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살아 움직이는 머신러닝 파이프라인 설계: 텐서플로와 함께하는 머신러닝 프로젝트 자동화
book

살아 움직이는 머신러닝 파이프라인 설계: 텐서플로와 함께하는 머신러닝 프로젝트 자동화

by 송호연, 하네스 하프케, 캐서린 넬슨
October 2021
Beginner to intermediate
416 pages
9h 3m
Korean
Hanbit Media, Inc.
Content preview from 살아 움직이는 머신러닝 파이프라인 설계: 텐서플로와 함께하는 머신러닝 프로젝트 자동화
79
3
데이터 수집
tfrecord_filepath = os.path.join(data_dir, tfrecord_filename)
# 기존 하위 디렉터리를 설정합니다.
input = example_gen_pb2.Input(splits=[
example_gen_pb2.Input.Split(name=’train’, pattern=’train/*’),
example_gen_pb2.Input.Split(name=’eval’, pattern=’eval/*’),
example_gen_pb2.Input.Split(name=’test’, pattern=’test/*’)
])
# input_config 인수를 추가합니다.
example_gen = CsvExampleGen(input_base=data_dir, input_config=input)
context.run(example_gen)
코드 위치:
chapters
/
chapter3
/
3
-
13
.
py
입력 구성을 정의한 후
input
_
config
인수를 정의하여
ExampleGen
컴포넌트에 설정을 전달
할 수 있습니다.
3.2.2
데이터셋 스패닝
새로운 데이터가 제공될 때 머신러닝 모델을 업데이트할 수 있다는 점은 머신러닝 파이프라인
의 중요한 사용 사례입니다. 이 시나리오에서는
ExampleGen
컴포넌트를 사용하여 스팬을 사
용할 ...
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Publisher Resources

ISBN: 9791162244814