Why training iterations are sometimes equal to the number of epochs specified plus one?

Sometimes, when I specify that I want the training to last for n epochs in the fit_one_cycle method, the training goes on for n+1 epochs. See the example in the picture. I have set the number of epochs equal to 3, but it seems I get 4 epochs of training.

That is something that I am experiencing both in version 1.0 and 2.0 of fastai software.

Without seeing the code of your do_fit function, it’s hard to understand what’s going on.

Sure, it’s nothing much. Something like that…

def do_fit(bs, sz, epochs, lr, freeze=True):
    if freeze:
        if learn.opt is not None: learn.opt.clear_state()
        learn.fit_one_cycle(epochs, slice(lr))
    learn.fit_one_cycle(epochs, slice(lr))

The learn variable comes from this:

learn = cnn_learner(dbch, xresnet50, loss_func=loss_func, opt_func=opt_func, metrics=metrics)

Never mind, it was a silly bug. I forgot the else after the if statement. So, after the fit_one_cycle with the model freezed it moved on to fit_one_cycle with the model unfreezed. Thus the one epoch became two epochs…