Comments (3)
Thank you for the comment!, it's my mistake and it should be that is_training = False on the evaluation of the virtual adversarial loss. I fixed the code, but the final result should not be influenced by this change.
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@takerum Thanks for the reply, I'm running the code with is_training set to True and False. However, so far, it seems that it will influence the training process. I will check the results in the following days. Thanks!
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I see, but the flags on evaluation affects only on its value, and does not affect on the learning parameters. Therefore, the learned model is not be changed by changing the flags on evaluation, while the printed values at each evaluation may be changed.
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Related Issues (16)
- Is there a queue runner to start training? HOT 2
- A concern about VAT and dropout HOT 3
- Unable to replicate results in the paper HOT 3
- PyTorch implementation HOT 3
- Why not update statistics in BatchNorm layer?
- VAT for MNIST
- VAT in segmentation tasks
- Test accuracy at the end of training
- usage of 'range' in 'reduce_max' and 'reduce_sum' needs to be debugged HOT 3
- Unstable training in MNIST
- What are vat.xi and vat.eplison optimal values?. HOT 2
- cannot reproduce your results
- Maybe a wrong loss function HOT 3
- GPU problem HOT 3
- I have a question about your paper HOT 1
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