Hello. May I ask about the loss functions inside the training_step?
The paper states that ||x||2 denotes L2 Norm. As I understand, L2 norm is the "square root of sum of squares of elements inside x".
However, the loss of the training step seems to be using MSE (mean((batch-w3)**2) etc).
Was this a change from the paper or did I not understand L2 norm correctly?
Thanks in advance!
Hello. May I ask about the loss functions inside the training_step? The paper states that ||x||2 denotes L2 Norm. As I understand, L2 norm is the "square root of sum of squares of elements inside x". However, the loss of the training step seems to be using MSE (mean((batch-w3)**2) etc). Was this a change from the paper or did I not understand L2 norm correctly? Thanks in advance!