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

show_doc(create_cnn, doc_string=False)

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

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

learner.save('one_epoch')

show_doc(Learner.predict)

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

data.export()

empty_data = ImageDataBunch.load_empty(path, tfms=get_transforms()[1]).normalize(imagenet_stats)
learn = create_cnn(empty_data, models.resnet18)
learn = learn.load('one_epoch')

show_doc(create_body)

show_doc(create_head, doc_string=False)

show_doc(ClassificationInterpretation)

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

show_doc(ClassificationInterpretation.from_learner)

jekyll_warn('In both those methods `sigmoid` is a deprecated argument and will be removed in a future version.')

show_doc(ClassificationInterpretation.plot_top_losses)

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

show_doc(ClassificationInterpretation.top_losses)

interp.top_losses(9)

show_doc(ClassificationInterpretation.plot_confusion_matrix)

interp.plot_confusion_matrix()

show_doc(ClassificationInterpretation.confusion_matrix)

interp.confusion_matrix()

show_doc(ClassificationInterpretation.most_confused)

interp.confusion_matrix(slice_size=10)

interp.plot_confusion_matrix(slice_size=10)

interp.most_confused(slice_size=10)

show_doc(create_cnn)