August 2021
Beginner to intermediate
720 pages
15h 32m
Korean
Content preview from fastai와 파이토치가 만나 꽃피운 딥러닝
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117
2
모델에서 제품까지
지금까지 모델이 학습하는 데 적합한 형태로 데이터를 구성했습니다. 이제는 데이터를 사용해
실제 이미지 분류 모델을 학습할 차례입니다.
2.4
‘곰’ 분류 모델의 학습에는
1
장에서와 같은 코드를 사용합니다. 곰의 유형별로
150
장씩 수집
된 데이터는 규모가 크지 않으므로
RandomResizedCrop
변형 및 디폴트값으로 구성된
aug
_
transforms
를 활용하여 모델을 학습시킵니다. 이때 모델로 입력된 이미지의 크기를
224px
로 지정했습니다. 이는 이미지 분류 문제에서 일반적으로 사용하는 표준 크기이며
aug
_
transforms
이 작동하는 기본 크기이기도 합니다.
bears = bears.new(item_tfms=RandomResizedCrop(224, min_scale=0.5),
batch_tfms=aug_transforms())
dls = bears.dataloaders(path)
이제 전과 같은 방식으로
Learner
객체
12
를 만들고 미세 조정할 수 있습니다.
learn = cnn_learner(dls, resnet18, metrics=error_rate)
learn.fine_tune(4)
epoch train
loss valid
loss error
rate
time
0 1
.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.