Non-determinisic results
I run the follow code before definition of modules. (My model uses Embeding, Dropout, LSTM, and Linear layers.) <SCODE>torch.manual_seed(1000)
torch.backends.cudnn.enabled = False
torch.cuda.manual_seed(1000)
<ECODE> However, the final results are still different for each trial (regardless I use CPU or GPU). Are there any other things I should do to get the deterministic results given the same input? Thanks.
supakjk
apaszke
apaszke
supakjk
fmassa
supakjk
fmassa
edgarriba
apaszke
edgarriba
apaszke
edgarriba
I am trying to understand the role of seeding in pytorch. For example, if I have a model trained with a specific seed, can I say, it will produce the same output for a specific input? While with no seed, its not guaranteed to produce the same output? One thing is also bothering me that if I train without setting any seed, why I would get different output for the same input given that there is no randomness associated with my model?
Samrat_Hasan
Houjing_Huang