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Home Page: http://pages.cs.wisc.edu/~fmu/gradfeat20
License: MIT License
Gradients as Features for Deep Representation Learning
Home Page: http://pages.cs.wisc.edu/~fmu/gradfeat20
License: MIT License
----- Training phase -----
Traceback (most recent call last):
File "./src/benchmark.py", line 228, in
main()
File "./src/benchmark.py", line 219, in main
args.niter, args.stepsize, losses, it=it)
TypeError: train() missing 1 required positional argument: 'losses'
Fix:
while it < args.niter:
it = train(
device=device, loader=train_loader, model=net, mode=args.mode, optimizer=optimizer,
niter=args.niter, stepsize=args.stepsize, losses=losses, it=it)
Thank you for sharing your code.
Thanks for this great work!
Why jvp has to be calculated via NTK parameterized linear layers? What if just using vanilla Conv2d for the jvp calculation?
https://github.com/fmu2/gradfeat20/blob/master/src/model.py#L106
I tried to run the script, but i cannot use torch.load('name.pt') to load the pre-trained model.
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