junya-chen / flatclr Goto Github PK

Hi, I was wondering if you could provide a list of dependencies in this repo (e.g., in the form of requirements.txt). I think this should help with the reproducibility of the results in the paper.

Hi，Below line 114 of flatclr.py, no matter what the value of v is, the value of loss_vec will be a vector of all ones. Therefore, in line 119, loss_vec.mean()-1 must be 0. So what is the significance of this item? At the same time, detach is added after the cross_entropy of the item after loss. According to my understanding, this means that the calculated value of this item will not return the gradient. So how does the loss value optimize the network?