The paper L. Smith created did some things premature because he had a trip, and no enough time to polish the paper. I think this was the main reason.

Ok…

I think measuring loss is better adoption in here.

Yes, maybe it’s something like when training the model, it is better to observe the loss value.

They save the model and then reload the initial state when done, check the  on_train_end .

So that means no updating for the weights in the model, during the learning rate finder 8 working, Is it right?

However, these batch data is very close if you check std and mean, particularly if the batch size is large enough, so I think this is the reason to slide trough batches as S. Googer wrote in algorithm.

I did not verify with my own model and input data.
But that, these batch data is very close if you check std and mean, particularly if the batch size is large enough, may be right.