from fastai.gen_doc.nbdoc import *
from fastai.vision import *
from fastai.callbacks import *

path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)

show_doc(TerminateOnNaNCallback)

model = simple_cnn((3,16,16,2))
learn = Learner(data, model, metrics=[accuracy])
learn.fit_one_cycle(1,1e4)

model = simple_cnn((3,16,16,2))
learn = Learner(data, model, metrics=[accuracy], callbacks=[TerminateOnNaNCallback()])
learn.fit(2,1e4)

show_doc(TerminateOnNaNCallback.on_batch_end)

show_doc(TerminateOnNaNCallback.on_epoch_end)

show_doc(EarlyStoppingCallback)

model = simple_cnn((3,16,16,2))
learn = Learner(data, model, metrics=[accuracy], 
                callback_fns=[partial(EarlyStoppingCallback, monitor='accuracy', min_delta=0.01, patience=3)])
learn.fit(50,1e-42)

show_doc(EarlyStoppingCallback.on_train_begin)

show_doc(EarlyStoppingCallback.on_epoch_end)

show_doc(SaveModelCallback)

model = simple_cnn((3,16,16,2))
learn = Learner(data, model, metrics=[accuracy])
learn.fit_one_cycle(5,1e-4, callbacks=[SaveModelCallback(learn, every='epoch', monitor='accuracy', name='model')])

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

learn.fit_one_cycle(5,1e-4, callbacks=[SaveModelCallback(learn, every='improvement', monitor='accuracy', name='best')])

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

show_doc(SaveModelCallback.on_epoch_end)

show_doc(SaveModelCallback.on_train_end)

show_doc(ReduceLROnPlateauCallback)

show_doc(ReduceLROnPlateauCallback.on_train_begin)

show_doc(ReduceLROnPlateauCallback.on_epoch_end)

show_doc(TrackerCallback)

show_doc(TrackerCallback.get_monitor_value)

show_doc(TrackerCallback.on_train_begin)