from fastai.vision.all import *
path = untar_data(URLs.PETS) / 'images'
print(path)


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), bs=5)
learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)

7439d5fbe785b69d49bb11308d8a32f51045aeaa
<mask token>


def is_cat(x):
    return x[0].isupper()


<mask token>

<mask token>
print(path)


def is_cat(x):
    return x[0].isupper()


<mask token>
learn.fine_tune(1)

<mask token>
path = untar_data(URLs.PETS) / 'images'
print(path)


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), bs=5)
learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)

from fastai.vision.all import *
path = untar_data(URLs.PETS) / 'images'
print(path)


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), bs=5)
learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)
