Learning Rate Finder¶
%matplotlib inline
from fastai.gen_doc.nbdoc import *
from fastai import *
from fastai.vision import *
from fastai.callbacks import *
Learning rate finder plots lr vs loss relationship for a Learner. The idea is to reduce the amount of guesswork on picking a good starting learning rate.
Overview:
- First run lr_find
learn.lr_find() - Plot the learning rate vs loss
learn.recorder.plot() - Pick a learning rate before it diverges then start training
Technical Details: (first described by Leslie Smith)
Train
Learnerover a few iterations. Start with a very lowstart_lrand change it at each mini-batch until it reaches a very highend_lr.Recorderwill record the loss at each iteration. Plot those losses against the learning rate to find the optimal value before it diverges.
Choosing a good learning rate¶
For a more intuitive explanation, please check out Sylvain Gugger's post
path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
def simple_learner(): return Learner(data, simple_cnn((3,16,16,2)), metrics=[accuracy])
learn = simple_learner()
First we run this command to launch the search:
show_doc(Learner.lr_find)
learn.lr_find(stop_div=False, num_it=200)
LR Finder complete, type {learner_name}.recorder.plot() to see the graph.
Then we plot the loss versus the learning rates. We're interested in finding a good order of magnitude of learning rate, so we plot with a log scale.
learn.recorder.plot()
Then, we choose a value that is approximately in the middle of the sharpest downward slope. In this case, training with 3e-2 looks like it should work well:
simple_learner().fit(2, 3e-2)
Total time: 00:03 epoch train_loss valid_loss accuracy 1 0.070224 0.039051 0.986752 (00:01) 2 0.038105 0.043696 0.985280 (00:01)
Don't just pick the minimum value from the plot!:
learn = simple_learner()
simple_learner().fit(2, 1e-0)
Total time: 00:03 epoch train_loss valid_loss accuracy 1 0.724437 0.693147 0.495584 (00:01) 2 0.693758 0.693147 0.495584 (00:01)
Picking a value before the downward slope results in slow training:
learn = simple_learner()
simple_learner().fit(2, 1e-3)
Total time: 00:03 epoch train_loss valid_loss accuracy 1 0.184354 0.168152 0.940137 (00:01) 2 0.146272 0.143661 0.946516 (00:01)
show_doc(LRFinder)
class LRFinder[source]
LRFinder(learn:Learner,start_lr:float=1e-07,end_lr:float=10,num_it:int=100,stop_div:bool=True) ::LearnerCallback
Causes learn to go on a mock training from start_lr to end_lr for num_it iterations. Training is interrupted if the loss diverges. Weights changes are reverted after run complete.
show_doc(LRFinder.on_train_end)
on_train_end[source]
on_train_end(kwargs:Any)
Cleanup learn model weights disturbed during LRFind exploration.
show_doc(LRFinder.on_batch_end)
on_batch_end[source]
on_batch_end(iteration:int,smooth_loss:TensorOrNumber,kwargs:Any)
Determine if loss has runaway and we should stop.
show_doc(LRFinder.on_train_begin)
on_train_begin[source]
on_train_begin(pbar,kwargs:Any)
Initialize optimizer and learner hyperparameters.
show_doc(LRFinder.on_epoch_end)