#hide
! [ -e /content ] && pip install -Uqq fastbook
import fastbook
fastbook.setup_book()

#hide
from fastbook import *
[[chapter_intro]]
#id first_training
#caption Results from the first training
# CLICK ME
from fastai.vision.all import *
path = untar_data(URLs.PETS)/'images'

def is_cat(x): return x[0].isupper()
dls = ImageDataLoaders.from_name_func(
    path, get_image_files(path), valid_pct=0.2, seed=42,
    label_func=is_cat, item_tfms=Resize(224))

learn = vision_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)

1+1

img = PILImage.create(image_cat())
img.to_thumb(192)

#hide_output
uploader = widgets.FileUpload()
uploader

#hide
# For the book, we can't actually click an upload button, so we fake it
uploader = SimpleNamespace(data = ['images/chapter1_cat_example.jpg'])

img = PILImage.create(uploader.data[0])
is_cat,_,probs = learn.predict(img)
print(f"Is this a cat?: {is_cat}.")
print(f"Probability it's a cat: {probs[1].item():.6f}")

#hide_input
#caption A traditional program
#id basic_program
#alt Pipeline inputs, program, results
gv('''program[shape=box3d width=1 height=0.7]
inputs->program->results''')

#hide_input
#caption A program using weight assignment
#id weight_assignment
gv('''model[shape=box3d width=1 height=0.7]
inputs->model->results; weights->model''')

#hide_input
#caption Training a machine learning model
#id training_loop
#alt The basic training loop
gv('''ordering=in
model[shape=box3d width=1 height=0.7]
inputs->model->results; weights->model; results->performance
performance->weights[constraint=false label=update]''')

#hide_input
#caption Using a trained model as a program
#id using_model
gv('''model[shape=box3d width=1 height=0.7]
inputs->model->results''')

#hide_input
#caption Detailed training loop
#id detailed_loop
gv('''ordering=in
model[shape=box3d width=1 height=0.7 label=architecture]
inputs->model->predictions; parameters->model; labels->loss; predictions->loss
loss->parameters[constraint=false label=update]''')

path = untar_data(URLs.CAMVID_TINY)
dls = SegmentationDataLoaders.from_label_func(
    path, bs=8, fnames = get_image_files(path/"images"),
    label_func = lambda o: path/'labels'/f'{o.stem}_P{o.suffix}',
    codes = np.loadtxt(path/'codes.txt', dtype=str)
)

learn = unet_learner(dls, resnet34)
learn.fine_tune(8)

learn.show_results(max_n=6, figsize=(7,8))

from fastai.text.all import *

dls = TextDataLoaders.from_folder(untar_data(URLs.IMDB), valid='test')
learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy)
learn.fine_tune(4, 1e-2)

learn.predict("I really liked that movie!")

from fastai.tabular.all import *
path = untar_data(URLs.ADULT_SAMPLE)

dls = TabularDataLoaders.from_csv(path/'adult.csv', path=path, y_names="salary",
    cat_names = ['workclass', 'education', 'marital-status', 'occupation',
                 'relationship', 'race'],
    cont_names = ['age', 'fnlwgt', 'education-num'],
    procs = [Categorify, FillMissing, Normalize])

learn = tabular_learner(dls, metrics=accuracy)

learn.fit_one_cycle(3)

from fastai.collab import *
path = untar_data(URLs.ML_SAMPLE)
dls = CollabDataLoaders.from_csv(path/'ratings.csv')
learn = collab_learner(dls, y_range=(0.5,5.5))
learn.fine_tune(10)

learn.show_results()