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


show_doc(cnn_learner)

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

learner = cnn_learner(data, models.resnet18, metrics=[accuracy])
learner.fit_one_cycle(1,1e-3)

learner.save('one_epoch')

show_doc(unet_learner)

jekyll_warn("The models created with this function won't work with pytorch `nn.DataParallel`, you have to use distributed training instead!")

show_doc(Learner.predict)

img = learner.data.train_ds[0][0]
learner.predict(img)

learner.export()

learn = load_learner(path)

show_doc(create_body)

show_doc(create_head, doc_string=False)

show_doc(ClassificationInterpretation, title_level=3)

learn = cnn_learner(data, models.resnet18)
learn.fit(1)
preds,y,losses = learn.get_preds(with_loss=True)
interp = ClassificationInterpretation(learn, preds, y, losses)

show_doc(ClassificationInterpretation.from_learner, full_name='from_learner')

show_doc(Learner.interpret, full_name='interpret')

show_doc(ClassificationInterpretation.plot_top_losses, full_name='plot_top_losses')

interp.plot_top_losses(9, figsize=(7,7))

show_doc(ClassificationInterpretation.plot_multi_top_losses, full_name='plot_multi_top_losses')