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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187
7
장
모델 분석 및 검증
resolver_class=latest_blessed_model_resolver.LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing)
)
context.run(model_resolver)
7.5.2
Evaluator
컴포넌트
Evaluator
컴포넌트는
TFMA
라이브러리를 사용하여 검증 데이터셋에 대한 모델의 예측을
평가합니다.
ExampleGen
컴포넌트의 입력 데이터,
EvalConfig
요소의 학습된 모델,
TFMA
용
EvalConfig
(
TFMA
를 독립 실행형 라이브러리로 사용할 때와 동일 )를 사용합니다.
먼저
EvalConfig
를 정의합니다.
import
tensorflow_model_analysis
as
tfma
eval_config=tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key=’consumer_disputed’)],
slicing_specs=[tfma.SlicingSpec(),
tfma.SlicingSpec(feature_keys=[‘product’])],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(class_name=’BinaryAccuracy’), ...
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