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[유쉘] 7.가중치 감소
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오버피팅을 개선할 수 있는 가중치 감소에 대한 기본적인 아이디어를 제공해 줍니다.
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[유쉘] 7.가중치 감소
1.
가중치 감소
2.
유쉘 산업시스템 공학과를 졸업하고 딥러닝에
대해 공부하고 있습니다 yushell0417@gmail.com
3.
3 언더피팅은 개선 하였지만 오버피팅이
일어날 경우에는 어떻게해야 할까요?
4.
4 오버피팅이란, 학습 데이터에 너무
적합하게(fit) 되어 실험 데이터에 정확도가 저하되는 현상을 말합니다.
5.
5 오버피팅이 일어나는 이유
중에 하나는 가중치(W) 가중치(W)가중치(W)가중치(W) 가중치의 매개변수의 값이 커져서 발생하는 경우가 많습니다.
6.
6 오버피팅에 대해 그림으로
이해해 볼까요?
7.
7 : 학습 데이터 정확도
: 99 % 학습 데이터를 99% 맞춘 모델입니다.
8.
8 이 모델을 가지고
실험 데이터에 적용해 보도록 하겠습니다.
9.
9 학습데이터에 적합한 모델을 실험
데이터에 정확도를 확인해 본 결과!! 정확도가 잘 나오지 않습니다.. ㅍ : 실험 데이터 정확도 : 71 %
10.
10 실험 데이터의 성능을
향상 시키기 위한 방법은 무엇이 있을까요?
11.
11 가중치 감소란, 학습
데이터를 학습시킬 때 가중치에 혼돈을 주어 범용성을 늘리자는 방법입니다.
12.
12 학습 데이터 학습
시 가중치에 혼돈을주어 모델의 범용성을 늘립니다. : 학습 데이터 정확도 : 90 %
13.
13 이 모델에 테스트
데이터의 정확도를 확인해 보면..??
14.
14 정확도가 전 보다
높아진 것을 확인할 수 있습니다. 또한 두 데이터 간의 정확도 차이도 줄었습니다. : 실험 데이터 정확도 : 83 % ㅍ
15.
15 그럼 가중치 감소의
종류는 어떤 것이 있을까요?
16.
16 가중치 감소의 종류는
17.
17 L1 정규화: 가중치
절대 값의 합 만큼 가중치에 혼돈을 주고
18.
18 L2 정규화: 가중치
제곱 값의 합 만큼 가중치에 혼돈을 줍니다.
19.
19 가중치에 혼돈을 주는 L1,
L2 정규화를 사용하면 오버피팅을 줄일 수 있습니다
20.
감사합니다
Download now