Another thing i tried was to save the model’s output of a single input batch before training and after training.
So, in the same kernel instance, after training i get good results. When i reload the instance, i get poor results for a single input batch. But if i save the results right after training and load in another instance, it would work as I am saving the output of a single input batch. This however is not what’s required.
I need my model outputs to stay consistent even after restarting a kernel and remove any randomness so that its reproducible.
Please give me some more insights as to what’s to be done next