TensorFlow Extended(TFX)에 대한 구성 요소별 소개
이 Colab 기반 튜토리얼은 TensorFlow Extended(TFX)의 각 기본 제공 구성요소를 대화식으로 안내합니다.
데이터 수집에서 모델 푸시, 제공에 이르기까지 종단 간 기계 학습 파이프라인의 모든 단계를 다룹니다.
완료되면 이 노트북의 콘텐츠를 TFX 파이프라인 소스 코드로 자동으로 내보낼 수 있으며, 이를 Apache Airflow 및 Apache Beam으로 오케스트레이션할 수 있습니다.
배경
이 노트북은 Jupyter/Colab 환경에서 TFX를 사용하는 방법을 보여줍니다. 여기에서는 대화형 노트북에서 Chicago Taxi의 예를 살펴보겠습니다.
대화형 노트북에서 작업하는 것은 TFX 파이프라인의 구조에 익숙해지는 데 유용한 방법입니다. 또한 경량 개발 환경으로 자체 파이프라인을 개발할 때 유용하지만 대화형 노트북이 오케스트레이션되는 방식과 메타데이터 아티팩트에 액세스하는 방식에 차이가 있음을 알아야 합니다.
관현악법
TFX의 프로덕션 배포에서는 Apache Airflow, Kubeflow Pipelines 또는 Apache Beam과 같은 오케스트레이터를 사용하여 TFX 구성 요소의 미리 정의된 파이프라인 그래프를 오케스트레이션합니다. 대화형 노트북에서 노트북 자체는 오케스트레이터이며 노트북 셀을 실행할 때 각 TFX 구성 요소를 실행합니다.
메타데이터
TFX의 프로덕션 배포에서는 ML 메타데이터(MLMD) API를 통해 메타데이터에 액세스합니다. MLMD는 메타데이터 속성을 MySQL 또는 SQLite와 같은 데이터베이스에 저장하고 메타데이터 페이로드를 파일 시스템과 같은 영구 저장소에 저장합니다. 대화 형 노트북에서 속성과 페이로드 모두에서 임시 SQLite 데이터베이스에 저장되어있는 /tmp
Jupyter 노트북 또는 Colab 서버의 디렉토리.
설정
먼저 필요한 패키지를 설치 및 가져오고, 경로를 설정하고, 데이터를 다운로드합니다.
핍 업그레이드
로컬에서 실행할 때 시스템에서 Pip를 업그레이드하지 않으려면 Colab에서 실행 중인지 확인하세요. 물론 로컬 시스템은 별도로 업그레이드할 수 있습니다.
try:
import colab
!pip install --upgrade pip
except:
pass
TFX 설치
pip install -U tfx
런타임을 다시 시작하셨습니까?
Google Colab을 사용하는 경우 위의 셀을 처음 실행할 때 런타임을 다시 시작해야 합니다(런타임 > 런타임 다시 시작...). Colab이 패키지를 로드하는 방식 때문입니다.
패키지 가져오기
표준 TFX 구성 요소 클래스를 포함하여 필요한 패키지를 가져옵니다.
import os
import pprint
import tempfile
import urllib
import absl
import tensorflow as tf
import tensorflow_model_analysis as tfma
tf.get_logger().propagate = False
pp = pprint.PrettyPrinter()
from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip
라이브러리 버전을 확인해보자.
print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.7.0 TFX version: 1.5.0
파이프라인 경로 설정
# This is the root directory for your TFX pip package installation.
_tfx_root = tfx.__path__[0]
# This is the directory containing the TFX Chicago Taxi Pipeline example.
_taxi_root = os.path.join(_tfx_root, 'examples/chicago_taxi_pipeline')
# This is the path where your model will be pushed for serving.
_serving_model_dir = os.path.join(
tempfile.mkdtemp(), 'serving_model/taxi_simple')
# Set up logging.
absl.logging.set_verbosity(absl.logging.INFO)
예제 데이터 다운로드
TFX 파이프라인에서 사용할 예제 데이터 세트를 다운로드합니다.
우리가 사용하고있는 데이터 세트는 것입니다 택시는 데이터 세트 여행 시카고시에서 발표합니다. 이 데이터세트의 열은 다음과 같습니다.
픽업_커뮤니티_영역 | 요금 | trip_start_month |
trip_start_hour | trip_start_day | trip_start_timestamp |
픽업 위도 | 픽업_경도 | dropoff_latitude |
dropoff_longitude | trip_miles | 픽업_센서스_트랙 |
dropoff_census_tract | 지불 유형 | 회사 |
trip_seconds | dropoff_community_area | 팁 |
이 데이터 집합으로, 우리는 예측하는 모델을 구축 할 예정 tips
여행의를.
_data_root = tempfile.mkdtemp(prefix='tfx-data')
DATA_PATH = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv'
_data_filepath = os.path.join(_data_root, "data.csv")
urllib.request.urlretrieve(DATA_PATH, _data_filepath)
('/tmp/tfx-datacz9xjro6/data.csv', <http.client.HTTPMessage at 0x7f889af49250>)
CSV 파일을 간단히 살펴보세요.
head {_data_filepath}
pickup_community_area,fare,trip_start_month,trip_start_hour,trip_start_day,trip_start_timestamp,pickup_latitude,pickup_longitude,dropoff_latitude,dropoff_longitude,trip_miles,pickup_census_tract,dropoff_census_tract,payment_type,company,trip_seconds,dropoff_community_area,tips ,12.45,5,19,6,1400269500,,,,,0.0,,,Credit Card,Chicago Elite Cab Corp. (Chicago Carriag,0,,0.0 ,0,3,19,5,1362683700,,,,,0,,,Unknown,Chicago Elite Cab Corp.,300,,0 60,27.05,10,2,3,1380593700,41.836150155,-87.648787952,,,12.6,,,Cash,Taxi Affiliation Services,1380,,0.0 10,5.85,10,1,2,1382319000,41.985015101,-87.804532006,,,0.0,,,Cash,Taxi Affiliation Services,180,,0.0 14,16.65,5,7,5,1369897200,41.968069,-87.721559063,,,0.0,,,Cash,Dispatch Taxi Affiliation,1080,,0.0 13,16.45,11,12,3,1446554700,41.983636307,-87.723583185,,,6.9,,,Cash,,780,,0.0 16,32.05,12,1,1,1417916700,41.953582125,-87.72345239,,,15.4,,,Cash,,1200,,0.0 30,38.45,10,10,5,1444301100,41.839086906,-87.714003807,,,14.6,,,Cash,,2580,,0.0 11,14.65,1,1,3,1358213400,41.978829526,-87.771166703,,,5.81,,,Cash,,1080,,0.0
면책 조항: 이 사이트는 시카고 시 공식 웹사이트인 www.cityofchicago.org의 원본 소스에서 사용하도록 수정된 데이터를 사용하는 응용 프로그램을 제공합니다. 시카고 시는 이 사이트에서 제공되는 데이터의 내용, 정확성, 적시성 또는 완전성에 대해 어떠한 주장도 하지 않습니다. 이 사이트에서 제공하는 데이터는 언제든지 변경될 수 있습니다. 이 사이트에서 제공되는 데이터는 자신의 책임하에 사용되는 것으로 이해됩니다.
InteractiveContext 생성
마지막으로 이 노트북에서 TFX 구성 요소를 대화식으로 실행할 수 있는 InteractiveContext를 만듭니다.
# Here, we create an InteractiveContext using default parameters. This will
# use a temporary directory with an ephemeral ML Metadata database instance.
# To use your own pipeline root or database, the optional properties
# `pipeline_root` and `metadata_connection_config` may be passed to
# InteractiveContext. Calls to InteractiveContext are no-ops outside of the
# notebook.
context = InteractiveContext()
WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq as root for pipeline outputs. WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/metadata.sqlite.
TFX 구성 요소를 대화형으로 실행
다음 셀에서 TFX 구성 요소를 하나씩 생성하고, 각각을 실행하고, 출력 아티팩트를 시각화합니다.
예제젠
ExampleGen
구성 요소는 TFX 파이프 라인의 시작에 보통이다. 다음 작업을 수행합니다.
- 데이터를 훈련 및 평가 세트로 분할(기본적으로 2/3 훈련 + 1/3 평가)
- 로 변환 데이터
tf.Example
형식 (자세히 알아 여기 ) - 에 데이터를 복사
_tfx_root
액세스 할 수있는 다른 구성 요소 디렉토리
ExampleGen
데이터 소스에 대한 입력으로 경로를합니다. 우리의 경우, 이것은이다 _data_root
다운로드 한 CSV를 포함 경로.
example_gen = tfx.components.CsvExampleGen(input_base=_data_root)
context.run(example_gen)
INFO:absl:Running driver for CsvExampleGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:select span and version = (0, None) INFO:absl:latest span and version = (0, None) INFO:absl:Running executor for CsvExampleGen INFO:absl:Generating examples. WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features. INFO:absl:Processing input csv data /tmp/tfx-datacz9xjro6/* to TFExample. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be. INFO:absl:Examples generated. INFO:absl:Running publisher for CsvExampleGen INFO:absl:MetadataStore with DB connection initialized
하자가의 출력 유물 조사 ExampleGen
. 이 구성 요소는 두 개의 아티팩트, 교육 예제 및 평가 예제를 생성합니다.
artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/CsvExampleGen/examples/1
또한 처음 세 가지 교육 예를 살펴볼 수도 있습니다.
# Get the URI of the output artifact representing the training examples, which is a directory
train_uri = os.path.join(example_gen.outputs['examples'].get()[0].uri, 'Split-train')
# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
for name in os.listdir(train_uri)]
# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")
# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
serialized_example = tfrecord.numpy()
example = tf.train.Example()
example.ParseFromString(serialized_example)
pp.pprint(example)
features { feature { key: "company" value { bytes_list { value: "Chicago Elite Cab Corp. (Chicago Carriag" } } } feature { key: "dropoff_census_tract" value { int64_list { } } } feature { key: "dropoff_community_area" value { int64_list { } } } feature { key: "dropoff_latitude" value { float_list { } } } feature { key: "dropoff_longitude" value { float_list { } } } feature { key: "fare" value { float_list { value: 12.449999809265137 } } } feature { key: "payment_type" value { bytes_list { value: "Credit Card" } } } feature { key: "pickup_census_tract" value { int64_list { } } } feature { key: "pickup_community_area" value { int64_list { } } } feature { key: "pickup_latitude" value { float_list { } } } feature { key: "pickup_longitude" value { float_list { } } } feature { key: "tips" value { float_list { value: 0.0 } } } feature { key: "trip_miles" value { float_list { value: 0.0 } } } feature { key: "trip_seconds" value { int64_list { value: 0 } } } feature { key: "trip_start_day" value { int64_list { value: 6 } } } feature { key: "trip_start_hour" value { int64_list { value: 19 } } } feature { key: "trip_start_month" value { int64_list { value: 5 } } } feature { key: "trip_start_timestamp" value { int64_list { value: 1400269500 } } } } features { feature { key: "company" value { bytes_list { value: "Taxi Affiliation Services" } } } feature { key: "dropoff_census_tract" value { int64_list { } } } feature { key: "dropoff_community_area" value { int64_list { } } } feature { key: "dropoff_latitude" value { float_list { } } } feature { key: "dropoff_longitude" value { float_list { } } } feature { key: "fare" value { float_list { value: 27.049999237060547 } } } feature { key: "payment_type" value { bytes_list { value: "Cash" } } } feature { key: "pickup_census_tract" value { int64_list { } } } feature { key: "pickup_community_area" value { int64_list { value: 60 } } } feature { key: "pickup_latitude" value { float_list { value: 41.836151123046875 } } } feature { key: "pickup_longitude" value { float_list { value: -87.64878845214844 } } } feature { key: "tips" value { float_list { value: 0.0 } } } feature { key: "trip_miles" value { float_list { value: 12.600000381469727 } } } feature { key: "trip_seconds" value { int64_list { value: 1380 } } } feature { key: "trip_start_day" value { int64_list { value: 3 } } } feature { key: "trip_start_hour" value { int64_list { value: 2 } } } feature { key: "trip_start_month" value { int64_list { value: 10 } } } feature { key: "trip_start_timestamp" value { int64_list { value: 1380593700 } } } } features { feature { key: "company" value { bytes_list { } } } feature { key: "dropoff_census_tract" value { int64_list { } } } feature { key: "dropoff_community_area" value { int64_list { } } } feature { key: "dropoff_latitude" value { float_list { } } } feature { key: "dropoff_longitude" value { float_list { } } } feature { key: "fare" value { float_list { value: 16.450000762939453 } } } feature { key: "payment_type" value { bytes_list { value: "Cash" } } } feature { key: "pickup_census_tract" value { int64_list { } } } feature { key: "pickup_community_area" value { int64_list { value: 13 } } } feature { key: "pickup_latitude" value { float_list { value: 41.98363494873047 } } } feature { key: "pickup_longitude" value { float_list { value: -87.72357940673828 } } } feature { key: "tips" value { float_list { value: 0.0 } } } feature { key: "trip_miles" value { float_list { value: 6.900000095367432 } } } feature { key: "trip_seconds" value { int64_list { value: 780 } } } feature { key: "trip_start_day" value { int64_list { value: 3 } } } feature { key: "trip_start_hour" value { int64_list { value: 12 } } } feature { key: "trip_start_month" value { int64_list { value: 11 } } } feature { key: "trip_start_timestamp" value { int64_list { value: 1446554700 } } } }
이제 ExampleGen
데이터를 섭취 완료, 다음 단계는 데이터 분석입니다.
통계 생성
StatisticsGen
다운 스트림 구성 요소에서 데이터 분석을위한 데이터 세트뿐만 아니라에 대한 사용에 구성 요소로 계산 통계. 그것은 사용 TensorFlow 데이터 유효성 검사의 라이브러리를.
StatisticsGen
입력으로 우리가 사용 섭취 데이터 세트 소요 ExampleGen
.
statistics_gen = tfx.components.StatisticsGen(
examples=example_gen.outputs['examples'])
context.run(statistics_gen)
INFO:absl:Excluding no splits because exclude_splits is not set. INFO:absl:Running driver for StatisticsGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for StatisticsGen INFO:absl:Generating statistics for split train. INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/StatisticsGen/statistics/2/Split-train. INFO:absl:Generating statistics for split eval. INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/StatisticsGen/statistics/2/Split-eval. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. INFO:absl:Running publisher for StatisticsGen INFO:absl:MetadataStore with DB connection initialized
후 StatisticsGen
실행이 완료, 우리는 출력 통계를 시각화 할 수 있습니다. 다양한 플롯으로 플레이해보세요!
context.show(statistics_gen.outputs['statistics'])
스키마젠
SchemaGen
구성 요소는 데이터 통계를 기반으로 스키마를 생성합니다. (A 스키마는 예상 범위, 유형 및 데이터 세트의 기능의 속성을 정의합니다.) 그것은 또한 사용 TensorFlow 데이터 유효성 검사의 라이브러리를.
SchemaGen
우리가 생성하는 통계 입력으로 소요됩니다 StatisticsGen
기본적으로 교육 분할보고.
schema_gen = tfx.components.SchemaGen(
statistics=statistics_gen.outputs['statistics'],
infer_feature_shape=False)
context.run(schema_gen)
INFO:absl:Excluding no splits because exclude_splits is not set. INFO:absl:Running driver for SchemaGen INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for SchemaGen INFO:absl:Processing schema from statistics for split train. INFO:absl:Processing schema from statistics for split eval. INFO:absl:Schema written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/SchemaGen/schema/3/schema.pbtxt. INFO:absl:Running publisher for SchemaGen INFO:absl:MetadataStore with DB connection initialized
후에 SchemaGen
수행을 완료, 우리는 테이블로 생성 된 스키마를 시각화 할 수 있습니다.
context.show(schema_gen.outputs['schema'])
데이터세트의 각 기능은 속성과 함께 스키마 테이블에 행으로 표시됩니다. 스키마는 또한 범주형 기능이 취하는 모든 값(해당 도메인으로 표시됨)을 캡처합니다.
스키마에 대한 자세한 내용은 다음 페이지를 참조 에서는 schemagen 문서를 .
예제검증기
ExampleValidator
구성 요소는 스키마에 의해 정의 된 기대에 따라, 데이터에 이상을 감지합니다. 또한 사용 TensorFlow 데이터 유효성 검사의 라이브러리를.
ExampleValidator
에서 입력으로 통계를 취할 것 StatisticsGen
과의 스키마 SchemaGen
.
example_validator = tfx.components.ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
context.run(example_validator)
INFO:absl:Excluding no splits because exclude_splits is not set. INFO:absl:Running driver for ExampleValidator INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for ExampleValidator INFO:absl:Validating schema against the computed statistics for split train. INFO:absl:Validation complete for split train. Anomalies written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/ExampleValidator/anomalies/4/Split-train. INFO:absl:Validating schema against the computed statistics for split eval. INFO:absl:Validation complete for split eval. Anomalies written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/ExampleValidator/anomalies/4/Split-eval. INFO:absl:Running publisher for ExampleValidator INFO:absl:MetadataStore with DB connection initialized
후 ExampleValidator
수행을 완료, 우리는 테이블과 이상을 시각화 할 수 있습니다.
context.show(example_validator.outputs['anomalies'])
변칙 테이블에서 변칙이 없음을 알 수 있습니다. 이것은 우리가 분석한 첫 번째 데이터 세트이고 스키마가 이에 맞게 조정되었기 때문에 예상한 것입니다. 이 스키마를 검토해야 합니다. 예상치 못한 것은 데이터의 이상을 의미합니다. 검토가 완료되면 스키마를 사용하여 향후 데이터를 보호할 수 있으며 여기에서 생성된 이상 항목을 사용하여 모델 성능을 디버그하고 시간 경과에 따라 데이터가 어떻게 발전하는지 이해하고 데이터 오류를 식별할 수 있습니다.
변환
는 Transform
훈련과 역할을 모두 구성 요소 수행하는 기능 엔지니어링. 그것은 사용 TensorFlow이 변환 라이브러리입니다.
Transform
입력으로의 데이터 걸릴 ExampleGen
에서 스키마 SchemaGen
뿐만 아니라 사용자 정의 된 코드를 포함하는 변환 모듈.
하자가의 예를 보려면 사용자 정의 (즉, TensorFlow에 대한 소개 API를 변환 아래 코드를 변환 자습서를 참조 ). 먼저 피쳐 엔지니어링을 위한 몇 가지 상수를 정의합니다.
_taxi_constants_module_file = 'taxi_constants.py'
%%writefile {_taxi_constants_module_file}
# Categorical features are assumed to each have a maximum value in the dataset.
MAX_CATEGORICAL_FEATURE_VALUES = [24, 31, 12]
CATEGORICAL_FEATURE_KEYS = [
'trip_start_hour', 'trip_start_day', 'trip_start_month',
'pickup_census_tract', 'dropoff_census_tract', 'pickup_community_area',
'dropoff_community_area'
]
DENSE_FLOAT_FEATURE_KEYS = ['trip_miles', 'fare', 'trip_seconds']
# Number of buckets used by tf.transform for encoding each feature.
FEATURE_BUCKET_COUNT = 10
BUCKET_FEATURE_KEYS = [
'pickup_latitude', 'pickup_longitude', 'dropoff_latitude',
'dropoff_longitude'
]
# Number of vocabulary terms used for encoding VOCAB_FEATURES by tf.transform
VOCAB_SIZE = 1000
# Count of out-of-vocab buckets in which unrecognized VOCAB_FEATURES are hashed.
OOV_SIZE = 10
VOCAB_FEATURE_KEYS = [
'payment_type',
'company',
]
# Keys
LABEL_KEY = 'tips'
FARE_KEY = 'fare'
Writing taxi_constants.py
다음으로, 우리는 쓰기 preprocessing_fn
입력으로 원시 데이터에 소요 반환 우리의 모델에 훈련 할 수있는 변환 기능 :
_taxi_transform_module_file = 'taxi_transform.py'
%%writefile {_taxi_transform_module_file}
import tensorflow as tf
import tensorflow_transform as tft
import taxi_constants
_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_FARE_KEY = taxi_constants.FARE_KEY
_LABEL_KEY = taxi_constants.LABEL_KEY
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
outputs = {}
for key in _DENSE_FLOAT_FEATURE_KEYS:
# If sparse make it dense, setting nan's to 0 or '', and apply zscore.
outputs[key] = tft.scale_to_z_score(
_fill_in_missing(inputs[key]))
for key in _VOCAB_FEATURE_KEYS:
# Build a vocabulary for this feature.
outputs[key] = tft.compute_and_apply_vocabulary(
_fill_in_missing(inputs[key]),
top_k=_VOCAB_SIZE,
num_oov_buckets=_OOV_SIZE)
for key in _BUCKET_FEATURE_KEYS:
outputs[key] = tft.bucketize(
_fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT)
for key in _CATEGORICAL_FEATURE_KEYS:
outputs[key] = _fill_in_missing(inputs[key])
# Was this passenger a big tipper?
taxi_fare = _fill_in_missing(inputs[_FARE_KEY])
tips = _fill_in_missing(inputs[_LABEL_KEY])
outputs[_LABEL_KEY] = tf.where(
tf.math.is_nan(taxi_fare),
tf.cast(tf.zeros_like(taxi_fare), tf.int64),
# Test if the tip was > 20% of the fare.
tf.cast(
tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))
return outputs
def _fill_in_missing(x):
"""Replace missing values in a SparseTensor.
Fills in missing values of `x` with '' or 0, and converts to a dense tensor.
Args:
x: A `SparseTensor` of rank 2. Its dense shape should have size at most 1
in the second dimension.
Returns:
A rank 1 tensor where missing values of `x` have been filled in.
"""
if not isinstance(x, tf.sparse.SparseTensor):
return x
default_value = '' if x.dtype == tf.string else 0
return tf.squeeze(
tf.sparse.to_dense(
tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),
default_value),
axis=1)
Writing taxi_transform.py
이제, 우리는이 기능 엔지니어링 코드를 전달 Transform
구성 요소 및 데이터 변환을 실행합니다.
transform = tfx.components.Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=os.path.abspath(_taxi_transform_module_file))
context.run(transform)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py' (including modules: ['taxi_transform', 'taxi_constants']). INFO:absl:User module package has hash fingerprint version f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp9qnpryw9/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmppaskl3va', '--dist-dir', '/tmp/tmpr6oorqji'] /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools. setuptools.SetuptoolsDeprecationWarning, INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'; target user module is 'taxi_transform'. INFO:absl:Full user module path is 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' INFO:absl:Running driver for Transform INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for Transform INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set. INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn' INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpbvbj9r5b', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'] running bdist_wheel running build running build_py creating build creating build/lib copying taxi_transform.py -> build/lib copying taxi_constants.py -> build/lib running install running install_lib running install_egg_info running egg_info creating tfx_user_code_Transform.egg-info writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt' writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt' Copying tfx_user_code_Transform.egg-info to /tmp/tmppaskl3va/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3.7.egg-info running install_scripts Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'. INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn' INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpbzwdie1a', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'] Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424 Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'. INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp09euava5', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'] Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424 Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. Installing collected packages: tfx-user-code-Transform Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424 INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:289: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Use ref() instead. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2 WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2 INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2 WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2 WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor. INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor. WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. 2021-12-21 10:10:18.679569: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them. INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5/.temp_path/tftransform_tmp/80dbc09e6ded4a93b5c506e252c8f536/assets INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5/.temp_path/tftransform_tmp/572eacb7c64f4f6e9262f7d496a95f86/assets INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation. INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. INFO:absl:Running publisher for Transform INFO:absl:MetadataStore with DB connection initialized
의는의 출력 유물 살펴 보자 Transform
. 이 구성 요소는 두 가지 유형의 출력을 생성합니다.
-
transform_graph
(이 그래프가 게재 및 평가 모델을 포함한다) 전처리 조작을 수행 할 수있는 그래프이다. -
transformed_examples
전처리 된 교육 및 평가 데이터를 나타냅니다.
transform.outputs
{'transform_graph': Channel( type_name: TransformGraph artifacts: [Artifact(artifact: id: 5 type_id: 22 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5" custom_properties { key: "name" value { string_value: "transform_graph" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 22 name: "TransformGraph" )] additional_properties: {} additional_custom_properties: {} ), 'transformed_examples': Channel( type_name: Examples artifacts: [Artifact(artifact: id: 6 type_id: 14 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transformed_examples/5" properties { key: "split_names" value { string_value: "[\"train\", \"eval\"]" } } custom_properties { key: "name" value { string_value: "transformed_examples" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 14 name: "Examples" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } properties { key: "version" value: INT } base_type: DATASET )] additional_properties: {} additional_custom_properties: {} ), 'updated_analyzer_cache': Channel( type_name: TransformCache artifacts: [Artifact(artifact: id: 7 type_id: 23 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/updated_analyzer_cache/5" custom_properties { key: "name" value { string_value: "updated_analyzer_cache" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 23 name: "TransformCache" )] additional_properties: {} additional_custom_properties: {} ), 'pre_transform_schema': Channel( type_name: Schema artifacts: [Artifact(artifact: id: 8 type_id: 18 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/pre_transform_schema/5" custom_properties { key: "name" value { string_value: "pre_transform_schema" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 18 name: "Schema" )] additional_properties: {} additional_custom_properties: {} ), 'pre_transform_stats': Channel( type_name: ExampleStatistics artifacts: [Artifact(artifact: id: 9 type_id: 16 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/pre_transform_stats/5" custom_properties { key: "name" value { string_value: "pre_transform_stats" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 16 name: "ExampleStatistics" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } base_type: STATISTICS )] additional_properties: {} additional_custom_properties: {} ), 'post_transform_schema': Channel( type_name: Schema artifacts: [Artifact(artifact: id: 10 type_id: 18 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_schema/5" custom_properties { key: "name" value { string_value: "post_transform_schema" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 18 name: "Schema" )] additional_properties: {} additional_custom_properties: {} ), 'post_transform_stats': Channel( type_name: ExampleStatistics artifacts: [Artifact(artifact: id: 11 type_id: 16 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_stats/5" custom_properties { key: "name" value { string_value: "post_transform_stats" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 16 name: "ExampleStatistics" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } base_type: STATISTICS )] additional_properties: {} additional_custom_properties: {} ), 'post_transform_anomalies': Channel( type_name: ExampleAnomalies artifacts: [Artifact(artifact: id: 12 type_id: 20 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_anomalies/5" custom_properties { key: "name" value { string_value: "post_transform_anomalies" } } custom_properties { key: "producer_component" value { string_value: "Transform" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 20 name: "ExampleAnomalies" properties { key: "span" value: INT } properties { key: "split_names" value: STRING } )] additional_properties: {} additional_custom_properties: {} )}
상기 들여다보세요 transform_graph
유물. 세 개의 하위 디렉토리가 포함된 디렉토리를 가리킵니다.
train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)
['transform_fn', 'transformed_metadata', 'metadata']
transformed_metadata
하위 디렉토리는 전처리 된 데이터의 스키마를 포함합니다. transform_fn
서브 디렉토리에는 실제 전처리 그래프를 포함한다. metadata
하위 디렉토리는 원본 데이터의 스키마를 포함합니다.
변환된 처음 세 가지 예도 살펴볼 수 있습니다.
# Get the URI of the output artifact representing the transformed examples, which is a directory
train_uri = os.path.join(transform.outputs['transformed_examples'].get()[0].uri, 'Split-train')
# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
for name in os.listdir(train_uri)]
# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")
# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
serialized_example = tfrecord.numpy()
example = tf.train.Example()
example.ParseFromString(serialized_example)
pp.pprint(example)
features { feature { key: "company" value { int64_list { value: 8 } } } feature { key: "dropoff_census_tract" value { int64_list { value: 0 } } } feature { key: "dropoff_community_area" value { int64_list { value: 0 } } } feature { key: "dropoff_latitude" value { int64_list { value: 0 } } } feature { key: "dropoff_longitude" value { int64_list { value: 9 } } } feature { key: "fare" value { float_list { value: 0.061060599982738495 } } } feature { key: "payment_type" value { int64_list { value: 1 } } } feature { key: "pickup_census_tract" value { int64_list { value: 0 } } } feature { key: "pickup_community_area" value { int64_list { value: 0 } } } feature { key: "pickup_latitude" value { int64_list { value: 0 } } } feature { key: "pickup_longitude" value { int64_list { value: 9 } } } feature { key: "tips" value { int64_list { value: 0 } } } feature { key: "trip_miles" value { float_list { value: -0.15886741876602173 } } } feature { key: "trip_seconds" value { float_list { value: -0.7118487358093262 } } } feature { key: "trip_start_day" value { int64_list { value: 6 } } } feature { key: "trip_start_hour" value { int64_list { value: 19 } } } feature { key: "trip_start_month" value { int64_list { value: 5 } } } } features { feature { key: "company" value { int64_list { value: 0 } } } feature { key: "dropoff_census_tract" value { int64_list { value: 0 } } } feature { key: "dropoff_community_area" value { int64_list { value: 0 } } } feature { key: "dropoff_latitude" value { int64_list { value: 0 } } } feature { key: "dropoff_longitude" value { int64_list { value: 9 } } } feature { key: "fare" value { float_list { value: 1.2521240711212158 } } } feature { key: "payment_type" value { int64_list { value: 0 } } } feature { key: "pickup_census_tract" value { int64_list { value: 0 } } } feature { key: "pickup_community_area" value { int64_list { value: 60 } } } feature { key: "pickup_latitude" value { int64_list { value: 0 } } } feature { key: "pickup_longitude" value { int64_list { value: 3 } } } feature { key: "tips" value { int64_list { value: 0 } } } feature { key: "trip_miles" value { float_list { value: 0.532160758972168 } } } feature { key: "trip_seconds" value { float_list { value: 0.5509493350982666 } } } feature { key: "trip_start_day" value { int64_list { value: 3 } } } feature { key: "trip_start_hour" value { int64_list { value: 2 } } } feature { key: "trip_start_month" value { int64_list { value: 10 } } } } features { feature { key: "company" value { int64_list { value: 48 } } } feature { key: "dropoff_census_tract" value { int64_list { value: 0 } } } feature { key: "dropoff_community_area" value { int64_list { value: 0 } } } feature { key: "dropoff_latitude" value { int64_list { value: 0 } } } feature { key: "dropoff_longitude" value { int64_list { value: 9 } } } feature { key: "fare" value { float_list { value: 0.3873794376850128 } } } feature { key: "payment_type" value { int64_list { value: 0 } } } feature { key: "pickup_census_tract" value { int64_list { value: 0 } } } feature { key: "pickup_community_area" value { int64_list { value: 13 } } } feature { key: "pickup_latitude" value { int64_list { value: 9 } } } feature { key: "pickup_longitude" value { int64_list { value: 0 } } } feature { key: "tips" value { int64_list { value: 0 } } } feature { key: "trip_miles" value { float_list { value: 0.21955277025699615 } } } feature { key: "trip_seconds" value { float_list { value: 0.0019067146349698305 } } } feature { key: "trip_start_day" value { int64_list { value: 3 } } } feature { key: "trip_start_hour" value { int64_list { value: 12 } } } feature { key: "trip_start_month" value { int64_list { value: 11 } } } }
애프터 Transform
구성 요소가 기능으로 데이터를 변환하고있다 다음 단계는 모델을 양성하는 것입니다.
훈련자
Trainer
구성 요소를 사용하면 TensorFlow에서 정의하는 모델을 학습합니다. Keras API를 사용하여, 강사 지원 견적 API 기본, 당신은 지정해야합니다 일반 트레이너를 설정하여 custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor)
트레이너의 생성자입니다.
Trainer
입력으로의 스키마 얻어 SchemaGen
, 행 변환 된 데이터 그래프는 Transform
파라미터뿐만 아니라 사용자 정의 모델 코드를 포함하는 모듈을 훈련.
하자합니다 (TensorFlow Keras API에 대한 소개는 아래의 사용자 정의 모델 코드의 예를 참조 자습서를 참조 )
_taxi_trainer_module_file = 'taxi_trainer.py'
%%writefile {_taxi_trainer_module_file}
from typing import List, Text
import os
from absl import logging
import datetime
import tensorflow as tf
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx_bsl.public import tfxio
import taxi_constants
_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_MAX_CATEGORICAL_FEATURE_VALUES = taxi_constants.MAX_CATEGORICAL_FEATURE_VALUES
_LABEL_KEY = taxi_constants.LABEL_KEY
def _get_tf_examples_serving_signature(model, tf_transform_output):
"""Returns a serving signature that accepts `tensorflow.Example`."""
# We need to track the layers in the model in order to save it.
# TODO(b/162357359): Revise once the bug is resolved.
model.tft_layer_inference = tf_transform_output.transform_features_layer()
@tf.function(input_signature=[
tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')
])
def serve_tf_examples_fn(serialized_tf_example):
"""Returns the output to be used in the serving signature."""
raw_feature_spec = tf_transform_output.raw_feature_spec()
# Remove label feature since these will not be present at serving time.
raw_feature_spec.pop(_LABEL_KEY)
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer_inference(raw_features)
logging.info('serve_transformed_features = %s', transformed_features)
outputs = model(transformed_features)
# TODO(b/154085620): Convert the predicted labels from the model using a
# reverse-lookup (opposite of transform.py).
return {'outputs': outputs}
return serve_tf_examples_fn
def _get_transform_features_signature(model, tf_transform_output):
"""Returns a serving signature that applies tf.Transform to features."""
# We need to track the layers in the model in order to save it.
# TODO(b/162357359): Revise once the bug is resolved.
model.tft_layer_eval = tf_transform_output.transform_features_layer()
@tf.function(input_signature=[
tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')
])
def transform_features_fn(serialized_tf_example):
"""Returns the transformed_features to be fed as input to evaluator."""
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer_eval(raw_features)
logging.info('eval_transformed_features = %s', transformed_features)
return transformed_features
return transform_features_fn
def _input_fn(file_pattern: List[Text],
data_accessor: tfx.components.DataAccessor,
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) -> tf.data.Dataset:
"""Generates features and label for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
tf_transform_output: A TFTransformOutput.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
return data_accessor.tf_dataset_factory(
file_pattern,
tfxio.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_LABEL_KEY),
tf_transform_output.transformed_metadata.schema)
def _build_keras_model(hidden_units: List[int] = None) -> tf.keras.Model:
"""Creates a DNN Keras model for classifying taxi data.
Args:
hidden_units: [int], the layer sizes of the DNN (input layer first).
Returns:
A keras Model.
"""
real_valued_columns = [
tf.feature_column.numeric_column(key, shape=())
for key in _DENSE_FLOAT_FEATURE_KEYS
]
categorical_columns = [
tf.feature_column.categorical_column_with_identity(
key, num_buckets=_VOCAB_SIZE + _OOV_SIZE, default_value=0)
for key in _VOCAB_FEATURE_KEYS
]
categorical_columns += [
tf.feature_column.categorical_column_with_identity(
key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
for key in _BUCKET_FEATURE_KEYS
]
categorical_columns += [
tf.feature_column.categorical_column_with_identity( # pylint: disable=g-complex-comprehension
key,
num_buckets=num_buckets,
default_value=0) for key, num_buckets in zip(
_CATEGORICAL_FEATURE_KEYS,
_MAX_CATEGORICAL_FEATURE_VALUES)
]
indicator_column = [
tf.feature_column.indicator_column(categorical_column)
for categorical_column in categorical_columns
]
model = _wide_and_deep_classifier(
# TODO(b/139668410) replace with premade wide_and_deep keras model
wide_columns=indicator_column,
deep_columns=real_valued_columns,
dnn_hidden_units=hidden_units or [100, 70, 50, 25])
return model
def _wide_and_deep_classifier(wide_columns, deep_columns, dnn_hidden_units):
"""Build a simple keras wide and deep model.
Args:
wide_columns: Feature columns wrapped in indicator_column for wide (linear)
part of the model.
deep_columns: Feature columns for deep part of the model.
dnn_hidden_units: [int], the layer sizes of the hidden DNN.
Returns:
A Wide and Deep Keras model
"""
# Following values are hard coded for simplicity in this example,
# However prefarably they should be passsed in as hparams.
# Keras needs the feature definitions at compile time.
# TODO(b/139081439): Automate generation of input layers from FeatureColumn.
input_layers = {
colname: tf.keras.layers.Input(name=colname, shape=(), dtype=tf.float32)
for colname in _DENSE_FLOAT_FEATURE_KEYS
}
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _VOCAB_FEATURE_KEYS
})
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _BUCKET_FEATURE_KEYS
})
input_layers.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
for colname in _CATEGORICAL_FEATURE_KEYS
})
# TODO(b/161952382): Replace with Keras preprocessing layers.
deep = tf.keras.layers.DenseFeatures(deep_columns)(input_layers)
for numnodes in dnn_hidden_units:
deep = tf.keras.layers.Dense(numnodes)(deep)
wide = tf.keras.layers.DenseFeatures(wide_columns)(input_layers)
output = tf.keras.layers.Dense(1)(
tf.keras.layers.concatenate([deep, wide]))
model = tf.keras.Model(input_layers, output)
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(lr=0.001),
metrics=[tf.keras.metrics.BinaryAccuracy()])
model.summary(print_fn=logging.info)
return model
# TFX Trainer will call this function.
def run_fn(fn_args: tfx.components.FnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
# Number of nodes in the first layer of the DNN
first_dnn_layer_size = 100
num_dnn_layers = 4
dnn_decay_factor = 0.7
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(fn_args.train_files, fn_args.data_accessor,
tf_transform_output, 40)
eval_dataset = _input_fn(fn_args.eval_files, fn_args.data_accessor,
tf_transform_output, 40)
model = _build_keras_model(
# Construct layers sizes with exponetial decay
hidden_units=[
max(2, int(first_dnn_layer_size * dnn_decay_factor**i))
for i in range(num_dnn_layers)
])
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=fn_args.model_run_dir, update_freq='batch')
model.fit(
train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps,
callbacks=[tensorboard_callback])
signatures = {
'serving_default':
_get_tf_examples_serving_signature(model, tf_transform_output),
'transform_features':
_get_transform_features_signature(model, tf_transform_output),
}
model.save(fn_args.serving_model_dir, save_format='tf', signatures=signatures)
Writing taxi_trainer.py
이제, 우리는이 모델 코드를 전달 Trainer
구성 요소와는 모델을 학습하기 위해 실행합니다.
trainer = tfx.components.Trainer(
module_file=os.path.abspath(_taxi_trainer_module_file),
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
train_args=tfx.proto.TrainArgs(num_steps=10000),
eval_args=tfx.proto.EvalArgs(num_steps=5000))
context.run(trainer)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py' (including modules: ['taxi_transform', 'taxi_constants', 'taxi_trainer']). INFO:absl:User module package has hash fingerprint version ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpzxd5b1yc/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpbg9ly6tr', '--dist-dir', '/tmp/tmpx43qh690'] /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools. setuptools.SetuptoolsDeprecationWarning, INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'; target user module is 'taxi_trainer'. INFO:absl:Full user module path is 'taxi_trainer@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl' INFO:absl:Running driver for Trainer INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for Trainer INFO:absl:Train on the 'train' split when train_args.splits is not set. INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set. WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE INFO:absl:udf_utils.get_fn {'train_args': '{\n "num_steps": 10000\n}', 'eval_args': '{\n "num_steps": 5000\n}', 'module_file': None, 'run_fn': None, 'trainer_fn': None, 'custom_config': 'null', 'module_path': 'taxi_trainer@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'} 'run_fn' INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl' to a temporary directory. INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp1osq6e1x', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'] running bdist_wheel running build running build_py creating build creating build/lib copying taxi_transform.py -> build/lib copying taxi_constants.py -> build/lib copying taxi_trainer.py -> build/lib running install running install_lib running install_egg_info running egg_info creating tfx_user_code_Trainer.egg-info writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt' writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt' Copying tfx_user_code_Trainer.egg-info to /tmp/tmpbg9ly6tr/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3.7.egg-info running install_scripts Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'. INFO:absl:Training model. INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. Installing collected packages: tfx-user-code-Trainer Successfully installed tfx-user-code-Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. INFO:absl:Feature company has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature fare has a shape . Setting to DenseTensor. INFO:absl:Feature payment_type has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor. INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor. INFO:absl:Feature tips has a shape . Setting to DenseTensor. INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor. INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor. INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor. /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead. super(Adam, self).__init__(name, **kwargs) INFO:absl:Model: "model" INFO:absl:__________________________________________________________________________________________________ INFO:absl: Layer (type) Output Shape Param # Connected to INFO:absl:================================================================================================== INFO:absl: company (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: dropoff_census_tract (InputLay [(None,)] 0 [] INFO:absl: er) INFO:absl: INFO:absl: dropoff_community_area (InputL [(None,)] 0 [] INFO:absl: ayer) INFO:absl: INFO:absl: dropoff_latitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: dropoff_longitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: fare (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: payment_type (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: pickup_census_tract (InputLaye [(None,)] 0 [] INFO:absl: r) INFO:absl: INFO:absl: pickup_community_area (InputLa [(None,)] 0 [] INFO:absl: yer) INFO:absl: INFO:absl: pickup_latitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: pickup_longitude (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_miles (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_seconds (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_start_day (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_start_hour (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: trip_start_month (InputLayer) [(None,)] 0 [] INFO:absl: INFO:absl: dense_features (DenseFeatures) (None, 3) 0 ['company[0][0]', INFO:absl: 'dropoff_census_tract[0][0]', INFO:absl: 'dropoff_community_area[0][0]', INFO:absl: 'dropoff_latitude[0][0]', INFO:absl: 'dropoff_longitude[0][0]', INFO:absl: 'fare[0][0]', INFO:absl: 'payment_type[0][0]', INFO:absl: 'pickup_census_tract[0][0]', INFO:absl: 'pickup_community_area[0][0]', INFO:absl: 'pickup_latitude[0][0]', INFO:absl: 'pickup_longitude[0][0]', INFO:absl: 'trip_miles[0][0]', INFO:absl: 'trip_seconds[0][0]', INFO:absl: 'trip_start_day[0][0]', INFO:absl: 'trip_start_hour[0][0]', INFO:absl: 'trip_start_month[0][0]'] INFO:absl: INFO:absl: dense (Dense) (None, 100) 400 ['dense_features[0][0]'] INFO:absl: INFO:absl: dense_1 (Dense) (None, 70) 7070 ['dense[0][0]'] INFO:absl: INFO:absl: dense_2 (Dense) (None, 48) 3408 ['dense_1[0][0]'] INFO:absl: INFO:absl: dense_3 (Dense) (None, 34) 1666 ['dense_2[0][0]'] INFO:absl: INFO:absl: dense_features_1 (DenseFeature (None, 2127) 0 ['company[0][0]', INFO:absl: s) 'dropoff_census_tract[0][0]', INFO:absl: 'dropoff_community_area[0][0]', INFO:absl: 'dropoff_latitude[0][0]', INFO:absl: 'dropoff_longitude[0][0]', INFO:absl: 'fare[0][0]', INFO:absl: 'payment_type[0][0]', INFO:absl: 'pickup_census_tract[0][0]', INFO:absl: 'pickup_community_area[0][0]', INFO:absl: 'pickup_latitude[0][0]', INFO:absl: 'pickup_longitude[0][0]', INFO:absl: 'trip_miles[0][0]', INFO:absl: 'trip_seconds[0][0]', INFO:absl: 'trip_start_day[0][0]', INFO:absl: 'trip_start_hour[0][0]', INFO:absl: 'trip_start_month[0][0]'] INFO:absl: INFO:absl: concatenate (Concatenate) (None, 2161) 0 ['dense_3[0][0]', INFO:absl: 'dense_features_1[0][0]'] INFO:absl: INFO:absl: dense_4 (Dense) (None, 1) 2162 ['concatenate[0][0]'] INFO:absl: INFO:absl:================================================================================================== INFO:absl:Total params: 14,706 INFO:absl:Trainable params: 14,706 INFO:absl:Non-trainable params: 0 INFO:absl:__________________________________________________________________________________________________ 10000/10000 [==============================] - 100s 10ms/step - loss: 0.2372 - binary_accuracy: 0.8605 - val_loss: 0.2222 - val_binary_accuracy: 0.8709 INFO:tensorflow:tensorflow_text is not available. INFO:tensorflow:tensorflow_decision_forests is not available. INFO:tensorflow:struct2tensor is not available. WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f88b5e27910>> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert WARNING: AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f88b5e27910>> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert INFO:absl:serve_transformed_features = {'pickup_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:9' shape=(None,) dtype=int64>, 'trip_start_hour': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:15' shape=(None,) dtype=int64>, 'fare': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:5' shape=(None,) dtype=float32>, 'trip_miles': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:12' shape=(None,) dtype=float32>, 'trip_start_day': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:14' shape=(None,) dtype=int64>, 'dropoff_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:3' shape=(None,) dtype=int64>, 'trip_start_month': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:16' shape=(None,) dtype=int64>, 'dropoff_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:2' shape=(None,) dtype=int64>, 'dropoff_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:4' shape=(None,) dtype=int64>, 'payment_type': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:6' shape=(None,) dtype=int64>, 'pickup_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:10' shape=(None,) dtype=int64>, 'pickup_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:8' shape=(None,) dtype=int64>, 'company': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:0' shape=(None,) dtype=int64>, 'pickup_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:7' shape=(None,) dtype=int64>, 'dropoff_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:1' shape=(None,) dtype=int64>, 'trip_seconds': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:13' shape=(None,) dtype=float32>} INFO:absl:eval_transformed_features = {'pickup_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:9' shape=(None,) dtype=int64>, 'trip_start_hour': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:15' shape=(None,) dtype=int64>, 'fare': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:5' shape=(None,) dtype=float32>, 'trip_miles': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:12' shape=(None,) dtype=float32>, 'trip_start_day': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:14' shape=(None,) dtype=int64>, 'dropoff_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:3' shape=(None,) dtype=int64>, 'trip_start_month': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:16' shape=(None,) dtype=int64>, 'dropoff_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:2' shape=(None,) dtype=int64>, 'dropoff_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:4' shape=(None,) dtype=int64>, 'payment_type': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:6' shape=(None,) dtype=int64>, 'pickup_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:10' shape=(None,) dtype=int64>, 'pickup_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:8' shape=(None,) dtype=int64>, 'company': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:0' shape=(None,) dtype=int64>, 'pickup_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:7' shape=(None,) dtype=int64>, 'tips': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:11' shape=(None,) dtype=int64>, 'dropoff_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:1' shape=(None,) dtype=int64>, 'trip_seconds': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:13' shape=(None,) dtype=float32>} INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving/assets INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model_run/6 INFO:absl:Running publisher for Trainer INFO:absl:MetadataStore with DB connection initialized
TensorBoard로 훈련 분석
트레이너 유물을 살펴보세요. 모델 하위 디렉터리가 포함된 디렉터리를 가리킵니다.
model_artifact_dir = trainer.outputs['model'].get()[0].uri
pp.pprint(os.listdir(model_artifact_dir))
model_dir = os.path.join(model_artifact_dir, 'Format-Serving')
pp.pprint(os.listdir(model_dir))
['Format-Serving'] ['variables', 'assets', 'keras_metadata.pb', 'saved_model.pb']
선택적으로 TensorBoard를 Trainer에 연결하여 모델의 훈련 곡선을 분석할 수 있습니다.
model_run_artifact_dir = trainer.outputs['model_run'].get()[0].uri
%load_ext tensorboard
%tensorboard --logdir {model_run_artifact_dir}
평가자
Evaluator
구성 요소는 평가 세트 이상 모델 성능 메트릭을 계산한다. 그것은 사용 TensorFlow 모델 분석 라이브러리를. Evaluator
선택적 새로 훈련 된 모델은 더 이전 모델보다 것을 확인할 수 있습니다. 이는 매일 자동으로 모델을 훈련하고 검증할 수 있는 프로덕션 파이프라인 설정에서 유용합니다. 그렇게이 노트북에서, 우리는 하나 개의 모델을 학습 Evaluator
자동으로 "좋은"와 같은 모델에 레이블을 것입니다.
Evaluator
입력으로의 데이터 걸릴 것 ExampleGen
에서 훈련 모델 Trainer
와 슬라이스 구성. 슬라이싱 구성을 사용하면 특성 값에 대한 메트릭을 슬라이싱할 수 있습니다(예: 오전 8시에 시작하는 택시 여행과 오후 8시에 시작하는 택시 여행에서 모델의 성능은 어떻습니까?). 아래에서 이 구성의 예를 참조하십시오.
eval_config = tfma.EvalConfig(
model_specs=[
# This assumes a serving model with signature 'serving_default'. If
# using estimator based EvalSavedModel, add signature_name: 'eval' and
# remove the label_key.
tfma.ModelSpec(
signature_name='serving_default',
label_key='tips',
preprocessing_function_names=['transform_features'],
)
],
metrics_specs=[
tfma.MetricsSpec(
# The metrics added here are in addition to those saved with the
# model (assuming either a keras model or EvalSavedModel is used).
# Any metrics added into the saved model (for example using
# model.compile(..., metrics=[...]), etc) will be computed
# automatically.
# To add validation thresholds for metrics saved with the model,
# add them keyed by metric name to the thresholds map.
metrics=[
tfma.MetricConfig(class_name='ExampleCount'),
tfma.MetricConfig(class_name='BinaryAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.5}),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
]
)
],
slicing_specs=[
# An empty slice spec means the overall slice, i.e. the whole dataset.
tfma.SlicingSpec(),
# Data can be sliced along a feature column. In this case, data is
# sliced along feature column trip_start_hour.
tfma.SlicingSpec(feature_keys=['trip_start_hour'])
])
다음으로, 우리는이 구성주고 Evaluator
실행하십시오.
# Use TFMA to compute a evaluation statistics over features of a model and
# validate them against a baseline.
# The model resolver is only required if performing model validation in addition
# to evaluation. In this case we validate against the latest blessed model. If
# no model has been blessed before (as in this case) the evaluator will make our
# candidate the first blessed model.
model_resolver = tfx.dsl.Resolver(
strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
model_blessing=tfx.dsl.Channel(
type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
'latest_blessed_model_resolver')
context.run(model_resolver)
evaluator = tfx.components.Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
context.run(evaluator)
INFO:absl:Running driver for latest_blessed_model_resolver INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running publisher for latest_blessed_model_resolver INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running driver for Evaluator INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for Evaluator INFO:absl:Nonempty beam arg extra_packages already includes dependency INFO:absl:udf_utils.get_fn {'eval_config': '{\n "metrics_specs": [\n {\n "metrics": [\n {\n "class_name": "ExampleCount"\n },\n {\n "class_name": "BinaryAccuracy",\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.5\n }\n }\n }\n ]\n }\n ],\n "model_specs": [\n {\n "label_key": "tips",\n "preprocessing_function_names": [\n "transform_features"\n ],\n "signature_name": "serving_default"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "trip_start_hour"\n ]\n }\n ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model' INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config= model_specs { signature_name: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } } INFO:absl:Using /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving as model. WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87bc0f5e50> and <keras.engine.input_layer.InputLayer object at 0x7f87bc0f5b50>). INFO:absl:The 'example_splits' parameter is not set, using 'eval' split. INFO:absl:Evaluating model. INFO:absl:udf_utils.get_fn {'eval_config': '{\n "metrics_specs": [\n {\n "metrics": [\n {\n "class_name": "ExampleCount"\n },\n {\n "class_name": "BinaryAccuracy",\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.5\n }\n }\n }\n ]\n }\n ],\n "model_specs": [\n {\n "label_key": "tips",\n "preprocessing_function_names": [\n "transform_features"\n ],\n "signature_name": "serving_default"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "trip_start_hour"\n ]\n }\n ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_extractors' INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config= model_specs { signature_name: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } model_names: "" } INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config= model_specs { signature_name: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } model_names: "" } INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config= model_specs { signature_name: "serving_default" label_key: "tips" preprocessing_function_names: "transform_features" } slicing_specs { } slicing_specs { feature_keys: "trip_start_hour" } metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" threshold { value_threshold { lower_bound { value: 0.5 } } } } model_names: "" } WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87b0102150> and <keras.engine.input_layer.InputLayer object at 0x7f875454e810>). WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter. WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87b06c9d50> and <keras.engine.input_layer.InputLayer object at 0x7f87d4041290>). WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f874c8d6a10> and <keras.engine.input_layer.InputLayer object at 0x7f874c8ac0d0>). WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f830dcf9fd0> and <keras.engine.input_layer.InputLayer object at 0x7f830dd87110>). WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f830dc8cad0> and <keras.engine.input_layer.InputLayer object at 0x7f830cf892d0>). WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87b041add0> and <keras.engine.input_layer.InputLayer object at 0x7f874d6b6d50>). WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program. Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f830c42a5d0> and <keras.engine.input_layer.InputLayer object at 0x7f830c3037d0>). INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/evaluation/8. INFO:absl:Checking validation results. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:107: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version. Instructions for updating: Use eager execution and: `tf.data.TFRecordDataset(path)` INFO:absl:Blessing result True written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/blessing/8. INFO:absl:Running publisher for Evaluator INFO:absl:MetadataStore with DB connection initialized
이제의 출력 유물 살펴 보자 Evaluator
.
evaluator.outputs
{'evaluation': Channel( type_name: ModelEvaluation artifacts: [Artifact(artifact: id: 15 type_id: 29 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/evaluation/8" custom_properties { key: "name" value { string_value: "evaluation" } } custom_properties { key: "producer_component" value { string_value: "Evaluator" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 29 name: "ModelEvaluation" )] additional_properties: {} additional_custom_properties: {} ), 'blessing': Channel( type_name: ModelBlessing artifacts: [Artifact(artifact: id: 16 type_id: 30 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/blessing/8" custom_properties { key: "blessed" value { int_value: 1 } } custom_properties { key: "current_model" value { string_value: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6" } } custom_properties { key: "current_model_id" value { int_value: 13 } } custom_properties { key: "name" value { string_value: "blessing" } } custom_properties { key: "producer_component" value { string_value: "Evaluator" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 30 name: "ModelBlessing" )] additional_properties: {} additional_custom_properties: {} )}
은 Using evaluation
출력하는 것은 우리는 전체 평가 세트에 세계 측정의 기본 시각화를 표시 할 수 있습니다.
context.show(evaluator.outputs['evaluation'])
슬라이스 평가 메트릭의 시각화를 보려면 TensorFlow 모델 분석 라이브러리를 직접 호출할 수 있습니다.
import tensorflow_model_analysis as tfma
# Get the TFMA output result path and load the result.
PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
tfma_result = tfma.load_eval_result(PATH_TO_RESULT)
# Show data sliced along feature column trip_start_hour.
tfma.view.render_slicing_metrics(
tfma_result, slicing_column='trip_start_hour')
SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'trip_start_hour:19',…
이 시각화는 동일한 메트릭을 나타내지 만, 모든 특징 량에서 계산 trip_start_hour
대신 전체 평가 세트.
TensorFlow 모델 분석은 공정성 지표 및 모델 성능의 시계열 플로팅과 같은 다른 많은 시각화를 지원합니다. 더 많은 내용을 참조 자습서를 .
구성에 임계값을 추가했으므로 유효성 검사 출력도 사용할 수 있습니다. a의 precence blessing
유물은 우리의 모델이 검증을 통과 함을 나타냅니다. 이것이 수행되는 첫 번째 검증이기 때문에 후보자는 자동으로 축복을 받습니다.
blessing_uri = evaluator.outputs['blessing'].get()[0].uri
!ls -l {blessing_uri}
total 0 -rw-rw-r-- 1 kbuilder kbuilder 0 Dec 21 10:13 BLESSED
이제 유효성 검사 결과 레코드를 로드하여 성공을 확인할 수도 있습니다.
PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
print(tfma.load_validation_result(PATH_TO_RESULT))
validation_ok: true validation_details { slicing_details { slicing_spec { } num_matching_slices: 25 } }
미는 사람
Pusher
구성 요소는 TFX 파이프 라인의 끝에 보통이다. 이 모델은, 수출 모델 검증을 통과, 만약 그렇다면 여부를 확인 _serving_model_dir
.
pusher = tfx.components.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
context.run(pusher)
INFO:absl:Running driver for Pusher INFO:absl:MetadataStore with DB connection initialized INFO:absl:Running executor for Pusher INFO:absl:Model version: 1640081600 INFO:absl:Model written to serving path /tmp/tmpkvhhk5j5/serving_model/taxi_simple/1640081600. INFO:absl:Model pushed to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Pusher/pushed_model/9. INFO:absl:Running publisher for Pusher INFO:absl:MetadataStore with DB connection initialized
하자가의 출력 유물 조사 Pusher
.
pusher.outputs
{'pushed_model': Channel( type_name: PushedModel artifacts: [Artifact(artifact: id: 17 type_id: 32 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Pusher/pushed_model/9" custom_properties { key: "name" value { string_value: "pushed_model" } } custom_properties { key: "producer_component" value { string_value: "Pusher" } } custom_properties { key: "pushed" value { int_value: 1 } } custom_properties { key: "pushed_destination" value { string_value: "/tmp/tmpkvhhk5j5/serving_model/taxi_simple/1640081600" } } custom_properties { key: "pushed_version" value { string_value: "1640081600" } } custom_properties { key: "state" value { string_value: "published" } } custom_properties { key: "tfx_version" value { string_value: "1.5.0" } } state: LIVE , artifact_type: id: 32 name: "PushedModel" )] additional_properties: {} additional_custom_properties: {} )}
특히, 푸셔는 다음과 같은 저장된 모델 형식으로 모델을 내보냅니다.
push_uri = pusher.outputs['pushed_model'].get()[0].uri
model = tf.saved_model.load(push_uri)
for item in model.signatures.items():
pp.pprint(item)
('serving_default', <ConcreteFunction signature_wrapper(*, examples) at 0x7F82F31FDE50>) ('transform_features', <ConcreteFunction signature_wrapper(*, examples) at 0x7F82F31AC410>)
내장 TFX 구성 요소 둘러보기를 마쳤습니다!