I was wondering if anyone else has noticed this but most examples on the pytorch repo only split the data two ways train/test and they use the test set to evaluate model performance after every epoch, isn’t that just plain wrong? Even splitting three ways train/valid/test with datasets of size similar to mnist and cifar I’m still concerned whether that’s enough? Any thoughts on the above issues or am I dead wrong on this?