TFX Estimator Component Tutorial

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

# TFX has a constraint of 1.16 due to the removal of tf.estimator support.
pip install "tfx<1.16"

import os
import pprint
import tempfile
import urllib

import absl
import tensorflow as tf
import tensorflow_model_analysis as tfma
tf.get_logger().propagate = False
pp = pprint.PrettyPrinter()

from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext

%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip

2024-08-02 09:18:45.037858: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-08-02 09:18:45.037906: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-08-02 09:18:45.039616: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

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

TensorFlow version: 2.15.1
TFX version: 1.15.1

# This is the root directory for your TFX pip package installation.
_tfx_root = tfx.__path__[0]

# This is the directory containing the TFX Chicago Taxi Pipeline example.
_taxi_root = os.path.join(_tfx_root, 'examples/chicago_taxi_pipeline')

# This is the path where your model will be pushed for serving.
_serving_model_dir = os.path.join(
    tempfile.mkdtemp(), 'serving_model/taxi_simple')

# Set up logging.
absl.logging.set_verbosity(absl.logging.INFO)

_data_root = tempfile.mkdtemp(prefix='tfx-data')
DATA_PATH = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv'
_data_filepath = os.path.join(_data_root, "data.csv")
urllib.request.urlretrieve(DATA_PATH, _data_filepath)

('/tmpfs/tmp/tfx-dataf8kc6jl6/data.csv',
 <http.client.HTTPMessage at 0x7fe738114ee0>)

head {_data_filepath}

pickup_community_area,fare,trip_start_month,trip_start_hour,trip_start_day,trip_start_timestamp,pickup_latitude,pickup_longitude,dropoff_latitude,dropoff_longitude,trip_miles,pickup_census_tract,dropoff_census_tract,payment_type,company,trip_seconds,dropoff_community_area,tips
,12.45,5,19,6,1400269500,,,,,0.0,,,Credit Card,Chicago Elite Cab Corp. (Chicago Carriag,0,,0.0
,0,3,19,5,1362683700,,,,,0,,,Unknown,Chicago Elite Cab Corp.,300,,0
60,27.05,10,2,3,1380593700,41.836150155,-87.648787952,,,12.6,,,Cash,Taxi Affiliation Services,1380,,0.0
10,5.85,10,1,2,1382319000,41.985015101,-87.804532006,,,0.0,,,Cash,Taxi Affiliation Services,180,,0.0
14,16.65,5,7,5,1369897200,41.968069,-87.721559063,,,0.0,,,Cash,Dispatch Taxi Affiliation,1080,,0.0
13,16.45,11,12,3,1446554700,41.983636307,-87.723583185,,,6.9,,,Cash,,780,,0.0
16,32.05,12,1,1,1417916700,41.953582125,-87.72345239,,,15.4,,,Cash,,1200,,0.0
30,38.45,10,10,5,1444301100,41.839086906,-87.714003807,,,14.6,,,Cash,,2580,,0.0
11,14.65,1,1,3,1358213400,41.978829526,-87.771166703,,,5.81,,,Cash,,1080,,0.0

# Here, we create an InteractiveContext using default parameters. This will
# use a temporary directory with an ephemeral ML Metadata database instance.
# To use your own pipeline root or database, the optional properties
# `pipeline_root` and `metadata_connection_config` may be passed to
# InteractiveContext. Calls to InteractiveContext are no-ops outside of the
# notebook.
context = InteractiveContext()

WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/metadata.sqlite.

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

INFO:absl:Running driver for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:Running executor for CsvExampleGen
INFO:absl:Generating examples.
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
INFO:absl:Processing input csv data /tmpfs/tmp/tfx-dataf8kc6jl6/* to TFExample.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Running publisher for CsvExampleGen
INFO:absl:MetadataStore with DB connection initialized

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

["train", "eval"] /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/CsvExampleGen/examples/1

# Get the URI of the output artifact representing the training examples, which is a directory
train_uri = os.path.join(example_gen.outputs['examples'].get()[0].uri, 'Split-train')

# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
                      for name in os.listdir(train_uri)]

# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")

# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
  serialized_example = tfrecord.numpy()
  example = tf.train.Example()
  example.ParseFromString(serialized_example)
  pp.pprint(example)

features {
  feature {
    key: "company"
    value {
      bytes_list {
        value: "Chicago Elite Cab Corp. (Chicago Carriag"
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 12.449999809265137
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      bytes_list {
        value: "Credit Card"
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "tips"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 6
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 19
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 5
      }
    }
  }
  feature {
    key: "trip_start_timestamp"
    value {
      int64_list {
        value: 1400269500
      }
    }
  }
}

features {
  feature {
    key: "company"
    value {
      bytes_list {
        value: "Taxi Affiliation Services"
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 27.049999237060547
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      bytes_list {
        value: "Cash"
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 60
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      float_list {
        value: 41.836151123046875
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      float_list {
        value: -87.64878845214844
      }
    }
  }
  feature {
    key: "tips"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 12.600000381469727
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      int64_list {
        value: 1380
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 2
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 10
      }
    }
  }
  feature {
    key: "trip_start_timestamp"
    value {
      int64_list {
        value: 1380593700
      }
    }
  }
}

features {
  feature {
    key: "company"
    value {
      bytes_list {
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      float_list {
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 16.450000762939453
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      bytes_list {
        value: "Cash"
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      float_list {
        value: 41.98363494873047
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      float_list {
        value: -87.72357940673828
      }
    }
  }
  feature {
    key: "tips"
    value {
      float_list {
        value: 0.0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 6.900000095367432
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      int64_list {
        value: 780
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 12
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 11
      }
    }
  }
  feature {
    key: "trip_start_timestamp"
    value {
      int64_list {
        value: 1446554700
      }
    }
  }
}

statistics_gen = tfx.components.StatisticsGen(examples=example_gen.outputs['examples'])
context.run(statistics_gen)

INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for StatisticsGen
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/StatisticsGen/statistics/2/Split-eval.
INFO:absl:Running publisher for StatisticsGen
INFO:absl:MetadataStore with DB connection initialized

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

schema_gen = tfx.components.SchemaGen(
    statistics=statistics_gen.outputs['statistics'],
    infer_feature_shape=False)
context.run(schema_gen)

INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for SchemaGen
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for SchemaGen
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/SchemaGen/schema/3/schema.pbtxt.
INFO:absl:Running publisher for SchemaGen
INFO:absl:MetadataStore with DB connection initialized

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

example_validator = tfx.components.ExampleValidator(
    statistics=statistics_gen.outputs['statistics'],
    schema=schema_gen.outputs['schema'])
context.run(example_validator)

INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Running driver for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for ExampleValidator
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Anomalies alerts created for split train.
INFO:absl:Validation complete for split train. Anomalies written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/ExampleValidator/anomalies/4/Split-train.
INFO:absl:Validating schema against the computed statistics for split eval.
INFO:absl:Anomalies alerts created for split eval.
INFO:absl:Validation complete for split eval. Anomalies written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/ExampleValidator/anomalies/4/Split-eval.
INFO:absl:Running publisher for ExampleValidator
INFO:absl:MetadataStore with DB connection initialized

context.show(example_validator.outputs['anomalies'])

_taxi_constants_module_file = 'taxi_constants.py'

%%writefile {_taxi_constants_module_file}

# Categorical features are assumed to each have a maximum value in the dataset.
MAX_CATEGORICAL_FEATURE_VALUES = [24, 31, 12]

CATEGORICAL_FEATURE_KEYS = [
    'trip_start_hour', 'trip_start_day', 'trip_start_month',
    'pickup_census_tract', 'dropoff_census_tract', 'pickup_community_area',
    'dropoff_community_area'
]

DENSE_FLOAT_FEATURE_KEYS = ['trip_miles', 'fare', 'trip_seconds']

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

BUCKET_FEATURE_KEYS = [
    'pickup_latitude', 'pickup_longitude', 'dropoff_latitude',
    'dropoff_longitude'
]

# Number of vocabulary terms used for encoding VOCAB_FEATURES by tf.transform
VOCAB_SIZE = 1000

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

VOCAB_FEATURE_KEYS = [
    'payment_type',
    'company',
]

# Keys
LABEL_KEY = 'tips'
FARE_KEY = 'fare'

Writing taxi_constants.py

_taxi_transform_module_file = 'taxi_transform.py'

%%writefile {_taxi_transform_module_file}

import tensorflow as tf
import tensorflow_transform as tft

import taxi_constants

_DENSE_FLOAT_FEATURE_KEYS = taxi_constants.DENSE_FLOAT_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = taxi_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = taxi_constants.VOCAB_SIZE
_OOV_SIZE = taxi_constants.OOV_SIZE
_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT
_BUCKET_FEATURE_KEYS = taxi_constants.BUCKET_FEATURE_KEYS
_CATEGORICAL_FEATURE_KEYS = taxi_constants.CATEGORICAL_FEATURE_KEYS
_FARE_KEY = taxi_constants.FARE_KEY
_LABEL_KEY = taxi_constants.LABEL_KEY


def preprocessing_fn(inputs):
  """tf.transform's callback function for preprocessing inputs.
  Args:
    inputs: map from feature keys to raw not-yet-transformed features.
  Returns:
    Map from string feature key to transformed feature operations.
  """
  outputs = {}
  for key in _DENSE_FLOAT_FEATURE_KEYS:
    # If sparse make it dense, setting nan's to 0 or '', and apply zscore.
    outputs[key] = tft.scale_to_z_score(
        _fill_in_missing(inputs[key]))

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

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

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

  # Was this passenger a big tipper?
  taxi_fare = _fill_in_missing(inputs[_FARE_KEY])
  tips = _fill_in_missing(inputs[_LABEL_KEY])
  outputs[_LABEL_KEY] = tf.where(
      tf.math.is_nan(taxi_fare),
      tf.cast(tf.zeros_like(taxi_fare), tf.int64),
      # Test if the tip was > 20% of the fare.
      tf.cast(
          tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))

  return outputs


def _fill_in_missing(x):
  """Replace missing values in a SparseTensor.
  Fills in missing values of `x` with '' or 0, and converts to a dense tensor.
  Args:
    x: A `SparseTensor` of rank 2.  Its dense shape should have size at most 1
      in the second dimension.
  Returns:
    A rank 1 tensor where missing values of `x` have been filled in.
  """
  if not isinstance(x, tf.sparse.SparseTensor):
    return x

  default_value = '' if x.dtype == tf.string else 0
  return tf.squeeze(
      tf.sparse.to_dense(
          tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),
          default_value),
      axis=1)

Writing taxi_transform.py

transform = tfx.components.Transform(
    examples=example_gen.outputs['examples'],
    schema=schema_gen.outputs['schema'],
    module_file=os.path.abspath(_taxi_transform_module_file))
context.run(transform)

INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py' (including modules: ['taxi_transform', 'taxi_constants']).
INFO:absl:User module package has hash fingerprint version f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmpfs/tmp/tmp3yrnrgjr/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmpfs/tmp/tmp0217gwc_', '--dist-dir', '/tmpfs/tmp/tmp9vevtaee']
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
!!

        ********************************************************************************
        Please avoid running ``setup.py`` directly.
        Instead, use pypa/build, pypa/installer or other
        standards-based tools.

        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
        ********************************************************************************

!!
  self.initialize_options()
INFO:absl:Successfully built user code wheel distribution at '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'; target user module is 'taxi_transform'.
INFO:absl:Full user module path is 'taxi_transform@/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'
INFO:absl:Running driver for Transform
INFO:absl:MetadataStore with DB connection initialized
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_transform.py -> build/lib
copying taxi_constants.py -> build/lib
installing to /tmpfs/tmp/tmp0217gwc_
running install
running install_lib
copying build/lib/taxi_transform.py -> /tmpfs/tmp/tmp0217gwc_
copying build/lib/taxi_constants.py -> /tmpfs/tmp/tmp0217gwc_
running install_egg_info
running egg_info
creating tfx_user_code_Transform.egg-info
writing tfx_user_code_Transform.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'
Copying tfx_user_code_Transform.egg-info to /tmpfs/tmp/tmp0217gwc_/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3.9.egg-info
running install_scripts
creating /tmpfs/tmp/tmp0217gwc_/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.dist-info/WHEEL
creating '/tmpfs/tmp/tmp9vevtaee/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' and adding '/tmpfs/tmp/tmp0217gwc_' to it
adding 'taxi_constants.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.dist-info/METADATA'
adding 'tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.dist-info/WHEEL'
adding 'tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.dist-info/top_level.txt'
adding 'tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424.dist-info/RECORD'
removing /tmpfs/tmp/tmp0217gwc_
INFO:absl:Running executor for Transform
INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'
INFO:absl:Installing '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmp64ci87py', '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl']
Processing /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl
INFO:absl:Successfully installed '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'.
INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'
INFO:absl:Installing '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmp6lu7e3bz', '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424
Processing /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl
INFO:absl:Successfully installed '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'.
INFO:absl:Installing '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmpia326ow3', '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl']
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424
Processing /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl
INFO:absl:Successfully installed '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424-py3-none-any.whl'.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.
Installing collected packages: tfx-user-code-Transform
Successfully installed tfx-user-code-Transform-0.0+f78e5f6b4988b5d5289aab277eceaff03bd38343154c2f602e06d95c6acd5424
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2
WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Transform/transform_graph/5/.temp_path/tftransform_tmp/f6e1c2a9c50f4ae389af51a32aa3d8bf/assets
INFO:absl:Writing fingerprint to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Transform/transform_graph/5/.temp_path/tftransform_tmp/f6e1c2a9c50f4ae389af51a32aa3d8bf/fingerprint.pb
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Transform/transform_graph/5/.temp_path/tftransform_tmp/ffdcaaffed72449194bac42d258bd914/assets
INFO:absl:Writing fingerprint to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Transform/transform_graph/5/.temp_path/tftransform_tmp/ffdcaaffed72449194bac42d258bd914/fingerprint.pb
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:absl:Running publisher for Transform
INFO:absl:MetadataStore with DB connection initialized

transform.outputs

{'transform_graph': OutputChannel(artifact_type=TransformGraph, producer_component_id=Transform, output_key=transform_graph, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
 'transformed_examples': OutputChannel(artifact_type=Examples, producer_component_id=Transform, output_key=transformed_examples, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
 'updated_analyzer_cache': OutputChannel(artifact_type=TransformCache, producer_component_id=Transform, output_key=updated_analyzer_cache, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
 'pre_transform_schema': OutputChannel(artifact_type=Schema, producer_component_id=Transform, output_key=pre_transform_schema, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
 'pre_transform_stats': OutputChannel(artifact_type=ExampleStatistics, producer_component_id=Transform, output_key=pre_transform_stats, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
 'post_transform_schema': OutputChannel(artifact_type=Schema, producer_component_id=Transform, output_key=post_transform_schema, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
 'post_transform_stats': OutputChannel(artifact_type=ExampleStatistics, producer_component_id=Transform, output_key=post_transform_stats, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
 'post_transform_anomalies': OutputChannel(artifact_type=ExampleAnomalies, producer_component_id=Transform, output_key=post_transform_anomalies, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)}

train_uri = transform.outputs['transform_graph'].get()[0].uri
os.listdir(train_uri)

['metadata', 'transformed_metadata', 'transform_fn']

# Get the URI of the output artifact representing the transformed examples, which is a directory
train_uri = os.path.join(transform.outputs['transformed_examples'].get()[0].uri, 'Split-train')

# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
                      for name in os.listdir(train_uri)]

# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")

# Iterate over the first 3 records and decode them.
for tfrecord in dataset.take(3):
  serialized_example = tfrecord.numpy()
  example = tf.train.Example()
  example.ParseFromString(serialized_example)
  pp.pprint(example)

features {
  feature {
    key: "company"
    value {
      int64_list {
        value: 8
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 0.061060599982738495
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      int64_list {
        value: 1
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "tips"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: -0.15886740386486053
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      float_list {
        value: -0.7118487358093262
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 6
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 19
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 5
      }
    }
  }
}

features {
  feature {
    key: "company"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 1.2521240711212158
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 60
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "tips"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 0.532160758972168
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      float_list {
        value: 0.5509493350982666
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 2
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 10
      }
    }
  }
}

features {
  feature {
    key: "company"
    value {
      int64_list {
        value: 48
      }
    }
  }
  feature {
    key: "dropoff_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_community_area"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_latitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "dropoff_longitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "fare"
    value {
      float_list {
        value: 0.3873794376850128
      }
    }
  }
  feature {
    key: "payment_type"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_census_tract"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "pickup_community_area"
    value {
      int64_list {
        value: 13
      }
    }
  }
  feature {
    key: "pickup_latitude"
    value {
      int64_list {
        value: 9
      }
    }
  }
  feature {
    key: "pickup_longitude"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "tips"
    value {
      int64_list {
        value: 0
      }
    }
  }
  feature {
    key: "trip_miles"
    value {
      float_list {
        value: 0.21955278515815735
      }
    }
  }
  feature {
    key: "trip_seconds"
    value {
      float_list {
        value: 0.0019067146349698305
      }
    }
  }
  feature {
    key: "trip_start_day"
    value {
      int64_list {
        value: 3
      }
    }
  }
  feature {
    key: "trip_start_hour"
    value {
      int64_list {
        value: 12
      }
    }
  }
  feature {
    key: "trip_start_month"
    value {
      int64_list {
        value: 11
      }
    }
  }
}

_taxi_trainer_module_file = 'taxi_trainer.py'

%%writefile {_taxi_trainer_module_file}

import tensorflow as tf
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx_bsl.tfxio import dataset_options

import taxi_constants

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


# Tf.Transform considers these features as "raw"
def _get_raw_feature_spec(schema):
  return schema_utils.schema_as_feature_spec(schema).feature_spec


def _build_estimator(config, hidden_units=None, warm_start_from=None):
  """Build an estimator for predicting the tipping behavior of taxi riders.
  Args:
    config: tf.estimator.RunConfig defining the runtime environment for the
      estimator (including model_dir).
    hidden_units: [int], the layer sizes of the DNN (input layer first)
    warm_start_from: Optional directory to warm start from.
  Returns:
    A dict of the following:
      - estimator: The estimator that will be used for training and eval.
      - train_spec: Spec for training.
      - eval_spec: Spec for eval.
      - eval_input_receiver_fn: Input function for eval.
  """
  real_valued_columns = [
      tf.feature_column.numeric_column(key, shape=())
      for key in _DENSE_FLOAT_FEATURE_KEYS
  ]
  categorical_columns = [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_VOCAB_SIZE + _OOV_SIZE, default_value=0)
      for key in _VOCAB_FEATURE_KEYS
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(
          key, num_buckets=_FEATURE_BUCKET_COUNT, default_value=0)
      for key in _BUCKET_FEATURE_KEYS
  ]
  categorical_columns += [
      tf.feature_column.categorical_column_with_identity(  # pylint: disable=g-complex-comprehension
          key,
          num_buckets=num_buckets,
          default_value=0) for key, num_buckets in zip(
              _CATEGORICAL_FEATURE_KEYS,
              _MAX_CATEGORICAL_FEATURE_VALUES)
  ]
  return tf.estimator.DNNLinearCombinedClassifier(
      config=config,
      linear_feature_columns=categorical_columns,
      dnn_feature_columns=real_valued_columns,
      dnn_hidden_units=hidden_units or [100, 70, 50, 25],
      warm_start_from=warm_start_from)


def _example_serving_receiver_fn(tf_transform_graph, schema):
  """Build the serving in inputs.
  Args:
    tf_transform_graph: A TFTransformOutput.
    schema: the schema of the input data.
  Returns:
    Tensorflow graph which parses examples, applying tf-transform to them.
  """
  raw_feature_spec = _get_raw_feature_spec(schema)
  raw_feature_spec.pop(_LABEL_KEY)

  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()

  transformed_features = tf_transform_graph.transform_raw_features(
      serving_input_receiver.features)

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


def _eval_input_receiver_fn(tf_transform_graph, schema):
  """Build everything needed for the tf-model-analysis to run the model.
  Args:
    tf_transform_graph: A TFTransformOutput.
    schema: the schema of the input data.
  Returns:
    EvalInputReceiver function, which contains:
      - Tensorflow graph which parses raw untransformed features, applies the
        tf-transform preprocessing operators.
      - Set of raw, untransformed features.
      - Label against which predictions will be compared.
  """
  # Notice that the inputs are raw features, not transformed features here.
  raw_feature_spec = _get_raw_feature_spec(schema)

  serialized_tf_example = tf.compat.v1.placeholder(
      dtype=tf.string, shape=[None], name='input_example_tensor')

  # Add a parse_example operator to the tensorflow graph, which will parse
  # raw, untransformed, tf examples.
  features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)

  # Now that we have our raw examples, process them through the tf-transform
  # function computed during the preprocessing step.
  transformed_features = tf_transform_graph.transform_raw_features(
      features)

  # The key name MUST be 'examples'.
  receiver_tensors = {'examples': serialized_tf_example}

  # NOTE: Model is driven by transformed features (since training works on the
  # materialized output of TFT, but slicing will happen on raw features.
  features.update(transformed_features)

  return tfma.export.EvalInputReceiver(
      features=features,
      receiver_tensors=receiver_tensors,
      labels=transformed_features[_LABEL_KEY])


def _input_fn(file_pattern, data_accessor, tf_transform_output, batch_size=200):
  """Generates features and label for tuning/training.

  Args:
    file_pattern: List of paths or patterns of input tfrecord files.
    data_accessor: DataAccessor for converting input to RecordBatch.
    tf_transform_output: A TFTransformOutput.
    batch_size: representing the number of consecutive elements of returned
      dataset to combine in a single batch

  Returns:
    A dataset that contains (features, indices) tuple where features is a
      dictionary of Tensors, and indices is a single Tensor of label indices.
  """
  return data_accessor.tf_dataset_factory(
      file_pattern,
      dataset_options.TensorFlowDatasetOptions(
          batch_size=batch_size, label_key=_LABEL_KEY),
      tf_transform_output.transformed_metadata.schema)


# TFX will call this function
def trainer_fn(trainer_fn_args, schema):
  """Build the estimator using the high level API.
  Args:
    trainer_fn_args: Holds args used to train the model as name/value pairs.
    schema: Holds the schema of the training examples.
  Returns:
    A dict of the following:
      - estimator: The estimator that will be used for training and eval.
      - train_spec: Spec for training.
      - eval_spec: Spec for eval.
      - eval_input_receiver_fn: Input function for eval.
  """
  # Number of nodes in the first layer of the DNN
  first_dnn_layer_size = 100
  num_dnn_layers = 4
  dnn_decay_factor = 0.7

  train_batch_size = 40
  eval_batch_size = 40

  tf_transform_graph = tft.TFTransformOutput(trainer_fn_args.transform_output)

  train_input_fn = lambda: _input_fn(  # pylint: disable=g-long-lambda
      trainer_fn_args.train_files,
      trainer_fn_args.data_accessor,
      tf_transform_graph,
      batch_size=train_batch_size)

  eval_input_fn = lambda: _input_fn(  # pylint: disable=g-long-lambda
      trainer_fn_args.eval_files,
      trainer_fn_args.data_accessor,
      tf_transform_graph,
      batch_size=eval_batch_size)

  train_spec = tf.estimator.TrainSpec(  # pylint: disable=g-long-lambda
      train_input_fn,
      max_steps=trainer_fn_args.train_steps)

  serving_receiver_fn = lambda: _example_serving_receiver_fn(  # pylint: disable=g-long-lambda
      tf_transform_graph, schema)

  exporter = tf.estimator.FinalExporter('chicago-taxi', serving_receiver_fn)
  eval_spec = tf.estimator.EvalSpec(
      eval_input_fn,
      steps=trainer_fn_args.eval_steps,
      exporters=[exporter],
      name='chicago-taxi-eval')

  run_config = tf.estimator.RunConfig(
      save_checkpoints_steps=999, keep_checkpoint_max=1)

  run_config = run_config.replace(model_dir=trainer_fn_args.serving_model_dir)

  estimator = _build_estimator(
      # Construct layers sizes with exponetial decay
      hidden_units=[
          max(2, int(first_dnn_layer_size * dnn_decay_factor**i))
          for i in range(num_dnn_layers)
      ],
      config=run_config,
      warm_start_from=trainer_fn_args.base_model)

  # Create an input receiver for TFMA processing
  receiver_fn = lambda: _eval_input_receiver_fn(  # pylint: disable=g-long-lambda
      tf_transform_graph, schema)

  return {
      'estimator': estimator,
      'train_spec': train_spec,
      'eval_spec': eval_spec,
      'eval_input_receiver_fn': receiver_fn
  }

Writing taxi_trainer.py

from tfx.components.trainer.executor import Executor
from tfx.dsl.components.base import executor_spec

trainer = tfx.components.Trainer(
    module_file=os.path.abspath(_taxi_trainer_module_file),
    custom_executor_spec=executor_spec.ExecutorClassSpec(Executor),
    examples=transform.outputs['transformed_examples'],
    schema=schema_gen.outputs['schema'],
    transform_graph=transform.outputs['transform_graph'],
    train_args=tfx.proto.TrainArgs(num_steps=10000),
    eval_args=tfx.proto.EvalArgs(num_steps=5000))
context.run(trainer)

WARNING:absl:`custom_executor_spec` is deprecated. Please customize component directly.
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py' (including modules: ['taxi_trainer', 'taxi_transform', 'taxi_constants']).
INFO:absl:User module package has hash fingerprint version e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmpfs/tmp/tmpxoj_le3k/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmpfs/tmp/tmpuykvsdzd', '--dist-dir', '/tmpfs/tmp/tmpyo330uub']
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
!!

        ********************************************************************************
        Please avoid running ``setup.py`` directly.
        Instead, use pypa/build, pypa/installer or other
        standards-based tools.

        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
        ********************************************************************************

!!
  self.initialize_options()
INFO:absl:Successfully built user code wheel distribution at '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl'; target user module is 'taxi_trainer'.
INFO:absl:Full user module path is 'taxi_trainer@/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl'
INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying taxi_trainer.py -> build/lib
copying taxi_transform.py -> build/lib
copying taxi_constants.py -> build/lib
installing to /tmpfs/tmp/tmpuykvsdzd
running install
running install_lib
copying build/lib/taxi_trainer.py -> /tmpfs/tmp/tmpuykvsdzd
copying build/lib/taxi_transform.py -> /tmpfs/tmp/tmpuykvsdzd
copying build/lib/taxi_constants.py -> /tmpfs/tmp/tmpuykvsdzd
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmpfs/tmp/tmpuykvsdzd/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3.9.egg-info
running install_scripts
creating /tmpfs/tmp/tmpuykvsdzd/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.dist-info/WHEEL
creating '/tmpfs/tmp/tmpyo330uub/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl' and adding '/tmpfs/tmp/tmpuykvsdzd' to it
adding 'taxi_constants.py'
adding 'taxi_trainer.py'
adding 'taxi_transform.py'
adding 'tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618.dist-info/RECORD'
removing /tmpfs/tmp/tmpuykvsdzd
INFO:absl:Running executor for Trainer
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE
INFO:absl:udf_utils.get_fn {'train_args': '{\n  "num_steps": 10000\n}', 'eval_args': '{\n  "num_steps": 5000\n}', 'module_file': None, 'run_fn': None, 'trainer_fn': None, 'custom_config': 'null', 'module_path': 'taxi_trainer@/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl'} 'trainer_fn'
INFO:absl:Installing '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmpqdwzchbf', '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl']
Processing /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl
INFO:absl:Successfully installed '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/_wheels/tfx_user_code_Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618-py3-none-any.whl'.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+e337a512821685b6d91445dbd0628b47de0e4c751e9e54edf78bcf0866309618
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py:188: TrainSpec.__new__ (from tensorflow_estimator.python.estimator.training) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py:195: FinalExporter.__init__ (from tensorflow_estimator.python.estimator.exporter) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py:196: EvalSpec.__new__ (from tensorflow_estimator.python.estimator.training) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py:202: RunConfig.__init__ (from tensorflow_estimator.python.estimator.run_config) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py:41: numeric_column (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py:45: categorical_column_with_identity (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py:62: DNNLinearCombinedClassifierV2.__init__ (from tensorflow_estimator.python.estimator.canned.dnn_linear_combined) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/head_utils.py:54: BinaryClassHead.__init__ (from tensorflow_estimator.python.estimator.head.binary_class_head) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/canned/dnn_linear_combined.py:586: Estimator.__init__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 999, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 1, '_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:absl:Training model.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tfx/components/trainer/executor.py:270: train_and_evaluate (from tensorflow_estimator.python.estimator.training) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 999 or save_checkpoints_secs None.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:385: StopAtStepHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tfx_bsl/tfxio/tf_example_record.py:343: parse_example_dataset (from tensorflow.python.data.experimental.ops.parsing_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map(tf.io.parse_example(...))` instead.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/optimizers/legacy/adagrad.py:93: 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/python3.9/site-packages/tensorflow_estimator/python/estimator/model_fn.py:250: EstimatorSpec.__new__ (from tensorflow_estimator.python.estimator.model_fn) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1416: NanTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1419: LoggingTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/basic_session_run_hooks.py:232: SecondOrStepTimer.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1456: CheckpointSaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Create CheckpointSaverHook.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:579: StepCounterHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:586: SummarySaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
2024-08-02 09:19:34.413317: W tensorflow/core/common_runtime/type_inference.cc:339] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: expected compatible input types, but input 1:
type_id: TFT_OPTIONAL
args {
  type_id: TFT_PRODUCT
  args {
    type_id: TFT_TENSOR
    args {
      type_id: TFT_INT64
    }
  }
}
 is neither a subtype nor a supertype of the combined inputs preceding it:
type_id: TFT_OPTIONAL
args {
  type_id: TFT_PRODUCT
  args {
    type_id: TFT_TENSOR
    args {
      type_id: TFT_INT32
    }
  }
}

    for Tuple type infernce function 0
    while inferring type of node 'dnn/zero_fraction/cond/output/_18'
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1455: SessionRunArgs.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1454: SessionRunContext.__init__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1474: SessionRunValues.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:loss = 0.68345356, step = 0
INFO:tensorflow:global_step/sec: 94.6219
INFO:tensorflow:loss = 0.5876302, step = 100 (1.058 sec)
INFO:tensorflow:global_step/sec: 126.837
INFO:tensorflow:loss = 0.54258287, step = 200 (0.789 sec)
INFO:tensorflow:global_step/sec: 128.101
INFO:tensorflow:loss = 0.42309752, step = 300 (0.780 sec)
INFO:tensorflow:global_step/sec: 126.804
INFO:tensorflow:loss = 0.49153805, step = 400 (0.789 sec)
INFO:tensorflow:global_step/sec: 127.62
INFO:tensorflow:loss = 0.4945454, step = 500 (0.784 sec)
INFO:tensorflow:global_step/sec: 127.584
INFO:tensorflow:loss = 0.5304156, step = 600 (0.784 sec)
INFO:tensorflow:global_step/sec: 130.578
INFO:tensorflow:loss = 0.5257536, step = 700 (0.766 sec)
INFO:tensorflow:global_step/sec: 131.19
INFO:tensorflow:loss = 0.39649287, step = 800 (0.762 sec)
INFO:tensorflow:global_step/sec: 131.381
INFO:tensorflow:loss = 0.48203364, step = 900 (0.761 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Saving checkpoints for 999 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/saver.py:1067: remove_checkpoint (from tensorflow.python.checkpoint.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2024-08-02T09:19:47
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/evaluation.py:260: FinalOpsHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt-999
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 30.09666s
INFO:tensorflow:Finished evaluation at 2024-08-02-09:20:17
INFO:tensorflow:Saving dict for global step 999: accuracy = 0.77127, accuracy_baseline = 0.77127, auc = 0.9157473, auc_precision_recall = 0.63793385, average_loss = 0.4631773, global_step = 999, label/mean = 0.22873, loss = 0.46317753, precision = 0.0, prediction/mean = 0.24352272, recall = 0.0
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt-999
INFO:tensorflow:global_step/sec: 3.0638
INFO:tensorflow:loss = 0.4464565, step = 1000 (32.639 sec)
INFO:tensorflow:global_step/sec: 127.029
INFO:tensorflow:loss = 0.34790123, step = 1100 (0.788 sec)
INFO:tensorflow:global_step/sec: 127.743
INFO:tensorflow:loss = 0.3965652, step = 1200 (0.783 sec)
INFO:tensorflow:global_step/sec: 126.222
INFO:tensorflow:loss = 0.41430426, step = 1300 (0.792 sec)
INFO:tensorflow:global_step/sec: 126.602
INFO:tensorflow:loss = 0.5473569, step = 1400 (0.790 sec)
INFO:tensorflow:global_step/sec: 127.742
INFO:tensorflow:loss = 0.4605174, step = 1500 (0.783 sec)
INFO:tensorflow:global_step/sec: 125.617
INFO:tensorflow:loss = 0.4138917, step = 1600 (0.796 sec)
INFO:tensorflow:global_step/sec: 129.248
INFO:tensorflow:loss = 0.4090398, step = 1700 (0.774 sec)
INFO:tensorflow:global_step/sec: 128.093
INFO:tensorflow:loss = 0.43102828, step = 1800 (0.781 sec)
INFO:tensorflow:global_step/sec: 126.057
INFO:tensorflow:loss = 0.46358317, step = 1900 (0.793 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Saving checkpoints for 1998 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1998...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 109.496
INFO:tensorflow:loss = 0.4306933, step = 2000 (0.913 sec)
INFO:tensorflow:global_step/sec: 128.816
INFO:tensorflow:loss = 0.4857188, step = 2100 (0.777 sec)
INFO:tensorflow:global_step/sec: 130.685
INFO:tensorflow:loss = 0.3482414, step = 2200 (0.765 sec)
INFO:tensorflow:global_step/sec: 127.638
INFO:tensorflow:loss = 0.43326974, step = 2300 (0.784 sec)
INFO:tensorflow:global_step/sec: 126.4
INFO:tensorflow:loss = 0.356163, step = 2400 (0.791 sec)
INFO:tensorflow:global_step/sec: 126.647
INFO:tensorflow:loss = 0.40226907, step = 2500 (0.790 sec)
INFO:tensorflow:global_step/sec: 126.972
INFO:tensorflow:loss = 0.45428342, step = 2600 (0.788 sec)
INFO:tensorflow:global_step/sec: 126.583
INFO:tensorflow:loss = 0.39020246, step = 2700 (0.790 sec)
INFO:tensorflow:global_step/sec: 128.646
INFO:tensorflow:loss = 0.40562025, step = 2800 (0.777 sec)
INFO:tensorflow:global_step/sec: 126.918
INFO:tensorflow:loss = 0.43240872, step = 2900 (0.788 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Saving checkpoints for 2997 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2997...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 109.498
INFO:tensorflow:loss = 0.46481556, step = 3000 (0.913 sec)
INFO:tensorflow:global_step/sec: 127.363
INFO:tensorflow:loss = 0.40305233, step = 3100 (0.785 sec)
INFO:tensorflow:global_step/sec: 124.665
INFO:tensorflow:loss = 0.40317774, step = 3200 (0.802 sec)
INFO:tensorflow:global_step/sec: 126.368
INFO:tensorflow:loss = 0.3879068, step = 3300 (0.791 sec)
INFO:tensorflow:global_step/sec: 127.044
INFO:tensorflow:loss = 0.45533687, step = 3400 (0.787 sec)
INFO:tensorflow:global_step/sec: 127.924
INFO:tensorflow:loss = 0.36743593, step = 3500 (0.782 sec)
INFO:tensorflow:global_step/sec: 127.528
INFO:tensorflow:loss = 0.3420141, step = 3600 (0.784 sec)
INFO:tensorflow:global_step/sec: 127.037
INFO:tensorflow:loss = 0.37507004, step = 3700 (0.787 sec)
INFO:tensorflow:global_step/sec: 125.288
INFO:tensorflow:loss = 0.4554777, step = 3800 (0.798 sec)
INFO:tensorflow:global_step/sec: 127.224
INFO:tensorflow:loss = 0.3540257, step = 3900 (0.786 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Saving checkpoints for 3996 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3996...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 107.582
INFO:tensorflow:loss = 0.39362353, step = 4000 (0.929 sec)
INFO:tensorflow:global_step/sec: 128.364
INFO:tensorflow:loss = 0.37329096, step = 4100 (0.780 sec)
INFO:tensorflow:global_step/sec: 126.761
INFO:tensorflow:loss = 0.41019115, step = 4200 (0.789 sec)
INFO:tensorflow:global_step/sec: 125.749
INFO:tensorflow:loss = 0.4272521, step = 4300 (0.796 sec)
INFO:tensorflow:global_step/sec: 126.224
INFO:tensorflow:loss = 0.3582986, step = 4400 (0.792 sec)
INFO:tensorflow:global_step/sec: 124.553
INFO:tensorflow:loss = 0.42920828, step = 4500 (0.803 sec)
INFO:tensorflow:global_step/sec: 126.126
INFO:tensorflow:loss = 0.399159, step = 4600 (0.793 sec)
INFO:tensorflow:global_step/sec: 126.574
INFO:tensorflow:loss = 0.337004, step = 4700 (0.790 sec)
INFO:tensorflow:global_step/sec: 128.435
INFO:tensorflow:loss = 0.3079869, step = 4800 (0.779 sec)
INFO:tensorflow:global_step/sec: 128.011
INFO:tensorflow:loss = 0.2898496, step = 4900 (0.781 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Saving checkpoints for 4995 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4995...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 107.802
INFO:tensorflow:loss = 0.30157006, step = 5000 (0.927 sec)
INFO:tensorflow:global_step/sec: 128.486
INFO:tensorflow:loss = 0.35911578, step = 5100 (0.779 sec)
INFO:tensorflow:global_step/sec: 128.123
INFO:tensorflow:loss = 0.42201638, step = 5200 (0.781 sec)
INFO:tensorflow:global_step/sec: 126.168
INFO:tensorflow:loss = 0.36438444, step = 5300 (0.792 sec)
INFO:tensorflow:global_step/sec: 127.968
INFO:tensorflow:loss = 0.3075903, step = 5400 (0.781 sec)
INFO:tensorflow:global_step/sec: 126.923
INFO:tensorflow:loss = 0.37466988, step = 5500 (0.788 sec)
INFO:tensorflow:global_step/sec: 125.858
INFO:tensorflow:loss = 0.45140833, step = 5600 (0.795 sec)
INFO:tensorflow:global_step/sec: 125.066
INFO:tensorflow:loss = 0.31925184, step = 5700 (0.800 sec)
INFO:tensorflow:global_step/sec: 128.717
INFO:tensorflow:loss = 0.3493313, step = 5800 (0.777 sec)
INFO:tensorflow:global_step/sec: 129.039
INFO:tensorflow:loss = 0.40627345, step = 5900 (0.775 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Saving checkpoints for 5994 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5994...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 108.053
INFO:tensorflow:loss = 0.28857297, step = 6000 (0.925 sec)
INFO:tensorflow:global_step/sec: 125.484
INFO:tensorflow:loss = 0.27557686, step = 6100 (0.797 sec)
INFO:tensorflow:global_step/sec: 126.448
INFO:tensorflow:loss = 0.26575455, step = 6200 (0.791 sec)
INFO:tensorflow:global_step/sec: 128.276
INFO:tensorflow:loss = 0.4027404, step = 6300 (0.780 sec)
INFO:tensorflow:global_step/sec: 128.328
INFO:tensorflow:loss = 0.37504548, step = 6400 (0.779 sec)
INFO:tensorflow:global_step/sec: 127.74
INFO:tensorflow:loss = 0.40821376, step = 6500 (0.783 sec)
INFO:tensorflow:global_step/sec: 128.458
INFO:tensorflow:loss = 0.35266423, step = 6600 (0.779 sec)
INFO:tensorflow:global_step/sec: 126.952
INFO:tensorflow:loss = 0.31729963, step = 6700 (0.788 sec)
INFO:tensorflow:global_step/sec: 127.488
INFO:tensorflow:loss = 0.3718481, step = 6800 (0.785 sec)
INFO:tensorflow:global_step/sec: 126.73
INFO:tensorflow:loss = 0.40504962, step = 6900 (0.789 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Saving checkpoints for 6993 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6993...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 108.832
INFO:tensorflow:loss = 0.25404018, step = 7000 (0.919 sec)
INFO:tensorflow:global_step/sec: 126.112
INFO:tensorflow:loss = 0.2731769, step = 7100 (0.793 sec)
INFO:tensorflow:global_step/sec: 128.987
INFO:tensorflow:loss = 0.2747486, step = 7200 (0.775 sec)
INFO:tensorflow:global_step/sec: 127.168
INFO:tensorflow:loss = 0.43343297, step = 7300 (0.786 sec)
INFO:tensorflow:global_step/sec: 126.696
INFO:tensorflow:loss = 0.35623556, step = 7400 (0.789 sec)
INFO:tensorflow:global_step/sec: 125.879
INFO:tensorflow:loss = 0.39246577, step = 7500 (0.795 sec)
INFO:tensorflow:global_step/sec: 126.881
INFO:tensorflow:loss = 0.400997, step = 7600 (0.788 sec)
INFO:tensorflow:global_step/sec: 128.105
INFO:tensorflow:loss = 0.4201835, step = 7700 (0.781 sec)
INFO:tensorflow:global_step/sec: 126.947
INFO:tensorflow:loss = 0.37048712, step = 7800 (0.788 sec)
INFO:tensorflow:global_step/sec: 125.791
INFO:tensorflow:loss = 0.3623591, step = 7900 (0.795 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Saving checkpoints for 7992 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7992...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 108.946
INFO:tensorflow:loss = 0.36777845, step = 8000 (0.918 sec)
INFO:tensorflow:global_step/sec: 129.235
INFO:tensorflow:loss = 0.2914999, step = 8100 (0.774 sec)
INFO:tensorflow:global_step/sec: 129.476
INFO:tensorflow:loss = 0.30548525, step = 8200 (0.772 sec)
INFO:tensorflow:global_step/sec: 129.06
INFO:tensorflow:loss = 0.3804708, step = 8300 (0.775 sec)
INFO:tensorflow:global_step/sec: 127.645
INFO:tensorflow:loss = 0.34999222, step = 8400 (0.784 sec)
INFO:tensorflow:global_step/sec: 131.021
INFO:tensorflow:loss = 0.37043446, step = 8500 (0.763 sec)
INFO:tensorflow:global_step/sec: 130.323
INFO:tensorflow:loss = 0.31763867, step = 8600 (0.767 sec)
INFO:tensorflow:global_step/sec: 130.619
INFO:tensorflow:loss = 0.3240898, step = 8700 (0.765 sec)
INFO:tensorflow:global_step/sec: 131.161
INFO:tensorflow:loss = 0.3534736, step = 8800 (0.763 sec)
INFO:tensorflow:global_step/sec: 131.406
INFO:tensorflow:loss = 0.352484, step = 8900 (0.761 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Saving checkpoints for 8991 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8991...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 109.642
INFO:tensorflow:loss = 0.27267218, step = 9000 (0.912 sec)
INFO:tensorflow:global_step/sec: 131.661
INFO:tensorflow:loss = 0.3245061, step = 9100 (0.760 sec)
INFO:tensorflow:global_step/sec: 131.599
INFO:tensorflow:loss = 0.43071085, step = 9200 (0.760 sec)
INFO:tensorflow:global_step/sec: 131.755
INFO:tensorflow:loss = 0.31766015, step = 9300 (0.759 sec)
INFO:tensorflow:global_step/sec: 132.721
INFO:tensorflow:loss = 0.37132785, step = 9400 (0.753 sec)
INFO:tensorflow:global_step/sec: 133.55
INFO:tensorflow:loss = 0.32427594, step = 9500 (0.749 sec)
INFO:tensorflow:global_step/sec: 133.19
INFO:tensorflow:loss = 0.4069633, step = 9600 (0.751 sec)
INFO:tensorflow:global_step/sec: 131.414
INFO:tensorflow:loss = 0.3321201, step = 9700 (0.761 sec)
INFO:tensorflow:global_step/sec: 129.794
INFO:tensorflow:loss = 0.3150528, step = 9800 (0.770 sec)
INFO:tensorflow:global_step/sec: 129.832
INFO:tensorflow:loss = 0.37696955, step = 9900 (0.770 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Saving checkpoints for 9990 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Saving checkpoints for 10000 into /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:absl:Feature company has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature dropoff_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature fare has a shape . Setting to DenseTensor.
INFO:absl:Feature payment_type has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_census_tract has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_community_area has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_latitude has a shape . Setting to DenseTensor.
INFO:absl:Feature pickup_longitude has a shape . Setting to DenseTensor.
INFO:absl:Feature tips has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_miles has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_seconds has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_day has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_hour has a shape . Setting to DenseTensor.
INFO:absl:Feature trip_start_month has a shape . Setting to DenseTensor.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2024-08-02T09:21:30
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 30.29653s
INFO:tensorflow:Finished evaluation at 2024-08-02-09:22:01
INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.78815, accuracy_baseline = 0.77124, auc = 0.93310654, auc_precision_recall = 0.70294404, average_loss = 0.34523568, global_step = 10000, label/mean = 0.22876, loss = 0.34523627, precision = 0.6943678, prediction/mean = 0.23057358, recall = 0.13203794
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Performing the final export in the end of training.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.
WARNING:tensorflow:From /tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py:81: build_parsing_serving_input_receiver_fn (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/export/export.py:312: ServingInputReceiver.__new__ (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/base_head.py:786: ClassificationOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py:561: RegressionOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py:563: PredictOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:168: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
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 Predict: ['predict']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/export/chicago-taxi/temp-1722590521/assets
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/export/chicago-taxi/temp-1722590521/saved_model.pb
INFO:tensorflow:Loss for final step: 0.33445308.
INFO:absl:Training complete. Model written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving. ModelRun written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6
INFO:absl:Exporting eval_savedmodel for TFMA.
INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.
INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.
WARNING:tensorflow:Loading a TF2 SavedModel but eager mode seems disabled.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:345: _SupervisedOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
INFO:tensorflow:Signatures INCLUDED in export for Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
WARNING:tensorflow:Export includes no default signature!
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-Serving/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-TFMA/temp-1722590523/assets
INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-TFMA/temp-1722590523/saved_model.pb
INFO:absl:Exported eval_savedmodel to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model_run/6/Format-TFMA.
WARNING:absl:Support for estimator-based executor and model export will be deprecated soon. Please use export structure <ModelExportPath>/serving_model_dir/saved_model.pb"
INFO:absl:Serving model copied to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model/6/Format-Serving.
WARNING:absl:Support for estimator-based executor and model export will be deprecated soon. Please use export structure <ModelExportPath>/eval_model_dir/saved_model.pb"
INFO:absl:Eval model copied to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model/6/Format-TFMA.
INFO:absl:Running publisher for Trainer
INFO:absl:MetadataStore with DB connection initialized

# Get the URI of the output artifact representing the training logs, which is a directory
model_run_dir = trainer.outputs['model_run'].get()[0].uri

%load_ext tensorboard
%tensorboard --logdir {model_run_dir}

eval_config = tfma.EvalConfig(
    model_specs=[
        # Using signature 'eval' implies the use of an EvalSavedModel. To use
        # a serving model remove the signature to defaults to 'serving_default'
        # and add a label_key.
        tfma.ModelSpec(signature_name='eval')
    ],
    metrics_specs=[
        tfma.MetricsSpec(
            # The metrics added here are in addition to those saved with the
            # model (assuming either a keras model or EvalSavedModel is used).
            # Any metrics added into the saved model (for example using
            # model.compile(..., metrics=[...]), etc) will be computed
            # automatically.
            metrics=[
                tfma.MetricConfig(class_name='ExampleCount')
            ],
            # To add validation thresholds for metrics saved with the model,
            # add them keyed by metric name to the thresholds map.
            thresholds = {
                'accuracy': tfma.MetricThreshold(
                    value_threshold=tfma.GenericValueThreshold(
                        lower_bound={'value': 0.5}),
                    # Change threshold will be ignored if there is no
                    # baseline model resolved from MLMD (first run).
                    change_threshold=tfma.GenericChangeThreshold(
                       direction=tfma.MetricDirection.HIGHER_IS_BETTER,
                       absolute={'value': -1e-10}))
            }
        )
    ],
    slicing_specs=[
        # An empty slice spec means the overall slice, i.e. the whole dataset.
        tfma.SlicingSpec(),
        # Data can be sliced along a feature column. In this case, data is
        # sliced along feature column trip_start_hour.
        tfma.SlicingSpec(feature_keys=['trip_start_hour'])
    ])

# Use TFMA to compute a evaluation statistics over features of a model and
# validate them against a baseline.

# The model resolver is only required if performing model validation in addition
# to evaluation. In this case we validate against the latest blessed model. If
# no model has been blessed before (as in this case) the evaluator will make our
# candidate the first blessed model.
model_resolver = tfx.dsl.Resolver(
      strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
      model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
      model_blessing=tfx.dsl.Channel(
          type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
              'latest_blessed_model_resolver')
context.run(model_resolver)

evaluator = tfx.components.Evaluator(
    examples=example_gen.outputs['examples'],
    model=trainer.outputs['model'],
    eval_config=eval_config)
context.run(evaluator)

INFO:absl:Running driver for latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running publisher for latest_blessed_model_resolver
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running driver for Evaluator
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Evaluator
INFO:absl:udf_utils.get_fn {'eval_config': '{\n  "metrics_specs": [\n    {\n      "metrics": [\n        {\n          "class_name": "ExampleCount"\n        }\n      ],\n      "thresholds": {\n        "accuracy": {\n          "change_threshold": {\n            "absolute": -1e-10,\n            "direction": "HIGHER_IS_BETTER"\n          },\n          "value_threshold": {\n            "lower_bound": 0.5\n          }\n        }\n      }\n    }\n  ],\n  "model_specs": [\n    {\n      "signature_name": "eval"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  thresholds {
    key: "accuracy"
    value {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}

INFO:absl:Using /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model/6/Format-TFMA as  model.
WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory.
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:udf_utils.get_fn {'eval_config': '{\n  "metrics_specs": [\n    {\n      "metrics": [\n        {\n          "class_name": "ExampleCount"\n        }\n      ],\n      "thresholds": {\n        "accuracy": {\n          "change_threshold": {\n            "absolute": -1e-10,\n            "direction": "HIGHER_IS_BETTER"\n          },\n          "value_threshold": {\n            "lower_bound": 0.5\n          }\n        }\n      }\n    }\n  ],\n  "model_specs": [\n    {\n      "signature_name": "eval"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "trip_start_hour"\n      ]\n    }\n  ]\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_extractors'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  model_names: ""
  thresholds {
    key: "accuracy"
    value {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  model_names: ""
  thresholds {
    key: "accuracy"
    value {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}

INFO:absl:eval_shared_models have model_types: {'tfma_eval'}
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  signature_name: "eval"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "trip_start_hour"
}
metrics_specs {
  metrics {
    class_name: "ExampleCount"
  }
  model_names: ""
  thresholds {
    key: "accuracy"
    value {
      value_threshold {
        lower_bound {
          value: 0.5
        }
      }
    }
  }
}
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:163: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.saved_model.load` instead.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Trainer/model/6/Format-TFMA/variables/variables
2024-08-02 09:22:08.724576: W tensorflow/c/c_api.cc:305] Operation '{name:'head/metrics/true_positives_1/Assign' id:1343 op device:{requested: '', assigned: ''} def:{ { {node head/metrics/true_positives_1/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](head/metrics/true_positives_1, head/metrics/true_positives_1/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
2024-08-02 09:22:08.909600: W tensorflow/c/c_api.cc:305] Operation '{name:'head/metrics/true_positives_1/Assign' id:1343 op device:{requested: '', assigned: ''} def:{ { {node head/metrics/true_positives_1/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](head/metrics/true_positives_1, head/metrics/true_positives_1/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
INFO:absl:Evaluation complete. Results written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Evaluator/evaluation/8.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:112: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`
INFO:absl:Blessing result True written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Evaluator/blessing/8.
INFO:absl:Running publisher for Evaluator
INFO:absl:MetadataStore with DB connection initialized

evaluator.outputs

{'evaluation': OutputChannel(artifact_type=ModelEvaluation, producer_component_id=Evaluator, output_key=evaluation, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),
 'blessing': OutputChannel(artifact_type=ModelBlessing, producer_component_id=Evaluator, output_key=blessing, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)}

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

SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'Overall', 'metrics':…

import tensorflow_model_analysis as tfma

# Get the TFMA output result path and load the result.
PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
tfma_result = tfma.load_eval_result(PATH_TO_RESULT)

# Show data sliced along feature column trip_start_hour.
tfma.view.render_slicing_metrics(
    tfma_result, slicing_column='trip_start_hour')

SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'trip_start_hour:19',…

blessing_uri = evaluator.outputs['blessing'].get()[0].uri
!ls -l {blessing_uri}

total 0
-rw-rw-r-- 1 kbuilder kbuilder 0 Aug  2 09:22 BLESSED

PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri
print(tfma.load_validation_result(PATH_TO_RESULT))

validation_ok: true
validation_details {
  slicing_details {
    slicing_spec {
    }
    num_matching_slices: 25
  }
}

pusher = tfx.components.Pusher(
    model=trainer.outputs['model'],
    model_blessing=evaluator.outputs['blessing'],
    push_destination=tfx.proto.PushDestination(
        filesystem=tfx.proto.PushDestination.Filesystem(
            base_directory=_serving_model_dir)))
context.run(pusher)

INFO:absl:Running driver for Pusher
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Pusher
INFO:absl:Model version: 1722590536
INFO:absl:Model written to serving path /tmpfs/tmp/tmp72buj3co/serving_model/taxi_simple/1722590536.
INFO:absl:Model pushed to /tmpfs/tmp/tfx-interactive-2024-08-02T09_18_50.903833-rtz7tiiy/Pusher/pushed_model/9.
INFO:absl:Running publisher for Pusher
INFO:absl:MetadataStore with DB connection initialized

pusher.outputs

{'pushed_model': OutputChannel(artifact_type=PushedModel, producer_component_id=Pusher, output_key=pushed_model, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)}

push_uri = pusher.outputs['pushed_model'].get()[0].uri
model = tf.saved_model.load(push_uri)

for item in model.signatures.items():
  pp.pprint(item)

INFO:absl:Fingerprint not found. Saved model loading will continue.
INFO:absl:path_and_singleprint metric could not be logged. Saved model loading will continue.
('predict',
 <ConcreteFunction () -> Dict[['all_class_ids', TensorSpec(shape=(None, 2), dtype=tf.int32, name=None)], ['class_ids', TensorSpec(shape=(None, 1), dtype=tf.int64, name=None)], ['logistic', TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)], ['probabilities', TensorSpec(shape=(None, 2), dtype=tf.float32, name=None)], ['all_classes', TensorSpec(shape=(None, 2), dtype=tf.string, name=None)], ['classes', TensorSpec(shape=(None, 1), dtype=tf.string, name=None)], ['logits', TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)]] at 0x7FE85FC74F10>)
('regression',
 <ConcreteFunction () -> Dict[['outputs', TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)]] at 0x7FE7205F5B20>)
('classification',
 <ConcreteFunction () -> Dict[['scores', TensorSpec(shape=(None, 2), dtype=tf.float32, name=None)], ['classes', TensorSpec(shape=(None, 2), dtype=tf.string, name=None)]] at 0x7FE738146C10>)
('serving_default',
 <ConcreteFunction () -> Dict[['classes', TensorSpec(shape=(None, 2), dtype=tf.string, name=None)], ['scores', TensorSpec(shape=(None, 2), dtype=tf.float32, name=None)]] at 0x7FE6BC5E6FD0>)