I want to train a model for more than 100 epochs, and want to see how the sequences of batch affects the result of model.
As far as I know, using SGD, if the models is initialized with fixed seed and if it is trained with fixed batch sequences, the model is trained in deterministic. So with the batch sequences fixed, I can check how the initialization affects the variation of the model’s performance. And as I want to train the model for 160 epochs, I have to save 160 of different fixed seeds of batches to compare each models’ differences.
I failed to find any pytorch dataloader functions or sources or codes that implemented this. Is it right?