MLコミュニティデーは11月9日です! TensorFlow、JAXからの更新のために私たちに参加し、より多くの詳細をご覧ください

TFXKerasコンポーネントチュートリアル

TensorFlow Extended(TFX)のコンポーネントごとの概要

このColabベースのチュートリアルでは、TensorFlow Extended(TFX)の各組み込みコンポーネントについてインタラクティブに説明します。

データの取り込みからモデルのプッシュ、サービス提供まで、エンドツーエンドの機械学習パイプラインのすべてのステップをカバーしています。

完了したら、このノートブックのコンテンツをTFXパイプラインソースコードとして自動的にエクスポートできます。これは、ApacheAirflowおよびApacheBeamとオーケストレーションできます。

バックグラウンド

このノートブックは、Jupyter / Colab環境でTFXを使用する方法を示しています。ここでは、インタラクティブなノートブックでシカゴのタクシーの例を見ていきます。

インタラクティブノートブックでの作業は、TFXパイプラインの構造に慣れるための便利な方法です。軽量の開発環境として独自のパイプラインの開発を行う場合にも役立ちますが、インタラクティブノートブックのオーケストレーション方法とメタデータアーティファクトへのアクセス方法には違いがあることに注意する必要があります。

オーケストレーション

TFXの本番デプロイメントでは、Apache Airflow、Kubeflow Pipelines、Apache Beamなどのオーケストレーターを使用して、TFXコンポーネントの事前定義されたパイプライングラフをオーケストします。インタラクティブノートブックでは、ノートブック自体がオーケストレーターであり、ノートブックセルを実行するときに各TFXコンポーネントを実行します。

メタデータ

TFXの本番デプロイメントでは、MLメタデータ(MLMD)APIを介してメタデータにアクセスします。 MLMDは、メタデータプロパティをMySQLやSQLiteなどのデータベースに保存し、メタデータペイロードをファイルシステムなどの永続ストアに保存します。インタラクティブノートに、特性とペイロードの両方がで短命SQLiteデータベースに格納されている/tmp Jupyterノートまたはコラボサーバ上のディレクトリ。

設定

まず、必要なパッケージをインストールしてインポートし、パスを設定して、データをダウンロードします。

アップグレードピップ

ローカルで実行しているときにシステムでPipをアップグレードしないようにするには、Colabで実行していることを確認してください。もちろん、ローカルシステムは個別にアップグレードできます。

try:
  import colab
  !pip install --upgrade pip
except:
  pass

TFXをインストールする

pip install -U tfx

ランタイムを再起動しましたか?

上記のセルを初めて実行するときにGoogleColabを使用している場合は、ランタイムを再起動する必要があります([ランタイム]> [ランタイムの再起動...])。これは、Colabがパッケージをロードする方法が原因です。

パッケージをインポートする

標準のTFXコンポーネントクラスを含む必要なパッケージをインポートします。

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

ライブラリのバージョンを確認しましょう。

print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.5.1
TFX version: 1.2.0

パイプラインパスを設定する

# 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)

サンプルデータをダウンロードする

TFXパイプラインで使用するサンプルデータセットをダウンロードします。

私たちが使っているデータセットがあるタクシーデータセットをTripsのシカゴ市が発表しました。このデータセットの列は次のとおりです。

Pickup_community_area運賃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払いの種類会社
trip_seconds dropoff_community_areaチップ

このデータセットでは、我々は予測するモデル構築するtips旅行のを。

_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-data3oncdzkk/data.csv', <http.client.HTTPMessage at 0x7ffac02f5e90>)

CSVファイルをざっと見てください。

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

免責事項:このサイトは、元のソースであるシカゴ市の公式Webサイトwww.cityofchicago.orgから使用するために変更されたデータを使用するアプリケーションを提供します。シカゴ市は、このサイトで提供されるデータの内容、正確性、適時性、または完全性について一切の主張を行いません。このサイトで提供されるデータは、いつでも変更される可能性があります。このサイトで提供されるデータは、自己責任で使用されていると理解されています。

InteractiveContextを作成する

最後に、InteractiveContextを作成します。これにより、このノートブックでTFXコンポーネントをインタラクティブに実行できます。

# 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-2021-09-30T02_22_38.015128-a3hzxsja as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/metadata.sqlite.

TFXコンポーネントをインタラクティブに実行する

次のセルでは、TFXコンポーネントを1つずつ作成し、それぞれを実行して、出力アーティファクトを視覚化します。

ExampleGen

ExampleGenコンポーネントは、TFXパイプラインの開始時に通常です。そうなる:

  1. データをトレーニングセットと評価セットに分割します(デフォルトでは、2/3トレーニング+ 1/3評価)
  2. 変換データtf.Exampleフォーマット(詳細はこちらこちら
  3. データをコピーし_tfx_rootアクセスに他のコンポーネント用のディレクトリ

ExampleGen入力として、データソースへのパスを取ります。我々の場合には、これはある_data_rootダウンロードCSVを含むパス。

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-data3oncdzkk/* 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

のがの出力アーティファクト調べてみましょうExampleGen 。このコンポーネントは、トレーニング例と評価例の2つのアーティファクトを生成します。

artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/CsvExampleGen/examples/1

また、最初の3つのトレーニング例を確認することもできます。

# 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
      }
    }
  }
}

今というExampleGenデータを摂取終了している、次のステップは、データ分析です。

StatisticsGen

StatisticsGenデータ分析のためだけでなく、下流の構成要素で使用するデータセット以上のコンポーネントを計算する統計。これは、使用していますTensorFlowデータ検証用ライブラリを。

StatisticsGen 、入力として、我々はちょうど使用して摂取したデータセット取り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-2021-09-30T02_22_38.015128-a3hzxsja/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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実行が終了し、我々は、出力された統計情報を視覚化することができます。さまざまなプロットで遊んでみてください!

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

SchemaGen

SchemaGenコンポーネントは、データの統計情報に基づいてスキーマを生成します。 (スキーマは予想範囲、種類、およびデータセットの機能のプロパティを定義します。)また、使用していますTensorFlowデータ検証ライブラリを。

SchemaGen入力として、我々が発生したことを統計かかりますStatisticsGenデフォルトでトレーニングスプリットを見て、。

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
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0930 02:22:48.211663 23719 rdbms_metadata_access_object.cc:686] 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-2021-09-30T02_22_38.015128-a3hzxsja/SchemaGen/schema/3/schema.pbtxt.
INFO:absl:Running publisher for SchemaGen
INFO:absl:MetadataStore with DB connection initialized

SchemaGen実行が終了、私たちはテーブルとして生成されたスキーマを視覚化することができます。

context.show(schema_gen.outputs['schema'])
/home/kbuilder/.local/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)

データセット内の各機能は、そのプロパティとともにスキーマテーブルの行として表示されます。スキーマは、ドメインとして示される、カテゴリ機能がとるすべての値もキャプチャします。

スキーマの詳細については、を参照SchemaGenのマニュアルを

ExampleValidator

ExampleValidatorコンポーネントは、スキーマで定義された予測に基づいて、あなたのデータの異常を検知します。また、使用していますTensorFlowデータ検証ライブラリを。

ExampleValidatorからの入力として統計を取るStatisticsGen 、およびからスキーマ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-2021-09-30T02_22_38.015128-a3hzxsja/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-2021-09-30T02_22_38.015128-a3hzxsja/ExampleValidator/anomalies/4/Split-eval.
INFO:absl:Running publisher for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized

ExampleValidator実行が終了、私たちはテーブルのように異常を視覚化することができます。

context.show(example_validator.outputs['anomalies'])
/home/kbuilder/.local/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)

異常表では、異常がないことがわかります。これは、私たちが分析した最初のデータセットであり、スキーマがそれに合わせて調整されているため、私たちが期待するものです。このスキーマを確認する必要があります。予期しないことは、データの異常を意味します。確認したら、スキーマを使用して将来のデータを保護できます。ここで生成された異常を使用して、モデルのパフォーマンスをデバッグし、データが時間の経過とともにどのように変化するかを理解し、データエラーを特定できます。

変身

Transformコンポーネントを実行するには、トレーニングとサービス提供の両方のためのエンジニアリングを備えています。これは、使用していますTensorFlowトランスフォームライブラリ。

Transform入力としてデータ取るExampleGen 、からスキーマSchemaGen 、ならびにユーザ定義の変換コードを含むモジュールを。

レッツは、例を参照してください、ユーザー定義(、TensorFlowへの導入のためのAPIを変換し、以下のコードを変換するチュートリアルを参照してください)。まず、特徴工学のいくつかの定数を定義します。

_taxi_constants_module_file = 'taxi_constants.py'
%%writefile {_taxi_constants_module_file}

# Categorical features are assumed to each have a maximum value in the dataset.
MAX_CATEGORICAL_FEATURE_VALUES = [24, 31, 12]

CATEGORICAL_FEATURE_KEYS = [
    'trip_start_hour', 'trip_start_day', 'trip_start_month',
    'pickup_census_tract', 'dropoff_census_tract', 'pickup_community_area',
    'dropoff_community_area'
]

DENSE_FLOAT_FEATURE_KEYS = ['trip_miles', 'fare', 'trip_seconds']

# Number of buckets used by tf.transform for encoding each feature.
FEATURE_BUCKET_COUNT = 10

BUCKET_FEATURE_KEYS = [
    'pickup_latitude', 'pickup_longitude', 'dropoff_latitude',
    'dropoff_longitude'
]

# Number of vocabulary terms used for encoding VOCAB_FEATURES by tf.transform
VOCAB_SIZE = 1000

# Count of out-of-vocab buckets in which unrecognized VOCAB_FEATURES are hashed.
OOV_SIZE = 10

VOCAB_FEATURE_KEYS = [
    'payment_type',
    'company',
]

# Keys
LABEL_KEY = 'tips'
FARE_KEY = 'fare'
Writing taxi_constants.py

次に、我々は書くpreprocessing_fn入力として生データを取り込み、戻り我々のモデルは、上で訓練することができ変換機能を:

_taxi_transform_module_file = 'taxi_transform.py'
%%writefile {_taxi_transform_module_file}

import tensorflow as tf
import tensorflow_transform as tft

import taxi_constants

_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_FARE_KEY = taxi_constants.FARE_KEY
_LABEL_KEY = taxi_constants.LABEL_KEY


def preprocessing_fn(inputs):
  """tf.transform's callback function for preprocessing inputs.
  Args:
    inputs: map from feature keys to raw not-yet-transformed features.
  Returns:
    Map from string feature key to transformed feature operations.
  """
  outputs = {}
  for key in _DENSE_FLOAT_FEATURE_KEYS:
    # Preserve this feature as a dense float, setting nan's to the mean.
    outputs[key] = tft.scale_to_z_score(
        _fill_in_missing(inputs[key]))

  for key in _VOCAB_FEATURE_KEYS:
    # Build a vocabulary for this feature.
    outputs[key] = tft.compute_and_apply_vocabulary(
        _fill_in_missing(inputs[key]),
        top_k=_VOCAB_SIZE,
        num_oov_buckets=_OOV_SIZE)

  for key in _BUCKET_FEATURE_KEYS:
    outputs[key] = tft.bucketize(
        _fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT)

  for key in _CATEGORICAL_FEATURE_KEYS:
    outputs[key] = _fill_in_missing(inputs[key])

  # Was this passenger a big tipper?
  taxi_fare = _fill_in_missing(inputs[_FARE_KEY])
  tips = _fill_in_missing(inputs[_LABEL_KEY])
  outputs[_LABEL_KEY] = tf.where(
      tf.math.is_nan(taxi_fare),
      tf.cast(tf.zeros_like(taxi_fare), tf.int64),
      # Test if the tip was > 20% of the fare.
      tf.cast(
          tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))

  return outputs


def _fill_in_missing(x):
  """Replace missing values in a SparseTensor.
  Fills in missing values of `x` with '' or 0, and converts to a dense tensor.
  Args:
    x: A `SparseTensor` of rank 2.  Its dense shape should have size at most 1
      in the second dimension.
  Returns:
    A rank 1 tensor where missing values of `x` have been filled in.
  """
  if not isinstance(x, tf.sparse.SparseTensor):
    return x

  default_value = '' if x.dtype == tf.string else 0
  return tf.squeeze(
      tf.sparse.to_dense(
          tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),
          default_value),
      axis=1)
Writing taxi_transform.py

今、私たちは、この機能のエンジニアリング・コードに渡しTransformコンポーネントと、あなたのデータを変換するためにそれを実行します。

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 8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp25n57aeg/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpv2ojslny', '--dist-dir', '/tmp/tmpduqs2o96']
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl'; target user module is 'taxi_transform'.
INFO:absl:Full user module path is 'taxi_transform@/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl'
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
I0930 02:22:48.774238 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Transform
I0930 02:22:48.777988 23719 rdbms_metadata_access_object.cc:686] 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-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp6f1bcbm7', '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-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_transform.py -> build/lib
installing to /tmp/tmpv2ojslny
running install
running install_lib
copying build/lib/taxi_constants.py -> /tmp/tmpv2ojslny
copying build/lib/taxi_transform.py -> /tmp/tmpv2ojslny
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/tmpv2ojslny/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3.7.egg-info
running install_scripts
creating /tmp/tmpv2ojslny/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.dist-info/WHEEL
creating '/tmp/tmpduqs2o96/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl' and adding '/tmp/tmpv2ojslny' to it
adding 'taxi_constants.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.dist-info/RECORD'
removing /tmp/tmpv2ojslny
Processing /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl'.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp55e42eqi', '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb
Processing /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl'.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb
WARNING:tensorflow:From /home/kbuilder/.local/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:261: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Installing '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp03chpfbr', '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl']
Processing /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-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+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
2021-09-30 02:23:01.717912: 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-2021-09-30T02_22_38.015128-a3hzxsja/Transform/transform_graph/5/.temp_path/tftransform_tmp/f846c938978244c591f21e5f90b088aa/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/transform_graph/5/.temp_path/tftransform_tmp/31cf107e852b44ddba4df6155ba9b0bc/assets
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized

のがの出力アーティファクト調べてみましょうTransform 。このコンポーネントは、次の2種類の出力を生成します。

  • transform_graph (このグラフは、サービング及び評価モデルに含まれる)前処理操作を行うことができたグラフです。
  • transformed_examples前処理されたトレーニングや評価データを表しています。
transform.outputs
{'transform_graph': Channel(
     type_name: TransformGraph
     artifacts: [Artifact(artifact: id: 5
 type_id: 22
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 22
 name: "TransformGraph"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'transformed_examples': Channel(
     type_name: Examples
     artifacts: [Artifact(artifact: id: 6
 type_id: 14
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 14
 name: "Examples"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 properties {
   key: "version"
   value: INT
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'updated_analyzer_cache': Channel(
     type_name: TransformCache
     artifacts: [Artifact(artifact: id: 7
 type_id: 23
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 23
 name: "TransformCache"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'pre_transform_schema': Channel(
     type_name: Schema
     artifacts: [Artifact(artifact: id: 8
 type_id: 18
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 18
 name: "Schema"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'pre_transform_stats': Channel(
     type_name: ExampleStatistics
     artifacts: [Artifact(artifact: id: 9
 type_id: 16
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 16
 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: 18
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 18
 name: "Schema"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_stats': Channel(
     type_name: ExampleStatistics
     artifacts: [Artifact(artifact: id: 11
 type_id: 16
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 16
 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: 20
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 20
 name: "ExampleAnomalies"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

覗いてくださいtransform_graphアーティファクトを。これは、3つのサブディレクトリを含むディレクトリを指します。

train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)
['transform_fn', 'transformed_metadata', 'metadata']

transformed_metadataサブディレクトリには、前処理データのスキーマが含まれています。 transform_fnサブディレクトリには、実際の前処理グラフが含まれています。 metadataサブディレクトリには、元のデータのスキーマが含まれています。

また、最初の3つの変換された例を見ることができます。

# Get the URI of the output artifact representing the transformed examples, which is a directory
train_uri = os.path.join(transform.outputs['transformed_examples'].get()[0].uri, 'Split-train')

# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
                      for name in os.listdir(train_uri)]

# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")

# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
  serialized_example = tfrecord.numpy()
  example = tf.train.Example()
  example.ParseFromString(serialized_example)
  pp.pprint(example)
features {
  feature {
    key: "company"
    value {
      int64_list {
        value: 8
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 0.061060599982738495
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      int64_list {
        value: 1
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "tips"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: -0.15886740386486053
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      float_list {
        value: -0.7118487358093262
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 6
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 19
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 5
      }
    }
  }
}

features {
  feature {
    key: "company"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 1.2521240711212158
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 60
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "tips"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 0.532160758972168
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      float_list {
        value: 0.5509493350982666
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 2
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 10
      }
    }
  }
}

features {
  feature {
    key: "company"
    value {
      int64_list {
        value: 48
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 0.3873794376850128
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "tips"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 0.21955278515815735
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      float_list {
        value: 0.0019067146349698305
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 12
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 11
      }
    }
  }
}

後にTransformコンポーネントの機能にデータを変換し、次のステップは、モデルを訓練することですしています。

トレーナー

Trainerコンポーネントを使用すると、TensorFlowで定義されていることモデルをトレーニングします。トレーナーのサポート見積もりAPIは、ユーザーが指定する必要がKerasのAPIを使用して、デフォルトの一般的なトレーナーを設定することによりcustom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor)トレーナーのコンストラクタで。

Trainerからの入力としてスキーマを取りSchemaGen 、変換されたデータとグラフからTransform 、トレーニングパラメータ、ならびにユーザ定義のモデルコードを含むモジュール。

のは、(TensorFlow KerasのAPIの概要については、以下のユーザ定義のモデルコードの例を見てみましょうチュートリアルを参照してください):

_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


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=_LABEL_KEY),
      tf_transform_output.transformed_metadata.schema)


def _build_keras_model(hidden_units: List[int] = None) -> tf.keras.Model:
  """Creates a DNN Keras model for classifying taxi data.

  Args:
    hidden_units: [int], the layer sizes of the DNN (input layer first).

  Returns:
    A keras Model.
  """
  real_valued_columns = [
      tf.feature_column.numeric_column(key, shape=())
      for key in _DENSE_FLOAT_FEATURE_KEYS
  ]
  categorical_columns = [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_VOCAB_SIZE + _OOV_SIZE, default_value=0)
      for key in _VOCAB_FEATURE_KEYS
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
      for key in _BUCKET_FEATURE_KEYS
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(  # pylint: disable=g-complex-comprehension
          key,
          num_buckets=num_buckets,
          default_value=0) for key, num_buckets in zip(
              _CATEGORICAL_FEATURE_KEYS,
              _MAX_CATEGORICAL_FEATURE_VALUES)
  ]
  indicator_column = [
      tf.feature_column.indicator_column(categorical_column)
      for categorical_column in categorical_columns
  ]

  model = _wide_and_deep_classifier(
      # TODO(b/139668410) replace with premade wide_and_deep keras model
      wide_columns=indicator_column,
      deep_columns=real_valued_columns,
      dnn_hidden_units=hidden_units or [100, 70, 50, 25])
  return model


def _wide_and_deep_classifier(wide_columns, deep_columns, dnn_hidden_units):
  """Build a simple keras wide and deep model.

  Args:
    wide_columns: Feature columns wrapped in indicator_column for wide (linear)
      part of the model.
    deep_columns: Feature columns for deep part of the model.
    dnn_hidden_units: [int], the layer sizes of the hidden DNN.

  Returns:
    A Wide and Deep Keras model
  """
  # Following values are hard coded for simplicity in this example,
  # However prefarably they should be passsed in as hparams.

  # Keras needs the feature definitions at compile time.
  # TODO(b/139081439): Automate generation of input layers from FeatureColumn.
  input_layers = {
      colname: tf.keras.layers.Input(name=colname, shape=(), dtype=tf.float32)
      for colname in _DENSE_FLOAT_FEATURE_KEYS
  }
  input_layers.update({
      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
      for colname in _VOCAB_FEATURE_KEYS
  })
  input_layers.update({
      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
      for colname in _BUCKET_FEATURE_KEYS
  })
  input_layers.update({
      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
      for colname in _CATEGORICAL_FEATURE_KEYS
  })

  # TODO(b/161952382): Replace with Keras preprocessing layers.
  deep = tf.keras.layers.DenseFeatures(deep_columns)(input_layers)
  for numnodes in dnn_hidden_units:
    deep = tf.keras.layers.Dense(numnodes)(deep)
  wide = tf.keras.layers.DenseFeatures(wide_columns)(input_layers)

  output = tf.keras.layers.Dense(1)(
          tf.keras.layers.concatenate([deep, wide]))

  model = tf.keras.Model(input_layers, output)
  model.compile(
      loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
      optimizer=tf.keras.optimizers.Adam(lr=0.001),
      metrics=[tf.keras.metrics.BinaryAccuracy()])
  model.summary(print_fn=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

今、私たちは、このモデルのコードに渡しTrainerコンポーネントとモデルを訓練するためにそれを実行します。

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_transform', 'taxi_trainer']).
INFO:absl:User module package has hash fingerprint version 60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpg7itmljo/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpwli3zykq', '--dist-dir', '/tmp/tmp70wvofh8']
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl'; target user module is 'taxi_trainer'.
INFO:absl:Full user module path is 'taxi_trainer@/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl'
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
I0930 02:23:13.167377 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Trainer
I0930 02:23:13.170449 23719 rdbms_metadata_access_object.cc:686] 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-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl'} 'run_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpc7e0fakf', '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-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_transform.py -> build/lib
copying taxi_trainer.py -> build/lib
installing to /tmp/tmpwli3zykq
running install
running install_lib
copying build/lib/taxi_constants.py -> /tmp/tmpwli3zykq
copying build/lib/taxi_transform.py -> /tmp/tmpwli3zykq
copying build/lib/taxi_trainer.py -> /tmp/tmpwli3zykq
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/tmpwli3zykq/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3.7.egg-info
running install_scripts
creating /tmp/tmpwli3zykq/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.dist-info/WHEEL
creating '/tmp/tmp70wvofh8/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl' and adding '/tmp/tmpwli3zykq' to it
adding 'taxi_constants.py'
adding 'taxi_trainer.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.dist-info/RECORD'
removing /tmp/tmpwli3zykq
Processing /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
/home/kbuilder/.local/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 (InputLayer)            [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_census_tract (InputLaye [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_community_area (InputLa [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_latitude (InputLayer)   [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_longitude (InputLayer)  [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:fare (InputLayer)               [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:payment_type (InputLayer)       [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_census_tract (InputLayer [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_community_area (InputLay [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_latitude (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_longitude (InputLayer)   [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_miles (InputLayer)         [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_seconds (InputLayer)       [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_day (InputLayer)     [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_hour (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_month (InputLayer)   [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features (DenseFeatures)  (None, 3)            0           company[0][0]                    
INFO:absl:                                                                 dropoff_census_tract[0][0]       
INFO:absl:                                                                 dropoff_community_area[0][0]     
INFO:absl:                                                                 dropoff_latitude[0][0]           
INFO:absl:                                                                 dropoff_longitude[0][0]          
INFO:absl:                                                                 fare[0][0]                       
INFO:absl:                                                                 payment_type[0][0]               
INFO:absl:                                                                 pickup_census_tract[0][0]        
INFO:absl:                                                                 pickup_community_area[0][0]      
INFO:absl:                                                                 pickup_latitude[0][0]            
INFO:absl:                                                                 pickup_longitude[0][0]           
INFO:absl:                                                                 trip_miles[0][0]                 
INFO:absl:                                                                 trip_seconds[0][0]               
INFO:absl:                                                                 trip_start_day[0][0]             
INFO:absl:                                                                 trip_start_hour[0][0]            
INFO:absl:                                                                 trip_start_month[0][0]           
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 100)          400         dense_features[0][0]             
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 70)           7070        dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 48)           3408        dense_1[0][0]                    
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_3 (Dense)                 (None, 34)           1666        dense_2[0][0]                    
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features_1 (DenseFeatures (None, 2127)         0           company[0][0]                    
INFO:absl:                                                                 dropoff_census_tract[0][0]       
INFO:absl:                                                                 dropoff_community_area[0][0]     
INFO:absl:                                                                 dropoff_latitude[0][0]           
INFO:absl:                                                                 dropoff_longitude[0][0]          
INFO:absl:                                                                 fare[0][0]                       
INFO:absl:                                                                 payment_type[0][0]               
INFO:absl:                                                                 pickup_census_tract[0][0]        
INFO:absl:                                                                 pickup_community_area[0][0]      
INFO:absl:                                                                 pickup_latitude[0][0]            
INFO:absl:                                                                 pickup_longitude[0][0]           
INFO:absl:                                                                 trip_miles[0][0]                 
INFO:absl:                                                                 trip_seconds[0][0]               
INFO:absl:                                                                 trip_start_day[0][0]             
INFO:absl:                                                                 trip_start_hour[0][0]            
INFO:absl:                                                                 trip_start_month[0][0]           
INFO:absl:__________________________________________________________________________________________________
INFO:absl: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:__________________________________________________________________________________________________
10000/10000 [==============================] - 75s 7ms/step - loss: 0.2374 - binary_accuracy: 0.8608 - val_loss: 0.2225 - val_binary_accuracy: 0.8759
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Trainer/model/6/Format-Serving/assets
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Trainer/model/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Trainer/model_run/6
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized

TensorBoardでトレーニングを分析する

トレーナーのアーティファクトをのぞいてみましょう。モデルのサブディレクトリを含むディレクトリを指します。

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']

オプションで、TensorBoardをTrainerに接続して、モデルのトレーニング曲線を分析できます。

model_run_artifact_dir = trainer.outputs['model_run'].get()[0].uri

%load_ext tensorboard
%tensorboard --logdir {model_run_artifact_dir}

評価者

Evaluatorコンポーネントは、評価セットの上にモデルのパフォーマンスメトリックを計算します。これは、使用していますTensorFlowモデル解析ライブラリを。 Evaluatorまた、必要に応じて、新たに訓練されたモデルは、より良い以前のモデルよりもあることを検証することができます。これは、モデルを毎日自動的にトレーニングおよび検証できる本番パイプライン設定で役立ちます。ので、このノートブックでは、我々は唯一、一つのモデルを訓練Evaluator自動的に「良い」などのモデルにラベルを付けます。

Evaluator 、入力としてのデータがかかりますExampleGen 、から訓練を受けたモデルTrainer 、およびスライスの設定を。スライス構成を使用すると、特徴値のメトリックをスライスできます(たとえば、午前8時と午後8時に開始するタクシー旅行でモデルがどのように機能するか)。以下のこの構成の例を参照してください。

eval_config = tfma.EvalConfig(
    model_specs=[
        # This assumes a serving model with signature 'serving_default'. If
        # using estimator based EvalSavedModel, add signature_name: 'eval' and 
        # remove the label_key.
        tfma.ModelSpec(
            signature_name='serving_default',
            label_key='tips'
            )
        ],
    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'])
    ])

次に、我々は、この設定を与えるEvaluatorと、それを実行します。

# 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
I0930 02:24:36.166139 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I0930 02:24:36.169533 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Evaluator
INFO:absl:Nonempty beam arg extra_packages already includes dependency
INFO:absl:udf_utils.get_fn {'eval_config': '{\n  "metrics_specs": [\n    {\n      "metrics": [\n        {\n          "class_name": "ExampleCount"\n        },\n        {\n          "class_name": "BinaryAccuracy",\n          "threshold": {\n            "change_threshold": {\n              "absolute": -1e-10,\n              "direction": "HIGHER_IS_BETTER"\n            },\n            "value_threshold": {\n              "lower_bound": 0.5\n            }\n          }\n        }\n      ]\n    }\n  ],\n  "model_specs": [\n    {\n      "label_key": "tips",\n      "signature_name": "serving_default"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model'
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 {
  signature_name: "serving_default"
  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-2021-09-30T02_22_38.015128-a3hzxsja/Trainer/model/6/Format-Serving as  model.
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ffa76ab2a90> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff9684cfa90>).
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      "signature_name": "serving_default"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_extractors'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "serving_default"
  label_key: "tips"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
    threshold {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
  model_names: ""
}

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "serving_default"
  label_key: "tips"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
    threshold {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
  model_names: ""
}

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "serving_default"
  label_key: "tips"
}
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 0x7ff960527ad0> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff960574a90>).
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
Exception ignored in: <function CapturableResource.__del__ at 0x7ffa2948f320>
Traceback (most recent call last):
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/home/kbuilder/.local/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 "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/kbuilder/.local/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 "/home/kbuilder/.local/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 0x7ffa2948f320>
Traceback (most recent call last):
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/home/kbuilder/.local/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 "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/kbuilder/.local/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 "/home/kbuilder/.local/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 0x7ffa2948f320>
Traceback (most recent call last):
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/home/kbuilder/.local/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 "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/kbuilder/.local/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 "/home/kbuilder/.local/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 0x7ffa2948f320>
Traceback (most recent call last):
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/home/kbuilder/.local/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 "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/kbuilder/.local/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 "/home/kbuilder/.local/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 0x7ff8fe92f910> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff8fe972dd0>).
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 0x7ff8fc09a150> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff8fc086350>).
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 0x7ff4fd133610> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff4fd0d9a50>).
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 0x7ff9d0263a50> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ffa761492d0>).
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 0x7ff9d04b4b90> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff960642390>).
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 0x7ff4fc86e990> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff4fc6f4650>).
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Evaluator/evaluation/8.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /home/kbuilder/.local/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-2021-09-30T02_22_38.015128-a3hzxsja/Evaluator/blessing/8.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized

今度は、の出力アーティファクト調べてみましょうEvaluator

evaluator.outputs
{'evaluation': Channel(
     type_name: ModelEvaluation
     artifacts: [Artifact(artifact: id: 15
 type_id: 29
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 29
 name: "ModelEvaluation"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'blessing': Channel(
     type_name: ModelBlessing
     artifacts: [Artifact(artifact: id: 16
 type_id: 30
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Evaluator/blessing/8"
 custom_properties {
   key: "blessed"
   value {
     int_value: 1
   }
 }
 custom_properties {
   key: "current_model"
   value {
     string_value: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 30
 name: "ModelBlessing"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

使用してevaluation出力することは、我々は全体の評価セットのグローバルメトリックのデフォルトの可視化を表示することができます。

context.show(evaluator.outputs['evaluation'])

スライスされた評価指標の視覚化を確認するには、TensorFlowモデル分析ライブラリを直接呼び出すことができます。

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',…

この可視化は、同じメトリックを示しているが、の全ての特徴値で計算trip_start_hourの代わりに、全体の評価セットに。

TensorFlow Model Analysisは、公平性インジケーターやモデルパフォーマンスの時系列のプロットなど、他の多くの視覚化をサポートしています。より多くを学ぶために、参照のチュートリアルを

構成にしきい値を追加したため、検証出力も利用できます。 precence blessingアーティファクトは、我々のモデルが検証に合格したことを示しています。これが実行される最初の検証であるため、候補者は自動的に祝福されます。

blessing_uri = evaluator.outputs['blessing'].get()[0].uri
!ls -l {blessing_uri}
total 0
-rw-rw-r-- 1 kbuilder kbuilder 0 Sep 30 02:24 BLESSED

検証結果レコードをロードして、成功を検証できるようになりました。

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
  }
}

プッシャー

Pusher成分はTFXパイプラインの終わりに通常です。このモデルが検証に合格し、そうであれば、にモデルをエクスポートしているかどうかをチェックし_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
I0930 02:24:56.889948 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Pusher
INFO:absl:Model version: 1632968696
INFO:absl:Model written to serving path /tmp/tmpi_ti963w/serving_model/taxi_simple/1632968696.
INFO:absl:Model pushed to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Pusher/pushed_model/9.
INFO:absl:Running publisher for Pusher
INFO:absl:MetadataStore with DB connection initialized

のがの出力アーティファクト調べてみましょうPusher

pusher.outputs
{'pushed_model': Channel(
     type_name: PushedModel
     artifacts: [Artifact(artifact: id: 17
 type_id: 32
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/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/tmpi_ti963w/serving_model/taxi_simple/1632968696"
   }
 }
 custom_properties {
   key: "pushed_version"
   value {
     string_value: "1632968696"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 32
 name: "PushedModel"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

特に、プッシャーはモデルをSavedModel形式でエクスポートします。これは次のようになります。

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 0x7FF4F5BBEB50>)

組み込みのTFXコンポーネントのツアーが終了しました。