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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153
6
장
모델 학습
이런 분산 전략은 단일
GPU
메모리에 맞지 않는 대규모 학습 작업에 유용합니다. 다음 절에서
논의할 모델 튜닝에 이런 전략들이 필요합니다.
6.5
모델 튜닝
하이퍼파라미터 튜닝은 정확한 머신러닝 모델을 달성하는 데 있어 중요한 부분입니다. 사용 사
례에 따라 초기 실험 중에 수행하거나 파이프라인에 포함할 수 있습니다.여기서는 모델 튜닝의
간략한 개요를 제공하고 모델 튜닝을 파이프라인에 어떻게 포함하는지 설명합니다.
6.5.1
하이퍼파라미터 튜닝 전략
파이프라인에 있는 모델 유형에 따라 하이퍼파라미터의 선택이 달라집니다. 모델이 심층 신경
망
deep
neural
network
일 때, 신경망으로 우수한 성능을 달성하려면 하이퍼파라미터 튜닝이 특히 중
요합니다. 튜닝해야 할 핵심 하이퍼파라미터 두 가지는 최적화와 네트워크 아키텍처를 제어하
는 것입니다.
최적화에는 기본적으로
Adam
이나
NAdam
을 사용하면 좋습니다. 학습률은 실험에 매우 중
요한 매개변수이며 학습률 스케줄러에는 여러 가지 옵션이 있습니다.
GPU
메모리에 맞는 가
장 큰 배치 크기를 사용하기를 권장합니다.
특히 대규모 모델에서는 아래의 방식을 제안합니다.
●
0
.
1
로 시작하여 초기 학습률
learning
rate
를 조정합니다.
●
학습 단계를 선택합니다 (인내
patience
가 허용하는 수만큼 ).
●
지정된 단계 수에 걸쳐 학습률을
0
으로 선형적으로 감소시킵니다.
소형 모델에는 조기 종료
early
stopping
를 사용해서 과적합
overfitting ...
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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.