Comments (3)
latstars
commented on June 26, 2024
对的，这个代码只支持unrolled=True。unrolled=False是不支持的，论文中一阶梯度是没有定义的，实际上使用一阶梯度也是不合理的。
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Trent-tangtao
commented on June 26, 2024
好哒，感谢
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Euphoria16
commented on June 26, 2024
按照作者现在的code，更新architecture parameters的时候就会没有关于ops weight和probabilities的梯度（因为这两个是通过sample得到的），而magnitude不一样所以有梯度也就可以更新，因为看起来code里每次augmentation都是离散的，没有用softmax给relax成连续的啊？这跟论文里说的是不是不一致？
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Related Issues (20)
- About method DifferentiableAugment HOT 5
- Update install instruction b to use == to specify the version of cudatoolkit instead of =
- How to add (weight, probability and magnitude) to the forward calculation to calculate the gradient？ HOT 3
- About DifferentiableAugment HOT 4
- No genotypes module HOT 1
- How to choose the final policy in the search phase? HOT 4
- Why in search-gumbel-architect.py-def _backward_step_unrolled(), dalpha is []? HOT 1
- Why you put two "elif dataset == 'reduced'": HOT 2
- why did you produce 5 splits in "sss = StratifiedShuffleSplit(n_splits=5, test_size=split, random_state=0)"
- A question about the gradients of sampling HOT 1
- Low accuracy while searching HOT 6
- Ops weights and probabilities not getting updated HOT 5
- Reduced ImageNet split HOT 2
- Error in search_gumbel HOT 2
- How to get the NetworkCIFAR ? HOT 2
- Explaination for Equation 10
- dataloader num_workers=0 HOT 6
- Could you provide ILSVRC/ImageSets/CLS-LOC/train_cls.txt? HOT 1
- Extract the found policy HOT 4
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