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Vorverarbeitung von Daten mit TensorFlow Transform

Die Feature Engineering-Komponente von TensorFlow Extended (TFX)

Dieses Colab-Beispiel-Notebook bietet ein etwas fortgeschritteneres Beispiel dafür, wie TensorFlow Transform ( tf.Transform ) verwendet werden kann, um Daten mit genau demselben Code tf.Transform um ein Modell zu trainieren und Inferenzen in der Produktion tf.Transform .

TensorFlow Transform ist eine Bibliothek zur Vorverarbeitung von Eingabedaten für TensorFlow, einschließlich der Erstellung von Features, die einen vollständigen Durchgang über das Trainings-Dataset erfordern. Mit TensorFlow Transform könnten Sie beispielsweise:

  • Normalisieren Sie einen Eingabewert, indem Sie den Mittelwert und die Standardabweichung verwenden
  • Konvertieren Sie Strings in ganze Zahlen, indem Sie ein Vokabular über alle Eingabewerte generieren
  • Konvertieren Sie Gleitkommazahlen in Ganzzahlen, indem Sie sie Buckets basierend auf der beobachteten Datenverteilung zuweisen

TensorFlow bietet integrierte Unterstützung für Manipulationen an einem einzelnen Beispiel oder einer Reihe von Beispielen. tf.Transform erweitert diese Funktionen, um vollständige Durchgänge über den gesamten Trainingsdatensatz zu unterstützen.

Die Ausgabe von tf.Transform wird als TensorFlow-Diagramm exportiert, das Sie sowohl für das Training als auch für die Bereitstellung verwenden können. Die Verwendung des gleichen Diagramms für Training und Bereitstellung kann Verzerrungen verhindern, da in beiden Phasen die gleichen Transformationen angewendet werden.

Was machen wir in diesem Beispiel

In diesem Beispiel verarbeiten wir ein weit verbreitetes Dataset mit Volkszählungsdaten und trainieren ein Modell für die Klassifizierung. Dabei transformieren wir die Daten mit tf.Transform .

Upgrade-Pip

Um zu vermeiden, dass Pip in einem System aktualisiert wird, wenn es lokal ausgeführt wird, stellen Sie sicher, dass wir in Colab ausgeführt werden. Lokale Systeme können natürlich separat nachgerüstet werden.

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

Installieren Sie TensorFlow Transform

pip install tensorflow-transform

Python-Prüfung, -Importe und -Globals

Zuerst stellen wir sicher, dass wir Python 3 verwenden, und installieren und importieren dann die benötigten Sachen.

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 math
import os
import pprint

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

import apache_beam as beam
print('Beam: {}'.format(beam.__version__))

import tensorflow_transform as tft
import tensorflow_transform.beam as tft_beam
print('Transform: {}'.format(tft.__version__))

from tfx_bsl.public import tfxio
from tfx_bsl.coders.example_coder import RecordBatchToExamples

!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: 2.5.0
Beam: 2.30.0
Transform: 1.0.0
--2021-06-22 09:10:56--  https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.data
Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.204.128, 64.233.188.128, 64.233.189.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.204.128|: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   

2021-06-22 09:10:57 (161 MB/s) - ‘adult.data’ saved [3974305/3974305]

--2021-06-22 09:10:57--  https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.test
Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.203.128, 74.125.23.128, 108.177.97.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.203.128|: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 0.01s   

2021-06-22 09:10:58 (157 MB/s) - ‘adult.test’ saved [2003153/2003153]

Benennen Sie unsere Spalten

Wir erstellen einige praktische Listen zum Verweisen auf die Spalten in unserem Dataset.

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

Definieren Sie unsere Funktionen und unser Schema

Lassen Sie uns ein Schema definieren, das auf den Typen der Spalten in unserer Eingabe basiert. Dies hilft unter anderem beim korrekten Importieren.

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))]
)

SCHEMA = tft.tf_metadata.dataset_metadata.DatasetMetadata(
    tft.tf_metadata.schema_utils.schema_from_feature_spec(RAW_DATA_FEATURE_SPEC)).schema

Einstellen von Hyperparametern und grundlegendem Housekeeping

Für das Training verwendete Konstanten und Hyperparameter. Die Bucket-Größe umfasst alle aufgeführten Kategorien in der Dataset-Beschreibung sowie eine zusätzliche für "?" was Unbekanntes darstellt.

testing = os.getenv("WEB_TEST_BROWSER", False)
NUM_OOV_BUCKETS = 1
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'

Vorverarbeitung mit tf.Transform

Erstellen Sie eine tf.Transform

Die Vorverarbeitungsfunktion ist das wichtigste Konzept von tf.Transform. Eine Vorverarbeitungsfunktion ist der Ort, an dem die Transformation des Datensatzes wirklich stattfindet. Es akzeptiert und gibt ein Wörterbuch von Tensoren zurück, wobei ein Tensor einen Tensor oder SparseTensor . Es gibt zwei Hauptgruppen von API-Aufrufen, die normalerweise das Herz einer Vorverarbeitungsfunktion bilden:

  1. TensorFlow-Ops: Jede Funktion, die Tensoren akzeptiert und zurückgibt, was normalerweise TensorFlow-Ops bedeutet. Diese fügen dem Graphen TensorFlow-Operationen hinzu, die Rohdaten in transformierte Daten umwandeln, einen Feature-Vektor nach dem anderen. Diese werden für jedes Beispiel ausgeführt, sowohl während des Trainings als auch während des Servierens.
  2. TensorFlow-Transformationsanalysatoren: Alle von tf.Transform bereitgestellten Analysatoren. Analysatoren akzeptieren und geben auch Tensoren zurück, werden jedoch im Gegensatz zu TensorFlow-Operationen nur einmal während des Trainings ausgeführt und durchlaufen normalerweise den gesamten Trainingsdatensatz. Sie erstellen Tensorkonstanten , die Ihrem Diagramm hinzugefügt werden. Beispielsweise berechnet tft.min das Minimum eines Tensors über dem Trainingsdatensatz. tf.Transform bietet einen festen Satz von Analysatoren, die jedoch in zukünftigen Versionen erweitert werden.
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(inputs[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(inputs[key].indices, inputs[key].values,
                                    [inputs[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:
    outputs[key] = tft.compute_and_apply_vocabulary(
        tf.strings.strip(inputs[key]),
        num_oov_buckets=NUM_OOV_BUCKETS,
        vocab_filename=key)

  # For the label column we provide the mapping from string to index.
  table_keys = ['>50K', '<=50K']
  with tf.init_scope():
    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)
  # Remove trailing periods for test data when the data is read with tf.data.
  label_str = tf.strings.regex_replace(inputs[LABEL_KEY], r'\.', '')
  label_str = tf.strings.strip(label_str)
  data_labels = table.lookup(label_str)
  transformed_label = tf.one_hot(
      indices=data_labels, depth=len(table_keys), on_value=1.0, off_value=0.0)
  outputs[LABEL_KEY] = tf.reshape(transformed_label, [-1, len(table_keys)])

  return outputs

Transformieren Sie die Daten

Jetzt können wir mit der Transformation unserer Daten in eine Apache Beam-Pipeline beginnen.

  1. Daten mit dem CSV-Reader einlesen
  2. Transformieren Sie es mithilfe einer Vorverarbeitungspipeline, die numerische Daten skaliert und kategoriale Daten von Strings in Indizes mit int64-Werten umwandelt, indem für jede Kategorie ein Vokabular erstellt wird
  3. Schreiben Sie das Ergebnis als TFRecord von Example , die wir später zum Trainieren eines Modells verwenden werden
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 TFXIO 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.
      # We first read CSV files and use BeamRecordCsvTFXIO whose .BeamSource()
      # accepts a PCollection[bytes] because we need to patch the records first
      # (see "FixCommasTrainData" below). Otherwise, tfxio.CsvTFXIO can be used
      # to both read the CSV files and parse them to TFT inputs:
      # csv_tfxio = tfxio.CsvTFXIO(...)
      # raw_data = (pipeline | 'ToRecordBatches' >> csv_tfxio.BeamSource())
      csv_tfxio = tfxio.BeamRecordCsvTFXIO(
          physical_format='text',
          column_names=ORDERED_CSV_COLUMNS,
          schema=SCHEMA)

      # Read in raw data and convert using CSV TFXIO.  Note that we apply
      # some Beam transformations here, which will not be encoded in the TF
      # graph since we don't do the from within tf.Transform's methods
      # (AnalyzeDataset, TransformDataset etc.).  These transformations are just
      # to get data into a format that the CSV TFXIO can read, in particular
      # removing spaces after commas.
      raw_data = (
          pipeline
          | 'ReadTrainData' >> beam.io.ReadFromText(
              train_data_file, coder=beam.coders.BytesCoder())
          | 'FixCommasTrainData' >> beam.Map(
              lambda line: line.replace(b', ', b','))
          | 'DecodeTrainData' >> csv_tfxio.BeamSource())

      # 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, csv_tfxio.TensorAdapterConfig())

      # The TFXIO output format is chosen for improved performance.
      transformed_dataset, transform_fn = (
          raw_dataset | tft_beam.AnalyzeAndTransformDataset(
              preprocessing_fn, output_record_batches=True))

      # Transformed metadata is not necessary for encoding.
      transformed_data, _ = transformed_dataset

      # Extract transformed RecordBatches, encode and write them to the given
      # directory.
      _ = (
          transformed_data
          | 'EncodeTrainData' >>
          beam.FlatMapTuple(lambda batch, _: RecordBatchToExamples(batch))
          | '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,
              coder=beam.coders.BytesCoder())
          | 'FixCommasTestData' >> beam.Map(
              lambda line: line.replace(b', ', b','))
          | 'RemoveTrailingPeriodsTestData' >> beam.Map(lambda line: line[:-1])
          | 'DecodeTestData' >> csv_tfxio.BeamSource())

      raw_test_dataset = (raw_test_data, csv_tfxio.TensorAdapterConfig())

      # The TFXIO output format is chosen for improved performance.
      transformed_test_dataset = (
          (raw_test_dataset, transform_fn)
          | tft_beam.TransformDataset(output_record_batches=True))

      # Transformed metadata is not necessary for encoding.
      transformed_test_data, _ = transformed_test_dataset

      # Extract transformed RecordBatches, encode and write them to the given
      # directory.
      _ = (
          transformed_test_data
          | 'EncodeTestData' >>
          beam.FlatMapTuple(lambda batch, _: RecordBatchToExamples(batch))
          | '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))

Verwenden unserer vorverarbeiteten Daten zum Trainieren eines Modells mit tf.keras

Um zu zeigen, wie es uns mit tf.Transform ermöglicht, denselben Code sowohl für das Training als auch für die Bereitstellung zu verwenden und so Verzerrungen zu vermeiden, trainieren wir ein Modell. Um unser Modell zu trainieren und unser trainiertes Modell für die Produktion vorzubereiten, müssen wir Eingabefunktionen erstellen. Der Hauptunterschied zwischen unserer Trainingseingabefunktion und unserer Serving-Eingabefunktion besteht darin, dass Trainingsdaten Labels enthalten und Produktionsdaten nicht. Die Argumente und Rückgaben sind ebenfalls etwas anders.

Erstellen Sie eine Eingabefunktion für das Training

def _make_training_input_fn(tf_transform_output, transformed_examples,
                            batch_size):
  """An input function reading from transformed data, converting to model input.

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

  Returns:
    The input data for training or eval, in the form of k.
  """
  def input_fn():
    return 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,
        label_key=LABEL_KEY,
        shuffle=True).prefetch(tf.data.experimental.AUTOTUNE)

  return input_fn

Erstellen Sie eine Eingabefunktion für die Bereitstellung

Lassen Sie uns eine Eingabefunktion erstellen, die wir in der Produktion verwenden können, und unser trainiertes Modell für die Bereitstellung vorbereiten.

def _make_serving_input_fn(tf_transform_output, raw_examples, batch_size):
  """An input function reading from raw data, converting to model input.

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

  Returns:
    The input data for training or eval, in the form of k.
  """

  def get_ordered_raw_data_dtypes():
    result = []
    for col in ORDERED_CSV_COLUMNS:
      if col not in RAW_DATA_FEATURE_SPEC:
        result.append(0.0)
        continue
      spec = RAW_DATA_FEATURE_SPEC[col]
      if isinstance(spec, tf.io.FixedLenFeature):
        result.append(spec.dtype)
      else:
        result.append(0.0)
    return result

  def input_fn():
    dataset = tf.data.experimental.make_csv_dataset(
        file_pattern=raw_examples,
        batch_size=batch_size,
        column_names=ORDERED_CSV_COLUMNS,
        column_defaults=get_ordered_raw_data_dtypes(),
        prefetch_buffer_size=0,
        ignore_errors=True)

    tft_layer = tf_transform_output.transform_features_layer()

    def transform_dataset(data):
      raw_features = {}
      for key, val in data.items():
        if key not in RAW_DATA_FEATURE_SPEC:
          continue
        if isinstance(RAW_DATA_FEATURE_SPEC[key], tf.io.VarLenFeature):
          raw_features[key] = tf.RaggedTensor.from_tensor(
              tf.expand_dims(val, -1)).to_sparse()
          continue
        raw_features[key] = val
      transformed_features = tft_layer(raw_features)
      data_labels = transformed_features.pop(LABEL_KEY)
      return (transformed_features, data_labels)

    return dataset.map(
        transform_dataset,
        num_parallel_calls=tf.data.experimental.AUTOTUNE).prefetch(
            tf.data.experimental.AUTOTUNE)

  return input_fn

Trainieren, bewerten und exportieren Sie unser Modell

def export_serving_model(tf_transform_output, model, output_dir):
  """Exports a keras model for serving.

  Args:
    tf_transform_output: Wrapper around output of tf.Transform.
    model: A keras model to export for serving.
    output_dir: A directory where the model will be exported to.
  """
  # The layer has to be saved to the model for keras tracking purpases.
  model.tft_layer = tf_transform_output.transform_features_layer()

  @tf.function
  def serve_tf_examples_fn(serialized_tf_examples):
    """Serving tf.function model wrapper."""
    feature_spec = RAW_DATA_FEATURE_SPEC.copy()
    feature_spec.pop(LABEL_KEY)
    parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
    transformed_features = model.tft_layer(parsed_features)
    outputs = model(transformed_features)
    classes_names = tf.constant([['0', '1']])
    classes = tf.tile(classes_names, [tf.shape(outputs)[0], 1])
    return {'classes': classes, 'scores': outputs}

  concrete_serving_fn = serve_tf_examples_fn.get_concrete_function(
      tf.TensorSpec(shape=[None], dtype=tf.string, name='inputs'))
  signatures = {'serving_default': concrete_serving_fn}

  # This is required in order to make this model servable with model_server.
  versioned_output_dir = os.path.join(output_dir, '1')
  model.save(versioned_output_dir, save_format='tf', signatures=signatures)
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: The location of the Transform output.
    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
  """
  train_data_path_pattern = os.path.join(working_dir,
                                 TRANSFORMED_TRAIN_DATA_FILEBASE + '*')
  eval_data_path_pattern = os.path.join(working_dir,
                            TRANSFORMED_TEST_DATA_FILEBASE + '*')
  tf_transform_output = tft.TFTransformOutput(working_dir)

  train_input_fn = _make_training_input_fn(
      tf_transform_output, train_data_path_pattern, batch_size=TRAIN_BATCH_SIZE)
  train_dataset = train_input_fn()

  # Evaluate model on test dataset.
  eval_input_fn = _make_training_input_fn(
      tf_transform_output, eval_data_path_pattern, batch_size=TRAIN_BATCH_SIZE)
  validation_dataset = eval_input_fn()

  feature_spec = tf_transform_output.transformed_feature_spec().copy()
  feature_spec.pop(LABEL_KEY)

  inputs = {}
  for key, spec in feature_spec.items():
    if isinstance(spec, tf.io.VarLenFeature):
      inputs[key] = tf.keras.layers.Input(
          shape=[None], name=key, dtype=spec.dtype, sparse=True)
    elif isinstance(spec, tf.io.FixedLenFeature):
      inputs[key] = tf.keras.layers.Input(
          shape=spec.shape, name=key, dtype=spec.dtype)
    else:
      raise ValueError('Spec type is not supported: ', key, spec)

  encoded_inputs = {}
  for key in inputs:
    feature = tf.expand_dims(inputs[key], -1)
    if key in CATEGORICAL_FEATURE_KEYS:
      num_buckets = tf_transform_output.num_buckets_for_transformed_feature(key)
      encoding_layer = (
          tf.keras.layers.experimental.preprocessing.CategoryEncoding(
              max_tokens=num_buckets, output_mode='binary', sparse=False))
      encoded_inputs[key] = encoding_layer(feature)
    else:
      encoded_inputs[key] = feature

  stacked_inputs = tf.concat(tf.nest.flatten(encoded_inputs), axis=1)
  output = tf.keras.layers.Dense(100, activation='relu')(stacked_inputs)
  output = tf.keras.layers.Dense(70, activation='relu')(output)
  output = tf.keras.layers.Dense(50, activation='relu')(output)
  output = tf.keras.layers.Dense(20, activation='relu')(output)
  output = tf.keras.layers.Dense(2, activation='sigmoid')(output)
  model = tf.keras.Model(inputs=inputs, outputs=output)

  model.compile(optimizer='adam',
                loss='binary_crossentropy',
                metrics=['accuracy'])
  pprint.pprint(model.summary())

  model.fit(train_dataset, validation_data=validation_dataset,
            epochs=TRAIN_NUM_EPOCHS,
            steps_per_epoch=math.ceil(num_train_instances / TRAIN_BATCH_SIZE),
            validation_steps=math.ceil(num_test_instances / TRAIN_BATCH_SIZE))

  # Export the model.
  exported_model_dir = os.path.join(working_dir, EXPORTED_MODEL_DIR)
  export_serving_model(tf_transform_output, model, exported_model_dir)

  metrics_values = model.evaluate(validation_dataset, steps=num_test_instances)
  metrics_labels = model.metrics_names
  return {l: v for l, v in zip(metrics_labels, metrics_values)}

Setzen Sie alles zusammen

Wir haben alles erstellt, was wir brauchen, um unsere Volkszählungsdaten vorzuverarbeiten, ein Modell zu trainieren und es für die Bereitstellung vorzubereiten. Bisher haben wir nur die Dinge vorbereitet. Es ist Zeit, mit dem Laufen zu beginnen!

import tempfile
temp = os.path.join(tempfile.gettempdir(), 'keras')

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:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:266: 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/python3.7/site-packages/tensorflow_transform/tf_utils.py:266: 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:tensorflow:Assets written to: /tmp/tmp6u9m6tga/tftransform_tmp/657983b839a3492c95a12bc74afda176/assets
INFO:tensorflow:Assets written to: /tmp/tmp6u9m6tga/tftransform_tmp/657983b839a3492c95a12bc74afda176/assets
INFO:tensorflow:Assets written to: /tmp/tmp6u9m6tga/tftransform_tmp/f41ad79d84ee45e2881b2429ee0cee31/assets
INFO:tensorflow:Assets written to: /tmp/tmp6u9m6tga/tftransform_tmp/f41ad79d84ee45e2881b2429ee0cee31/assets
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:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
education (InputLayer)          [(None,)]            0                                            
__________________________________________________________________________________________________
marital-status (InputLayer)     [(None,)]            0                                            
__________________________________________________________________________________________________
native-country (InputLayer)     [(None,)]            0                                            
__________________________________________________________________________________________________
occupation (InputLayer)         [(None,)]            0                                            
__________________________________________________________________________________________________
race (InputLayer)               [(None,)]            0                                            
__________________________________________________________________________________________________
relationship (InputLayer)       [(None,)]            0                                            
__________________________________________________________________________________________________
sex (InputLayer)                [(None,)]            0                                            
__________________________________________________________________________________________________
workclass (InputLayer)          [(None,)]            0                                            
__________________________________________________________________________________________________
age (InputLayer)                [(None,)]            0                                            
__________________________________________________________________________________________________
capital-gain (InputLayer)       [(None,)]            0                                            
__________________________________________________________________________________________________
capital-loss (InputLayer)       [(None,)]            0                                            
__________________________________________________________________________________________________
tf.expand_dims_3 (TFOpLambda)   (None, 1)            0           education[0][0]                  
__________________________________________________________________________________________________
education-num (InputLayer)      [(None,)]            0                                            
__________________________________________________________________________________________________
hours-per-week (InputLayer)     [(None,)]            0                                            
__________________________________________________________________________________________________
tf.expand_dims_6 (TFOpLambda)   (None, 1)            0           marital-status[0][0]             
__________________________________________________________________________________________________
tf.expand_dims_7 (TFOpLambda)   (None, 1)            0           native-country[0][0]             
__________________________________________________________________________________________________
tf.expand_dims_8 (TFOpLambda)   (None, 1)            0           occupation[0][0]                 
__________________________________________________________________________________________________
tf.expand_dims_9 (TFOpLambda)   (None, 1)            0           race[0][0]                       
__________________________________________________________________________________________________
tf.expand_dims_10 (TFOpLambda)  (None, 1)            0           relationship[0][0]               
__________________________________________________________________________________________________
tf.expand_dims_11 (TFOpLambda)  (None, 1)            0           sex[0][0]                        
__________________________________________________________________________________________________
tf.expand_dims_12 (TFOpLambda)  (None, 1)            0           workclass[0][0]                  
__________________________________________________________________________________________________
tf.expand_dims (TFOpLambda)     (None, 1)            0           age[0][0]                        
__________________________________________________________________________________________________
tf.expand_dims_1 (TFOpLambda)   (None, 1)            0           capital-gain[0][0]               
__________________________________________________________________________________________________
tf.expand_dims_2 (TFOpLambda)   (None, 1)            0           capital-loss[0][0]               
__________________________________________________________________________________________________
category_encoding (CategoryEnco (None, 17)           0           tf.expand_dims_3[0][0]           
__________________________________________________________________________________________________
tf.expand_dims_4 (TFOpLambda)   (None, 1)            0           education-num[0][0]              
__________________________________________________________________________________________________
tf.expand_dims_5 (TFOpLambda)   (None, 1)            0           hours-per-week[0][0]             
__________________________________________________________________________________________________
category_encoding_1 (CategoryEn (None, 8)            0           tf.expand_dims_6[0][0]           
__________________________________________________________________________________________________
category_encoding_2 (CategoryEn (None, 43)           0           tf.expand_dims_7[0][0]           
__________________________________________________________________________________________________
category_encoding_3 (CategoryEn (None, 16)           0           tf.expand_dims_8[0][0]           
__________________________________________________________________________________________________
category_encoding_4 (CategoryEn (None, 6)            0           tf.expand_dims_9[0][0]           
__________________________________________________________________________________________________
category_encoding_5 (CategoryEn (None, 7)            0           tf.expand_dims_10[0][0]          
__________________________________________________________________________________________________
category_encoding_6 (CategoryEn (None, 3)            0           tf.expand_dims_11[0][0]          
__________________________________________________________________________________________________
category_encoding_7 (CategoryEn (None, 10)           0           tf.expand_dims_12[0][0]          
__________________________________________________________________________________________________
tf.concat (TFOpLambda)          (None, 115)          0           tf.expand_dims[0][0]             
                                                                 tf.expand_dims_1[0][0]           
                                                                 tf.expand_dims_2[0][0]           
                                                                 category_encoding[0][0]          
                                                                 tf.expand_dims_4[0][0]           
                                                                 tf.expand_dims_5[0][0]           
                                                                 category_encoding_1[0][0]        
                                                                 category_encoding_2[0][0]        
                                                                 category_encoding_3[0][0]        
                                                                 category_encoding_4[0][0]        
                                                                 category_encoding_5[0][0]        
                                                                 category_encoding_6[0][0]        
                                                                 category_encoding_7[0][0]        
__________________________________________________________________________________________________
dense (Dense)                   (None, 100)          11600       tf.concat[0][0]                  
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 70)           7070        dense[0][0]                      
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 50)           3550        dense_1[0][0]                    
__________________________________________________________________________________________________
dense_3 (Dense)                 (None, 20)           1020        dense_2[0][0]                    
__________________________________________________________________________________________________
dense_4 (Dense)                 (None, 2)            42          dense_3[0][0]                    
==================================================================================================
Total params: 23,282
Trainable params: 23,282
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/16
255/255 [==============================] - 3s 7ms/step - loss: 0.3870 - accuracy: 0.8180 - val_loss: 0.3410 - val_accuracy: 0.8399
Epoch 2/16
255/255 [==============================] - 2s 6ms/step - loss: 0.3337 - accuracy: 0.8419 - val_loss: 0.3270 - val_accuracy: 0.8469
Epoch 3/16
255/255 [==============================] - 2s 6ms/step - loss: 0.3211 - accuracy: 0.8494 - val_loss: 0.3242 - val_accuracy: 0.8478
Epoch 4/16
255/255 [==============================] - 2s 6ms/step - loss: 0.3142 - accuracy: 0.8521 - val_loss: 0.3223 - val_accuracy: 0.8509
Epoch 5/16
255/255 [==============================] - 2s 6ms/step - loss: 0.3091 - accuracy: 0.8540 - val_loss: 0.3262 - val_accuracy: 0.8480
Epoch 6/16
255/255 [==============================] - 2s 6ms/step - loss: 0.3073 - accuracy: 0.8547 - val_loss: 0.3260 - val_accuracy: 0.8453
Epoch 7/16
255/255 [==============================] - 2s 6ms/step - loss: 0.3023 - accuracy: 0.8589 - val_loss: 0.3252 - val_accuracy: 0.8475
Epoch 8/16
255/255 [==============================] - 2s 6ms/step - loss: 0.2982 - accuracy: 0.8612 - val_loss: 0.3273 - val_accuracy: 0.8516
Epoch 9/16
255/255 [==============================] - 2s 6ms/step - loss: 0.2936 - accuracy: 0.8624 - val_loss: 0.3314 - val_accuracy: 0.8502
Epoch 10/16
255/255 [==============================] - 2s 6ms/step - loss: 0.2907 - accuracy: 0.8636 - val_loss: 0.3300 - val_accuracy: 0.8476
Epoch 11/16
255/255 [==============================] - 2s 6ms/step - loss: 0.2894 - accuracy: 0.8634 - val_loss: 0.3323 - val_accuracy: 0.8468
Epoch 12/16
255/255 [==============================] - 2s 6ms/step - loss: 0.2853 - accuracy: 0.8668 - val_loss: 0.3368 - val_accuracy: 0.8446
Epoch 13/16
255/255 [==============================] - 2s 6ms/step - loss: 0.2824 - accuracy: 0.8684 - val_loss: 0.3378 - val_accuracy: 0.8490
Epoch 14/16
255/255 [==============================] - 2s 6ms/step - loss: 0.2791 - accuracy: 0.8680 - val_loss: 0.3476 - val_accuracy: 0.8411
Epoch 15/16
255/255 [==============================] - 2s 6ms/step - loss: 0.2768 - accuracy: 0.8689 - val_loss: 0.3419 - val_accuracy: 0.8448
Epoch 16/16
255/255 [==============================] - 2s 7ms/step - loss: 0.2752 - accuracy: 0.8703 - val_loss: 0.3414 - val_accuracy: 0.8476
INFO:tensorflow:Assets written to: /tmp/keras/exported_model_dir/1/assets
INFO:tensorflow:Assets written to: /tmp/keras/exported_model_dir/1/assets
16281/16281 [==============================] - 62s 4ms/step - loss: 0.3416 - accuracy: 0.8476
{'accuracy': 0.8475523591041565, 'loss': 0.34160903096199036}

(Optional) Verwenden unserer vorverarbeiteten Daten zum Trainieren eines Modells mit tf.estimator

Wenn Sie statt eines Keras-Modells lieber ein Estimator-Modell verwenden möchten, zeigt der Code in diesem Abschnitt, wie das geht.

Erstellen Sie eine Eingabefunktion für das Training

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 = tf.where(
        tf.equal(transformed_features.pop(LABEL_KEY), 1))

    return transformed_features, transformed_labels[:,1]

  return input_fn

Erstellen Sie eine Eingabefunktion für die Bereitstellung

Lassen Sie uns eine Eingabefunktion erstellen, die wir in der Produktion verwenden können, und unser trainiertes Modell für die Bereitstellung vorbereiten.

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

Wickeln Sie unsere Eingabedaten in FeatureColumns ein

Unser Modell erwartet unsere Daten in 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.indicator_column(
          tf.feature_column.categorical_column_with_identity(
              key=key,
              num_buckets=(NUM_OOV_BUCKETS +
                  tf_transform_output.vocabulary_size_by_name(
                      vocab_filename=key))))
      for key in CATEGORICAL_FEATURE_KEYS]

  return real_valued_columns + one_hot_columns

Trainieren, bewerten und exportieren Sie unser Modell

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)

Setzen Sie alles zusammen

Wir haben alles erstellt, was wir brauchen, um unsere Volkszählungsdaten vorzuverarbeiten, ein Modell zu trainieren und es für die Bereitstellung vorzubereiten. Bisher haben wir nur die Dinge vorbereitet. Es ist Zeit, mit dem Laufen zu beginnen!

import tempfile
temp = os.path.join(tempfile.gettempdir(), 'estimator')

transform_data(train, test, temp)
results = train_and_evaluate(temp)
pprint.pprint(results)
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
Exception ignored in: <function CapturableResource.__del__ at 0x7f10f07a5710>
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py", line 277, in __del__
    self._destroy_resource()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
    result = self._call(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 924, in _call
    results = self._stateful_fn(*args, **kwds)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3022, in __call__
    filtered_flat_args) = self._maybe_define_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3289, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
AttributeError: 'NoneType' object has no attribute '__wrapped__'
INFO:tensorflow:Assets written to: /tmp/tmpbeo2jzpd/tftransform_tmp/dbfd9d68a3e1400db66637a39b58d0e2/assets
INFO:tensorflow:Assets written to: /tmp/tmpbeo2jzpd/tftransform_tmp/dbfd9d68a3e1400db66637a39b58d0e2/assets
INFO:tensorflow:Assets written to: /tmp/tmpbeo2jzpd/tftransform_tmp/5e411e0d9f984ca8bfc7236b2e749210/assets
INFO:tensorflow:Assets written to: /tmp/tmpbeo2jzpd/tftransform_tmp/5e411e0d9f984ca8bfc7236b2e749210/assets
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpjhndn_69
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpjhndn_69
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpjhndn_69', '_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, '_checkpoint_save_graph_def': True, '_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/tmpjhndn_69', '_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, '_checkpoint_save_graph_def': True, '_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/python3.7/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/python3.7/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.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1700: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.
  warnings.warn('`layer.add_variable` is deprecated and '
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/ftrl.py:149: 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/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/ftrl.py:149: 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/tmpjhndn_69/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpjhndn_69/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: 182.658
INFO:tensorflow:global_step/sec: 182.658
INFO:tensorflow:loss = 54.77435, step = 100 (0.549 sec)
INFO:tensorflow:loss = 54.77435, step = 100 (0.549 sec)
INFO:tensorflow:global_step/sec: 231.18
INFO:tensorflow:global_step/sec: 231.18
INFO:tensorflow:loss = 40.807327, step = 200 (0.432 sec)
INFO:tensorflow:loss = 40.807327, step = 200 (0.432 sec)
INFO:tensorflow:global_step/sec: 245.067
INFO:tensorflow:global_step/sec: 245.067
INFO:tensorflow:loss = 42.116783, step = 300 (0.408 sec)
INFO:tensorflow:loss = 42.116783, step = 300 (0.408 sec)
INFO:tensorflow:global_step/sec: 243.445
INFO:tensorflow:global_step/sec: 243.445
INFO:tensorflow:loss = 46.41303, step = 400 (0.411 sec)
INFO:tensorflow:loss = 46.41303, step = 400 (0.411 sec)
INFO:tensorflow:global_step/sec: 242.042
INFO:tensorflow:global_step/sec: 242.042
INFO:tensorflow:loss = 52.615246, step = 500 (0.413 sec)
INFO:tensorflow:loss = 52.615246, step = 500 (0.413 sec)
INFO:tensorflow:global_step/sec: 239.243
INFO:tensorflow:global_step/sec: 239.243
INFO:tensorflow:loss = 48.1847, step = 600 (0.418 sec)
INFO:tensorflow:loss = 48.1847, step = 600 (0.418 sec)
INFO:tensorflow:global_step/sec: 241.24
INFO:tensorflow:global_step/sec: 241.24
INFO:tensorflow:loss = 48.38508, step = 700 (0.415 sec)
INFO:tensorflow:loss = 48.38508, step = 700 (0.415 sec)
INFO:tensorflow:global_step/sec: 246.833
INFO:tensorflow:global_step/sec: 246.833
INFO:tensorflow:loss = 43.00689, step = 800 (0.405 sec)
INFO:tensorflow:loss = 43.00689, step = 800 (0.405 sec)
INFO:tensorflow:global_step/sec: 236.843
INFO:tensorflow:global_step/sec: 236.843
INFO:tensorflow:loss = 38.905403, step = 900 (0.422 sec)
INFO:tensorflow:loss = 38.905403, step = 900 (0.422 sec)
INFO:tensorflow:global_step/sec: 232.326
INFO:tensorflow:global_step/sec: 232.326
INFO:tensorflow:loss = 42.327198, step = 1000 (0.430 sec)
INFO:tensorflow:loss = 42.327198, step = 1000 (0.430 sec)
INFO:tensorflow:global_step/sec: 234.032
INFO:tensorflow:global_step/sec: 234.032
INFO:tensorflow:loss = 45.145943, step = 1100 (0.427 sec)
INFO:tensorflow:loss = 45.145943, step = 1100 (0.427 sec)
INFO:tensorflow:global_step/sec: 241.151
INFO:tensorflow:global_step/sec: 241.151
INFO:tensorflow:loss = 33.759712, step = 1200 (0.415 sec)
INFO:tensorflow:loss = 33.759712, step = 1200 (0.415 sec)
INFO:tensorflow:global_step/sec: 235.258
INFO:tensorflow:global_step/sec: 235.258
INFO:tensorflow:loss = 48.59608, step = 1300 (0.425 sec)
INFO:tensorflow:loss = 48.59608, step = 1300 (0.425 sec)
INFO:tensorflow:global_step/sec: 230.981
INFO:tensorflow:global_step/sec: 230.981
INFO:tensorflow:loss = 43.324547, step = 1400 (0.433 sec)
INFO:tensorflow:loss = 43.324547, step = 1400 (0.433 sec)
INFO:tensorflow:global_step/sec: 231.788
INFO:tensorflow:global_step/sec: 231.788
INFO:tensorflow:loss = 47.836075, step = 1500 (0.431 sec)
INFO:tensorflow:loss = 47.836075, step = 1500 (0.431 sec)
INFO:tensorflow:global_step/sec: 235.388
INFO:tensorflow:global_step/sec: 235.388
INFO:tensorflow:loss = 39.878197, step = 1600 (0.425 sec)
INFO:tensorflow:loss = 39.878197, step = 1600 (0.425 sec)
INFO:tensorflow:global_step/sec: 240.218
INFO:tensorflow:global_step/sec: 240.218
INFO:tensorflow:loss = 43.730515, step = 1700 (0.416 sec)
INFO:tensorflow:loss = 43.730515, step = 1700 (0.416 sec)
INFO:tensorflow:global_step/sec: 242.466
INFO:tensorflow:global_step/sec: 242.466
INFO:tensorflow:loss = 36.19258, step = 1800 (0.413 sec)
INFO:tensorflow:loss = 36.19258, step = 1800 (0.413 sec)
INFO:tensorflow:global_step/sec: 242.914
INFO:tensorflow:global_step/sec: 242.914
INFO:tensorflow:loss = 54.65744, step = 1900 (0.411 sec)
INFO:tensorflow:loss = 54.65744, step = 1900 (0.411 sec)
INFO:tensorflow:global_step/sec: 242.666
INFO:tensorflow:global_step/sec: 242.666
INFO:tensorflow:loss = 41.011486, step = 2000 (0.412 sec)
INFO:tensorflow:loss = 41.011486, step = 2000 (0.412 sec)
INFO:tensorflow:global_step/sec: 241.373
INFO:tensorflow:global_step/sec: 241.373
INFO:tensorflow:loss = 48.27359, step = 2100 (0.414 sec)
INFO:tensorflow:loss = 48.27359, step = 2100 (0.414 sec)
INFO:tensorflow:global_step/sec: 245.825
INFO:tensorflow:global_step/sec: 245.825
INFO:tensorflow:loss = 41.024265, step = 2200 (0.407 sec)
INFO:tensorflow:loss = 41.024265, step = 2200 (0.407 sec)
INFO:tensorflow:global_step/sec: 240.988
INFO:tensorflow:global_step/sec: 240.988
INFO:tensorflow:loss = 45.679882, step = 2300 (0.415 sec)
INFO:tensorflow:loss = 45.679882, step = 2300 (0.415 sec)
INFO:tensorflow:global_step/sec: 236.994
INFO:tensorflow:global_step/sec: 236.994
INFO:tensorflow:loss = 37.19581, step = 2400 (0.422 sec)
INFO:tensorflow:loss = 37.19581, step = 2400 (0.422 sec)
INFO:tensorflow:global_step/sec: 231.945
INFO:tensorflow:global_step/sec: 231.945
INFO:tensorflow:loss = 46.33335, step = 2500 (0.431 sec)
INFO:tensorflow:loss = 46.33335, step = 2500 (0.431 sec)
INFO:tensorflow:global_step/sec: 241.035
INFO:tensorflow:global_step/sec: 241.035
INFO:tensorflow:loss = 54.012684, step = 2600 (0.415 sec)
INFO:tensorflow:loss = 54.012684, step = 2600 (0.415 sec)
INFO:tensorflow:global_step/sec: 241.701
INFO:tensorflow:global_step/sec: 241.701
INFO:tensorflow:loss = 39.641083, step = 2700 (0.414 sec)
INFO:tensorflow:loss = 39.641083, step = 2700 (0.414 sec)
INFO:tensorflow:global_step/sec: 236.669
INFO:tensorflow:global_step/sec: 236.669
INFO:tensorflow:loss = 42.626823, step = 2800 (0.423 sec)
INFO:tensorflow:loss = 42.626823, step = 2800 (0.423 sec)
INFO:tensorflow:global_step/sec: 239.643
INFO:tensorflow:global_step/sec: 239.643
INFO:tensorflow:loss = 31.745514, step = 2900 (0.417 sec)
INFO:tensorflow:loss = 31.745514, step = 2900 (0.417 sec)
INFO:tensorflow:global_step/sec: 242.553
INFO:tensorflow:global_step/sec: 242.553
INFO:tensorflow:loss = 34.658115, step = 3000 (0.413 sec)
INFO:tensorflow:loss = 34.658115, step = 3000 (0.413 sec)
INFO:tensorflow:global_step/sec: 243.198
INFO:tensorflow:global_step/sec: 243.198
INFO:tensorflow:loss = 49.728905, step = 3100 (0.411 sec)
INFO:tensorflow:loss = 49.728905, step = 3100 (0.411 sec)
INFO:tensorflow:global_step/sec: 243.938
INFO:tensorflow:global_step/sec: 243.938
INFO:tensorflow:loss = 38.7268, step = 3200 (0.410 sec)
INFO:tensorflow:loss = 38.7268, step = 3200 (0.410 sec)
INFO:tensorflow:global_step/sec: 240.607
INFO:tensorflow:global_step/sec: 240.607
INFO:tensorflow:loss = 42.869728, step = 3300 (0.416 sec)
INFO:tensorflow:loss = 42.869728, step = 3300 (0.416 sec)
INFO:tensorflow:global_step/sec: 243.879
INFO:tensorflow:global_step/sec: 243.879
INFO:tensorflow:loss = 43.34453, step = 3400 (0.410 sec)
INFO:tensorflow:loss = 43.34453, step = 3400 (0.410 sec)
INFO:tensorflow:global_step/sec: 236.059
INFO:tensorflow:global_step/sec: 236.059
INFO:tensorflow:loss = 35.733303, step = 3500 (0.423 sec)
INFO:tensorflow:loss = 35.733303, step = 3500 (0.423 sec)
INFO:tensorflow:global_step/sec: 231.983
INFO:tensorflow:global_step/sec: 231.983
INFO:tensorflow:loss = 41.160187, step = 3600 (0.431 sec)
INFO:tensorflow:loss = 41.160187, step = 3600 (0.431 sec)
INFO:tensorflow:global_step/sec: 230.322
INFO:tensorflow:global_step/sec: 230.322
INFO:tensorflow:loss = 40.32483, step = 3700 (0.434 sec)
INFO:tensorflow:loss = 40.32483, step = 3700 (0.434 sec)
INFO:tensorflow:global_step/sec: 235.372
INFO:tensorflow:global_step/sec: 235.372
INFO:tensorflow:loss = 37.755905, step = 3800 (0.425 sec)
INFO:tensorflow:loss = 37.755905, step = 3800 (0.425 sec)
INFO:tensorflow:global_step/sec: 237.268
INFO:tensorflow:global_step/sec: 237.268
INFO:tensorflow:loss = 32.770317, step = 3900 (0.421 sec)
INFO:tensorflow:loss = 32.770317, step = 3900 (0.421 sec)
INFO:tensorflow:global_step/sec: 240.301
INFO:tensorflow:global_step/sec: 240.301
INFO:tensorflow:loss = 27.438885, step = 4000 (0.417 sec)
INFO:tensorflow:loss = 27.438885, step = 4000 (0.417 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/tmpjhndn_69/model.ckpt.
INFO:tensorflow:Saving checkpoints for 4071 into /tmp/tmpjhndn_69/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: 43.916992.
INFO:tensorflow:Loss for final step: 43.916992.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:145: 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/python3.7/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:145: 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: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/tmpjhndn_69/model.ckpt-4071
INFO:tensorflow:Restoring parameters from /tmp/tmpjhndn_69/model.ckpt-4071
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/estimator/exported_model_dir/temp-1624353210/assets
INFO:tensorflow:Assets written to: /tmp/estimator/exported_model_dir/temp-1624353210/assets
INFO:tensorflow:SavedModel written to: /tmp/estimator/exported_model_dir/temp-1624353210/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/estimator/exported_model_dir/temp-1624353210/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 2021-06-22T09:13:33
INFO:tensorflow:Starting evaluation at 2021-06-22T09:13:33
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpjhndn_69/model.ckpt-4071
INFO:tensorflow:Restoring parameters from /tmp/tmpjhndn_69/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 : 64.71752s
INFO:tensorflow:Inference Time : 64.71752s
INFO:tensorflow:Finished evaluation at 2021-06-22-09:14:37
INFO:tensorflow:Finished evaluation at 2021-06-22-09:14:37
INFO:tensorflow:Saving dict for global step 4071: accuracy = 0.8500092, accuracy_baseline = 0.76377374, auc = 0.9015345, auc_precision_recall = 0.96711934, average_loss = 0.32456586, global_step = 4071, label/mean = 0.76377374, loss = 0.32456586, precision = 0.8794138, prediction/mean = 0.76127905, recall = 0.9313229
INFO:tensorflow:Saving dict for global step 4071: accuracy = 0.8500092, accuracy_baseline = 0.76377374, auc = 0.9015345, auc_precision_recall = 0.96711934, average_loss = 0.32456586, global_step = 4071, label/mean = 0.76377374, loss = 0.32456586, precision = 0.8794138, prediction/mean = 0.76127905, recall = 0.9313229
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4071: /tmp/tmpjhndn_69/model.ckpt-4071
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4071: /tmp/tmpjhndn_69/model.ckpt-4071
{'accuracy': 0.8500092,
 'accuracy_baseline': 0.76377374,
 'auc': 0.9015345,
 'auc_precision_recall': 0.96711934,
 'average_loss': 0.32456586,
 'global_step': 4071,
 'label/mean': 0.76377374,
 'loss': 0.32456586,
 'precision': 0.8794138,
 'prediction/mean': 0.76127905,
 'recall': 0.9313229}

Was wir gemacht haben

In diesem Beispiel haben wir tf.Transform um einen Datensatz mit Volkszählungsdaten tf.Transform und ein Modell mit den bereinigten und transformierten Daten zu trainieren. Wir haben auch eine Eingabefunktion erstellt, die wir verwenden können, wenn wir unser trainiertes Modell in einer Produktionsumgebung bereitstellen, um Inferenz durchzuführen. Durch die Verwendung des gleichen Codes für Training und Inferenz vermeiden wir Probleme mit der Datenverzerrung. Dabei haben wir gelernt, wie wir eine Apache Beam-Transformation erstellen, um die Transformation durchzuführen, die wir zum Bereinigen der Daten benötigten. Wir haben auch gesehen, wie diese transformierten Daten verwendet werden, um ein Modell mit tf.keras oder tf.estimator . Dies ist nur ein kleiner Teil dessen, was TensorFlow Transform leisten kann! Wir tf.Transform Ihnen, in tf.Transform und tf.Transform , was es für Sie tun kann.