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 don’t understand is how did the authors jump to a learning rate of 3e-03 from Minimum/10 being 8.32e-03? Shouldn’t it be
learn.fine_tune(2, base_lr=8.32e-03)
as per the authors’ above tips on finding the learning rate?
I don’t have too much mathematical background so please forgive me if it’s supposed to be a no-brainer.