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TFX Keras-Komponenten-Tutorial

Eine komponentenweise Einführung in TensorFlow Extended (TFX)

Dieses Colab-basierte Tutorial führt interaktiv durch jede integrierte Komponente von TensorFlow Extended (TFX).

Es deckt jeden Schritt in einer End-to-End-Pipeline für maschinelles Lernen ab, von der Datenaufnahme über das Pushen eines Modells bis zur Bereitstellung.

Wenn Sie fertig sind, kann der Inhalt dieses Notebooks automatisch als TFX-Pipeline-Quellcode exportiert werden, den Sie mit Apache Airflow und Apache Beam orchestrieren können.

Hintergrund

Dieses Notebook demonstriert die Verwendung von TFX in einer Jupyter/Colab-Umgebung. Hier gehen wir das Chicago Taxi-Beispiel in einem interaktiven Notizbuch durch.

Die Arbeit in einem interaktiven Notizbuch ist eine nützliche Methode, um sich mit der Struktur einer TFX-Pipeline vertraut zu machen. Es ist auch nützlich, wenn Sie Ihre eigenen Pipelines als einfache Entwicklungsumgebung entwickeln, aber Sie sollten sich bewusst sein, dass es Unterschiede in der Art und Weise gibt, wie interaktive Notebooks orchestriert werden und wie sie auf Metadatenartefakte zugreifen.

Orchestrierung

In einer Produktionsbereitstellung von TFX verwenden Sie einen Orchestrator wie Apache Airflow, Kubeflow Pipelines oder Apache Beam, um ein vordefiniertes Pipeline-Diagramm von TFX-Komponenten zu orchestrieren. In einem interaktiven Notebook ist das Notebook selbst der Orchestrator, der jede TFX-Komponente ausführt, während Sie die Notebook-Zellen ausführen.

Metadaten

In einer Produktionsbereitstellung von TFX greifen Sie über die ML Metadata (MLMD) API auf Metadaten zu. MLMD speichert Metadateneigenschaften in einer Datenbank wie MySQL oder SQLite und speichert die Metadaten-Nutzlasten in einem dauerhaften Speicher wie in Ihrem Dateisystem. In einem interaktiven Notebook werden sowohl Eigenschaften als auch Nutzlasten in einer kurzlebigen SQLite-Datenbank im Verzeichnis /tmp auf dem Jupyter-Notebook oder Colab-Server gespeichert.

Einrichten

Zuerst installieren und importieren wir die erforderlichen Pakete, richten Pfade ein und laden Daten herunter.

Upgrade-Pip

Um zu vermeiden, dass Pip in einem System aktualisiert wird, wenn es lokal ausgeführt wird, stellen Sie sicher, dass wir in Colab ausgeführt werden. Lokale Systeme können natürlich separat nachgerüstet werden.

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

TFX installieren

pip install -U tfx

Hast du die Laufzeit neu gestartet?

Wenn Sie Google Colab verwenden, müssen Sie beim ersten Ausführen der obigen Zelle die Laufzeit neu starten (Laufzeit > Laufzeit neu starten ...). Dies liegt an der Art und Weise, wie Colab Pakete lädt.

Pakete importieren

Wir importieren notwendige Pakete, einschließlich Standard-TFX-Komponentenklassen.

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
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.

Lassen Sie uns die Bibliotheksversionen überprüfen.

print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.4.2
TFX version: 0.30.0

Pipelinepfade einrichten

# 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)

Beispieldaten herunterladen

Wir laden den Beispieldatensatz zur Verwendung in unserer TFX-Pipeline herunter.

Der von uns verwendete Datensatz ist der von der Stadt Chicago veröffentlichte Datensatz zu Taxifahrten . Die Spalten in diesem Datensatz sind:

pickup_community_area Fahrpreis trip_start_month
trip_start_hour trip_start_day trip_start_timestamp
Pickup_Latitude Pickup_Längengrad dropoff_latitude
dropoff_longitude trip_miles pickup_census_tract
dropoff_census_tract Zahlungsart Unternehmen
trip_seconds dropoff_community_area Tipps

Mit diesem Datensatz erstellen wir ein Modell, das die tips einer Reise vorhersagt.

_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-datac4ibbtgw/data.csv', <http.client.HTTPMessage at 0x7fb148721190>)

Sehen Sie sich die CSV-Datei kurz an.

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

Haftungsausschluss: Diese Website bietet Anwendungen, die Daten verwenden, die für die Verwendung von ihrer ursprünglichen Quelle, www.cityofchicago.org, der offiziellen Website der Stadt Chicago, modifiziert wurden. Die City of Chicago erhebt keinen Anspruch auf Inhalt, Richtigkeit, Aktualität oder Vollständigkeit der auf dieser Site bereitgestellten Daten. Die auf dieser Site bereitgestellten Daten können sich jederzeit ändern. Es versteht sich, dass die Nutzung der auf dieser Site bereitgestellten Daten auf eigene Gefahr erfolgt.

Erstellen Sie den InteractiveContext

Zuletzt erstellen wir einen InteractiveContext, der es uns ermöglicht, TFX-Komponenten interaktiv in diesem Notebook auszuführen.

# 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-2021-06-18T09_15_50.141015-46onlp08 as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/metadata.sqlite.

TFX-Komponenten interaktiv ausführen

In den folgenden Zellen erstellen wir nacheinander TFX-Komponenten, führen jede von ihnen aus und visualisieren ihre Ausgabeartefakte.

BeispielGen

Die ExampleGen Komponente befindet sich normalerweise am Anfang einer TFX-Pipeline. Es wird:

  1. Daten in Trainings- und Bewertungssätze aufteilen (standardmäßig 2/3 Training + 1/3 Bewertung)
  2. Konvertieren von Daten in das tf.Example Format (weitere Informationen hier )
  3. Kopieren Sie die Daten in das Verzeichnis _tfx_root damit andere Komponenten darauf zugreifen können

ExampleGen als Eingabe den Pfad zu Ihrer Datenquelle. In unserem Fall ist dies der Pfad _data_root , der die heruntergeladene CSV- _data_root enthält.

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-datac4ibbtgw/* to TFExample.
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

Untersuchen wir die Ausgabeartefakte von ExampleGen . Diese Komponente erzeugt zwei Artefakte, Trainingsbeispiele und Evaluierungsbeispiele:

artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/CsvExampleGen/examples/1

Schauen wir uns auch die ersten drei Trainingsbeispiele an:

# 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
      }
    }
  }
}

ExampleGen die Datenaufnahme nun ExampleGen hat, ist der nächste Schritt die Datenanalyse.

StatistikGen

Die StatisticsGen Komponente berechnet Statistiken über Ihr Dataset zur Datenanalyse sowie zur Verwendung in nachgelagerten Komponenten. Es verwendet die TensorFlow Data Validation- Bibliothek.

StatisticsGen als Eingabe den Datensatz, den wir gerade mit ExampleGen aufgenommen 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-2021-06-18T09_15_50.141015-46onlp08/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/StatisticsGen/statistics/2/Split-eval.
INFO:absl:Running publisher for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized

Nachdem StatisticsGen Ausführung beendet hat, können wir die ausgegebenen Statistiken visualisieren. Versuchen Sie, mit den verschiedenen Plots zu spielen!

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

SchemaGen

Die SchemaGen Komponente generiert ein Schema basierend auf Ihren Datenstatistiken. (Ein Schema definiert die erwarteten Grenzen, Typen und Eigenschaften der Features in Ihrem Dataset.) Es verwendet auch die TensorFlow Data Validation- Bibliothek.

SchemaGen als Eingabe die Statistiken, die wir mit StatisticsGen generiert haben, wobei standardmäßig die Trainingsaufteilung SchemaGen wird.

schema_gen = tfx.components.SchemaGen(
    statistics=statistics_gen.outputs['statistics'],
    infer_feature_shape=False)
context.run(schema_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for SchemaGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for SchemaGen
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/SchemaGen/schema/3/schema.pbtxt.
INFO:absl:Running publisher for SchemaGen
INFO:absl:MetadataStore with DB connection initialized

Nachdem SchemaGen Ausführung beendet hat, können wir das generierte Schema als Tabelle visualisieren.

context.show(schema_gen.outputs['schema'])
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_data_validation/utils/display_util.py:180: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
  pd.set_option('max_colwidth', -1)

Jedes Feature in Ihrem Dataset wird neben seinen Eigenschaften als Zeile in der Schematabelle angezeigt. Das Schema erfasst auch alle Werte, die ein kategoriales Merkmal annimmt, das als seine Domäne bezeichnet wird.

Weitere Informationen zu Schemas finden Sie in der SchemaGen-Dokumentation .

BeispielValidator

Die ExampleValidator Komponente erkennt Anomalien in Ihren Daten basierend auf den vom Schema definierten Erwartungen. Es verwendet auch die TensorFlow Data Validation- Bibliothek.

ExampleValidator nimmt die Statistiken von StatisticsGen und das Schema von SchemaGen als Eingabe.

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-2021-06-18T09_15_50.141015-46onlp08/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-2021-06-18T09_15_50.141015-46onlp08/ExampleValidator/anomalies/4/Split-eval.
INFO:absl:Running publisher for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized

Nachdem ExampleValidator Ausführung von ExampleValidator , können wir die Anomalien als Tabelle visualisieren.

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

In der Anomalientabelle können wir sehen, dass es keine Anomalien gibt. Dies ist, was wir erwarten würden, da dies der erste Datensatz ist, den wir analysiert haben und das Schema darauf zugeschnitten ist. Sie sollten dieses Schema überprüfen – alles Unerwartete bedeutet eine Anomalie in den Daten. Nach der Überprüfung kann das Schema verwendet werden, um zukünftige Daten zu schützen, und hier erzeugte Anomalien können verwendet werden, um die Modellleistung zu debuggen, zu verstehen, wie sich Ihre Daten im Laufe der Zeit entwickeln, und Datenfehler zu identifizieren.

Verwandeln

Die Transform Komponente führt Feature-Engineering für Training und Bereitstellung durch. Es verwendet die TensorFlow Transform- Bibliothek.

Transform nimmt als Eingabe die Daten von ExampleGen , das Schema von SchemaGen sowie ein Modul, das benutzerdefinierten Transform-Code enthält.

Sehen wir uns unten ein Beispiel für benutzerdefinierten Transformationscode an (eine Einführung in die TensorFlow-Transformations-APIs finden Sie im Tutorial ). Zunächst definieren wir einige Konstanten für das Feature Engineering:

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

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

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

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

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

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

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

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

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

# Keys
LABEL_KEY = 'tips'
FARE_KEY = 'fare'

def transformed_name(key):
  return key + '_xf'
Writing taxi_constants.py

Als Nächstes schreiben wir preprocessing_fn , das Rohdaten als Eingabe aufnimmt und transformierte Features zurückgibt, auf denen unser Modell trainieren kann:

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

import tensorflow as tf
import tensorflow_transform as tft

import taxi_constants

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


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

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

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

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

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

  return outputs


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

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

Nun übergeben wir diesen Feature-Engineering-Code an die Transform Komponente und führen sie aus, um Ihre Daten zu transformieren.

transform = tfx.components.Transform(
    examples=example_gen.outputs['examples'],
    schema=schema_gen.outputs['schema'],
    module_file=os.path.abspath(_taxi_transform_module_file))
context.run(transform)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py' (including modules: ['taxi_constants', 'taxi_transform']).
INFO:absl:User module package has hash fingerprint version ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpar2aa58d/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpyjygr64v', '--dist-dir', '/tmp/tmplbizr143']
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'; target user module is 'taxi_transform'.
INFO:absl:Full user module path is 'taxi_transform@/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Transform
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
ERROR:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmponl7ni7l', '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl']
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:266: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Installing '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpw4s_hpw3', '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl']
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Transform-0.0+ba15fceb350294024553cb2f31d9929992f91dcaa3af4f05811c926d31c25e8f-py3-none-any.whl'.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:tensorflow:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:tensorflow:Tensorflow version (2.4.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:Tensorflow version (2.4.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:Tensorflow version (2.4.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Transform/transform_graph/5/.temp_path/tftransform_tmp/96e91f8423b041868095573682bba094/assets
WARNING:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1ad95d560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:6 out of the last 6 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1442d30e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Transform/transform_graph/5/.temp_path/tftransform_tmp/49e5bb5269ec495a83b3c255c69aaf51/assets
WARNING:tensorflow:7 out of the last 7 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc1d9200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:8 out of the last 8 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc1e1b00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:9 out of the last 9 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc1d5440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:10 out of the last 10 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc1d98c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc1ddd40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc164200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bce4f170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bce56a70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bce493b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bce4f830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bce50cb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bce59170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized

Untersuchen wir die Ausgabeartefakte von Transform . Diese Komponente erzeugt zwei Arten von Ausgaben:

  • transform_graph ist der Graph, der die Vorverarbeitungsoperationen ausführen kann (dieser Graph wird in die Bereitstellungs- und Bewertungsmodelle aufgenommen).
  • transformed_examples repräsentiert die vorverarbeiteten Trainings- und Bewertungsdaten.
transform.outputs
{'transform_graph': Channel(
    type_name: TransformGraph
    artifacts: [Artifact(artifact: id: 5
type_id: 13
uri: "/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/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: "0.30.0"
  }
}
state: LIVE
, artifact_type: id: 13
name: "TransformGraph"
)]
    additional_properties: {}
    additional_custom_properties: {}
), 'transformed_examples': Channel(
    type_name: Examples
    artifacts: [Artifact(artifact: id: 6
type_id: 5
uri: "/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/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: "0.30.0"
  }
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]
    additional_properties: {}
    additional_custom_properties: {}
), 'updated_analyzer_cache': Channel(
    type_name: TransformCache
    artifacts: [Artifact(artifact: id: 7
type_id: 14
uri: "/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/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: "0.30.0"
  }
}
state: LIVE
, artifact_type: id: 14
name: "TransformCache"
)]
    additional_properties: {}
    additional_custom_properties: {}
)}

Werfen Sie einen Blick auf das transform_graph Artefakt. Es zeigt auf ein Verzeichnis, das drei Unterverzeichnisse enthält.

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

Das Unterverzeichnis transformed_metadata enthält das Schema der vorverarbeiteten Daten. Das Unterverzeichnis transform_fn enthält den eigentlichen Vorverarbeitungsgraphen. Das metadata Unterverzeichnis enthält das Schema der Originaldaten.

Wir können uns auch die ersten drei transformierten Beispiele ansehen:

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

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

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

# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
  serialized_example = tfrecord.numpy()
  example = tf.train.Example()
  example.ParseFromString(serialized_example)
  pp.pprint(example)
features {
  feature {
    key: "company_xf"
    value {
      int64_list {
        value: 8
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 0.06106060370802879
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 1
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: -0.15886741876602173
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: -0.7118487358093262
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 6
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 19
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    value {
      int64_list {
        value: 5
      }
    }
  }
}

features {
  feature {
    key: "company_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 1.2521241903305054
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 60
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: 0.532160758972168
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: 0.5509493350982666
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 2
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    value {
      int64_list {
        value: 10
      }
    }
  }
}

features {
  feature {
    key: "company_xf"
    value {
      int64_list {
        value: 48
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 0.3873794674873352
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: 0.21955277025699615
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: 0.0019067146349698305
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 12
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    value {
      int64_list {
        value: 11
      }
    }
  }
}

Nachdem die Transform Komponente Ihre Daten in Features umgewandelt hat, besteht der nächste Schritt darin, ein Modell zu trainieren.

Trainer

Die Trainer Komponente trainiert ein Modell, das Sie in TensorFlow definieren. Standard-Trainer-Unterstützung Estimator-API, um die Keras-API zu verwenden, müssen Sie Generic Trainer durch setup custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor) im Trainer- custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor) angeben.

Trainer nimmt als Eingabe das Schema von SchemaGen , die transformierten Daten und den Graphen von Transform , Trainingsparameter sowie ein Modul, das benutzerdefinierten Modellcode enthält.

Sehen wir uns unten ein Beispiel für benutzerdefinierten Modellcode an (eine Einführung in die TensorFlow Keras-APIs finden Sie im Tutorial ):

_taxi_trainer_module_file = 'taxi_trainer.py'
%%writefile {_taxi_trainer_module_file}

from typing import List, Text

import os
import absl
import datetime
import tensorflow as tf
import tensorflow_transform as tft

from tfx import v1 as tfx
from tfx_bsl.public import tfxio

import taxi_constants

_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_MAX_CATEGORICAL_FEATURE_VALUES = taxi_constants.MAX_CATEGORICAL_FEATURE_VALUES
_LABEL_KEY = taxi_constants.LABEL_KEY
_transformed_name = taxi_constants.transformed_name


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


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

  model.tft_layer = tf_transform_output.transform_features_layer()

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

  return serve_tf_examples_fn


def _input_fn(file_pattern: List[Text],
              data_accessor: tfx.components.DataAccessor,
              tf_transform_output: tft.TFTransformOutput,
              batch_size: int = 200) -> tf.data.Dataset:
  """Generates features and label for tuning/training.

  Args:
    file_pattern: List of paths or patterns of input tfrecord files.
    data_accessor: DataAccessor for converting input to RecordBatch.
    tf_transform_output: A TFTransformOutput.
    batch_size: representing the number of consecutive elements of returned
      dataset to combine in a single batch

  Returns:
    A dataset that contains (features, indices) tuple where features is a
      dictionary of Tensors, and indices is a single Tensor of label indices.
  """
  return data_accessor.tf_dataset_factory(
      file_pattern,
      tfxio.TensorFlowDatasetOptions(
          batch_size=batch_size, label_key=_transformed_name(_LABEL_KEY)),
      tf_transform_output.transformed_metadata.schema)


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

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

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

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


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

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

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

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

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

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

  model = tf.keras.Model(input_layers, output)
  model.compile(
      loss='binary_crossentropy',
      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

Nun übergeben wir diesen Modellcode an die Trainer Komponente und führen ihn aus, um das Modell zu trainieren.

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_trainer', 'taxi_constants', 'taxi_transform']).
INFO:absl:User module package has hash fingerprint version 71a2d59f4e2e9cfb196b1c8c593aaba8e4b6854a168a82a30b66693104bb01a8.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpjpkiz_x5/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmp2qam7t7m', '--dist-dir', '/tmp/tmpuz0alqo0']
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Trainer-0.0+71a2d59f4e2e9cfb196b1c8c593aaba8e4b6854a168a82a30b66693104bb01a8-py3-none-any.whl'; target user module is 'taxi_trainer'.
INFO:absl:Full user module path is 'taxi_trainer@/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Trainer-0.0+71a2d59f4e2e9cfb196b1c8c593aaba8e4b6854a168a82a30b66693104bb01a8-py3-none-any.whl'
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Trainer
INFO:absl:Nonempty beam arg extra_packages already includes dependency
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
ERROR: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-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Trainer-0.0+71a2d59f4e2e9cfb196b1c8c593aaba8e4b6854a168a82a30b66693104bb01a8-py3-none-any.whl'} 'run_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Trainer-0.0+71a2d59f4e2e9cfb196b1c8c593aaba8e4b6854a168a82a30b66693104bb01a8-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp3b7ia03m', '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Trainer-0.0+71a2d59f4e2e9cfb196b1c8c593aaba8e4b6854a168a82a30b66693104bb01a8-py3-none-any.whl']
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/_wheels/tfx_user_code_Trainer-0.0+71a2d59f4e2e9cfb196b1c8c593aaba8e4b6854a168a82a30b66693104bb01a8-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature company_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature tips_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month_xf has a shape . Setting to DenseTensor.
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type)                    Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl:company_xf (InputLayer)         [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_census_tract_xf (InputL [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_community_area_xf (Inpu [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_latitude_xf (InputLayer [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_longitude_xf (InputLaye [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:fare_xf (InputLayer)            [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:payment_type_xf (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_census_tract_xf (InputLa [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_community_area_xf (Input [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_latitude_xf (InputLayer) [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_longitude_xf (InputLayer [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_miles_xf (InputLayer)      [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_seconds_xf (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_day_xf (InputLayer)  [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_hour_xf (InputLayer) [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_month_xf (InputLayer [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features (DenseFeatures)  (None, 3)            0           company_xf[0][0]                 
INFO:absl:                                                                 dropoff_census_tract_xf[0][0]    
INFO:absl:                                                                 dropoff_community_area_xf[0][0]  
INFO:absl:                                                                 dropoff_latitude_xf[0][0]        
INFO:absl:                                                                 dropoff_longitude_xf[0][0]       
INFO:absl:                                                                 fare_xf[0][0]                    
INFO:absl:                                                                 payment_type_xf[0][0]            
INFO:absl:                                                                 pickup_census_tract_xf[0][0]     
INFO:absl:                                                                 pickup_community_area_xf[0][0]   
INFO:absl:                                                                 pickup_latitude_xf[0][0]         
INFO:absl:                                                                 pickup_longitude_xf[0][0]        
INFO:absl:                                                                 trip_miles_xf[0][0]              
INFO:absl:                                                                 trip_seconds_xf[0][0]            
INFO:absl:                                                                 trip_start_day_xf[0][0]          
INFO:absl:                                                                 trip_start_hour_xf[0][0]         
INFO:absl:                                                                 trip_start_month_xf[0][0]        
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 100)          400         dense_features[0][0]             
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 70)           7070        dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 48)           3408        dense_1[0][0]                    
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_3 (Dense)                 (None, 34)           1666        dense_2[0][0]                    
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features_1 (DenseFeatures (None, 2127)         0           company_xf[0][0]                 
INFO:absl:                                                                 dropoff_census_tract_xf[0][0]    
INFO:absl:                                                                 dropoff_community_area_xf[0][0]  
INFO:absl:                                                                 dropoff_latitude_xf[0][0]        
INFO:absl:                                                                 dropoff_longitude_xf[0][0]       
INFO:absl:                                                                 fare_xf[0][0]                    
INFO:absl:                                                                 payment_type_xf[0][0]            
INFO:absl:                                                                 pickup_census_tract_xf[0][0]     
INFO:absl:                                                                 pickup_community_area_xf[0][0]   
INFO:absl:                                                                 pickup_latitude_xf[0][0]         
INFO:absl:                                                                 pickup_longitude_xf[0][0]        
INFO:absl:                                                                 trip_miles_xf[0][0]              
INFO:absl:                                                                 trip_seconds_xf[0][0]            
INFO:absl:                                                                 trip_start_day_xf[0][0]          
INFO:absl:                                                                 trip_start_hour_xf[0][0]         
INFO:absl:                                                                 trip_start_month_xf[0][0]        
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate)       (None, 2161)         0           dense_3[0][0]                    
INFO:absl:                                                                 dense_features_1[0][0]           
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_4 (Dense)                 (None, 1)            2162        concatenate[0][0]                
INFO:absl:==================================================================================================
INFO:absl:Total params: 14,706
INFO:absl:Trainable params: 14,706
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
10000/10000 [==============================] - 27s 3ms/step - loss: 0.3070 - binary_accuracy: 0.8482 - val_loss: 0.2220 - val_binary_accuracy: 0.8826
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be7320e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be72f200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be72f320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be7327a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be7324d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be72a950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Trainer/model/6/Format-Serving/assets
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Trainer/model/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Trainer/model_run/6
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized

Analysieren Sie das Training mit TensorBoard

Werfen Sie einen Blick auf das Trainer-Artefakt. Es zeigt auf ein Verzeichnis, das die Modellunterverzeichnisse enthält.

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', 'saved_model.pb']

Optional können wir TensorBoard mit dem Trainer verbinden, um die Trainingskurven unseres Modells zu analysieren.

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

%load_ext tensorboard
%tensorboard --logdir {model_run_artifact_dir}

Bewerter

Die Evaluator Komponente berechnet Modellleistungsmetriken über den Evaluierungssatz. Es verwendet die TensorFlow-Modellanalysebibliothek . Optional kann der Evaluator auch validieren, dass ein neu trainiertes Modell besser ist als das vorherige Modell. Dies ist in einer Produktionspipeline-Einstellung nützlich, in der Sie ein Modell täglich automatisch trainieren und validieren können. In diesem Notebook trainieren wir nur ein Modell, sodass der Evaluator das Modell automatisch als "gut" Evaluator .

Evaluator nimmt als Eingabe die Daten von ExampleGen , das trainierte Modell von Trainer und die Slicing-Konfiguration. Mit der Slicing-Konfiguration können Sie Ihre Metriken auf Merkmalswerte aufteilen (z. B. wie verhält sich Ihr Modell bei Taxifahrten, die um 8 Uhr morgens beginnen, gegenüber 20 Uhr abends?). Sehen Sie unten ein Beispiel für diese Konfiguration:

eval_config = tfma.EvalConfig(
    model_specs=[
        # This assumes a serving model with signature 'serving_default'. If
        # using estimator based EvalSavedModel, add signature_name: 'eval' and 
        # remove the label_key.
        tfma.ModelSpec(label_key='tips')
    ],
    metrics_specs=[
        tfma.MetricsSpec(
            # The metrics added here are in addition to those saved with the
            # model (assuming either a keras model or EvalSavedModel is used).
            # Any metrics added into the saved model (for example using
            # model.compile(..., metrics=[...]), etc) will be computed
            # automatically.
            # To add validation thresholds for metrics saved with the model,
            # add them keyed by metric name to the thresholds map.
            metrics=[
                tfma.MetricConfig(class_name='ExampleCount'),
                tfma.MetricConfig(class_name='BinaryAccuracy',
                  threshold=tfma.MetricThreshold(
                      value_threshold=tfma.GenericValueThreshold(
                          lower_bound={'value': 0.5}),
                      # Change threshold will be ignored if there is no
                      # baseline model resolved from MLMD (first run).
                      change_threshold=tfma.GenericChangeThreshold(
                          direction=tfma.MetricDirection.HIGHER_IS_BETTER,
                          absolute={'value': -1e-10})))
            ]
        )
    ],
    slicing_specs=[
        # An empty slice spec means the overall slice, i.e. the whole dataset.
        tfma.SlicingSpec(),
        # Data can be sliced along a feature column. In this case, data is
        # sliced along feature column trip_start_hour.
        tfma.SlicingSpec(feature_keys=['trip_start_hour'])
    ])

Als Nächstes übergeben wir diese Konfiguration an Evaluator und führen sie aus.

# Use TFMA to compute a evaluation statistics over features of a model and
# validate them against a baseline.

# The model resolver is only required if performing model validation in addition
# to evaluation. In this case we validate against the latest blessed model. If
# no model has been blessed before (as in this case) the evaluator will make our
# candidate the first blessed model.
model_resolver = tfx.dsl.Resolver(
      strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
      model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
      model_blessing=tfx.dsl.Channel(
          type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
              'latest_blessed_model_resolver')
context.run(model_resolver)

evaluator = tfx.components.Evaluator(
    examples=example_gen.outputs['examples'],
    model=trainer.outputs['model'],
    baseline_model=model_resolver.outputs['model'],
    eval_config=eval_config)
context.run(evaluator)
INFO:absl:Running driver for latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running publisher for latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running driver for Evaluator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Evaluator
INFO:absl:Nonempty beam arg extra_packages already includes dependency
ERROR:absl:udf_utils.get_fn {'eval_config': '{\n  "metrics_specs": [\n    {\n      "metrics": [\n        {\n          "class_name": "ExampleCount"\n        },\n        {\n          "class_name": "BinaryAccuracy",\n          "threshold": {\n            "change_threshold": {\n              "absolute": -1e-10,\n              "direction": "HIGHER_IS_BETTER"\n            },\n            "value_threshold": {\n              "lower_bound": 0.5\n            }\n          }\n        }\n      ]\n    }\n  ],\n  "model_specs": [\n    {\n      "label_key": "tips"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': None, 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model'
ERROR:absl:There are change thresholds, but the baseline is missing. This is allowed only when rubber stamping (first run).
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "tips"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
    threshold {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}

INFO:absl:Using /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Trainer/model/6/Format-Serving as  model.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be72a5f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be72fb90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be732e60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be72f9e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc4ee4d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bce469e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
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 0x7fb120290750> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7fb0bcbc1310>).
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1474af830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb14755f0e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc4ee680> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb12063e200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
ERROR:absl:udf_utils.get_fn {'eval_config': '{\n  "metrics_specs": [\n    {\n      "metrics": [\n        {\n          "class_name": "ExampleCount"\n        },\n        {\n          "class_name": "BinaryAccuracy",\n          "threshold": {\n            "change_threshold": {\n              "absolute": -1e-10,\n              "direction": "HIGHER_IS_BETTER"\n            },\n            "value_threshold": {\n              "lower_bound": 0.5\n            }\n          }\n        }\n      ]\n    }\n  ],\n  "model_specs": [\n    {\n      "label_key": "tips"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': None, 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_extractors'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "tips"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
    threshold {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
  model_names: ""
}

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

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "tips"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
    threshold {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
  model_names: ""
}
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1ad118b90> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1ad4bfd40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
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 0x7fb1207e2450> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7fb1204e0c90>).
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1ad422680> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1483de710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1ad40cc20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1ad4dbf80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
Exception ignored in: <function CapturableResourceDeleter.__del__ at 0x7fb185c663b0>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 208, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 871, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 726, in _initialize
    *args, **kwds))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2969, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3361, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3206, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 990, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 634, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 253, in restored_function_body
    return _call_concrete_function(function, inputs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/function_deserialization.py", line 75, in _call_concrete_function
    result = function._call_flat(tensor_inputs, function._captured_inputs)  # pylint: disable=protected-access
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 116, in _call_flat
    cancellation_manager)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 1932, in _call_flat
    flat_outputs = forward_function.call(ctx, args_with_tangents)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 589, in call
    executor_type=executor_type)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py", line 1206, in partitioned_call
    f.add_to_graph(graph)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 505, in add_to_graph
    g._add_function(self)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3396, in _add_function
    gradient)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 'func' argument to TF_GraphCopyFunction cannot be null
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1ad5c3200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1ad5ae9e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
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 0x7fb0bd281d10> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7fb0bd2bfb50>).
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bc4f0d40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bc44b050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bc396050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1ad52fd40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb120226cb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb06a66b7a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
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 0x7fb0bc047d10> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7fb0bc0961d0>).
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0be578cb0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb12027cb00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb06a6529e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1adbc08c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc1dd050> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bc3c6e60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
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 0x7fb06a493990> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7fb0bc03e090>).
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc2f2290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc080440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bcd29560> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bc4cb950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bd8488c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb14755a8c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
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 0x7fb0bc347f50> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7fb0dc13add0>).
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bcec1b00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bcd19ef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bcec1950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0bcb49440> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb069b51170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb069b628c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
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 0x7fb0dc4c1810> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7fb0dc4c1290>).
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb069b67dd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb069b6c950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb069b6ca70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb069b5d8c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0690a28c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb069050f80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
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 0x7fb069908d50> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7fb069951f50>).
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb069094170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb06909e4d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb069050200> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb069050b00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Evaluator/evaluation/8.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:113: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`
INFO:absl:Blessing result True written to /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Evaluator/blessing/8.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized

Untersuchen wir nun die Ausgabeartefakte von Evaluator .

evaluator.outputs
{'evaluation': Channel(
    type_name: ModelEvaluation
    artifacts: [Artifact(artifact: id: 10
type_id: 20
uri: "/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/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: "0.30.0"
  }
}
state: LIVE
, artifact_type: id: 20
name: "ModelEvaluation"
)]
    additional_properties: {}
    additional_custom_properties: {}
), 'blessing': Channel(
    type_name: ModelBlessing
    artifacts: [Artifact(artifact: id: 11
type_id: 21
uri: "/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Evaluator/blessing/8"
custom_properties {
  key: "blessed"
  value {
    int_value: 1
  }
}
custom_properties {
  key: "current_model"
  value {
    string_value: "/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Trainer/model/6"
  }
}
custom_properties {
  key: "current_model_id"
  value {
    int_value: 8
  }
}
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: "0.30.0"
  }
}
state: LIVE
, artifact_type: id: 21
name: "ModelBlessing"
)]
    additional_properties: {}
    additional_custom_properties: {}
)}

Mit der evaluation können wir die Standardvisualisierung globaler Metriken für das gesamte Evaluierungsset anzeigen.

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

Um die Visualisierung für aufgeteilte Bewertungsmetriken anzuzeigen, können wir direkt die TensorFlow-Modellanalysebibliothek aufrufen.

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',…

Diese Visualisierung zeigt die gleichen Metriken, die jedoch bei jedem Merkmalswert von trip_start_hour anstelle des gesamten Auswertungssatzes berechnet werden.

Die TensorFlow-Modellanalyse unterstützt viele andere Visualisierungen, z. B. Fairness-Indikatoren und das Zeichnen einer Zeitreihe der Modellleistung. Weitere Informationen finden Sie im Tutorial .

Da wir unserer Konfiguration Schwellenwerte hinzugefügt haben, ist auch eine Validierungsausgabe verfügbar. Die precence eines blessing Artefakt zeigt an, dass unsere Modellvalidierung übergeben. Da dies die erste Validierung ist, die durchgeführt wird, wird der Kandidat automatisch gesegnet.

blessing_uri = evaluator.outputs.blessing.get()[0].uri
!ls -l {blessing_uri}
total 0
-rw-rw-r-- 1 kbuilder kbuilder 0 Jun 18 09:17 BLESSED

Jetzt können Sie den Erfolg auch überprüfen, indem Sie den Validierungsergebnissatz laden:

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
  }
}

Pusher

Die Pusher Komponente befindet sich normalerweise am Ende einer TFX-Pipeline. Es prüft, ob ein Modell die Validierung bestanden hat, und exportiert das Modell in diesem _serving_model_dir nach _serving_model_dir .

pusher = tfx.components.Pusher(
    model=trainer.outputs['model'],
    model_blessing=evaluator.outputs['blessing'],
    push_destination=tfx.proto.PushDestination(
        filesystem=tfx.proto.PushDestination.Filesystem(
            base_directory=_serving_model_dir)))
context.run(pusher)
INFO:absl:Running driver for Pusher
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Pusher
INFO:absl:Nonempty beam arg extra_packages already includes dependency
INFO:absl:Model version: 1624007840
INFO:absl:Model written to serving path /tmp/tmpw2egxpah/serving_model/taxi_simple/1624007840.
INFO:absl:Model pushed to /tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/Pusher/pushed_model/9.
INFO:absl:Running publisher for Pusher
INFO:absl:MetadataStore with DB connection initialized

Lassen Sie uns die Ausgabeartefakte von Pusher .

pusher.outputs
{'pushed_model': Channel(
    type_name: PushedModel
    artifacts: [Artifact(artifact: id: 12
type_id: 23
uri: "/tmp/tfx-interactive-2021-06-18T09_15_50.141015-46onlp08/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/tmpw2egxpah/serving_model/taxi_simple/1624007840"
  }
}
custom_properties {
  key: "pushed_version"
  value {
    string_value: "1624007840"
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "0.30.0"
  }
}
state: LIVE
, artifact_type: id: 23
name: "PushedModel"
)]
    additional_properties: {}
    additional_custom_properties: {}
)}

Insbesondere exportiert der Pusher Ihr Modell im SavedModel-Format, das wie folgt aussieht:

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)
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc091c20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb068439320> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1473bf0e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0dc5544d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb1201fbef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7fb0684399e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
('serving_default',
 <ConcreteFunction signature_wrapper(*, examples) at 0x7FB06859EC50>)

Wir sind mit unserer Tour durch eingebaute TFX-Komponenten fertig!