I made my own custom model and now run with online training (batch size = 1). As a next step, I want extend the model to take mini-batch. Then I want to study about how to support mini-batch on PyTorch.
Does it need to extend parameter size holding for all indentical one propagation?
I think that it is natural to have amount of mini-batch size for input and its label. But regarding parameters, I think the model should not have the size because of use the model and parameter for identical inference after its deploying.
Yes, the size of the parameters is independent of the batch size.
Your code is most likely already using “batched” inputs with a batch size of 1, so increasing the batch size should work out of the box.
Again, thank you for your comment, before running, I did replace constant “1” in loading input with hyper-parameter being set to large number. And now it works (^ - ^).
My model did not work on TensorFlow but now it works fine with PyTorch.