from fastai.gen_doc.nbdoc import *
from fastai.callbacks import *
from fastai.basic_train import *
from fastai.train import *
from fastai import callbacks
fastai's training loop is highly extensible, with a rich callback system. See the callback docs if you're interested in writing your own callback. See below for a list of callbacks that are provided with fastai, grouped by the module they're defined in.
Every callback that is passed to Learner with the callback_fns parameter will be automatically stored as an attribute. The attribute name is snake-cased, so for instance ActivationStats will appear as learn.activation_stats (assuming your object is named learn).
Callback¶This sub-package contains more sophisticated callbacks that each are in their own module. They are (click the link for more details):
OneCycleScheduler¶Train with Leslie Smith's 1cycle annealing method.
MixedPrecision¶Use fp16 to take advantage of tensor cores on recent NVIDIA GPUs for a 200% or more speedup.
GeneralScheduler¶Create your own multi-stage annealing schemes with a convenient API.
MixUpCallback¶Data augmentation using the method from mixup: Beyond Empirical Risk Minimization
LRFinder¶Use Leslie Smith's learning rate finder to find a good learning rate for training your model.
HookCallback¶Convenient wrapper for registering and automatically deregistering PyTorch hooks. Also contains pre-defined hook callback: ActivationStats.