October 2021
Beginner to intermediate
416 pages
9h 3m
Korean
Content preview from 살아 움직이는 머신러닝 파이프라인 설계: 텐서플로와 함께하는 머신러닝 프로젝트 자동화
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115
5
장
데이터 전처리
5.2.3
모범 사례
TFT
로 작업하면서 우리는 많은 교훈을 얻었습니다. 그중 몇 가지만 살펴보죠.
●
피처 이름은 중요합니다.
전처리의 출력 피처의 이름은 중요합니다. 다음
TFT
구현처럼 입력 피처의 이름을 재사용하고
_xf
를 추
가합니다. 또한 텐서플로 모델의 입력 노드 이름은
preprocessing_fn
함수의 출력 피처 이름과 일치
해야 합니다.
●
데이터 유형을 잘 체크해야 합니다.
TFT
는 출력 피처의 데이터 유형을 제한합니다. 전처리된 모든 피처를
tf.string
,
tf.float32
,
tf.int64
값으로 내보냅니다. 이는 모델이 이런 데이터 유형을 사용할 수 없을 때 중요합니다. 텐서플로
허브의 일부 모델에서는
tf.int32
값으로 입력해야 합니다 (예:
BERT
모델 ). 모델 내의 데이터 유형으로
입력값을 타입 캐스팅하거나 추정기 입력 피처에서 데이터 유형을 변환하면 이런 상황을 방지할 수 있습니다.
●
배치 단위로 전처리해야 합니다.
전처리 함수를 작성할 때 한 번에 한 데이터 행을 처리한다고 생각할 수 있습니다. 실제로
TFT
는 일괄적
으로 작업을 수행합니다. 따라서 변환 컴포넌트의 콘텍스트에서
preprocessing_fn()
함수의 출력을
Tensor
또는
SparseTensor
로 재구성해야 합니다.
●
텐서플로의 즉시 실행
eager
execution
은 사용하지 마세요.
preprocessing_fn()
함수의 내부 함수는 텐서플로의 ...
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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.