List of callbacks¶

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from fastai.gen_doc.nbdoc import *
from fastai.vision import *
from fastai.text 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):

`LRFinder`¶

Use Leslie Smith's learning rate finder to find a good learning rate for training your model. Let's see an example of use on the MNIST dataset with a simple CNN.

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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()

The fastai librairy already has a Learner method called lr_find that uses LRFinder to plot the loss as a function of the learning rate

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learn.lr_find()

LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.

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learn.recorder.plot()

In this example, a learning rate around 2e-2 seems like the right fit.

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lr = 2e-2

`OneCycleScheduler`¶

Train with Leslie Smith's 1cycle annealing method. Let's train our simple learner using the one cycle policy.

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learn.fit_one_cycle(3, lr)

Total time: 00:07

epoch	train_loss	valid_loss	accuracy	time
0	0.109439	0.059349	0.980864	00:02
1	0.039582	0.023152	0.992149	00:02
2	0.019009	0.021239	0.991659	00:02

The learning rate and the momentum were changed during the epochs as follows (more info on the dedicated documentation page).

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learn.recorder.plot_lr(show_moms=True)

`MixUpCallback`¶

Data augmentation using the method from mixup: Beyond Empirical Risk Minimization. It is very simple to add mixup in fastai :

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learn = Learner(data, simple_cnn((3, 16, 16, 2)), metrics=[accuracy]).mixup()

`CSVLogger`¶

Log the results of training in a csv file. Simply pass the CSVLogger callback to the Learner.

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learn = Learner(data, simple_cnn((3, 16, 16, 2)), metrics=[accuracy, error_rate], callback_fns=[CSVLogger])

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learn.fit(3)

Total time: 00:07

epoch	train_loss	valid_loss	accuracy	error_rate	time
0	0.127259	0.098069	0.969578	0.030422	00:02
1	0.084601	0.068024	0.974975	0.025025	00:02
2	0.055074	0.047266	0.983317	0.016683	00:02

You can then read the csv.

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learn.csv_logger.read_logged_file()

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	epoch	train_loss	valid_loss	accuracy	error_rate
0	0	0.127259	0.098069	0.969578	0.030422
1	1	0.084601	0.068024	0.974975	0.025025
2	2	0.055074	0.047266	0.983317	0.016683

`GeneralScheduler`¶

Create your own multi-stage annealing schemes with a convenient API. To illustrate, let's implement a 2 phase schedule.

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def fit_odd_shedule(learn, lr):
    n = len(learn.data.train_dl)
    phases = [TrainingPhase(n).schedule_hp('lr', lr, anneal=annealing_cos), 
              TrainingPhase(n*2).schedule_hp('lr', lr, anneal=annealing_poly(2))]
    sched = GeneralScheduler(learn, phases)
    learn.callbacks.append(sched)
    total_epochs = 3
    learn.fit(total_epochs)

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learn = Learner(data, simple_cnn((3,16,16,2)), metrics=accuracy)
fit_odd_shedule(learn, 1e-3)

Total time: 00:07

epoch	train_loss	valid_loss	accuracy	time
0	0.176607	0.157229	0.946025	00:02
1	0.140903	0.133690	0.954367	00:02
2	0.130910	0.131156	0.956820	00:02

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learn.recorder.plot_lr()

`MixedPrecision`¶

Use fp16 to take advantage of tensor cores on recent NVIDIA GPUs for a 200% or more speedup.

`HookCallback`¶

Convenient wrapper for registering and automatically deregistering PyTorch hooks. Also contains pre-defined hook callback: ActivationStats.

`RNNTrainer`¶

Callback taking care of all the tweaks to train an RNN.

`TerminateOnNaNCallback`¶

Stop training if the loss reaches NaN.

`EarlyStoppingCallback`¶

Stop training if a given metric/validation loss doesn't improve.

`SaveModelCallback`¶

Save the model at every epoch, or the best model for a given metric/validation loss.

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learn = Learner(data, simple_cnn((3,16,16,2)), metrics=accuracy)
learn.fit_one_cycle(3,1e-4, callbacks=[SaveModelCallback(learn, every='epoch', monitor='accuracy')])

Total time: 00:07

epoch	train_loss	valid_loss	accuracy	time
0	0.679189	0.646599	0.804220	00:02
1	0.527475	0.497290	0.908243	00:02
2	0.464756	0.462471	0.917076	00:02

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!ls ~/.fastai/data/mnist_sample/models

best.pth	 bestmodel_2.pth  model_1.pth  model_4.pth    stage-1.pth
bestmodel_0.pth  bestmodel_3.pth  model_2.pth  model_5.pth    tmp.pth
bestmodel_1.pth  model_0.pth	  model_3.pth  one_epoch.pth  trained_model.pth

List of callbacks¶

`Callback`¶

`LRFinder`¶

`OneCycleScheduler`¶

`MixUpCallback`¶

`CSVLogger`¶

`GeneralScheduler`¶

`MixedPrecision`¶

`HookCallback`¶

`RNNTrainer`¶

`TerminateOnNaNCallback`¶

`EarlyStoppingCallback`¶

`SaveModelCallback`¶

`ReduceLROnPlateauCallback`¶

`PeakMemMetric`¶

`StopAfterNBatches`¶

`LearnerTensorboardWriter`¶

`train` and `basic_train`¶

`Recorder`¶

`ShowGraph`¶

`BnFreeze`¶

`GradientClipping`¶

List of callbacks¶

train and basic_train¶

`train` and `basic_train`¶