SIG TFX-Addons 커뮤니티에 가입하고 TFX를 더욱 향상시키는 데 도움을 주세요! SIG TFX 애드온 가입

TFX Keras 구성 요소 자습서

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 알고 있어야 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 배포에서 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 시작했습니까?

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
2021-07-27 09:07:32.686219: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0

라이브러리 버전을 확인해보자.

print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.5.0
TFX version: 1.0.0

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

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-datazpmc_2k0/data.csv', <http.client.HTTPMessage at 0x7f29e4316290>)

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-2021-07-27T09_07_38.527065-m86gazca as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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. 그것은:

  1. 데이터를 훈련 및 평가 세트로 분할(기본적으로 2/3 훈련 + 1/3 평가)
  2. 로 변환 데이터 tf.Example 형식 (자세히 알아 여기 )
  3. 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-datazpmc_2k0/* 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-2021-07-27T09_07_38.527065-m86gazca/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
      }
    }
  }
}
2021-07-27 09:07:45.320714: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-07-27 09:07:46.182366: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.183343: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: 
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-27 09:07:46.183380: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-27 09:07:46.186392: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
2021-07-27 09:07:46.186495: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2021-07-27 09:07:46.187463: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10
2021-07-27 09:07:46.187792: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10
2021-07-27 09:07:46.188571: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11
2021-07-27 09:07:46.189318: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11
2021-07-27 09:07:46.189509: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8
2021-07-27 09:07:46.189639: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.190658: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.191561: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-27 09:07:46.192211: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-07-27 09:07:46.192702: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.193636: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: 
pciBusID: 0000:00:05.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-27 09:07:46.193733: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.194748: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.195660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-27 09:07:46.195706: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-27 09:07:46.821426: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-27 09:07:46.821464: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 
2021-07-27 09:07:46.821473: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N 
2021-07-27 09:07:46.821754: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.822812: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.823824: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-27 09:07:46.824737: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
2021-07-27 09:07:46.854033: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
2021-07-27 09:07:46.854452: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2000185000 Hz

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-2021-07-27T09_07_38.527065-m86gazca/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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

StatisticsGen 실행이 완료, 우리는 출력 통계를 시각화 할 수 있습니다. 다양한 플롯으로 플레이해보세요!

context.show(statistics_gen.outputs['statistics'])

스키마 생성

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
2021-07-27 09:07:50.046440: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
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-2021-07-27T09_07_38.527065-m86gazca/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'])
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_data_validation/utils/display_util.py:180: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
  pd.set_option('max_colwidth', -1)

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-2021-07-27T09_07_38.527065-m86gazca/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-2021-07-27T09_07_38.527065-m86gazca/ExampleValidator/anomalies/4/Split-eval.
INFO:absl:Running publisher for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized

ExampleValidator 수행을 완료, 우리는 테이블과 이상을 시각화 할 수 있습니다.

context.show(example_validator.outputs['anomalies'])
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_data_validation/utils/display_util.py:217: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
  pd.set_option('max_colwidth', -1)

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 이상을 사용하여 모델 성능을 디버그하고 시간이 지남에 따라 데이터가 어떻게 발전하는지 이해하고 데이터 오류를 식별할 수 있습니다.

변환

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'

def transformed_name(key):
  return key + '_xf'
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
_transformed_name = taxi_constants.transformed_name


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:
    # Preserve this feature as a dense float, setting nan's to the mean.
    outputs[_transformed_name(key)] = tft.scale_to_z_score(
        _fill_in_missing(inputs[key]))

  for key in _VOCAB_FEATURE_KEYS:
    # Build a vocabulary for this feature.
    outputs[_transformed_name(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[_transformed_name(key)] = tft.bucketize(
        _fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT)

  for key in _CATEGORICAL_FEATURE_KEYS:
    outputs[_transformed_name(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[_transformed_name(_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_constants', 'taxi_transform']).
INFO:absl:User module package has hash fingerprint version ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpoqmg142s/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpanjcjub4', '--dist-dir', '/tmp/tmpdsj16ttp']
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'; target user module is 'taxi_transform'.
INFO:absl:Full user module path is 'taxi_transform@/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
2021-07-27 09:07:50.744965: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Transform
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_constants.py -> build/lib
copying taxi_transform.py -> build/lib
installing to /tmp/tmpanjcjub4
running install
running install_lib
copying build/lib/taxi_constants.py -> /tmp/tmpanjcjub4
copying build/lib/taxi_transform.py -> /tmp/tmpanjcjub4
running install_egg_info
running egg_info
creating tfx_user_code_Transform.egg-info
writing tfx_user_code_Transform.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
reading 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/tmpanjcjub4/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3.7.egg-info
running install_scripts
creating /tmp/tmpanjcjub4/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.dist-info/WHEEL
creating '/tmp/tmpdsj16ttp/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl' and adding '/tmp/tmpanjcjub4' to it
adding 'taxi_constants.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.dist-info/RECORD'
removing /tmp/tmpanjcjub4
2021-07-27 09:07:50.749018: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
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-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpgm2vzpks', '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl']
Processing /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl
WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpgvos8jkh', '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f
Processing /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl
WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type 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 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+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:266: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow: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`.
WARNING:tensorflow: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`.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type 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 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:tensorflow: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`.
WARNING:tensorflow: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`.
WARNING:tensorflow: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`.
WARNING:tensorflow: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`.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type 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 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 payment_type 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 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 payment_type 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 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 payment_type 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 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:Installing '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpfzcjc_89', '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl']
Processing /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl
WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'.
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]] instead.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f
WARNING:tensorflow: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`.
WARNING:tensorflow: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`.
WARNING:tensorflow: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`.
WARNING:tensorflow: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`.
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]] instead.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type 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 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 payment_type 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 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:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
2021-07-27 09:08:04.817364: W tensorflow/python/util/util.cc:348] 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-2021-07-27T09_07_38.527065-m86gazca/Transform/transform_graph/5/.temp_path/tftransform_tmp/da700272a3d54e20a2b7ccdc18a4fecc/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Transform/transform_graph/5/.temp_path/tftransform_tmp/688d8c4944014a3cb6680dd2e0499418/assets
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: 13
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 13
 name: "TransformGraph"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'transformed_examples': Channel(
     type_name: Examples
     artifacts: [Artifact(artifact: id: 6
 type_id: 5
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 5
 name: "Examples"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 properties {
   key: "version"
   value: INT
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'updated_analyzer_cache': Channel(
     type_name: TransformCache
     artifacts: [Artifact(artifact: id: 7
 type_id: 14
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 14
 name: "TransformCache"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'pre_transform_schema': Channel(
     type_name: Schema
     artifacts: [Artifact(artifact: id: 8
 type_id: 9
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 9
 name: "Schema"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'pre_transform_stats': Channel(
     type_name: ExampleStatistics
     artifacts: [Artifact(artifact: id: 9
 type_id: 7
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 7
 name: "ExampleStatistics"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_schema': Channel(
     type_name: Schema
     artifacts: [Artifact(artifact: id: 10
 type_id: 9
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 9
 name: "Schema"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_stats': Channel(
     type_name: ExampleStatistics
     artifacts: [Artifact(artifact: id: 11
 type_id: 7
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 7
 name: "ExampleStatistics"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_anomalies': Channel(
     type_name: ExampleAnomalies
     artifacts: [Artifact(artifact: id: 12
 type_id: 11
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 11
 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.

또한 처음 세 가지 변형된 예를 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_xf"
    value {
      int64_list {
        value: 8
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 0.061060599982738495
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 1
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: -0.15886740386486053
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: -0.7118487358093262
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 6
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 19
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    value {
      int64_list {
        value: 5
      }
    }
  }
}

features {
  feature {
    key: "company_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 1.2521240711212158
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 60
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: 0.532160758972168
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: 0.5509493350982666
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 2
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    value {
      int64_list {
        value: 10
      }
    }
  }
}

features {
  feature {
    key: "company_xf"
    value {
      int64_list {
        value: 48
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 0.3873794376850128
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: 0.21955278515815735
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: 0.0019067146349698305
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 12
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    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
import absl
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
_transformed_name = taxi_constants.transformed_name


def _transformed_names(keys):
  return [_transformed_name(key) for key in keys]


def _get_serve_tf_examples_fn(model, tf_transform_output):
  """Returns a function that parses a serialized tf.Example and applies TFT."""

  model.tft_layer = tf_transform_output.transform_features_layer()

  @tf.function
  def serve_tf_examples_fn(serialized_tf_examples):
    """Returns the output to be used in the serving signature."""
    feature_spec = tf_transform_output.raw_feature_spec()
    feature_spec.pop(_LABEL_KEY)
    parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
    transformed_features = model.tft_layer(parsed_features)
    return model(transformed_features)

  return serve_tf_examples_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=_transformed_name(_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 _transformed_names(_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 _transformed_names(_VOCAB_FEATURE_KEYS)
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
      for key in _transformed_names(_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(
              _transformed_names(_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 _transformed_names(_DENSE_FLOAT_FEATURE_KEYS)
  }
  input_layers.update({
      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
      for colname in _transformed_names(_VOCAB_FEATURE_KEYS)
  })
  input_layers.update({
      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
      for colname in _transformed_names(_BUCKET_FEATURE_KEYS)
  })
  input_layers.update({
      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
      for colname in _transformed_names(_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=absl.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_serve_tf_examples_fn(model,
                                    tf_transform_output).get_concrete_function(
                                        tf.TensorSpec(
                                            shape=[None],
                                            dtype=tf.string,
                                            name='examples')),
  }
  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_constants', 'taxi_trainer', 'taxi_transform']).
INFO:absl:User module package has hash fingerprint version 3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp_lnvffb5/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpc26kvw4n', '--dist-dir', '/tmp/tmpjsv1y_4u']
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl'; target user module is 'taxi_trainer'.
INFO:absl:Full user module path is 'taxi_trainer@/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl'
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
2021-07-27 09:08:19.212010: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Trainer
2021-07-27 09:08:19.215726: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
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-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl'} 'run_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp7xh_pre7', '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_constants.py -> build/lib
copying taxi_trainer.py -> build/lib
copying taxi_transform.py -> build/lib
installing to /tmp/tmpc26kvw4n
running install
running install_lib
copying build/lib/taxi_constants.py -> /tmp/tmpc26kvw4n
copying build/lib/taxi_transform.py -> /tmp/tmpc26kvw4n
copying build/lib/taxi_trainer.py -> /tmp/tmpc26kvw4n
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading 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/tmpc26kvw4n/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3.7.egg-info
running install_scripts
creating /tmp/tmpc26kvw4n/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.dist-info/WHEEL
creating '/tmp/tmpjsv1y_4u/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl' and adding '/tmp/tmpc26kvw4n' to it
adding 'taxi_constants.py'
adding 'taxi_trainer.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7.dist-info/RECORD'
removing /tmp/tmpc26kvw4n
Processing /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl
WARNING: You are using pip version 21.1.3; however, version 21.2.1 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/_wheels/tfx_user_code_Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+3acd02058a78fc9e40d70144d392b74161d6b10802fdd25a94793cf0145193b7
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  "The `lr` argument is deprecated, use `learning_rate` instead.")
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type)                    Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl:company_xf (InputLayer)         [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_census_tract_xf (InputL [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_community_area_xf (Inpu [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_latitude_xf (InputLayer [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_longitude_xf (InputLaye [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:fare_xf (InputLayer)            [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:payment_type_xf (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_census_tract_xf (InputLa [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_community_area_xf (Input [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_latitude_xf (InputLayer) [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_longitude_xf (InputLayer [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_miles_xf (InputLayer)      [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_seconds_xf (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_day_xf (InputLayer)  [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_hour_xf (InputLayer) [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_month_xf (InputLayer [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features (DenseFeatures)  (None, 3)            0           company_xf[0][0]                 
INFO:absl:                                                                 dropoff_census_tract_xf[0][0]    
INFO:absl:                                                                 dropoff_community_area_xf[0][0]  
INFO:absl:                                                                 dropoff_latitude_xf[0][0]        
INFO:absl:                                                                 dropoff_longitude_xf[0][0]       
INFO:absl:                                                                 fare_xf[0][0]                    
INFO:absl:                                                                 payment_type_xf[0][0]            
INFO:absl:                                                                 pickup_census_tract_xf[0][0]     
INFO:absl:                                                                 pickup_community_area_xf[0][0]   
INFO:absl:                                                                 pickup_latitude_xf[0][0]         
INFO:absl:                                                                 pickup_longitude_xf[0][0]        
INFO:absl:                                                                 trip_miles_xf[0][0]              
INFO:absl:                                                                 trip_seconds_xf[0][0]            
INFO:absl:                                                                 trip_start_day_xf[0][0]          
INFO:absl:                                                                 trip_start_hour_xf[0][0]         
INFO:absl:                                                                 trip_start_month_xf[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 (DenseFeatures (None, 2127)         0           company_xf[0][0]                 
INFO:absl:                                                                 dropoff_census_tract_xf[0][0]    
INFO:absl:                                                                 dropoff_community_area_xf[0][0]  
INFO:absl:                                                                 dropoff_latitude_xf[0][0]        
INFO:absl:                                                                 dropoff_longitude_xf[0][0]       
INFO:absl:                                                                 fare_xf[0][0]                    
INFO:absl:                                                                 payment_type_xf[0][0]            
INFO:absl:                                                                 pickup_census_tract_xf[0][0]     
INFO:absl:                                                                 pickup_community_area_xf[0][0]   
INFO:absl:                                                                 pickup_latitude_xf[0][0]         
INFO:absl:                                                                 pickup_longitude_xf[0][0]        
INFO:absl:                                                                 trip_miles_xf[0][0]              
INFO:absl:                                                                 trip_seconds_xf[0][0]            
INFO:absl:                                                                 trip_start_day_xf[0][0]          
INFO:absl:                                                                 trip_start_hour_xf[0][0]         
INFO:absl:                                                                 trip_start_month_xf[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:Total params: 14,706
INFO:absl:Trainable params: 14,706
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
2021-07-27 09:08:21.580710: I tensorflow/core/profiler/lib/profiler_session.cc:126] Profiler session initializing.
2021-07-27 09:08:21.580762: I tensorflow/core/profiler/lib/profiler_session.cc:141] Profiler session started.
2021-07-27 09:08:21.580858: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1611] Profiler found 1 GPUs
2021-07-27 09:08:21.643763: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcupti.so.11.2
2021-07-27 09:08:21.847952: I tensorflow/core/profiler/lib/profiler_session.cc:159] Profiler session tear down.
2021-07-27 09:08:21.853003: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1743] CUPTI activity buffer flushed
2021-07-27 09:08:23.104739: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
1/10000 [..............................] - ETA: 4:30:47 - loss: 0.6922 - binary_accuracy: 0.7750
2021-07-27 09:08:23.544669: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2021-07-27 09:08:23.585375: I tensorflow/core/profiler/lib/profiler_session.cc:126] Profiler session initializing.
2021-07-27 09:08:23.585425: I tensorflow/core/profiler/lib/profiler_session.cc:141] Profiler session started.
21/10000 [..............................] - ETA: 3:42 - loss: 0.6493 - binary_accuracy: 0.7821
2021-07-27 09:08:23.797730: I tensorflow/core/profiler/lib/profiler_session.cc:66] Profiler session collecting data.
2021-07-27 09:08:23.800405: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1743] CUPTI activity buffer flushed
2021-07-27 09:08:23.833723: I tensorflow/core/profiler/internal/gpu/cupti_collector.cc:673]  GpuTracer has collected 301 callback api events and 298 activity events. 
2021-07-27 09:08:23.841253: I tensorflow/core/profiler/lib/profiler_session.cc:159] Profiler session tear down.
2021-07-27 09:08:23.850367: I tensorflow/core/profiler/rpc/client/save_profile.cc:137] Creating directory: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23
2021-07-27 09:08:23.857266: I tensorflow/core/profiler/rpc/client/save_profile.cc:143] Dumped gzipped tool data for trace.json.gz to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.trace.json.gz
2021-07-27 09:08:23.876579: I tensorflow/core/profiler/rpc/client/save_profile.cc:137] Creating directory: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23
2021-07-27 09:08:23.879755: I tensorflow/core/profiler/rpc/client/save_profile.cc:143] Dumped gzipped tool data for memory_profile.json.gz to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.memory_profile.json.gz
2021-07-27 09:08:23.880489: I tensorflow/core/profiler/rpc/client/capture_profile.cc:251] Creating directory: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23Dumped tool data for xplane.pb to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.xplane.pb
Dumped tool data for overview_page.pb to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.overview_page.pb
Dumped tool data for input_pipeline.pb to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.input_pipeline.pb
Dumped tool data for tensorflow_stats.pb to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.tensorflow_stats.pb
Dumped tool data for kernel_stats.pb to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model_run/6/train/plugins/profile/2021_07_27_09_08_23/kokoro-gcp-ubuntu-prod-762616165.kernel_stats.pb
10000/10000 [==============================] - 83s 8ms/step - loss: 0.2376 - binary_accuracy: 0.8600 - val_loss: 0.2218 - val_binary_accuracy: 0.8729
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model/6/Format-Serving/assets
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Trainer/model/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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 기능 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(label_key='tips')
    ],
    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
2021-07-27 09:09:55.670102: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Evaluator
2021-07-27 09:09:55.673778: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
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    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': None, 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model'
ERROR:absl:There are change thresholds, but the baseline is missing. This is allowed only when rubber stamping (first run).
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 {
  label_key: "tips"
}
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-2021-07-27T09_07_38.527065-m86gazca/Trainer/model/6/Format-Serving as  model.
Exception ignored in: <function CapturableResource.__del__ at 0x7f2a1f16d9e0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f2a1f16d9e0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f2a1f16d9e0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f2a1f16d9e0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
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 (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f2a90368b90> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f29e04590d0>).
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    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': None, '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 {
  label_key: "tips"
}
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 {
  label_key: "tips"
}
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 {
  label_key: "tips"
}
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 (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f29e3987050> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f29e011d590>).
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 (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f29c83c43d0> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f29c8161ed0>).
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 (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f295867efd0> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f295868bd10>).
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 (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f2526140410> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f2526309cd0>).
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 (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f29587df150> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f2a902f1490>).
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 (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f2960342a10> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f2a63a7af10>).
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 (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f250d20c090> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7f250d230fd0>).
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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:113: 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-2021-07-27T09_07_38.527065-m86gazca/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: 20
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 20
 name: "ModelEvaluation"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'blessing': Channel(
     type_name: ModelBlessing
     artifacts: [Artifact(artifact: id: 16
 type_id: 21
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/Evaluator/blessing/8"
 custom_properties {
   key: "blessed"
   value {
     int_value: 1
   }
 }
 custom_properties {
   key: "current_model"
   value {
     string_value: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 21
 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'])

분할된 평가 메트릭에 대한 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 Jul 27 09:10 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
2021-07-27 09:10:20.780757: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Pusher
INFO:absl:Model version: 1627377020
INFO:absl:Model written to serving path /tmp/tmpf2y8jc9r/serving_model/taxi_simple/1627377020.
INFO:absl:Model pushed to /tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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: 23
 uri: "/tmp/tfx-interactive-2021-07-27T09_07_38.527065-m86gazca/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/tmpf2y8jc9r/serving_model/taxi_simple/1627377020"
   }
 }
 custom_properties {
   key: "pushed_version"
   value {
     string_value: "1627377020"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.0.0"
   }
 }
 state: LIVE
 , artifact_type: id: 23
 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 0x7F250C448490>)

내장 TFX 구성 요소 둘러보기를 마쳤습니다!