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starting new practical learning branch using nbdev
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# %%
#id first_training
#caption Results from the first training
# CLICK ME
#|export
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)
# %%
from IPython.display import display
from ipywidgets import widgets
uploader = widgets.FileUpload()
display(uploader)
# %%
#|export
# For the book, we can't actually click an upload button, so we fake it
uploader = SimpleNamespace(data = ['data/interim/th-1.webp'])
# %%
#|export
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}")