บทนำแบบส่วนประกอบต่อส่วนประกอบสู่ TensorFlow Extended (TFX)
ดูบน TensorFlow.org
ทำงานใน Google Colab
ดูแหล่งที่มาบน GitHub
ดาวน์โหลดโน๊ตบุ๊คบทแนะนำที่ใช้ Colab นี้จะอธิบายแบบโต้ตอบผ่านแต่ละองค์ประกอบในตัวของ TensorFlow Extended (TFX)
ครอบคลุมทุกขั้นตอนในไปป์ไลน์แมชชีนเลิร์นนิงแบบ end-to-end ตั้งแต่การนำเข้าข้อมูลไปจนถึงการพุชโมเดลไปจนถึงการให้บริการ
เมื่อเสร็จแล้ว เนื้อหาของสมุดบันทึกนี้สามารถส่งออกโดยอัตโนมัติเป็นซอร์สโค้ดไปป์ไลน์ TFX ซึ่งคุณสามารถควบคุมด้วย Apache Airflow และ Apache Beam
พื้นหลัง
สมุดบันทึกนี้สาธิตวิธีใช้ TFX ในสภาพแวดล้อม Jupyter/Colab ที่นี่ เราเดินผ่านตัวอย่าง Chicago Taxi ในสมุดบันทึกแบบโต้ตอบ
การทำงานในสมุดบันทึกแบบโต้ตอบเป็นวิธีที่มีประโยชน์ในการทำความคุ้นเคยกับโครงสร้างของไปป์ไลน์ TFX นอกจากนี้ยังมีประโยชน์เมื่อทำการพัฒนาไปป์ไลน์ของคุณเองในฐานะสภาพแวดล้อมการพัฒนาแบบใช้ทรัพยากรน้อย แต่คุณควรระวังว่าวิธีจัดการโน้ตบุ๊กแบบโต้ตอบนั้นมีความแตกต่างกัน และวิธีที่พวกเขาเข้าถึงสิ่งประดิษฐ์ของข้อมูลเมตา
ประสานเสียง
ในการใช้งานจริงของ TFX คุณจะใช้ผู้ประสานงาน เช่น Apache Airflow, Kubeflow Pipelines หรือ Apache Beam เพื่อจัดการกราฟไปป์ไลน์ที่กำหนดไว้ล่วงหน้าของส่วนประกอบ TFX ในสมุดบันทึกแบบโต้ตอบ สมุดบันทึกคือตัวประสาน โดยเรียกใช้แต่ละองค์ประกอบ TFX ในขณะที่คุณดำเนินการเซลล์ของสมุดบันทึก
ข้อมูลเมตา
ในการใช้งานจริงของ TFX คุณจะเข้าถึงข้อมูลเมตาผ่าน ML Metadata (MLMD) API MLMD จัดเก็บคุณสมบัติข้อมูลเมตาในฐานข้อมูล เช่น MySQL หรือ SQLite และจัดเก็บเพย์โหลดข้อมูลเมตาในที่จัดเก็บถาวร เช่น บนระบบไฟล์ของคุณ ในสมุดบันทึกการโต้ตอบทั้งคุณสมบัติและ payloads ถูกเก็บไว้ในฐานข้อมูล SQLite ชั่วคราวใน /tmp ไดเรกทอรีบนโน้ตบุ๊ค Jupyter หรือเซิร์ฟเวอร์ Colab
ติดตั้ง
ขั้นแรก เราติดตั้งและนำเข้าแพ็คเกจที่จำเป็น ตั้งค่าเส้นทาง และดาวน์โหลดข้อมูล
อัพเกรด Pip
เพื่อหลีกเลี่ยงการอัพเกรด Pip ในระบบเมื่อรันในเครื่อง ให้ตรวจสอบว่าเรากำลังทำงานใน Colab แน่นอนว่าระบบในพื้นที่สามารถอัพเกรดแยกกันได้
try:
import colab
!pip install --upgrade pip
except:
pass
ติดตั้ง TFX
pip install -U tfx
คุณรีสตาร์ทรันไทม์หรือไม่
หากคุณกำลังใช้ Google Colab ในครั้งแรกที่คุณเรียกใช้เซลล์ด้านบน คุณต้องเริ่มรันไทม์ใหม่ (รันไทม์ > รีสตาร์ทรันไทม์ ...) นี่เป็นเพราะวิธีที่ Colab โหลดแพ็กเกจ
นำเข้าแพ็คเกจ
เรานำเข้าแพ็คเกจที่จำเป็น รวมถึงคลาสส่วนประกอบ TFX มาตรฐาน
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
มาตรวจสอบเวอร์ชั่นของห้องสมุดกัน
print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.7.0 TFX version: 1.5.0
ตั้งค่าเส้นทางไปป์ไลน์
# 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)
ดาวน์โหลดข้อมูลตัวอย่าง
เราดาวน์โหลดชุดข้อมูลตัวอย่างเพื่อใช้ในไปป์ไลน์ TFX ของเรา
ชุดข้อมูลที่เรากำลังใช้เป็น รถแท็กซี่การเดินทางชุดข้อมูลที่ ปล่อยออกมาจากเมืองชิคาโก คอลัมน์ในชุดข้อมูลนี้คือ:
| รถปิคอัพ_ชุมชน_พื้นที่ | ค่าโดยสาร | trip_start_month |
| trip_start_hour | trip_start_day | trip_start_timestamp |
| รถปิคอัพ_ละติจูด | รถกระบะ_ลองจิจูด | dropoff_latitude |
| dropoff_longitude | trip_miles | pickup_census_tract |
| dropoff_census_tract | ประเภทการชำระเงิน | บริษัท |
| trip_seconds | dropoff_community_area | เคล็ดลับ |
ด้วยชุดนี้เราจะสร้างรูปแบบที่คาดการณ์เป็น tips ของการเดินทาง
_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>)
ดูไฟล์ CSV อย่างรวดเร็ว
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
ข้อจำกัดความรับผิดชอบ: ไซต์นี้จัดเตรียมแอปพลิเคชันโดยใช้ข้อมูลที่ได้รับการแก้ไขเพื่อใช้จากแหล่งที่มาดั้งเดิม www.cityofchicago.org ซึ่งเป็นเว็บไซต์อย่างเป็นทางการของเมืองชิคาโก เมืองชิคาโกไม่ได้อ้างสิทธิ์ในเนื้อหา ความถูกต้อง ความตรงต่อเวลา หรือความสมบูรณ์ของข้อมูลใดๆ ที่ให้ไว้ในเว็บไซต์นี้ ข้อมูลที่ให้ไว้ในเว็บไซต์นี้อาจเปลี่ยนแปลงได้ตลอดเวลา เป็นที่เข้าใจกันว่าข้อมูลที่ให้ไว้ในไซต์นี้กำลังถูกใช้โดยความเสี่ยงของตัวเอง
สร้าง InteractiveContext
สุดท้าย เราสร้าง InteractiveContext ซึ่งจะทำให้เราสามารถเรียกใช้ส่วนประกอบ TFX แบบโต้ตอบได้ในสมุดบันทึกนี้
# 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.
เรียกใช้คอมโพเนนต์ TFX แบบโต้ตอบ
ในเซลล์ที่ตามมา เราสร้างองค์ประกอบ TFX ทีละรายการ เรียกใช้แต่ละองค์ประกอบ และแสดงภาพสิ่งประดิษฐ์เอาต์พุต
ตัวอย่างGen
ExampleGen ส่วนประกอบมักจะเป็นจุดเริ่มต้นของท่อ TFX มันจะ:
- แยกข้อมูลออกเป็นชุดการฝึกและการประเมิน (โดยค่าเริ่มต้น การฝึก 2/3 + การประเมิน 1/3)
- ข้อมูลแปลงลงใน
tf.Exampleรูปแบบ (เรียนรู้เพิ่มเติม ที่นี่ ) - คัดลอกข้อมูลลงใน
_tfx_rootไดเรกทอรีสำหรับส่วนประกอบอื่น ๆ ในการเข้าถึง
ExampleGen ใช้เวลาเป็น input เส้นทางไปยังแหล่งข้อมูลของคุณ ในกรณีของเรานี้เป็น _data_root เส้นทางที่ประกอบด้วย CSV ดาวน์โหลด
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
ขอตรวจสอบสิ่งประดิษฐ์การส่งออกของ ExampleGen ส่วนประกอบนี้สร้างสิ่งประดิษฐ์สองชิ้น ตัวอย่างการฝึกอบรม และตัวอย่างการประเมิน:
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
นอกจากนี้เรายังสามารถดูตัวอย่างการฝึกอบรมสามตัวอย่างแรก:
# 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
}
}
}
}
ตอนนี้ที่ ExampleGen ได้เสร็จสิ้นการบริโภคข้อมูลขั้นตอนต่อไปคือการวิเคราะห์ข้อมูล
สถิติGen
StatisticsGen สถิติคำนวณส่วนประกอบมากกว่าชุดของคุณสำหรับการวิเคราะห์ข้อมูลเช่นเดียวกับการใช้งานในส่วนปลายน้ำ มันใช้ TensorFlow การตรวจสอบข้อมูล ห้องสมุด
StatisticsGen ใช้เวลาเป็น input ชุดข้อมูลที่เราเพิ่งกินใช้ 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
หลังจาก StatisticsGen เสร็จสิ้นการทำงานเราสามารถเห็นภาพสถิติเอาท์พุต ลองเล่นกับพล็อตที่แตกต่างกัน!
context.show(statistics_gen.outputs['statistics'])
สคีมาGen
SchemaGen องค์ประกอบสร้างสคีมาขึ้นอยู่กับสถิติข้อมูลของคุณ (โครงสร้างกำหนดที่คาดว่าขอบเขตประเภทและคุณสมบัติของคุณสมบัติในชุดข้อมูลที่คุณ.) นอกจากนี้ยังใช้ TensorFlow การตรวจสอบข้อมูล ห้องสมุด
SchemaGen จะใช้เป็นข้อมูลสถิติที่เราสร้างขึ้นด้วย StatisticsGen มองที่แยกการฝึกอบรมโดยค่าเริ่มต้น
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
หลังจาก SchemaGen เสร็จสิ้นการทำงานเราสามารถเห็นภาพคีมาสร้างเป็นตาราง
context.show(schema_gen.outputs['schema'])
แต่ละฟีเจอร์ในชุดข้อมูลของคุณจะแสดงเป็นแถวในตารางสคีมา ข้างคุณสมบัติของมัน สคีมายังรวบรวมค่าทั้งหมดที่คุณลักษณะหมวดหมู่ใช้ ซึ่งแสดงเป็นโดเมน
ต้องการเรียนรู้เพิ่มเติมเกี่ยวกับสกีมาดู เอกสาร SchemaGen
ตัวอย่างValidator
ExampleValidator ส่วนประกอบตรวจหาความผิดปกติในข้อมูลของคุณขึ้นอยู่กับความคาดหวังที่กำหนดโดยสคีมา นอกจากนี้ยังใช้ TensorFlow การตรวจสอบข้อมูล ห้องสมุด
ExampleValidator จะใช้เป็นข้อมูลสถิติจาก StatisticsGen และคีมาจาก 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
หลังจาก ExampleValidator เสร็จสิ้นการทำงานเราสามารถเห็นภาพความผิดปกติเป็นตาราง
context.show(example_validator.outputs['anomalies'])
ในตารางความผิดปกติ เราจะเห็นว่าไม่มีความผิดปกติ นี่คือสิ่งที่เราคาดหวัง เนื่องจากเป็นชุดข้อมูลแรกที่เราได้วิเคราะห์และสคีมาได้รับการปรับแต่งให้เหมาะสม คุณควรตรวจสอบสคีมานี้ สิ่งที่ไม่คาดคิดหมายถึงความผิดปกติในข้อมูล เมื่อตรวจสอบแล้ว สามารถใช้สคีมาเพื่อป้องกันข้อมูลในอนาคต และความผิดปกติที่เกิดขึ้นที่นี่สามารถใช้เพื่อดีบักประสิทธิภาพของโมเดล ทำความเข้าใจว่าข้อมูลของคุณมีวิวัฒนาการอย่างไรเมื่อเวลาผ่านไป และระบุข้อผิดพลาดของข้อมูล
แปลง
Transform ดำเนินส่วนประกอบวิศวกรรมคุณลักษณะสำหรับทั้งการฝึกอบรมและการให้บริการ มันใช้ TensorFlow Transform ห้องสมุด
Transform จะใช้เวลาเป็น input ข้อมูลจาก ExampleGen คีจาก SchemaGen เช่นเดียวกับโมดูลที่มีผู้ใช้กำหนดเปลี่ยนรหัส
ลองมาดูตัวอย่างของการที่ผู้ใช้กำหนดแปลงโค้ดด้านล่าง (สำหรับการแนะนำให้ TensorFlow แปลง APIs, ดูกวดวิชา ) ขั้นแรก เรากำหนดค่าคงที่สองสามค่าสำหรับวิศวกรรมคุณลักษณะ:
_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
ต่อไปเราจะเขียน preprocessing_fn ที่ใช้เวลาในข้อมูลดิบเป็น input และผลตอบแทนจากคุณลักษณะที่เปลี่ยนรูปแบบของเราสามารถฝึกอบรมเมื่อ:
_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
ตอนนี้เราส่งผ่านรหัสคุณลักษณะวิศวกรรมนี้เพื่อ Transform องค์ประกอบและเรียกใช้ในการแปลงข้อมูลของคุณ
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
ขอตรวจสอบสิ่งประดิษฐ์การส่งออกของ Transform ส่วนประกอบนี้สร้างเอาต์พุตสองประเภท:
-
transform_graphเป็นกราฟที่สามารถดำเนินการการดำเนินงาน preprocessing (กราฟนี้จะรวมอยู่ในการให้บริการและการประเมินผลรูปแบบ) -
transformed_examplesหมายถึง preprocessed การฝึกอบรมและการประเมินผลข้อมูล
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: {}
)}
จะมองที่ transform_graph สิ่งประดิษฐ์ มันชี้ไปที่ไดเร็กทอรีที่มีสามไดเร็กทอรีย่อย
train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)
['transform_fn', 'transformed_metadata', 'metadata']
transformed_metadata ไดเรกทอรีย่อยมีคีมาของข้อมูล preprocessed ที่ transform_fn ไดเรกทอรีย่อยมีกราฟประมวลผลเบื้องต้นที่เกิดขึ้นจริง metadata ไดเรกทอรีย่อยมีคีมาของข้อมูลเดิม
เราสามารถดูตัวอย่างที่แปลงแล้วสามตัวอย่างแรกได้:
# 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
}
}
}
}
หลังจากที่ Transform องค์ประกอบได้เปลี่ยนข้อมูลของคุณเป็นคุณสมบัติและขั้นตอนต่อไปคือการฝึกอบรมแบบ
เทรนเนอร์
Trainer องค์ประกอบที่จะฝึกรูปแบบที่คุณกำหนดใน TensorFlow เริ่มต้นการสนับสนุนเทรนเนอร์ประมาณการ API ในการใช้ Keras API คุณต้องระบุ ทั่วไปเทรนเนอร์ โดยการติดตั้ง custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor) ใน contructor เทรนเนอร์
Trainer จะใช้เวลาเป็น input คีมาจาก SchemaGen ข้อมูลเปลี่ยนและกราฟจาก Transform , การฝึกอบรมพารามิเตอร์เช่นเดียวกับโมดูลที่มีผู้ใช้กำหนดรหัสรูปแบบ
ลองมาดูตัวอย่างของการที่ผู้ใช้กำหนดรหัสรุ่นด้านล่าง (สำหรับการแนะนำไปยัง TensorFlow Keras APIs การ ดูการกวดวิชา ):
_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
ตอนนี้เราผ่านในรหัสรุ่นนี้กับ Trainer องค์ประกอบและเรียกใช้ในการฝึกอบรมรุ่น
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
วิเคราะห์การฝึกอบรมด้วย TensorBoard
ลองดูสิ่งประดิษฐ์ของผู้ฝึกสอน มันชี้ไปที่ไดเร็กทอรีที่มีไดเร็กทอรีย่อยของโมเดล
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']
อีกทางเลือกหนึ่ง เราสามารถเชื่อมต่อ TensorBoard กับ Trainer เพื่อวิเคราะห์เส้นโค้งการฝึกของแบบจำลองของเรา
model_run_artifact_dir = trainer.outputs['model_run'].get()[0].uri
%load_ext tensorboard
%tensorboard --logdir {model_run_artifact_dir}
ผู้ประเมิน
Evaluator ตัวชี้วัดประสิทธิภาพคำนวณองค์ประกอบรูปแบบมากกว่าชุดการประเมินผล มันใช้ TensorFlow รุ่นวิเคราะห์ ห้องสมุด Evaluator ยังสามารถเลือกที่จะตรวจสอบว่ารูปแบบการฝึกอบรมใหม่จะดีกว่ารุ่นก่อนหน้านี้ สิ่งนี้มีประโยชน์ในการตั้งค่าไปป์ไลน์การผลิต ซึ่งคุณสามารถฝึกอบรมและตรวจสอบแบบจำลองได้โดยอัตโนมัติทุกวัน ในสมุดบันทึกนี้เราฝึกรูปแบบหนึ่งดังนั้นการ Evaluator โดยอัตโนมัติจะติดป้ายรูปแบบขณะที่ "ดี"
Evaluator จะใช้เป็น input ข้อมูลจาก ExampleGen รูปแบบการฝึกอบรมจาก Trainer และการกำหนดค่าหั่น การกำหนดค่าการแบ่งส่วนช่วยให้คุณสามารถแบ่งส่วนเมตริกของคุณตามค่าคุณลักษณะ (เช่น โมเดลของคุณทำงานอย่างไรในการเดินทางโดยรถแท็กซี่ที่เริ่มเวลา 8.00 น. เทียบกับ 20.00 น.) ดูตัวอย่างของการกำหนดค่านี้ด้านล่าง:
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'])
])
ต่อไปเราจะให้การกำหนดค่านี้เพื่อ Evaluator และเรียกใช้
# 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
ตอนนี้ขอตรวจสอบสิ่งประดิษฐ์การส่งออกของ 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: {}
)}
โดยใช้ evaluation การส่งออกเราสามารถแสดงให้เห็นถึงการสร้างภาพเริ่มต้นของตัวชี้วัดทั่วโลกในการประเมินผลทั้งชุด
context.show(evaluator.outputs['evaluation'])
หากต้องการดูการแสดงภาพสำหรับเมตริกการประเมินแบบแบ่งส่วน เราสามารถเรียกไลบรารี TensorFlow Model Analysis ได้โดยตรง
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',…
การสร้างภาพนี้แสดงเมตริกเดียวกัน แต่คำนวณมูลค่าที่คุณลักษณะของทุก trip_start_hour แทนในชุดการประเมินทั้งหมด
การวิเคราะห์แบบจำลอง TensorFlow รองรับการแสดงภาพอื่นๆ มากมาย เช่น ตัวบ่งชี้ความเป็นธรรม และการพล็อตอนุกรมเวลาของประสิทธิภาพของแบบจำลอง ต้องการเรียนรู้เพิ่มเติมโปรดดูที่ การกวดวิชา
เนื่องจากเราได้เพิ่มเกณฑ์ในการกำหนดค่าของเรา เอาต์พุตการตรวจสอบก็สามารถใช้ได้เช่นกัน precence ของ blessing สิ่งประดิษฐ์ที่แสดงให้เห็นว่ารูปแบบของเราผ่านการตรวจสอบ เนื่องจากเป็นการตรวจสอบความถูกต้องครั้งแรก ผู้สมัครจะได้รับพรโดยอัตโนมัติ
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
ตอนนี้ยังตรวจสอบความสำเร็จได้ด้วยการโหลดเรกคอร์ดผลการตรวจสอบ:
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
}
}
พุชเชอร์
Pusher ส่วนประกอบมักจะเป็นในตอนท้ายของท่อ TFX มันจะตรวจสอบไม่ว่าจะเป็นรูปแบบที่ได้ผ่านการตรวจสอบและถ้าเป็นเช่นนั้นการส่งออกรูปแบบการ _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
ขอตรวจสอบสิ่งประดิษฐ์การส่งออกของ 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: {}
)}
โดยเฉพาะอย่างยิ่ง Pusher จะส่งออกโมเดลของคุณในรูปแบบ SavedModel ซึ่งมีลักษณะดังนี้:
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>)
เราเสร็จสิ้นการทัวร์ชมส่วนประกอบ TFX ในตัวแล้ว!