from fastai.gen_doc.nbdoc import *

from fastai.vision import *

path = untar_data(URLs.MNIST_SAMPLE)
path

data = ImageDataBunch.from_folder(path)

learn = cnn_learner(data, models.resnet18, metrics=accuracy)

learn.fit(1)

data = ImageDataBunch.from_folder(path)

ds = data.train_ds

img,label = ds[0]
img

img.show(figsize=(2,2), title='MNIST digit')

img.rotate(35)

help(get_transforms)

tfms = [rotate(degrees=(-20,20)), symmetric_warp(magnitude=(-0.3,0.3))]

fig,axes = plt.subplots(1,4,figsize=(8,2))
for ax in axes: ds[0][0].apply_tfms(tfms).show(ax=ax)

data = ImageDataBunch.from_folder(path, ds_tfms=(tfms, []))

learn = cnn_learner(data, models.resnet18, metrics=accuracy)
learn.fit(1)

interp = ClassificationInterpretation.from_learner(learn)

interp.plot_top_losses(9, figsize=(6,6))

interp.plot_confusion_matrix()

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