MLMD 模型卡工具包演示

在 TensorFlow.org 上查看

在 Google Colab 中运行

在 GitHub 上查看

下载笔记本

背景

此笔记本演示了如何在 Jupyter/Colab 环境中使用 MLMD 和 TFX 管道，使用模型卡工具包生成模型卡。您可以在 https://modelcards.withgoogle.com/about 了解更多关于模型卡的信息

设置

首先，我们需要 a) 安装并导入必要的包，以及 b) 下载数据。

升级到 Pip 21（或更高版本）并安装模型卡工具包

pip install --upgrade pip==21.3
pip install model-card-toolkit

您是否重新启动了运行时？

如果您使用的是 Google Colab，则在安装新包后必须重新启动运行时。

导入包

我们导入必要的包，包括标准 TFX 组件类，并检查库版本。

import os
import pprint
import tempfile
import urllib

import absl
import random
import tensorflow.compat.v2 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 SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.components import Transform
from tfx.components.trainer.executor import GenericExecutor
from tfx.dsl.components.base import executor_spec
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

import ml_metadata as mlmd

确保 TensorFlow 2 正在运行并以急切模式执行。

tf.enable_v2_behavior()
tf.executing_eagerly()

True

print('TensorFlow version: {}'.format(tf.__version__))
print('TFX version: {}'.format(tfx.version.__version__))
print('MLMD version: {}'.format(mlmd.__version__))

TensorFlow version: 2.7.1
TFX version: 1.5.0
MLMD version: 1.5.0

设置管道路径

# 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)

下载示例数据

我们下载示例数据集以供我们的 TFX 管道使用。

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)

快速查看 CSV 文件。

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

创建 InteractiveContext

最后，我们创建一个 InteractiveContext，它将允许我们在此笔记本中以交互方式运行 TFX 组件。

# 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(pipeline_name="Census Income Classification Pipeline")

WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/metadata.sqlite.

以交互方式运行 TFX 组件

在接下来的单元格中，我们逐个创建 TFX 组件，运行每个组件，并可视化其输出工件。在此笔记本中，我们不会提供对每个 TFX 组件的详细解释，但您可以在 TFX Colab 工作坊 中看到每个组件的作用。

ExampleGen

创建 ExampleGen 组件以将数据拆分为训练集和评估集，将数据转换为 tf.Example 格式，并将数据复制到 _tfx_root 目录中，以便其他组件可以访问。

example_gen = CsvExampleGen(input_base=_data_root)
context.run(example_gen)

INFO:absl:Running driver for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:Running executor for CsvExampleGen
INFO:absl:Generating examples.
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
INFO:absl:Processing input csv data /tmpfs/tmp/tfx-datawz3771q2/* 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

artifact = example_gen.outputs['examples'].get()[0]
print(artifact.split_names, artifact.uri)

["train", "eval"] /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/CsvExampleGen/examples/1

让我们看一下前三个训练示例

# 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: "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
      }
    }
  }
}

StatisticsGen

StatisticsGen 以我们刚刚使用 ExampleGen 摄取的数据集作为输入，并允许您使用 TensorFlow 数据验证 (TFDV) 对数据集进行一些分析。

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 /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/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

在 StatisticsGen 完成运行后，我们可以可视化输出的统计信息。尝试使用不同的图表！

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

SchemaGen

SchemaGen 将以我们使用 StatisticsGen 生成的统计信息作为输入，默认情况下查看训练拆分。

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 /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/SchemaGen/schema/3/schema.pbtxt.
INFO:absl:Running publisher for SchemaGen
INFO:absl:MetadataStore with DB connection initialized

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

要了解有关模式的更多信息，请参阅 SchemaGen 文档。

Transform

Transform 将以来自 ExampleGen 的数据、来自 SchemaGen 的模式以及包含用户定义的 Transform 代码的模块作为输入。

让我们在下面查看用户定义的 Transform 代码示例（有关 TensorFlow Transform API 的介绍，请参阅 教程）。

_census_income_constants_module_file = 'census_income_constants.py'

%%writefile {_census_income_constants_module_file}

# Categorical features are assumed to each have a maximum value in the dataset.
MAX_CATEGORICAL_FEATURE_VALUES = [20]

CATEGORICAL_FEATURE_KEYS = ["Education-Num"]


DENSE_FLOAT_FEATURE_KEYS = ["Capital-Gain", "Hours-per-week", "Capital-Loss"]

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

BUCKET_FEATURE_KEYS = ["Age"]

# Number of vocabulary terms used for encoding VOCAB_FEATURES by tf.transform
VOCAB_SIZE = 200

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

VOCAB_FEATURE_KEYS = ["Workclass", "Education", "Marital-Status", "Occupation", 
                      "Relationship", "Race", "Sex", "Country"]

# Keys
LABEL_KEY = "Over-50K"

def transformed_name(key):
  return key + '_xf'

Writing census_income_constants.py

_census_income_transform_module_file = 'census_income_transform.py'

%%writefile {_census_income_transform_module_file}

import tensorflow as tf
import tensorflow_transform as tft

import census_income_constants

_DENSE_FLOAT_FEATURE_KEYS = census_income_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = census_income_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = census_income_constants.VOCAB_SIZE
_OOV_SIZE = census_income_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = census_income_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = census_income_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = census_income_constants.CATEGORICAL_FEATURE_KEYS
_LABEL_KEY = census_income_constants.LABEL_KEY
_transformed_name = census_income_constants.transformed_name


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

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

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

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

  label = _fill_in_missing(inputs[_LABEL_KEY])
  outputs[_transformed_name(_LABEL_KEY)] = label

  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.
  """
  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 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:Generating ephemeral wheel package for '/tmpfs/src/temp/model_card_toolkit/documentation/examples/census_income_transform.py' (including modules: ['census_income_transform', 'census_income_constants']).
INFO:absl:User module package has hash fingerprint version 9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmpfs/tmp/tmp6ykorptc/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmpfs/tmp/tmppxg6tk5f', '--dist-dir', '/tmpfs/tmp/tmpmcwpffer']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying census_income_transform.py -> build/lib
copying census_income_constants.py -> build/lib
installing to /tmpfs/tmp/tmppxg6tk5f
running install
running install_lib
copying build/lib/census_income_transform.py -> /tmpfs/tmp/tmppxg6tk5f
copying build/lib/census_income_constants.py -> /tmpfs/tmp/tmppxg6tk5f
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
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
INFO:absl:Successfully built user code wheel distribution at '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl'; target user module is 'census_income_transform'.
INFO:absl:Full user module path is 'census_income_transform@/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl'
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
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 /tmpfs/tmp/tmppxg6tk5f/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3.7.egg-info
running install_scripts
creating /tmpfs/tmp/tmppxg6tk5f/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0.dist-info/WHEEL
creating '/tmpfs/tmp/tmpmcwpffer/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl' and adding '/tmpfs/tmp/tmppxg6tk5f' to it
adding 'census_income_constants.py'
adding 'census_income_transform.py'
adding 'tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0.dist-info/RECORD'
removing /tmpfs/tmp/tmppxg6tk5f
INFO:absl:Running executor for Transform
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'census_income_transform@/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmpp2fgxex6', '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl']
Processing /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl
WARNING: You are using pip version 21.3; however, version 22.0.4 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl'.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'census_income_transform@/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'
INFO:absl:Installing '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmp08sebpcy', '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0
Processing /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl
WARNING: You are using pip version 21.3; however, version 22.0.4 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl'.
INFO:absl:Installing '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmpyzdmu59r', '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0
Processing /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl
WARNING: You are using pip version 21.3; however, version 22.0.4 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0-py3-none-any.whl'.
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:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+9ad4c9c61fb069aa72a9c3dbadb0b29c6470126efbcfd2f85e87e9c2355a96d0
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:289: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
INFO:absl:Feature 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:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:Feature 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:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
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_2/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_3/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_4/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_5/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_6/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_7/apply_vocab/text_file_init/InitializeTableFromTextFileV2
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING: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_2/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_3/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_4/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_5/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_6/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_7/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:Feature 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:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
2022-04-27 09:11:17.569552: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/Transform/transform_graph/4/.temp_path/tftransform_tmp/3ec9e5378ce447b5b7959c94b9b4bfda/assets
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/Transform/transform_graph/4/.temp_path/tftransform_tmp/3ebfabff57b2413b9071df6af8e0070b/assets
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized

transform.outputs['transform_graph']

Trainer

让我们在下面查看用户定义的模型代码示例（有关 TensorFlow Keras API 的介绍，请参阅 教程）

_census_income_trainer_module_file = 'census_income_trainer.py'

%%writefile {_census_income_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.components.trainer.executor import TrainerFnArgs

import census_income_constants

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


def _transformed_names(keys):
  return [_transformed_name(key) for key in keys]


def _gzip_reader_fn(filenames):
  """Small utility returning a record reader that can read gzip'ed files."""
  return tf.data.TFRecordDataset(
      filenames,
      compression_type='GZIP')


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)
    if _transformed_name(_LABEL_KEY) in transformed_features:
      transformed_features.pop(_transformed_name(_LABEL_KEY))

    return model(transformed_features)

  return serve_tf_examples_fn


def _input_fn(file_pattern: List[Text],
              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.
    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.
  """
  transformed_feature_spec = (
      tf_transform_output.transformed_feature_spec().copy())

  dataset = tf.data.experimental.make_batched_features_dataset(
      file_pattern=file_pattern,
      batch_size=batch_size,
      features=transformed_feature_spec,
      reader=_gzip_reader_fn,
      label_key=_transformed_name(_LABEL_KEY))

  return dataset


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

  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 _transformed_names(_DENSE_FLOAT_FEATURE_KEYS)
  ]
  categorical_columns = [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_VOCAB_SIZE + _OOV_SIZE, default_value=0)
      for key in _transformed_names(_VOCAB_FEATURE_KEYS)
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
      for key in _transformed_names(_BUCKET_FEATURE_KEYS)
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(  # pylint: disable=g-complex-comprehension
          key,
          num_buckets=num_buckets,
          default_value=0) for key, num_buckets in zip(
              _transformed_names(_CATEGORICAL_FEATURE_KEYS),
              _MAX_CATEGORICAL_FEATURE_VALUES)
  ]
  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 _transformed_names(_DENSE_FLOAT_FEATURE_KEYS)
  }
  input_layers.update({
      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
      for colname in _transformed_names(_VOCAB_FEATURE_KEYS)
  })
  input_layers.update({
      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
      for colname in _transformed_names(_BUCKET_FEATURE_KEYS)
  })
  input_layers.update({
      colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int32')
      for colname in _transformed_names(_CATEGORICAL_FEATURE_KEYS)
  })

  # TODO(b/161816639): SparseFeatures for feature columns + Keras.
  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, activation='sigmoid')(
          tf.keras.layers.concatenate([deep, wide]))

  model = tf.keras.Model(input_layers, output)
  model.compile(
      loss='binary_crossentropy',
      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: TrainerFnArgs):
  """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, tf_transform_output, 40)
  eval_dataset = _input_fn(fn_args.eval_files, 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)
      ])

  # This log path might change in the future.
  log_dir = os.path.join(os.path.dirname(fn_args.serving_model_dir), 'logs')
  tensorboard_callback = tf.keras.callbacks.TensorBoard(
     log_dir=log_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 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:`custom_executor_spec` is deprecated. Please customize component directly.
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/model_card_toolkit/documentation/examples/census_income_trainer.py' (including modules: ['census_income_transform', 'census_income_trainer', 'census_income_constants']).
INFO:absl:User module package has hash fingerprint version aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmpfs/tmp/tmpwqql_i8g/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmpfs/tmp/tmpzpmolcsr', '--dist-dir', '/tmpfs/tmp/tmpnkgc7yz1']
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying census_income_transform.py -> build/lib
copying census_income_trainer.py -> build/lib
copying census_income_constants.py -> build/lib
installing to /tmpfs/tmp/tmpzpmolcsr
running install
running install_lib
copying build/lib/census_income_transform.py -> /tmpfs/tmp/tmpzpmolcsr
copying build/lib/census_income_trainer.py -> /tmpfs/tmp/tmpzpmolcsr
copying build/lib/census_income_constants.py -> /tmpfs/tmp/tmpzpmolcsr
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
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
INFO:absl:Successfully built user code wheel distribution at '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe-py3-none-any.whl'; target user module is 'census_income_trainer'.
INFO:absl:Full user module path is 'census_income_trainer@/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe-py3-none-any.whl'
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
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 /tmpfs/tmp/tmpzpmolcsr/tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe-py3.7.egg-info
running install_scripts
creating /tmpfs/tmp/tmpzpmolcsr/tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe.dist-info/WHEEL
creating '/tmpfs/tmp/tmpnkgc7yz1/tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe-py3-none-any.whl' and adding '/tmpfs/tmp/tmpzpmolcsr' to it
adding 'census_income_constants.py'
adding 'census_income_trainer.py'
adding 'census_income_transform.py'
adding 'tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe.dist-info/RECORD'
removing /tmpfs/tmp/tmpzpmolcsr
INFO:absl:Running executor for Trainer
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
INFO:absl:udf_utils.get_fn {'train_args': '{\n  "num_steps": 100\n}', 'eval_args': '{\n  "num_steps": 50\n}', 'module_file': None, 'run_fn': None, 'trainer_fn': None, 'custom_config': 'null', 'module_path': 'census_income_trainer@/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe-py3-none-any.whl'} 'run_fn'
INFO:absl:Installing '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmpj80rqe5y', '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe-py3-none-any.whl']
Processing /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe-py3-none-any.whl
WARNING: You are using pip version 21.3; however, version 22.0.4 is available.
You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.
INFO:absl:Successfully installed '/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/_wheels/tfx_user_code_Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe-py3-none-any.whl'.
INFO:absl:Training model.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+aa1f5233b0e0a112365f42ff9488eec93da45611d414b4dc3c0a3a28e1aaa3fe
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl: Layer (type)                   Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl: 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 (DenseFeature  (None, 1710)        0           ['Age_xf[0][0]',                 
INFO:absl: s)                                                               '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:==================================================================================================
INFO:absl:Total params: 14,289
INFO:absl:Trainable params: 14,289
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
100/100 [==============================] - 4s 14ms/step - loss: 0.4904 - binary_accuracy: 0.7855 - val_loss: 0.4341 - val_binary_accuracy: 0.8115
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/Trainer/model/5/Format-Serving/assets
INFO:absl:Training complete. Model written to /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/Trainer/model/5/Format-Serving. ModelRun written to /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/Trainer/model_run/5
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized

trainer.outputs

{'model': Channel(
     type_name: Model
     artifacts: [Artifact(artifact: id: 12
 type_id: 24
 uri: "/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/Trainer/model/5"
 custom_properties {
   key: "name"
   value {
     string_value: "model"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Trainer"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 24
 name: "Model"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'model_run': Channel(
     type_name: ModelRun
     artifacts: [Artifact(artifact: id: 13
 type_id: 25
 uri: "/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/Trainer/model_run/5"
 custom_properties {
   key: "name"
   value {
     string_value: "model_run"
   }
 }
 custom_properties {
   key: "producer_component"
   value {
     string_value: "Trainer"
   }
 }
 custom_properties {
   key: "state"
   value {
     string_value: "published"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 25
 name: "ModelRun"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

Evaluator

Evaluator 组件计算评估集上的模型性能指标。它使用 TensorFlow 模型分析 库。

Evaluator 将以来自 ExampleGen 的数据、来自 Trainer 的训练模型以及切片配置作为输入。切片配置允许您根据特征值对指标进行切片。请参阅以下切片配置示例

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.

# TODO(b/226656838) Fix the inconsistent references warnings.
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
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        },\n        {\n          "class_name": "FairnessIndicators",\n          "config": "{ \\"thresholds\\": [0.5] }"\n        }\n      ]\n    }\n  ],\n  "model_specs": [\n    {\n      "label_key": "Over-50K"\n    }\n  ],\n  "options": {\n    "compute_confidence_intervals": true\n  },\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "Race"\n      ]\n    },\n    {\n      "feature_keys": [\n        "Sex"\n      ]\n    },\n    {\n      "feature_keys": [\n        "Race",\n        "Sex"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  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 /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/Trainer/model/5/Format-Serving as  model.
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f49ad48c2d0> and <keras.engine.input_layer.InputLayer object at 0x7f48005301d0>).
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        },\n        {\n          "class_name": "FairnessIndicators",\n          "config": "{ \\"thresholds\\": [0.5] }"\n        }\n      ]\n    }\n  ],\n  "model_specs": [\n    {\n      "label_key": "Over-50K"\n    }\n  ],\n  "options": {\n    "compute_confidence_intervals": true\n  },\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "Race"\n      ]\n    },\n    {\n      "feature_keys": [\n        "Sex"\n      ]\n    },\n    {\n      "feature_keys": [\n        "Race",\n        "Sex"\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 {
  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:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f499c6a02d0> and <keras.engine.input_layer.InputLayer object at 0x7f471c402990>).
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f46fc38e050> and <keras.engine.input_layer.InputLayer object at 0x7f46fc3cd0d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4993eb72d0> and <keras.engine.input_layer.InputLayer object at 0x7f48005cf910>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4898291450> and <keras.engine.input_layer.InputLayer object at 0x7f471c635b90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f46c4408f50> and <keras.engine.input_layer.InputLayer object at 0x7f46c4453b50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f46466f7890> and <keras.engine.input_layer.InputLayer object at 0x7f4646733b50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4644f58cd0> and <keras.engine.input_layer.InputLayer object at 0x7f4645068210>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4644148710> and <keras.engine.input_layer.InputLayer object at 0x7f46c446ae10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4245492f50> and <keras.engine.input_layer.InputLayer object at 0x7f42454d8b10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4244641f90> and <keras.engine.input_layer.InputLayer object at 0x7f4244609bd0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f423f6905d0> and <keras.engine.input_layer.InputLayer object at 0x7f423f7a6190>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f423e8c24d0> and <keras.engine.input_layer.InputLayer object at 0x7f423e8ddc10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f423d9c6f50> and <keras.engine.input_layer.InputLayer object at 0x7f423da0ead0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f423cb6b490> and <keras.engine.input_layer.InputLayer object at 0x7f423cbbfe10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4223bcd850> and <keras.engine.input_layer.InputLayer object at 0x7f423cb13d90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4222e030d0> and <keras.engine.input_layer.InputLayer object at 0x7f4222e12310>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4221f0bb10> and <keras.engine.input_layer.InputLayer object at 0x7f4221f51810>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f42210b0690> and <keras.engine.input_layer.InputLayer object at 0x7f4221084590>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4220126750> and <keras.engine.input_layer.InputLayer object at 0x7f4220235d90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f41fb19e0d0> and <keras.engine.input_layer.InputLayer object at 0x7f41fb1f49d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f41fa44a910> and <keras.engine.input_layer.InputLayer object at 0x7f41fa495f50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f41f8549890> and <keras.engine.input_layer.InputLayer object at 0x7f41f850bfd0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4179781610> and <keras.engine.input_layer.InputLayer object at 0x7f41797da990>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4178855810> and <keras.engine.input_layer.InputLayer object at 0x7f417888d890>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f414b966a90> and <keras.engine.input_layer.InputLayer object at 0x7f414b94f090>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f414a9f94d0> and <keras.engine.input_layer.InputLayer object at 0x7f414aa31810>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4149aa5510> and <keras.engine.input_layer.InputLayer object at 0x7f4149afe8d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4148d5d810> and <keras.engine.input_layer.InputLayer object at 0x7f4148d92850>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4143e1cf50> and <keras.engine.input_layer.InputLayer object at 0x7f4143e62950>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4142e1d910> and <keras.engine.input_layer.InputLayer object at 0x7f4142e6a790>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4141ee57d0> and <keras.engine.input_layer.InputLayer object at 0x7f4141f38650>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4140f52650> and <keras.engine.input_layer.InputLayer object at 0x7f4141022ad0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f414001ad90> and <keras.engine.input_layer.InputLayer object at 0x7f414005bf50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f41031a6d10> and <keras.engine.input_layer.InputLayer object at 0x7f41031bcf90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4143eef6d0> and <keras.engine.input_layer.InputLayer object at 0x7f4148ec9590>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4100f7bd50> and <keras.engine.input_layer.InputLayer object at 0x7f4100ed3c90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40e3f7c9d0> and <keras.engine.input_layer.InputLayer object at 0x7f40e3ee6950>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40e3090710> and <keras.engine.input_layer.InputLayer object at 0x7f40e30bb250>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40e20cbe90> and <keras.engine.input_layer.InputLayer object at 0x7f40e2082750>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40e12aa710> and <keras.engine.input_layer.InputLayer object at 0x7f40e1221810>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40e02e0310> and <keras.engine.input_layer.InputLayer object at 0x7f40e02f7a10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d74aca90> and <keras.engine.input_layer.InputLayer object at 0x7f40d734cc10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d64bc8d0> and <keras.engine.input_layer.InputLayer object at 0x7f40d64f25d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d5608a50> and <keras.engine.input_layer.InputLayer object at 0x7f40d5546090>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d46a6a50> and <keras.engine.input_layer.InputLayer object at 0x7f40d46f23d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d37e6fd0> and <keras.engine.input_layer.InputLayer object at 0x7f40d381ced0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d2842050> and <keras.engine.input_layer.InputLayer object at 0x7f40d2887810>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d1923dd0> and <keras.engine.input_layer.InputLayer object at 0x7f40d195f590>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d09e5b50> and <keras.engine.input_layer.InputLayer object at 0x7f40d0a27b90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40cfa51a50> and <keras.engine.input_layer.InputLayer object at 0x7f40cfa87a50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40ceb09f90> and <keras.engine.input_layer.InputLayer object at 0x7f40ceb54c50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40cdb1a690> and <keras.engine.input_layer.InputLayer object at 0x7f40cdb643d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f423ce15310> and <keras.engine.input_layer.InputLayer object at 0x7f423f6b3550>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40cc202350> and <keras.engine.input_layer.InputLayer object at 0x7f40cc1c1f50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40cb25bb50> and <keras.engine.input_layer.InputLayer object at 0x7f40cb225d90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40ca414650> and <keras.engine.input_layer.InputLayer object at 0x7f40ca3402d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c943b850> and <keras.engine.input_layer.InputLayer object at 0x7f40c9413c10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c8b60290> and <keras.engine.input_layer.InputLayer object at 0x7f40c8a6e550>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c7bb3110> and <keras.engine.input_layer.InputLayer object at 0x7f40c7bf7910>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c6caa4d0> and <keras.engine.input_layer.InputLayer object at 0x7f40c6cf5190>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c5da5950> and <keras.engine.input_layer.InputLayer object at 0x7f40c5def7d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c4e7ccd0> and <keras.engine.input_layer.InputLayer object at 0x7f40c4eba850>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c3f28a50> and <keras.engine.input_layer.InputLayer object at 0x7f40c3f61a90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c2ffcb10> and <keras.engine.input_layer.InputLayer object at 0x7f40c3036cd0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c206df10> and <keras.engine.input_layer.InputLayer object at 0x7f40c2169a10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c118d410> and <keras.engine.input_layer.InputLayer object at 0x7f40c11534d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40c02b8810> and <keras.engine.input_layer.InputLayer object at 0x7f40c111a0d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f42238f6510> and <keras.engine.input_layer.InputLayer object at 0x7f41fb2bff90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f423da6ab10> and <keras.engine.input_layer.InputLayer object at 0x7f4223daf310>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4245111550> and <keras.engine.input_layer.InputLayer object at 0x7f4244646e90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f46fc11a050> and <keras.engine.input_layer.InputLayer object at 0x7f46fc2e9d50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4993f132d0> and <keras.engine.input_layer.InputLayer object at 0x7f46fc2f8190>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f423dca6450> and <keras.engine.input_layer.InputLayer object at 0x7f41fb3ce310>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4644f1ab50> and <keras.engine.input_layer.InputLayer object at 0x7f4644f103d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f423fcc5910> and <keras.engine.input_layer.InputLayer object at 0x7f4245484710>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f423c958f50> and <keras.engine.input_layer.InputLayer object at 0x7f422351b0d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f422275c650> and <keras.engine.input_layer.InputLayer object at 0x7f4220c98490>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4644b46510> and <keras.engine.input_layer.InputLayer object at 0x7f4644c1f750>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4221afbed0> and <keras.engine.input_layer.InputLayer object at 0x7f4223796590>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f414b96dcd0> and <keras.engine.input_layer.InputLayer object at 0x7f41431a5f50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4222baee10> and <keras.engine.input_layer.InputLayer object at 0x7f4222ab9710>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4221518410> and <keras.engine.input_layer.InputLayer object at 0x7f4221b0b9d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f42451d14d0> and <keras.engine.input_layer.InputLayer object at 0x7f4645550050>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f423f431f50> and <keras.engine.input_layer.InputLayer object at 0x7f423f47a9d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4223732b50> and <keras.engine.input_layer.InputLayer object at 0x7f422376cd50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4220a2e690> and <keras.engine.input_layer.InputLayer object at 0x7f4220a75410>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f41f9e9f850> and <keras.engine.input_layer.InputLayer object at 0x7f41f9f33710>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40becc6b50> and <keras.engine.input_layer.InputLayer object at 0x7f40bed138d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40cf148150> and <keras.engine.input_layer.InputLayer object at 0x7f40d1015c50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d29e9f10> and <keras.engine.input_layer.InputLayer object at 0x7f40d2c37cd0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d70e50d0> and <keras.engine.input_layer.InputLayer object at 0x7f40d6c42910>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40e3232710> and <keras.engine.input_layer.InputLayer object at 0x7f40e22895d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4148316650> and <keras.engine.input_layer.InputLayer object at 0x7f4142a029d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f417846d590> and <keras.engine.input_layer.InputLayer object at 0x7f414a8a1a10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40e062c290> and <keras.engine.input_layer.InputLayer object at 0x7f40d6b25310>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4143dd2fd0> and <keras.engine.input_layer.InputLayer object at 0x7f4141fbe150>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40ce91d110> and <keras.engine.input_layer.InputLayer object at 0x7f40ce290810>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40cfc7c6d0> and <keras.engine.input_layer.InputLayer object at 0x7f4149781250>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f414a6f8250> and <keras.engine.input_layer.InputLayer object at 0x7f41433f9250>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40cf4c9210> and <keras.engine.input_layer.InputLayer object at 0x7f40cf16b6d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f414848c750> and <keras.engine.input_layer.InputLayer object at 0x7f4148a776d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40e0b71950> and <keras.engine.input_layer.InputLayer object at 0x7f40e0b26890>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f414a0e7990> and <keras.engine.input_layer.InputLayer object at 0x7f414be51a90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d0f63250> and <keras.engine.input_layer.InputLayer object at 0x7f40d14ec1d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d2cdff50> and <keras.engine.input_layer.InputLayer object at 0x7f4148337d50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d209af50> and <keras.engine.input_layer.InputLayer object at 0x7f40e0c22050>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d05f7290> and <keras.engine.input_layer.InputLayer object at 0x7f40d44e6490>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d402d050> and <keras.engine.input_layer.InputLayer object at 0x7f40d4e5fdd0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f414ac9e8d0> and <keras.engine.input_layer.InputLayer object at 0x7f414accfbd0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d4112f10> and <keras.engine.input_layer.InputLayer object at 0x7f40d43c2c50>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4101a65cd0> and <keras.engine.input_layer.InputLayer object at 0x7f40d61bd150>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f417942ef50> and <keras.engine.input_layer.InputLayer object at 0x7f4179479990>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f414a5457d0> and <keras.engine.input_layer.InputLayer object at 0x7f414a750610>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4143601a50> and <keras.engine.input_layer.InputLayer object at 0x7f414377ac10>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4102633610> and <keras.engine.input_layer.InputLayer object at 0x7f41026093d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40e112a4d0> and <keras.engine.input_layer.InputLayer object at 0x7f40e113f250>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d461cd10> and <keras.engine.input_layer.InputLayer object at 0x7f40d455a750>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40d0808110> and <keras.engine.input_layer.InputLayer object at 0x7f40d085dad0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40bdf44590> and <keras.engine.input_layer.InputLayer object at 0x7f40bdf904d0>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40bd0a99d0> and <keras.engine.input_layer.InputLayer object at 0x7f40bd0e3c90>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40bc232090> and <keras.engine.input_layer.InputLayer object at 0x7f40bc26c250>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f40bb26af50> and <keras.engine.input_layer.InputLayer object at 0x7f40bb2a0290>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f414bf80450> and <keras.engine.input_layer.InputLayer object at 0x7f4824302450>).
WARNING:tensorflow:Inconsistent references when loading the checkpoint into this object graph. Either the Trackable object references in the Python program have changed in an incompatible way, or the checkpoint was generated in an incompatible program.

Two checkpoint references resolved to different objects (<keras.saving.saved_model.load.TensorFlowTransform>TransformFeaturesLayer object at 0x7f4646281050> and <keras.engine.input_layer.InputLayer object at 0x7f464c7b39d0>).
INFO:absl:Evaluation complete. Results written to /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/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: 14
 type_id: 27
 uri: "/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/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"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 27
 name: "ModelEvaluation"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 ),
 'blessing': Channel(
     type_name: ModelBlessing
     artifacts: [Artifact(artifact: id: 15
 type_id: 28
 uri: "/tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/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"
   }
 }
 custom_properties {
   key: "tfx_version"
   value {
     string_value: "1.5.0"
   }
 }
 state: LIVE
 , artifact_type: id: 28
 name: "ModelBlessing"
 )]
     additional_properties: {}
     additional_custom_properties: {}
 )}

使用 evaluation 输出，我们可以显示整个评估集上全局指标的默认可视化。

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

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:107: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`

模型卡生成器

Model Card 组件是一个 TFX 组件，它从 StatisticGen 输出、Evaluator 输出和准备好的 json 注释生成模型卡 - 关于机器学习模型的关键信息的简短文档。可以选择提供推送的模型或模板。

模型卡资产保存到 ModelCard 工件中，可以从 outputs['model_card]' 属性中获取。

为模型卡准备注释 Json

记录对下游用户可能重要的模型信息也很重要，例如其局限性、预期用例、权衡和道德考量。因此，我们将以 json 格式准备此信息，以便在模型卡生成步骤中使用。

import json
import model_card_toolkit as mctlib

model_card_json = {'model_details': {'name': 'Census Income Classifier'}, 
                   '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_details': {'owners': [{"name": "Model Cards Team", "contact": "[email protected]"}]},
                   'considerations': {'use_cases':[{"description":'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.'}]},
                   'considerations': {'limitations': [{'description':
                      '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.'}]}, 
                    '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.'}]}
                  }

生成模型卡。

from model_card_toolkit.tfx.component import ModelCardGenerator

mct_gen = ModelCardGenerator(statistics=statistics_gen.outputs['statistics'],
                             evaluation=evaluator.outputs['evaluation'],
                             json=json.dumps(model_card_json))
context.run(mct_gen)

INFO:absl:Running driver for ModelCardGenerator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for ModelCardGenerator
INFO:absl:EvalResult found at path /tmpfs/tmp/tfx-Census Income Classification Pipeline-9ikbb3ld/Evaluator/evaluation/6
INFO:absl:Reading stats artifact from Split-train
INFO:absl:Reading stats artifact from Split-eval
INFO:absl:Running publisher for ModelCardGenerator
INFO:absl:MetadataStore with DB connection initialized

mct_gen.outputs['model_card']

模型卡显示

最后，我们从模型卡生成器工件中隔离 uri，并使用它来显示模型卡。

from IPython import display

mct_artifact = mct_gen.outputs['model_card'].get()[0]
mct_uri = mct_artifact.uri

print(os.listdir(mct_uri))

mct_path = os.path.join(mct_uri, 'model_cards', 'model_card.html')
with open(mct_path) as f:
  mct_content = f.read()


display.display(display.HTML(mct_content))

['template', 'model_cards', 'data']