January 2017
Beginner to intermediate
312 pages
7h 3m
Korean
Content preview from 밑바닥부터 시작하는 딥러닝
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72
밑바닥부터 시작하는 딥러닝
3.2.4
시그모이드 함수 구현하기
이어서 시그모이드 함수를 구현합시다. [식
3
.
6
]의 시그모이드 함수는 파이썬으로 다음과 같
이 작성할 수 있습니다.
def sigmoid
(
x
):
return 1
/
(
1
+
np
.
exp
(-
x
))
여기서
np
.
exp
(-
x
)는
exp
(-
x
) 수식에 해당합니다. 이 구현에서 특별히 어려운 건 없고, 인
수
x
가 넘파이 배열이어도 올바른 결과가 나온다는 정도만 기억해둡시다. 실제로 넘파이 배열
을 제대로 처리하는지 실험해보죠.
>>>
x
=
np
.
array
([-
1
.
0
,
1
.
0
,
2
.
0
])
>>>
sigmoid
(
x
)
array
([
0
.
26894142
,
0
.
73105858
,
0
.
88079708
])
이 함수가 넘파이 배열도 훌륭히 처리해줄 수 있는 비밀은 넘파이의 브로드캐스트에 있습니다
(“
1
.
5
.
5
브로드캐스트” 참고). 브로드캐스트 기능이란 넘파이 배열과 스칼라값의 연산을 넘파
이 배열의 원소 각각과 스칼라값의 연산으로 바꿔 수행하는 것입니다. 복습 겸 구체적인 예를
하나 보시죠.
>>>
t
=
np
.
array
([
1
.
0
,
2
.
0
,
3
.
0
])
>>>
1
.
0
+
t
array
([
2
.,
3
.,
4
.])
>>>
1
.
0
/
t
array
([
1
.
,
0
.
5
,
0
.
33333333
])
이 예에서는 스칼라값
1
.
0
과 넘파이 배열 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.