एमएल समुदाय दिवस 9 नवंबर है! TensorFlow, JAX से नई जानकारी के लिए हमसे जुड़ें, और अधिक जानें

TFX केरस घटक ट्यूटोरियल

एक घटक-दर-घटक TensorFlow विस्तारित (TFX) का परिचय

यह Colab- आधारित ट्यूटोरियल TensorFlow Extended (TFX) के प्रत्येक अंतर्निर्मित घटक के माध्यम से अंतःक्रियात्मक रूप से चलेगा।

यह एंड-टू-एंड मशीन लर्निंग पाइपलाइन के हर चरण को कवर करता है, जिसमें डेटा अंतर्ग्रहण से लेकर मॉडल को पेश करने तक शामिल है।

जब आप कर लें, तो इस नोटबुक की सामग्री को स्वचालित रूप से TFX पाइपलाइन स्रोत कोड के रूप में निर्यात किया जा सकता है, जिसे आप Apache Airflow और Apache Beam के साथ व्यवस्थित कर सकते हैं।

पृष्ठभूमि

यह नोटबुक दर्शाता है कि Jupyter/Colab परिवेश में TFX का उपयोग कैसे किया जाता है। यहां, हम एक इंटरैक्टिव नोटबुक में शिकागो टैक्सी उदाहरण के माध्यम से चलते हैं।

एक इंटरेक्टिव नोटबुक में कार्य करना TFX पाइपलाइन की संरचना से परिचित होने का एक उपयोगी तरीका है। हल्के विकास के माहौल के रूप में अपनी खुद की पाइपलाइनों का विकास करते समय भी यह उपयोगी होता है, लेकिन आपको पता होना चाहिए कि इंटरैक्टिव नोटबुक को व्यवस्थित करने के तरीके और मेटाडेटा कलाकृतियों तक पहुंचने के तरीके में अंतर हैं।

वाद्य-स्थान

TFX के उत्पादन परिनियोजन में, आप TFX घटकों के पूर्व-निर्धारित पाइपलाइन ग्राफ़ को व्यवस्थित करने के लिए Apache Airflow, Kubeflow Pipelines, या Apache Beam जैसे ऑर्केस्ट्रेटर का उपयोग करेंगे। एक इंटरेक्टिव नोटबुक में, नोटबुक स्वयं ऑर्केस्ट्रेटर है, प्रत्येक TFX घटक को चला रहा है जब आप नोटबुक सेल निष्पादित करते हैं।

मेटाडाटा

टीएफएक्स के उत्पादन परिनियोजन में, आप एमएल मेटाडेटा (एमएलएमडी) एपीआई के माध्यम से मेटाडेटा तक पहुंचेंगे। MLMD मेटाडेटा गुणों को MySQL या SQLite जैसे डेटाबेस में संग्रहीत करता है, और मेटाडेटा पेलोड को एक सतत स्टोर में संग्रहीत करता है जैसे कि आपके फाइल सिस्टम पर। एक इंटरैक्टिव नोटबुक में, दोनों गुण और पेलोड में एक अल्पकालिक SQLite डेटाबेस में संग्रहीत हैं /tmp Jupyter नोटबुक या Colab सर्वर पर निर्देशिका।

सेट अप

सबसे पहले, हम आवश्यक पैकेज स्थापित और आयात करते हैं, पथ सेट करते हैं, और डेटा डाउनलोड करते हैं।

पिप अपग्रेड करें

स्थानीय रूप से चलते समय सिस्टम में पिप को अपग्रेड करने से बचने के लिए, यह सुनिश्चित करने के लिए जांचें कि हम कोलाब में चल रहे हैं। स्थानीय प्रणालियों को निश्चित रूप से अलग से अपग्रेड किया जा सकता है।

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

टीएफएक्स स्थापित करें

pip install -U tfx

क्या आपने रनटाइम को पुनरारंभ किया?

यदि आप Google Colab का उपयोग कर रहे हैं, जब आप पहली बार ऊपर सेल चलाते हैं, तो आपको रनटाइम को पुनरारंभ करना होगा (रनटाइम> रनटाइम पुनरारंभ करें ...)। ऐसा इसलिए है क्योंकि Colab संकुल को लोड करता है।

पैकेज आयात करें

हम मानक TFX घटक वर्गों सहित आवश्यक पैकेज आयात करते हैं।

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

आइए पुस्तकालय संस्करणों की जांच करें।

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

पाइपलाइन पथ सेट करें

# 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)

उदाहरण डेटा डाउनलोड करें

हम अपने TFX पाइपलाइन में उपयोग के लिए उदाहरण डेटासेट डाउनलोड करते हैं।

डाटासेट हम प्रयोग कर रहे है टैक्सी डाटासेट Trips शिकागो शहर के द्वारा जारी किया। इस डेटासेट में कॉलम हैं:

पिकअप_समुदाय_क्षेत्र किराया ट्रिप_स्टार्ट_माह
यात्रा_शुरू_घंटा यात्रा_शुरू_दिन ट्रिप_स्टार्ट_टाइमस्टैम्प
पिकअप_अक्षांश पिकअप_देशांतर ड्रॉपऑफ_अक्षांश
ड्रॉपऑफ_देशांतर यात्रा_मील पिकअप_सेंसस_ट्रैक्ट
ड्रॉपऑफ़_सेंसस_ट्रैक्ट भुगतान के प्रकार कंपनी
यात्रा_सेकंड ड्रॉपऑफ़_समुदाय_क्षेत्र टिप्स

इस डेटासेट के साथ, हम एक मॉडल है कि भविष्यवाणी का निर्माण करेगा tips एक यात्रा की।

_data_root = tempfile.mkdtemp(prefix='tfx-data')
DATA_PATH = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv'
_data_filepath = os.path.join(_data_root, "data.csv")
urllib.request.urlretrieve(DATA_PATH, _data_filepath)
('/tmp/tfx-data3oncdzkk/data.csv', <http.client.HTTPMessage at 0x7ffac02f5e90>)

सीएसवी फ़ाइल पर एक त्वरित नज़र डालें।

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

अस्वीकरण: यह साइट डेटा का उपयोग करके एप्लिकेशन प्रदान करती है जिसे इसके मूल स्रोत, www.cityofchicago.org, शिकागो शहर की आधिकारिक वेबसाइट से उपयोग के लिए संशोधित किया गया है। शिकागो शहर इस साइट पर उपलब्ध कराए गए किसी भी डेटा की सामग्री, सटीकता, समयबद्धता या पूर्णता के बारे में कोई दावा नहीं करता है। इस साइट पर उपलब्ध कराए गए डेटा किसी भी समय परिवर्तन के अधीन हैं। यह समझा जाता है कि इस साइट पर उपलब्ध कराए गए डेटा का उपयोग अपने जोखिम पर किया जा रहा है।

इंटरएक्टिव कॉन्टेक्स्ट बनाएं

अंत में, हम एक इंटरएक्टिव कॉन्टेक्स्ट बनाते हैं, जो हमें इस नोटबुक में टीएफएक्स घटकों को अंतःक्रियात्मक रूप से चलाने की अनुमति देगा।

# Here, we create an InteractiveContext using default parameters. This will
# use a temporary directory with an ephemeral ML Metadata database instance.
# To use your own pipeline root or database, the optional properties
# `pipeline_root` and `metadata_connection_config` may be passed to
# InteractiveContext. Calls to InteractiveContext are no-ops outside of the
# notebook.
context = InteractiveContext()
WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/metadata.sqlite.

अंतःक्रियात्मक रूप से TFX घटकों को चलाएं

अनुसरण करने वाली कोशिकाओं में, हम एक-एक करके TFX घटक बनाते हैं, उनमें से प्रत्येक को चलाते हैं, और उनके आउटपुट कलाकृतियों की कल्पना करते हैं।

उदाहरण Gen

ExampleGen घटक एक TFX पाइप लाइन के शुरू में आम तौर पर है। यह:

  1. डेटा को प्रशिक्षण और मूल्यांकन सेट में विभाजित करें (डिफ़ॉल्ट रूप से, 2/3 प्रशिक्षण + 1/3 eval)
  2. में तब्दील डेटा tf.Example प्रारूप (अधिक जानने के लिए यहाँ )
  3. में डेटा कॉपी _tfx_root का उपयोग करने के अन्य घटकों के लिए निर्देशिका

ExampleGen अपने डेटा स्रोत के लिए पथ इनपुट के रूप में लेता है। हमारे मामले में, यह है _data_root रास्ता है कि डाउनलोड की गई CSV में शामिल है।

example_gen = tfx.components.CsvExampleGen(input_base=_data_root)
context.run(example_gen)
INFO:absl:Running driver for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:Running executor for CsvExampleGen
INFO:absl:Generating examples.
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
INFO:absl:Processing input csv data /tmp/tfx-data3oncdzkk/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Running publisher for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized

चलो के उत्पादन में कलाकृतियों की जांच ExampleGen । यह घटक दो कलाकृतियों, प्रशिक्षण उदाहरणों और मूल्यांकन उदाहरणों का उत्पादन करता है:

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

हम पहले तीन प्रशिक्षण उदाहरणों पर भी एक नज़र डाल सकते हैं:

# 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
      }
    }
  }
}

अब जब कि ExampleGen डेटा अंतर्ग्रहण समाप्त हो गया है, अगले कदम के डेटा विश्लेषण है।

सांख्यिकीGen

StatisticsGen नीचे की ओर घटकों में डेटा विश्लेषण के लिए आपके डेटासेट, साथ ही के लिए उपयोग पर घटक computes आंकड़े। यह का उपयोग करता है TensorFlow डेटा मान्यता पुस्तकालय।

StatisticsGen डाटासेट हम सिर्फ का उपयोग कर किया जाता इनपुट के रूप में लेता है ExampleGen

statistics_gen = tfx.components.StatisticsGen(
    examples=example_gen.outputs['examples'])
context.run(statistics_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for StatisticsGen
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/StatisticsGen/statistics/2/Split-eval.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Running publisher for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized

बाद StatisticsGen चल समाप्त कर ले, हम outputted आंकड़े कल्पना कर सकते हैं। विभिन्न भूखंडों के साथ खेलने का प्रयास करें!

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

स्कीमाजेन

SchemaGen घटक अपने डेटा आंकड़ों के आधार पर एक स्कीमा उत्पन्न करता है। (स्कीमा की उम्मीद सीमा, प्रकार, और अपने डेटासेट में सुविधाओं के गुणों को परिभाषित करता।) यह भी उपयोग करता TensorFlow डेटा मान्यता पुस्तकालय।

SchemaGen आँकड़े इनपुट के रूप में ले जाएगा कि हम साथ उत्पन्न StatisticsGen , डिफ़ॉल्ट रूप से प्रशिक्षण विभाजन को देखकर।

schema_gen = tfx.components.SchemaGen(
    statistics=statistics_gen.outputs['statistics'],
    infer_feature_shape=False)
context.run(schema_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for SchemaGen
INFO:absl:MetadataStore with DB connection initialized
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0930 02:22:48.211663 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for SchemaGen
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/SchemaGen/schema/3/schema.pbtxt.
INFO:absl:Running publisher for SchemaGen
INFO:absl:MetadataStore with DB connection initialized

बाद SchemaGen चल खत्म, हम एक तालिका के रूप में उत्पन्न स्कीमा कल्पना कर सकते हैं।

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

आपके डेटासेट की प्रत्येक सुविधा स्कीमा तालिका में उसके गुणों के साथ एक पंक्ति के रूप में दिखाई देती है। स्कीमा उन सभी मानों को भी कैप्चर करती है जो एक श्रेणीबद्ध विशेषता अपने डोमेन के रूप में निरूपित करती है।

स्कीमा के बारे में अधिक जानने के लिए, SchemaGen प्रलेखन

उदाहरण सत्यापनकर्ता

ExampleValidator घटक उम्मीदों स्कीमा द्वारा परिभाषित के आधार पर, आपके डेटा में विसंगतियों का पता लगाता है। यह भी उपयोग करता TensorFlow डेटा मान्यता पुस्तकालय।

ExampleValidator से इनपुट के रूप में आंकड़े ले जाएगा StatisticsGen , और से स्कीमा SchemaGen

example_validator = tfx.components.ExampleValidator(
    statistics=statistics_gen.outputs['statistics'],
    schema=schema_gen.outputs['schema'])
context.run(example_validator)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for ExampleValidator
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Validation complete for split train. Anomalies written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/ExampleValidator/anomalies/4/Split-train.
INFO:absl:Validating schema against the computed statistics for split eval.
INFO:absl:Validation complete for split eval. Anomalies written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/ExampleValidator/anomalies/4/Split-eval.
INFO:absl:Running publisher for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized

बाद ExampleValidator चल खत्म, हम एक तालिका के रूप में विसंगतियों कल्पना कर सकते हैं।

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

विसंगतियों की तालिका में, हम देख सकते हैं कि कोई विसंगतियाँ नहीं हैं। हम यही उम्मीद करेंगे, क्योंकि यह पहला डेटासेट है जिसका हमने विश्लेषण किया है और स्कीमा इसके अनुरूप है। आपको इस स्कीमा की समीक्षा करनी चाहिए -- कुछ भी अनपेक्षित का मतलब डेटा में एक विसंगति है। एक बार समीक्षा करने के बाद, स्कीमा का उपयोग भविष्य के डेटा की सुरक्षा के लिए किया जा सकता है, और यहां उत्पन्न विसंगतियों का उपयोग मॉडल प्रदर्शन को डीबग करने, यह समझने के लिए किया जा सकता है कि आपका डेटा समय के साथ कैसे विकसित होता है, और डेटा त्रुटियों की पहचान करता है।

परिवर्तन

Transform दोनों प्रशिक्षण और की सेवा के लिए घटक प्रदर्शन सुविधा इंजीनियरिंग। यह का उपयोग करता है TensorFlow रूपांतरण पुस्तकालय।

Transform इनपुट के रूप में से डेटा ले जाएगा ExampleGen , से स्कीमा SchemaGen है, साथ ही एक मॉड्यूल उपयोगकर्ता परिभाषित कोड रूपांतरण होता है।

चलो का एक उदाहरण देख उपयोगकर्ता परिभाषित नीचे कोड रूपांतरण (TensorFlow के परिचय के लिए एपीआई रूपांतरण, ट्यूटोरियल देख )। सबसे पहले, हम फीचर इंजीनियरिंग के लिए कुछ स्थिरांक परिभाषित करते हैं:

_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

इसके बाद, हम एक लिखने preprocessing_fn कि इनपुट के रूप में कच्चे डेटा में लेता है, और रिटर्न तब्दील विशेषताएं है कि हमारे मॉडल पर प्रशिक्षित कर सकते हैं:

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

import tensorflow as tf
import tensorflow_transform as tft

import taxi_constants

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


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

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

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

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

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

  return outputs


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

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

अब, हम करने के लिए इस सुविधा इंजीनियरिंग कोड में पास Transform घटक और अपने डेटा को बदलने के लिए इसे चलाने के।

transform = tfx.components.Transform(
    examples=example_gen.outputs['examples'],
    schema=schema_gen.outputs['schema'],
    module_file=os.path.abspath(_taxi_transform_module_file))
context.run(transform)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py' (including modules: ['taxi_constants', 'taxi_transform']).
INFO:absl:User module package has hash fingerprint version 8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp25n57aeg/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpv2ojslny', '--dist-dir', '/tmp/tmpduqs2o96']
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl'; target user module is 'taxi_transform'.
INFO:absl:Full user module path is 'taxi_transform@/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl'
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
I0930 02:22:48.774238 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Transform
I0930 02:22:48.777988 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp6f1bcbm7', '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_constants.py -> build/lib
copying taxi_transform.py -> build/lib
installing to /tmp/tmpv2ojslny
running install
running install_lib
copying build/lib/taxi_constants.py -> /tmp/tmpv2ojslny
copying build/lib/taxi_transform.py -> /tmp/tmpv2ojslny
running install_egg_info
running egg_info
creating tfx_user_code_Transform.egg-info
writing tfx_user_code_Transform.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
Copying tfx_user_code_Transform.egg-info to /tmp/tmpv2ojslny/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3.7.egg-info
running install_scripts
creating /tmp/tmpv2ojslny/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.dist-info/WHEEL
creating '/tmp/tmpduqs2o96/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl' and adding '/tmp/tmpv2ojslny' to it
adding 'taxi_constants.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb.dist-info/RECORD'
removing /tmp/tmpv2ojslny
Processing /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl'.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp55e42eqi', '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb
Processing /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl'.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb
WARNING:tensorflow:From /home/kbuilder/.local/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:261: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Installing '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp03chpfbr', '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl']
Processing /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb-py3-none-any.whl'.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+8b9ed99f61c7fd5fe1360ed191b2bbcb433767cc03c399a85cc941e091e40bdb
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature company has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fare has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature payment_type has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature tips has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_miles has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_seconds has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_day has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_month has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to VarLenSparseTensor.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
2021-09-30 02:23:01.717912: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/transform_graph/5/.temp_path/tftransform_tmp/f846c938978244c591f21e5f90b088aa/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/transform_graph/5/.temp_path/tftransform_tmp/31cf107e852b44ddba4df6155ba9b0bc/assets
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized

के के उत्पादन में कलाकृतियों जांच Transform । यह घटक दो प्रकार के आउटपुट उत्पन्न करता है:

  • transform_graph ग्राफ कि preprocessing कार्रवाई कर सकते हैं (इस ग्राफ सेवारत और मूल्यांकन मॉडल में शामिल किया जाएगा) है।
  • transformed_examples preprocessed प्रशिक्षण और मूल्यांकन डेटा प्रतिनिधित्व करता है।
transform.outputs
{'transform_graph': Channel(
     type_name: TransformGraph
     artifacts: [Artifact(artifact: id: 5
 type_id: 22
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/transform_graph/5"
 custom_properties {
   key: "name"
   value {
     string_value: "transform_graph"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 22
 name: "TransformGraph"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'transformed_examples': Channel(
     type_name: Examples
     artifacts: [Artifact(artifact: id: 6
 type_id: 14
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/transformed_examples/5"
 properties {
   key: "split_names"
   value {
     string_value: "[\"train\", \"eval\"]"
   }
 }
 custom_properties {
   key: "name"
   value {
     string_value: "transformed_examples"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 14
 name: "Examples"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 properties {
   key: "version"
   value: INT
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'updated_analyzer_cache': Channel(
     type_name: TransformCache
     artifacts: [Artifact(artifact: id: 7
 type_id: 23
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/updated_analyzer_cache/5"
 custom_properties {
   key: "name"
   value {
     string_value: "updated_analyzer_cache"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 23
 name: "TransformCache"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'pre_transform_schema': Channel(
     type_name: Schema
     artifacts: [Artifact(artifact: id: 8
 type_id: 18
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/pre_transform_schema/5"
 custom_properties {
   key: "name"
   value {
     string_value: "pre_transform_schema"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 18
 name: "Schema"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'pre_transform_stats': Channel(
     type_name: ExampleStatistics
     artifacts: [Artifact(artifact: id: 9
 type_id: 16
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/pre_transform_stats/5"
 custom_properties {
   key: "name"
   value {
     string_value: "pre_transform_stats"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 16
 name: "ExampleStatistics"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_schema': Channel(
     type_name: Schema
     artifacts: [Artifact(artifact: id: 10
 type_id: 18
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/post_transform_schema/5"
 custom_properties {
   key: "name"
   value {
     string_value: "post_transform_schema"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 18
 name: "Schema"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_stats': Channel(
     type_name: ExampleStatistics
     artifacts: [Artifact(artifact: id: 11
 type_id: 16
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/post_transform_stats/5"
 custom_properties {
   key: "name"
   value {
     string_value: "post_transform_stats"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 16
 name: "ExampleStatistics"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'post_transform_anomalies': Channel(
     type_name: ExampleAnomalies
     artifacts: [Artifact(artifact: id: 12
 type_id: 20
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Transform/post_transform_anomalies/5"
 custom_properties {
   key: "name"
   value {
     string_value: "post_transform_anomalies"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Transform"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 20
 name: "ExampleAnomalies"
 properties {
   key: "span"
   value: INT
 }
 properties {
   key: "split_names"
   value: STRING
 }
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

पर पर नज़र transform_graph विरूपण साक्ष्य। यह तीन उपनिर्देशिकाओं वाली निर्देशिका की ओर इशारा करता है।

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

transformed_metadata उपनिर्देशिका preprocessed डेटा की स्कीमा में शामिल है। transform_fn उपनिर्देशिका वास्तविक preprocessing ग्राफ में शामिल है। metadata उपनिर्देशिका मूल डेटा का स्कीमा में शामिल है।

हम पहले तीन रूपांतरित उदाहरणों पर भी एक नज़र डाल सकते हैं:

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

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

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

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

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

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

बाद Transform घटक सुविधाओं में अपने डेटा को बदल दिया है, और अगले कदम के लिए एक मॉडल को प्रशिक्षित करने के लिए है।

ट्रेनर

Trainer घटक एक मॉडल है कि आप TensorFlow में परिभाषित प्रशिक्षित करेगा। Default ट्रेनर समर्थन अनुमानक एपीआई, Keras एपीआई का उपयोग करने के लिए, आप निर्दिष्ट करने की आवश्यकता जेनेरिक ट्रेनर सेटअप द्वारा custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor) ट्रेनर के contructor में।

Trainer से स्कीमा इनपुट के रूप में लेता है SchemaGen , से बदल डेटा और ग्राफ Transform , मानकों, साथ ही एक मॉड्यूल है कि उपयोगकर्ता परिभाषित मॉडल कोड शामिल प्रशिक्षण।

चलो नीचे (TensorFlow Keras एपीआई के परिचय के लिए, उपयोगकर्ता परिभाषित मॉडल कोड का एक उदाहरण देख ट्यूटोरियल देख ):

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

from typing import List, Text

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

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

import taxi_constants

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


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

  model.tft_layer = tf_transform_output.transform_features_layer()

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

  return serve_tf_examples_fn


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

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

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


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

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

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

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


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

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

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

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

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

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

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


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

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

  tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)

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

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

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

  signatures = {
      'serving_default':
          _get_serve_tf_examples_fn(model,
                                    tf_transform_output).get_concrete_function(
                                        tf.TensorSpec(
                                            shape=[None],
                                            dtype=tf.string,
                                            name='examples')),
  }
  model.save(fn_args.serving_model_dir, save_format='tf', signatures=signatures)
Writing taxi_trainer.py

अब, हम करने के लिए इस मॉडल कोड में पास Trainer घटक और यह मॉडल प्रशिक्षित करने के लिए चलाते हैं।

trainer = tfx.components.Trainer(
    module_file=os.path.abspath(_taxi_trainer_module_file),
    examples=transform.outputs['transformed_examples'],
    transform_graph=transform.outputs['transform_graph'],
    schema=schema_gen.outputs['schema'],
    train_args=tfx.proto.TrainArgs(num_steps=10000),
    eval_args=tfx.proto.EvalArgs(num_steps=5000))
context.run(trainer)
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py' (including modules: ['taxi_constants', 'taxi_transform', 'taxi_trainer']).
INFO:absl:User module package has hash fingerprint version 60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpg7itmljo/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpwli3zykq', '--dist-dir', '/tmp/tmp70wvofh8']
INFO:absl:Successfully built user code wheel distribution at '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl'; target user module is 'taxi_trainer'.
INFO:absl:Full user module path is 'taxi_trainer@/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl'
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
I0930 02:23:13.167377 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Trainer
I0930 02:23:13.170449 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
INFO:absl:udf_utils.get_fn {'train_args': '{\n  "num_steps": 10000\n}', 'eval_args': '{\n  "num_steps": 5000\n}', 'module_file': None, 'run_fn': None, 'trainer_fn': None, 'custom_config': 'null', 'module_path': 'taxi_trainer@/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl'} 'run_fn'
INFO:absl:Installing '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpc7e0fakf', '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_constants.py -> build/lib
copying taxi_transform.py -> build/lib
copying taxi_trainer.py -> build/lib
installing to /tmp/tmpwli3zykq
running install
running install_lib
copying build/lib/taxi_constants.py -> /tmp/tmpwli3zykq
copying build/lib/taxi_transform.py -> /tmp/tmpwli3zykq
copying build/lib/taxi_trainer.py -> /tmp/tmpwli3zykq
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmp/tmpwli3zykq/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3.7.egg-info
running install_scripts
creating /tmp/tmpwli3zykq/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.dist-info/WHEEL
creating '/tmp/tmp70wvofh8/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl' and adding '/tmp/tmpwli3zykq' to it
adding 'taxi_constants.py'
adding 'taxi_trainer.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642.dist-info/RECORD'
removing /tmp/tmpwli3zykq
Processing /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl
INFO:absl:Successfully installed '/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/_wheels/tfx_user_code_Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+60db236063636ded20f7a151f21b12e9e9a6a73f66f88475ce105d630e827642
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  "The `lr` argument is deprecated, use `learning_rate` instead.")
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type)                    Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl:company (InputLayer)            [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_census_tract (InputLaye [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_community_area (InputLa [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_latitude (InputLayer)   [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dropoff_longitude (InputLayer)  [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:fare (InputLayer)               [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:payment_type (InputLayer)       [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_census_tract (InputLayer [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_community_area (InputLay [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_latitude (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:pickup_longitude (InputLayer)   [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_miles (InputLayer)         [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_seconds (InputLayer)       [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_day (InputLayer)     [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_hour (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:trip_start_month (InputLayer)   [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features (DenseFeatures)  (None, 3)            0           company[0][0]                    
INFO:absl:                                                                 dropoff_census_tract[0][0]       
INFO:absl:                                                                 dropoff_community_area[0][0]     
INFO:absl:                                                                 dropoff_latitude[0][0]           
INFO:absl:                                                                 dropoff_longitude[0][0]          
INFO:absl:                                                                 fare[0][0]                       
INFO:absl:                                                                 payment_type[0][0]               
INFO:absl:                                                                 pickup_census_tract[0][0]        
INFO:absl:                                                                 pickup_community_area[0][0]      
INFO:absl:                                                                 pickup_latitude[0][0]            
INFO:absl:                                                                 pickup_longitude[0][0]           
INFO:absl:                                                                 trip_miles[0][0]                 
INFO:absl:                                                                 trip_seconds[0][0]               
INFO:absl:                                                                 trip_start_day[0][0]             
INFO:absl:                                                                 trip_start_hour[0][0]            
INFO:absl:                                                                 trip_start_month[0][0]           
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 100)          400         dense_features[0][0]             
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 70)           7070        dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 48)           3408        dense_1[0][0]                    
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_3 (Dense)                 (None, 34)           1666        dense_2[0][0]                    
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features_1 (DenseFeatures (None, 2127)         0           company[0][0]                    
INFO:absl:                                                                 dropoff_census_tract[0][0]       
INFO:absl:                                                                 dropoff_community_area[0][0]     
INFO:absl:                                                                 dropoff_latitude[0][0]           
INFO:absl:                                                                 dropoff_longitude[0][0]          
INFO:absl:                                                                 fare[0][0]                       
INFO:absl:                                                                 payment_type[0][0]               
INFO:absl:                                                                 pickup_census_tract[0][0]        
INFO:absl:                                                                 pickup_community_area[0][0]      
INFO:absl:                                                                 pickup_latitude[0][0]            
INFO:absl:                                                                 pickup_longitude[0][0]           
INFO:absl:                                                                 trip_miles[0][0]                 
INFO:absl:                                                                 trip_seconds[0][0]               
INFO:absl:                                                                 trip_start_day[0][0]             
INFO:absl:                                                                 trip_start_hour[0][0]            
INFO:absl:                                                                 trip_start_month[0][0]           
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate)       (None, 2161)         0           dense_3[0][0]                    
INFO:absl:                                                                 dense_features_1[0][0]           
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_4 (Dense)                 (None, 1)            2162        concatenate[0][0]                
INFO:absl:==================================================================================================
INFO:absl:Total params: 14,706
INFO:absl:Trainable params: 14,706
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
10000/10000 [==============================] - 75s 7ms/step - loss: 0.2374 - binary_accuracy: 0.8608 - val_loss: 0.2225 - val_binary_accuracy: 0.8759
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Trainer/model/6/Format-Serving/assets
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Trainer/model/6/Format-Serving. ModelRun written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Trainer/model_run/6
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized

TensorBoard के साथ प्रशिक्षण का विश्लेषण करें

ट्रेनर आर्टिफैक्ट पर एक नज़र डालें। यह मॉडल उपनिर्देशिका वाली निर्देशिका को इंगित करता है।

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']

वैकल्पिक रूप से, हम अपने मॉडल के प्रशिक्षण वक्रों का विश्लेषण करने के लिए TensorBoard को ट्रेनर से जोड़ सकते हैं।

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

%load_ext tensorboard
%tensorboard --logdir {model_run_artifact_dir}

मूल्यांकनकर्ता

Evaluator घटक मूल्यांकन सेट पर मॉडल प्रदर्शन मेट्रिक्स गणना करता है। यह का उपयोग करता है TensorFlow मॉडल विश्लेषण पुस्तकालय। Evaluator भी वैकल्पिक मान्य कर सकते हैं कि एक नव प्रशिक्षित मॉडल पिछले मॉडल की तुलना में बेहतर है। यह एक उत्पादन पाइपलाइन सेटिंग में उपयोगी है जहां आप हर दिन एक मॉडल को स्वचालित रूप से प्रशिक्षित और मान्य कर सकते हैं। इस नोटबुक में, हम केवल एक मॉडल को प्रशिक्षित, इसलिए Evaluator अपने आप ही "अच्छा" मॉडल लेबल होगा।

Evaluator से डेटा इनपुट के रूप में ले जाएगा ExampleGen , से प्रशिक्षित मॉडल Trainer , और टुकड़ा करने की क्रिया विन्यास। स्लाइसिंग कॉन्फ़िगरेशन आपको फीचर मानों पर अपने मीट्रिक को स्लाइस करने की अनुमति देता है (उदाहरण के लिए आपका मॉडल सुबह 8 बजे से शाम 8 बजे शुरू होने वाली टैक्सी यात्राओं पर कैसा प्रदर्शन करता है?) नीचे इस कॉन्फ़िगरेशन का एक उदाहरण देखें:

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

इसके बाद, हम करने के लिए इस विन्यास दे Evaluator और इसे चलाने के।

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

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

evaluator = tfx.components.Evaluator(
    examples=example_gen.outputs['examples'],
    model=trainer.outputs['model'],
    baseline_model=model_resolver.outputs['model'],
    eval_config=eval_config)
context.run(evaluator)
INFO:absl:Running driver for latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running publisher for latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running driver for Evaluator
INFO:absl:MetadataStore with DB connection initialized
I0930 02:24:36.166139 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I0930 02:24:36.169533 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Evaluator
INFO:absl:Nonempty beam arg extra_packages already includes dependency
INFO:absl:udf_utils.get_fn {'eval_config': '{\n  "metrics_specs": [\n    {\n      "metrics": [\n        {\n          "class_name": "ExampleCount"\n        },\n        {\n          "class_name": "BinaryAccuracy",\n          "threshold": {\n            "change_threshold": {\n              "absolute": -1e-10,\n              "direction": "HIGHER_IS_BETTER"\n            },\n            "value_threshold": {\n              "lower_bound": 0.5\n            }\n          }\n        }\n      ]\n    }\n  ],\n  "model_specs": [\n    {\n      "label_key": "tips",\n      "signature_name": "serving_default"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model'
ERROR:absl:There are change thresholds, but the baseline is missing. This is allowed only when rubber stamping (first run).
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "serving_default"
  label_key: "tips"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
    threshold {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}

INFO:absl:Using /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Trainer/model/6/Format-Serving as  model.
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ffa76ab2a90> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff9684cfa90>).
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:udf_utils.get_fn {'eval_config': '{\n  "metrics_specs": [\n    {\n      "metrics": [\n        {\n          "class_name": "ExampleCount"\n        },\n        {\n          "class_name": "BinaryAccuracy",\n          "threshold": {\n            "change_threshold": {\n              "absolute": -1e-10,\n              "direction": "HIGHER_IS_BETTER"\n            },\n            "value_threshold": {\n              "lower_bound": 0.5\n            }\n          }\n        }\n      ]\n    }\n  ],\n  "model_specs": [\n    {\n      "label_key": "tips",\n      "signature_name": "serving_default"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_extractors'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "serving_default"
  label_key: "tips"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
    threshold {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
  model_names: ""
}

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

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

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff960527ad0> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff960574a90>).
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
Exception ignored in: <function CapturableResource.__del__ at 0x7ffa2948f320>
Traceback (most recent call last):
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7ffa2948f320>
Traceback (most recent call last):
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7ffa2948f320>
Traceback (most recent call last):
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7ffa2948f320>
Traceback (most recent call last):
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/kbuilder/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff8fe92f910> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff8fe972dd0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff8fc09a150> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff8fc086350>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff4fd133610> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff4fd0d9a50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff9d0263a50> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ffa761492d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff9d04b4b90> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff960642390>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<tensorflow.python.keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7ff4fc86e990> and <tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7ff4fc6f4650>).
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Evaluator/evaluation/8.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /home/kbuilder/.local/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:113: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`
INFO:absl:Blessing result True written to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Evaluator/blessing/8.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized

अब हम के उत्पादन में कलाकृतियों की जांच करते हैं Evaluator

evaluator.outputs
{'evaluation': Channel(
     type_name: ModelEvaluation
     artifacts: [Artifact(artifact: id: 15
 type_id: 29
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Evaluator/evaluation/8"
 custom_properties {
   key: "name"
   value {
     string_value: "evaluation"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Evaluator"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 29
 name: "ModelEvaluation"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'blessing': Channel(
     type_name: ModelBlessing
     artifacts: [Artifact(artifact: id: 16
 type_id: 30
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Evaluator/blessing/8"
 custom_properties {
   key: "blessed"
   value {
     int_value: 1
   }
 }
 custom_properties {
   key: "current_model"
   value {
     string_value: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Trainer/model/6"
   }
 }
 custom_properties {
   key: "current_model_id"
   value {
     int_value: 13
   }
 }
 custom_properties {
   key: "name"
   value {
     string_value: "blessing"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Evaluator"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 30
 name: "ModelBlessing"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

का उपयोग करते हुए evaluation उत्पादन हम पूरी मूल्यांकन सेट पर वैश्विक मैट्रिक्स की डिफ़ॉल्ट दृश्य दिखा सकते हैं।

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

कटा हुआ मूल्यांकन मैट्रिक्स के विज़ुअलाइज़ेशन को देखने के लिए, हम सीधे TensorFlow मॉडल विश्लेषण लाइब्रेरी को कॉल कर सकते हैं।

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',…

यह दृश्य एक ही मीट्रिक दिखाती है, लेकिन की हर सुविधा मूल्य पर गणना की trip_start_hour के बजाय पूरे मूल्यांकन सेट पर।

TensorFlow मॉडल विश्लेषण कई अन्य विज़ुअलाइज़ेशन का समर्थन करता है, जैसे कि निष्पक्षता संकेतक और मॉडल प्रदर्शन की एक समय श्रृंखला की साजिश। अधिक जानने के लिए, ट्यूटोरियल

चूंकि हमने अपने कॉन्फिगर में थ्रेसहोल्ड जोड़े हैं, इसलिए सत्यापन आउटपुट भी उपलब्ध है। एक की precence blessing विरूपण साक्ष्य इंगित करता है कि हमारे मॉडल सत्यापन पारित कर दिया। चूंकि यह पहला सत्यापन किया जा रहा है, इसलिए उम्मीदवार स्वचालित रूप से धन्य हो जाता है।

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

अब सत्यापन परिणाम रिकॉर्ड लोड करके भी सफलता की पुष्टि कर सकते हैं:

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
  }
}

ढकेलनेवाला

Pusher घटक एक TFX पाइप लाइन के अंत में आम तौर पर है। यह जाँच करता है एक मॉडल सत्यापन पारित कर दिया है कि क्या है, और यदि हां, तो निर्यात के लिए मॉडल _serving_model_dir

pusher = tfx.components.Pusher(
    model=trainer.outputs['model'],
    model_blessing=evaluator.outputs['blessing'],
    push_destination=tfx.proto.PushDestination(
        filesystem=tfx.proto.PushDestination.Filesystem(
            base_directory=_serving_model_dir)))
context.run(pusher)
INFO:absl:Running driver for Pusher
INFO:absl:MetadataStore with DB connection initialized
I0930 02:24:56.889948 23719 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Running executor for Pusher
INFO:absl:Model version: 1632968696
INFO:absl:Model written to serving path /tmp/tmpi_ti963w/serving_model/taxi_simple/1632968696.
INFO:absl:Model pushed to /tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Pusher/pushed_model/9.
INFO:absl:Running publisher for Pusher
INFO:absl:MetadataStore with DB connection initialized

चलो के उत्पादन में कलाकृतियों की जांच Pusher

pusher.outputs
{'pushed_model': Channel(
     type_name: PushedModel
     artifacts: [Artifact(artifact: id: 17
 type_id: 32
 uri: "/tmp/tfx-interactive-2021-09-30T02_22_38.015128-a3hzxsja/Pusher/pushed_model/9"
 custom_properties {
   key: "name"
   value {
     string_value: "pushed_model"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Pusher"
   }
 }
 custom_properties {
   key: "pushed"
   value {
     int_value: 1
   }
 }
 custom_properties {
   key: "pushed_destination"
   value {
     string_value: "/tmp/tmpi_ti963w/serving_model/taxi_simple/1632968696"
   }
 }
 custom_properties {
   key: "pushed_version"
   value {
     string_value: "1632968696"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.2.0"
   }
 }
 state: LIVE
 , artifact_type: id: 32
 name: "PushedModel"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

विशेष रूप से, पुशर आपके मॉडल को सहेजे गए मॉडल प्रारूप में निर्यात करेगा, जो इस तरह दिखता है:

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

for item in model.signatures.items():
  pp.pprint(item)
('serving_default',
 <ConcreteFunction signature_wrapper(*, examples) at 0x7FF4F5BBEB50>)

हमने अंतर्निर्मित TFX घटकों का अपना दौरा पूरा कर लिया है!