Torch downgrade removed from W3D3 (NeuromatchAcademy#905) #934
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This commit fixes issue NeuromatchAcademy#905. `torch 2.0` introduced an out of memory bug in the tutorial, which was addressed with commit ed8f9f2 by downgrading to `torch 1.11`. The pytorch bug was fixed in `torch 2.1` through commit [fff4a9](pytorch/pytorch@fff4a9d). Since Google Colab now comes with `torch 2.1` installed by default, the out of memory bug no longer occurs when running the notebook without the pytorch downgrade. I also changed the supervised learning performance reported in table from `98.67%` to `98.70%`. Unlike the other methods (VAE, SimCLR, etc.), the supervised result has never been perfectly identical on each run. It varies a little bit around `98.70%` (from `98.67%` to `98.73%`, when I've run it). To avoid confusion for users, the first time this result is reported, I now report it with a `~` (i.e., as `~98.70%`) to signal to users that their result may differ slightly from the one reported.
colleenjg
changed the title
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Tagging @spirosChv to let you know about this PR reversing the TL;DR: |
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Fixes this #905 |
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This commit fixes issue #905.
torch 2.0introduced an out of memory bug in the tutorial, which was addressed with commit ed8f9f2 by downgrading totorch 1.11. The pytorch bug was fixed intorch 2.1through commit fff4a9. Since Google Colab now comes withtorch 2.1installed by default, the downgrade is no longer needed.I also changed the supervised learning performance reported in table from
98.67%to98.70%. Unlike the other methods (VAE, SimCLR, etc.), the supervised result has never been perfectly identical on each run. It varies a little bit around98.70%(from98.67%to98.73%, when I've run it). To avoid confusion for users, the first time this result is reported, it is now reported with a~(i.e., as~98.70%) to signal to users that their result may differ slightly from it.