Indicatori di equità Caso di studio sul lignaggio

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Set di dati COMPAS

COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) è un set di dati pubblico, che contiene circa 18.000 casi penali della contea di Broward, in Florida, tra gennaio 2013 e dicembre 2014. I dati contengono informazioni su 11.000 imputati unici, inclusi dati demografici sulla storia criminale e un punteggio di rischio inteso a rappresentare la probabilità del convenuto di recidiva (recidività). Un modello di apprendimento automatico addestrato su questi dati è stato utilizzato da giudici e agenti per la libertà vigilata per determinare se impostare o meno la cauzione e se concedere o meno la libertà condizionale.

Nel 2016, un articolo pubblicato su ProPublica ha rilevato che il modello COMPAS prevedeva erroneamente che gli imputati afroamericani avrebbero recidivato a tassi molto più alti rispetto alle loro controparti bianche mentre i caucasici non avrebbero recidivato a un tasso molto più alto. Per gli imputati caucasici, il modello ha commesso errori nella direzione opposta, facendo previsioni errate che non avrebbero commesso un altro crimine. Gli autori hanno continuato a dimostrare che questi pregiudizi erano probabilmente dovuti a una distribuzione non uniforme dei dati tra afro-americani e imputati caucasici. Specificamente, l'etichetta verità terra di un esempio negativo (il convenuto non commettere un altro reato) e un esempio positivo (convenuto sarebbe commettere un altro crimine) erano sproporzionate tra i due anelli. Dal 2016, il set di dati COMPAS è apparso frequentemente nella letteratura sull'equità ML 1, 2, 3 , con i ricercatori che lo utilizzano per dimostrare tecniche per identificare e porre rimedio ai problemi di equità. Questo tutorial della conferenza FAT * 2018 illustra come COMPAS può avere un impatto drammatico sulle prospettive di un imputato nel mondo reale.

È importante notare che lo sviluppo di un modello di apprendimento automatico per prevedere la custodia cautelare ha una serie di importanti considerazioni etiche. Puoi saperne di più su questi problemi nel " Report on Algorithmic Risk Assessment Tools in the US Criminal Justice System " del Partnership on AI. The Partnership on AI è un'organizzazione multi-stakeholder - di cui Google è membro - che crea linee guida sull'IA.

Usiamo il set di dati COMPAS solo come esempio di come identificare e porre rimedio ai problemi di equità nei dati. Questo set di dati è canonico nella letteratura sull'equità algoritmica.

Informazioni sugli strumenti in questo caso di studio

  • TensorFlow Extended (TFX) è una piattaforma di machine learning su scala di produzione di Google basata su TensorFlow. Fornisce un framework di configurazione e librerie condivise per integrare i componenti comuni necessari per definire, avviare e monitorare il tuo sistema di machine learning.

  • TensorFlow Model Analysis è una libreria per la valutazione dei modelli di machine learning. Gli utenti possono valutare i loro modelli su una grande quantità di dati in modo distribuito e visualizzare le metriche su sezioni diverse all'interno di un notebook.

  • Fairness Indicators è una suite di strumenti basata sull'analisi del modello TensorFlow che consente una valutazione regolare delle metriche di equità nelle pipeline di prodotti.

  • ML Metadata è una libreria per la registrazione e il recupero della derivazione e dei metadati di artefatti ML come modelli, set di dati e metriche. All'interno di TFX ML i metadati ci aiuteranno a comprendere gli artefatti creati in una pipeline, che è un'unità di dati che viene trasmessa tra i componenti TFX.

  • TensorFlow Data Validation è una libreria per analizzare i dati e verificare la presenza di errori che possono influenzare l'addestramento o l'elaborazione del modello.

Panoramica del case study

Per la durata di questo caso di studio definiremo le "preoccupazioni di equità" come un pregiudizio all'interno di un modello che ha un impatto negativo su una fetta dei nostri dati. Nello specifico, stiamo cercando di limitare qualsiasi previsione di recidività che potrebbe essere prevenuta nei confronti della razza.

La procedura dettagliata del caso di studio procederà come segue:

  1. Scarica i dati, elaborali in anticipo ed esplora il set di dati iniziale.
  2. Crea una pipeline TFX con il set di dati COMPAS utilizzando un classificatore binario di Keras.
  3. Esegui i nostri risultati attraverso l'analisi del modello TensorFlow, la convalida dei dati TensorFlow e gli indicatori di equità del carico per esplorare eventuali problemi di equità all'interno del nostro modello.
  4. Usa i metadati ML per tenere traccia di tutti gli artefatti per un modello che abbiamo addestrato con TFX.
  5. Ponderare il set di dati COMPAS iniziale per il nostro secondo modello per tenere conto della distribuzione non uniforme tra recidività e razza.
  6. Esamina le modifiche alle prestazioni all'interno del nuovo set di dati.
  7. Controlla le modifiche alla base della nostra pipeline TFX con i metadati ML per capire quali modifiche sono state apportate tra i due modelli.

Risorse utili

Questo caso di studio è un'estensione dei seguenti casi di studio. Si consiglia di esaminare prima i casi di studio seguenti.

Impostare

Per iniziare, installeremo i pacchetti necessari, scaricheremo i dati e importeremo i moduli richiesti per il case study.

Per installare i pacchetti richiesti per questo case study nel notebook, eseguire il comando PIP riportato di seguito.


  1. Wadsworth, C., Vera, F., Piech, C. (2017). Raggiungere l'equità attraverso l'apprendimento antagonistico: un'applicazione alla previsione della recidiva. https://arxiv.org/abs/1807.00199

  2. Chouldechova, A., G'Sell, M., (2017). Più giusto e più preciso, ma per chi? https://arxiv.org/abs/1707.00046

  3. Berk et al., (2017), Fairness in Criminal Justice Risk Assessments: The State of the Art, https://arxiv.org/abs/1703.09207

!python -m pip install -q -U pip==20.2

!python -m pip install -q -U \
  tensorflow==2.4.1 \
  tfx==0.28.0 \
  tensorflow-model-analysis==0.28.0 \
  tensorflow_data_validation==0.28.0 \
  tensorflow-metadata==0.28.0 \
  tensorflow-transform==0.28.0 \
  ml-metadata==0.28.0 \
  tfx-bsl==0.28.1 \
  absl-py==0.9

 # If prompted, please restart the Colab environment after the pip installs
 # as you might run into import errors.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import tempfile
import six.moves.urllib as urllib

from ml_metadata.metadata_store import metadata_store
from ml_metadata.proto import metadata_store_pb2

import pandas as pd
from google.protobuf import text_format
from sklearn.utils import shuffle
import tensorflow as tf
import tensorflow_data_validation as tfdv

import tensorflow_model_analysis as tfma
from tensorflow_model_analysis.addons.fairness.post_export_metrics import fairness_indicators
from tensorflow_model_analysis.addons.fairness.view import widget_view

import tfx
from tfx.components.evaluator.component import Evaluator
from tfx.components.example_gen.csv_example_gen.component import CsvExampleGen
from tfx.components.schema_gen.component import SchemaGen
from tfx.components.statistics_gen.component import StatisticsGen
from tfx.components.trainer.component import Trainer
from tfx.components.transform.component import Transform
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.proto import evaluator_pb2
from tfx.proto import trainer_pb2

Scarica e pre-elabora il set di dati

# Download the COMPAS dataset and setup the required filepaths.
_DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data')
_DATA_PATH = 'https://storage.googleapis.com/compas_dataset/cox-violent-parsed.csv'
_DATA_FILEPATH = os.path.join(_DATA_ROOT, 'compas-scores-two-years.csv')

data = urllib.request.urlopen(_DATA_PATH)
_COMPAS_DF = pd.read_csv(data)

# To simpliy the case study, we will only use the columns that will be used for
# our model.
_COLUMN_NAMES = [
  'age',
  'c_charge_desc',
  'c_charge_degree',
  'c_days_from_compas',
  'is_recid',
  'juv_fel_count',
  'juv_misd_count',
  'juv_other_count',
  'priors_count',
  'r_days_from_arrest',
  'race',
  'sex',
  'vr_charge_desc',                
]
_COMPAS_DF = _COMPAS_DF[_COLUMN_NAMES]

# We will use 'is_recid' as our ground truth lable, which is boolean value
# indicating if a defendant committed another crime. There are some rows with -1
# indicating that there is no data. These rows we will drop from training.
_COMPAS_DF = _COMPAS_DF[_COMPAS_DF['is_recid'] != -1]

# Given the distribution between races in this dataset we will only focuse on
# recidivism for African-Americans and Caucasians.
_COMPAS_DF = _COMPAS_DF[
  _COMPAS_DF['race'].isin(['African-American', 'Caucasian'])]

# Adding we weight feature that will be used during the second part of this
# case study to help improve fairness concerns.
_COMPAS_DF['sample_weight'] = 0.8

# Load the DataFrame back to a CSV file for our TFX model.
_COMPAS_DF.to_csv(_DATA_FILEPATH, index=False, na_rep='')

Costruire una pipeline TFX


Esistono diversi componenti della pipeline TFX che possono essere utilizzati per un modello di produzione, ma allo scopo questo caso di studio si concentrerà sull'utilizzo solo dei componenti seguenti:

  • ExampleGen per leggere il nostro set di dati.
  • StatisticsGen per calcolare le statistiche del nostro set di dati.
  • SchemaGen per creare uno schema di dati.
  • Trasforma per l'ingegneria delle funzionalità.
  • Trainer per eseguire il nostro modello di machine learning.

Crea il InteractiveContext

Per eseguire TFX all'interno di un notebook, dobbiamo prima creare un InteractiveContext per eseguire i componenti in modo interattivo.

InteractiveContext utilizzerà una directory temporanea con un'istanza di database di metadati ML temporanea. Per utilizzare la radice della pipeline o il database, le proprietà facoltative pipeline_root e metadata_connection_config possono essere passate a InteractiveContext .

context = InteractiveContext()
WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r as root for pipeline outputs.
WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/metadata.sqlite.

Componente TFX ExampleGen

# The ExampleGen TFX Pipeline component ingests data into TFX pipelines.
# It consumes external files/services to generate Examples which will be read by
# other TFX components. It also provides consistent and configurable partition,
# and shuffles the dataset for ML best practice.

example_gen = CsvExampleGen(input_base=_DATA_ROOT)
context.run(example_gen)
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.

Componente TFX StatisticsGen

# The StatisticsGen TFX pipeline component generates features statistics over
# both training and serving data, which can be used by other pipeline
# components. StatisticsGen uses Beam to scale to large datasets.

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

Componente TFX SchemaGen

# Some TFX components use a description of your input data called a schema. The
# schema is an instance of schema.proto. It can specify data types for feature
# values, whether a feature has to be present in all examples, allowed value
# ranges, and other properties. A SchemaGen pipeline component will
# automatically generate a schema by inferring types, categories, and ranges
# from the training data.

infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'])
context.run(infer_schema)
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_data_validation/utils/stats_util.py:247: 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)`
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_data_validation/utils/stats_util.py:247: 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)`

Componente di trasformazione TFX

Il componente Transform esegue le trasformazioni dei dati e la progettazione delle funzionalità. I risultati includono un grafico TensorFlow di input che viene utilizzato sia durante l'addestramento che durante la pre-elaborazione dei dati prima dell'addestramento o dell'inferenza. Questo grafico diventa parte del SavedModel che è il risultato dell'addestramento del modello. Poiché lo stesso grafico di input viene utilizzato sia per l'addestramento che per il servizio, la preelaborazione sarà sempre la stessa e deve essere scritta solo una volta.

Il componente Transform richiede più codice rispetto a molti altri componenti a causa della complessità arbitraria dell'ingegneria delle funzionalità di cui potresti aver bisogno per i dati e / o il modello con cui stai lavorando.

Definisci alcune costanti e funzioni sia per il componente Transform che per il componente Trainer . Definiteli in un modulo Python, in questo caso %%writefile su disco usando il comando magico %%writefile visto che state lavorando su un notebook.

Le trasformazioni che eseguiremo in questo caso di studio sono le seguenti:

  • Per i valori stringa genereremo un vocabolario che mappa a un numero intero tramite tft.compute_and_apply_vocabulary.
  • Per i valori interi standardizzeremo la media della colonna 0 e la varianza 1 tramite tft.scale_to_z_score.
  • Rimuovi i valori delle righe vuote e sostituiscili con una stringa vuota o 0 a seconda del tipo di elemento.
  • Aggiungi "_xf" ai nomi delle colonne per indicare le caratteristiche che sono state elaborate nel componente di trasformazione.

Definiamo ora un modulo contenente la funzione preprocessing_fn() che passeremo al componente Transform :

# Setup paths for the Transform Component.
_transform_module_file = 'compas_transform.py'
%%writefile {_transform_module_file}
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import tensorflow_transform as tft

CATEGORICAL_FEATURE_KEYS = [
    'sex',
    'race',
    'c_charge_desc',
    'c_charge_degree',
]

INT_FEATURE_KEYS = [
    'age',
    'c_days_from_compas',
    'juv_fel_count',
    'juv_misd_count',
    'juv_other_count',
    'priors_count',
    'sample_weight',
]

LABEL_KEY = 'is_recid'

# List of the unique values for the items within CATEGORICAL_FEATURE_KEYS.
MAX_CATEGORICAL_FEATURE_VALUES = [
    2,
    6,
    513,
    14,
]


def transformed_name(key):
  return '{}_xf'.format(key)


def preprocessing_fn(inputs):
  """tf.transform's callback function for preprocessing inputs.

  Args:
    inputs: Map from feature keys to raw features.

  Returns:
    Map from string feature key to transformed feature operations.
  """
  outputs = {}
  for key in CATEGORICAL_FEATURE_KEYS:
    outputs[transformed_name(key)] = tft.compute_and_apply_vocabulary(
        _fill_in_missing(inputs[key]),
        vocab_filename=key)

  for key in INT_FEATURE_KEYS:
    outputs[transformed_name(key)] = tft.scale_to_z_score(
        _fill_in_missing(inputs[key]))

  # Target label will be to see if the defendant is charged for another crime.
  outputs[transformed_name(LABEL_KEY)] = _fill_in_missing(inputs[LABEL_KEY])
  return outputs


def _fill_in_missing(tensor_value):
  """Replaces a missing values in a SparseTensor.

  Fills in missing values of `tensor_value` with '' or 0, and converts to a
  dense tensor.

  Args:
    tensor_value: 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 `tensor_value` are filled in.
  """
  if not isinstance(tensor_value, tf.sparse.SparseTensor):
    return tensor_value
  default_value = '' if tensor_value.dtype == tf.string else 0
  sparse_tensor = tf.SparseTensor(
      tensor_value.indices,
      tensor_value.values,
      [tensor_value.dense_shape[0], 1])
  dense_tensor = tf.sparse.to_dense(sparse_tensor, default_value)
  return tf.squeeze(dense_tensor, axis=1)
Writing compas_transform.py
# Build and run the Transform Component.
transform = Transform(
    examples=example_gen.outputs['examples'],
    schema=infer_schema.outputs['schema'],
    module_file=_transform_module_file
)
context.run(transform)
WARNING:absl:The default value of `force_tf_compat_v1` will change in a future release from `True` to `False`. Since this pipeline has TF 2 behaviors enabled, Transform will use native TF 2 at that point. You can test this behavior now by passing `force_tf_compat_v1=False` or disable it by explicitly setting `force_tf_compat_v1=True` in the Transform component.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tfx/components/transform/executor.py:573: Schema (from tensorflow_transform.tf_metadata.dataset_schema) is deprecated and will be removed in a future version.
Instructions for updating:
Schema is a deprecated, use schema_utils.schema_from_feature_spec to create a `Schema`
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tfx/components/transform/executor.py:573: Schema (from tensorflow_transform.tf_metadata.dataset_schema) is deprecated and will be removed in a future version.
Instructions for updating:
Schema is a deprecated, use schema_utils.schema_from_feature_spec to create a `Schema`
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:266: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_transform/tf_utils.py:266: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType]] instead.
WARNING:tensorflow:Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Transform/transform_graph/4/.temp_path/tftransform_tmp/34923099dd2444f1a12dd79e9e93b9d2/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Transform/transform_graph/4/.temp_path/tftransform_tmp/34923099dd2444f1a12dd79e9e93b9d2/saved_model.pb
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:No assets to write.
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
WARNING:tensorflow:Issue encountered when serializing tft_mapper_use.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Counter' object has no attribute 'name'
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Transform/transform_graph/4/.temp_path/tftransform_tmp/2d5bc9f0641646379cb0c6d04efedee6/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Transform/transform_graph/4/.temp_path/tftransform_tmp/2d5bc9f0641646379cb0c6d04efedee6/saved_model.pb
WARNING:tensorflow:Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:tensorflow:Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended.
WARNING:tensorflow:Tensorflow version (2.4.1) found. Note that Tensorflow Transform support for TF 2.0 is currently in beta, and features such as tf.function may not work as intended. 
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring send_type hint: <class 'NoneType'>
WARNING:apache_beam.typehints.typehints:Ignoring return_type hint: <class 'NoneType'>
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Transform/transform_graph/4/.temp_path/tftransform_tmp/8fb9d0492a5f4c0b994fd3acb409dff6/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Transform/transform_graph/4/.temp_path/tftransform_tmp/8fb9d0492a5f4c0b994fd3acb409dff6/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Transform/transform_graph/4/.temp_path/tftransform_tmp/8fb9d0492a5f4c0b994fd3acb409dff6/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Transform/transform_graph/4/.temp_path/tftransform_tmp/8fb9d0492a5f4c0b994fd3acb409dff6/saved_model.pb
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore

Componente TFX Trainer

Il componente Trainer addestra un modello TensorFlow specificato.

Per eseguire il componente trainer dobbiamo creare un modulo Python contenente una funzione trainer_fn che restituirà uno stimatore per il nostro modello. Se preferisci creare un modello Keras, puoi farlo e poi convertirlo in uno stimatore usando keras.model_to_estimator() .

Il componente Trainer addestra un modello TensorFlow specificato. Per eseguire il modello dobbiamo creare un modulo Python contenente una funzione chiamata trainer_fn funzione che TFX chiamerà.

Per il nostro caso di studio costruiremo un modello Keras che restituirà keras.model_to_estimator() .

# Setup paths for the Trainer Component.
_trainer_module_file = 'compas_trainer.py'
%%writefile {_trainer_module_file}
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils

from compas_transform import *

_BATCH_SIZE = 1000
_LEARNING_RATE = 0.00001
_MAX_CHECKPOINTS = 1
_SAVE_CHECKPOINT_STEPS = 999


def transformed_names(keys):
  return [transformed_name(key) for key in keys]


def transformed_name(key):
  return '{}_xf'.format(key)


def _gzip_reader_fn(filenames):
  """Returns a record reader that can read gzip'ed files.

  Args:
    filenames: A tf.string tensor or tf.data.Dataset containing one or more
      filenames.

  Returns: A nested structure of tf.TypeSpec objects matching the structure of
    an element of this dataset and specifying the type of individual components.
  """
  return tf.data.TFRecordDataset(filenames, compression_type='GZIP')


# Tf.Transform considers these features as "raw".
def _get_raw_feature_spec(schema):
  """Generates a feature spec from a Schema proto.

  Args:
    schema: A Schema proto.

  Returns:
    A feature spec defined as a dict whose keys are feature names and values are
      instances of FixedLenFeature, VarLenFeature or SparseFeature.
  """
  return schema_utils.schema_as_feature_spec(schema).feature_spec


def _example_serving_receiver_fn(tf_transform_output, schema):
  """Builds the serving in inputs.

  Args:
    tf_transform_output: 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)
  serving_input_receiver = raw_input_fn()

  transformed_features = tf_transform_output.transform_raw_features(
      serving_input_receiver.features)
  transformed_features.pop(transformed_name(LABEL_KEY))
  return tf.estimator.export.ServingInputReceiver(
      transformed_features, serving_input_receiver.receiver_tensors)


def _eval_input_receiver_fn(tf_transform_output, schema):
  """Builds everything needed for the tf-model-analysis to run the model.

  Args:
    tf_transform_output: 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=serialized_tf_example, features=raw_feature_spec)

  transformed_features = tf_transform_output.transform_raw_features(features)
  labels = transformed_features.pop(transformed_name(LABEL_KEY))

  receiver_tensors = {'examples': serialized_tf_example}

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


def _input_fn(filenames, tf_transform_output, batch_size=200):
  """Generates features and labels for training or evaluation.

  Args:
    filenames: List of CSV files to read data from.
    tf_transform_output: A TFTransformOutput.
    batch_size: First dimension size of the Tensors returned by input_fn.

  Returns:
    A (features, indices) tuple where features is a dictionary of
      Tensors, and indices is a single Tensor of label indices.
  """
  transformed_feature_spec = (
      tf_transform_output.transformed_feature_spec().copy())

  dataset = tf.compat.v1.data.experimental.make_batched_features_dataset(
      filenames,
      batch_size,
      transformed_feature_spec,
      shuffle=False,
      reader=_gzip_reader_fn)

  transformed_features = dataset.make_one_shot_iterator().get_next()

  # We pop the label because we do not want to use it as a feature while we're
  # training.
  return transformed_features, transformed_features.pop(
      transformed_name(LABEL_KEY))


def _keras_model_builder():
  """Build a keras model for COMPAS dataset classification.

  Returns:
    A compiled Keras model.
  """
  feature_columns = []
  feature_layer_inputs = {}

  for key in transformed_names(INT_FEATURE_KEYS):
    feature_columns.append(tf.feature_column.numeric_column(key))
    feature_layer_inputs[key] = tf.keras.Input(shape=(1,), name=key)

  for key, num_buckets in zip(transformed_names(CATEGORICAL_FEATURE_KEYS),
                              MAX_CATEGORICAL_FEATURE_VALUES):
    feature_columns.append(
        tf.feature_column.indicator_column(
            tf.feature_column.categorical_column_with_identity(
                key, num_buckets=num_buckets)))
    feature_layer_inputs[key] = tf.keras.Input(
        shape=(1,), name=key, dtype=tf.dtypes.int32)

  feature_columns_input = tf.keras.layers.DenseFeatures(feature_columns)
  feature_layer_outputs = feature_columns_input(feature_layer_inputs)

  dense_layers = tf.keras.layers.Dense(
      20, activation='relu', name='dense_1')(feature_layer_outputs)
  dense_layers = tf.keras.layers.Dense(
      10, activation='relu', name='dense_2')(dense_layers)
  output = tf.keras.layers.Dense(
      1, name='predictions')(dense_layers)

  model = tf.keras.Model(
      inputs=[v for v in feature_layer_inputs.values()], outputs=output)

  model.compile(
      loss=tf.keras.losses.MeanAbsoluteError(),
      optimizer=tf.optimizers.Adam(learning_rate=_LEARNING_RATE))

  return model


# TFX will call this function.
def trainer_fn(hparams, schema):
  """Build the estimator using the high level API.

  Args:
    hparams: Hyperparameters 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.
  """
  tf_transform_output = tft.TFTransformOutput(hparams.transform_output)

  train_input_fn = lambda: _input_fn(
      hparams.train_files,
      tf_transform_output,
      batch_size=_BATCH_SIZE)

  eval_input_fn = lambda: _input_fn(
      hparams.eval_files,
      tf_transform_output,
      batch_size=_BATCH_SIZE)

  train_spec = tf.estimator.TrainSpec(
      train_input_fn,
      max_steps=hparams.train_steps)

  serving_receiver_fn = lambda: _example_serving_receiver_fn(
      tf_transform_output, schema)

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

  run_config = tf.estimator.RunConfig(
      save_checkpoints_steps=_SAVE_CHECKPOINT_STEPS,
      keep_checkpoint_max=_MAX_CHECKPOINTS)

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

  estimator = tf.keras.estimator.model_to_estimator(
      keras_model=_keras_model_builder(), config=run_config)

  # Create an input receiver for TFMA processing.
  receiver_fn = lambda: _eval_input_receiver_fn(tf_transform_output, schema)

  return {
      'estimator': estimator,
      'train_spec': train_spec,
      'eval_spec': eval_spec,
      'eval_input_receiver_fn': receiver_fn
  }
Writing compas_trainer.py
# Uses user-provided Python function that implements a model using TensorFlow's
# Estimators API.
trainer = Trainer(
    module_file=_trainer_module_file,
    transformed_examples=transform.outputs['transformed_examples'],
    schema=infer_schema.outputs['schema'],
    transform_graph=transform.outputs['transform_graph'],
    train_args=trainer_pb2.TrainArgs(num_steps=10000),
    eval_args=trainer_pb2.EvalArgs(num_steps=5000)
)
context.run(trainer)
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:tensorflow:Using the Keras model provided.
INFO:tensorflow:Using the Keras model provided.
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/backend.py:434: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir', '_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:tensorflow:Using config: {'_model_dir': '/tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir', '_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:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
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.
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/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From compas_trainer.py:136: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
This is a deprecated API that should only be used in TF 1 graph mode and legacy TF 2 graph mode available through `tf.compat.v1`. In all other situations -- namely, eager mode and inside `tf.function` -- you can consume dataset elements using `for elem in dataset: ...` or by explicitly creating iterator via `iterator = iter(dataset)` and fetching its elements via `values = next(iterator)`. Furthermore, this API is not available in TF 2. During the transition from TF 1 to TF 2 you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)` to create a TF 1 graph mode style iterator for a dataset created through TF 2 APIs. Note that this should be a transient state of your code base as there are in general no guarantees about the interoperability of TF 1 and TF 2 code.
WARNING:tensorflow:From compas_trainer.py:136: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
This is a deprecated API that should only be used in TF 1 graph mode and legacy TF 2 graph mode available through `tf.compat.v1`. In all other situations -- namely, eager mode and inside `tf.function` -- you can consume dataset elements using `for elem in dataset: ...` or by explicitly creating iterator via `iterator = iter(dataset)` and fetching its elements via `values = next(iterator)`. Furthermore, this API is not available in TF 2. During the transition from TF 1 to TF 2 you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)` to create a TF 1 graph mode style iterator for a dataset created through TF 2 APIs. Note that this should be a transient state of your code base as there are in general no guarantees about the interoperability of TF 1 and TF 2 code.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting from: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting from: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-started 6 variables.
INFO:tensorflow:Warm-started 6 variables.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 0.47416827, step = 0
INFO:tensorflow:loss = 0.47416827, step = 0
INFO:tensorflow:global_step/sec: 103.552
INFO:tensorflow:global_step/sec: 103.552
INFO:tensorflow:loss = 0.4922419, step = 100 (0.968 sec)
INFO:tensorflow:loss = 0.4922419, step = 100 (0.968 sec)
INFO:tensorflow:global_step/sec: 106.369
INFO:tensorflow:global_step/sec: 106.369
INFO:tensorflow:loss = 0.50697845, step = 200 (0.939 sec)
INFO:tensorflow:loss = 0.50697845, step = 200 (0.939 sec)
INFO:tensorflow:global_step/sec: 108.028
INFO:tensorflow:global_step/sec: 108.028
INFO:tensorflow:loss = 0.50335556, step = 300 (0.926 sec)
INFO:tensorflow:loss = 0.50335556, step = 300 (0.926 sec)
INFO:tensorflow:global_step/sec: 106.316
INFO:tensorflow:global_step/sec: 106.316
INFO:tensorflow:loss = 0.47721145, step = 400 (0.941 sec)
INFO:tensorflow:loss = 0.47721145, step = 400 (0.941 sec)
INFO:tensorflow:global_step/sec: 107.036
INFO:tensorflow:global_step/sec: 107.036
INFO:tensorflow:loss = 0.45895657, step = 500 (0.934 sec)
INFO:tensorflow:loss = 0.45895657, step = 500 (0.934 sec)
INFO:tensorflow:global_step/sec: 106.896
INFO:tensorflow:global_step/sec: 106.896
INFO:tensorflow:loss = 0.45208624, step = 600 (0.935 sec)
INFO:tensorflow:loss = 0.45208624, step = 600 (0.935 sec)
INFO:tensorflow:global_step/sec: 105.365
INFO:tensorflow:global_step/sec: 105.365
INFO:tensorflow:loss = 0.4489294, step = 700 (0.949 sec)
INFO:tensorflow:loss = 0.4489294, step = 700 (0.949 sec)
INFO:tensorflow:global_step/sec: 107.341
INFO:tensorflow:global_step/sec: 107.341
INFO:tensorflow:loss = 0.46455735, step = 800 (0.932 sec)
INFO:tensorflow:loss = 0.46455735, step = 800 (0.932 sec)
INFO:tensorflow:global_step/sec: 103.443
INFO:tensorflow:global_step/sec: 103.443
INFO:tensorflow:loss = 0.47789398, step = 900 (0.967 sec)
INFO:tensorflow:loss = 0.47789398, step = 900 (0.967 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:2325: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
  warnings.warn('`Model.state_updates` will be removed in a future version. '
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-04-23T09:10:14Z
INFO:tensorflow:Starting evaluation at 2021-04-23T09:10:14Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-999
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-999
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 48.79983s
INFO:tensorflow:Inference Time : 48.79983s
INFO:tensorflow:Finished evaluation at 2021-04-23-09:11:03
INFO:tensorflow:Finished evaluation at 2021-04-23-09:11:03
INFO:tensorflow:Saving dict for global step 999: global_step = 999, loss = 0.4798829
INFO:tensorflow:Saving dict for global step 999: global_step = 999, loss = 0.4798829
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-999
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-999
INFO:tensorflow:global_step/sec: 1.99761
INFO:tensorflow:global_step/sec: 1.99761
INFO:tensorflow:loss = 0.49395803, step = 1000 (50.059 sec)
INFO:tensorflow:loss = 0.49395803, step = 1000 (50.059 sec)
INFO:tensorflow:global_step/sec: 103.094
INFO:tensorflow:global_step/sec: 103.094
INFO:tensorflow:loss = 0.48954606, step = 1100 (0.970 sec)
INFO:tensorflow:loss = 0.48954606, step = 1100 (0.970 sec)
INFO:tensorflow:global_step/sec: 101.109
INFO:tensorflow:global_step/sec: 101.109
INFO:tensorflow:loss = 0.49123546, step = 1200 (0.989 sec)
INFO:tensorflow:loss = 0.49123546, step = 1200 (0.989 sec)
INFO:tensorflow:global_step/sec: 100.528
INFO:tensorflow:global_step/sec: 100.528
INFO:tensorflow:loss = 0.4701535, step = 1300 (0.995 sec)
INFO:tensorflow:loss = 0.4701535, step = 1300 (0.995 sec)
INFO:tensorflow:global_step/sec: 100.192
INFO:tensorflow:global_step/sec: 100.192
INFO:tensorflow:loss = 0.46582404, step = 1400 (0.999 sec)
INFO:tensorflow:loss = 0.46582404, step = 1400 (0.999 sec)
INFO:tensorflow:global_step/sec: 100.13
INFO:tensorflow:global_step/sec: 100.13
INFO:tensorflow:loss = 0.45980436, step = 1500 (0.998 sec)
INFO:tensorflow:loss = 0.45980436, step = 1500 (0.998 sec)
INFO:tensorflow:global_step/sec: 101.085
INFO:tensorflow:global_step/sec: 101.085
INFO:tensorflow:loss = 0.46045718, step = 1600 (0.989 sec)
INFO:tensorflow:loss = 0.46045718, step = 1600 (0.989 sec)
INFO:tensorflow:global_step/sec: 100.746
INFO:tensorflow:global_step/sec: 100.746
INFO:tensorflow:loss = 0.47194332, step = 1700 (0.995 sec)
INFO:tensorflow:loss = 0.47194332, step = 1700 (0.995 sec)
INFO:tensorflow:global_step/sec: 99.8541
INFO:tensorflow:global_step/sec: 99.8541
INFO:tensorflow:loss = 0.45978338, step = 1800 (0.999 sec)
INFO:tensorflow:loss = 0.45978338, step = 1800 (0.999 sec)
INFO:tensorflow:global_step/sec: 97.982
INFO:tensorflow:global_step/sec: 97.982
INFO:tensorflow:loss = 0.45745283, step = 1900 (1.021 sec)
INFO:tensorflow:loss = 0.45745283, step = 1900 (1.021 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1998...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1998...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 96.2637
INFO:tensorflow:global_step/sec: 96.2637
INFO:tensorflow:loss = 0.44210017, step = 2000 (1.039 sec)
INFO:tensorflow:loss = 0.44210017, step = 2000 (1.039 sec)
INFO:tensorflow:global_step/sec: 104.181
INFO:tensorflow:global_step/sec: 104.181
INFO:tensorflow:loss = 0.4267306, step = 2100 (0.960 sec)
INFO:tensorflow:loss = 0.4267306, step = 2100 (0.960 sec)
INFO:tensorflow:global_step/sec: 100.628
INFO:tensorflow:global_step/sec: 100.628
INFO:tensorflow:loss = 0.43270233, step = 2200 (0.994 sec)
INFO:tensorflow:loss = 0.43270233, step = 2200 (0.994 sec)
INFO:tensorflow:global_step/sec: 102.274
INFO:tensorflow:global_step/sec: 102.274
INFO:tensorflow:loss = 0.42014548, step = 2300 (0.978 sec)
INFO:tensorflow:loss = 0.42014548, step = 2300 (0.978 sec)
INFO:tensorflow:global_step/sec: 99.5664
INFO:tensorflow:global_step/sec: 99.5664
INFO:tensorflow:loss = 0.42362845, step = 2400 (1.004 sec)
INFO:tensorflow:loss = 0.42362845, step = 2400 (1.004 sec)
INFO:tensorflow:global_step/sec: 101.008
INFO:tensorflow:global_step/sec: 101.008
INFO:tensorflow:loss = 0.43012613, step = 2500 (0.990 sec)
INFO:tensorflow:loss = 0.43012613, step = 2500 (0.990 sec)
INFO:tensorflow:global_step/sec: 102.62
INFO:tensorflow:global_step/sec: 102.62
INFO:tensorflow:loss = 0.435121, step = 2600 (0.974 sec)
INFO:tensorflow:loss = 0.435121, step = 2600 (0.974 sec)
INFO:tensorflow:global_step/sec: 102.1
INFO:tensorflow:global_step/sec: 102.1
INFO:tensorflow:loss = 0.42686707, step = 2700 (0.981 sec)
INFO:tensorflow:loss = 0.42686707, step = 2700 (0.981 sec)
INFO:tensorflow:global_step/sec: 103.746
INFO:tensorflow:global_step/sec: 103.746
INFO:tensorflow:loss = 0.41858014, step = 2800 (0.964 sec)
INFO:tensorflow:loss = 0.41858014, step = 2800 (0.964 sec)
INFO:tensorflow:global_step/sec: 102.04
INFO:tensorflow:global_step/sec: 102.04
INFO:tensorflow:loss = 0.41823772, step = 2900 (0.978 sec)
INFO:tensorflow:loss = 0.41823772, step = 2900 (0.978 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2997...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2997...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 100.291
INFO:tensorflow:global_step/sec: 100.291
INFO:tensorflow:loss = 0.40824187, step = 3000 (0.997 sec)
INFO:tensorflow:loss = 0.40824187, step = 3000 (0.997 sec)
INFO:tensorflow:global_step/sec: 106.907
INFO:tensorflow:global_step/sec: 106.907
INFO:tensorflow:loss = 0.40978715, step = 3100 (0.936 sec)
INFO:tensorflow:loss = 0.40978715, step = 3100 (0.936 sec)
INFO:tensorflow:global_step/sec: 104.101
INFO:tensorflow:global_step/sec: 104.101
INFO:tensorflow:loss = 0.417184, step = 3200 (0.960 sec)
INFO:tensorflow:loss = 0.417184, step = 3200 (0.960 sec)
INFO:tensorflow:global_step/sec: 99.6517
INFO:tensorflow:global_step/sec: 99.6517
INFO:tensorflow:loss = 0.43127513, step = 3300 (1.004 sec)
INFO:tensorflow:loss = 0.43127513, step = 3300 (1.004 sec)
INFO:tensorflow:global_step/sec: 99.7764
INFO:tensorflow:global_step/sec: 99.7764
INFO:tensorflow:loss = 0.41585788, step = 3400 (1.002 sec)
INFO:tensorflow:loss = 0.41585788, step = 3400 (1.002 sec)
INFO:tensorflow:global_step/sec: 104.479
INFO:tensorflow:global_step/sec: 104.479
INFO:tensorflow:loss = 0.40642825, step = 3500 (0.957 sec)
INFO:tensorflow:loss = 0.40642825, step = 3500 (0.957 sec)
INFO:tensorflow:global_step/sec: 99.2027
INFO:tensorflow:global_step/sec: 99.2027
INFO:tensorflow:loss = 0.40078893, step = 3600 (1.008 sec)
INFO:tensorflow:loss = 0.40078893, step = 3600 (1.008 sec)
INFO:tensorflow:global_step/sec: 99.5083
INFO:tensorflow:global_step/sec: 99.5083
INFO:tensorflow:loss = 0.4084859, step = 3700 (1.005 sec)
INFO:tensorflow:loss = 0.4084859, step = 3700 (1.005 sec)
INFO:tensorflow:global_step/sec: 101.837
INFO:tensorflow:global_step/sec: 101.837
INFO:tensorflow:loss = 0.38706055, step = 3800 (0.982 sec)
INFO:tensorflow:loss = 0.38706055, step = 3800 (0.982 sec)
INFO:tensorflow:global_step/sec: 100.761
INFO:tensorflow:global_step/sec: 100.761
INFO:tensorflow:loss = 0.38369697, step = 3900 (0.992 sec)
INFO:tensorflow:loss = 0.38369697, step = 3900 (0.992 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3996...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3996...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 99.897
INFO:tensorflow:global_step/sec: 99.897
INFO:tensorflow:loss = 0.4063977, step = 4000 (1.001 sec)
INFO:tensorflow:loss = 0.4063977, step = 4000 (1.001 sec)
INFO:tensorflow:global_step/sec: 99.4043
INFO:tensorflow:global_step/sec: 99.4043
INFO:tensorflow:loss = 0.42966503, step = 4100 (1.005 sec)
INFO:tensorflow:loss = 0.42966503, step = 4100 (1.005 sec)
INFO:tensorflow:global_step/sec: 99.4718
INFO:tensorflow:global_step/sec: 99.4718
INFO:tensorflow:loss = 0.43339205, step = 4200 (1.006 sec)
INFO:tensorflow:loss = 0.43339205, step = 4200 (1.006 sec)
INFO:tensorflow:global_step/sec: 99.881
INFO:tensorflow:global_step/sec: 99.881
INFO:tensorflow:loss = 0.41945544, step = 4300 (1.001 sec)
INFO:tensorflow:loss = 0.41945544, step = 4300 (1.001 sec)
INFO:tensorflow:global_step/sec: 99.7086
INFO:tensorflow:global_step/sec: 99.7086
INFO:tensorflow:loss = 0.39942062, step = 4400 (1.003 sec)
INFO:tensorflow:loss = 0.39942062, step = 4400 (1.003 sec)
INFO:tensorflow:global_step/sec: 100.605
INFO:tensorflow:global_step/sec: 100.605
INFO:tensorflow:loss = 0.40324017, step = 4500 (0.994 sec)
INFO:tensorflow:loss = 0.40324017, step = 4500 (0.994 sec)
INFO:tensorflow:global_step/sec: 103.285
INFO:tensorflow:global_step/sec: 103.285
INFO:tensorflow:loss = 0.40799192, step = 4600 (0.968 sec)
INFO:tensorflow:loss = 0.40799192, step = 4600 (0.968 sec)
INFO:tensorflow:global_step/sec: 105.19
INFO:tensorflow:global_step/sec: 105.19
INFO:tensorflow:loss = 0.4159081, step = 4700 (0.951 sec)
INFO:tensorflow:loss = 0.4159081, step = 4700 (0.951 sec)
INFO:tensorflow:global_step/sec: 104.719
INFO:tensorflow:global_step/sec: 104.719
INFO:tensorflow:loss = 0.43424368, step = 4800 (0.955 sec)
INFO:tensorflow:loss = 0.43424368, step = 4800 (0.955 sec)
INFO:tensorflow:global_step/sec: 107.189
INFO:tensorflow:global_step/sec: 107.189
INFO:tensorflow:loss = 0.41860652, step = 4900 (0.933 sec)
INFO:tensorflow:loss = 0.41860652, step = 4900 (0.933 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/saver.py:970: remove_checkpoint (from tensorflow.python.training.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.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/saver.py:970: remove_checkpoint (from tensorflow.python.training.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 4995...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4995...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 103.085
INFO:tensorflow:global_step/sec: 103.085
INFO:tensorflow:loss = 0.3955871, step = 5000 (0.970 sec)
INFO:tensorflow:loss = 0.3955871, step = 5000 (0.970 sec)
INFO:tensorflow:global_step/sec: 102.244
INFO:tensorflow:global_step/sec: 102.244
INFO:tensorflow:loss = 0.38054687, step = 5100 (0.979 sec)
INFO:tensorflow:loss = 0.38054687, step = 5100 (0.979 sec)
INFO:tensorflow:global_step/sec: 102.199
INFO:tensorflow:global_step/sec: 102.199
INFO:tensorflow:loss = 0.37835938, step = 5200 (0.979 sec)
INFO:tensorflow:loss = 0.37835938, step = 5200 (0.979 sec)
INFO:tensorflow:global_step/sec: 102.192
INFO:tensorflow:global_step/sec: 102.192
INFO:tensorflow:loss = 0.3742793, step = 5300 (0.978 sec)
INFO:tensorflow:loss = 0.3742793, step = 5300 (0.978 sec)
INFO:tensorflow:global_step/sec: 100.049
INFO:tensorflow:global_step/sec: 100.049
INFO:tensorflow:loss = 0.37766984, step = 5400 (0.999 sec)
INFO:tensorflow:loss = 0.37766984, step = 5400 (0.999 sec)
INFO:tensorflow:global_step/sec: 101.413
INFO:tensorflow:global_step/sec: 101.413
INFO:tensorflow:loss = 0.37288016, step = 5500 (0.989 sec)
INFO:tensorflow:loss = 0.37288016, step = 5500 (0.989 sec)
INFO:tensorflow:global_step/sec: 99.4785
INFO:tensorflow:global_step/sec: 99.4785
INFO:tensorflow:loss = 0.39033508, step = 5600 (1.002 sec)
INFO:tensorflow:loss = 0.39033508, step = 5600 (1.002 sec)
INFO:tensorflow:global_step/sec: 101.706
INFO:tensorflow:global_step/sec: 101.706
INFO:tensorflow:loss = 0.3888662, step = 5700 (0.983 sec)
INFO:tensorflow:loss = 0.3888662, step = 5700 (0.983 sec)
INFO:tensorflow:global_step/sec: 103.171
INFO:tensorflow:global_step/sec: 103.171
INFO:tensorflow:loss = 0.39443827, step = 5800 (0.969 sec)
INFO:tensorflow:loss = 0.39443827, step = 5800 (0.969 sec)
INFO:tensorflow:global_step/sec: 100.242
INFO:tensorflow:global_step/sec: 100.242
INFO:tensorflow:loss = 0.3824133, step = 5900 (0.998 sec)
INFO:tensorflow:loss = 0.3824133, step = 5900 (0.998 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5994...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5994...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 101.746
INFO:tensorflow:global_step/sec: 101.746
INFO:tensorflow:loss = 0.38710442, step = 6000 (0.983 sec)
INFO:tensorflow:loss = 0.38710442, step = 6000 (0.983 sec)
INFO:tensorflow:global_step/sec: 100.1
INFO:tensorflow:global_step/sec: 100.1
INFO:tensorflow:loss = 0.37636378, step = 6100 (0.999 sec)
INFO:tensorflow:loss = 0.37636378, step = 6100 (0.999 sec)
INFO:tensorflow:global_step/sec: 99.9325
INFO:tensorflow:global_step/sec: 99.9325
INFO:tensorflow:loss = 0.37966123, step = 6200 (1.001 sec)
INFO:tensorflow:loss = 0.37966123, step = 6200 (1.001 sec)
INFO:tensorflow:global_step/sec: 99.0218
INFO:tensorflow:global_step/sec: 99.0218
INFO:tensorflow:loss = 0.36940622, step = 6300 (1.010 sec)
INFO:tensorflow:loss = 0.36940622, step = 6300 (1.010 sec)
INFO:tensorflow:global_step/sec: 102.772
INFO:tensorflow:global_step/sec: 102.772
INFO:tensorflow:loss = 0.37147108, step = 6400 (0.972 sec)
INFO:tensorflow:loss = 0.37147108, step = 6400 (0.972 sec)
INFO:tensorflow:global_step/sec: 105.027
INFO:tensorflow:global_step/sec: 105.027
INFO:tensorflow:loss = 0.36456805, step = 6500 (0.952 sec)
INFO:tensorflow:loss = 0.36456805, step = 6500 (0.952 sec)
INFO:tensorflow:global_step/sec: 103.18
INFO:tensorflow:global_step/sec: 103.18
INFO:tensorflow:loss = 0.3684589, step = 6600 (0.969 sec)
INFO:tensorflow:loss = 0.3684589, step = 6600 (0.969 sec)
INFO:tensorflow:global_step/sec: 99.3375
INFO:tensorflow:global_step/sec: 99.3375
INFO:tensorflow:loss = 0.376545, step = 6700 (1.007 sec)
INFO:tensorflow:loss = 0.376545, step = 6700 (1.007 sec)
INFO:tensorflow:global_step/sec: 105.682
INFO:tensorflow:global_step/sec: 105.682
INFO:tensorflow:loss = 0.3895915, step = 6800 (0.947 sec)
INFO:tensorflow:loss = 0.3895915, step = 6800 (0.947 sec)
INFO:tensorflow:global_step/sec: 114.848
INFO:tensorflow:global_step/sec: 114.848
INFO:tensorflow:loss = 0.37849602, step = 6900 (0.870 sec)
INFO:tensorflow:loss = 0.37849602, step = 6900 (0.870 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6993...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6993...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 109.616
INFO:tensorflow:global_step/sec: 109.616
INFO:tensorflow:loss = 0.35964197, step = 7000 (0.912 sec)
INFO:tensorflow:loss = 0.35964197, step = 7000 (0.912 sec)
INFO:tensorflow:global_step/sec: 105.581
INFO:tensorflow:global_step/sec: 105.581
INFO:tensorflow:loss = 0.36216918, step = 7100 (0.947 sec)
INFO:tensorflow:loss = 0.36216918, step = 7100 (0.947 sec)
INFO:tensorflow:global_step/sec: 106.131
INFO:tensorflow:global_step/sec: 106.131
INFO:tensorflow:loss = 0.3937424, step = 7200 (0.942 sec)
INFO:tensorflow:loss = 0.3937424, step = 7200 (0.942 sec)
INFO:tensorflow:global_step/sec: 105.7
INFO:tensorflow:global_step/sec: 105.7
INFO:tensorflow:loss = 0.38952962, step = 7300 (0.946 sec)
INFO:tensorflow:loss = 0.38952962, step = 7300 (0.946 sec)
INFO:tensorflow:global_step/sec: 102.797
INFO:tensorflow:global_step/sec: 102.797
INFO:tensorflow:loss = 0.37355947, step = 7400 (0.973 sec)
INFO:tensorflow:loss = 0.37355947, step = 7400 (0.973 sec)
INFO:tensorflow:global_step/sec: 102.454
INFO:tensorflow:global_step/sec: 102.454
INFO:tensorflow:loss = 0.36603284, step = 7500 (0.976 sec)
INFO:tensorflow:loss = 0.36603284, step = 7500 (0.976 sec)
INFO:tensorflow:global_step/sec: 103.682
INFO:tensorflow:global_step/sec: 103.682
INFO:tensorflow:loss = 0.3693564, step = 7600 (0.964 sec)
INFO:tensorflow:loss = 0.3693564, step = 7600 (0.964 sec)
INFO:tensorflow:global_step/sec: 104.262
INFO:tensorflow:global_step/sec: 104.262
INFO:tensorflow:loss = 0.37061787, step = 7700 (0.959 sec)
INFO:tensorflow:loss = 0.37061787, step = 7700 (0.959 sec)
INFO:tensorflow:global_step/sec: 104.767
INFO:tensorflow:global_step/sec: 104.767
INFO:tensorflow:loss = 0.39289498, step = 7800 (0.955 sec)
INFO:tensorflow:loss = 0.39289498, step = 7800 (0.955 sec)
INFO:tensorflow:global_step/sec: 105.669
INFO:tensorflow:global_step/sec: 105.669
INFO:tensorflow:loss = 0.39648676, step = 7900 (0.946 sec)
INFO:tensorflow:loss = 0.39648676, step = 7900 (0.946 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7992...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7992...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 105.931
INFO:tensorflow:global_step/sec: 105.931
INFO:tensorflow:loss = 0.4102661, step = 8000 (0.944 sec)
INFO:tensorflow:loss = 0.4102661, step = 8000 (0.944 sec)
INFO:tensorflow:global_step/sec: 104.541
INFO:tensorflow:global_step/sec: 104.541
INFO:tensorflow:loss = 0.38024917, step = 8100 (0.957 sec)
INFO:tensorflow:loss = 0.38024917, step = 8100 (0.957 sec)
INFO:tensorflow:global_step/sec: 102.663
INFO:tensorflow:global_step/sec: 102.663
INFO:tensorflow:loss = 0.37263972, step = 8200 (0.974 sec)
INFO:tensorflow:loss = 0.37263972, step = 8200 (0.974 sec)
INFO:tensorflow:global_step/sec: 101.803
INFO:tensorflow:global_step/sec: 101.803
INFO:tensorflow:loss = 0.35875428, step = 8300 (0.982 sec)
INFO:tensorflow:loss = 0.35875428, step = 8300 (0.982 sec)
INFO:tensorflow:global_step/sec: 101.443
INFO:tensorflow:global_step/sec: 101.443
INFO:tensorflow:loss = 0.35559803, step = 8400 (0.986 sec)
INFO:tensorflow:loss = 0.35559803, step = 8400 (0.986 sec)
INFO:tensorflow:global_step/sec: 100.077
INFO:tensorflow:global_step/sec: 100.077
INFO:tensorflow:loss = 0.3563253, step = 8500 (0.999 sec)
INFO:tensorflow:loss = 0.3563253, step = 8500 (0.999 sec)
INFO:tensorflow:global_step/sec: 100.147
INFO:tensorflow:global_step/sec: 100.147
INFO:tensorflow:loss = 0.34861985, step = 8600 (0.998 sec)
INFO:tensorflow:loss = 0.34861985, step = 8600 (0.998 sec)
INFO:tensorflow:global_step/sec: 99.9734
INFO:tensorflow:global_step/sec: 99.9734
INFO:tensorflow:loss = 0.35559162, step = 8700 (1.000 sec)
INFO:tensorflow:loss = 0.35559162, step = 8700 (1.000 sec)
INFO:tensorflow:global_step/sec: 99.5136
INFO:tensorflow:global_step/sec: 99.5136
INFO:tensorflow:loss = 0.36242756, step = 8800 (1.005 sec)
INFO:tensorflow:loss = 0.36242756, step = 8800 (1.005 sec)
INFO:tensorflow:global_step/sec: 104.811
INFO:tensorflow:global_step/sec: 104.811
INFO:tensorflow:loss = 0.3742514, step = 8900 (0.954 sec)
INFO:tensorflow:loss = 0.3742514, step = 8900 (0.954 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8991...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8991...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 106.372
INFO:tensorflow:global_step/sec: 106.372
INFO:tensorflow:loss = 0.3587474, step = 9000 (0.940 sec)
INFO:tensorflow:loss = 0.3587474, step = 9000 (0.940 sec)
INFO:tensorflow:global_step/sec: 104.249
INFO:tensorflow:global_step/sec: 104.249
INFO:tensorflow:loss = 0.35512, step = 9100 (0.960 sec)
INFO:tensorflow:loss = 0.35512, step = 9100 (0.960 sec)
INFO:tensorflow:global_step/sec: 106.583
INFO:tensorflow:global_step/sec: 106.583
INFO:tensorflow:loss = 0.35559082, step = 9200 (0.938 sec)
INFO:tensorflow:loss = 0.35559082, step = 9200 (0.938 sec)
INFO:tensorflow:global_step/sec: 105.826
INFO:tensorflow:global_step/sec: 105.826
INFO:tensorflow:loss = 0.35460055, step = 9300 (0.945 sec)
INFO:tensorflow:loss = 0.35460055, step = 9300 (0.945 sec)
INFO:tensorflow:global_step/sec: 106.072
INFO:tensorflow:global_step/sec: 106.072
INFO:tensorflow:loss = 0.34970692, step = 9400 (0.944 sec)
INFO:tensorflow:loss = 0.34970692, step = 9400 (0.944 sec)
INFO:tensorflow:global_step/sec: 105.836
INFO:tensorflow:global_step/sec: 105.836
INFO:tensorflow:loss = 0.3449042, step = 9500 (0.943 sec)
INFO:tensorflow:loss = 0.3449042, step = 9500 (0.943 sec)
INFO:tensorflow:global_step/sec: 108.679
INFO:tensorflow:global_step/sec: 108.679
INFO:tensorflow:loss = 0.34985757, step = 9600 (0.920 sec)
INFO:tensorflow:loss = 0.34985757, step = 9600 (0.920 sec)
INFO:tensorflow:global_step/sec: 106.07
INFO:tensorflow:global_step/sec: 106.07
INFO:tensorflow:loss = 0.3453308, step = 9700 (0.943 sec)
INFO:tensorflow:loss = 0.3453308, step = 9700 (0.943 sec)
INFO:tensorflow:global_step/sec: 100.979
INFO:tensorflow:global_step/sec: 100.979
INFO:tensorflow:loss = 0.34995228, step = 9800 (0.990 sec)
INFO:tensorflow:loss = 0.34995228, step = 9800 (0.990 sec)
INFO:tensorflow:global_step/sec: 104.247
INFO:tensorflow:global_step/sec: 104.247
INFO:tensorflow:loss = 0.35693988, step = 9900 (0.959 sec)
INFO:tensorflow:loss = 0.35693988, step = 9900 (0.959 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-04-23T09:12:31Z
INFO:tensorflow:Starting evaluation at 2021-04-23T09:12:31Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 47.01670s
INFO:tensorflow:Inference Time : 47.01670s
INFO:tensorflow:Finished evaluation at 2021-04-23-09:13:18
INFO:tensorflow:Finished evaluation at 2021-04-23-09:13:18
INFO:tensorflow:Saving dict for global step 10000: global_step = 10000, loss = 0.39696866
INFO:tensorflow:Saving dict for global step 10000: global_step = 10000, loss = 0.39696866
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Performing the final export in the end of training.
INFO:tensorflow:Performing the final export in the end of training.
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
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 Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/export/compas/temp-1619169198/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/export/compas/temp-1619169198/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/export/compas/temp-1619169198/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/export/compas/temp-1619169198/saved_model.pb
INFO:tensorflow:Loss for final step: 0.3658929.
INFO:tensorflow:Loss for final step: 0.3658929.
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
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 Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: 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 Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
WARNING:tensorflow:Export includes no default signature!
WARNING:tensorflow:Export includes no default signature!
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/eval_model_dir/temp-1619169198/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/eval_model_dir/temp-1619169198/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/eval_model_dir/temp-1619169198/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/5/eval_model_dir/temp-1619169198/saved_model.pb
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"
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"

Analisi del modello TensorFlow

Ora che il nostro modello è stato sviluppato e addestrato all'interno di TFX, possiamo utilizzare diversi componenti aggiuntivi all'interno dell'esosistema TFX per comprendere le prestazioni dei nostri modelli in modo un po 'più dettagliato. Osservando metriche diverse siamo in grado di ottenere un quadro migliore di come si comporta il modello complessivo per sezioni diverse all'interno del nostro modello per assicurarci che il nostro modello non abbia prestazioni inferiori per nessun sottogruppo.

Per prima cosa esamineremo TensorFlow Model Analysis, che è una libreria per la valutazione dei modelli TensorFlow. Consente agli utenti di valutare i propri modelli su grandi quantità di dati in modo distribuito, utilizzando le stesse metriche definite nel proprio trainer. Queste metriche possono essere calcolate su diverse sezioni di dati e visualizzate in un notebook.

Per un elenco delle possibili metriche che possono essere aggiunte in TensorFlow Model Analysis, vedere qui .

# Uses TensorFlow Model Analysis to compute a evaluation statistics over
# features of a model.
model_analyzer = Evaluator(
    examples=example_gen.outputs['examples'],
    model=trainer.outputs['model'],

    eval_config = text_format.Parse("""
      model_specs {
        label_key: 'is_recid'
      }
      metrics_specs {
        metrics {class_name: "BinaryAccuracy"}
        metrics {class_name: "AUC"}
        metrics {
          class_name: "FairnessIndicators"
          config: '{"thresholds": [0.25, 0.5, 0.75]}'
        }
      }
      slicing_specs {
        feature_keys: 'race'
      }
    """, tfma.EvalConfig())
)
context.run(model_analyzer)

Indicatori di equità

Indicatori di equità del carico per esaminare i dati sottostanti.

evaluation_uri = model_analyzer.outputs['evaluation'].get()[0].uri
eval_result = tfma.load_eval_result(evaluation_uri)
tfma.addons.fairness.view.widget_view.render_fairness_indicator(eval_result)
FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Caucasian', 'slice': 'race:Caucasian', 'metrics': {'bi…

Gli indicatori di equità ci consentiranno di analizzare in dettaglio le prestazioni di diversi segmenti ed è progettato per supportare i team nella valutazione e nel miglioramento dei modelli per i problemi di equità. Consente un facile calcolo di classificatori binari e multiclasse e ti consentirà di valutare qualsiasi caso d'uso di qualsiasi dimensione.

Caricheremo gli indicatori di equità in questo taccuino, analizzeremo i risultati e daremo uno sguardo ai risultati. Dopo aver esplorato un momento con gli indicatori di equità, esaminare le schede Tasso di falsi positivi e Tasso di falsi negativi nello strumento. In questo caso di studio, ci interessa cercare di ridurre il numero di false previsioni di recidività, corrispondenti al tasso di falsi positivi .

Errori di tipo I e tipo II

Nello strumento Indicatore di equità vedrai due opzioni a discesa:

  1. Un'opzione "Baseline" impostata da column_for_slicing .
  2. Un'opzione "Thresholds" impostata da fairness_indicator_thresholds .

"Linea di base" è la sezione con cui vuoi confrontare tutte le altre sezioni. Più comunemente, è rappresentato dalla sezione complessiva, ma può anche essere una delle sezioni specifiche.

"Soglia" è un valore impostato all'interno di un dato modello di classificazione binaria per indicare dove deve essere collocata una previsione. Quando si imposta una soglia ci sono due cose da tenere a mente.

  1. Precisione: qual è lo svantaggio se la tua previsione genera un errore di tipo 1? In questo caso di studio una soglia più alta significherebbe stiamo prevedendo più imputati saranno commettere un altro crimine quando in realtà non lo fanno.
  2. Ricorda: qual è lo svantaggio di un errore di tipo II? In questo caso di studio una soglia più alta significherebbe che prevediamo che più imputati non commetteranno un altro crimine quando lo fanno effettivamente.

Imposteremo soglie arbitrarie a 0,75 e ci concentreremo solo sulle metriche di equità per gli imputati afro-americani e caucasici date le piccole dimensioni del campione per le altre razze, che non sono abbastanza grandi per trarre conclusioni statisticamente significative.

I tassi di seguito potrebbero differire leggermente in base a come i dati sono stati mescolati all'inizio di questo caso di studio, ma dai un'occhiata alla differenza tra i dati tra imputati afro-americani e caucasici. A una soglia inferiore il nostro modello è più probabile che predice che un difeso caucasico commetterà un secondo crimine rispetto a un difeso afroamericano. Tuttavia questa previsione si inverte quando aumentiamo la nostra soglia.

  • Tasso di falsi positivi a 0,75
    • Afro-americano: ~ 30%
      • AUC: 0,71
      • Precisione binaria: 0,67
    • Caucasica: ~ 8%
      • AUC: 0,71
      • AUC: 0,67

Ulteriori informazioni sugli errori di tipo I / II e sull'impostazione della soglia sono disponibili qui .

Metadati ML

Per capire da dove potrebbe provenire la disparità e per acquisire un'istantanea del nostro modello attuale, possiamo utilizzare i metadati ML per registrare e recuperare i metadati associati al nostro modello. ML Metadata è parte integrante di TFX, ma è progettato in modo da poter essere utilizzato in modo indipendente.

Per questo caso di studio, elencheremo tutti gli artefatti che abbiamo sviluppato in precedenza all'interno di questo caso di studio. Passando in rassegna gli artefatti, le esecuzioni e il contesto, avremo una visione di alto livello del nostro modello TFX per approfondire l'origine di eventuali problemi. Questo ci fornirà una panoramica di base di come è stato sviluppato il nostro modello e quali componenti TFX hanno contribuito a sviluppare il nostro modello iniziale.

Inizieremo disponendo prima gli artefatti di alto livello, l'esecuzione e i tipi di contesto nel nostro modello.

# Connect to the TFX database.
connection_config = metadata_store_pb2.ConnectionConfig()

connection_config.sqlite.filename_uri = os.path.join(
  context.pipeline_root, 'metadata.sqlite')
store = metadata_store.MetadataStore(connection_config)

def _mlmd_type_to_dataframe(mlmd_type):
  """Helper function to turn MLMD into a Pandas DataFrame.

  Args:
    mlmd_type: Metadata store type.

  Returns:
    DataFrame containing type ID, Name, and Properties.
  """
  pd.set_option('display.max_columns', None)  
  pd.set_option('display.expand_frame_repr', False)

  column_names = ['ID', 'Name', 'Properties']
  df = pd.DataFrame(columns=column_names)
  for a_type in mlmd_type:
    mlmd_row = pd.DataFrame([[a_type.id, a_type.name, a_type.properties]],
                            columns=column_names)
    df = df.append(mlmd_row)
  return df

# ML Metadata stores strong-typed Artifacts, Executions, and Contexts.
# First, we can use type APIs to understand what is defined in ML Metadata
# by the current version of TFX. We'll be able to view all the previous runs
# that created our initial model.
print('Artifact Types:')
display(_mlmd_type_to_dataframe(store.get_artifact_types()))

print('\nExecution Types:')
display(_mlmd_type_to_dataframe(store.get_execution_types()))

print('\nContext Types:')
display(_mlmd_type_to_dataframe(store.get_context_types()))
Artifact Types:
Execution Types:
Context Types:

Individua l'origine del problema dell'equità

Per ciascuno dei suddetti artefatti, esecuzione e tipi di contesto possiamo utilizzare i metadati ML per approfondire gli attributi e il modo in cui è stata sviluppata ogni parte della nostra pipeline ML.

Inizieremo immergendoci in StatisticsGen per esaminare i dati sottostanti che inizialmente abbiamo inserito nel modello. Conoscendo gli artefatti all'interno del nostro modello, possiamo utilizzare ML Metadata e TensorFlow Data Validation per guardare avanti e indietro all'interno del modello per identificare l'origine di un potenziale problema.

Dopo aver eseguito la cella sottostante, seleziona Lift (Y=1) nel secondo grafico della scheda Chart to show per vedere l' aumento tra le diverse sezioni di dati. All'interno della race , l'incremento per gli afroamericani è di circa 1,08 mentre il Caucaso è di circa 0,86.

statistics_gen = StatisticsGen(
    examples=example_gen.outputs['examples'],
    schema=infer_schema.outputs['schema'],
    stats_options=tfdv.StatsOptions(label_feature='is_recid'))
exec_result = context.run(statistics_gen)

for event in store.get_events_by_execution_ids([exec_result.execution_id]):
  if event.path.steps[0].key == 'statistics':
    statistics_w_schema_uri = store.get_artifacts_by_id([event.artifact_id])[0].uri

model_stats = tfdv.load_statistics(
    os.path.join(statistics_w_schema_uri, 'eval/stats_tfrecord/'))
tfdv.visualize_statistics(model_stats)
WARNING:root:This input type hint will be ignored and not used for type-checking purposes. Typically, input type hints for a PTransform are single (or nested) types wrapped by a PCollection, or PBegin. Got: Tuple[Tuple[Union[NoneType, str], RecordBatch], _SlicedYKey] instead.
WARNING:root:This input type hint will be ignored and not used for type-checking purposes. Typically, input type hints for a PTransform are single (or nested) types wrapped by a PCollection, or PBegin. Got: Tuple[Tuple[_SlicedXKey, Union[float, int]], _SlicedYKey] instead.
WARNING:root:This input type hint will be ignored and not used for type-checking purposes. Typically, input type hints for a PTransform are single (or nested) types wrapped by a PCollection, or PBegin. Got: Tuple[Tuple[_SlicedXKey, Union[float, int]], _SlicedYKey] instead.
WARNING:root:This input type hint will be ignored and not used for type-checking purposes. Typically, input type hints for a PTransform are single (or nested) types wrapped by a PCollection, or PBegin. Got: Tuple[Tuple[Union[NoneType, str], RecordBatch], _SlicedYKey] instead.
WARNING:root:This input type hint will be ignored and not used for type-checking purposes. Typically, input type hints for a PTransform are single (or nested) types wrapped by a PCollection, or PBegin. Got: Tuple[Tuple[Union[NoneType, str], RecordBatch], _SlicedYKey] instead.
WARNING:root:This input type hint will be ignored and not used for type-checking purposes. Typically, input type hints for a PTransform are single (or nested) types wrapped by a PCollection, or PBegin. Got: Tuple[Tuple[_SlicedXKey, Union[float, int]], _SlicedYKey] instead.
WARNING:root:This input type hint will be ignored and not used for type-checking purposes. Typically, input type hints for a PTransform are single (or nested) types wrapped by a PCollection, or PBegin. Got: Tuple[Tuple[_SlicedXKey, Union[float, int]], _SlicedYKey] instead.
WARNING:root:This input type hint will be ignored and not used for type-checking purposes. Typically, input type hints for a PTransform are single (or nested) types wrapped by a PCollection, or PBegin. Got: Tuple[Tuple[Union[NoneType, str], RecordBatch], _SlicedYKey] instead.

Monitoraggio di una modifica del modello

Ora che abbiamo un'idea su come migliorare l'equità del nostro modello, documenteremo prima la nostra esecuzione iniziale all'interno dei metadati ML per i nostri dati e per chiunque altro possa rivedere le nostre modifiche in futuro.

I metadati ML possono tenere un registro dei nostri modelli passati insieme a qualsiasi nota che vorremmo aggiungere tra le esecuzioni. Aggiungeremo una semplice nota alla nostra prima esecuzione che denota che questa esecuzione è stata eseguita sul set di dati COMPAS completo

_MODEL_NOTE_TO_ADD = 'First model that contains fairness concerns in the model.'

first_trained_model = store.get_artifacts_by_type('Model')[-1]

# Add the two notes above to the ML metadata.
first_trained_model.custom_properties['note'].string_value = _MODEL_NOTE_TO_ADD
store.put_artifacts([first_trained_model])

def _mlmd_model_to_dataframe(model, model_number):
  """Helper function to turn a MLMD modle into a Pandas DataFrame.

  Args:
    model: Metadata store model.
    model_number: Number of model run within ML Metadata.

  Returns:
    DataFrame containing the ML Metadata model.
  """
  pd.set_option('display.max_columns', None)  
  pd.set_option('display.expand_frame_repr', False)

  df = pd.DataFrame()
  custom_properties = ['name', 'note', 'state', 'producer_component',
                       'pipeline_name']
  df['id'] = [model[model_number].id]
  df['uri'] = [model[model_number].uri]
  for prop in custom_properties:
    df[prop] = model[model_number].custom_properties.get(prop)
    df[prop] = df[prop].astype(str).map(
        lambda x: x.lstrip('string_value: "').rstrip('"\n'))
  return df

# Print the current model to see the results of the ML Metadata for the model.
display(_mlmd_model_to_dataframe(store.get_artifacts_by_type('Model'), 0))

Migliorare i problemi di equità ponderando il modello

Esistono diversi modi in cui possiamo affrontare la risoluzione dei problemi di equità all'interno di un modello. La manipolazione di dati / etichette osservati, l'implementazione di vincoli di equità o la rimozione di pregiudizi mediante regolarizzazione sono alcune tecniche 1 che sono state utilizzate per risolvere i problemi di equità. In questo caso di studio ripondereremo il modello implementando una funzione di perdita personalizzata in Keras.

Il codice seguente è lo stesso del componente Transform di cui sopra, ma con l'eccezione di una nuova classe chiamata LogisticEndpoint che useremo per la nostra perdita all'interno di Keras e alcune modifiche ai parametri.


  1. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, N. (2019). Un'indagine su pregiudizi ed equità nell'apprendimento automatico. https://arxiv.org/pdf/1908.09635.pdf
%%writefile {_trainer_module_file}
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf

import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils

from compas_transform import *

_BATCH_SIZE = 1000
_LEARNING_RATE = 0.00001
_MAX_CHECKPOINTS = 1
_SAVE_CHECKPOINT_STEPS = 999


def transformed_names(keys):
  return [transformed_name(key) for key in keys]


def transformed_name(key):
  return '{}_xf'.format(key)


def _gzip_reader_fn(filenames):
  """Returns a record reader that can read gzip'ed files.

  Args:
    filenames: A tf.string tensor or tf.data.Dataset containing one or more
      filenames.

  Returns: A nested structure of tf.TypeSpec objects matching the structure of
    an element of this dataset and specifying the type of individual components.
  """
  return tf.data.TFRecordDataset(filenames, compression_type='GZIP')


# Tf.Transform considers these features as "raw".
def _get_raw_feature_spec(schema):
  """Generates a feature spec from a Schema proto.

  Args:
    schema: A Schema proto.

  Returns:
    A feature spec defined as a dict whose keys are feature names and values are
      instances of FixedLenFeature, VarLenFeature or SparseFeature.
  """
  return schema_utils.schema_as_feature_spec(schema).feature_spec


def _example_serving_receiver_fn(tf_transform_output, schema):
  """Builds the serving in inputs.

  Args:
    tf_transform_output: 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)
  serving_input_receiver = raw_input_fn()

  transformed_features = tf_transform_output.transform_raw_features(
      serving_input_receiver.features)
  transformed_features.pop(transformed_name(LABEL_KEY))
  return tf.estimator.export.ServingInputReceiver(
      transformed_features, serving_input_receiver.receiver_tensors)


def _eval_input_receiver_fn(tf_transform_output, schema):
  """Builds everything needed for the tf-model-analysis to run the model.

  Args:
    tf_transform_output: 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=serialized_tf_example, features=raw_feature_spec)

  transformed_features = tf_transform_output.transform_raw_features(features)
  labels = transformed_features.pop(transformed_name(LABEL_KEY))

  receiver_tensors = {'examples': serialized_tf_example}

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


def _input_fn(filenames, tf_transform_output, batch_size=200):
  """Generates features and labels for training or evaluation.

  Args:
    filenames: List of CSV files to read data from.
    tf_transform_output: A TFTransformOutput.
    batch_size: First dimension size of the Tensors returned by input_fn.

  Returns:
    A (features, indices) tuple where features is a dictionary of
      Tensors, and indices is a single Tensor of label indices.
  """
  transformed_feature_spec = (
      tf_transform_output.transformed_feature_spec().copy())

  dataset = tf.compat.v1.data.experimental.make_batched_features_dataset(
      filenames,
      batch_size,
      transformed_feature_spec,
      shuffle=False,
      reader=_gzip_reader_fn)

  transformed_features = dataset.make_one_shot_iterator().get_next()

  # We pop the label because we do not want to use it as a feature while we're
  # training.
  return transformed_features, transformed_features.pop(
      transformed_name(LABEL_KEY))


# TFX will call this function.
def trainer_fn(hparams, schema):
  """Build the estimator using the high level API.

  Args:
    hparams: Hyperparameters 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.
  """
  tf_transform_output = tft.TFTransformOutput(hparams.transform_output)

  train_input_fn = lambda: _input_fn(
      hparams.train_files,
      tf_transform_output,
      batch_size=_BATCH_SIZE)

  eval_input_fn = lambda: _input_fn(
      hparams.eval_files,
      tf_transform_output,
      batch_size=_BATCH_SIZE)

  train_spec = tf.estimator.TrainSpec(
      train_input_fn,
      max_steps=hparams.train_steps)

  serving_receiver_fn = lambda: _example_serving_receiver_fn(
      tf_transform_output, schema)

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

  run_config = tf.estimator.RunConfig(
      save_checkpoints_steps=_SAVE_CHECKPOINT_STEPS,
      keep_checkpoint_max=_MAX_CHECKPOINTS)

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

  estimator = tf.keras.estimator.model_to_estimator(
      keras_model=_keras_model_builder(), config=run_config)

  # Create an input receiver for TFMA processing.
  receiver_fn = lambda: _eval_input_receiver_fn(tf_transform_output, schema)

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


def _keras_model_builder():
  """Build a keras model for COMPAS dataset classification.

  Returns:
    A compiled Keras model.
  """
  feature_columns = []
  feature_layer_inputs = {}

  for key in transformed_names(INT_FEATURE_KEYS):
    feature_columns.append(tf.feature_column.numeric_column(key))
    feature_layer_inputs[key] = tf.keras.Input(shape=(1,), name=key)

  for key, num_buckets in zip(transformed_names(CATEGORICAL_FEATURE_KEYS),
                              MAX_CATEGORICAL_FEATURE_VALUES):
    feature_columns.append(
        tf.feature_column.indicator_column(
            tf.feature_column.categorical_column_with_identity(
                key, num_buckets=num_buckets)))
    feature_layer_inputs[key] = tf.keras.Input(
        shape=(1,), name=key, dtype=tf.dtypes.int32)

  feature_columns_input = tf.keras.layers.DenseFeatures(feature_columns)
  feature_layer_outputs = feature_columns_input(feature_layer_inputs)

  dense_layers = tf.keras.layers.Dense(
      20, activation='relu', name='dense_1')(feature_layer_outputs)
  dense_layers = tf.keras.layers.Dense(
      10, activation='relu', name='dense_2')(dense_layers)
  output = tf.keras.layers.Dense(
      1, name='predictions')(dense_layers)

  model = tf.keras.Model(
      inputs=[v for v in feature_layer_inputs.values()], outputs=output)

  # To weight our model we will develop a custom loss class within Keras.
  # The old loss is commented out below and the new one is added in below.
  model.compile(
      # loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
      loss=LogisticEndpoint(),
      optimizer=tf.optimizers.Adam(learning_rate=_LEARNING_RATE))

  return model


class LogisticEndpoint(tf.keras.layers.Layer):

  def __init__(self, name=None):
    super(LogisticEndpoint, self).__init__(name=name)
    self.loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)

  def __call__(self, y_true, y_pred, sample_weight=None):
    inputs = [y_true, y_pred]
    inputs += sample_weight or ['sample_weight_xf']
    return super(LogisticEndpoint, self).__call__(inputs)

  def call(self, inputs):
    y_true, y_pred = inputs[0], inputs[1]
    if len(inputs) == 3:
      sample_weight = inputs[2]
    else:
      sample_weight = None
    loss = self.loss_fn(y_true, y_pred, sample_weight)
    self.add_loss(loss)
    reduce_loss = tf.math.divide_no_nan(
        tf.math.reduce_sum(tf.nn.softmax(y_pred)), _BATCH_SIZE)
    return reduce_loss
Overwriting compas_trainer.py

Addestra nuovamente il modello TFX con il modello ponderato

In questa parte successiva useremo il componente Transform pesato per rieseguire lo stesso modello Trainer di prima per vedere il miglioramento dell'equità dopo l'applicazione della ponderazione.

trainer_weighted = Trainer(
    module_file=_trainer_module_file,
    transformed_examples=transform.outputs['transformed_examples'],
    schema=infer_schema.outputs['schema'],
    transform_graph=transform.outputs['transform_graph'],
    train_args=trainer_pb2.TrainArgs(num_steps=10000),
    eval_args=trainer_pb2.EvalArgs(num_steps=5000)
)
context.run(trainer_weighted)
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:tensorflow:Using the Keras model provided.
INFO:tensorflow:Using the Keras model provided.
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/backend.py:434: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir', '_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:tensorflow:Using config: {'_model_dir': '/tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir', '_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:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
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.
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.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting from: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting from: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-started 6 variables.
INFO:tensorflow:Warm-started 6 variables.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 0.47077793, step = 0
INFO:tensorflow:loss = 0.47077793, step = 0
INFO:tensorflow:global_step/sec: 103.682
INFO:tensorflow:global_step/sec: 103.682
INFO:tensorflow:loss = 0.49240756, step = 100 (0.966 sec)
INFO:tensorflow:loss = 0.49240756, step = 100 (0.966 sec)
INFO:tensorflow:global_step/sec: 107.004
INFO:tensorflow:global_step/sec: 107.004
INFO:tensorflow:loss = 0.5130932, step = 200 (0.934 sec)
INFO:tensorflow:loss = 0.5130932, step = 200 (0.934 sec)
INFO:tensorflow:global_step/sec: 107.626
INFO:tensorflow:global_step/sec: 107.626
INFO:tensorflow:loss = 0.50732946, step = 300 (0.929 sec)
INFO:tensorflow:loss = 0.50732946, step = 300 (0.929 sec)
INFO:tensorflow:global_step/sec: 109.147
INFO:tensorflow:global_step/sec: 109.147
INFO:tensorflow:loss = 0.478406, step = 400 (0.917 sec)
INFO:tensorflow:loss = 0.478406, step = 400 (0.917 sec)
INFO:tensorflow:global_step/sec: 106.691
INFO:tensorflow:global_step/sec: 106.691
INFO:tensorflow:loss = 0.46235517, step = 500 (0.937 sec)
INFO:tensorflow:loss = 0.46235517, step = 500 (0.937 sec)
INFO:tensorflow:global_step/sec: 105.369
INFO:tensorflow:global_step/sec: 105.369
INFO:tensorflow:loss = 0.45720923, step = 600 (0.949 sec)
INFO:tensorflow:loss = 0.45720923, step = 600 (0.949 sec)
INFO:tensorflow:global_step/sec: 108.051
INFO:tensorflow:global_step/sec: 108.051
INFO:tensorflow:loss = 0.45070276, step = 700 (0.925 sec)
INFO:tensorflow:loss = 0.45070276, step = 700 (0.925 sec)
INFO:tensorflow:global_step/sec: 109.233
INFO:tensorflow:global_step/sec: 109.233
INFO:tensorflow:loss = 0.46355185, step = 800 (0.915 sec)
INFO:tensorflow:loss = 0.46355185, step = 800 (0.915 sec)
INFO:tensorflow:global_step/sec: 109.367
INFO:tensorflow:global_step/sec: 109.367
INFO:tensorflow:loss = 0.48339045, step = 900 (0.914 sec)
INFO:tensorflow:loss = 0.48339045, step = 900 (0.914 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 999...
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 999 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 999...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:2325: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
  warnings.warn('`Model.state_updates` will be removed in a future version. '
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-04-23T09:13:43Z
INFO:tensorflow:Starting evaluation at 2021-04-23T09:13:43Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-999
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-999
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 46.00220s
INFO:tensorflow:Inference Time : 46.00220s
INFO:tensorflow:Finished evaluation at 2021-04-23-09:14:29
INFO:tensorflow:Finished evaluation at 2021-04-23-09:14:29
INFO:tensorflow:Saving dict for global step 999: global_step = 999, loss = 0.48788843
INFO:tensorflow:Saving dict for global step 999: global_step = 999, loss = 0.48788843
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-999
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 999: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-999
INFO:tensorflow:global_step/sec: 2.11897
INFO:tensorflow:global_step/sec: 2.11897
INFO:tensorflow:loss = 0.5041351, step = 1000 (47.193 sec)
INFO:tensorflow:loss = 0.5041351, step = 1000 (47.193 sec)
INFO:tensorflow:global_step/sec: 112.962
INFO:tensorflow:global_step/sec: 112.962
INFO:tensorflow:loss = 0.5043556, step = 1100 (0.885 sec)
INFO:tensorflow:loss = 0.5043556, step = 1100 (0.885 sec)
INFO:tensorflow:global_step/sec: 106.062
INFO:tensorflow:global_step/sec: 106.062
INFO:tensorflow:loss = 0.49965087, step = 1200 (0.943 sec)
INFO:tensorflow:loss = 0.49965087, step = 1200 (0.943 sec)
INFO:tensorflow:global_step/sec: 107.054
INFO:tensorflow:global_step/sec: 107.054
INFO:tensorflow:loss = 0.479686, step = 1300 (0.934 sec)
INFO:tensorflow:loss = 0.479686, step = 1300 (0.934 sec)
INFO:tensorflow:global_step/sec: 110.532
INFO:tensorflow:global_step/sec: 110.532
INFO:tensorflow:loss = 0.47265288, step = 1400 (0.905 sec)
INFO:tensorflow:loss = 0.47265288, step = 1400 (0.905 sec)
INFO:tensorflow:global_step/sec: 109.283
INFO:tensorflow:global_step/sec: 109.283
INFO:tensorflow:loss = 0.4670694, step = 1500 (0.915 sec)
INFO:tensorflow:loss = 0.4670694, step = 1500 (0.915 sec)
INFO:tensorflow:global_step/sec: 108.905
INFO:tensorflow:global_step/sec: 108.905
INFO:tensorflow:loss = 0.45940527, step = 1600 (0.918 sec)
INFO:tensorflow:loss = 0.45940527, step = 1600 (0.918 sec)
INFO:tensorflow:global_step/sec: 107.007
INFO:tensorflow:global_step/sec: 107.007
INFO:tensorflow:loss = 0.4766834, step = 1700 (0.936 sec)
INFO:tensorflow:loss = 0.4766834, step = 1700 (0.936 sec)
INFO:tensorflow:global_step/sec: 107.121
INFO:tensorflow:global_step/sec: 107.121
INFO:tensorflow:loss = 0.46949837, step = 1800 (0.932 sec)
INFO:tensorflow:loss = 0.46949837, step = 1800 (0.932 sec)
INFO:tensorflow:global_step/sec: 109.537
INFO:tensorflow:global_step/sec: 109.537
INFO:tensorflow:loss = 0.47130463, step = 1900 (0.913 sec)
INFO:tensorflow:loss = 0.47130463, step = 1900 (0.913 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1998...
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1998 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1998...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1998...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 105.565
INFO:tensorflow:global_step/sec: 105.565
INFO:tensorflow:loss = 0.45515984, step = 2000 (0.947 sec)
INFO:tensorflow:loss = 0.45515984, step = 2000 (0.947 sec)
INFO:tensorflow:global_step/sec: 111.265
INFO:tensorflow:global_step/sec: 111.265
INFO:tensorflow:loss = 0.43437228, step = 2100 (0.899 sec)
INFO:tensorflow:loss = 0.43437228, step = 2100 (0.899 sec)
INFO:tensorflow:global_step/sec: 108.639
INFO:tensorflow:global_step/sec: 108.639
INFO:tensorflow:loss = 0.4414773, step = 2200 (0.920 sec)
INFO:tensorflow:loss = 0.4414773, step = 2200 (0.920 sec)
INFO:tensorflow:global_step/sec: 103.783
INFO:tensorflow:global_step/sec: 103.783
INFO:tensorflow:loss = 0.4223846, step = 2300 (0.964 sec)
INFO:tensorflow:loss = 0.4223846, step = 2300 (0.964 sec)
INFO:tensorflow:global_step/sec: 109.882
INFO:tensorflow:global_step/sec: 109.882
INFO:tensorflow:loss = 0.4259975, step = 2400 (0.910 sec)
INFO:tensorflow:loss = 0.4259975, step = 2400 (0.910 sec)
INFO:tensorflow:global_step/sec: 108.38
INFO:tensorflow:global_step/sec: 108.38
INFO:tensorflow:loss = 0.43732366, step = 2500 (0.923 sec)
INFO:tensorflow:loss = 0.43732366, step = 2500 (0.923 sec)
INFO:tensorflow:global_step/sec: 106.671
INFO:tensorflow:global_step/sec: 106.671
INFO:tensorflow:loss = 0.44364113, step = 2600 (0.937 sec)
INFO:tensorflow:loss = 0.44364113, step = 2600 (0.937 sec)
INFO:tensorflow:global_step/sec: 107.267
INFO:tensorflow:global_step/sec: 107.267
INFO:tensorflow:loss = 0.43038422, step = 2700 (0.932 sec)
INFO:tensorflow:loss = 0.43038422, step = 2700 (0.932 sec)
INFO:tensorflow:global_step/sec: 110.393
INFO:tensorflow:global_step/sec: 110.393
INFO:tensorflow:loss = 0.41958278, step = 2800 (0.906 sec)
INFO:tensorflow:loss = 0.41958278, step = 2800 (0.906 sec)
INFO:tensorflow:global_step/sec: 105.96
INFO:tensorflow:global_step/sec: 105.96
INFO:tensorflow:loss = 0.41283488, step = 2900 (0.944 sec)
INFO:tensorflow:loss = 0.41283488, step = 2900 (0.944 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2997...
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 2997 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2997...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2997...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 104.287
INFO:tensorflow:global_step/sec: 104.287
INFO:tensorflow:loss = 0.39609566, step = 3000 (0.958 sec)
INFO:tensorflow:loss = 0.39609566, step = 3000 (0.958 sec)
INFO:tensorflow:global_step/sec: 108.021
INFO:tensorflow:global_step/sec: 108.021
INFO:tensorflow:loss = 0.39362195, step = 3100 (0.926 sec)
INFO:tensorflow:loss = 0.39362195, step = 3100 (0.926 sec)
INFO:tensorflow:global_step/sec: 108.451
INFO:tensorflow:global_step/sec: 108.451
INFO:tensorflow:loss = 0.40350518, step = 3200 (0.922 sec)
INFO:tensorflow:loss = 0.40350518, step = 3200 (0.922 sec)
INFO:tensorflow:global_step/sec: 107.884
INFO:tensorflow:global_step/sec: 107.884
INFO:tensorflow:loss = 0.42621797, step = 3300 (0.927 sec)
INFO:tensorflow:loss = 0.42621797, step = 3300 (0.927 sec)
INFO:tensorflow:global_step/sec: 108.506
INFO:tensorflow:global_step/sec: 108.506
INFO:tensorflow:loss = 0.41866535, step = 3400 (0.921 sec)
INFO:tensorflow:loss = 0.41866535, step = 3400 (0.921 sec)
INFO:tensorflow:global_step/sec: 107.08
INFO:tensorflow:global_step/sec: 107.08
INFO:tensorflow:loss = 0.4116188, step = 3500 (0.934 sec)
INFO:tensorflow:loss = 0.4116188, step = 3500 (0.934 sec)
INFO:tensorflow:global_step/sec: 107.495
INFO:tensorflow:global_step/sec: 107.495
INFO:tensorflow:loss = 0.4095764, step = 3600 (0.931 sec)
INFO:tensorflow:loss = 0.4095764, step = 3600 (0.931 sec)
INFO:tensorflow:global_step/sec: 107.481
INFO:tensorflow:global_step/sec: 107.481
INFO:tensorflow:loss = 0.40515175, step = 3700 (0.930 sec)
INFO:tensorflow:loss = 0.40515175, step = 3700 (0.930 sec)
INFO:tensorflow:global_step/sec: 107.701
INFO:tensorflow:global_step/sec: 107.701
INFO:tensorflow:loss = 0.37928, step = 3800 (0.929 sec)
INFO:tensorflow:loss = 0.37928, step = 3800 (0.929 sec)
INFO:tensorflow:global_step/sec: 106.99
INFO:tensorflow:global_step/sec: 106.99
INFO:tensorflow:loss = 0.3782839, step = 3900 (0.934 sec)
INFO:tensorflow:loss = 0.3782839, step = 3900 (0.934 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3996...
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 3996 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3996...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3996...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 106.371
INFO:tensorflow:global_step/sec: 106.371
INFO:tensorflow:loss = 0.40979695, step = 4000 (0.940 sec)
INFO:tensorflow:loss = 0.40979695, step = 4000 (0.940 sec)
INFO:tensorflow:global_step/sec: 110.509
INFO:tensorflow:global_step/sec: 110.509
INFO:tensorflow:loss = 0.4390851, step = 4100 (0.905 sec)
INFO:tensorflow:loss = 0.4390851, step = 4100 (0.905 sec)
INFO:tensorflow:global_step/sec: 109.02
INFO:tensorflow:global_step/sec: 109.02
INFO:tensorflow:loss = 0.43913904, step = 4200 (0.918 sec)
INFO:tensorflow:loss = 0.43913904, step = 4200 (0.918 sec)
INFO:tensorflow:global_step/sec: 109.836
INFO:tensorflow:global_step/sec: 109.836
INFO:tensorflow:loss = 0.41836765, step = 4300 (0.910 sec)
INFO:tensorflow:loss = 0.41836765, step = 4300 (0.910 sec)
INFO:tensorflow:global_step/sec: 112.894
INFO:tensorflow:global_step/sec: 112.894
INFO:tensorflow:loss = 0.402948, step = 4400 (0.886 sec)
INFO:tensorflow:loss = 0.402948, step = 4400 (0.886 sec)
INFO:tensorflow:global_step/sec: 108.879
INFO:tensorflow:global_step/sec: 108.879
INFO:tensorflow:loss = 0.40872148, step = 4500 (0.918 sec)
INFO:tensorflow:loss = 0.40872148, step = 4500 (0.918 sec)
INFO:tensorflow:global_step/sec: 108.843
INFO:tensorflow:global_step/sec: 108.843
INFO:tensorflow:loss = 0.41156477, step = 4600 (0.919 sec)
INFO:tensorflow:loss = 0.41156477, step = 4600 (0.919 sec)
INFO:tensorflow:global_step/sec: 108.463
INFO:tensorflow:global_step/sec: 108.463
INFO:tensorflow:loss = 0.41628867, step = 4700 (0.922 sec)
INFO:tensorflow:loss = 0.41628867, step = 4700 (0.922 sec)
INFO:tensorflow:global_step/sec: 105.419
INFO:tensorflow:global_step/sec: 105.419
INFO:tensorflow:loss = 0.43485588, step = 4800 (0.948 sec)
INFO:tensorflow:loss = 0.43485588, step = 4800 (0.948 sec)
INFO:tensorflow:global_step/sec: 108.522
INFO:tensorflow:global_step/sec: 108.522
INFO:tensorflow:loss = 0.42932, step = 4900 (0.922 sec)
INFO:tensorflow:loss = 0.42932, step = 4900 (0.922 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4995...
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 4995 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4995...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4995...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 106.885
INFO:tensorflow:global_step/sec: 106.885
INFO:tensorflow:loss = 0.40682846, step = 5000 (0.935 sec)
INFO:tensorflow:loss = 0.40682846, step = 5000 (0.935 sec)
INFO:tensorflow:global_step/sec: 111.019
INFO:tensorflow:global_step/sec: 111.019
INFO:tensorflow:loss = 0.38750562, step = 5100 (0.901 sec)
INFO:tensorflow:loss = 0.38750562, step = 5100 (0.901 sec)
INFO:tensorflow:global_step/sec: 108.979
INFO:tensorflow:global_step/sec: 108.979
INFO:tensorflow:loss = 0.38564628, step = 5200 (0.917 sec)
INFO:tensorflow:loss = 0.38564628, step = 5200 (0.917 sec)
INFO:tensorflow:global_step/sec: 109.045
INFO:tensorflow:global_step/sec: 109.045
INFO:tensorflow:loss = 0.37906387, step = 5300 (0.919 sec)
INFO:tensorflow:loss = 0.37906387, step = 5300 (0.919 sec)
INFO:tensorflow:global_step/sec: 108.653
INFO:tensorflow:global_step/sec: 108.653
INFO:tensorflow:loss = 0.38417932, step = 5400 (0.919 sec)
INFO:tensorflow:loss = 0.38417932, step = 5400 (0.919 sec)
INFO:tensorflow:global_step/sec: 110.857
INFO:tensorflow:global_step/sec: 110.857
INFO:tensorflow:loss = 0.37717777, step = 5500 (0.902 sec)
INFO:tensorflow:loss = 0.37717777, step = 5500 (0.902 sec)
INFO:tensorflow:global_step/sec: 107.849
INFO:tensorflow:global_step/sec: 107.849
INFO:tensorflow:loss = 0.3948313, step = 5600 (0.927 sec)
INFO:tensorflow:loss = 0.3948313, step = 5600 (0.927 sec)
INFO:tensorflow:global_step/sec: 109.597
INFO:tensorflow:global_step/sec: 109.597
INFO:tensorflow:loss = 0.39357123, step = 5700 (0.912 sec)
INFO:tensorflow:loss = 0.39357123, step = 5700 (0.912 sec)
INFO:tensorflow:global_step/sec: 109.138
INFO:tensorflow:global_step/sec: 109.138
INFO:tensorflow:loss = 0.39145112, step = 5800 (0.916 sec)
INFO:tensorflow:loss = 0.39145112, step = 5800 (0.916 sec)
INFO:tensorflow:global_step/sec: 109.651
INFO:tensorflow:global_step/sec: 109.651
INFO:tensorflow:loss = 0.38264394, step = 5900 (0.914 sec)
INFO:tensorflow:loss = 0.38264394, step = 5900 (0.914 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5994...
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 5994 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5994...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5994...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 105.747
INFO:tensorflow:global_step/sec: 105.747
INFO:tensorflow:loss = 0.37979886, step = 6000 (0.944 sec)
INFO:tensorflow:loss = 0.37979886, step = 6000 (0.944 sec)
INFO:tensorflow:global_step/sec: 107.903
INFO:tensorflow:global_step/sec: 107.903
INFO:tensorflow:loss = 0.37065622, step = 6100 (0.927 sec)
INFO:tensorflow:loss = 0.37065622, step = 6100 (0.927 sec)
INFO:tensorflow:global_step/sec: 109.687
INFO:tensorflow:global_step/sec: 109.687
INFO:tensorflow:loss = 0.37019882, step = 6200 (0.912 sec)
INFO:tensorflow:loss = 0.37019882, step = 6200 (0.912 sec)
INFO:tensorflow:global_step/sec: 111.749
INFO:tensorflow:global_step/sec: 111.749
INFO:tensorflow:loss = 0.3635425, step = 6300 (0.895 sec)
INFO:tensorflow:loss = 0.3635425, step = 6300 (0.895 sec)
INFO:tensorflow:global_step/sec: 109.591
INFO:tensorflow:global_step/sec: 109.591
INFO:tensorflow:loss = 0.37183607, step = 6400 (0.913 sec)
INFO:tensorflow:loss = 0.37183607, step = 6400 (0.913 sec)
INFO:tensorflow:global_step/sec: 110.09
INFO:tensorflow:global_step/sec: 110.09
INFO:tensorflow:loss = 0.36981124, step = 6500 (0.908 sec)
INFO:tensorflow:loss = 0.36981124, step = 6500 (0.908 sec)
INFO:tensorflow:global_step/sec: 111.705
INFO:tensorflow:global_step/sec: 111.705
INFO:tensorflow:loss = 0.37439653, step = 6600 (0.895 sec)
INFO:tensorflow:loss = 0.37439653, step = 6600 (0.895 sec)
INFO:tensorflow:global_step/sec: 111.733
INFO:tensorflow:global_step/sec: 111.733
INFO:tensorflow:loss = 0.38192895, step = 6700 (0.895 sec)
INFO:tensorflow:loss = 0.38192895, step = 6700 (0.895 sec)
INFO:tensorflow:global_step/sec: 110.939
INFO:tensorflow:global_step/sec: 110.939
INFO:tensorflow:loss = 0.39505512, step = 6800 (0.901 sec)
INFO:tensorflow:loss = 0.39505512, step = 6800 (0.901 sec)
INFO:tensorflow:global_step/sec: 108.696
INFO:tensorflow:global_step/sec: 108.696
INFO:tensorflow:loss = 0.37721425, step = 6900 (0.920 sec)
INFO:tensorflow:loss = 0.37721425, step = 6900 (0.920 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6993...
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 6993 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6993...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6993...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 108.787
INFO:tensorflow:global_step/sec: 108.787
INFO:tensorflow:loss = 0.35651168, step = 7000 (0.919 sec)
INFO:tensorflow:loss = 0.35651168, step = 7000 (0.919 sec)
INFO:tensorflow:global_step/sec: 110.463
INFO:tensorflow:global_step/sec: 110.463
INFO:tensorflow:loss = 0.35931125, step = 7100 (0.906 sec)
INFO:tensorflow:loss = 0.35931125, step = 7100 (0.906 sec)
INFO:tensorflow:global_step/sec: 110.653
INFO:tensorflow:global_step/sec: 110.653
INFO:tensorflow:loss = 0.4005883, step = 7200 (0.903 sec)
INFO:tensorflow:loss = 0.4005883, step = 7200 (0.903 sec)
INFO:tensorflow:global_step/sec: 109.584
INFO:tensorflow:global_step/sec: 109.584
INFO:tensorflow:loss = 0.39476267, step = 7300 (0.914 sec)
INFO:tensorflow:loss = 0.39476267, step = 7300 (0.914 sec)
INFO:tensorflow:global_step/sec: 110.296
INFO:tensorflow:global_step/sec: 110.296
INFO:tensorflow:loss = 0.38155714, step = 7400 (0.905 sec)
INFO:tensorflow:loss = 0.38155714, step = 7400 (0.905 sec)
INFO:tensorflow:global_step/sec: 112.264
INFO:tensorflow:global_step/sec: 112.264
INFO:tensorflow:loss = 0.3660822, step = 7500 (0.891 sec)
INFO:tensorflow:loss = 0.3660822, step = 7500 (0.891 sec)
INFO:tensorflow:global_step/sec: 107.973
INFO:tensorflow:global_step/sec: 107.973
INFO:tensorflow:loss = 0.37184823, step = 7600 (0.926 sec)
INFO:tensorflow:loss = 0.37184823, step = 7600 (0.926 sec)
INFO:tensorflow:global_step/sec: 112.386
INFO:tensorflow:global_step/sec: 112.386
INFO:tensorflow:loss = 0.37022683, step = 7700 (0.890 sec)
INFO:tensorflow:loss = 0.37022683, step = 7700 (0.890 sec)
INFO:tensorflow:global_step/sec: 108.054
INFO:tensorflow:global_step/sec: 108.054
INFO:tensorflow:loss = 0.39397115, step = 7800 (0.926 sec)
INFO:tensorflow:loss = 0.39397115, step = 7800 (0.926 sec)
INFO:tensorflow:global_step/sec: 109.51
INFO:tensorflow:global_step/sec: 109.51
INFO:tensorflow:loss = 0.4014641, step = 7900 (0.913 sec)
INFO:tensorflow:loss = 0.4014641, step = 7900 (0.913 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7992...
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 7992 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7992...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7992...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 110.755
INFO:tensorflow:global_step/sec: 110.755
INFO:tensorflow:loss = 0.41632578, step = 8000 (0.903 sec)
INFO:tensorflow:loss = 0.41632578, step = 8000 (0.903 sec)
INFO:tensorflow:global_step/sec: 111.974
INFO:tensorflow:global_step/sec: 111.974
INFO:tensorflow:loss = 0.38964537, step = 8100 (0.893 sec)
INFO:tensorflow:loss = 0.38964537, step = 8100 (0.893 sec)
INFO:tensorflow:global_step/sec: 109.464
INFO:tensorflow:global_step/sec: 109.464
INFO:tensorflow:loss = 0.3786476, step = 8200 (0.914 sec)
INFO:tensorflow:loss = 0.3786476, step = 8200 (0.914 sec)
INFO:tensorflow:global_step/sec: 110.488
INFO:tensorflow:global_step/sec: 110.488
INFO:tensorflow:loss = 0.36360282, step = 8300 (0.905 sec)
INFO:tensorflow:loss = 0.36360282, step = 8300 (0.905 sec)
INFO:tensorflow:global_step/sec: 111.241
INFO:tensorflow:global_step/sec: 111.241
INFO:tensorflow:loss = 0.35523522, step = 8400 (0.899 sec)
INFO:tensorflow:loss = 0.35523522, step = 8400 (0.899 sec)
INFO:tensorflow:global_step/sec: 109.894
INFO:tensorflow:global_step/sec: 109.894
INFO:tensorflow:loss = 0.36030933, step = 8500 (0.910 sec)
INFO:tensorflow:loss = 0.36030933, step = 8500 (0.910 sec)
INFO:tensorflow:global_step/sec: 110.548
INFO:tensorflow:global_step/sec: 110.548
INFO:tensorflow:loss = 0.35474238, step = 8600 (0.905 sec)
INFO:tensorflow:loss = 0.35474238, step = 8600 (0.905 sec)
INFO:tensorflow:global_step/sec: 108.786
INFO:tensorflow:global_step/sec: 108.786
INFO:tensorflow:loss = 0.36295354, step = 8700 (0.919 sec)
INFO:tensorflow:loss = 0.36295354, step = 8700 (0.919 sec)
INFO:tensorflow:global_step/sec: 110.613
INFO:tensorflow:global_step/sec: 110.613
INFO:tensorflow:loss = 0.370992, step = 8800 (0.905 sec)
INFO:tensorflow:loss = 0.370992, step = 8800 (0.905 sec)
INFO:tensorflow:global_step/sec: 110.296
INFO:tensorflow:global_step/sec: 110.296
INFO:tensorflow:loss = 0.37704998, step = 8900 (0.907 sec)
INFO:tensorflow:loss = 0.37704998, step = 8900 (0.907 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8991...
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 8991 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8991...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8991...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:global_step/sec: 109.913
INFO:tensorflow:global_step/sec: 109.913
INFO:tensorflow:loss = 0.35852998, step = 9000 (0.908 sec)
INFO:tensorflow:loss = 0.35852998, step = 9000 (0.908 sec)
INFO:tensorflow:global_step/sec: 110.748
INFO:tensorflow:global_step/sec: 110.748
INFO:tensorflow:loss = 0.3526183, step = 9100 (0.903 sec)
INFO:tensorflow:loss = 0.3526183, step = 9100 (0.903 sec)
INFO:tensorflow:global_step/sec: 109.463
INFO:tensorflow:global_step/sec: 109.463
INFO:tensorflow:loss = 0.35498005, step = 9200 (0.914 sec)
INFO:tensorflow:loss = 0.35498005, step = 9200 (0.914 sec)
INFO:tensorflow:global_step/sec: 109.903
INFO:tensorflow:global_step/sec: 109.903
INFO:tensorflow:loss = 0.35461825, step = 9300 (0.909 sec)
INFO:tensorflow:loss = 0.35461825, step = 9300 (0.909 sec)
INFO:tensorflow:global_step/sec: 110.685
INFO:tensorflow:global_step/sec: 110.685
INFO:tensorflow:loss = 0.34659553, step = 9400 (0.904 sec)
INFO:tensorflow:loss = 0.34659553, step = 9400 (0.904 sec)
INFO:tensorflow:global_step/sec: 102.877
INFO:tensorflow:global_step/sec: 102.877
INFO:tensorflow:loss = 0.34350696, step = 9500 (0.972 sec)
INFO:tensorflow:loss = 0.34350696, step = 9500 (0.972 sec)
INFO:tensorflow:global_step/sec: 104.166
INFO:tensorflow:global_step/sec: 104.166
INFO:tensorflow:loss = 0.354497, step = 9600 (0.960 sec)
INFO:tensorflow:loss = 0.354497, step = 9600 (0.960 sec)
INFO:tensorflow:global_step/sec: 108.578
INFO:tensorflow:global_step/sec: 108.578
INFO:tensorflow:loss = 0.35038272, step = 9700 (0.921 sec)
INFO:tensorflow:loss = 0.35038272, step = 9700 (0.921 sec)
INFO:tensorflow:global_step/sec: 108.338
INFO:tensorflow:global_step/sec: 108.338
INFO:tensorflow:loss = 0.36009234, step = 9800 (0.923 sec)
INFO:tensorflow:loss = 0.36009234, step = 9800 (0.923 sec)
INFO:tensorflow:global_step/sec: 112.09
INFO:tensorflow:global_step/sec: 112.09
INFO:tensorflow:loss = 0.36380777, step = 9900 (0.892 sec)
INFO:tensorflow:loss = 0.36380777, step = 9900 (0.892 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9990...
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 9990 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9990...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-04-23T09:15:52Z
INFO:tensorflow:Starting evaluation at 2021-04-23T09:15:52Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [500/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1000/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [1500/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2000/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [2500/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3000/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [3500/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4000/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [4500/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Evaluation [5000/5000]
INFO:tensorflow:Inference Time : 45.40978s
INFO:tensorflow:Inference Time : 45.40978s
INFO:tensorflow:Finished evaluation at 2021-04-23-09:16:37
INFO:tensorflow:Finished evaluation at 2021-04-23-09:16:37
INFO:tensorflow:Saving dict for global step 10000: global_step = 10000, loss = 0.40231007
INFO:tensorflow:Saving dict for global step 10000: global_step = 10000, loss = 0.40231007
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Performing the final export in the end of training.
INFO:tensorflow:Performing the final export in the end of training.
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
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 Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']
INFO:tensorflow:Signatures INCLUDED in export for Predict: ['serving_default']
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: None
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/export/compas/temp-1619169397/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/export/compas/temp-1619169397/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/export/compas/temp-1619169397/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/export/compas/temp-1619169397/saved_model.pb
INFO:tensorflow:Loss for final step: 0.37667033.
INFO:tensorflow:Loss for final step: 0.37667033.
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_3:0\022\003sex"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_5:0\022\004race"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_7:0\022\rc_charge_desc"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
WARNING:tensorflow:Expected binary or unicode string, got type_url: "type.googleapis.com/tensorflow.AssetFileDef"
value: "\n\013\n\tConst_9:0\022\017c_charge_degree"
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Signatures INCLUDED in export for Classify: None
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 Regress: None
INFO:tensorflow:Signatures INCLUDED in export for Predict: 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 Train: None
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
INFO:tensorflow:Signatures INCLUDED in export for Eval: ['eval']
WARNING:tensorflow:Export includes no default signature!
WARNING:tensorflow:Export includes no default signature!
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Restoring parameters from /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/serving_model_dir/model.ckpt-10000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/eval_model_dir/temp-1619169397/assets
INFO:tensorflow:Assets written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/eval_model_dir/temp-1619169397/assets
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/eval_model_dir/temp-1619169397/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfx-interactive-2021-04-23T09_09_30.909861-b_me_83r/Trainer/model_run/8/eval_model_dir/temp-1619169397/saved_model.pb
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"
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"
# Again, we will run TensorFlow Model Analysis and load Fairness Indicators
# to examine the performance change in our weighted model.
model_analyzer_weighted = Evaluator(
    examples=example_gen.outputs['examples'],
    model=trainer_weighted.outputs['model'],

    eval_config = text_format.Parse("""
      model_specs {
        label_key: 'is_recid'
      }
      metrics_specs {
        metrics {class_name: 'BinaryAccuracy'}
        metrics {class_name: 'AUC'}
        metrics {
          class_name: 'FairnessIndicators'
          config: '{"thresholds": [0.25, 0.5, 0.75]}'
        }
      }
      slicing_specs {
        feature_keys: 'race'
      }
    """, tfma.EvalConfig())
)
context.run(model_analyzer_weighted)
evaluation_uri_weighted = model_analyzer_weighted.outputs['evaluation'].get()[0].uri
eval_result_weighted = tfma.load_eval_result(evaluation_uri_weighted)

multi_eval_results = {
    'Unweighted Model': eval_result,
    'Weighted Model': eval_result_weighted
}
tfma.addons.fairness.view.widget_view.render_fairness_indicator(
    multi_eval_results=multi_eval_results)
FairnessIndicatorViewer(evalName='Unweighted Model', evalNameCompare='Weighted Model', slicingMetrics=[{'slice…

Dopo aver riqualificato i nostri risultati con il modello ponderato, possiamo ancora una volta esaminare le metriche di equità per valutare eventuali miglioramenti nel modello. Questa volta, tuttavia, utilizzeremo la funzione di confronto del modello all'interno degli indicatori di equità per vedere la differenza tra il modello ponderato e non ponderato. Sebbene stiamo ancora riscontrando alcuni problemi di equità con il modello ponderato, la discrepanza è molto meno pronunciata.

Lo svantaggio, tuttavia, è che anche la nostra AUC e la precisione binaria sono diminuite dopo aver ponderato il modello.

  • Tasso di falsi positivi a 0,75
    • Afro-americano: ~ 1%
      • AUC: 0,47
      • Precisione binaria: 0,59
    • Caucasico: ~ 0%
      • AUC: 0,47
      • Precisione binaria: 0,58

Esamina i dati della seconda corsa

Infine, possiamo visualizzare i dati con TensorFlow Data Validation e sovrapporre le modifiche ai dati tra i due modelli e aggiungere una nota aggiuntiva ai metadati ML che indica che questo modello ha migliorato i problemi di equità.

# Pull the URI for the two models that we ran in this case study.
first_model_uri = store.get_artifacts_by_type('ExampleStatistics')[-1].uri
second_model_uri = store.get_artifacts_by_type('ExampleStatistics')[0].uri

# Load the stats for both models.
first_model_uri = tfdv.load_statistics(os.path.join(
    first_model_uri, 'eval/stats_tfrecord/'))
second_model_stats = tfdv.load_statistics(os.path.join(
    second_model_uri, 'eval/stats_tfrecord/'))

# Visualize the statistics between the two models.
tfdv.visualize_statistics(
    lhs_statistics=second_model_stats,
    lhs_name='Sampled Model',
    rhs_statistics=first_model_uri,
    rhs_name='COMPAS Orginal')
# Add a new note within ML Metadata describing the weighted model.
_NOTE_TO_ADD = 'Weighted model between race and is_recid.'

# Pulling the URI for the weighted trained model.
second_trained_model = store.get_artifacts_by_type('Model')[-1]

# Add the note to ML Metadata.
second_trained_model.custom_properties['note'].string_value = _NOTE_TO_ADD
store.put_artifacts([second_trained_model])

display(_mlmd_model_to_dataframe(store.get_artifacts_by_type('Model'), -1))
display(_mlmd_model_to_dataframe(store.get_artifacts_by_type('Model'), 0))

Conclusione

All'interno di questo caso di studio abbiamo sviluppato un classificatore Keras all'interno di una pipeline TFX con il set di dati COMPAS per esaminare eventuali problemi di equità all'interno del set di dati. Dopo aver sviluppato inizialmente il TFX, i problemi di equità non erano immediatamente evidenti fino a quando non abbiamo esaminato le singole sezioni all'interno del nostro modello in base alle nostre caratteristiche sensibili, nella nostra gara di casi. Dopo aver identificato i problemi, siamo stati in grado di rintracciare la fonte del problema di equità con TensorFlow DataValidation per identificare un metodo per mitigare i problemi di equità tramite la ponderazione del modello durante il monitoraggio e l'annotazione delle modifiche tramite i metadati ML. Sebbene non siamo in grado di risolvere completamente tutti i problemi di equità all'interno del set di dati, l'aggiunta di una nota da seguire per i futuri sviluppatori consentirà ad altri di comprendere i problemi che abbiamo affrontato durante lo sviluppo di questo modello.

Infine è importante notare che questo caso di studio non ha risolto i problemi di equità presenti nel dataset COMPAS. Migliorando i problemi di equità nel modello, abbiamo anche ridotto l'AUC e l'accuratezza nelle prestazioni del modello. Quello che siamo stati in grado di fare, tuttavia, è stato creare un modello che mostrasse i problemi di equità e rintracciare da dove potrebbero derivare i problemi monitorando o il lignaggio del modello mentre annotava eventuali problemi del modello all'interno dei metadati.

Per ulteriori informazioni sulle questioni che la previsione della custodia cautelare può avere, vedere il discorso FAT * 2018 su "Comprendere il contesto e le conseguenze della custodia cautelare "