البرنامج التعليمي لمكون TFX Keras

تنظيم صفحاتك في مجموعات يمكنك حفظ المحتوى وتصنيفه حسب إعداداتك المفضّلة.

مقدمة مكونة من مكون إلى TensorFlow Extended (TFX)

سيعمل هذا البرنامج التعليمي المستند إلى Colab بشكل تفاعلي خلال كل مكون مدمج في TensorFlow Extended (TFX).

يغطي كل خطوة في خط أنابيب التعلم الآلي الشامل ، من استيعاب البيانات إلى دفع النموذج إلى العرض.

عند الانتهاء ، يمكن تصدير محتويات هذا الكمبيوتر الدفتري تلقائيًا كرمز مصدر لخط أنابيب TFX ، والذي يمكنك تنسيقه باستخدام Apache Airflow و Apache Beam.

خلفية

يوضح هذا الكمبيوتر الدفتري كيفية استخدام TFX في بيئة Jupyter / Colab. هنا ، نسير عبر مثال Chicago Taxi في دفتر ملاحظات تفاعلي.

يعد العمل في دفتر ملاحظات تفاعلي طريقة مفيدة للتعرف على بنية خط أنابيب TFX. إنه مفيد أيضًا عند القيام بتطوير خطوط الأنابيب الخاصة بك كبيئة تطوير خفيفة الوزن ، ولكن يجب أن تدرك أن هناك اختلافات في طريقة تنظيم دفاتر الملاحظات التفاعلية وكيفية وصولها إلى عناصر البيانات الوصفية.

تنسيق

في نشر إنتاج TFX ، ستستخدم منظمًا مثل Apache Airflow أو Kubeflow Pipelines أو Apache Beam لتنسيق رسم بياني لخطوط الأنابيب محدد مسبقًا لمكونات TFX. في أي كمبيوتر دفتري تفاعلي ، يكون الكمبيوتر الدفتري نفسه هو المنسق ، حيث يقوم بتشغيل كل مكون من مكونات TFX أثناء تنفيذ خلايا الكمبيوتر الدفتري.

البيانات الوصفية

في عملية نشر TFX ، ستصل إلى البيانات الوصفية من خلال واجهة برمجة تطبيقات ML Metadata (MLMD). يخزن MLMD خصائص البيانات الوصفية في قاعدة بيانات مثل MySQL أو SQLite ، ويخزن حمولات البيانات الوصفية في مخزن دائم مثل على نظام الملفات الخاص بك. في دفتر التفاعلي، ويتم تخزين كل الممتلكات والحمولات في قاعدة بيانات SQLite سريعة الزوال في /tmp الدليل على دفتر Jupyter أو خادم Colab.

يثبت

أولاً ، نقوم بتثبيت واستيراد الحزم اللازمة ، وإعداد المسارات ، وتنزيل البيانات.

ترقية النقطة

لتجنب ترقية Pip في نظام عند التشغيل محليًا ، تحقق للتأكد من أننا نعمل في Colab. يمكن بالطبع ترقية الأنظمة المحلية بشكل منفصل.

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

قم بتثبيت TFX

pip install -U tfx

هل أعدت تشغيل وقت التشغيل؟

إذا كنت تستخدم Google Colab ، في المرة الأولى التي تقوم فيها بتشغيل الخلية أعلاه ، يجب إعادة تشغيل وقت التشغيل (Runtime> Restart runtime ...). هذا بسبب الطريقة التي يقوم بها كولاب بتحميل الحزم.

حزم الاستيراد

نقوم باستيراد الحزم الضرورية ، بما في ذلك فئات مكونات TFX القياسية.

import os
import pprint
import tempfile
import urllib

import absl
import tensorflow as tf
import tensorflow_model_analysis as tfma
tf.get_logger().propagate = False
pp = pprint.PrettyPrinter()

from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext

%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip

دعنا نتحقق من إصدارات المكتبة.

print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.7.0
TFX version: 1.5.0

قم بإعداد مسارات خطوط الأنابيب

# 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)

تنزيل نموذج البيانات

نقوم بتنزيل مجموعة البيانات النموذجية لاستخدامها في خط أنابيب TFX الخاص بنا.

مجموعة البيانات نستخدمه هو تاكسي رحلات بيانات الصادرة عن مدينة شيكاغو. الأعمدة في مجموعة البيانات هذه هي:

pickup_community_area أجرة رحلة_الشهر
رحلة_بدء_ساعة رحلة_البدء_اليوم trip_start_timestamp
pickup_latitude pickup_longitude dropoff_latitude
dropoff_longitude رحلة_مايلز pickup_census_tract
dropoff_census_tract نوع الدفع شركة
رحلة_ثواني منطقة_المجتمع نصائح

مع هذه البينات، سوف نبني النموذج الذي يتنبأ tips للرحلة.

_data_root = tempfile.mkdtemp(prefix='tfx-data')
DATA_PATH = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv'
_data_filepath = os.path.join(_data_root, "data.csv")
urllib.request.urlretrieve(DATA_PATH, _data_filepath)
('/tmp/tfx-datacz9xjro6/data.csv', <http.client.HTTPMessage at 0x7f889af49250>)

ألق نظرة سريعة على ملف CSV.

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

إخلاء المسؤولية: يوفر هذا الموقع تطبيقات تستخدم البيانات التي تم تعديلها للاستخدام من مصدرها الأصلي ، www.cityofchicago.org ، الموقع الرسمي لمدينة شيكاغو. لا تقدم مدينة شيكاغو أي مطالبات فيما يتعلق بمحتوى أو دقة أو توقيت أو اكتمال أي من البيانات المقدمة في هذا الموقع. البيانات الواردة في هذا الموقع عرضة للتغيير في أي وقت. من المفهوم أن البيانات المقدمة في هذا الموقع يتم استخدامها على مسؤوليتك الخاصة.

قم بإنشاء InteractiveContext

أخيرًا ، قمنا بإنشاء InteractiveContext ، والذي سيسمح لنا بتشغيل مكونات TFX بشكل تفاعلي في هذا الكمبيوتر المحمول.

# Here, we create an InteractiveContext using default parameters. This will
# use a temporary directory with an ephemeral ML Metadata database instance.
# To use your own pipeline root or database, the optional properties
# `pipeline_root` and `metadata_connection_config` may be passed to
# InteractiveContext. Calls to InteractiveContext are no-ops outside of the
# notebook.
context = InteractiveContext()
WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/metadata.sqlite.

قم بتشغيل مكونات TFX بشكل تفاعلي

في الخلايا التالية ، نقوم بإنشاء مكونات TFX واحدًا تلو الآخر ، ونشغل كل منها ، ونتخيل نتائج الإخراج الخاصة بهم.

المثال

و ExampleGen العنصر هو عادة في بداية خط أنابيب TFX. سوف تكون:

  1. قسّم البيانات إلى مجموعات تدريب وتقييم (افتراضيًا ، 2/3 تدريب + 1/3 تقييم)
  2. البيانات تحويل البيانات إلى tf.Example شكل (معرفة المزيد هنا )
  3. نسخ البيانات إلى _tfx_root الدليل للمكونات الأخرى للوصول إلى

ExampleGen يأخذ كمدخل الطريق إلى مصدر البيانات. في حالتنا، وهذا هو _data_root المسار الذي يحتوي على CSV تحميلها.

example_gen = tfx.components.CsvExampleGen(input_base=_data_root)
context.run(example_gen)
INFO:absl:Running driver for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:Running executor for CsvExampleGen
INFO:absl:Generating examples.
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
INFO:absl:Processing input csv data /tmp/tfx-datacz9xjro6/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Running publisher for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized

دعونا نتفحص القطع الأثرية إخراج ExampleGen . ينتج هذا المكون اثنين من القطع الأثرية وأمثلة للتدريب وأمثلة للتقييم:

artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/CsvExampleGen/examples/1

يمكننا أيضًا إلقاء نظرة على الأمثلة التدريبية الثلاثة الأولى:

# 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
      }
    }
  }
}

والآن بعد أن ExampleGen انتهاء ادخال البيانات، فإن الخطوة التالية هي تحليل البيانات.

الإحصاء

و StatisticsGen يحسب المكونة إحصاءات على مجموعة البيانات الخاصة بك لتحليل البيانات، فضلا عن استخدامها في مكونات المصب. ويستخدم TensorFlow بيانات التحقق من صحة مكتبة.

StatisticsGen يأخذ كمدخل للبيانات نحن تناولها فقط باستخدام ExampleGen .

statistics_gen = tfx.components.StatisticsGen(
    examples=example_gen.outputs['examples'])
context.run(statistics_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for StatisticsGen
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/StatisticsGen/statistics/2/Split-eval.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Running publisher for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized

بعد StatisticsGen انتهاء تشغيل، ونحن يمكن تصور الإحصاءات أنتج. جرب اللعب بالمؤامرات المختلفة!

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

مخطط

و SchemaGen عنصر يولد مخطط بناء على البيانات الإحصائية الخاصة بك. (A مخطط يحدد حدود المتوقع، وأنواع، وخصائص ميزات في مجموعة البيانات.) ويستخدم أيضا TensorFlow بيانات التحقق من صحة مكتبة.

SchemaGen سيستغرق كمدخل الإحصائيات التي ولدت لنا مع StatisticsGen ، والنظر في تقسيم التدريب بشكل افتراضي.

schema_gen = tfx.components.SchemaGen(
    statistics=statistics_gen.outputs['statistics'],
    infer_feature_shape=False)
context.run(schema_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for SchemaGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for SchemaGen
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/SchemaGen/schema/3/schema.pbtxt.
INFO:absl:Running publisher for SchemaGen
INFO:absl:MetadataStore with DB connection initialized

بعد SchemaGen ينتهي تشغيل، ونحن يمكن تصور المخطط إنشاؤها كجدول.

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

تظهر كل ميزة في مجموعة البيانات كصف في جدول المخطط ، جنبًا إلى جنب مع خصائصها. يلتقط المخطط أيضًا جميع القيم التي تأخذها الميزة الفئوية ، ويُشار إليها على أنها مجالها.

لمعرفة المزيد حول مخططات، انظر الوثائق SchemaGen .

exampleValidator

و ExampleValidator عنصر بالكشف عن الحالات الشاذة في البيانات الخاصة بك، استنادا إلى التوقعات التي حددها المخطط. ويستخدم أيضا TensorFlow بيانات التحقق من صحة مكتبة.

ExampleValidator سيستغرق كمدخل الإحصاءات من StatisticsGen ، والمخطط من SchemaGen .

example_validator = tfx.components.ExampleValidator(
    statistics=statistics_gen.outputs['statistics'],
    schema=schema_gen.outputs['schema'])
context.run(example_validator)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for ExampleValidator
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Validation complete for split train. Anomalies written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/ExampleValidator/anomalies/4/Split-train.
INFO:absl:Validating schema against the computed statistics for split eval.
INFO:absl:Validation complete for split eval. Anomalies written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/ExampleValidator/anomalies/4/Split-eval.
INFO:absl:Running publisher for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized

بعد ExampleValidator ينتهي تشغيل، ونحن يمكن تصور الحالات الشاذة كجدول.

context.show(example_validator.outputs['anomalies'])

في جدول الحالات الشاذة ، يمكننا أن نرى أنه لا توجد حالات شاذة. هذا ما كنا نتوقعه ، نظرًا لأن هذه هي مجموعة البيانات الأولى التي حللناها والمخطط مصمم خصيصًا لها. يجب عليك مراجعة هذا المخطط - أي شيء غير متوقع يعني وجود شذوذ في البيانات. بمجرد المراجعة ، يمكن استخدام المخطط لحماية البيانات المستقبلية ، ويمكن استخدام الحالات الشاذة الناتجة هنا لتصحيح أداء النموذج ، وفهم كيفية تطور بياناتك بمرور الوقت ، وتحديد أخطاء البيانات.

تحول

و Transform ينفذ المكونة الهندسة ميزة للتدريب وخدمة. ويستخدم TensorFlow تحويل المكتبة.

Transform سيستغرق كمدخل البيانات من ExampleGen ، المخطط من SchemaGen ، وكذلك وحدة نمطية تحتوي تحويل رمز المعرفة من قبل المستخدم.

ترى دعونا مثال على تحويل الشفرة ادناه (لمقدمة لTensorFlow تحويل واجهات برمجة التطبيقات، المعرفة من قبل المستخدم رؤية البرنامج التعليمي ). أولاً ، نحدد بعض الثوابت لهندسة الميزات:

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

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

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

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

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

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

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

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

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

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

بعد ذلك، نكتب preprocessing_fn أن يأخذ في البيانات الأولية كمدخل، والعوائد ملامح تحول هذا النموذج لدينا يمكن أن يتدرب على:

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

import tensorflow as tf
import tensorflow_transform as tft

import taxi_constants

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


def preprocessing_fn(inputs):
  """tf.transform's callback function for preprocessing inputs.
  Args:
    inputs: map from feature keys to raw not-yet-transformed features.
  Returns:
    Map from string feature key to transformed feature operations.
  """
  outputs = {}
  for key in _DENSE_FLOAT_FEATURE_KEYS:
    # If sparse make it dense, setting nan's to 0 or '', and apply zscore.
    outputs[key] = tft.scale_to_z_score(
        _fill_in_missing(inputs[key]))

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

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

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

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

  return outputs


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

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

الآن، نحن نمر في هذا الرمز الهندسي ميزة ل Transform عنصر وتشغيله لتحويل البيانات الخاصة بك.

transform = tfx.components.Transform(
    examples=example_gen.outputs['examples'],
    schema=schema_gen.outputs['schema'],
    module_file=os.path.abspath(_taxi_transform_module_file))
context.run(transform)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py' (including modules: ['taxi_transform', 'taxi_constants']).
INFO:absl:User module package has hash fingerprint version f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp9qnpryw9/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmppaskl3va', '--dist-dir', '/tmp/tmpr6oorqji']
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'; target user module is 'taxi_transform'.
INFO:absl:Full user module path is 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Transform
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpbvbj9r5b', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_transform.py -> build/lib
copying taxi_constants.py -> build/lib
running install
running install_lib
running install_egg_info
running egg_info
creating tfx_user_code_Transform.egg-info
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
Copying tfx_user_code_Transform.egg-info to /tmp/tmppaskl3va/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3.7.egg-info
running install_scripts
Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpbzwdie1a', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424
Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'.
INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp09euava5', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424
Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:289: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
2021-12-21 10:10:18.679569: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5/.temp_path/tftransform_tmp/80dbc09e6ded4a93b5c506e252c8f536/assets
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5/.temp_path/tftransform_tmp/572eacb7c64f4f6e9262f7d496a95f86/assets
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized

دعونا نتفحص القطع الأثرية الناتج من Transform . ينتج هذا المكون نوعين من المخرجات:

  • transform_graph هو الرسم البياني التي يمكن أن تؤدي عمليات تجهيزها (وسيتم إدراج هذا الرسم البياني في نماذج خدمة والتقييم).
  • transformed_examples يمثل بيانات التدريب والتقييم preprocessed.
transform.outputs
{'transform_graph': Channel(
     type_name: TransformGraph
     artifacts: [Artifact(artifact: id: 5
 type_id: 22
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transform_graph/5"
 custom_properties {
   key: "name"
   value {
     string_value: "transform_graph"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 22
 name: "TransformGraph"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'transformed_examples': Channel(
     type_name: Examples
     artifacts: [Artifact(artifact: id: 6
 type_id: 14
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/transformed_examples/5"
 properties {
   key: "split_names"
   value {
     string_value: "[\"train\", \"eval\"]"
   }
 }
 custom_properties {
   key: "name"
   value {
     string_value: "transformed_examples"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 14
 name: "Examples"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 properties {
   key: "version"
   value: INT
 }
 base_type: DATASET
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'updated_analyzer_cache': Channel(
     type_name: TransformCache
     artifacts: [Artifact(artifact: id: 7
 type_id: 23
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/updated_analyzer_cache/5"
 custom_properties {
   key: "name"
   value {
     string_value: "updated_analyzer_cache"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 23
 name: "TransformCache"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'pre_transform_schema': Channel(
     type_name: Schema
     artifacts: [Artifact(artifact: id: 8
 type_id: 18
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/pre_transform_schema/5"
 custom_properties {
   key: "name"
   value {
     string_value: "pre_transform_schema"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 18
 name: "Schema"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'pre_transform_stats': Channel(
     type_name: ExampleStatistics
     artifacts: [Artifact(artifact: id: 9
 type_id: 16
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/pre_transform_stats/5"
 custom_properties {
   key: "name"
   value {
     string_value: "pre_transform_stats"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 16
 name: "ExampleStatistics"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 base_type: STATISTICS
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_schema': Channel(
     type_name: Schema
     artifacts: [Artifact(artifact: id: 10
 type_id: 18
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_schema/5"
 custom_properties {
   key: "name"
   value {
     string_value: "post_transform_schema"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 18
 name: "Schema"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_stats': Channel(
     type_name: ExampleStatistics
     artifacts: [Artifact(artifact: id: 11
 type_id: 16
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_stats/5"
 custom_properties {
   key: "name"
   value {
     string_value: "post_transform_stats"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 16
 name: "ExampleStatistics"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 base_type: STATISTICS
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_anomalies': Channel(
     type_name: ExampleAnomalies
     artifacts: [Artifact(artifact: id: 12
 type_id: 20
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Transform/post_transform_anomalies/5"
 custom_properties {
   key: "name"
   value {
     string_value: "post_transform_anomalies"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 20
 name: "ExampleAnomalies"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

نلقي نظرة خاطفة على transform_graph قطعة أثرية. يشير إلى دليل يحتوي على ثلاثة أدلة فرعية.

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

و transformed_metadata يحتوي الدليل مخطط البيانات preprocessed. و transform_fn يحتوي الدليل على الرسم البياني تجهيزها الفعلي. و metadata ويتضمن الدليل مخطط البيانات الأصلية.

يمكننا أيضًا إلقاء نظرة على الأمثلة الثلاثة الأولى المحوّلة:

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

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

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

# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
  serialized_example = tfrecord.numpy()
  example = tf.train.Example()
  example.ParseFromString(serialized_example)
  pp.pprint(example)
features {
  feature {
    key: "company"
    value {
      int64_list {
        value: 8
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 0.061060599982738495
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      int64_list {
        value: 1
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "tips"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: -0.15886741876602173
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      float_list {
        value: -0.7118487358093262
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 6
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 19
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 5
      }
    }
  }
}

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

features {
  feature {
    key: "company"
    value {
      int64_list {
        value: 48
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 0.3873794376850128
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "tips"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 0.21955277025699615
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      float_list {
        value: 0.0019067146349698305
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 12
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 11
      }
    }
  }
}

بعد Transform عنصر حولت البيانات إلى الميزات، والخطوة التالية هي تدريب نموذجا.

مدرب

و Trainer وعنصر تدريب النموذج الذي تعرفه في TensorFlow. افتراضي دعم المدرب مقدر API، لاستخدام Keras API، تحتاج إلى تحديد عام المدرب بواسطة الإعداد custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor) في دالة البناء المدرب.

Trainer يأخذ كمدخل المخطط من SchemaGen ، والبيانات المحولة والرسم البياني من Transform ، وتدريب المعلمات، وكذلك وحدة نمطية يحتوي على التعليمات البرمجية نموذج المعرفة من قبل المستخدم.

ترى دعونا مثال من التعليمات البرمجية نموذج المعرفة أدناه (بالنسبة للمقدمة لKeras واجهات برمجة التطبيقات TensorFlow، انظر تعليمي ):

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

from typing import List, Text

import os
from absl import logging

import datetime
import tensorflow as tf
import tensorflow_transform as tft

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

import taxi_constants

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


def _get_tf_examples_serving_signature(model, tf_transform_output):
  """Returns a serving signature that accepts `tensorflow.Example`."""

  # We need to track the layers in the model in order to save it.
  # TODO(b/162357359): Revise once the bug is resolved.
  model.tft_layer_inference = tf_transform_output.transform_features_layer()

  @tf.function(input_signature=[
      tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')
  ])
  def serve_tf_examples_fn(serialized_tf_example):
    """Returns the output to be used in the serving signature."""
    raw_feature_spec = tf_transform_output.raw_feature_spec()
    # Remove label feature since these will not be present at serving time.
    raw_feature_spec.pop(_LABEL_KEY)
    raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
    transformed_features = model.tft_layer_inference(raw_features)
    logging.info('serve_transformed_features = %s', transformed_features)

    outputs = model(transformed_features)
    # TODO(b/154085620): Convert the predicted labels from the model using a
    # reverse-lookup (opposite of transform.py).
    return {'outputs': outputs}

  return serve_tf_examples_fn


def _get_transform_features_signature(model, tf_transform_output):
  """Returns a serving signature that applies tf.Transform to features."""

  # We need to track the layers in the model in order to save it.
  # TODO(b/162357359): Revise once the bug is resolved.
  model.tft_layer_eval = tf_transform_output.transform_features_layer()

  @tf.function(input_signature=[
      tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')
  ])
  def transform_features_fn(serialized_tf_example):
    """Returns the transformed_features to be fed as input to evaluator."""
    raw_feature_spec = tf_transform_output.raw_feature_spec()
    raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
    transformed_features = model.tft_layer_eval(raw_features)
    logging.info('eval_transformed_features = %s', transformed_features)
    return transformed_features

  return transform_features_fn


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

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

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


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

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

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

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


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

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

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

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

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

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

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


# TFX Trainer will call this function.
def run_fn(fn_args: tfx.components.FnArgs):
  """Train the model based on given args.

  Args:
    fn_args: Holds args used to train the model as name/value pairs.
  """
  # Number of nodes in the first layer of the DNN
  first_dnn_layer_size = 100
  num_dnn_layers = 4
  dnn_decay_factor = 0.7

  tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)

  train_dataset = _input_fn(fn_args.train_files, fn_args.data_accessor, 
                            tf_transform_output, 40)
  eval_dataset = _input_fn(fn_args.eval_files, fn_args.data_accessor, 
                           tf_transform_output, 40)

  model = _build_keras_model(
      # Construct layers sizes with exponetial decay
      hidden_units=[
          max(2, int(first_dnn_layer_size * dnn_decay_factor**i))
          for i in range(num_dnn_layers)
      ])

  tensorboard_callback = tf.keras.callbacks.TensorBoard(
      log_dir=fn_args.model_run_dir, update_freq='batch')
  model.fit(
      train_dataset,
      steps_per_epoch=fn_args.train_steps,
      validation_data=eval_dataset,
      validation_steps=fn_args.eval_steps,
      callbacks=[tensorboard_callback])

  signatures = {
      'serving_default':
          _get_tf_examples_serving_signature(model, tf_transform_output),
      'transform_features':
          _get_transform_features_signature(model, tf_transform_output),
  }
  model.save(fn_args.serving_model_dir, save_format='tf', signatures=signatures)
Writing taxi_trainer.py

الآن، نحن نمر في هذه المدونة النموذجية لل Trainer مكون وتشغيله لتدريب النموذج.

trainer = tfx.components.Trainer(
    module_file=os.path.abspath(_taxi_trainer_module_file),
    examples=transform.outputs['transformed_examples'],
    transform_graph=transform.outputs['transform_graph'],
    schema=schema_gen.outputs['schema'],
    train_args=tfx.proto.TrainArgs(num_steps=10000),
    eval_args=tfx.proto.EvalArgs(num_steps=5000))
context.run(trainer)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py' (including modules: ['taxi_transform', 'taxi_constants', 'taxi_trainer']).
INFO:absl:User module package has hash fingerprint version ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpzxd5b1yc/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpbg9ly6tr', '--dist-dir', '/tmp/tmpx43qh690']
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'; target user module is 'taxi_trainer'.
INFO:absl:Full user module path is 'taxi_trainer@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Trainer
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
INFO:absl:udf_utils.get_fn {'train_args': '{\n  "num_steps": 10000\n}', 'eval_args': '{\n  "num_steps": 5000\n}', 'module_file': None, 'run_fn': None, 'trainer_fn': None, 'custom_config': 'null', 'module_path': 'taxi_trainer@/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'} 'run_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp1osq6e1x', '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_transform.py -> build/lib
copying taxi_constants.py -> build/lib
copying taxi_trainer.py -> build/lib
running install
running install_lib
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmp/tmpbg9ly6tr/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3.7.egg-info
running install_scripts
Processing /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/_wheels/tfx_user_code_Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+ace8eb563ff2ae66112acc05232b33344bcb925cdc0a0847df64c544323b99af
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl: Layer (type)                   Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl: company (InputLayer)           [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: dropoff_census_tract (InputLay  [(None,)]           0           []                               
INFO:absl: er)                                                                                              
INFO:absl:                                                                                                  
INFO:absl: dropoff_community_area (InputL  [(None,)]           0           []                               
INFO:absl: ayer)                                                                                            
INFO:absl:                                                                                                  
INFO:absl: dropoff_latitude (InputLayer)  [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: dropoff_longitude (InputLayer)  [(None,)]           0           []                               
INFO:absl:                                                                                                  
INFO:absl: fare (InputLayer)              [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: payment_type (InputLayer)      [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: pickup_census_tract (InputLaye  [(None,)]           0           []                               
INFO:absl: r)                                                                                               
INFO:absl:                                                                                                  
INFO:absl: pickup_community_area (InputLa  [(None,)]           0           []                               
INFO:absl: yer)                                                                                             
INFO:absl:                                                                                                  
INFO:absl: pickup_latitude (InputLayer)   [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: pickup_longitude (InputLayer)  [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: trip_miles (InputLayer)        [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: trip_seconds (InputLayer)      [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: trip_start_day (InputLayer)    [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: trip_start_hour (InputLayer)   [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: trip_start_month (InputLayer)  [(None,)]            0           []                               
INFO:absl:                                                                                                  
INFO:absl: dense_features (DenseFeatures)  (None, 3)           0           ['company[0][0]',                
INFO:absl:                                                                  'dropoff_census_tract[0][0]',   
INFO:absl:                                                                  'dropoff_community_area[0][0]', 
INFO:absl:                                                                  'dropoff_latitude[0][0]',       
INFO:absl:                                                                  'dropoff_longitude[0][0]',      
INFO:absl:                                                                  'fare[0][0]',                   
INFO:absl:                                                                  'payment_type[0][0]',           
INFO:absl:                                                                  'pickup_census_tract[0][0]',    
INFO:absl:                                                                  'pickup_community_area[0][0]',  
INFO:absl:                                                                  'pickup_latitude[0][0]',        
INFO:absl:                                                                  'pickup_longitude[0][0]',       
INFO:absl:                                                                  'trip_miles[0][0]',             
INFO:absl:                                                                  'trip_seconds[0][0]',           
INFO:absl:                                                                  'trip_start_day[0][0]',         
INFO:absl:                                                                  'trip_start_hour[0][0]',        
INFO:absl:                                                                  'trip_start_month[0][0]']       
INFO:absl:                                                                                                  
INFO:absl: dense (Dense)                  (None, 100)          400         ['dense_features[0][0]']         
INFO:absl:                                                                                                  
INFO:absl: dense_1 (Dense)                (None, 70)           7070        ['dense[0][0]']                  
INFO:absl:                                                                                                  
INFO:absl: dense_2 (Dense)                (None, 48)           3408        ['dense_1[0][0]']                
INFO:absl:                                                                                                  
INFO:absl: dense_3 (Dense)                (None, 34)           1666        ['dense_2[0][0]']                
INFO:absl:                                                                                                  
INFO:absl: dense_features_1 (DenseFeature  (None, 2127)        0           ['company[0][0]',                
INFO:absl: s)                                                               'dropoff_census_tract[0][0]',   
INFO:absl:                                                                  'dropoff_community_area[0][0]', 
INFO:absl:                                                                  'dropoff_latitude[0][0]',       
INFO:absl:                                                                  'dropoff_longitude[0][0]',      
INFO:absl:                                                                  'fare[0][0]',                   
INFO:absl:                                                                  'payment_type[0][0]',           
INFO:absl:                                                                  'pickup_census_tract[0][0]',    
INFO:absl:                                                                  'pickup_community_area[0][0]',  
INFO:absl:                                                                  'pickup_latitude[0][0]',        
INFO:absl:                                                                  'pickup_longitude[0][0]',       
INFO:absl:                                                                  'trip_miles[0][0]',             
INFO:absl:                                                                  'trip_seconds[0][0]',           
INFO:absl:                                                                  'trip_start_day[0][0]',         
INFO:absl:                                                                  'trip_start_hour[0][0]',        
INFO:absl:                                                                  'trip_start_month[0][0]']       
INFO:absl:                                                                                                  
INFO:absl: concatenate (Concatenate)      (None, 2161)         0           ['dense_3[0][0]',                
INFO:absl:                                                                  'dense_features_1[0][0]']       
INFO:absl:                                                                                                  
INFO:absl: dense_4 (Dense)                (None, 1)            2162        ['concatenate[0][0]']            
INFO:absl:                                                                                                  
INFO:absl:==================================================================================================
INFO:absl:Total params: 14,706
INFO:absl:Trainable params: 14,706
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
10000/10000 [==============================] - 100s 10ms/step - loss: 0.2372 - binary_accuracy: 0.8605 - val_loss: 0.2222 - val_binary_accuracy: 0.8709
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f88b5e27910>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING: AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f88b5e27910>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
INFO:absl:serve_transformed_features = {'pickup_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:9' shape=(None,) dtype=int64>, 'trip_start_hour': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:15' shape=(None,) dtype=int64>, 'fare': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:5' shape=(None,) dtype=float32>, 'trip_miles': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:12' shape=(None,) dtype=float32>, 'trip_start_day': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:14' shape=(None,) dtype=int64>, 'dropoff_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:3' shape=(None,) dtype=int64>, 'trip_start_month': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:16' shape=(None,) dtype=int64>, 'dropoff_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:2' shape=(None,) dtype=int64>, 'dropoff_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:4' shape=(None,) dtype=int64>, 'payment_type': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:6' shape=(None,) dtype=int64>, 'pickup_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:10' shape=(None,) dtype=int64>, 'pickup_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:8' shape=(None,) dtype=int64>, 'company': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:0' shape=(None,) dtype=int64>, 'pickup_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:7' shape=(None,) dtype=int64>, 'dropoff_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:1' shape=(None,) dtype=int64>, 'trip_seconds': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:13' shape=(None,) dtype=float32>}
INFO:absl:eval_transformed_features = {'pickup_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:9' shape=(None,) dtype=int64>, 'trip_start_hour': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:15' shape=(None,) dtype=int64>, 'fare': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:5' shape=(None,) dtype=float32>, 'trip_miles': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:12' shape=(None,) dtype=float32>, 'trip_start_day': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:14' shape=(None,) dtype=int64>, 'dropoff_latitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:3' shape=(None,) dtype=int64>, 'trip_start_month': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:16' shape=(None,) dtype=int64>, 'dropoff_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:2' shape=(None,) dtype=int64>, 'dropoff_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:4' shape=(None,) dtype=int64>, 'payment_type': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:6' shape=(None,) dtype=int64>, 'pickup_longitude': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:10' shape=(None,) dtype=int64>, 'pickup_community_area': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:8' shape=(None,) dtype=int64>, 'company': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:0' shape=(None,) dtype=int64>, 'pickup_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:7' shape=(None,) dtype=int64>, 'tips': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:11' shape=(None,) dtype=int64>, 'dropoff_census_tract': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:1' shape=(None,) dtype=int64>, 'trip_seconds': <tf.Tensor 'transform_features_layer/StatefulPartitionedCall:13' shape=(None,) dtype=float32>}
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving/assets
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model_run/6
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized

تحليل التدريب باستخدام TensorBoard

إلقاء نظرة خاطفة على قطعة أثرية المدرب. يشير إلى دليل يحتوي على نماذج الدلائل الفرعية.

model_artifact_dir = trainer.outputs['model'].get()[0].uri
pp.pprint(os.listdir(model_artifact_dir))
model_dir = os.path.join(model_artifact_dir, 'Format-Serving')
pp.pprint(os.listdir(model_dir))
['Format-Serving']
['variables', 'assets', 'keras_metadata.pb', 'saved_model.pb']

اختياريًا ، يمكننا توصيل TensorBoard بـ Trainer لتحليل منحنيات التدريب الخاصة بنموذجنا.

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

%load_ext tensorboard
%tensorboard --logdir {model_run_artifact_dir}

المقيم

و Evaluator عنصر يحسب مقاييس الأداء نموذجية على مجموعة التقييم. ويستخدم TensorFlow تحليل نموذج المكتبة. و Evaluator يمكن أيضا التحقق من صحة اختياريا أن نموذج المدربين حديثا هو أفضل من النموذج السابق. هذا مفيد في إعداد خط أنابيب الإنتاج حيث يمكنك تلقائيًا تدريب نموذج والتحقق من صحته كل يوم. في هذه المفكرة، ندرب فقط نموذج واحد، وبالتالي فإن Evaluator تلقائيا سوف تسمية نموذج بأنها "جيدة".

Evaluator سيستغرق كمدخل البيانات من ExampleGen ، ونموذج تدريب من Trainer ، والتكوين تشريح. يسمح لك تكوين التقطيع بتقسيم المقاييس الخاصة بك إلى قيم الميزات (على سبيل المثال ، كيف يعمل نموذجك في رحلات التاكسي التي تبدأ في الساعة 8 صباحًا مقابل 8 مساءً؟). شاهد مثالاً على هذا التكوين أدناه:

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

بعد ذلك، نعطي هذا التكوين إلى Evaluator وتشغيله.

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

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

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

INFO:absl:Using /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6/Format-Serving as  model.
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87bc0f5e50> and <keras.engine.input_layer.InputLayer object at 0x7f87bc0f5b50>).
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:udf_utils.get_fn {'eval_config': '{\n  "metrics_specs": [\n    {\n      "metrics": [\n        {\n          "class_name": "ExampleCount"\n        },\n        {\n          "class_name": "BinaryAccuracy",\n          "threshold": {\n            "change_threshold": {\n              "absolute": -1e-10,\n              "direction": "HIGHER_IS_BETTER"\n            },\n            "value_threshold": {\n              "lower_bound": 0.5\n            }\n          }\n        }\n      ]\n    }\n  ],\n  "model_specs": [\n    {\n      "label_key": "tips",\n      "preprocessing_function_names": [\n        "transform_features"\n      ],\n      "signature_name": "serving_default"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_extractors'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "serving_default"
  label_key: "tips"
  preprocessing_function_names: "transform_features"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
    threshold {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
  model_names: ""
}

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

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "serving_default"
  label_key: "tips"
  preprocessing_function_names: "transform_features"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
    threshold {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
  model_names: ""
}
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87b0102150> and <keras.engine.input_layer.InputLayer object at 0x7f875454e810>).
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87b06c9d50> and <keras.engine.input_layer.InputLayer object at 0x7f87d4041290>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f874c8d6a10> and <keras.engine.input_layer.InputLayer object at 0x7f874c8ac0d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f830dcf9fd0> and <keras.engine.input_layer.InputLayer object at 0x7f830dd87110>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f830dc8cad0> and <keras.engine.input_layer.InputLayer object at 0x7f830cf892d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f87b041add0> and <keras.engine.input_layer.InputLayer object at 0x7f874d6b6d50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f830c42a5d0> and <keras.engine.input_layer.InputLayer object at 0x7f830c3037d0>).
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/evaluation/8.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:107: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`
INFO:absl:Blessing result True written to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/blessing/8.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized

الآن دعونا فحص القطع الأثرية إخراج Evaluator .

evaluator.outputs
{'evaluation': Channel(
     type_name: ModelEvaluation
     artifacts: [Artifact(artifact: id: 15
 type_id: 29
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/evaluation/8"
 custom_properties {
   key: "name"
   value {
     string_value: "evaluation"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Evaluator"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 29
 name: "ModelEvaluation"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'blessing': Channel(
     type_name: ModelBlessing
     artifacts: [Artifact(artifact: id: 16
 type_id: 30
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Evaluator/blessing/8"
 custom_properties {
   key: "blessed"
   value {
     int_value: 1
   }
 }
 custom_properties {
   key: "current_model"
   value {
     string_value: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Trainer/model/6"
   }
 }
 custom_properties {
   key: "current_model_id"
   value {
     int_value: 13
   }
 }
 custom_properties {
   key: "name"
   value {
     string_value: "blessing"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Evaluator"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 30
 name: "ModelBlessing"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

استخدام evaluation الناتج يمكننا أن تظهر التصور الافتراضي المقاييس العالمية على مجموعة تقييم كامل.

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

لرؤية التصور لمقاييس التقييم المقطعة ، يمكننا الاتصال مباشرة بمكتبة تحليل نموذج TensorFlow.

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',…

يوضح هذا التصور نفس المقاييس، ولكن يحسب في كل قيمة سمة من سمات trip_start_hour بدلا من مجموعة تقييم كامل.

يدعم تحليل نموذج TensorFlow العديد من التصورات الأخرى ، مثل مؤشرات الإنصاف ورسم سلسلة زمنية لأداء النموذج. لمعرفة المزيد، راجع البرنامج التعليمي .

نظرًا لأننا أضفنا عتبات إلى التكوين الخاص بنا ، فإن مخرجات التحقق متاحة أيضًا. وprecence من blessing القطع الأثرية تشير إلى أن نموذجنا مرت التحقق من الصحة. نظرًا لأن هذا هو التحقق الأول الذي يتم إجراؤه ، تتم تلقائيًا مباركتك المرشح.

blessing_uri = evaluator.outputs['blessing'].get()[0].uri
!ls -l {blessing_uri}
total 0
-rw-rw-r-- 1 kbuilder kbuilder 0 Dec 21 10:13 BLESSED

الآن يمكن أيضًا التحقق من النجاح عن طريق تحميل سجل نتيجة التحقق:

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
  }
}

انتهازي

و Pusher العنصر هو عادة في نهاية خط أنابيب TFX. فإنه يتحقق ما إذا كان النموذج قد مرت التحقق من صحة، وإذا كان الأمر كذلك، صادرات نموذج ل _serving_model_dir .

pusher = tfx.components.Pusher(
    model=trainer.outputs['model'],
    model_blessing=evaluator.outputs['blessing'],
    push_destination=tfx.proto.PushDestination(
        filesystem=tfx.proto.PushDestination.Filesystem(
            base_directory=_serving_model_dir)))
context.run(pusher)
INFO:absl:Running driver for Pusher
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Pusher
INFO:absl:Model version: 1640081600
INFO:absl:Model written to serving path /tmp/tmpkvhhk5j5/serving_model/taxi_simple/1640081600.
INFO:absl:Model pushed to /tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Pusher/pushed_model/9.
INFO:absl:Running publisher for Pusher
INFO:absl:MetadataStore with DB connection initialized

دعونا نتفحص القطع الأثرية الناتج من Pusher .

pusher.outputs
{'pushed_model': Channel(
     type_name: PushedModel
     artifacts: [Artifact(artifact: id: 17
 type_id: 32
 uri: "/tmp/tfx-interactive-2021-12-21T10_09_51.902969-bvucg0eq/Pusher/pushed_model/9"
 custom_properties {
   key: "name"
   value {
     string_value: "pushed_model"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Pusher"
   }
 }
 custom_properties {
   key: "pushed"
   value {
     int_value: 1
   }
 }
 custom_properties {
   key: "pushed_destination"
   value {
     string_value: "/tmp/tmpkvhhk5j5/serving_model/taxi_simple/1640081600"
   }
 }
 custom_properties {
   key: "pushed_version"
   value {
     string_value: "1640081600"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 32
 name: "PushedModel"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

على وجه الخصوص ، سيقوم Pusher بتصدير نموذجك بتنسيق SavedModel ، والذي يبدو كالتالي:

push_uri = pusher.outputs['pushed_model'].get()[0].uri
model = tf.saved_model.load(push_uri)

for item in model.signatures.items():
  pp.pprint(item)
('serving_default',
 <ConcreteFunction signature_wrapper(*, examples) at 0x7F82F31FDE50>)
('transform_features',
 <ConcreteFunction signature_wrapper(*, examples) at 0x7F82F31AC410>)

لقد انتهينا من جولتنا لمكونات TFX المدمجة!