Patch for DSGAN example #494
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…g in DCGAN example
…the other for real data
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I have run both scripts and found that only patch_1 (clipping gradients for both fake and real data) works. I think it is reasonable that the second approach, which uses two optimizers, makes the gradients for actual data too small compared to real data due to the gradient clipping. I also tried applying |
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@Koukyosyumei has updated the pull request. You must reimport the pull request before landing. |
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@facebook-github-bot has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
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@Koukyosyumei Thank you very much for working on this! |
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@Koukyosyumei has updated the pull request. You must reimport the pull request before landing. |
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@facebook-github-bot has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
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@Koukyosyumei has updated the pull request. You must reimport the pull request before landing. |
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@facebook-github-bot has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
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@Koukyosyumei has updated the pull request. You must reimport the pull request before landing. |
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@facebook-github-bot has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
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@Koukyosyumei has updated the pull request. You must reimport the pull request before landing. |
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@facebook-github-bot has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
Types of changes
Motivation and Context / Related issue
I would like to solve #418. I quickly implemented two solutions suggested in the forum. Though I have created two separate py scripts for the solutions, I will experiment with the model performance of each solution and merge the better one. Since this is the first PR for opacus, any suggestions and help are welcome!
How Has This Been Tested (if it applies)
1. Loss (and accuracy)
The losses of the original implementation, solution 1, and solution 2 correspond to green, yellow, and blur lines. Due to the memory limitation, solution 2 works only batch size = 32for my environment (google colab).
2. Examples of generated images
batch size 64
batch size 32
3. Final$\epsilon$
batch size 64
Checklist