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TFX Estimator-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, beide Eigenschaften und Nutzlasten in einer ephemeren SQLite - Datenbank in dem gespeicherten /tmp - Verzeichnis auf dem Jupyter Notebook oder Colab Server.

Installieren

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

# tfx<=0.29.0 doesn't work well with the new pip resolver.
# TODO(b/186700845): Update tutorial for TFX 0.30.0 or later.
pip install -q -U --use-deprecated=legacy-resolver tfx==0.29.0

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()

import tfx
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.components import Transform
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental import latest_blessed_model_resolver
from tfx.orchestration import metadata
from tfx.orchestration import pipeline
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.proto import pusher_pb2
from tfx.proto import trainer_pb2
from tfx.proto.evaluator_pb2 import SingleSlicingSpec
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing

%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip
2021-07-24 09:22:15.850606: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
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: 2.5.0
TFX version: 0.29.0

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 Datensatz verwenden wir das Taxi - Datensatz Ausflüge von der Stadt Chicago veröffentlicht. 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 werden wir ein Modell erstellen, das die vorhersagt tips einer Reise.

_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-datah76faz2s/data.csv', <http.client.HTTPMessage at 0x7f88a7362850>)

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-2021-07-24T09_22_20.157950-zveef4oj as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/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 ist in der Regel zu Beginn 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 von Daten in das _tfx_root Verzeichnis für andere Komponenten für den Zugriff

ExampleGen nimmt als Eingabe den Pfad zur Datenquelle. In unserem Fall ist dies der _data_root Pfad, der die heruntergeladene CSV enthält.

example_gen = 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-datah76faz2s/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Running publisher for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized

Betrachten sie den Ausgang Artefakte 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-2021-07-24T09_22_20.157950-zveef4oj/CsvExampleGen/examples/1

Wir können uns auch die ersten drei Trainingsbeispiele anschauen:

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

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

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

# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
  serialized_example = tfrecord.numpy()
  example = tf.train.Example()
  example.ParseFromString(serialized_example)
  pp.pprint(example)
features {
  feature {
    key: "company"
    value {
      bytes_list {
        value: "Chicago Elite Cab Corp. (Chicago Carriag"
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 12.449999809265137
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      bytes_list {
        value: "Credit Card"
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "tips"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 6
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 19
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 5
      }
    }
  }
  feature {
    key: "trip_start_timestamp"
    value {
      int64_list {
        value: 1400269500
      }
    }
  }
}

features {
  feature {
    key: "company"
    value {
      bytes_list {
        value: "Taxi Affiliation Services"
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 27.049999237060547
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      bytes_list {
        value: "Cash"
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 60
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      float_list {
        value: 41.836151123046875
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      float_list {
        value: -87.64878845214844
      }
    }
  }
  feature {
    key: "tips"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 12.600000381469727
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      int64_list {
        value: 1380
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 2
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 10
      }
    }
  }
  feature {
    key: "trip_start_timestamp"
    value {
      int64_list {
        value: 1380593700
      }
    }
  }
}

features {
  feature {
    key: "company"
    value {
      bytes_list {
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 16.450000762939453
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      bytes_list {
        value: "Cash"
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      float_list {
        value: 41.98363494873047
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      float_list {
        value: -87.72357940673828
      }
    }
  }
  feature {
    key: "tips"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 6.900000095367432
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      int64_list {
        value: 780
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 12
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 11
      }
    }
  }
  feature {
    key: "trip_start_timestamp"
    value {
      int64_list {
        value: 1446554700
      }
    }
  }
}
2021-07-24 09:22:26.748246: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-07-24 09:22:26.751910: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_SYSTEM_DRIVER_MISMATCH: system has unsupported display driver / cuda driver combination
2021-07-24 09:22:26.751940: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: kokoro-gcp-ubuntu-prod-559609198
2021-07-24 09:22:26.751947: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: kokoro-gcp-ubuntu-prod-559609198
2021-07-24 09:22:26.752076: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 470.57.2
2021-07-24 09:22:26.752099: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 465.27.0
2021-07-24 09:22:26.752106: E tensorflow/stream_executor/cuda/cuda_diagnostics.cc:313] kernel version 465.27.0 does not match DSO version 470.57.2 -- cannot find working devices in this configuration
2021-07-24 09:22:26.752724: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-07-24 09:22:26.779918: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
2021-07-24 09:22:26.780263: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2000179999 Hz

Nun , da ExampleGen die Daten Einnahme beendet hat, ist der nächste Schritt der Datenanalyse.

StatistikGen

Die StatisticsGen Komponente berechnet Statistiken über Ihre Datenmenge für die Datenanalyse sowie für die Verwendung in nachgelagerten Komponenten. Es nutzt die TensorFlow Data Validation - Bibliothek.

StatisticsGen nimmt als Eingabe den Datensatz wir mit nur eingenommen ExampleGen .

statistics_gen = StatisticsGen(
    examples=example_gen.outputs['examples'])
context.run(statistics_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for StatisticsGen
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/StatisticsGen/statistics/2/Split-eval.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Running publisher for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized

Nach StatisticsGen Lauf abgeschlossen ist , können wir die ausgegebenen Statistiken visualisieren. Versuchen Sie, mit den verschiedenen Plots zu spielen!

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

SchemaGen

Die SchemaGen Komponente erzeugt ein Schema auf der Grundlage Ihrer Daten Statistiken. (A - Schema definiert die erwarteten Grenzen, Typen und Eigenschaften der Funktionen in Ihrem Daten - Set) . Es nutzt auch die TensorFlow Data Validation - Bibliothek.

SchemaGen werden die Statistiken als Eingabe , dass wir mit generierten StatisticsGen , bei der Ausbildung Split standardmäßig suchen.

schema_gen = SchemaGen(
    statistics=statistics_gen.outputs['statistics'],
    infer_feature_shape=False)
context.run(schema_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for SchemaGen
INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:22:29.685348: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for SchemaGen
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/SchemaGen/schema/3/schema.pbtxt.
INFO:absl:Running publisher for SchemaGen
INFO:absl:MetadataStore with DB connection initialized

Nach SchemaGen Ausführung beendet ist , können wir das generierte Schema als Tabelle visualisieren.

context.show(schema_gen.outputs['schema'])

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 die SchemaGen Dokumentation .

BeispielValidator

Die ExampleValidator Komponente erkennt Anomalien in den Daten auf der Grundlage der Erwartungen durch das Schema definiert. Es nutzt auch die TensorFlow Data Validation - Bibliothek.

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

example_validator = ExampleValidator(
    statistics=statistics_gen.outputs['statistics'],
    schema=schema_gen.outputs['schema'])
context.run(example_validator)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for ExampleValidator
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Validation complete for split train. Anomalies written to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/ExampleValidator/anomalies/4/Split-train.
INFO:absl:Validating schema against the computed statistics for split eval.
INFO:absl:Validation complete for split eval. Anomalies written to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/ExampleValidator/anomalies/4/Split-eval.
INFO:absl:Running publisher for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized

Nach ExampleValidator Ausführung beendet ist , 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:188: 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, die Entwicklung Ihrer Daten im Laufe der Zeit zu verstehen und Datenfehler zu identifizieren.

Verwandeln

Die Transform Komponente führt Feature - Engineering für Training und Servieren. Es nutzt die TensorFlow Transformation Bibliothek.

Transform werden aus den Daten als Eingabe ExampleGen , das Schema aus SchemaGen , sowie ein Modul , das Code - Transformation definiert benutzer enthält.

Mal sehen , ein Beispiel für benutzerdefinierten Code - Transformation unten (für eine Einführung in die TensorFlow Trans 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 eine preprocessing_fn , die in Rohdaten als Eingabe und kehrt transformierten Merkmale , dass unser Modell auf 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 gehen wir in dieser Funktion Engineering Code an die Transform Komponente und führen Sie es , Ihre Daten zu transformieren.

transform = 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:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:22:29.866820: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
2021-07-24 09:22:29.870148: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Transform
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
WARNING:absl:The default value of `force_tf_compat_v1` will change in a future release from `True` to `False`. Since this pipeline has TF 2 behaviors enabled, Transform will use native TF 2 at that point. You can test this behavior now by passing `force_tf_compat_v1=False` or disable it by explicitly setting `force_tf_compat_v1=True` in the Transform component.
INFO:absl:Loading source_path /tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py as name user_module_0 because it has not been loaded before.
INFO:absl:/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py is already loaded, reloading
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.
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: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: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: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 (2.5.0) 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:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Transform/transform_graph/5/.temp_path/tftransform_tmp/7f422ea1bed146139bd0f9718e796203/saved_model.pb
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Transform/transform_graph/5/.temp_path/tftransform_tmp/36e5c25ee1274624af2c8110c1c5a833/saved_model.pb
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 (2.5.0) 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 (2.5.0) 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'>
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Transform/transform_graph/5/.temp_path/tftransform_tmp/2a3b1f0ef1b941fda11433f23eb5d0bc/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Transform/transform_graph/5/.temp_path/tftransform_tmp/2a3b1f0ef1b941fda11433f23eb5d0bc/saved_model.pb
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized

Lassen Sie uns die Ausgabe Artefakte untersuchen Transform . Diese Komponente erzeugt zwei Arten von Ausgaben:

  • transform_graph ist der Graph, der die Vorverarbeitung Operationen durchführen kann (Dieser Graph wird in der bedienenden und Bewertungsmodelle enthalten sein).
  • transformed_examples stellt die vorverarbeiteten Ausbildung und Bewertungsdaten.
transform.outputs
{'transform_graph': Channel(
    type_name: TransformGraph
    artifacts: [Artifact(artifact: id: 5
type_id: 13
uri: "/tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/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: "0.29.0"
  }
}
state: LIVE
, artifact_type: id: 13
name: "TransformGraph"
)]
    additional_properties: {}
    additional_custom_properties: {}
), 'transformed_examples': Channel(
    type_name: Examples
    artifacts: [Artifact(artifact: id: 6
type_id: 5
uri: "/tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/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: "0.29.0"
  }
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]
    additional_properties: {}
    additional_custom_properties: {}
), 'updated_analyzer_cache': Channel(
    type_name: TransformCache
    artifacts: [Artifact(artifact: id: 7
type_id: 14
uri: "/tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/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: "0.29.0"
  }
}
state: LIVE
, artifact_type: id: 14
name: "TransformCache"
)]
    additional_properties: {}
    additional_custom_properties: {}
)}

Werfen Sie einen Blick auf die 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 transformed_metadata Unterverzeichnis enthält das Schema der vorverarbeiteten Daten. Das transform_fn Unterverzeichnis enthält die eigentliche Vorverarbeitung Graph. Das metadata - Unterverzeichnis enthält das Schema der ursprünglichen Daten.

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: 0.061060599982738495
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 1
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: -0.15886741876602173
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: -0.711848795413971
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 6
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 19
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    value {
      int64_list {
        value: 5
      }
    }
  }
}

features {
  feature {
    key: "company_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 1.2521240711212158
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 60
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: 0.532160758972168
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: 0.5509493947029114
      }
    }
  }
  feature {
    key: "trip_start_day_xf"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour_xf"
    value {
      int64_list {
        value: 2
      }
    }
  }
  feature {
    key: "trip_start_month_xf"
    value {
      int64_list {
        value: 10
      }
    }
  }
}

features {
  feature {
    key: "company_xf"
    value {
      int64_list {
        value: 48
      }
    }
  }
  feature {
    key: "dropoff_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare_xf"
    value {
      float_list {
        value: 0.3873794376850128
      }
    }
  }
  feature {
    key: "payment_type_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area_xf"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "pickup_latitude_xf"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "pickup_longitude_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "tips_xf"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles_xf"
    value {
      float_list {
        value: 0.21955275535583496
      }
    }
  }
  feature {
    key: "trip_seconds_xf"
    value {
      float_list {
        value: 0.0019067147513851523
      }
    }
  }
  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
      }
    }
  }
}

Nach der Transform Komponente Ihrer Daten in Funktionen umgewandelt hat, und der nächste Schritt ist es, ein Modell zu trainieren.

Trainer

Der Trainer Komponente wird ein Modell trainieren , dass Sie in TensorFlow (entweder mit dem Estimator - API oder die Keras API mit definieren model_to_estimator ).

Trainer als Eingabe das Schema aus SchemaGen , die transformierten Daten und einem Graph von Transform - Parameter Ausbildung, sowie ein Modul , das benutzerdefinierte Modellcode enthält.

Mal sehen , ein Beispiel für benutzerdefinierten Modell Code (für eine Einführung in die TensorFlow Estimator - APIs finden Sie im Tutorial ):

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

import tensorflow as tf
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx_bsl.tfxio import dataset_options

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]


# Tf.Transform considers these features as "raw"
def _get_raw_feature_spec(schema):
  return schema_utils.schema_as_feature_spec(schema).feature_spec


def _build_estimator(config, hidden_units=None, warm_start_from=None):
  """Build an estimator for predicting the tipping behavior of taxi riders.
  Args:
    config: tf.estimator.RunConfig defining the runtime environment for the
      estimator (including model_dir).
    hidden_units: [int], the layer sizes of the DNN (input layer first)
    warm_start_from: Optional directory to warm start from.
  Returns:
    A dict of the following:

      - estimator: The estimator that will be used for training and eval.
      - train_spec: Spec for training.
      - eval_spec: Spec for eval.
      - eval_input_receiver_fn: Input function for eval.
  """
  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)
  ]
  return tf.estimator.DNNLinearCombinedClassifier(
      config=config,
      linear_feature_columns=categorical_columns,
      dnn_feature_columns=real_valued_columns,
      dnn_hidden_units=hidden_units or [100, 70, 50, 25],
      warm_start_from=warm_start_from)


def _example_serving_receiver_fn(tf_transform_graph, schema):
  """Build the serving in inputs.
  Args:
    tf_transform_graph: A TFTransformOutput.
    schema: the schema of the input data.
  Returns:
    Tensorflow graph which parses examples, applying tf-transform to them.
  """
  raw_feature_spec = _get_raw_feature_spec(schema)
  raw_feature_spec.pop(_LABEL_KEY)

  raw_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
      raw_feature_spec, default_batch_size=None)
  serving_input_receiver = raw_input_fn()

  transformed_features = tf_transform_graph.transform_raw_features(
      serving_input_receiver.features)

  return tf.estimator.export.ServingInputReceiver(
      transformed_features, serving_input_receiver.receiver_tensors)


def _eval_input_receiver_fn(tf_transform_graph, schema):
  """Build everything needed for the tf-model-analysis to run the model.
  Args:
    tf_transform_graph: A TFTransformOutput.
    schema: the schema of the input data.
  Returns:
    EvalInputReceiver function, which contains:

      - Tensorflow graph which parses raw untransformed features, applies the
        tf-transform preprocessing operators.
      - Set of raw, untransformed features.
      - Label against which predictions will be compared.
  """
  # Notice that the inputs are raw features, not transformed features here.
  raw_feature_spec = _get_raw_feature_spec(schema)

  serialized_tf_example = tf.compat.v1.placeholder(
      dtype=tf.string, shape=[None], name='input_example_tensor')

  # Add a parse_example operator to the tensorflow graph, which will parse
  # raw, untransformed, tf examples.
  features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)

  # Now that we have our raw examples, process them through the tf-transform
  # function computed during the preprocessing step.
  transformed_features = tf_transform_graph.transform_raw_features(
      features)

  # The key name MUST be 'examples'.
  receiver_tensors = {'examples': serialized_tf_example}

  # NOTE: Model is driven by transformed features (since training works on the
  # materialized output of TFT, but slicing will happen on raw features.
  features.update(transformed_features)

  return tfma.export.EvalInputReceiver(
      features=features,
      receiver_tensors=receiver_tensors,
      labels=transformed_features[_transformed_name(_LABEL_KEY)])


def _input_fn(file_pattern, data_accessor, tf_transform_output, batch_size=200):
  """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,
      dataset_options.TensorFlowDatasetOptions(
          batch_size=batch_size, label_key=_transformed_name(_LABEL_KEY)),
      tf_transform_output.transformed_metadata.schema)


# TFX will call this function
def trainer_fn(trainer_fn_args, schema):
  """Build the estimator using the high level API.
  Args:
    trainer_fn_args: Holds args used to train the model as name/value pairs.
    schema: Holds the schema of the training examples.
  Returns:
    A dict of the following:

      - estimator: The estimator that will be used for training and eval.
      - train_spec: Spec for training.
      - eval_spec: Spec for eval.
      - eval_input_receiver_fn: Input function for eval.
  """
  # Number of nodes in the first layer of the DNN
  first_dnn_layer_size = 100
  num_dnn_layers = 4
  dnn_decay_factor = 0.7

  train_batch_size = 40
  eval_batch_size = 40

  tf_transform_graph = tft.TFTransformOutput(trainer_fn_args.transform_output)

  train_input_fn = lambda: _input_fn(  # pylint: disable=g-long-lambda
      trainer_fn_args.train_files,
      trainer_fn_args.data_accessor,
      tf_transform_graph,
      batch_size=train_batch_size)

  eval_input_fn = lambda: _input_fn(  # pylint: disable=g-long-lambda
      trainer_fn_args.eval_files,
      trainer_fn_args.data_accessor,
      tf_transform_graph,
      batch_size=eval_batch_size)

  train_spec = tf.estimator.TrainSpec(  # pylint: disable=g-long-lambda
      train_input_fn,
      max_steps=trainer_fn_args.train_steps)

  serving_receiver_fn = lambda: _example_serving_receiver_fn(  # pylint: disable=g-long-lambda
      tf_transform_graph, schema)

  exporter = tf.estimator.FinalExporter('chicago-taxi', serving_receiver_fn)
  eval_spec = tf.estimator.EvalSpec(
      eval_input_fn,
      steps=trainer_fn_args.eval_steps,
      exporters=[exporter],
      name='chicago-taxi-eval')

  run_config = tf.estimator.RunConfig(
      save_checkpoints_steps=999, keep_checkpoint_max=1)

  run_config = run_config.replace(model_dir=trainer_fn_args.serving_model_dir)

  estimator = _build_estimator(
      # 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)
      ],
      config=run_config,
      warm_start_from=trainer_fn_args.base_model)

  # Create an input receiver for TFMA processing
  receiver_fn = lambda: _eval_input_receiver_fn(  # pylint: disable=g-long-lambda
      tf_transform_graph, schema)

  return {
      'estimator': estimator,
      'train_spec': train_spec,
      'eval_spec': eval_spec,
      'eval_input_receiver_fn': receiver_fn
  }
Writing taxi_trainer.py

Nun gehen wir in diesem Modell Code an die Trainer - Komponente und führen Sie sich um das Modell zu trainieren.

trainer = Trainer(
    module_file=os.path.abspath(_taxi_trainer_module_file),
    transformed_examples=transform.outputs['transformed_examples'],
    schema=schema_gen.outputs['schema'],
    transform_graph=transform.outputs['transform_graph'],
    train_args=trainer_pb2.TrainArgs(num_steps=10000),
    eval_args=trainer_pb2.EvalArgs(num_steps=5000))
context.run(trainer)
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:22:42.532418: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
2021-07-24 09:22:42.535869: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Trainer
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
INFO:absl:Loading source_path /tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py as name user_module_1 because it has not been loaded before.
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 999, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:absl:Training model.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 999 or save_checkpoints_secs None.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
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:tensorflow:Calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1700: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.
  warnings.warn('`layer.add_variable` is deprecated and '
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/adagrad.py:88: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 0.6816494, step = 0
INFO:tensorflow:global_step/sec: 234.161
INFO:tensorflow:loss = 0.50976276, step = 100 (0.428 sec)
INFO:tensorflow:global_step/sec: 569.949
INFO:tensorflow:loss = 0.47649044, step = 200 (0.175 sec)
INFO:tensorflow:global_step/sec: 576.73
INFO:tensorflow:loss = 0.47491264, step = 300 (0.174 sec)
INFO:tensorflow:global_step/sec: 566.786
INFO:tensorflow:loss = 0.59975225, step = 400 (0.176 sec)
INFO:tensorflow:global_step/sec: 584.289
INFO:tensorflow:loss = 0.4452181, step = 500 (0.171 sec)
INFO:tensorflow:global_step/sec: 579.691
INFO:tensorflow:loss = 0.48753548, step = 600 (0.172 sec)
INFO:tensorflow:global_step/sec: 577.202
INFO:tensorflow:loss = 0.5024217, step = 700 (0.173 sec)
INFO:tensorflow:global_step/sec: 590.578
INFO:tensorflow:loss = 0.4357342, step = 800 (0.169 sec)
INFO:tensorflow:global_step/sec: 582.913
INFO:tensorflow:loss = 0.4996794, step = 900 (0.172 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/saver.py:971: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
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:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-07-24T09:22:50
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt-999
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 6.83225s
INFO:tensorflow:Finished evaluation at 2021-07-24-09:22:56
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.771205, accuracy_baseline = 0.771205, auc = 0.91518056, auc_precision_recall = 0.64471316, average_loss = 0.461031, global_step = 999, label/mean = 0.228795, loss = 0.4610313, precision = 0.0, prediction/mean = 0.24308255, recall = 0.0
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt-999
INFO:tensorflow:global_step/sec: 11.5575
INFO:tensorflow:loss = 0.5172368, step = 1000 (8.652 sec)
INFO:tensorflow:global_step/sec: 574.751
INFO:tensorflow:loss = 0.40844625, step = 1100 (0.174 sec)
INFO:tensorflow:global_step/sec: 575.562
INFO:tensorflow:loss = 0.45662904, step = 1200 (0.174 sec)
INFO:tensorflow:global_step/sec: 578.545
INFO:tensorflow:loss = 0.38425204, step = 1300 (0.173 sec)
INFO:tensorflow:global_step/sec: 576.133
INFO:tensorflow:loss = 0.4589347, step = 1400 (0.174 sec)
INFO:tensorflow:global_step/sec: 582.624
INFO:tensorflow:loss = 0.48272824, step = 1500 (0.173 sec)
INFO:tensorflow:global_step/sec: 569.869
INFO:tensorflow:loss = 0.4339346, step = 1600 (0.175 sec)
INFO:tensorflow:global_step/sec: 580.312
INFO:tensorflow:loss = 0.47492296, step = 1700 (0.172 sec)
INFO:tensorflow:global_step/sec: 584.658
INFO:tensorflow:loss = 0.36550814, step = 1800 (0.171 sec)
INFO:tensorflow:global_step/sec: 579.197
INFO:tensorflow:loss = 0.39077607, step = 1900 (0.173 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1998...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 332.482
INFO:tensorflow:loss = 0.41298097, step = 2000 (0.301 sec)
INFO:tensorflow:global_step/sec: 564.253
INFO:tensorflow:loss = 0.39654365, step = 2100 (0.178 sec)
INFO:tensorflow:global_step/sec: 586.525
INFO:tensorflow:loss = 0.41430712, step = 2200 (0.170 sec)
INFO:tensorflow:global_step/sec: 582.958
INFO:tensorflow:loss = 0.4734439, step = 2300 (0.172 sec)
INFO:tensorflow:global_step/sec: 588.486
INFO:tensorflow:loss = 0.35521457, step = 2400 (0.170 sec)
INFO:tensorflow:global_step/sec: 572.729
INFO:tensorflow:loss = 0.38978812, step = 2500 (0.175 sec)
INFO:tensorflow:global_step/sec: 572.59
INFO:tensorflow:loss = 0.37490422, step = 2600 (0.175 sec)
INFO:tensorflow:global_step/sec: 579.111
INFO:tensorflow:loss = 0.3578423, step = 2700 (0.173 sec)
INFO:tensorflow:global_step/sec: 579.297
INFO:tensorflow:loss = 0.34661508, step = 2800 (0.173 sec)
INFO:tensorflow:global_step/sec: 576.741
INFO:tensorflow:loss = 0.33115488, step = 2900 (0.173 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2997...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 331.693
INFO:tensorflow:loss = 0.36451238, step = 3000 (0.301 sec)
INFO:tensorflow:global_step/sec: 580.291
INFO:tensorflow:loss = 0.40079427, step = 3100 (0.173 sec)
INFO:tensorflow:global_step/sec: 577.005
INFO:tensorflow:loss = 0.457141, step = 3200 (0.173 sec)
INFO:tensorflow:global_step/sec: 582.291
INFO:tensorflow:loss = 0.3652722, step = 3300 (0.172 sec)
INFO:tensorflow:global_step/sec: 582.986
INFO:tensorflow:loss = 0.4494985, step = 3400 (0.172 sec)
INFO:tensorflow:global_step/sec: 569.291
INFO:tensorflow:loss = 0.431526, step = 3500 (0.176 sec)
INFO:tensorflow:global_step/sec: 561.541
INFO:tensorflow:loss = 0.438737, step = 3600 (0.178 sec)
INFO:tensorflow:global_step/sec: 564.196
INFO:tensorflow:loss = 0.39757258, step = 3700 (0.177 sec)
INFO:tensorflow:global_step/sec: 549.332
INFO:tensorflow:loss = 0.37423712, step = 3800 (0.182 sec)
INFO:tensorflow:global_step/sec: 579.082
INFO:tensorflow:loss = 0.45341128, step = 3900 (0.173 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3996...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 336.437
INFO:tensorflow:loss = 0.39152098, step = 4000 (0.297 sec)
INFO:tensorflow:global_step/sec: 556.42
INFO:tensorflow:loss = 0.3671503, step = 4100 (0.180 sec)
INFO:tensorflow:global_step/sec: 576.207
INFO:tensorflow:loss = 0.45925093, step = 4200 (0.174 sec)
INFO:tensorflow:global_step/sec: 585.748
INFO:tensorflow:loss = 0.29328844, step = 4300 (0.171 sec)
INFO:tensorflow:global_step/sec: 594.021
INFO:tensorflow:loss = 0.37301713, step = 4400 (0.168 sec)
INFO:tensorflow:global_step/sec: 573.815
INFO:tensorflow:loss = 0.37060982, step = 4500 (0.174 sec)
INFO:tensorflow:global_step/sec: 559.489
INFO:tensorflow:loss = 0.46553117, step = 4600 (0.179 sec)
INFO:tensorflow:global_step/sec: 577.827
INFO:tensorflow:loss = 0.32584053, step = 4700 (0.173 sec)
INFO:tensorflow:global_step/sec: 580.136
INFO:tensorflow:loss = 0.4986774, step = 4800 (0.172 sec)
INFO:tensorflow:global_step/sec: 584.689
INFO:tensorflow:loss = 0.41229, step = 4900 (0.171 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4995...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 344.518
INFO:tensorflow:loss = 0.3996284, step = 5000 (0.290 sec)
INFO:tensorflow:global_step/sec: 583.693
INFO:tensorflow:loss = 0.4486746, step = 5100 (0.171 sec)
INFO:tensorflow:global_step/sec: 566.622
INFO:tensorflow:loss = 0.3108496, step = 5200 (0.177 sec)
INFO:tensorflow:global_step/sec: 563.967
INFO:tensorflow:loss = 0.33118144, step = 5300 (0.177 sec)
INFO:tensorflow:global_step/sec: 575.507
INFO:tensorflow:loss = 0.42952842, step = 5400 (0.174 sec)
INFO:tensorflow:global_step/sec: 583.897
INFO:tensorflow:loss = 0.38152784, step = 5500 (0.171 sec)
INFO:tensorflow:global_step/sec: 572.333
INFO:tensorflow:loss = 0.3680429, step = 5600 (0.175 sec)
INFO:tensorflow:global_step/sec: 571.275
INFO:tensorflow:loss = 0.395936, step = 5700 (0.175 sec)
INFO:tensorflow:global_step/sec: 575.721
INFO:tensorflow:loss = 0.44940987, step = 5800 (0.174 sec)
INFO:tensorflow:global_step/sec: 576.401
INFO:tensorflow:loss = 0.4078973, step = 5900 (0.174 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5994...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 340.407
INFO:tensorflow:loss = 0.3904915, step = 6000 (0.294 sec)
INFO:tensorflow:global_step/sec: 570.248
INFO:tensorflow:loss = 0.35723388, step = 6100 (0.175 sec)
INFO:tensorflow:global_step/sec: 578.413
INFO:tensorflow:loss = 0.41861263, step = 6200 (0.173 sec)
INFO:tensorflow:global_step/sec: 580.312
INFO:tensorflow:loss = 0.3050785, step = 6300 (0.173 sec)
INFO:tensorflow:global_step/sec: 564.376
INFO:tensorflow:loss = 0.32811323, step = 6400 (0.177 sec)
INFO:tensorflow:global_step/sec: 573.006
INFO:tensorflow:loss = 0.32332134, step = 6500 (0.175 sec)
INFO:tensorflow:global_step/sec: 566.395
INFO:tensorflow:loss = 0.33091322, step = 6600 (0.177 sec)
INFO:tensorflow:global_step/sec: 570.873
INFO:tensorflow:loss = 0.37212968, step = 6700 (0.176 sec)
INFO:tensorflow:global_step/sec: 571.135
INFO:tensorflow:loss = 0.41529113, step = 6800 (0.175 sec)
INFO:tensorflow:global_step/sec: 578.561
INFO:tensorflow:loss = 0.36724982, step = 6900 (0.173 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6993...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 344.941
INFO:tensorflow:loss = 0.34240094, step = 7000 (0.290 sec)
INFO:tensorflow:global_step/sec: 576.526
INFO:tensorflow:loss = 0.37134916, step = 7100 (0.174 sec)
INFO:tensorflow:global_step/sec: 559.253
INFO:tensorflow:loss = 0.39360422, step = 7200 (0.179 sec)
INFO:tensorflow:global_step/sec: 561.936
INFO:tensorflow:loss = 0.36754823, step = 7300 (0.178 sec)
INFO:tensorflow:global_step/sec: 563.687
INFO:tensorflow:loss = 0.41373166, step = 7400 (0.178 sec)
INFO:tensorflow:global_step/sec: 575.717
INFO:tensorflow:loss = 0.32954282, step = 7500 (0.173 sec)
INFO:tensorflow:global_step/sec: 569.188
INFO:tensorflow:loss = 0.3834055, step = 7600 (0.176 sec)
INFO:tensorflow:global_step/sec: 585.873
INFO:tensorflow:loss = 0.3756402, step = 7700 (0.171 sec)
INFO:tensorflow:global_step/sec: 576.162
INFO:tensorflow:loss = 0.2930138, step = 7800 (0.174 sec)
INFO:tensorflow:global_step/sec: 575.647
INFO:tensorflow:loss = 0.29789674, step = 7900 (0.174 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7992...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 330.627
INFO:tensorflow:loss = 0.3864488, step = 8000 (0.302 sec)
INFO:tensorflow:global_step/sec: 569.624
INFO:tensorflow:loss = 0.37080488, step = 8100 (0.176 sec)
INFO:tensorflow:global_step/sec: 583.881
INFO:tensorflow:loss = 0.34841514, step = 8200 (0.171 sec)
INFO:tensorflow:global_step/sec: 587.852
INFO:tensorflow:loss = 0.27459455, step = 8300 (0.170 sec)
INFO:tensorflow:global_step/sec: 585.64
INFO:tensorflow:loss = 0.2897858, step = 8400 (0.171 sec)
INFO:tensorflow:global_step/sec: 581.485
INFO:tensorflow:loss = 0.35457927, step = 8500 (0.172 sec)
INFO:tensorflow:global_step/sec: 574.684
INFO:tensorflow:loss = 0.40731177, step = 8600 (0.174 sec)
INFO:tensorflow:global_step/sec: 582.097
INFO:tensorflow:loss = 0.37380633, step = 8700 (0.172 sec)
INFO:tensorflow:global_step/sec: 589.141
INFO:tensorflow:loss = 0.35380566, step = 8800 (0.170 sec)
INFO:tensorflow:global_step/sec: 580.597
INFO:tensorflow:loss = 0.3771058, step = 8900 (0.172 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8991...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 335.135
INFO:tensorflow:loss = 0.334621, step = 9000 (0.298 sec)
INFO:tensorflow:global_step/sec: 579.147
INFO:tensorflow:loss = 0.37119967, step = 9100 (0.173 sec)
INFO:tensorflow:global_step/sec: 581.955
INFO:tensorflow:loss = 0.274684, step = 9200 (0.172 sec)
INFO:tensorflow:global_step/sec: 592.385
INFO:tensorflow:loss = 0.36168328, step = 9300 (0.169 sec)
INFO:tensorflow:global_step/sec: 578.86
INFO:tensorflow:loss = 0.31686133, step = 9400 (0.173 sec)
INFO:tensorflow:global_step/sec: 580.456
INFO:tensorflow:loss = 0.3768372, step = 9500 (0.173 sec)
INFO:tensorflow:global_step/sec: 580.462
INFO:tensorflow:loss = 0.40613213, step = 9600 (0.172 sec)
INFO:tensorflow:global_step/sec: 578.64
INFO:tensorflow:loss = 0.2891247, step = 9700 (0.173 sec)
INFO:tensorflow:global_step/sec: 578.044
INFO:tensorflow:loss = 0.35419783, step = 9800 (0.173 sec)
INFO:tensorflow:global_step/sec: 584.336
INFO:tensorflow:loss = 0.3706368, step = 9900 (0.171 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
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:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-07-24T09:23:15
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 6.97432s
INFO:tensorflow:Finished evaluation at 2021-07-24-09:23:22
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.78937, accuracy_baseline = 0.771265, auc = 0.9336785, auc_precision_recall = 0.70868444, average_loss = 0.34470296, global_step = 10000, label/mean = 0.228735, loss = 0.344703, precision = 0.7043684, prediction/mean = 0.23046845, recall = 0.13640238
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Performing the final export in the end of training.
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification']
INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/export/chicago-taxi/temp-1627118602/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/export/chicago-taxi/temp-1627118602/saved_model.pb
INFO:tensorflow:Loss for final step: 0.2737777.
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6
INFO:absl:Exporting eval_savedmodel for TFMA.
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_4:0\022/vocab_compute_and_apply_vocabulary_1_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
WARNING:tensorflow:Export includes no default signature!
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-TFMA/temp-1627118603/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-TFMA/temp-1627118603/saved_model.pb
INFO:absl:Exported eval_savedmodel to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model_run/6/Format-TFMA.
WARNING:absl:Support for estimator-based executor and model export will be deprecated soon. Please use export structure <ModelExportPath>/serving_model_dir/saved_model.pb"
INFO:absl:Serving model copied to: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model/6/Format-Serving.
WARNING:absl:Support for estimator-based executor and model export will be deprecated soon. Please use export structure <ModelExportPath>/eval_model_dir/saved_model.pb"
INFO:absl:Eval model copied to: /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model/6/Format-TFMA.
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized

Analysieren Sie das Training mit TensorBoard

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

# Get the URI of the output artifact representing the training logs, which is a directory
model_run_dir = trainer.outputs['model_run'].get()[0].uri

%load_ext tensorboard
%tensorboard --logdir {model_run_dir}

Bewerter

Die Evaluator - Komponente berechnet Modell Performance - Metriken über den Auswertsatz. Es nutzt die TensorFlow Modellanalyse Bibliothek. Der Evaluator kann optional auch bestätigen , dass ein neu ausgebildetes Modell besser ist als das Vorgängermodell. 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, so dass der Evaluator automatisch das Modell als „gut“ bezeichnen.

Evaluator wird als Eingabe die Daten aus ExampleGen , das trainierte Modell aus Trainer und 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, im Vergleich zu 20 Uhr abends?). Sehen Sie unten ein Beispiel für diese Konfiguration:

eval_config = tfma.EvalConfig(
    model_specs=[
        # Using signature 'eval' implies the use of an EvalSavedModel. To use
        # a serving model remove the signature to defaults to 'serving_default'
        # and add a label_key.
        tfma.ModelSpec(signature_name='eval')
    ],
    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.
            metrics=[
                tfma.MetricConfig(class_name='ExampleCount')
            ],
            # To add validation thresholds for metrics saved with the model,
            # add them keyed by metric name to the thresholds map.
            thresholds = {
                'accuracy': 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 geben wir diese Konfiguration Evaluator und ausführen.

# 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 = resolver.Resolver(
      instance_name='latest_blessed_model_resolver',
      strategy_class=latest_blessed_model_resolver.LatestBlessedModelResolver,
      model=Channel(type=Model),
      model_blessing=Channel(type=ModelBlessing))
context.run(model_resolver)

evaluator = Evaluator(
    examples=example_gen.outputs['examples'],
    model=trainer.outputs['model'],
    #baseline_model=model_resolver.outputs['model'],
    eval_config=eval_config)
context.run(evaluator)
WARNING:absl:`instance_name` is deprecated, please set the node id directly using `with_id()` or the `.id` setter.
INFO:absl:Running driver for Resolver.latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running publisher for Resolver.latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running driver for Evaluator
INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:23:28.456077: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Evaluator
2021-07-24 09:23:28.459715: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
ERROR:absl:There are change thresholds, but the baseline is missing. This is allowed only when rubber stamping (first run).
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  thresholds {
    key: "accuracy"
    value {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}

INFO:absl:Using /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model/6/Format-TFMA as  model.
WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory.
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  model_names: ""
  thresholds {
    key: "accuracy"
    value {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}

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

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

WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:169: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Trainer/model/6/Format-TFMA/variables/variables
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:189: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.get_tensor_from_tensor_info or tf.compat.v1.saved_model.get_tensor_from_tensor_info.
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Evaluator/evaluation/8.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:113: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`
INFO:absl:Blessing result True written to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Evaluator/blessing/8.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized

Lassen Sie sich nun den Ausgang Artefakte untersuchen Evaluator .

evaluator.outputs
{'evaluation': Channel(
    type_name: ModelEvaluation
    artifacts: [Artifact(artifact: id: 10
type_id: 20
uri: "/tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/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: "0.29.0"
  }
}
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-2021-07-24T09_22_20.157950-zveef4oj/Evaluator/blessing/8"
custom_properties {
  key: "blessed"
  value {
    int_value: 1
  }
}
custom_properties {
  key: "current_model"
  value {
    string_value: "/tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/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: "0.29.0"
  }
}
state: LIVE
, artifact_type: id: 21
name: "ModelBlessing"
)]
    additional_properties: {}
    additional_custom_properties: {}
)}

Die Verwendung von evaluation Ausgabe können wir die Standard - Visualisierung der globalen Metriken auf der gesamten Auswertsatz zeigen.

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, sondern bei jedem Merkmalswert berechnet trip_start_hour statt auf dem gesamten Auswertsatz.

Die TensorFlow-Modellanalyse unterstützt viele andere Visualisierungen, z. B. Fairness-Indikatoren und das Zeichnen einer Zeitreihe der Modellleistung. Um mehr zu erfahren, finden Sie das 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 Jul 24 09:23 BLESSED

Jetzt kann der Erfolg auch durch Laden des Validierungsergebnissatzes überprüft werden:

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 ist in der Regel am Ende einer TFX - Pipeline. Es wird überprüft , ob eine Modellvalidierung bestanden hat, und wenn ja, die Exporte um das Modell zu _serving_model_dir .

pusher = Pusher(
    model=trainer.outputs['model'],
    model_blessing=evaluator.outputs['blessing'],
    push_destination=pusher_pb2.PushDestination(
        filesystem=pusher_pb2.PushDestination.Filesystem(
            base_directory=_serving_model_dir)))
context.run(pusher)
INFO:absl:Running driver for Pusher
INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:23:37.476756: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Running executor for Pusher
INFO:absl:Model version: 1627118617
INFO:absl:Model written to serving path /tmp/tmpqi5o_miu/serving_model/taxi_simple/1627118617.
INFO:absl:Model pushed to /tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/Pusher/pushed_model/9.
INFO:absl:Running publisher for Pusher
INFO:absl:MetadataStore with DB connection initialized

Betrachten sie den Ausgang Artefakte von Pusher .

pusher.outputs
{'pushed_model': Channel(
    type_name: PushedModel
    artifacts: [Artifact(artifact: id: 12
type_id: 23
uri: "/tmp/tfx-interactive-2021-07-24T09_22_20.157950-zveef4oj/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/tmpqi5o_miu/serving_model/taxi_simple/1627118617"
  }
}
custom_properties {
  key: "pushed_version"
  value {
    string_value: "1627118617"
  }
}
custom_properties {
  key: "state"
  value {
    string_value: "published"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "0.29.0"
  }
}
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)
('classification', <ConcreteFunction pruned(inputs) at 0x7F88A40F63D0>)
('regression', <ConcreteFunction pruned(inputs) at 0x7F88D7645450>)
('serving_default', <ConcreteFunction pruned(inputs) at 0x7F8879BD9510>)
('predict', <ConcreteFunction pruned(examples) at 0x7F8879824290>)

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