thalitadru / ldmnet-pytorch Goto Github PK

Thank you for your code. I'm a freshman in pytorch. I want to ask if I want to extract the kernels in each iteration and use them in another class, how to do that? I mean are there some methods and functions that can help me with it.

when I tried "python main.py with cifar train_size=1000 device=cuda dropout=0.5 alphaupdate.lambda_bar=0.01", the train loss rapidly increased and the program broke down. I found this can be avoided by decreasing "mu". However, after doing so, the accuracy first increased (to around 21%) but then decreased back to around 18.0% during the whole training process (500 epochs). Have you checked that?

Hi! I think your LDMNet-pytorch project is really excellent, but I found out that if I ran using 'python main.py with mnist' then the train loss would greatly increase at last. I think this may be due to that you're confused by these two different concepts——"epoch" and "iteration". And in the paper the author use the term 'epoch' instead of 'iteration'.
When I change the hyparameters by using 'python main.py with mnist epochs_update=200 max_epochs=50000' (which means I use 100 iterations for every epoch), I find that the train loss will steadily decrease rather than greatly increase.
Looking forward to your reply, thanks a lot!