การประมวลผลข้อมูลล่วงหน้าด้วย TensorFlow Transform

ส่วนประกอบวิศวกรรมคุณลักษณะของ TensorFlow Extended (TFX)

โน๊ตบุ๊คตัวอย่าง Colab นี้ให้เป็นตัวอย่างที่ค่อนข้างสูงขึ้นของวิธี TensorFlow Transform ( tf.Transform ) สามารถใช้ข้อมูล preprocess ใช้ว่ารหัสเดียวกันสำหรับการฝึกอบรมทั้งรูปแบบและการให้บริการการหาข้อสรุปในการผลิต

TensorFlow Transform เป็นไลบรารีสำหรับประมวลผลข้อมูลอินพุตล่วงหน้าสำหรับ TensorFlow รวมถึงการสร้างคุณสมบัติที่ต้องใช้ชุดข้อมูลการฝึกอบรมแบบเต็ม ตัวอย่างเช่น การใช้ TensorFlow Transform คุณสามารถ:

  • ปรับค่าอินพุตให้เป็นมาตรฐานโดยใช้ค่าเฉลี่ยและค่าเบี่ยงเบนมาตรฐาน
  • แปลงสตริงเป็นจำนวนเต็มโดยสร้างคำศัพท์เหนือค่าอินพุตทั้งหมด
  • แปลงจำนวนเต็มเป็นจำนวนเต็มโดยกำหนดให้กับบัคเก็ต ตามการกระจายข้อมูลที่สังเกตได้

TensorFlow มีการสนับสนุนในตัวสำหรับการปรับเปลี่ยนในตัวอย่างเดียวหรือกลุ่มตัวอย่าง tf.Transform ขยายขีดความสามารถเหล่านี้ให้การสนับสนุนผ่านเต็มรูปแบบผ่านชุดการฝึกอบรมทั้งหมด

การส่งออกของ tf.Transform มีการส่งออกเป็นกราฟ TensorFlow ซึ่งคุณสามารถใช้สำหรับทั้งการฝึกอบรมและการให้บริการ การใช้กราฟเดียวกันสำหรับการฝึกและการเสิร์ฟสามารถป้องกันการเอียงได้ เนื่องจากมีการนำการแปลงแบบเดียวกันมาใช้ในทั้งสองขั้นตอน

เรากำลังทำอะไรในตัวอย่างนี้

ในตัวอย่างนี้เราจะได้รับการประมวลผล ชุดข้อมูลที่ใช้กันอย่างแพร่หลายมีข้อมูลการสำรวจสำมะโนประชากร และการฝึกอบรมรุ่นที่จะทำการจัดหมวดหมู่ ไปตามทางที่เราจะเปลี่ยนข้อมูลโดยใช้ tf.Transform

อัพเกรด Pip

เพื่อหลีกเลี่ยงการอัพเกรด Pip ในระบบเมื่อรันในเครื่อง ให้ตรวจสอบว่าเรากำลังทำงานใน Colab แน่นอนว่าระบบในพื้นที่สามารถอัพเกรดแยกกันได้

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

ติดตั้ง TensorFlow Transform

pip install tensorflow-transform

ตรวจสอบ Python นำเข้าและ globals

อันดับแรก เราจะตรวจสอบให้แน่ใจว่าเราใช้ Python 3 จากนั้นจึงดำเนินการติดตั้งและนำเข้าสิ่งที่เราต้องการ

import sys

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

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

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

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

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

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

train = './adult.data'
test = './adult.test'
TF: 2.4.4
Beam: 2.34.0
Transform: 0.29.0
--2021-12-04 10:43:05--  https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.data
Resolving storage.googleapis.com (storage.googleapis.com)... 142.251.8.128, 74.125.204.128, 64.233.189.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|142.251.8.128|: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 0.03s   

2021-12-04 10:43:05 (135 MB/s) - ‘adult.data’ saved [3974305/3974305]

--2021-12-04 10:43:05--  https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/census/adult.test
Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.157.128, 108.177.125.128, 64.233.189.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.157.128|: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   

2021-12-04 10:43:05 (177 MB/s) - ‘adult.test’ saved [2003153/2003153]

ตั้งชื่อคอลัมน์ของเรา

เราจะสร้างรายการที่มีประโยชน์สำหรับอ้างอิงคอลัมน์ในชุดข้อมูลของเรา

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'

กำหนดคุณสมบัติและสคีมาของเรา

มากำหนดสคีมาโดยพิจารณาจากประเภทของคอลัมน์ในอินพุตของเรา เหนือสิ่งอื่นใด สิ่งนี้จะช่วยในการนำเข้าอย่างถูกต้อง

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

การตั้งค่าไฮเปอร์พารามิเตอร์และการดูแลทำความสะอาดขั้นพื้นฐาน

ค่าคงที่และไฮเปอร์พารามิเตอร์ที่ใช้สำหรับการฝึก ขนาดที่ฝากข้อมูลรวมถึงหมวดหมู่ที่แสดงรายการทั้งหมดในคำอธิบายชุดข้อมูลและอีกหนึ่งรายการสำหรับ "?" ซึ่งแสดงถึงสิ่งที่ไม่รู้จัก

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'

preprocessing กับ tf.Transform

สร้าง tf.Transform preprocessing_fn

ฟังก์ชั่น preprocessing เป็นแนวคิดที่สำคัญที่สุดของ tf.Transform ฟังก์ชันการประมวลผลล่วงหน้าเป็นที่ที่การแปลงชุดข้อมูลเกิดขึ้นจริง มันรับและผลตอบแทนในพจนานุกรมของเทนเซอร์ที่เมตริกซ์หมายถึงการเป็น Tensor หรือ SparseTensor การเรียก API มีสองกลุ่มหลักที่โดยทั่วไปแล้วจะเป็นหัวใจของฟังก์ชันการประมวลผลล่วงหน้า:

  1. TensorFlow Ops: ฟังก์ชั่นที่ยอมรับและผลตอบแทนเทนเซอร์ซึ่งมักจะหมายถึง Ops TensorFlow สิ่งเหล่านี้เพิ่มการดำเนินการ TensorFlow ให้กับกราฟที่แปลงข้อมูลดิบเป็นข้อมูลที่แปลงแล้วเวกเตอร์คุณลักษณะครั้งละหนึ่งรายการ สิ่งเหล่านี้จะทำงานในทุกตัวอย่าง ระหว่างการฝึกและการเสิร์ฟ
  2. TensorFlow Transform วิเคราะห์: ใด ๆ ของการวิเคราะห์ที่มีให้โดย tf.Transform เครื่องวิเคราะห์ยังยอมรับและส่งคืนเทนเซอร์ แต่ต่างจาก TensorFlow ops ที่ทำงานเพียงครั้งเดียว ระหว่างการฝึก และโดยทั่วไปแล้วจะส่งผ่านชุดข้อมูลการฝึกทั้งหมดอย่างสมบูรณ์ พวกเขาสร้าง ค่าคงที่เมตริกซ์ ซึ่งจะมีการเพิ่มกราฟของคุณ ยกตัวอย่างเช่น tft.min คำนวณต่ำสุดของเมตริกซ์กว่าชุดการฝึกอบรม tf.Transform มีชุดเครื่องมือวิเคราะห์แบบตายตัว แต่จะขยายเพิ่มเติมในเวอร์ชันต่อๆ ไป
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

แปลงข้อมูล

ตอนนี้เราพร้อมที่จะเริ่มแปลงข้อมูลของเราในไปป์ไลน์ Apache Beam แล้ว

  1. อ่านข้อมูลโดยใช้โปรแกรมอ่าน CSV
  2. แปลงโดยใช้ไปป์ไลน์การประมวลผลล่วงหน้าที่ปรับขนาดข้อมูลตัวเลขและแปลงข้อมูลหมวดหมู่จากสตริงเป็นดัชนีค่า int64 โดยสร้างคำศัพท์สำหรับแต่ละหมวดหมู่
  3. เขียนออกผลเป็น TFRecord ของ Example Protos ซึ่งเราจะใช้สำหรับการฝึกอบรมรุ่นต่อมา
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))

การใช้ข้อมูลที่ประมวลผลล่วงหน้าของเราในการฝึกโมเดลโดยใช้ tf.keras

แสดงให้เห็นว่า tf.Transform ช่วยให้เราสามารถใช้รหัสเดียวกันสำหรับทั้งการฝึกอบรมและการให้บริการและทำให้การป้องกันไม่ให้เอียงเรากำลังจะไปฝึกอบรมรูปแบบ ในการฝึกโมเดลของเราและเตรียมโมเดลที่ได้รับการฝึกอบรมสำหรับการผลิต เราจำเป็นต้องสร้างฟังก์ชันอินพุต ความแตกต่างที่สำคัญระหว่างฟังก์ชันป้อนข้อมูลการฝึกอบรมและฟังก์ชันป้อนข้อมูลการให้บริการของเราคือ ข้อมูลการฝึกอบรมประกอบด้วยป้ายกำกับ และข้อมูลการผลิตไม่มี อาร์กิวเมนต์และผลตอบแทนก็แตกต่างกันบ้าง

สร้างฟังก์ชันอินพุตสำหรับการฝึกอบรม

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

สร้างฟังก์ชันอินพุตสำหรับเสิร์ฟ

มาสร้างฟังก์ชันอินพุตที่เราใช้ในการผลิตกันเถอะ และเตรียมโมเดลที่ผ่านการฝึกอบรมสำหรับการให้บริการ

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

ฝึกฝน ประเมิน และส่งออกแบบจำลองของเรา

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)}

เอามารวมกัน

เราได้สร้างทุกสิ่งที่จำเป็นในการประมวลผลข้อมูลสำมะโนของเราล่วงหน้า ฝึกอบรมแบบจำลอง และเตรียมข้อมูลดังกล่าวสำหรับการให้บริการ เท่านี้เราก็เตรียมของให้พร้อมแล้ว ได้เวลาเริ่มวิ่งแล้ว!

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:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:266: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_transform/tf_utils.py:266: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
2021-12-04 10:43:07.088016: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2021-12-04 10:43:07.089022: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tmpwtmrrrxa/tftransform_tmp/3dfb612abc894c0ab0ae6895d85b5084/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tmpwtmrrrxa/tftransform_tmp/3dfb612abc894c0ab0ae6895d85b5084/saved_model.pb
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tmpwtmrrrxa/tftransform_tmp/c76371e6c4104068b035f1ba7ac0c160/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tmpwtmrrrxa/tftransform_tmp/c76371e6c4104068b035f1ba7ac0c160/saved_model.pb
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2021-12-04 10:43:12.129285: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2021-12-04 10:43:12.129350: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tmpwtmrrrxa/tftransform_tmp/a447c39aff834eaa8b3df63abd6a0d29/assets
INFO:tensorflow:Assets written to: /tmp/tmpwtmrrrxa/tftransform_tmp/a447c39aff834eaa8b3df63abd6a0d29/assets
INFO:tensorflow:SavedModel written to: /tmp/tmpwtmrrrxa/tftransform_tmp/a447c39aff834eaa8b3df63abd6a0d29/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tmpwtmrrrxa/tftransform_tmp/a447c39aff834eaa8b3df63abd6a0d29/saved_model.pb
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2021-12-04 10:43:17.368791: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2021-12-04 10:43:17.368851: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
2021-12-04 10:43:18.716754: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2021-12-04 10:43:18.716809: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
education (InputLayer)          [(None,)]            0                                            
__________________________________________________________________________________________________
marital-status (InputLayer)     [(None,)]            0                                            
__________________________________________________________________________________________________
native-country (InputLayer)     [(None,)]            0                                            
__________________________________________________________________________________________________
occupation (InputLayer)         [(None,)]            0                                            
__________________________________________________________________________________________________
race (InputLayer)               [(None,)]            0                                            
__________________________________________________________________________________________________
relationship (InputLayer)       [(None,)]            0                                            
__________________________________________________________________________________________________
sex (InputLayer)                [(None,)]            0                                            
__________________________________________________________________________________________________
workclass (InputLayer)          [(None,)]            0                                            
__________________________________________________________________________________________________
age (InputLayer)                [(None,)]            0                                            
__________________________________________________________________________________________________
capital-gain (InputLayer)       [(None,)]            0                                            
__________________________________________________________________________________________________
capital-loss (InputLayer)       [(None,)]            0                                            
__________________________________________________________________________________________________
tf.expand_dims_3 (TFOpLambda)   (None, 1)            0           education[0][0]                  
__________________________________________________________________________________________________
education-num (InputLayer)      [(None,)]            0                                            
__________________________________________________________________________________________________
hours-per-week (InputLayer)     [(None,)]            0                                            
__________________________________________________________________________________________________
tf.expand_dims_6 (TFOpLambda)   (None, 1)            0           marital-status[0][0]             
__________________________________________________________________________________________________
tf.expand_dims_7 (TFOpLambda)   (None, 1)            0           native-country[0][0]             
__________________________________________________________________________________________________
tf.expand_dims_8 (TFOpLambda)   (None, 1)            0           occupation[0][0]                 
__________________________________________________________________________________________________
tf.expand_dims_9 (TFOpLambda)   (None, 1)            0           race[0][0]                       
__________________________________________________________________________________________________
tf.expand_dims_10 (TFOpLambda)  (None, 1)            0           relationship[0][0]               
__________________________________________________________________________________________________
tf.expand_dims_11 (TFOpLambda)  (None, 1)            0           sex[0][0]                        
__________________________________________________________________________________________________
tf.expand_dims_12 (TFOpLambda)  (None, 1)            0           workclass[0][0]                  
__________________________________________________________________________________________________
tf.expand_dims (TFOpLambda)     (None, 1)            0           age[0][0]                        
__________________________________________________________________________________________________
tf.expand_dims_1 (TFOpLambda)   (None, 1)            0           capital-gain[0][0]               
__________________________________________________________________________________________________
tf.expand_dims_2 (TFOpLambda)   (None, 1)            0           capital-loss[0][0]               
__________________________________________________________________________________________________
category_encoding (CategoryEnco (None, 17)           0           tf.expand_dims_3[0][0]           
__________________________________________________________________________________________________
tf.expand_dims_4 (TFOpLambda)   (None, 1)            0           education-num[0][0]              
__________________________________________________________________________________________________
tf.expand_dims_5 (TFOpLambda)   (None, 1)            0           hours-per-week[0][0]             
__________________________________________________________________________________________________
category_encoding_1 (CategoryEn (None, 8)            0           tf.expand_dims_6[0][0]           
__________________________________________________________________________________________________
category_encoding_2 (CategoryEn (None, 43)           0           tf.expand_dims_7[0][0]           
__________________________________________________________________________________________________
category_encoding_3 (CategoryEn (None, 16)           0           tf.expand_dims_8[0][0]           
__________________________________________________________________________________________________
category_encoding_4 (CategoryEn (None, 6)            0           tf.expand_dims_9[0][0]           
__________________________________________________________________________________________________
category_encoding_5 (CategoryEn (None, 7)            0           tf.expand_dims_10[0][0]          
__________________________________________________________________________________________________
category_encoding_6 (CategoryEn (None, 3)            0           tf.expand_dims_11[0][0]          
__________________________________________________________________________________________________
category_encoding_7 (CategoryEn (None, 10)           0           tf.expand_dims_12[0][0]          
__________________________________________________________________________________________________
tf.concat (TFOpLambda)          (None, 115)          0           tf.expand_dims[0][0]             
                                                                 tf.expand_dims_1[0][0]           
                                                                 tf.expand_dims_2[0][0]           
                                                                 category_encoding[0][0]          
                                                                 tf.expand_dims_4[0][0]           
                                                                 tf.expand_dims_5[0][0]           
                                                                 category_encoding_1[0][0]        
                                                                 category_encoding_2[0][0]        
                                                                 category_encoding_3[0][0]        
                                                                 category_encoding_4[0][0]        
                                                                 category_encoding_5[0][0]        
                                                                 category_encoding_6[0][0]        
                                                                 category_encoding_7[0][0]        
__________________________________________________________________________________________________
dense (Dense)                   (None, 100)          11600       tf.concat[0][0]                  
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 70)           7070        dense[0][0]                      
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 50)           3550        dense_1[0][0]                    
__________________________________________________________________________________________________
dense_3 (Dense)                 (None, 20)           1020        dense_2[0][0]                    
__________________________________________________________________________________________________
dense_4 (Dense)                 (None, 2)            42          dense_3[0][0]                    
==================================================================================================
Total params: 23,282
Trainable params: 23,282
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/16
255/255 [==============================] - 2s 5ms/step - loss: 0.4575 - accuracy: 0.7892 - val_loss: 0.3393 - val_accuracy: 0.8425
Epoch 2/16
255/255 [==============================] - 1s 3ms/step - loss: 0.3390 - accuracy: 0.8420 - val_loss: 0.3367 - val_accuracy: 0.8442
Epoch 3/16
255/255 [==============================] - 1s 3ms/step - loss: 0.3278 - accuracy: 0.8478 - val_loss: 0.3256 - val_accuracy: 0.8490
Epoch 4/16
255/255 [==============================] - 1s 3ms/step - loss: 0.3182 - accuracy: 0.8494 - val_loss: 0.3246 - val_accuracy: 0.8481
Epoch 5/16
255/255 [==============================] - 1s 3ms/step - loss: 0.3133 - accuracy: 0.8527 - val_loss: 0.3204 - val_accuracy: 0.8484
Epoch 6/16
255/255 [==============================] - 1s 3ms/step - loss: 0.3054 - accuracy: 0.8566 - val_loss: 0.3232 - val_accuracy: 0.8480
Epoch 7/16
255/255 [==============================] - 1s 4ms/step - loss: 0.3024 - accuracy: 0.8568 - val_loss: 0.3248 - val_accuracy: 0.8488
Epoch 8/16
255/255 [==============================] - 1s 3ms/step - loss: 0.2970 - accuracy: 0.8595 - val_loss: 0.3310 - val_accuracy: 0.8470
Epoch 9/16
255/255 [==============================] - 1s 3ms/step - loss: 0.2932 - accuracy: 0.8619 - val_loss: 0.3277 - val_accuracy: 0.8465
Epoch 10/16
255/255 [==============================] - 1s 3ms/step - loss: 0.2946 - accuracy: 0.8617 - val_loss: 0.3292 - val_accuracy: 0.8495
Epoch 11/16
255/255 [==============================] - 1s 3ms/step - loss: 0.2914 - accuracy: 0.8606 - val_loss: 0.3334 - val_accuracy: 0.8511
Epoch 12/16
255/255 [==============================] - 1s 3ms/step - loss: 0.2864 - accuracy: 0.8631 - val_loss: 0.3328 - val_accuracy: 0.8490
Epoch 13/16
255/255 [==============================] - 1s 3ms/step - loss: 0.2811 - accuracy: 0.8671 - val_loss: 0.3386 - val_accuracy: 0.8503
Epoch 14/16
255/255 [==============================] - 1s 3ms/step - loss: 0.2738 - accuracy: 0.8720 - val_loss: 0.3397 - val_accuracy: 0.8483
Epoch 15/16
255/255 [==============================] - 1s 3ms/step - loss: 0.2709 - accuracy: 0.8745 - val_loss: 0.3429 - val_accuracy: 0.8491
Epoch 16/16
255/255 [==============================] - 1s 3ms/step - loss: 0.2705 - accuracy: 0.8724 - val_loss: 0.3467 - val_accuracy: 0.8491
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2021-12-04 10:43:37.584301: 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/keras/exported_model_dir/1/assets
INFO:tensorflow:Assets written to: /tmp/keras/exported_model_dir/1/assets
16281/16281 [==============================] - 21s 1ms/step - loss: 0.3470 - accuracy: 0.8491
{'accuracy': 0.8490878939628601, 'loss': 0.34699547290802}

(ไม่บังคับ) การใช้ข้อมูลที่ประมวลผลล่วงหน้าเพื่อฝึกโมเดลโดยใช้ tf.estimator

หากคุณต้องการใช้โมเดล Estimator แทนโมเดล Keras โค้ดในส่วนนี้จะแสดงวิธีการทำเช่นนั้น

สร้างฟังก์ชันอินพุตสำหรับการฝึกอบรม

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

สร้างฟังก์ชันอินพุตสำหรับเสิร์ฟ

มาสร้างฟังก์ชันอินพุตที่เราใช้ในการผลิตกันเถอะ และเตรียมโมเดลที่ผ่านการฝึกอบรมสำหรับการให้บริการ

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

ล้อมข้อมูลอินพุตของเราใน FeatureColumns

โมเดลของเราจะคาดหวังข้อมูลของเราใน TensorFlow FeatureColumns

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

  Args:
    tf_transform_output: A `TFTransformOutput` object.

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

  # Wrap categorical columns.
  one_hot_columns = [
      tf.feature_column.indicator_column(
          tf.feature_column.categorical_column_with_identity(
              key=key,
              num_buckets=(NUM_OOV_BUCKETS +
                  tf_transform_output.vocabulary_size_by_name(
                      vocab_filename=key))))
      for key in CATEGORICAL_FEATURE_KEYS]

  return real_valued_columns + one_hot_columns

ฝึกฝน ประเมิน และส่งออกแบบจำลองของเรา

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)

เอามารวมกัน

เราได้สร้างทุกสิ่งที่จำเป็นในการประมวลผลข้อมูลสำมะโนของเราล่วงหน้า ฝึกอบรมแบบจำลอง และเตรียมข้อมูลดังกล่าวสำหรับการให้บริการ เท่านี้เราก็เตรียมของให้พร้อมแล้ว ได้เวลาเริ่มวิ่งแล้ว!

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

transform_data(train, test, temp)
results = train_and_evaluate(temp)
pprint.pprint(results)
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tmpi7o66bl8/tftransform_tmp/a7f3726df5bf498ca24bd528eebca9e9/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tmpi7o66bl8/tftransform_tmp/a7f3726df5bf498ca24bd528eebca9e9/saved_model.pb
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tmpi7o66bl8/tftransform_tmp/3466a3517ec243a39102fa6ad6e5fec2/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tmpi7o66bl8/tftransform_tmp/3466a3517ec243a39102fa6ad6e5fec2/saved_model.pb
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.4) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2021-12-04 10:44:05.733070: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2021-12-04 10:44:05.733123: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tmpi7o66bl8/tftransform_tmp/96186aa415404f0884cb3766b270b9b2/assets
INFO:tensorflow:Assets written to: /tmp/tmpi7o66bl8/tftransform_tmp/96186aa415404f0884cb3766b270b9b2/assets
INFO:tensorflow:SavedModel written to: /tmp/tmpi7o66bl8/tftransform_tmp/96186aa415404f0884cb3766b270b9b2/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tmpi7o66bl8/tftransform_tmp/96186aa415404f0884cb3766b270b9b2/saved_model.pb
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
2021-12-04 10:44:10.983401: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2021-12-04 10:44:10.983461: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
2021-12-04 10:44:12.469671: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2021-12-04 10:44:12.469756: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpwufx88ji
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpwufx88ji
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpwufx88ji', '_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/tmpwufx88ji', '_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:1727: 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:134: 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:134: 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.
2021-12-04 10:44:15.191355: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2021-12-04 10:44:15.191419: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
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/tmpwufx88ji/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpwufx88ji/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: 432.87
INFO:tensorflow:global_step/sec: 432.87
INFO:tensorflow:loss = 33.484627, step = 100 (0.233 sec)
INFO:tensorflow:loss = 33.484627, step = 100 (0.233 sec)
INFO:tensorflow:global_step/sec: 764.774
INFO:tensorflow:global_step/sec: 764.774
INFO:tensorflow:loss = 42.72283, step = 200 (0.130 sec)
INFO:tensorflow:loss = 42.72283, step = 200 (0.130 sec)
INFO:tensorflow:global_step/sec: 763.549
INFO:tensorflow:global_step/sec: 763.549
INFO:tensorflow:loss = 55.91174, step = 300 (0.131 sec)
INFO:tensorflow:loss = 55.91174, step = 300 (0.131 sec)
INFO:tensorflow:global_step/sec: 755.175
INFO:tensorflow:global_step/sec: 755.175
INFO:tensorflow:loss = 39.204643, step = 400 (0.133 sec)
INFO:tensorflow:loss = 39.204643, step = 400 (0.133 sec)
INFO:tensorflow:global_step/sec: 792.262
INFO:tensorflow:global_step/sec: 792.262
INFO:tensorflow:loss = 41.268295, step = 500 (0.126 sec)
INFO:tensorflow:loss = 41.268295, step = 500 (0.126 sec)
INFO:tensorflow:global_step/sec: 743.725
INFO:tensorflow:global_step/sec: 743.725
INFO:tensorflow:loss = 51.267006, step = 600 (0.135 sec)
INFO:tensorflow:loss = 51.267006, step = 600 (0.135 sec)
INFO:tensorflow:global_step/sec: 806.716
INFO:tensorflow:global_step/sec: 806.716
INFO:tensorflow:loss = 42.03744, step = 700 (0.124 sec)
INFO:tensorflow:loss = 42.03744, step = 700 (0.124 sec)
INFO:tensorflow:global_step/sec: 763.135
INFO:tensorflow:global_step/sec: 763.135
INFO:tensorflow:loss = 42.66994, step = 800 (0.131 sec)
INFO:tensorflow:loss = 42.66994, step = 800 (0.131 sec)
INFO:tensorflow:global_step/sec: 779.496
INFO:tensorflow:global_step/sec: 779.496
INFO:tensorflow:loss = 48.643982, step = 900 (0.129 sec)
INFO:tensorflow:loss = 48.643982, step = 900 (0.129 sec)
INFO:tensorflow:global_step/sec: 787.431
INFO:tensorflow:global_step/sec: 787.431
INFO:tensorflow:loss = 41.668102, step = 1000 (0.127 sec)
INFO:tensorflow:loss = 41.668102, step = 1000 (0.127 sec)
INFO:tensorflow:global_step/sec: 737.697
INFO:tensorflow:global_step/sec: 737.697
INFO:tensorflow:loss = 40.340927, step = 1100 (0.135 sec)
INFO:tensorflow:loss = 40.340927, step = 1100 (0.135 sec)
INFO:tensorflow:global_step/sec: 755.647
INFO:tensorflow:global_step/sec: 755.647
INFO:tensorflow:loss = 31.146494, step = 1200 (0.133 sec)
INFO:tensorflow:loss = 31.146494, step = 1200 (0.133 sec)
INFO:tensorflow:global_step/sec: 785.653
INFO:tensorflow:global_step/sec: 785.653
INFO:tensorflow:loss = 30.96864, step = 1300 (0.127 sec)
INFO:tensorflow:loss = 30.96864, step = 1300 (0.127 sec)
INFO:tensorflow:global_step/sec: 759.461
INFO:tensorflow:global_step/sec: 759.461
INFO:tensorflow:loss = 38.621964, step = 1400 (0.132 sec)
INFO:tensorflow:loss = 38.621964, step = 1400 (0.132 sec)
INFO:tensorflow:global_step/sec: 777.328
INFO:tensorflow:global_step/sec: 777.328
INFO:tensorflow:loss = 44.518555, step = 1500 (0.129 sec)
INFO:tensorflow:loss = 44.518555, step = 1500 (0.129 sec)
INFO:tensorflow:global_step/sec: 741.005
INFO:tensorflow:global_step/sec: 741.005
INFO:tensorflow:loss = 45.997204, step = 1600 (0.135 sec)
INFO:tensorflow:loss = 45.997204, step = 1600 (0.135 sec)
INFO:tensorflow:global_step/sec: 734.846
INFO:tensorflow:global_step/sec: 734.846
INFO:tensorflow:loss = 50.39132, step = 1700 (0.136 sec)
INFO:tensorflow:loss = 50.39132, step = 1700 (0.136 sec)
INFO:tensorflow:global_step/sec: 752.826
INFO:tensorflow:global_step/sec: 752.826
INFO:tensorflow:loss = 45.41472, step = 1800 (0.133 sec)
INFO:tensorflow:loss = 45.41472, step = 1800 (0.133 sec)
INFO:tensorflow:global_step/sec: 757.018
INFO:tensorflow:global_step/sec: 757.018
INFO:tensorflow:loss = 46.133186, step = 1900 (0.132 sec)
INFO:tensorflow:loss = 46.133186, step = 1900 (0.132 sec)
INFO:tensorflow:global_step/sec: 700.757
INFO:tensorflow:global_step/sec: 700.757
INFO:tensorflow:loss = 34.684982, step = 2000 (0.143 sec)
INFO:tensorflow:loss = 34.684982, step = 2000 (0.143 sec)
INFO:tensorflow:global_step/sec: 741.709
INFO:tensorflow:global_step/sec: 741.709
INFO:tensorflow:loss = 39.637863, step = 2100 (0.135 sec)
INFO:tensorflow:loss = 39.637863, step = 2100 (0.135 sec)
INFO:tensorflow:global_step/sec: 772.066
INFO:tensorflow:global_step/sec: 772.066
INFO:tensorflow:loss = 45.70813, step = 2200 (0.129 sec)
INFO:tensorflow:loss = 45.70813, step = 2200 (0.129 sec)
INFO:tensorflow:global_step/sec: 776.263
INFO:tensorflow:global_step/sec: 776.263
INFO:tensorflow:loss = 39.104668, step = 2300 (0.129 sec)
INFO:tensorflow:loss = 39.104668, step = 2300 (0.129 sec)
INFO:tensorflow:global_step/sec: 768.016
INFO:tensorflow:global_step/sec: 768.016
INFO:tensorflow:loss = 36.262817, step = 2400 (0.130 sec)
INFO:tensorflow:loss = 36.262817, step = 2400 (0.130 sec)
INFO:tensorflow:global_step/sec: 754.04
INFO:tensorflow:global_step/sec: 754.04
INFO:tensorflow:loss = 43.80282, step = 2500 (0.132 sec)
INFO:tensorflow:loss = 43.80282, step = 2500 (0.132 sec)
INFO:tensorflow:global_step/sec: 742.917
INFO:tensorflow:global_step/sec: 742.917
INFO:tensorflow:loss = 48.113125, step = 2600 (0.135 sec)
INFO:tensorflow:loss = 48.113125, step = 2600 (0.135 sec)
INFO:tensorflow:global_step/sec: 753.394
INFO:tensorflow:global_step/sec: 753.394
INFO:tensorflow:loss = 43.442005, step = 2700 (0.133 sec)
INFO:tensorflow:loss = 43.442005, step = 2700 (0.133 sec)
INFO:tensorflow:global_step/sec: 768.985
INFO:tensorflow:global_step/sec: 768.985
INFO:tensorflow:loss = 34.593086, step = 2800 (0.130 sec)
INFO:tensorflow:loss = 34.593086, step = 2800 (0.130 sec)
INFO:tensorflow:global_step/sec: 756.393
INFO:tensorflow:global_step/sec: 756.393
INFO:tensorflow:loss = 38.085594, step = 2900 (0.132 sec)
INFO:tensorflow:loss = 38.085594, step = 2900 (0.132 sec)
INFO:tensorflow:global_step/sec: 792.717
INFO:tensorflow:global_step/sec: 792.717
INFO:tensorflow:loss = 42.41484, step = 3000 (0.126 sec)
INFO:tensorflow:loss = 42.41484, step = 3000 (0.126 sec)
INFO:tensorflow:global_step/sec: 763.25
INFO:tensorflow:global_step/sec: 763.25
INFO:tensorflow:loss = 42.457626, step = 3100 (0.131 sec)
INFO:tensorflow:loss = 42.457626, step = 3100 (0.131 sec)
INFO:tensorflow:global_step/sec: 747.998
INFO:tensorflow:global_step/sec: 747.998
INFO:tensorflow:loss = 52.64791, step = 3200 (0.134 sec)
INFO:tensorflow:loss = 52.64791, step = 3200 (0.134 sec)
INFO:tensorflow:global_step/sec: 733.804
INFO:tensorflow:global_step/sec: 733.804
INFO:tensorflow:loss = 36.78949, step = 3300 (0.136 sec)
INFO:tensorflow:loss = 36.78949, step = 3300 (0.136 sec)
INFO:tensorflow:global_step/sec: 747.473
INFO:tensorflow:global_step/sec: 747.473
INFO:tensorflow:loss = 43.02353, step = 3400 (0.134 sec)
INFO:tensorflow:loss = 43.02353, step = 3400 (0.134 sec)
INFO:tensorflow:global_step/sec: 766.967
INFO:tensorflow:global_step/sec: 766.967
INFO:tensorflow:loss = 42.971584, step = 3500 (0.131 sec)
INFO:tensorflow:loss = 42.971584, step = 3500 (0.131 sec)
INFO:tensorflow:global_step/sec: 759.238
INFO:tensorflow:global_step/sec: 759.238
INFO:tensorflow:loss = 31.898714, step = 3600 (0.133 sec)
INFO:tensorflow:loss = 31.898714, step = 3600 (0.133 sec)
INFO:tensorflow:global_step/sec: 770.209
INFO:tensorflow:global_step/sec: 770.209
INFO:tensorflow:loss = 43.47151, step = 3700 (0.128 sec)
INFO:tensorflow:loss = 43.47151, step = 3700 (0.128 sec)
INFO:tensorflow:global_step/sec: 750.127
INFO:tensorflow:global_step/sec: 750.127
INFO:tensorflow:loss = 40.073875, step = 3800 (0.133 sec)
INFO:tensorflow:loss = 40.073875, step = 3800 (0.133 sec)
INFO:tensorflow:global_step/sec: 731.607
INFO:tensorflow:global_step/sec: 731.607
INFO:tensorflow:loss = 33.494003, step = 3900 (0.137 sec)
INFO:tensorflow:loss = 33.494003, step = 3900 (0.137 sec)
INFO:tensorflow:global_step/sec: 753.01
INFO:tensorflow:global_step/sec: 753.01
INFO:tensorflow:loss = 40.401936, step = 4000 (0.133 sec)
INFO:tensorflow:loss = 40.401936, step = 4000 (0.133 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/tmpwufx88ji/model.ckpt.
INFO:tensorflow:Saving checkpoints for 4071 into /tmp/tmpwufx88ji/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: 51.911263.
INFO:tensorflow:Loss for final step: 51.911263.
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\tworkclass"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\teducation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\016marital-status"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\noccupation"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_11:0\022\014relationship"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_13:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_15:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\014\n\nConst_17:0\022\016native-country"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification']
INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification']
INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression']
INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Restoring parameters from /tmp/tmpwufx88ji/model.ckpt-4071
2021-12-04 10:44:22.080737: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2021-12-04 10:44:22.080796: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
INFO:tensorflow:Restoring parameters from /tmp/tmpwufx88ji/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-1638614661/assets
INFO:tensorflow:Assets written to: /tmp/estimator/exported_model_dir/temp-1638614661/assets
INFO:tensorflow:SavedModel written to: /tmp/estimator/exported_model_dir/temp-1638614661/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/estimator/exported_model_dir/temp-1638614661/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 2021-12-04T10:44:23Z
INFO:tensorflow:Starting evaluation at 2021-12-04T10:44:23Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpwufx88ji/model.ckpt-4071
2021-12-04 10:44:23.300547: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2021-12-04 10:44:23.300668: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
INFO:tensorflow:Restoring parameters from /tmp/tmpwufx88ji/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.
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INFO:tensorflow:Inference Time : 12.76048s
INFO:tensorflow:Inference Time : 12.76048s
INFO:tensorflow:Finished evaluation at 2021-12-04-10:44:35
INFO:tensorflow:Finished evaluation at 2021-12-04-10:44:35
INFO:tensorflow:Saving dict for global step 4071: accuracy = 0.85123765, accuracy_baseline = 0.76377374, auc = 0.9019859, auc_precision_recall = 0.9672531, average_loss = 0.32398567, global_step = 4071, label/mean = 0.76377374, loss = 0.32398567, precision = 0.8828477, prediction/mean = 0.75662553, recall = 0.9284278
INFO:tensorflow:Saving dict for global step 4071: accuracy = 0.85123765, accuracy_baseline = 0.76377374, auc = 0.9019859, auc_precision_recall = 0.9672531, average_loss = 0.32398567, global_step = 4071, label/mean = 0.76377374, loss = 0.32398567, precision = 0.8828477, prediction/mean = 0.75662553, recall = 0.9284278
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4071: /tmp/tmpwufx88ji/model.ckpt-4071
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4071: /tmp/tmpwufx88ji/model.ckpt-4071
{'accuracy': 0.85123765,
 'accuracy_baseline': 0.76377374,
 'auc': 0.9019859,
 'auc_precision_recall': 0.9672531,
 'average_loss': 0.32398567,
 'global_step': 4071,
 'label/mean': 0.76377374,
 'loss': 0.32398567,
 'precision': 0.8828477,
 'prediction/mean': 0.75662553,
 'recall': 0.9284278}

สิ่งที่เราทำ

ในตัวอย่างนี้เราใช้ tf.Transform เพื่อ preprocess ชุดของข้อมูลการสำรวจสำมะโนประชากรและการฝึกอบรมรุ่นที่มีข้อมูลการทำความสะอาดและเปลี่ยน เรายังได้สร้างฟังก์ชันอินพุตที่เราสามารถใช้ได้เมื่อเราปรับใช้โมเดลที่ได้รับการฝึกอบรมในสภาพแวดล้อมการผลิตเพื่อทำการอนุมาน ด้วยการใช้รหัสเดียวกันสำหรับทั้งการฝึกอบรมและการอนุมาน เราจะหลีกเลี่ยงปัญหาใดๆ เกี่ยวกับข้อมูลเอียง ระหว่างที่เราได้เรียนรู้เกี่ยวกับการสร้างการแปลง Apache Beam เพื่อดำเนินการแปลงที่เราต้องการสำหรับการล้างข้อมูล นอกจากนี้เรายังได้เห็นวิธีการใช้ข้อมูลเปลี่ยนนี้ในการฝึกอบรมรุ่นที่ใช้อย่างใดอย่างหนึ่ง tf.keras หรือ tf.estimator นี่เป็นเพียงส่วนเล็กๆ ที่ TensorFlow Transform สามารถทำได้! เราขอแนะนำให้คุณดำน้ำใน tf.Transform และค้นพบสิ่งที่มันสามารถทำเพื่อคุณ