In fastai, when calling Learner.fit_one_cycle(n), and seeing the learning rate evolution at the end of training, I am under the impression, that we only go through one cycle, and n is the number of epoch that it takes to perform that cycle.
My understanding of Leslie N. Smith paper and one cycle policy is the following. Instead of having one constant or decreasing learning rate, you modify the learning rate during training in a cyclic manner, going from low learning rates to higher learning rates. From what I read in the paper, the idea is to go through many cycle.
For instance, if I want to train my model fors 50 epochs, my understanding of the Leslie N. Smith paper makes me think that I would get better result by doing let’s say 5 cycles of 10 epochs that 1 cycle of 50 epochs. If I wanted to do those 5 cycles in fastai, I would have to call Learner.fit_one_cycle(10) 5 times which seems very weird to me.
Is my understanding of fit_one_cycle correct? And if so, what’s the best way to train a model for like 50 epochs?