Hey, i’m a newcomer to Pytorch. I created a dummy dataset after generating random weight vector and bias, and obtaining ‘y’ using WXt + b. After that I implemented a simple linear regression using Pytorch’s single linear layer, and trained the data with same X and y generated above. The rmse is extremely small and the output is also virtually same as original y, but weight vector is completely different than the original. Bias is same though.
I tried to see if Pytorch somewhat normalizes the weights, so i divided each index of Original W with corresponding index of trained W, and got a constant.
For example:
Original_W = [1.85623121, 0.02777083, 0.32021133]
Trained_W = [11.1365,  0.1659,  1.9205]
Trained_W / Original_W = [5.99952201, 5.97389396, 5.99760163]

(The deviation is probably due to training error)
Can someone please explain?
Thanks!
Saif