def lr_find(learn:Learner, start_lr:Floats=1e-7, end_lr:Floats=10, num_it:int=100, stop_div:bool=True, wd:float=None):
    "Explore lr from `start_lr` to `end_lr` over `num_it` iterations in `learn`. If `stop_div`, stops when loss diverges."
    start_lr = learn.lr_range(start_lr)
    start_lr = np.array(start_lr) if is_listy(start_lr) else start_lr
    end_lr = learn.lr_range(end_lr)
    end_lr = np.array(end_lr) if is_listy(end_lr) else end_lr
    cb = LRFinder(learn, start_lr, end_lr, num_it, stop_div)
    epochs = int(np.ceil(num_it/len(learn.data.train_dl)))
    learn.fit(epochs, start_lr, callbacks=[cb], wd=wd)
def lr_find ( learn : Learner , start_lr : Floats = 1e-7 , end_lr : Floats = 10 , num_it : int = 100 , stop_div : bool = True , wd : float = None ) : start_lr = learn . lr_range ( start_lr ) start_lr = np . array ( start_lr ) if is_listy ( start_lr ) else start_lr end_lr = learn . lr_range ( end_lr ) end_lr = np . array ( end_lr ) if is_listy ( end_lr ) else end_lr cb = LRFinder ( learn , start_lr , end_lr , num_it , stop_div ) epochs = int ( np . ceil ( num_it / len ( learn . data . train_dl ) ) ) learn . fit ( epochs , start_lr , callbacks = [ cb ] , wd = wd )
Explore lr from `start_lr` to `end_lr` over `num_it` iterations in `learn`. If `stop_div`, stops when loss diverges.