Hi.

I am having trouble understanding the explanation in the official fastai book(p.206~207) concerning how to find an appropriate learning rate using the learning rate finder.

When I run the learning rate finder using:

`learn = cnn_learner(dls, resnet34, metrics=error_rate)`

`lr_min,lr_steep = learn.lr_find()`

`print(f"Minimum/10: {lr_min:.2e}, steepest point: {lr_steep:.2e}")`

I get:

` Minimum/10: 8.32e-03, steepest point: 6.31e-03`

The author mentions that the best way to find the learning rate is:

Our advice is to pick either of these:

*One order of magnitude less than where the minimum loss was achieved(i.e., the minimum divided by 10)

*The last point where the loss was clearly decreasing

What I donâ€™t understand is how did the authors jump to a learning rate of 3e-03 from Minimum/10 being 8.32e-03? Shouldnâ€™t it be

`learn.fine_tune(2, base_lr=8.32e-03)`

as per the authorsâ€™ above tips on finding the learning rate?

I donâ€™t have too much mathematical background so please forgive me if itâ€™s supposed to be a no-brainer.