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Wskaźniki uczciwości FaceSSD Przykład Colab

Zobacz na TensorFlow.org Uruchom w Google Colab Wyświetl w serwisie GitHub Pobierz notatnik

Przegląd

W tym ćwiczeniu użyjesz wskaźników uczciwości, aby zbadać przewidywania FaceSSD w zestawie danych z etykietami twarzy w środowisku naturalnym . Wskaźniki uczciwości to zestaw narzędzi zbudowanych na podstawie analizy modelu TensorFlow, które umożliwiają regularną ocenę wskaźników uczciwości w potokach produktów.

O zbiorze danych

W tym ćwiczeniu będziesz pracować z zestawem danych przewidywania FaceSSD, około 200 000 różnych prognoz obrazu i prawdami podstawowymi wygenerowanymi przez interfejs API FaceSSD.

O narzędziach

Analiza modelu TensorFlow to biblioteka służąca do oceny modeli uczenia maszynowego TensorFlow i innych niż TensorFlow. Pozwala użytkownikom oceniać swoje modele na dużych ilościach danych w sposób rozproszony, obliczając wykresy i inne metryki na różnych wycinkach danych i wizualizować w notatnikach.

Walidacja danych TensorFlow to narzędzie, którego możesz użyć do analizy danych. Możesz go użyć, aby znaleźć potencjalne problemy w danych, takie jak brakujące wartości i nierównowaga danych, które mogą prowadzić do rozbieżności w uczciwości.

Dzięki wskaźnikom uczciwości użytkownicy będą mogli:

  • Oceń wydajność modelu podzieloną na zdefiniowane grupy użytkowników
  • Poczuj pewność co do wyników dzięki przedziałom ufności i ocenom przy wielu progach

Importowanie

Uruchom następujący kod, aby zainstalować bibliotekę fairness_indicators. Ten pakiet zawiera narzędzia, których będziemy używać w tym ćwiczeniu. Może być wymagane ponowne uruchomienie środowiska wykonawczego, ale nie jest to konieczne.

pip install -q -U pip==20.2
pip install fairness-indicators
import os
import tempfile
import apache_beam as beam
import numpy as np
import pandas as pd
from datetime import datetime

import tensorflow_hub as hub
import tensorflow as tf
import tensorflow_model_analysis as tfma
import tensorflow_data_validation as tfdv
from tensorflow_model_analysis.addons.fairness.post_export_metrics import fairness_indicators
from tensorflow_model_analysis.addons.fairness.view import widget_view
from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_predict as agnostic_predict
from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_evaluate_graph
from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_extractor

from witwidget.notebook.visualization import WitConfigBuilder
from witwidget.notebook.visualization import WitWidget

Pobierz i zrozum dane

Oznaczone twarze na wolności to publiczny zestaw danych wzorcowych do weryfikacji twarzy, znanego również jako dopasowywanie par. LFW zawiera ponad 13 000 zdjęć twarzy zebranych z sieci.

Przeprowadziliśmy przewidywania FaceSSD na tym zbiorze danych, aby przewidzieć, czy twarz jest obecna na danym zdjęciu. W tej kolumnie podzielimy dane według płci, aby zaobserwować, czy istnieją jakiekolwiek znaczące różnice między wynikami modelu dla różnych grup płci.

Jeśli na obrazie jest więcej niż jedna twarz, płeć jest oznaczana jako „BRAK”.

Dla wygody udostępniliśmy zbiór danych w Google Cloud Platform. Uruchom poniższy kod, aby pobrać dane z GCP, pobranie i analiza danych zajmie około minuty.

data_location = tf.keras.utils.get_file('lfw_dataset.tf', 'https://storage.googleapis.com/facessd_dataset/lfw_dataset.tfrecord')

stats = tfdv.generate_statistics_from_tfrecord(data_location=data_location)
tfdv.visualize_statistics(stats)
Downloading data from https://storage.googleapis.com/facessd_dataset/lfw_dataset.tfrecord
200835072/200828483 [==============================] - 1s 0us/step
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.
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)`

Definiowanie stałych

BASE_DIR = tempfile.gettempdir()

tfma_eval_result_path = os.path.join(BASE_DIR, 'tfma_eval_result')

compute_confidence_intervals = True

slice_key = 'object/groundtruth/Gender'
label_key = 'object/groundtruth/face'
prediction_key = 'object/prediction/face'

feature_map = {
    slice_key:
        tf.io.FixedLenFeature([], tf.string, default_value=['none']),
    label_key:
        tf.io.FixedLenFeature([], tf.float32, default_value=[0.0]),
    prediction_key:
        tf.io.FixedLenFeature([], tf.float32, default_value=[0.0]),
}

Konfiguracja niezależna od modelu dla TFMA

model_agnostic_config = agnostic_predict.ModelAgnosticConfig(
    label_keys=[label_key],
    prediction_keys=[prediction_key],
    feature_spec=feature_map)

model_agnostic_extractors = [
    model_agnostic_extractor.ModelAgnosticExtractor(
        model_agnostic_config=model_agnostic_config, desired_batch_size=3),
    tfma.extractors.slice_key_extractor.SliceKeyExtractor(
          [tfma.slicer.SingleSliceSpec(),
           tfma.slicer.SingleSliceSpec(columns=[slice_key])])
]

Fairness Callback i Computing Fairness Metrics

# Helper class for counting examples in beam PCollection
class CountExamples(beam.CombineFn):
    def __init__(self, message):
      self.message = message

    def create_accumulator(self):
      return 0

    def add_input(self, current_sum, element):
      return current_sum + 1

    def merge_accumulators(self, accumulators): 
      return sum(accumulators)

    def extract_output(self, final_sum):
      if final_sum:
        print("%s: %d"%(self.message, final_sum))
metrics_callbacks = [
  tfma.post_export_metrics.fairness_indicators(
      thresholds=[0.1, 0.3, 0.5, 0.7, 0.9],
      labels_key=label_key,
      target_prediction_keys=[prediction_key]),
  tfma.post_export_metrics.auc(
      curve='PR',
      labels_key=label_key,
      target_prediction_keys=[prediction_key]),
]

eval_shared_model = tfma.types.EvalSharedModel(
    add_metrics_callbacks=metrics_callbacks,
    construct_fn=model_agnostic_evaluate_graph.make_construct_fn(
        add_metrics_callbacks=metrics_callbacks,
        config=model_agnostic_config))

with beam.Pipeline() as pipeline:
  # Read data.
  data = (
      pipeline
      | 'ReadData' >> beam.io.ReadFromTFRecord(data_location))

  # Count all examples.
  data_count = (
      data | 'Count number of examples' >> beam.CombineGlobally(
          CountExamples('Before filtering "Gender:MISSING"')))

  # If there are more than one face in image, the gender feature is 'MISSING'
  # and we are filtering that image out.
  def filter_missing_gender(element):
    example = tf.train.Example.FromString(element)
    if example.features.feature[slice_key].bytes_list.value[0] != b'MISSING':
      yield element

  filtered_data = (
      data
      | 'Filter Missing Gender' >> beam.ParDo(filter_missing_gender))

  # Count after filtering "Gender:MISSING".
  filtered_data_count = (
      filtered_data | 'Count number of examples after filtering'
      >> beam.CombineGlobally(
          CountExamples('After filtering "Gender:MISSING"')))

  # Because LFW data set has always faces by default, we are adding
  # labels as 1.0 for all images.
  def add_face_groundtruth(element):
    example = tf.train.Example.FromString(element)
    example.features.feature[label_key].float_list.value[:] = [1.0]
    yield example.SerializeToString()

  final_data = (
      filtered_data
      | 'Add Face Groundtruth' >> beam.ParDo(add_face_groundtruth))

  # Run TFMA.
  _ = (
      final_data
      | 'ExtractEvaluateAndWriteResults' >>
       tfma.ExtractEvaluateAndWriteResults(
                 eval_shared_model=eval_shared_model,
                 compute_confidence_intervals=compute_confidence_intervals,
                 output_path=tfma_eval_result_path,
                 extractors=model_agnostic_extractors))

eval_result = tfma.load_eval_result(output_path=tfma_eval_result_path)
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WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_model_analysis/post_export_metrics/post_export_metrics.py:178: auc (from tensorflow.python.ops.metrics_impl) is deprecated and will be removed in a future version.
Instructions for updating:
The value of AUC returned by this may race with the update so this is deprecated. Please use tf.keras.metrics.AUC instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_model_analysis/post_export_metrics/post_export_metrics.py:178: auc (from tensorflow.python.ops.metrics_impl) is deprecated and will be removed in a future version.
Instructions for updating:
The value of AUC returned by this may race with the update so this is deprecated. Please use tf.keras.metrics.AUC instead.
Before filtering "Gender:MISSING": 13836
After filtering "Gender:MISSING": 11544
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Wskaźniki rzetelności renderowania

Renderuj widżet Wskaźniki rzetelności z wyeksportowanymi wynikami oceny.

Poniżej zobaczysz wykresy słupkowe przedstawiające wydajność każdego wycinka danych w wybranych metrykach. Możesz dostosować wycinek porównania linii bazowej, a także wyświetlane progi, korzystając z menu rozwijanych u góry wizualizacji.

Istotną miarą dla tego przypadku użycia jest prawdziwy współczynnik dodatni, znany również jako wycofanie. Użyj selektora po lewej stronie, aby wybrać wykres dla wartości true_positive_rate. Te wartości metryki odpowiadają wartościom wyświetlanym na karcie modelu .

W przypadku niektórych zdjęć płeć jest oznaczana jako młoda, a nie jako mężczyzna lub kobieta, jeśli osoba na zdjęciu jest zbyt młoda, aby można było ją dokładnie opisać.

widget_view.render_fairness_indicator(eval_result=eval_result,
                                      slicing_column=slice_key)
FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'post_export…