Open kuoluo1995 opened 4 years ago
And initializer is very important. Your method will be exploding gradients when pg = 6.
thx for reporting the issue!
some of my implementations are not maintained and there're several issues like PGGAN impl of mine (not perfectly impled) :(
someday, i'll fix the issues as possible as i can. thank you in advance!
I find PGGAN can not train. When pg ==1, generator make the fake image which shape is [-1,16,16, 3]. And i find reason: because generator has upsample twice on line of block the pggan_model.py . repair