一下网络搜索完之后如何得到需要的网络结构代码? HOT 3
- About the license of this repository
- Hello, whether PC-DARTS likes DARTS with extra dropout?
- Not Enough Comments in the Code
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