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Procesamiento previo de datos con TensorFlow Transform

El componente de ingeniería de funciones de TensorFlow Extended (TFX)

Este cuaderno de ejemplo de Colab proporciona un ejemplo algo más avanzado de cómo TensorFlow Transform ( tf.Transform ) se puede usar para preprocesar datos usando exactamente el mismo código para entrenar un modelo y entregar inferencias en producción.

TensorFlow Transform es una biblioteca para preprocesar datos de entrada para TensorFlow, incluida la creación de funciones que requieren un pase completo sobre el conjunto de datos de entrenamiento. Por ejemplo, con TensorFlow Transform podrías:

  • Normalizar un valor de entrada utilizando la media y la desviación estándar
  • Convierta cadenas en números enteros generando un vocabulario sobre todos los valores de entrada
  • Convierta flotantes en enteros asignándolos a depósitos, según la distribución de datos observada

TensorFlow tiene soporte integrado para manipulaciones en un solo ejemplo o un lote de ejemplos. tf.Transform amplía estas capacidades para admitir pases completos en todo el conjunto de datos de entrenamiento.

La salida de tf.Transform se exporta como un gráfico de TensorFlow que puede usar tanto para el entrenamiento como para la tf.Transform . Usar el mismo gráfico tanto para el entrenamiento como para el servicio puede evitar el sesgo, ya que se aplican las mismas transformaciones en ambas etapas.

Qué estamos haciendo en este ejemplo

En este ejemplo, procesaremos un conjunto de datos ampliamente utilizado que contiene datos del censo y prepararemos un modelo para realizar la clasificación. En el camino, transformaremos los datos usando tf.Transform .

Actualizar Pip

Para evitar actualizar Pip en un sistema cuando se ejecuta localmente, verifique que estemos ejecutando en Colab. Por supuesto, los sistemas locales se pueden actualizar por separado.

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

Instalar TensorFlow

pip install tensorflow==2.2.0
Collecting tensorflow==2.2.0
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Successfully installed tensorboard-2.2.2 tensorflow-2.2.0 tensorflow-estimator-2.2.0

Comprobación, importaciones y globales de Python

Primero nos aseguraremos de que estamos usando Python 3, y luego vamos a instalar e importar lo que necesitamos.

import sys

# Confirm that we're using Python 3
assert sys.version_info.major is 3, 'Oops, not running Python 3. Use Runtime > Change runtime type'
import os
import pprint

import tensorflow as tf
print('TF: {}'.format(tf.__version__))

print('Installing Apache Beam')
!pip install -Uq apache_beam==2.21.0
import apache_beam as beam
print('Beam: {}'.format(beam.__version__))

print('Installing TensorFlow Transform')
!pip install -q tensorflow-transform==0.22.0
import tensorflow_transform as tft
print('Transform: {}'.format(tft.__version__))

import tensorflow_transform.beam as tft_beam

!wget https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.data
!wget https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.test

train = './adult.data'
test = './adult.test'
TF: 2.2.0
Installing Apache Beam
Beam: 2.21.0
Installing TensorFlow Transform
ERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.

We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.

tensorflow-serving-api 2.3.0 requires tensorflow<3,>=2.3, but you'll have tensorflow 2.2.0 which is incompatible.
Transform: 0.22.0
--2020-08-18 09:24:09--  https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.data
Resolving storage.googleapis.com (storage.googleapis.com)... 64.233.189.128, 108.177.97.128, 108.177.125.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.189.128|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 3974305 (3.8M) [application/octet-stream]
Saving to: ‘adult.data’

adult.data          100%[===================>]   3.79M  --.-KB/s    in 0.02s   

2020-08-18 09:24:10 (216 MB/s) - ‘adult.data’ saved [3974305/3974305]

--2020-08-18 09:24:10--  https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.test
Resolving storage.googleapis.com (storage.googleapis.com)... 64.233.188.128, 74.125.203.128, 108.177.125.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.188.128|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 2003153 (1.9M) [application/octet-stream]
Saving to: ‘adult.test’

adult.test          100%[===================>]   1.91M  --.-KB/s    in 0.02s   

2020-08-18 09:24:11 (96.5 MB/s) - ‘adult.test’ saved [2003153/2003153]


Nombra nuestras columnas

Crearemos algunas listas útiles para hacer referencia a las columnas en nuestro conjunto de datos.

CATEGORICAL_FEATURE_KEYS = [
    'workclass',
    'education',
    'marital-status',
    'occupation',
    'relationship',
    'race',
    'sex',
    'native-country',
]
NUMERIC_FEATURE_KEYS = [
    'age',
    'capital-gain',
    'capital-loss',
    'hours-per-week',
]
OPTIONAL_NUMERIC_FEATURE_KEYS = [
    'education-num',
]
LABEL_KEY = 'label'

Definir nuestras características y esquema

Definamos un esquema basado en los tipos de columnas en nuestra entrada. Entre otras cosas, esto ayudará a importarlos correctamente.

RAW_DATA_FEATURE_SPEC = dict(
    [(name, tf.io.FixedLenFeature([], tf.string))
     for name in CATEGORICAL_FEATURE_KEYS] +
    [(name, tf.io.FixedLenFeature([], tf.float32))
     for name in NUMERIC_FEATURE_KEYS] +
    [(name, tf.io.VarLenFeature(tf.float32))
     for name in OPTIONAL_NUMERIC_FEATURE_KEYS] +
    [(LABEL_KEY, tf.io.FixedLenFeature([], tf.string))]
)

RAW_DATA_METADATA = tft.tf_metadata.dataset_metadata.DatasetMetadata(
    tft.tf_metadata.dataset_schema.schema_utils.schema_from_feature_spec(RAW_DATA_FEATURE_SPEC))

Configuración de hiperparámetros y limpieza básica

Constantes e hiperparámetros utilizados para el entrenamiento. El tamaño del depósito incluye todas las categorías enumeradas en la descripción del conjunto de datos, así como una adicional para "?" que representa desconocido.

testing = os.getenv("WEB_TEST_BROWSER", False)
if testing:
  TRAIN_NUM_EPOCHS = 1
  NUM_TRAIN_INSTANCES = 1
  TRAIN_BATCH_SIZE = 1
  NUM_TEST_INSTANCES = 1
else:
  TRAIN_NUM_EPOCHS = 16
  NUM_TRAIN_INSTANCES = 32561
  TRAIN_BATCH_SIZE = 128
  NUM_TEST_INSTANCES = 16281

# Names of temp files
TRANSFORMED_TRAIN_DATA_FILEBASE = 'train_transformed'
TRANSFORMED_TEST_DATA_FILEBASE = 'test_transformed'
EXPORTED_MODEL_DIR = 'exported_model_dir'

Limpieza

Cree una transformación de haz para limpiar nuestros datos de entrada

Crearemos una transformación de haz creando una subclase de la clase PTransform de Apache Beam y anulando el método de expand para especificar la lógica de procesamiento real. Un PTransform representa una operación de procesamiento de datos, o un paso, en su canalización. Cada PTransform toma uno o más objetos PCollection como entrada, realiza una función de procesamiento que usted proporciona en los elementos de esa PCollection y produce cero o más objetos PCollection de salida.

Nuestra clase transformar aplicará de haz ParDo en la entrada PCollection que contiene nuestro conjunto de datos del censo, la producción de datos limpios en una salida PCollection .

class MapAndFilterErrors(beam.PTransform):
  """Like beam.Map but filters out errors in the map_fn."""

  class _MapAndFilterErrorsDoFn(beam.DoFn):
    """Count the bad examples using a beam metric."""

    def __init__(self, fn):
      self._fn = fn
      # Create a counter to measure number of bad elements.
      self._bad_elements_counter = beam.metrics.Metrics.counter(
          'census_example', 'bad_elements')

    def process(self, element):
      try:
        yield self._fn(element)
      except Exception:  # pylint: disable=broad-except
        # Catch any exception the above call.
        self._bad_elements_counter.inc(1)

  def __init__(self, fn):
    self._fn = fn

  def expand(self, pcoll):
    return pcoll | beam.ParDo(self._MapAndFilterErrorsDoFn(self._fn))

Preprocesamiento con tf.Transform

Crea un tf.Transform preprocessing_fn

La función de preprocesamiento es el concepto más importante de tf.Transform. Una función de preprocesamiento es donde realmente ocurre la transformación del conjunto de datos. Acepta y devuelve un diccionario de tensores, donde un tensor significa Tensor o SparseTensor . Hay dos grupos principales de llamadas a API que normalmente forman el corazón de una función de preprocesamiento:

  1. TensorFlow Ops: cualquier función que acepte y devuelva tensores, lo que generalmente significa operaciones de TensorFlow. Estos agregan operaciones de TensorFlow al gráfico que transforma los datos sin procesar en datos transformados, un vector de características a la vez. Estos se ejecutarán para cada ejemplo, tanto durante el entrenamiento como durante el servicio.
  2. Analizadores de transformación de TensorFlow: cualquiera de los analizadores proporcionados por tf.Transform. Los analizadores también aceptan y devuelven tensores, pero a diferencia de las operaciones de TensorFlow, solo se ejecutan una vez, durante el entrenamiento, y por lo general hacen un pase completo sobre todo el conjunto de datos de entrenamiento. Crean constantes tensoriales , que se agregan a su gráfico. Por ejemplo, tft.min calcula el mínimo de un tensor sobre el conjunto de datos de entrenamiento. tf.Transform proporciona un conjunto fijo de analizadores, pero esto se ampliará en versiones futuras.
def preprocessing_fn(inputs):
  """Preprocess input columns into transformed columns."""
  # Since we are modifying some features and leaving others unchanged, we
  # start by setting `outputs` to a copy of `inputs.
  outputs = inputs.copy()

  # Scale numeric columns to have range [0, 1].
  for key in NUMERIC_FEATURE_KEYS:
    outputs[key] = tft.scale_to_0_1(outputs[key])

  for key in OPTIONAL_NUMERIC_FEATURE_KEYS:
    # This is a SparseTensor because it is optional. Here we fill in a default
    # value when it is missing.
    sparse = tf.sparse.SparseTensor(outputs[key].indices, outputs[key].values,
                                    [outputs[key].dense_shape[0], 1])
    dense = tf.sparse.to_dense(sp_input=sparse, default_value=0.)
    # Reshaping from a batch of vectors of size 1 to a batch to scalars.
    dense = tf.squeeze(dense, axis=1)
    outputs[key] = tft.scale_to_0_1(dense)

  # For all categorical columns except the label column, we generate a
  # vocabulary but do not modify the feature.  This vocabulary is instead
  # used in the trainer, by means of a feature column, to convert the feature
  # from a string to an integer id.
  for key in CATEGORICAL_FEATURE_KEYS:
    tft.vocabulary(inputs[key], vocab_filename=key)

  # For the label column we provide the mapping from string to index.
  table_keys = ['>50K', '<=50K']
  initializer = tf.lookup.KeyValueTensorInitializer(
      keys=table_keys,
      values=tf.cast(tf.range(len(table_keys)), tf.int64),
      key_dtype=tf.string,
      value_dtype=tf.int64)
  table = tf.lookup.StaticHashTable(initializer, default_value=-1)
  outputs[LABEL_KEY] = table.lookup(outputs[LABEL_KEY])

  return outputs

Transforma los datos

Ahora estamos listos para comenzar a transformar nuestros datos en una canalización de Apache Beam.

  1. Leer los datos usando el lector CSV
  2. Límpielo usando nuestra nueva transformación MapAndFilterErrors
  3. Transfórmelo utilizando una canalización de preprocesamiento que escale datos numéricos y convierta datos categóricos de cadenas a índices de valores int64, creando un vocabulario para cada categoría
  4. Escriba el resultado como un TFRecord of Example protos, que usaremos para entrenar un modelo más adelante
def transform_data(train_data_file, test_data_file, working_dir):
  """Transform the data and write out as a TFRecord of Example protos.

  Read in the data using the CSV reader, and transform it using a
  preprocessing pipeline that scales numeric data and converts categorical data
  from strings to int64 values indices, by creating a vocabulary for each
  category.

  Args:
    train_data_file: File containing training data
    test_data_file: File containing test data
    working_dir: Directory to write transformed data and metadata to
  """

  # The "with" block will create a pipeline, and run that pipeline at the exit
  # of the block.
  with beam.Pipeline() as pipeline:
    with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
      # Create a coder to read the census data with the schema.  To do this we
      # need to list all columns in order since the schema doesn't specify the
      # order of columns in the csv.
      ordered_columns = [
          'age', 'workclass', 'fnlwgt', 'education', 'education-num',
          'marital-status', 'occupation', 'relationship', 'race', 'sex',
          'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',
          'label'
      ]
      converter = tft.coders.CsvCoder(ordered_columns, RAW_DATA_METADATA.schema)

      # Read in raw data and convert using CSV converter.  Note that we apply
      # some Beam transformations here, which will not be encoded in the TF
      # graph since we don't do them from within tf.Transform's methods
      # (AnalyzeDataset, TransformDataset etc.).  These transformations are just
      # to get data into a format that the CSV converter can read, in particular
      # removing spaces after commas.
      #
      # We use MapAndFilterErrors instead of Map to filter out decode errors in
      # convert.decode which should only occur for the trailing blank line.
      raw_data = (
          pipeline
          | 'ReadTrainData' >> beam.io.ReadFromText(train_data_file)
          | 'FixCommasTrainData' >> beam.Map(
              lambda line: line.replace(', ', ','))
          | 'DecodeTrainData' >> MapAndFilterErrors(converter.decode))

      # Combine data and schema into a dataset tuple.  Note that we already used
      # the schema to read the CSV data, but we also need it to interpret
      # raw_data.
      raw_dataset = (raw_data, RAW_DATA_METADATA)
      transformed_dataset, transform_fn = (
          raw_dataset | tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
      transformed_data, transformed_metadata = transformed_dataset
      transformed_data_coder = tft.coders.ExampleProtoCoder(
          transformed_metadata.schema)

      _ = (
          transformed_data
          | 'EncodeTrainData' >> beam.Map(transformed_data_coder.encode)
          | 'WriteTrainData' >> beam.io.WriteToTFRecord(
              os.path.join(working_dir, TRANSFORMED_TRAIN_DATA_FILEBASE)))

      # Now apply transform function to test data.  In this case we remove the
      # trailing period at the end of each line, and also ignore the header line
      # that is present in the test data file.
      raw_test_data = (
          pipeline
          | 'ReadTestData' >> beam.io.ReadFromText(test_data_file,
                                                   skip_header_lines=1)
          | 'FixCommasTestData' >> beam.Map(
              lambda line: line.replace(', ', ','))
          | 'RemoveTrailingPeriodsTestData' >> beam.Map(lambda line: line[:-1])
          | 'DecodeTestData' >> MapAndFilterErrors(converter.decode))

      raw_test_dataset = (raw_test_data, RAW_DATA_METADATA)

      transformed_test_dataset = (
          (raw_test_dataset, transform_fn) | tft_beam.TransformDataset())
      # Don't need transformed data schema, it's the same as before.
      transformed_test_data, _ = transformed_test_dataset

      _ = (
          transformed_test_data
          | 'EncodeTestData' >> beam.Map(transformed_data_coder.encode)
          | 'WriteTestData' >> beam.io.WriteToTFRecord(
              os.path.join(working_dir, TRANSFORMED_TEST_DATA_FILEBASE)))

      # Will write a SavedModel and metadata to working_dir, which can then
      # be read by the tft.TFTransformOutput class.
      _ = (
          transform_fn
          | 'WriteTransformFn' >> tft_beam.WriteTransformFn(working_dir))

Usando nuestros datos preprocesados ​​para entrenar un modelo

Para mostrar cómo tf.Transform nos permite usar el mismo código tanto para el entrenamiento como para el servicio, y así evitar el sesgo, vamos a entrenar un modelo. Para entrenar nuestro modelo y preparar nuestro modelo entrenado para la producción, necesitamos crear funciones de entrada. La principal diferencia entre nuestra función de entrada de entrenamiento y nuestra función de entrada de servicio es que los datos de entrenamiento contienen las etiquetas y los datos de producción no. Los argumentos y las devoluciones también son algo diferentes.

Crea una función de entrada para entrenamiento

def _make_training_input_fn(tf_transform_output, transformed_examples,
                            batch_size):
  """Creates an input function reading from transformed data.

  Args:
    tf_transform_output: Wrapper around output of tf.Transform.
    transformed_examples: Base filename of examples.
    batch_size: Batch size.

  Returns:
    The input function for training or eval.
  """
  def input_fn():
    """Input function for training and eval."""
    dataset = tf.data.experimental.make_batched_features_dataset(
        file_pattern=transformed_examples,
        batch_size=batch_size,
        features=tf_transform_output.transformed_feature_spec(),
        reader=tf.data.TFRecordDataset,
        shuffle=True)

    transformed_features = tf.compat.v1.data.make_one_shot_iterator(
        dataset).get_next()

    # Extract features and label from the transformed tensors.
    transformed_labels = transformed_features.pop(LABEL_KEY)

    return transformed_features, transformed_labels

  return input_fn

Crea una función de entrada para servir

Creemos una función de entrada que podamos usar en producción y preparemos nuestro modelo entrenado para servir.

def _make_serving_input_fn(tf_transform_output):
  """Creates an input function reading from raw data.

  Args:
    tf_transform_output: Wrapper around output of tf.Transform.

  Returns:
    The serving input function.
  """
  raw_feature_spec = RAW_DATA_FEATURE_SPEC.copy()
  # Remove label since it is not available during serving.
  raw_feature_spec.pop(LABEL_KEY)

  def serving_input_fn():
    """Input function for serving."""
    # Get raw features by generating the basic serving input_fn and calling it.
    # Here we generate an input_fn that expects a parsed Example proto to be fed
    # to the model at serving time.  See also
    # tf.estimator.export.build_raw_serving_input_receiver_fn.
    raw_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
        raw_feature_spec, default_batch_size=None)
    serving_input_receiver = raw_input_fn()

    # Apply the transform function that was used to generate the materialized
    # data.
    raw_features = serving_input_receiver.features
    transformed_features = tf_transform_output.transform_raw_features(
        raw_features)

    return tf.estimator.export.ServingInputReceiver(
        transformed_features, serving_input_receiver.receiver_tensors)

  return serving_input_fn

Envuelva nuestros datos de entrada en FeatureColumns

Nuestro modelo esperará nuestros datos en TensorFlow FeatureColumns.

def get_feature_columns(tf_transform_output):
  """Returns the FeatureColumns for the model.

  Args:
    tf_transform_output: A `TFTransformOutput` object.

  Returns:
    A list of FeatureColumns.
  """
  # Wrap scalars as real valued columns.
  real_valued_columns = [tf.feature_column.numeric_column(key, shape=())
                         for key in NUMERIC_FEATURE_KEYS]

  # Wrap categorical columns.
  one_hot_columns = [
      tf.feature_column.categorical_column_with_vocabulary_file(
          key=key,
          vocabulary_file=tf_transform_output.vocabulary_file_by_name(
              vocab_filename=key))
      for key in CATEGORICAL_FEATURE_KEYS]

  return real_valued_columns + one_hot_columns

Capacitar, evaluar y exportar nuestro modelo

def train_and_evaluate(working_dir, num_train_instances=NUM_TRAIN_INSTANCES,
                       num_test_instances=NUM_TEST_INSTANCES):
  """Train the model on training data and evaluate on test data.

  Args:
    working_dir: Directory to read transformed data and metadata from and to
        write exported model to.
    num_train_instances: Number of instances in train set
    num_test_instances: Number of instances in test set

  Returns:
    The results from the estimator's 'evaluate' method
  """
  tf_transform_output = tft.TFTransformOutput(working_dir)

  run_config = tf.estimator.RunConfig()

  estimator = tf.estimator.LinearClassifier(
      feature_columns=get_feature_columns(tf_transform_output),
      config=run_config,
      loss_reduction=tf.losses.Reduction.SUM)

  # Fit the model using the default optimizer.
  train_input_fn = _make_training_input_fn(
      tf_transform_output,
      os.path.join(working_dir, TRANSFORMED_TRAIN_DATA_FILEBASE + '*'),
      batch_size=TRAIN_BATCH_SIZE)
  estimator.train(
      input_fn=train_input_fn,
      max_steps=TRAIN_NUM_EPOCHS * num_train_instances / TRAIN_BATCH_SIZE)

  # Evaluate model on test dataset.
  eval_input_fn = _make_training_input_fn(
      tf_transform_output,
      os.path.join(working_dir, TRANSFORMED_TEST_DATA_FILEBASE + '*'),
      batch_size=1)

  # Export the model.
  serving_input_fn = _make_serving_input_fn(tf_transform_output)
  exported_model_dir = os.path.join(working_dir, EXPORTED_MODEL_DIR)
  estimator.export_saved_model(exported_model_dir, serving_input_fn)

  return estimator.evaluate(input_fn=eval_input_fn, steps=num_test_instances)

Ponlo todo junto

Hemos creado todo lo que necesitamos para procesar previamente los datos del censo, entrenar un modelo y prepararlo para su publicación. Hasta ahora solo hemos estado preparando las cosas. ¡Es hora de empezar a correr!

import tempfile
temp = tempfile.gettempdir()

transform_data(train, test, temp)
results = train_and_evaluate(temp)
pprint.pprint(results)
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.

Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:220: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:220: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:No assets to write.

INFO:tensorflow:No assets to write.

Warning:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'

Warning:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'

Warning:tensorflow:Issue encountered when serializing tft_analyzer_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'

Warning:tensorflow:Issue encountered when serializing tft_analyzer_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'

INFO:tensorflow:SavedModel written to: /tmp/tmptntmkgtc/tftransform_tmp/6807e6f02233499db5d910c34462bba9/saved_model.pb

INFO:tensorflow:SavedModel written to: /tmp/tmptntmkgtc/tftransform_tmp/6807e6f02233499db5d910c34462bba9/saved_model.pb

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:No assets to write.

INFO:tensorflow:No assets to write.

Warning:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'

Warning:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'

Warning:tensorflow:Issue encountered when serializing tft_analyzer_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'

Warning:tensorflow:Issue encountered when serializing tft_analyzer_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'

INFO:tensorflow:SavedModel written to: /tmp/tmptntmkgtc/tftransform_tmp/d350b2f9afca405f80c7f6069b138eb0/saved_model.pb

INFO:tensorflow:SavedModel written to: /tmp/tmptntmkgtc/tftransform_tmp/d350b2f9afca405f80c7f6069b138eb0/saved_model.pb

Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 

Warning:tensorflow:Tensorflow version (2.2.0) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 
WARNING:apache_beam.utils.interactive_utils:Failed to alter the label of a transform with the ipython prompt metadata. Cannot figure out the pipeline that the given pvalueish ((<PCollection[DecodeTestData/ParDo(_MapAndFilterErrorsDoFn).None] at 0x7f042c2f0be0>, {'_schema': feature {
  name: "age"
  type: FLOAT
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "capital-gain"
  type: FLOAT
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "capital-loss"
  type: FLOAT
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "education"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "education-num"
  type: FLOAT
}
feature {
  name: "hours-per-week"
  type: FLOAT
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "label"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "marital-status"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "native-country"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "occupation"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "race"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "relationship"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "sex"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "workclass"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
}), (<PCollection[AnalyzeAndTransformDataset/AnalyzeDataset/CreateSavedModel/BindTensors/ReplaceWithConstants.None] at 0x7f042c318550>, BeamDatasetMetadata(dataset_metadata={'_schema': feature {
  name: "age"
  type: FLOAT
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "capital-gain"
  type: FLOAT
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "capital-loss"
  type: FLOAT
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "education"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "education-num"
  type: FLOAT
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "hours-per-week"
  type: FLOAT
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "label"
  type: INT
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "marital-status"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "native-country"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "occupation"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "race"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "relationship"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "sex"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
feature {
  name: "workclass"
  type: BYTES
  presence {
    min_fraction: 1.0
  }
  shape {
  }
}
}, deferred_metadata=<PCollection[AnalyzeAndTransformDataset/AnalyzeDataset/ComputeDeferredMetadata.None] at 0x7f042c2fce48>))) belongs to. Thus noop.

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:Assets written to: /tmp/tmptntmkgtc/tftransform_tmp/a838335ddf08440faef27b83689d05cf/assets

INFO:tensorflow:Assets written to: /tmp/tmptntmkgtc/tftransform_tmp/a838335ddf08440faef27b83689d05cf/assets

INFO:tensorflow:SavedModel written to: /tmp/tmptntmkgtc/tftransform_tmp/a838335ddf08440faef27b83689d05cf/saved_model.pb

INFO:tensorflow:SavedModel written to: /tmp/tmptntmkgtc/tftransform_tmp/a838335ddf08440faef27b83689d05cf/saved_model.pb

Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"


INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"


INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
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.

Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"


INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:vocabulary_size = 9 in workclass is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/workclass.

INFO:tensorflow:vocabulary_size = 9 in workclass is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/workclass.

INFO:tensorflow:vocabulary_size = 16 in education is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/education.

INFO:tensorflow:vocabulary_size = 16 in education is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/education.

INFO:tensorflow:vocabulary_size = 7 in marital-status is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/marital-status.

INFO:tensorflow:vocabulary_size = 7 in marital-status is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/marital-status.

INFO:tensorflow:vocabulary_size = 15 in occupation is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/occupation.

INFO:tensorflow:vocabulary_size = 15 in occupation is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/occupation.

INFO:tensorflow:vocabulary_size = 6 in relationship is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/relationship.

INFO:tensorflow:vocabulary_size = 6 in relationship is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/relationship.

INFO:tensorflow:vocabulary_size = 5 in race is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/race.

INFO:tensorflow:vocabulary_size = 5 in race is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/race.

INFO:tensorflow:vocabulary_size = 2 in sex is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/sex.

INFO:tensorflow:vocabulary_size = 2 in sex is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/sex.

INFO:tensorflow:vocabulary_size = 42 in native-country is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/native-country.

INFO:tensorflow:vocabulary_size = 42 in native-country is inferred from the number of elements in the vocabulary_file /tmp/transform_fn/assets/native-country.

Warning:tensorflow:Using temporary folder as model directory: /tmp/tmputnc4riv

Warning:tensorflow:Using temporary folder as model directory: /tmp/tmputnc4riv

INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmputnc4riv', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}

INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmputnc4riv', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.

INFO:tensorflow:Calling model_fn.

INFO:tensorflow:Calling model_fn.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:540: Layer.add_variable (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.add_weight` method instead.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:540: Layer.add_variable (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.add_weight` method instead.

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/ftrl.py:144: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor

Warning:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/ftrl.py:144: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor

INFO:tensorflow:Done calling model_fn.

INFO:tensorflow:Done calling model_fn.

INFO:tensorflow:Create CheckpointSaverHook.

INFO:tensorflow:Create CheckpointSaverHook.

INFO:tensorflow:Graph was finalized.

INFO:tensorflow:Graph was finalized.

INFO:tensorflow:Running local_init_op.

INFO:tensorflow:Running local_init_op.

INFO:tensorflow:Done running local_init_op.

INFO:tensorflow:Done running local_init_op.

INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...

INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...

INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmputnc4riv/model.ckpt.

INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmputnc4riv/model.ckpt.

INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...

INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...

INFO:tensorflow:loss = 88.72284, step = 0

INFO:tensorflow:loss = 88.72284, step = 0

INFO:tensorflow:global_step/sec: 76.9862

INFO:tensorflow:global_step/sec: 76.9862

INFO:tensorflow:loss = 38.703667, step = 100 (1.301 sec)

INFO:tensorflow:loss = 38.703667, step = 100 (1.301 sec)

INFO:tensorflow:global_step/sec: 99.9878

INFO:tensorflow:global_step/sec: 99.9878

INFO:tensorflow:loss = 45.19542, step = 200 (1.000 sec)

INFO:tensorflow:loss = 45.19542, step = 200 (1.000 sec)

INFO:tensorflow:global_step/sec: 99.859

INFO:tensorflow:global_step/sec: 99.859

INFO:tensorflow:loss = 45.45166, step = 300 (1.001 sec)

INFO:tensorflow:loss = 45.45166, step = 300 (1.001 sec)

INFO:tensorflow:global_step/sec: 100.861

INFO:tensorflow:global_step/sec: 100.861

INFO:tensorflow:loss = 43.172836, step = 400 (0.991 sec)

INFO:tensorflow:loss = 43.172836, step = 400 (0.991 sec)

INFO:tensorflow:global_step/sec: 100.344

INFO:tensorflow:global_step/sec: 100.344

INFO:tensorflow:loss = 47.976437, step = 500 (0.997 sec)

INFO:tensorflow:loss = 47.976437, step = 500 (0.997 sec)

INFO:tensorflow:global_step/sec: 100.028

INFO:tensorflow:global_step/sec: 100.028

INFO:tensorflow:loss = 36.716545, step = 600 (0.999 sec)

INFO:tensorflow:loss = 36.716545, step = 600 (0.999 sec)

INFO:tensorflow:global_step/sec: 101.002

INFO:tensorflow:global_step/sec: 101.002

INFO:tensorflow:loss = 49.73072, step = 700 (0.990 sec)

INFO:tensorflow:loss = 49.73072, step = 700 (0.990 sec)

INFO:tensorflow:global_step/sec: 100.215

INFO:tensorflow:global_step/sec: 100.215

INFO:tensorflow:loss = 51.18441, step = 800 (0.998 sec)

INFO:tensorflow:loss = 51.18441, step = 800 (0.998 sec)

INFO:tensorflow:global_step/sec: 100.284

INFO:tensorflow:global_step/sec: 100.284

INFO:tensorflow:loss = 43.9189, step = 900 (0.997 sec)

INFO:tensorflow:loss = 43.9189, step = 900 (0.997 sec)

INFO:tensorflow:global_step/sec: 100.351

INFO:tensorflow:global_step/sec: 100.351

INFO:tensorflow:loss = 42.52917, step = 1000 (0.997 sec)

INFO:tensorflow:loss = 42.52917, step = 1000 (0.997 sec)

INFO:tensorflow:global_step/sec: 112.56

INFO:tensorflow:global_step/sec: 112.56

INFO:tensorflow:loss = 44.90046, step = 1100 (0.888 sec)

INFO:tensorflow:loss = 44.90046, step = 1100 (0.888 sec)

INFO:tensorflow:global_step/sec: 119.925

INFO:tensorflow:global_step/sec: 119.925

INFO:tensorflow:loss = 43.706593, step = 1200 (0.834 sec)

INFO:tensorflow:loss = 43.706593, step = 1200 (0.834 sec)

INFO:tensorflow:global_step/sec: 120.519

INFO:tensorflow:global_step/sec: 120.519

INFO:tensorflow:loss = 44.676178, step = 1300 (0.829 sec)

INFO:tensorflow:loss = 44.676178, step = 1300 (0.829 sec)

INFO:tensorflow:global_step/sec: 119.963

INFO:tensorflow:global_step/sec: 119.963

INFO:tensorflow:loss = 43.093307, step = 1400 (0.834 sec)

INFO:tensorflow:loss = 43.093307, step = 1400 (0.834 sec)

INFO:tensorflow:global_step/sec: 119.763

INFO:tensorflow:global_step/sec: 119.763

INFO:tensorflow:loss = 42.738785, step = 1500 (0.835 sec)

INFO:tensorflow:loss = 42.738785, step = 1500 (0.835 sec)

INFO:tensorflow:global_step/sec: 121.425

INFO:tensorflow:global_step/sec: 121.425

INFO:tensorflow:loss = 40.920486, step = 1600 (0.824 sec)

INFO:tensorflow:loss = 40.920486, step = 1600 (0.824 sec)

INFO:tensorflow:global_step/sec: 119.283

INFO:tensorflow:global_step/sec: 119.283

INFO:tensorflow:loss = 41.593758, step = 1700 (0.838 sec)

INFO:tensorflow:loss = 41.593758, step = 1700 (0.838 sec)

INFO:tensorflow:global_step/sec: 120.527

INFO:tensorflow:global_step/sec: 120.527

INFO:tensorflow:loss = 52.07851, step = 1800 (0.830 sec)

INFO:tensorflow:loss = 52.07851, step = 1800 (0.830 sec)

INFO:tensorflow:global_step/sec: 121.508

INFO:tensorflow:global_step/sec: 121.508

INFO:tensorflow:loss = 47.40565, step = 1900 (0.823 sec)

INFO:tensorflow:loss = 47.40565, step = 1900 (0.823 sec)

INFO:tensorflow:global_step/sec: 120.495

INFO:tensorflow:global_step/sec: 120.495

INFO:tensorflow:loss = 37.15222, step = 2000 (0.830 sec)

INFO:tensorflow:loss = 37.15222, step = 2000 (0.830 sec)

INFO:tensorflow:global_step/sec: 121.44

INFO:tensorflow:global_step/sec: 121.44

INFO:tensorflow:loss = 40.43554, step = 2100 (0.824 sec)

INFO:tensorflow:loss = 40.43554, step = 2100 (0.824 sec)

INFO:tensorflow:global_step/sec: 121.327

INFO:tensorflow:global_step/sec: 121.327

INFO:tensorflow:loss = 34.854286, step = 2200 (0.824 sec)

INFO:tensorflow:loss = 34.854286, step = 2200 (0.824 sec)

INFO:tensorflow:global_step/sec: 121.412

INFO:tensorflow:global_step/sec: 121.412

INFO:tensorflow:loss = 39.517056, step = 2300 (0.824 sec)

INFO:tensorflow:loss = 39.517056, step = 2300 (0.824 sec)

INFO:tensorflow:global_step/sec: 121.074

INFO:tensorflow:global_step/sec: 121.074

INFO:tensorflow:loss = 42.165592, step = 2400 (0.826 sec)

INFO:tensorflow:loss = 42.165592, step = 2400 (0.826 sec)

INFO:tensorflow:global_step/sec: 120.49

INFO:tensorflow:global_step/sec: 120.49

INFO:tensorflow:loss = 41.839676, step = 2500 (0.830 sec)

INFO:tensorflow:loss = 41.839676, step = 2500 (0.830 sec)

INFO:tensorflow:global_step/sec: 120.712

INFO:tensorflow:global_step/sec: 120.712

INFO:tensorflow:loss = 39.65165, step = 2600 (0.829 sec)

INFO:tensorflow:loss = 39.65165, step = 2600 (0.829 sec)

INFO:tensorflow:global_step/sec: 119.787

INFO:tensorflow:global_step/sec: 119.787

INFO:tensorflow:loss = 42.94733, step = 2700 (0.835 sec)

INFO:tensorflow:loss = 42.94733, step = 2700 (0.835 sec)

INFO:tensorflow:global_step/sec: 121.19

INFO:tensorflow:global_step/sec: 121.19

INFO:tensorflow:loss = 41.764587, step = 2800 (0.825 sec)

INFO:tensorflow:loss = 41.764587, step = 2800 (0.825 sec)

INFO:tensorflow:global_step/sec: 120.891

INFO:tensorflow:global_step/sec: 120.891

INFO:tensorflow:loss = 51.07626, step = 2900 (0.827 sec)

INFO:tensorflow:loss = 51.07626, step = 2900 (0.827 sec)

INFO:tensorflow:global_step/sec: 120.676

INFO:tensorflow:global_step/sec: 120.676

INFO:tensorflow:loss = 44.027252, step = 3000 (0.829 sec)

INFO:tensorflow:loss = 44.027252, step = 3000 (0.829 sec)

INFO:tensorflow:global_step/sec: 120.657

INFO:tensorflow:global_step/sec: 120.657

INFO:tensorflow:loss = 39.74543, step = 3100 (0.829 sec)

INFO:tensorflow:loss = 39.74543, step = 3100 (0.829 sec)

INFO:tensorflow:global_step/sec: 119.515

INFO:tensorflow:global_step/sec: 119.515

INFO:tensorflow:loss = 37.307327, step = 3200 (0.837 sec)

INFO:tensorflow:loss = 37.307327, step = 3200 (0.837 sec)

INFO:tensorflow:global_step/sec: 119.363

INFO:tensorflow:global_step/sec: 119.363

INFO:tensorflow:loss = 38.945374, step = 3300 (0.838 sec)

INFO:tensorflow:loss = 38.945374, step = 3300 (0.838 sec)

INFO:tensorflow:global_step/sec: 120.96

INFO:tensorflow:global_step/sec: 120.96

INFO:tensorflow:loss = 35.931507, step = 3400 (0.827 sec)

INFO:tensorflow:loss = 35.931507, step = 3400 (0.827 sec)

INFO:tensorflow:global_step/sec: 119.252

INFO:tensorflow:global_step/sec: 119.252

INFO:tensorflow:loss = 40.045303, step = 3500 (0.838 sec)

INFO:tensorflow:loss = 40.045303, step = 3500 (0.838 sec)

INFO:tensorflow:global_step/sec: 121.723

INFO:tensorflow:global_step/sec: 121.723

INFO:tensorflow:loss = 38.345215, step = 3600 (0.822 sec)

INFO:tensorflow:loss = 38.345215, step = 3600 (0.822 sec)

INFO:tensorflow:global_step/sec: 122.203

INFO:tensorflow:global_step/sec: 122.203

INFO:tensorflow:loss = 47.337906, step = 3700 (0.818 sec)

INFO:tensorflow:loss = 47.337906, step = 3700 (0.818 sec)

INFO:tensorflow:global_step/sec: 120.313

INFO:tensorflow:global_step/sec: 120.313

INFO:tensorflow:loss = 51.29358, step = 3800 (0.831 sec)

INFO:tensorflow:loss = 51.29358, step = 3800 (0.831 sec)

INFO:tensorflow:global_step/sec: 120.656

INFO:tensorflow:global_step/sec: 120.656

INFO:tensorflow:loss = 43.02392, step = 3900 (0.829 sec)

INFO:tensorflow:loss = 43.02392, step = 3900 (0.829 sec)

INFO:tensorflow:global_step/sec: 121.049

INFO:tensorflow:global_step/sec: 121.049

INFO:tensorflow:loss = 34.73887, step = 4000 (0.826 sec)

INFO:tensorflow:loss = 34.73887, step = 4000 (0.826 sec)

INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4071...

INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4071...

INFO:tensorflow:Saving checkpoints for 4071 into /tmp/tmputnc4riv/model.ckpt.

INFO:tensorflow:Saving checkpoints for 4071 into /tmp/tmputnc4riv/model.ckpt.

INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4071...

INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4071...

INFO:tensorflow:Loss for final step: 38.014317.

INFO:tensorflow:Loss for final step: 38.014317.

Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"


Warning:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"


INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

INFO:tensorflow:Calling model_fn.

INFO:tensorflow:Calling model_fn.

INFO:tensorflow:Done calling model_fn.

INFO:tensorflow:Done calling model_fn.

INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification']

INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification']

INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression']

INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression']

INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']

INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']

INFO:tensorflow:Signatures INCLUDED in export for Train: None

INFO:tensorflow:Signatures INCLUDED in export for Train: None

INFO:tensorflow:Signatures INCLUDED in export for Eval: None

INFO:tensorflow:Signatures INCLUDED in export for Eval: None

INFO:tensorflow:Restoring parameters from /tmp/tmputnc4riv/model.ckpt-4071

INFO:tensorflow:Restoring parameters from /tmp/tmputnc4riv/model.ckpt-4071

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:Assets added to graph.

INFO:tensorflow:Assets written to: /tmp/exported_model_dir/temp-1597742714/assets

INFO:tensorflow:Assets written to: /tmp/exported_model_dir/temp-1597742714/assets

INFO:tensorflow:SavedModel written to: /tmp/exported_model_dir/temp-1597742714/saved_model.pb

INFO:tensorflow:SavedModel written to: /tmp/exported_model_dir/temp-1597742714/saved_model.pb

INFO:tensorflow:Calling model_fn.

INFO:tensorflow:Calling model_fn.

INFO:tensorflow:Done calling model_fn.

INFO:tensorflow:Done calling model_fn.

INFO:tensorflow:Starting evaluation at 2020-08-18T09:25:17Z

INFO:tensorflow:Starting evaluation at 2020-08-18T09:25:17Z

INFO:tensorflow:Graph was finalized.

INFO:tensorflow:Graph was finalized.

INFO:tensorflow:Restoring parameters from /tmp/tmputnc4riv/model.ckpt-4071

INFO:tensorflow:Restoring parameters from /tmp/tmputnc4riv/model.ckpt-4071

INFO:tensorflow:Running local_init_op.

INFO:tensorflow:Running local_init_op.

INFO:tensorflow:Done running local_init_op.

INFO:tensorflow:Done running local_init_op.

INFO:tensorflow:Evaluation [1628/16281]

INFO:tensorflow:Evaluation [1628/16281]

INFO:tensorflow:Evaluation [3256/16281]

INFO:tensorflow:Evaluation [3256/16281]

INFO:tensorflow:Evaluation [4884/16281]

INFO:tensorflow:Evaluation [4884/16281]

INFO:tensorflow:Evaluation [6512/16281]

INFO:tensorflow:Evaluation [6512/16281]

INFO:tensorflow:Evaluation [8140/16281]

INFO:tensorflow:Evaluation [8140/16281]

INFO:tensorflow:Evaluation [9768/16281]

INFO:tensorflow:Evaluation [9768/16281]

INFO:tensorflow:Evaluation [11396/16281]

INFO:tensorflow:Evaluation [11396/16281]

INFO:tensorflow:Evaluation [13024/16281]

INFO:tensorflow:Evaluation [13024/16281]

INFO:tensorflow:Evaluation [14652/16281]

INFO:tensorflow:Evaluation [14652/16281]

INFO:tensorflow:Evaluation [16280/16281]

INFO:tensorflow:Evaluation [16280/16281]

INFO:tensorflow:Evaluation [16281/16281]

INFO:tensorflow:Evaluation [16281/16281]

INFO:tensorflow:Inference Time : 129.68261s

INFO:tensorflow:Inference Time : 129.68261s

INFO:tensorflow:Finished evaluation at 2020-08-18-09:27:26

INFO:tensorflow:Finished evaluation at 2020-08-18-09:27:26

INFO:tensorflow:Saving dict for global step 4071: accuracy = 0.85037774, accuracy_baseline = 0.76377374, auc = 0.901911, auc_precision_recall = 0.96723056, average_loss = 0.3240134, global_step = 4071, label/mean = 0.76377374, loss = 0.3240134, precision = 0.8783774, prediction/mean = 0.7628007, recall = 0.93333334

INFO:tensorflow:Saving dict for global step 4071: accuracy = 0.85037774, accuracy_baseline = 0.76377374, auc = 0.901911, auc_precision_recall = 0.96723056, average_loss = 0.3240134, global_step = 4071, label/mean = 0.76377374, loss = 0.3240134, precision = 0.8783774, prediction/mean = 0.7628007, recall = 0.93333334

INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4071: /tmp/tmputnc4riv/model.ckpt-4071

INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4071: /tmp/tmputnc4riv/model.ckpt-4071

{'accuracy': 0.85037774,
 'accuracy_baseline': 0.76377374,
 'auc': 0.901911,
 'auc_precision_recall': 0.96723056,
 'average_loss': 0.3240134,
 'global_step': 4071,
 'label/mean': 0.76377374,
 'loss': 0.3240134,
 'precision': 0.8783774,
 'prediction/mean': 0.7628007,
 'recall': 0.93333334}

Lo que hicimos

En este ejemplo, usamos tf.Transform para preprocesar un conjunto de datos de censos y entrenar un modelo con los datos limpios y transformados. También creamos una función de entrada que podríamos usar cuando implementamos nuestro modelo entrenado en un entorno de producción para realizar inferencias. Al usar el mismo código tanto para el entrenamiento como para la inferencia, evitamos cualquier problema con la desviación de datos. A lo largo del camino, aprendimos sobre cómo crear una transformación de Apache Beam para realizar la transformación que necesitábamos para limpiar los datos y empaquetamos nuestros datos en TensorFlow FeatureColumns . ¡Esto es solo una pequeña parte de lo que TensorFlow Transform puede hacer! Le animamos a sumergirse en tf.Transform y descubrir lo que puede hacer por usted.