Demo Toolkit Kartu Model MLMD

Tetap teratur dengan koleksi Simpan dan kategorikan konten berdasarkan preferensi Anda.

Lihat di TensorFlow.org Jalankan di Google Colab Lihat di GitHub Unduh buku catatan

Latar belakang

Notebook ini menunjukkan cara membuat kartu model menggunakan Model Card Toolkit dengan pipeline MLMD dan TFX di lingkungan Jupyter/Colab. Anda dapat mempelajari lebih lanjut tentang kartu model pada https://modelcards.withgoogle.com/about

Mempersiapkan

Pertama-tama kita perlu a) menginstal dan mengimpor paket yang diperlukan, dan b) mengunduh data.

Tingkatkan ke Pip 20.2 dan Instal TFX

pip install -q --upgrade pip==20.2
pip install -q "tfx==0.26.0"
pip install -q model-card-toolkit

Apakah Anda me-restart runtime?

Jika Anda menggunakan Google Colab, pertama kali menjalankan sel di atas, Anda harus memulai ulang runtime (Runtime > Restart runtime ...). Ini karena cara Colab memuat paket.

paket impor

Kami mengimpor paket yang diperlukan, termasuk kelas komponen TFX standar dan memeriksa versi perpustakaan.

import os
import pprint
import tempfile
import urllib

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

import tfx
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.components import ResolverNode
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.components import Transform
from tfx.components.base import executor_spec
from tfx.components.trainer.executor import GenericExecutor
from tfx.dsl.experimental import latest_blessed_model_resolver
from tfx.orchestration import metadata
from tfx.orchestration import pipeline
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.proto import pusher_pb2
from tfx.proto import trainer_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.utils.dsl_utils import external_input

import ml_metadata as mlmd
WARNING:absl:RuntimeParameter is only supported on Cloud-based DAG runner currently.
print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.version.__version__))
print('MLMD version: {}'.format(mlmd.__version__))
TensorFlow version: 2.3.2
TFX version: 0.26.0
MLMD version: 0.26.0

Siapkan jalur pipa

# This is the root directory for your TFX pip package installation.
_tfx_root = tfx.__path__

# Set up logging.
absl.logging.set_verbosity(absl.logging.INFO)

Unduh contoh data

Kami mengunduh contoh kumpulan data untuk digunakan dalam saluran TFX kami.

DATA_PATH = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/' \
   'adult.data'
_data_root = tempfile.mkdtemp(prefix='tfx-data')
_data_filepath = os.path.join(_data_root, "data.csv")
urllib.request.urlretrieve(DATA_PATH, _data_filepath)

columns = [
  "Age", "Workclass", "fnlwgt", "Education", "Education-Num", "Marital-Status",
  "Occupation", "Relationship", "Race", "Sex", "Capital-Gain", "Capital-Loss",
  "Hours-per-week", "Country", "Over-50K"]

with open(_data_filepath, 'r') as f:
  content = f.read()
  content = content.replace(", <=50K", ', 0').replace(", >50K", ', 1')

with open(_data_filepath, 'w') as f:
  f.write(','.join(columns) + '\n' + content)

Lihat sekilas file CSV.

head {_data_filepath}
Age,Workclass,fnlwgt,Education,Education-Num,Marital-Status,Occupation,Relationship,Race,Sex,Capital-Gain,Capital-Loss,Hours-per-week,Country,Over-50K
39, State-gov, 77516, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 2174, 0, 40, United-States, 0
50, Self-emp-not-inc, 83311, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 13, United-States, 0
38, Private, 215646, HS-grad, 9, Divorced, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, 0
53, Private, 234721, 11th, 7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, 0
28, Private, 338409, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, Black, Female, 0, 0, 40, Cuba, 0
37, Private, 284582, Masters, 14, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, 0
49, Private, 160187, 9th, 5, Married-spouse-absent, Other-service, Not-in-family, Black, Female, 0, 0, 16, Jamaica, 0
52, Self-emp-not-inc, 209642, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, 1
31, Private, 45781, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 14084, 0, 50, United-States, 1

Buat Konteks Interaktif

Terakhir, kami membuat InteractiveContext, yang memungkinkan kami menjalankan komponen TFX secara interaktif di notebook ini.

# 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-2021-02-05T22_01_17.129037-teowo3b1 as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/metadata.sqlite.

Jalankan komponen TFX secara interaktif

Di sel berikutnya, kami membuat komponen TFX satu per satu, menjalankan masing-masing komponen, dan memvisualisasikan artefak keluarannya. Dalam notebook ini, kami tidak akan memberikan penjelasan rinci dari masing-masing komponen TFX, tetapi Anda dapat melihat apa yang masing-masing tidak di TFX CoLab lokakarya .

ContohGen

Buat ExampleGen komponen data dibagi menjadi pelatihan dan evaluasi set, mengkonversi data ke dalam tf.Example format, dan menyalin data ke dalam _tfx_root direktori untuk komponen lain untuk mengakses.

example_gen = CsvExampleGen(input=external_input(_data_root))
context.run(example_gen)
WARNING:absl:From <ipython-input-1-2e0190c2dd16>:1: external_input (from tfx.utils.dsl_utils) is deprecated and will be removed in a future version.
Instructions for updating:
external_input is deprecated, directly pass the uri to ExampleGen.
WARNING:absl:The "input" argument to the CsvExampleGen component has been deprecated by "input_base". Please update your usage as support for this argument will be removed soon.
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-datajjx_v0dr/* to TFExample.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Running publisher for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized
artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)
["train", "eval"] /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/CsvExampleGen/examples/1

Mari kita lihat tiga contoh pelatihan pertama:

# 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, '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: "Age"
    value {
      int64_list {
        value: 39
      }
    }
  }
  feature {
    key: "Capital-Gain"
    value {
      int64_list {
        value: 2174
      }
    }
  }
  feature {
    key: "Capital-Loss"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Country"
    value {
      bytes_list {
        value: " United-States"
      }
    }
  }
  feature {
    key: "Education"
    value {
      bytes_list {
        value: " Bachelors"
      }
    }
  }
  feature {
    key: "Education-Num"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "Hours-per-week"
    value {
      int64_list {
        value: 40
      }
    }
  }
  feature {
    key: "Marital-Status"
    value {
      bytes_list {
        value: " Never-married"
      }
    }
  }
  feature {
    key: "Occupation"
    value {
      bytes_list {
        value: " Adm-clerical"
      }
    }
  }
  feature {
    key: "Over-50K"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Race"
    value {
      bytes_list {
        value: " White"
      }
    }
  }
  feature {
    key: "Relationship"
    value {
      bytes_list {
        value: " Not-in-family"
      }
    }
  }
  feature {
    key: "Sex"
    value {
      bytes_list {
        value: " Male"
      }
    }
  }
  feature {
    key: "Workclass"
    value {
      bytes_list {
        value: " State-gov"
      }
    }
  }
  feature {
    key: "fnlwgt"
    value {
      int64_list {
        value: 77516
      }
    }
  }
}

features {
  feature {
    key: "Age"
    value {
      int64_list {
        value: 50
      }
    }
  }
  feature {
    key: "Capital-Gain"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Capital-Loss"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Country"
    value {
      bytes_list {
        value: " United-States"
      }
    }
  }
  feature {
    key: "Education"
    value {
      bytes_list {
        value: " Bachelors"
      }
    }
  }
  feature {
    key: "Education-Num"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "Hours-per-week"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "Marital-Status"
    value {
      bytes_list {
        value: " Married-civ-spouse"
      }
    }
  }
  feature {
    key: "Occupation"
    value {
      bytes_list {
        value: " Exec-managerial"
      }
    }
  }
  feature {
    key: "Over-50K"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Race"
    value {
      bytes_list {
        value: " White"
      }
    }
  }
  feature {
    key: "Relationship"
    value {
      bytes_list {
        value: " Husband"
      }
    }
  }
  feature {
    key: "Sex"
    value {
      bytes_list {
        value: " Male"
      }
    }
  }
  feature {
    key: "Workclass"
    value {
      bytes_list {
        value: " Self-emp-not-inc"
      }
    }
  }
  feature {
    key: "fnlwgt"
    value {
      int64_list {
        value: 83311
      }
    }
  }
}

features {
  feature {
    key: "Age"
    value {
      int64_list {
        value: 38
      }
    }
  }
  feature {
    key: "Capital-Gain"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Capital-Loss"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Country"
    value {
      bytes_list {
        value: " United-States"
      }
    }
  }
  feature {
    key: "Education"
    value {
      bytes_list {
        value: " HS-grad"
      }
    }
  }
  feature {
    key: "Education-Num"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "Hours-per-week"
    value {
      int64_list {
        value: 40
      }
    }
  }
  feature {
    key: "Marital-Status"
    value {
      bytes_list {
        value: " Divorced"
      }
    }
  }
  feature {
    key: "Occupation"
    value {
      bytes_list {
        value: " Handlers-cleaners"
      }
    }
  }
  feature {
    key: "Over-50K"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "Race"
    value {
      bytes_list {
        value: " White"
      }
    }
  }
  feature {
    key: "Relationship"
    value {
      bytes_list {
        value: " Not-in-family"
      }
    }
  }
  feature {
    key: "Sex"
    value {
      bytes_list {
        value: " Male"
      }
    }
  }
  feature {
    key: "Workclass"
    value {
      bytes_list {
        value: " Private"
      }
    }
  }
  feature {
    key: "fnlwgt"
    value {
      int64_list {
        value: 215646
      }
    }
  }
}

StatistikGen

StatisticsGen mengambil input dataset kita hanya tertelan menggunakan ExampleGen dan memungkinkan Anda untuk melakukan beberapa analisis dataset Anda menggunakan TensorFlow Validasi Data (TFDV).

statistics_gen = StatisticsGen(
    examples=example_gen.outputs['examples'])
context.run(statistics_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for StatisticsGen
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/StatisticsGen/statistics/2/train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/StatisticsGen/statistics/2/eval.
INFO:absl:Running publisher for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized

Setelah StatisticsGen selesai berjalan, kita dapat memvisualisasikan statistik yang dikeluarkan. Cobalah bermain dengan plot yang berbeda!

context.show(statistics_gen.outputs['statistics'])
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_data_validation/utils/stats_util.py:247: 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)`

SkemaGen

SchemaGen akan mengambil sebagai masukan statistik yang kami dihasilkan dengan StatisticsGen , melihat perpecahan pelatihan secara default.

schema_gen = SchemaGen(
    statistics=statistics_gen.outputs['statistics'],
    infer_feature_shape=False)
context.run(schema_gen)
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for SchemaGen
INFO:absl:MetadataStore with DB connection initialized
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-2021-02-05T22_01_17.129037-teowo3b1/SchemaGen/schema/3/schema.pbtxt.
INFO:absl:Running publisher for SchemaGen
INFO:absl:MetadataStore with DB connection initialized
context.show(schema_gen.outputs['schema'])
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_data_validation/utils/display_util.py:151: 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)

Untuk mempelajari lebih lanjut tentang skema, lihat dokumentasi SchemaGen .

Mengubah

Transform akan mengambil sebagai masukan data dari ExampleGen , skema dari SchemaGen , serta modul yang berisi mengubah kode yang ditetapkan pengguna.

Mari kita lihat contoh-ditetapkan pengguna Transform kode di bawah ini (untuk pengenalan TensorFlow Transform API, lihat tutorial ).

_census_income_constants_module_file = 'census_income_constants.py'

Writing census_income_constants.py
_census_income_transform_module_file = 'census_income_transform.py'

Writing census_income_transform.py
transform = Transform(
    examples=example_gen.outputs['examples'],
    schema=schema_gen.outputs['schema'],
    module_file=os.path.abspath(_census_income_transform_module_file))
context.run(transform)
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Transform
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
WARNING:absl:The default value of `force_tf_compat_v1` will change in a future release from `True` to `False`. Since this pipeline has TF 2 behaviors enabled, Transform will use native TF 2 at that point. You can test this behavior now by passing `force_tf_compat_v1=False` or disable it by explicitly setting `force_tf_compat_v1=True` in the Transform component.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tfx/components/transform/executor.py:541: Schema (from tensorflow_transform.tf_metadata.dataset_schema) is deprecated and will be removed in a future version.
Instructions for updating:
Schema is a deprecated, use schema_utils.schema_from_feature_spec to create a `Schema`
INFO:absl:Loading /tmpfs/src/temp/model_card_toolkit/documentation/examples/census_income_transform.py because it has not been loaded before.
INFO:absl:/tmpfs/src/temp/model_card_toolkit/documentation/examples/census_income_transform.py is already loaded.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/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 Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:TFT beam APIs accept both the TFXIO format and the instance dict format now. There is no need to set use_tfxio any more and it will be removed soon.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:tensorflow:Tensorflow version (2.3.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transform_graph/4/.temp_path/tftransform_tmp/259914d385c64e718981569626d0274c/saved_model.pb
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transform_graph/4/.temp_path/tftransform_tmp/5a8d2f32189a42faa1a8db83c514c74e/saved_model.pb
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:Tensorflow version (2.3.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
INFO:absl:Feature Age has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Gain has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Capital-Loss has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Country has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Education-Num has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Hours-per-week has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Marital-Status has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Occupation has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Over-50K has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Race has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Relationship has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Sex has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature Workclass has no shape. Setting to VarLenSparseTensor.
INFO:absl:Feature fnlwgt has no shape. Setting to VarLenSparseTensor.
WARNING:tensorflow:Tensorflow version (2.3.2) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transform_graph/4/.temp_path/tftransform_tmp/f4e3edcb37534b60889ae56fa82df09f/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transform_graph/4/.temp_path/tftransform_tmp/f4e3edcb37534b60889ae56fa82df09f/saved_model.pb
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_2:0\022-vocab_compute_and_apply_vocabulary_vocabulary"

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

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_6:0\022/vocab_compute_and_apply_vocabulary_2_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_8:0\022/vocab_compute_and_apply_vocabulary_3_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_10:0\022/vocab_compute_and_apply_vocabulary_4_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_12:0\022/vocab_compute_and_apply_vocabulary_5_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_14:0\022/vocab_compute_and_apply_vocabulary_6_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_16:0\022/vocab_compute_and_apply_vocabulary_7_vocabulary"

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

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

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_6:0\022/vocab_compute_and_apply_vocabulary_2_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_8:0\022/vocab_compute_and_apply_vocabulary_3_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_10:0\022/vocab_compute_and_apply_vocabulary_4_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_12:0\022/vocab_compute_and_apply_vocabulary_5_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_14:0\022/vocab_compute_and_apply_vocabulary_6_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_16:0\022/vocab_compute_and_apply_vocabulary_7_vocabulary"

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

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

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_6:0\022/vocab_compute_and_apply_vocabulary_2_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_8:0\022/vocab_compute_and_apply_vocabulary_3_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_10:0\022/vocab_compute_and_apply_vocabulary_4_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_12:0\022/vocab_compute_and_apply_vocabulary_5_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_14:0\022/vocab_compute_and_apply_vocabulary_6_vocabulary"

WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_16:0\022/vocab_compute_and_apply_vocabulary_7_vocabulary"

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized
transform.outputs
{'transform_graph': Channel(
    type_name: TransformGraph
    artifacts: [Artifact(artifact: id: 4
type_id: 11
uri: "/tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transform_graph/4"
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"
  }
}
state: LIVE
, artifact_type: id: 11
name: "TransformGraph"
)]
), 'transformed_examples': Channel(
    type_name: Examples
    artifacts: [Artifact(artifact: id: 5
type_id: 5
uri: "/tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/transformed_examples/4"
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"
  }
}
state: LIVE
, artifact_type: id: 5
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]
), 'updated_analyzer_cache': Channel(
    type_name: TransformCache
    artifacts: [Artifact(artifact: id: 6
type_id: 12
uri: "/tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Transform/updated_analyzer_cache/4"
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"
  }
}
state: LIVE
, artifact_type: id: 12
name: "TransformCache"
)]
)}

Pelatih

Mari kita lihat contoh model yang ditetapkan pengguna kode di bawah ini (untuk pengenalan TensorFlow Keras API, lihat tutorial ):

_census_income_trainer_module_file = 'census_income_trainer.py'

Writing census_income_trainer.py
trainer = Trainer(
    module_file=os.path.abspath(_census_income_trainer_module_file),
    custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),
    examples=transform.outputs['transformed_examples'],
    transform_graph=transform.outputs['transform_graph'],
    schema=schema_gen.outputs['schema'],
    train_args=trainer_pb2.TrainArgs(num_steps=100),
    eval_args=trainer_pb2.EvalArgs(num_steps=50))
context.run(trainer)
WARNING:absl:From <ipython-input-1-6ab3bbf2f5a0>:3: The name tfx.components.base.executor_spec.ExecutorClassSpec is deprecated. Please use tfx.dsl.components.base.executor_spec.ExecutorClassSpec instead.
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Trainer
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
INFO:absl:Loading /tmpfs/src/temp/model_card_toolkit/documentation/examples/census_income_trainer.py because it has not been loaded before.
INFO:absl:Training model.
INFO:absl:Model: "functional_1"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type)                    Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl:Age_xf (InputLayer)             [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Capital-Gain_xf (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Capital-Loss_xf (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Country_xf (InputLayer)         [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Education-Num_xf (InputLayer)   [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Education_xf (InputLayer)       [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Hours-per-week_xf (InputLayer)  [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Marital-Status_xf (InputLayer)  [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Occupation_xf (InputLayer)      [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Race_xf (InputLayer)            [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Relationship_xf (InputLayer)    [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Sex_xf (InputLayer)             [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Workclass_xf (InputLayer)       [(None,)]            0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features (DenseFeatures)  (None, 3)            0           Age_xf[0][0]                     
INFO:absl:                                                                 Capital-Gain_xf[0][0]            
INFO:absl:                                                                 Capital-Loss_xf[0][0]            
INFO:absl:                                                                 Country_xf[0][0]                 
INFO:absl:                                                                 Education-Num_xf[0][0]           
INFO:absl:                                                                 Education_xf[0][0]               
INFO:absl:                                                                 Hours-per-week_xf[0][0]          
INFO:absl:                                                                 Marital-Status_xf[0][0]          
INFO:absl:                                                                 Occupation_xf[0][0]              
INFO:absl:                                                                 Race_xf[0][0]                    
INFO:absl:                                                                 Relationship_xf[0][0]            
INFO:absl:                                                                 Sex_xf[0][0]                     
INFO:absl:                                                                 Workclass_xf[0][0]               
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 100)          400         dense_features[0][0]             
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 70)           7070        dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 48)           3408        dense_1[0][0]                    
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_3 (Dense)                 (None, 34)           1666        dense_2[0][0]                    
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_features_1 (DenseFeatures (None, 1710)         0           Age_xf[0][0]                     
INFO:absl:                                                                 Capital-Gain_xf[0][0]            
INFO:absl:                                                                 Capital-Loss_xf[0][0]            
INFO:absl:                                                                 Country_xf[0][0]                 
INFO:absl:                                                                 Education-Num_xf[0][0]           
INFO:absl:                                                                 Education_xf[0][0]               
INFO:absl:                                                                 Hours-per-week_xf[0][0]          
INFO:absl:                                                                 Marital-Status_xf[0][0]          
INFO:absl:                                                                 Occupation_xf[0][0]              
INFO:absl:                                                                 Race_xf[0][0]                    
INFO:absl:                                                                 Relationship_xf[0][0]            
INFO:absl:                                                                 Sex_xf[0][0]                     
INFO:absl:                                                                 Workclass_xf[0][0]               
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate)       (None, 1744)         0           dense_3[0][0]                    
INFO:absl:                                                                 dense_features_1[0][0]           
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_4 (Dense)                 (None, 1)            1745        concatenate[0][0]                
INFO:absl:==================================================================================================
INFO:absl:Total params: 14,289
INFO:absl:Trainable params: 14,289
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
1/100 [..............................] - ETA: 0s - loss: 0.7236 - binary_accuracy: 0.2250WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.
Instructions for updating:
use `tf.profiler.experimental.stop` instead.
WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0029s vs `on_train_batch_end` time: 0.0135s). Check your callbacks.
100/100 [==============================] - 1s 9ms/step - loss: 0.5104 - binary_accuracy: 0.7710 - val_loss: 0.4469 - val_binary_accuracy: 0.8005
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Trainer/model/5/serving_model_dir/assets
INFO:absl:Training complete. Model written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Trainer/model/5/serving_model_dir. ModelRun written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Trainer/model_run/5
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized

Penilai

The Evaluator komponen menghitung metrik kinerja model atas evaluasi set. Ia menggunakan Analisis Model TensorFlow perpustakaan.

Evaluator akan mengambil sebagai masukan data dari ExampleGen , model terlatih dari Trainer , dan konfigurasi mengiris. Konfigurasi slicing memungkinkan Anda untuk mengiris metrik Anda pada nilai fitur. Lihat contoh konfigurasi di bawah ini:

from google.protobuf.wrappers_pb2 import BoolValue

eval_config = tfma.EvalConfig(
    model_specs=[
        # This assumes a serving model with signature 'serving_default'. If
        # using estimator based EvalSavedModel, add signature_name: 'eval' and 
        # remove the label_key.
        tfma.ModelSpec(label_key="Over-50K")
    ],
    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'),
                tfma.MetricConfig(class_name='FairnessIndicators',
                                  config='{ "thresholds": [0.5] }'),
            ]
        )
    ],
    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 by feature column Race and Sex.
        tfma.SlicingSpec(feature_keys=['Race']),
        tfma.SlicingSpec(feature_keys=['Sex']),
        tfma.SlicingSpec(feature_keys=['Race', 'Sex']),
    ],
    options = tfma.Options(compute_confidence_intervals=BoolValue(value=True))
)
# Use TFMA to compute a evaluation statistics over features of a model and
# validate them against a baseline.
evaluator = Evaluator(
    examples=example_gen.outputs['examples'],
    model=trainer.outputs['model'],
    eval_config=eval_config)
context.run(evaluator)
INFO:absl:Running driver for Evaluator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Evaluator
WARNING:absl:"maybe_add_baseline" and "maybe_remove_baseline" are deprecated,
        please use "has_baseline" instead.
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "Over-50K"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "Race"
}
slicing_specs {
  feature_keys: "Sex"
}
slicing_specs {
  feature_keys: "Race"
  feature_keys: "Sex"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
  }
  metrics {
    class_name: "FairnessIndicators"
    config: "{ \"thresholds\": [0.5] }"
  }
}
options {
  compute_confidence_intervals {
    value: true
  }
  confidence_intervals {
    method: JACKKNIFE
  }
}

INFO:absl:Using /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Trainer/model/5/serving_model_dir as  model.
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "Over-50K"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "Race"
}
slicing_specs {
  feature_keys: "Sex"
}
slicing_specs {
  feature_keys: "Race"
  feature_keys: "Sex"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
  }
  metrics {
    class_name: "FairnessIndicators"
    config: "{ \"thresholds\": [0.5] }"
  }
  model_names: ""
}
options {
  compute_confidence_intervals {
    value: true
  }
  confidence_intervals {
    method: JACKKNIFE
  }
}

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "Over-50K"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "Race"
}
slicing_specs {
  feature_keys: "Sex"
}
slicing_specs {
  feature_keys: "Race"
  feature_keys: "Sex"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
  }
  metrics {
    class_name: "FairnessIndicators"
    config: "{ \"thresholds\": [0.5] }"
  }
  model_names: ""
}
options {
  compute_confidence_intervals {
    value: true
  }
  confidence_intervals {
    method: JACKKNIFE
  }
}

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "Over-50K"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "Race"
}
slicing_specs {
  feature_keys: "Sex"
}
slicing_specs {
  feature_keys: "Race"
  feature_keys: "Sex"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  metrics {
    class_name: "BinaryAccuracy"
  }
  metrics {
    class_name: "FairnessIndicators"
    config: "{ \"thresholds\": [0.5] }"
  }
  model_names: ""
}
options {
  compute_confidence_intervals {
    value: true
  }
  confidence_intervals {
    method: JACKKNIFE
  }
}
WARNING:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7f77a02d2510> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
INFO:absl:Evaluation complete. Results written to /tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Evaluator/evaluation/6.
INFO:absl:No threshold configured, will not validate model.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized
evaluator.outputs
{'evaluation': Channel(
    type_name: ModelEvaluation
    artifacts: [Artifact(artifact: id: 9
type_id: 17
uri: "/tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Evaluator/evaluation/6"
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"
  }
}
state: LIVE
, artifact_type: id: 17
name: "ModelEvaluation"
)]
), 'blessing': Channel(
    type_name: ModelBlessing
    artifacts: [Artifact(artifact: id: 10
type_id: 18
uri: "/tmp/tfx-interactive-2021-02-05T22_01_17.129037-teowo3b1/Evaluator/blessing/6"
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"
  }
}
state: LIVE
, artifact_type: id: 18
name: "ModelBlessing"
)]
)}

Menggunakan evaluation output yang kita dapat menunjukkan visualisasi default metrik global seluruh set evaluasi.

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

Isi Properti dari ModelCard dengan Model Card Toolkit

Sekarang setelah kami menyiapkan saluran TFX kami, kami akan menggunakan Model Card Toolkit untuk mengekstrak artefak utama dari proses dan mengisi Model Card.

Hubungkan ke toko MLMD yang digunakan oleh InteractiveContext

from ml_metadata.metadata_store import metadata_store
from IPython import display

mlmd_store = metadata_store.MetadataStore(context.metadata_connection_config)
model_uri = trainer.outputs["model"].get()[0].uri
INFO:absl:MetadataStore with DB connection initialized

Gunakan Toolkit Kartu Model

Inisialisasi Toolkit Kartu Model.

from model_card_toolkit import ModelCardToolkit

mct = ModelCardToolkit(mlmd_store=mlmd_store, model_uri=model_uri)

Buat ruang kerja Model Card.

model_card = mct.scaffold_assets()

Beri keterangan lebih banyak informasi ke dalam Model Card.

Penting juga untuk mendokumentasikan informasi model yang mungkin penting bagi pengguna hilir, seperti keterbatasannya, kasus penggunaan yang dimaksudkan, trade off, dan pertimbangan etis. Untuk setiap bagian ini, kita bisa langsung menambahkan objek JSON baru untuk mewakili informasi ini.

model_card.model_details.name = 'Census Income Classifier'
model_card.model_details.overview = (
    'This is a wide and deep Keras model which aims to classify whether or not '
    'an individual has an income of over $50,000 based on various demographic '
    'features. The model is trained on the UCI Census Income Dataset. This is '
    'not a production model, and this dataset has traditionally only been used '
    'for research purposes. In this Model Card, you can review quantitative '
    'components of the model’s performance and data, as well as information '
    'about the model’s intended uses, limitations, and ethical considerations.'
)
model_card.model_details.owners = [
  {'name': 'Model Cards Team', 'contact': 'model-cards@google.com'}
]
model_card.considerations.use_cases = [
    'This dataset that this model was trained on was originally created to '
    'support the machine learning community in conducting empirical analysis '
    'of ML algorithms. The Adult Data Set can be used in fairness-related '
    'studies that compare inequalities across sex and race, based on '
    'people’s annual incomes.'
]
model_card.considerations.limitations = [
    'This is a class-imbalanced dataset across a variety of sensitive classes.'
    ' The ratio of male-to-female examples is about 2:1 and there are far more'
    ' examples with the “white” attribute than every other race combined. '
    'Furthermore, the ratio of $50,000 or less earners to $50,000 or more '
    'earners is just over 3:1. Due to the imbalance across income levels, we '
    'can see that our true negative rate seems quite high, while our true '
    'positive rate seems quite low. This is true to an even greater degree '
    'when we only look at the “female” sub-group, because there are even '
    'fewer female examples in the $50,000+ earner group, causing our model to '
    'overfit these examples. To avoid this, we can try various remediation '
    'strategies in future iterations (e.g. undersampling, hyperparameter '
    'tuning, etc), but we may not be able to fix all of the fairness issues.'
]
model_card.considerations.ethical_considerations = [{
    'name':
        'We risk expressing the viewpoint that the attributes in this dataset '
        'are the only ones that are predictive of someone’s income, even '
        'though we know this is not the case.',
    'mitigation_strategy':
        'As mentioned, some interventions may need to be performed to address '
        'the class imbalances in the dataset.'
}]

Filter dan Tambahkan Grafik.

Kami dapat memfilter grafik yang dihasilkan oleh komponen TFX untuk menyertakan yang paling relevan untuk Kartu Model menggunakan fungsi yang ditentukan di bawah ini. Dalam contoh ini, kita menyaring untuk race dan sex , dua atribut berpotensi sensitif.

Setiap Kartu Model akan memiliki hingga tiga bagian untuk grafik -- statistik kumpulan data pelatihan, statistik kumpulan data evaluasi, dan analisis kuantitatif kinerja model kami.

# These are the graphs that will appear in the Quantiative Analysis portion of 
# the Model Card. Feel free to add or remove from this list. 
TARGET_EVAL_GRAPH_NAMES = [
  'fairness_indicators_metrics/false_positive_rate@0.5',
  'fairness_indicators_metrics/false_negative_rate@0.5',
  'binary_accuracy',
  'example_count | Race_X_Sex',
]

# These are the graphs that will appear in both the Train Set and Eval Set 
# portions of the Model Card. Feel free to add or remove from this list. 
TARGET_DATASET_GRAPH_NAMES = [
  'counts | Race',
  'counts | Sex',
]

def filter_graphs(graphics, target_graph_names):
  result = []
  for graph in graphics:
    for target_graph_name in target_graph_names:
      if graph.name.startswith(target_graph_name):
        result.append(graph)
  result.sort(key=lambda g: g.name)
  return result

# Populating the three different sections using the filter defined above. To 
# see all the graphs available in a section, we can iterate through each of the
# different collections. 
model_card.quantitative_analysis.graphics.collection = filter_graphs(
    model_card.quantitative_analysis.graphics.collection, TARGET_EVAL_GRAPH_NAMES)
model_card.model_parameters.data.eval.graphics.collection = filter_graphs(
    model_card.model_parameters.data.eval.graphics.collection, TARGET_DATASET_GRAPH_NAMES)
model_card.model_parameters.data.train.graphics.collection = filter_graphs(
    model_card.model_parameters.data.train.graphics.collection, TARGET_DATASET_GRAPH_NAMES)

Kami kemudian menambahkan deskripsi (opsional) untuk masing-masing bagian grafik.

model_card.model_parameters.data.train.graphics.description = (
    'This section includes graphs displaying the class distribution for the '
    '“Race” and “Sex” attributes in our training dataset. We chose to '
    'show these graphs in particular because we felt it was important that '
    'users see the class imbalance.'
)
model_card.model_parameters.data.eval.graphics.description = (
    'Like the training set, we provide graphs showing the class distribution '
    'of the data we used to evaluate our model’s performance. '
)
model_card.quantitative_analysis.graphics.description = (
    'These graphs show how the model performs for data sliced by “Race”, '
    '“Sex” and the intersection of these attributes. The metrics we chose '
    'to display are “Accuracy”, “False Positive Rate”, and “False '
    'Negative Rate”, because we anticipated that the class imbalances might '
    'cause our model to underperform for certain groups.'
)
mct.update_model_card_json(model_card)

Buat Kartu Model.

Sekarang kita dapat menampilkan Model Card dalam format HTML.

html = mct.export_format()
display.display(display.HTML(html))