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TensorFlow Transform으로 데이터 전처리

TensorFlow Extended(TFX)의 기능 엔지니어링 구성요소

이 예제 colab 노트북 방법에 대한 좀 더 진보 된 예를 제공 TensorFlow가 변환 ( tf.Transform 모두 모델을 훈련 및 생산 추론를 제공 정확히 동일한 코드를 사용하여 전처리 데이터를 사용할 수 있습니다).

TensorFlow Transform은 학습 데이터 세트 전체를 통과해야하는 기능을 만드는 것을 포함하여 TensorFlow의 입력 데이터를 사전 처리하기위한 라이브러리입니다. 예를 들어 TensorFlow Transform을 사용하여 다음을 수행 할 수 있습니다.

  • 평균과 표준편차를 이용하여 입력값 정규화
  • 모든 입력 값에 대해 어휘를 생성하여 문자열을 정수로 변환
  • 관찰된 데이터 분포를 기반으로 부동 소수점을 버킷에 할당하여 정수로 변환

TensorFlow에는 단일 예제 또는 예제 배치에 대한 조작 지원이 내장되어 있습니다. tf.Transform 전체 훈련 데이터 세트를 완벽하게 패스를 지원하기 위해 이러한 기능을 확장합니다.

의 출력 tf.Transform 당신이 훈련과 봉사에 모두 사용할 수있는 TensorFlow 그래프로 내보내집니다. 훈련과 제공 모두에 동일한 그래프를 사용하면 두 단계에 동일한 변환이 적용되므로 왜곡을 방지할 수 있습니다.

이 예에서 우리가하는 일

이 예에서 우리는 처리됩니다 널리 사용되는 데이터 집합에 포함 된 인구 조사 데이터 및 분류를 할 수있는 모델을 훈련. 길을 따라 우리는 사용하여 데이터를 변환 할 수 있습니다 tf.Transform .

Pip 업그레이드

로컬에서 실행할 때 시스템에서 Pip를 업그레이드하지 않으려면 Colab에서 실행 중인지 확인하세요. 물론 로컬 시스템은 별도로 업그레이드 할 수 있습니다.

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

TensorFlow 변환 설치

pip install tensorflow-transform

Python 검사, 가져오기 및 전역

먼저 Python 3을 사용하고 있는지 확인한 다음 필요한 항목을 설치하고 가져옵니다.

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'
2021-07-17 09:16:30.134565: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
TF: 2.5.0
Beam: 2.31.0
Transform: 1.1.1
--2021-07-17 09:16:33--  https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.data
Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.23.128, 74.125.203.128, 74.125.204.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.23.128|: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   

2021-07-17 09:16:33 (206 MB/s) - ‘adult.data’ saved [3974305/3974305]

--2021-07-17 09:16:33--  https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.test
Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.97.128, 64.233.189.128, 64.233.188.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.97.128|: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   

2021-07-17 09:16:34 (136 MB/s) - ‘adult.test’ saved [2003153/2003153]

열 이름 지정

데이터 세트의 열을 참조하기위한 몇 가지 편리한 목록을 만들 것입니다.

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'

기능 및 스키마 정의

입력에있는 열 유형을 기반으로 스키마를 정의 해 보겠습니다. 무엇보다도 이것은 올바르게 가져 오는 데 도움이됩니다.

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

초 매개 변수 및 기본 관리 설정

훈련에 사용되는 상수 및 초 매개 변수. 버킷 크기에는 데이터 세트 설명에 나열된 모든 카테고리와 "?"에 대한 추가 카테고리가 포함됩니다. 알 수 없음을 나타냅니다.

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'

함께 전처리 tf.Transform

크리에이트 tf.Transform preprocessing_fn을

전처리 기능은 tf.Transform의 가장 중요한 개념이다. 전처리 기능은 데이터 세트의 변환이 실제로 발생하는 곳입니다. 그것은 받아 텐서는 의미 텐서의 사전, 반환 Tensor 또는 SparseTensor . 일반적으로 전처리 기능의 핵심을 형성하는 두 가지 주요 API 호출 그룹이 있습니다.

  1. TensorFlow 옵스 : 보통 TensorFlow 작전을 의미하는 텐서를 받아 반환하는 모든 기능. 이것은 원시 데이터를 변환된 데이터로 한 번에 하나의 특성 벡터로 변환하는 TensorFlow 작업을 그래프에 추가합니다. 이는 교육 및 봉사 기간 동안 모든 예에 대해 실행됩니다.
  2. 분석기를 변환 TensorFlow : tf.Transform에서 제공하는 분석기 중 하나를. 분석기는 또한 텐서를 수락하고 반환하지만 TensorFlow 작업과 달리 훈련 중에 한 번만 실행되며 일반적으로 전체 훈련 데이터 세트를 완전히 통과합니다. 그들은 만들 텐서 상수 그래프에 추가됩니다. 예를 들어, tft.min 훈련 데이터 세트를 통해 텐서의 최소를 계산합니다. tf.Transform은 고정 된 분석기 세트를 제공하지만 향후 버전에서 확장 될 예정입니다.
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

데이터 변환

이제 Apache Beam 파이프 라인에서 데이터 변환을 시작할 준비가되었습니다.

  1. CSV 리더를 사용하여 데이터 읽기
  2. 각 범주에 대한 어휘를 생성하여 숫자 데이터를 확장하고 범주형 데이터를 문자열에서 int64 값 인덱스로 변환하는 전처리 파이프라인을 사용하여 변환
  3. A와 결과를 기록 TFRecordExample 나중에 모델을 훈련에 사용할 PROTOS,
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))

전처리 된 데이터를 사용하여 tf.keras를 사용하여 모델 학습

방법을 보여 tf.Transform 교육과 봉사에 동일한 코드를 사용하여 우리를 가능하게하고, 따라서 왜곡 방지, 우리는 모델을 학습하는 것입니다. 모델을 훈련시키고 훈련된 모델을 프로덕션용으로 준비하려면 입력 함수를 생성해야 합니다. 훈련 입력 함수와 서빙 입력 함수의 주요 차이점은 훈련 데이터에는 레이블이 포함되고 프로덕션 데이터에는 포함되지 않는다는 것입니다. 인수와 반환도 약간 다릅니다.

훈련을 위한 입력 함수 생성

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

서빙을 위한 입력 함수 생성

프로덕션에서 사용할 수 있는 입력 함수를 만들고 훈련된 모델을 제공하도록 준비하겠습니다.

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

모델 학습, 평가 및 내보내기

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)}

모두 함께 넣어

우리는 인구 조사 데이터를 사전 처리하고, 모델을 훈련하고, 제공을 위해 준비하는 데 필요한 모든 것을 만들었습니다. 지금까지 우리는 단지 준비하고 있었습니다. 달리기를 시작할 시간입니다!

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:261: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
2021-07-17 09:16:34.561315: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-07-17 09:16:35.220190: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:16:35.220832: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: 
pciBusID: 0000:00:05.0 name: NVIDIA Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-17 09:16:35.220873: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-17 09:16:35.224564: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
2021-07-17 09:16:35.224669: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2021-07-17 09:16:35.225782: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10
2021-07-17 09:16:35.226153: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10
2021-07-17 09:16:35.227215: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11
2021-07-17 09:16:35.228122: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11
2021-07-17 09:16:35.228294: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8
2021-07-17 09:16:35.228414: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:16:35.229096: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node rWARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:261: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
ead from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:16:35.229734: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-17 09:16:35.230464: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-07-17 09:16:35.231032: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:16:35.231646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: 
pciBusID: 0000:00:05.0 name: NVIDIA Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-17 09:16:35.231731: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:16:35.232404: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:16:35.233011: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-17 09:16:35.233068: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-17 09:16:35.834587: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-17 09:16:35.834621: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 
2021-07-17 09:16:35.834630: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N 
2021-07-17 09:16:35.834875: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:16:35.835601: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:16:35.836274: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:16:35.836920: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: NVIDIA Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
2021-07-17 09:16:38.699564: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
2021-07-17 09:16:38.700291: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2000185000 Hz
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
2021-07-17 09:16:41.769833: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmp/tmpaqz2xu3b/tftransform_tmp/d1c9a9e4d8aa4c12ba85a26c45478cb9/assets
INFO:tensorflow:Assets written to: /tmp/tmpaqz2xu3b/tftransform_tmp/d1c9a9e4d8aa4c12ba85a26c45478cb9/assets
INFO:tensorflow:Assets written to: /tmp/tmpaqz2xu3b/tftransform_tmp/ec72323ba15942bfb7f4d39ee8039f74/assets
INFO:tensorflow:Assets written to: /tmp/tmpaqz2xu3b/tftransform_tmp/ec72323ba15942bfb7f4d39ee8039f74/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
2021-07-17 09:16:56.041945: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
36/255 [===>..........................] - ETA: 0s - loss: 0.5431 - accuracy: 0.7491
2021-07-17 09:16:56.484081: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
255/255 [==============================] - 4s 8ms/step - loss: 0.3891 - accuracy: 0.8165 - val_loss: 0.3404 - val_accuracy: 0.8425
Epoch 2/16
255/255 [==============================] - 2s 7ms/step - loss: 0.3353 - accuracy: 0.8408 - val_loss: 0.3291 - val_accuracy: 0.8455
Epoch 3/16
255/255 [==============================] - 2s 7ms/step - loss: 0.3236 - accuracy: 0.8485 - val_loss: 0.3256 - val_accuracy: 0.8466
Epoch 4/16
255/255 [==============================] - 2s 7ms/step - loss: 0.3167 - accuracy: 0.8512 - val_loss: 0.3225 - val_accuracy: 0.8483
Epoch 5/16
255/255 [==============================] - 2s 7ms/step - loss: 0.3105 - accuracy: 0.8523 - val_loss: 0.3205 - val_accuracy: 0.8477
Epoch 6/16
255/255 [==============================] - 2s 7ms/step - loss: 0.3053 - accuracy: 0.8567 - val_loss: 0.3275 - val_accuracy: 0.8499
Epoch 7/16
255/255 [==============================] - 2s 7ms/step - loss: 0.3047 - accuracy: 0.8572 - val_loss: 0.3205 - val_accuracy: 0.8522
Epoch 8/16
255/255 [==============================] - 2s 7ms/step - loss: 0.3003 - accuracy: 0.8585 - val_loss: 0.3214 - val_accuracy: 0.8517
Epoch 9/16
255/255 [==============================] - 2s 7ms/step - loss: 0.2975 - accuracy: 0.8607 - val_loss: 0.3217 - val_accuracy: 0.8519
Epoch 10/16
255/255 [==============================] - 2s 7ms/step - loss: 0.2928 - accuracy: 0.8628 - val_loss: 0.3286 - val_accuracy: 0.8484
Epoch 11/16
255/255 [==============================] - 2s 7ms/step - loss: 0.2892 - accuracy: 0.8644 - val_loss: 0.3328 - val_accuracy: 0.8496
Epoch 12/16
255/255 [==============================] - 2s 7ms/step - loss: 0.2848 - accuracy: 0.8669 - val_loss: 0.3271 - val_accuracy: 0.8514
Epoch 13/16
255/255 [==============================] - 2s 7ms/step - loss: 0.2806 - accuracy: 0.8674 - val_loss: 0.3329 - val_accuracy: 0.8524
Epoch 14/16
255/255 [==============================] - 2s 7ms/step - loss: 0.2790 - accuracy: 0.8688 - val_loss: 0.3421 - val_accuracy: 0.8475
Epoch 15/16
255/255 [==============================] - 2s 6ms/step - loss: 0.2772 - accuracy: 0.8703 - val_loss: 0.3374 - val_accuracy: 0.8514
Epoch 16/16
255/255 [==============================] - 2s 7ms/step - loss: 0.2734 - accuracy: 0.8716 - val_loss: 0.3395 - val_accuracy: 0.8494
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 [==============================] - 65s 4ms/step - loss: 0.3399 - accuracy: 0.8492
{'accuracy': 0.8492107391357422, 'loss': 0.3398820161819458}

(선택 사항) 사전 처리 된 데이터를 사용하여 tf.estimator를 사용하여 모델 학습

Keras 모델 대신 Estimator 모델을 사용하려는 경우 이 섹션의 코드에서 그 방법을 보여줍니다.

훈련을 위한 입력 함수 생성

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

제공하기위한 입력 함수 만들기

프로덕션에서 사용할 수 있는 입력 함수를 만들고 훈련된 모델을 제공하도록 준비하겠습니다.

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

입력 데이터를 FeatureColumns로 래핑

모델은 TensorFlow FeatureColumns에서 데이터를 예상합니다.

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

모델 학습, 평가 및 내보내기

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)

모두 모아

우리는 인구 조사 데이터를 사전 처리하고, 모델을 훈련하고, 제공을 위해 준비하는 데 필요한 모든 것을 만들었습니다. 지금까지 우리는 준비를 마쳤습니다. 달리기를 시작할 시간입니다!

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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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 0x7ff6dd76f050>
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__'
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:tensorflow:Assets written to: /tmp/tmpui3h3qkj/tftransform_tmp/98871089de0646f2a924d9224586d14a/assets
INFO:tensorflow:Assets written to: /tmp/tmpui3h3qkj/tftransform_tmp/98871089de0646f2a924d9224586d14a/assets
INFO:tensorflow:Assets written to: /tmp/tmpui3h3qkj/tftransform_tmp/7a20fc83eb074022b5357cf2d979a7e2/assets
INFO:tensorflow:Assets written to: /tmp/tmpui3h3qkj/tftransform_tmp/7a20fc83eb074022b5357cf2d979a7e2/assets
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpcudcuz0p
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpcudcuz0p
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpcudcuz0p', '_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/tmpcudcuz0p', '_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.
2021-07-17 09:18:55.706440: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:18:55.706934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: 
pciBusID: 0000:00:05.0 name: NVIDIA Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-17 09:18:55.707146: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:18:55.707602: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:18:55.707952: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-17 09:18:55.708018: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-17 09:18:55.708033: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 
2021-07-17 09:18:55.708044: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N 
2021-07-17 09:18:55.708219: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:18:55.708661: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:18:55.709051: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: NVIDIA Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
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/tmpcudcuz0p/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpcudcuz0p/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: 175.699
INFO:tensorflow:global_step/sec: 175.699
INFO:tensorflow:loss = 46.000416, step = 100 (0.571 sec)
INFO:tensorflow:loss = 46.000416, step = 100 (0.571 sec)
INFO:tensorflow:global_step/sec: 228.95
INFO:tensorflow:global_step/sec: 228.95
INFO:tensorflow:loss = 36.560204, step = 200 (0.436 sec)
INFO:tensorflow:loss = 36.560204, step = 200 (0.436 sec)
INFO:tensorflow:global_step/sec: 233.996
INFO:tensorflow:global_step/sec: 233.996
INFO:tensorflow:loss = 36.249218, step = 300 (0.427 sec)
INFO:tensorflow:loss = 36.249218, step = 300 (0.427 sec)
INFO:tensorflow:global_step/sec: 236.696
INFO:tensorflow:global_step/sec: 236.696
INFO:tensorflow:loss = 47.485767, step = 400 (0.422 sec)
INFO:tensorflow:loss = 47.485767, step = 400 (0.422 sec)
INFO:tensorflow:global_step/sec: 237.265
INFO:tensorflow:global_step/sec: 237.265
INFO:tensorflow:loss = 44.86882, step = 500 (0.421 sec)
INFO:tensorflow:loss = 44.86882, step = 500 (0.421 sec)
INFO:tensorflow:global_step/sec: 234.798
INFO:tensorflow:global_step/sec: 234.798
INFO:tensorflow:loss = 43.45087, step = 600 (0.426 sec)
INFO:tensorflow:loss = 43.45087, step = 600 (0.426 sec)
INFO:tensorflow:global_step/sec: 234.522
INFO:tensorflow:global_step/sec: 234.522
INFO:tensorflow:loss = 46.665726, step = 700 (0.426 sec)
INFO:tensorflow:loss = 46.665726, step = 700 (0.426 sec)
INFO:tensorflow:global_step/sec: 233.732
INFO:tensorflow:global_step/sec: 233.732
INFO:tensorflow:loss = 40.407955, step = 800 (0.428 sec)
INFO:tensorflow:loss = 40.407955, step = 800 (0.428 sec)
INFO:tensorflow:global_step/sec: 227.531
INFO:tensorflow:global_step/sec: 227.531
INFO:tensorflow:loss = 48.43808, step = 900 (0.440 sec)
INFO:tensorflow:loss = 48.43808, step = 900 (0.440 sec)
INFO:tensorflow:global_step/sec: 229.512
INFO:tensorflow:global_step/sec: 229.512
INFO:tensorflow:loss = 47.670822, step = 1000 (0.436 sec)
INFO:tensorflow:loss = 47.670822, step = 1000 (0.436 sec)
INFO:tensorflow:global_step/sec: 222.945
INFO:tensorflow:global_step/sec: 222.945
INFO:tensorflow:loss = 35.43522, step = 1100 (0.448 sec)
INFO:tensorflow:loss = 35.43522, step = 1100 (0.448 sec)
INFO:tensorflow:global_step/sec: 226.611
INFO:tensorflow:global_step/sec: 226.611
INFO:tensorflow:loss = 39.898304, step = 1200 (0.441 sec)
INFO:tensorflow:loss = 39.898304, step = 1200 (0.441 sec)
INFO:tensorflow:global_step/sec: 226.901
INFO:tensorflow:global_step/sec: 226.901
INFO:tensorflow:loss = 43.98366, step = 1300 (0.441 sec)
INFO:tensorflow:loss = 43.98366, step = 1300 (0.441 sec)
INFO:tensorflow:global_step/sec: 224.554
INFO:tensorflow:global_step/sec: 224.554
INFO:tensorflow:loss = 38.598232, step = 1400 (0.445 sec)
INFO:tensorflow:loss = 38.598232, step = 1400 (0.445 sec)
INFO:tensorflow:global_step/sec: 227.57
INFO:tensorflow:global_step/sec: 227.57
INFO:tensorflow:loss = 53.68925, step = 1500 (0.439 sec)
INFO:tensorflow:loss = 53.68925, step = 1500 (0.439 sec)
INFO:tensorflow:global_step/sec: 228.672
INFO:tensorflow:global_step/sec: 228.672
INFO:tensorflow:loss = 41.286385, step = 1600 (0.437 sec)
INFO:tensorflow:loss = 41.286385, step = 1600 (0.437 sec)
INFO:tensorflow:global_step/sec: 230.426
INFO:tensorflow:global_step/sec: 230.426
INFO:tensorflow:loss = 42.005077, step = 1700 (0.434 sec)
INFO:tensorflow:loss = 42.005077, step = 1700 (0.434 sec)
INFO:tensorflow:global_step/sec: 227.496
INFO:tensorflow:global_step/sec: 227.496
INFO:tensorflow:loss = 40.119736, step = 1800 (0.440 sec)
INFO:tensorflow:loss = 40.119736, step = 1800 (0.440 sec)
INFO:tensorflow:global_step/sec: 226.626
INFO:tensorflow:global_step/sec: 226.626
INFO:tensorflow:loss = 46.97238, step = 1900 (0.441 sec)
INFO:tensorflow:loss = 46.97238, step = 1900 (0.441 sec)
INFO:tensorflow:global_step/sec: 227.996
INFO:tensorflow:global_step/sec: 227.996
INFO:tensorflow:loss = 40.328735, step = 2000 (0.438 sec)
INFO:tensorflow:loss = 40.328735, step = 2000 (0.438 sec)
INFO:tensorflow:global_step/sec: 231.026
INFO:tensorflow:global_step/sec: 231.026
INFO:tensorflow:loss = 34.11062, step = 2100 (0.434 sec)
INFO:tensorflow:loss = 34.11062, step = 2100 (0.434 sec)
INFO:tensorflow:global_step/sec: 234.52
INFO:tensorflow:global_step/sec: 234.52
INFO:tensorflow:loss = 35.958435, step = 2200 (0.425 sec)
INFO:tensorflow:loss = 35.958435, step = 2200 (0.425 sec)
INFO:tensorflow:global_step/sec: 230.927
INFO:tensorflow:global_step/sec: 230.927
INFO:tensorflow:loss = 32.133068, step = 2300 (0.433 sec)
INFO:tensorflow:loss = 32.133068, step = 2300 (0.433 sec)
INFO:tensorflow:global_step/sec: 237.994
INFO:tensorflow:global_step/sec: 237.994
INFO:tensorflow:loss = 50.483704, step = 2400 (0.420 sec)
INFO:tensorflow:loss = 50.483704, step = 2400 (0.420 sec)
INFO:tensorflow:global_step/sec: 238.501
INFO:tensorflow:global_step/sec: 238.501
INFO:tensorflow:loss = 40.067295, step = 2500 (0.420 sec)
INFO:tensorflow:loss = 40.067295, step = 2500 (0.420 sec)
INFO:tensorflow:global_step/sec: 227.306
INFO:tensorflow:global_step/sec: 227.306
INFO:tensorflow:loss = 40.04756, step = 2600 (0.440 sec)
INFO:tensorflow:loss = 40.04756, step = 2600 (0.440 sec)
INFO:tensorflow:global_step/sec: 228.174
INFO:tensorflow:global_step/sec: 228.174
INFO:tensorflow:loss = 35.271015, step = 2700 (0.438 sec)
INFO:tensorflow:loss = 35.271015, step = 2700 (0.438 sec)
INFO:tensorflow:global_step/sec: 229.061
INFO:tensorflow:global_step/sec: 229.061
INFO:tensorflow:loss = 38.12026, step = 2800 (0.437 sec)
INFO:tensorflow:loss = 38.12026, step = 2800 (0.437 sec)
INFO:tensorflow:global_step/sec: 225.413
INFO:tensorflow:global_step/sec: 225.413
INFO:tensorflow:loss = 35.26473, step = 2900 (0.443 sec)
INFO:tensorflow:loss = 35.26473, step = 2900 (0.443 sec)
INFO:tensorflow:global_step/sec: 207.679
INFO:tensorflow:global_step/sec: 207.679
INFO:tensorflow:loss = 33.684696, step = 3000 (0.482 sec)
INFO:tensorflow:loss = 33.684696, step = 3000 (0.482 sec)
INFO:tensorflow:global_step/sec: 220.284
INFO:tensorflow:global_step/sec: 220.284
INFO:tensorflow:loss = 41.094524, step = 3100 (0.454 sec)
INFO:tensorflow:loss = 41.094524, step = 3100 (0.454 sec)
INFO:tensorflow:global_step/sec: 226.876
INFO:tensorflow:global_step/sec: 226.876
INFO:tensorflow:loss = 33.792892, step = 3200 (0.441 sec)
INFO:tensorflow:loss = 33.792892, step = 3200 (0.441 sec)
INFO:tensorflow:global_step/sec: 231.382
INFO:tensorflow:global_step/sec: 231.382
INFO:tensorflow:loss = 41.686043, step = 3300 (0.432 sec)
INFO:tensorflow:loss = 41.686043, step = 3300 (0.432 sec)
INFO:tensorflow:global_step/sec: 229.275
INFO:tensorflow:global_step/sec: 229.275
INFO:tensorflow:loss = 37.32172, step = 3400 (0.436 sec)
INFO:tensorflow:loss = 37.32172, step = 3400 (0.436 sec)
INFO:tensorflow:global_step/sec: 227.149
INFO:tensorflow:global_step/sec: 227.149
INFO:tensorflow:loss = 34.77407, step = 3500 (0.440 sec)
INFO:tensorflow:loss = 34.77407, step = 3500 (0.440 sec)
INFO:tensorflow:global_step/sec: 224.853
INFO:tensorflow:global_step/sec: 224.853
INFO:tensorflow:loss = 44.490417, step = 3600 (0.445 sec)
INFO:tensorflow:loss = 44.490417, step = 3600 (0.445 sec)
INFO:tensorflow:global_step/sec: 223.457
INFO:tensorflow:global_step/sec: 223.457
INFO:tensorflow:loss = 34.61316, step = 3700 (0.447 sec)
INFO:tensorflow:loss = 34.61316, step = 3700 (0.447 sec)
INFO:tensorflow:global_step/sec: 220.931
INFO:tensorflow:global_step/sec: 220.931
INFO:tensorflow:loss = 39.123787, step = 3800 (0.453 sec)
INFO:tensorflow:loss = 39.123787, step = 3800 (0.453 sec)
INFO:tensorflow:global_step/sec: 221.335
INFO:tensorflow:global_step/sec: 221.335
INFO:tensorflow:loss = 52.335667, step = 3900 (0.452 sec)
INFO:tensorflow:loss = 52.335667, step = 3900 (0.452 sec)
INFO:tensorflow:global_step/sec: 223.469
INFO:tensorflow:global_step/sec: 223.469
INFO:tensorflow:loss = 44.82943, step = 4000 (0.447 sec)
INFO:tensorflow:loss = 44.82943, step = 4000 (0.447 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/tmpcudcuz0p/model.ckpt.
INFO:tensorflow:Saving checkpoints for 4071 into /tmp/tmpcudcuz0p/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: 43.16082.
INFO:tensorflow:Loss for final step: 43.16082.
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/tmpcudcuz0p/model.ckpt-4071
2021-07-17 09:19:15.711890: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:19:15.712394: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: 
pciBusID: 0000:00:05.0 name: NVIDIA Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-17 09:19:15.712598: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:19:15.713126: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:19:15.713517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-17 09:19:15.713574: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-17 09:19:15.713588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 
2021-07-17 09:19:15.713597: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N 
2021-07-17 09:19:15.713783: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:19:15.714280: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:19:15.714704: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: NVIDIA Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
INFO:tensorflow:Restoring parameters from /tmp/tmpcudcuz0p/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-1626513554/assets
INFO:tensorflow:Assets written to: /tmp/estimator/exported_model_dir/temp-1626513554/assets
INFO:tensorflow:SavedModel written to: /tmp/estimator/exported_model_dir/temp-1626513554/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/estimator/exported_model_dir/temp-1626513554/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 2021-07-17T09:19:17
INFO:tensorflow:Starting evaluation at 2021-07-17T09:19:17
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpcudcuz0p/model.ckpt-4071
2021-07-17 09:19:17.119589: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:19:17.119988: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: 
pciBusID: 0000:00:05.0 name: NVIDIA Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-07-17 09:19:17.120121: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:19:17.120445: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:19:17.120737: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2021-07-17 09:19:17.120781: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-17 09:19:17.120789: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 
2021-07-17 09:19:17.120813: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N 
2021-07-17 09:19:17.120922: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:19:17.121327: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-17 09:19:17.121624: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14646 MB memory) -> physical GPU (device: 0, name: NVIDIA Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0)
INFO:tensorflow:Restoring parameters from /tmp/tmpcudcuz0p/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 : 68.48405s
INFO:tensorflow:Inference Time : 68.48405s
INFO:tensorflow:Finished evaluation at 2021-07-17-09:20:25
INFO:tensorflow:Finished evaluation at 2021-07-17-09:20:25
INFO:tensorflow:Saving dict for global step 4071: accuracy = 0.8507463, accuracy_baseline = 0.76377374, auc = 0.9017595, auc_precision_recall = 0.967162, average_loss = 0.3242954, global_step = 4071, label/mean = 0.76377374, loss = 0.3242954, precision = 0.88189936, prediction/mean = 0.7576821, recall = 0.9289908
INFO:tensorflow:Saving dict for global step 4071: accuracy = 0.8507463, accuracy_baseline = 0.76377374, auc = 0.9017595, auc_precision_recall = 0.967162, average_loss = 0.3242954, global_step = 4071, label/mean = 0.76377374, loss = 0.3242954, precision = 0.88189936, prediction/mean = 0.7576821, recall = 0.9289908
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4071: /tmp/tmpcudcuz0p/model.ckpt-4071
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4071: /tmp/tmpcudcuz0p/model.ckpt-4071
{'accuracy': 0.8507463,
 'accuracy_baseline': 0.76377374,
 'auc': 0.9017595,
 'auc_precision_recall': 0.967162,
 'average_loss': 0.3242954,
 'global_step': 4071,
 'label/mean': 0.76377374,
 'loss': 0.3242954,
 'precision': 0.88189936,
 'prediction/mean': 0.7576821,
 'recall': 0.9289908}

우리가 한 일

이 예에서 우리는 사용 tf.Transform 세정 및 변환 된 데이터와 모델을 인구 조사 데이터의 데이터 집합을 사전 처리 및 훈련. 또한 학습 된 모델을 프로덕션 환경에 배포하여 추론을 수행 할 때 사용할 수있는 입력 함수를 만들었습니다. 훈련과 추론 모두에 동일한 코드를 사용함으로써 데이터 왜곡 문제를 피할 수 있습니다. 그 과정에서 데이터를 정리하는 데 필요한 변환을 수행하기 위해 Apache Beam 변환을 만드는 방법을 배웠습니다. 우리는 또한 중 하나를 사용하여 모델 훈련이 변환 된 데이터를 사용하는 방법을 살펴 보았다 tf.keras 또는 tf.estimator . 이것은 TensorFlow Transform이 할 수있는 일의 작은 부분 일뿐입니다! 우리는에 다이빙에 좋습니다 tf.Transform 하고 당신을 위해 무엇을 할 수 있는지 알아보세요.