 TensorFlow.org'da görüntüleyin |  Google Colab'da çalıştırın |  GitHub'da görüntüle |  Not defterini indir |
genel bakış
Bu aktivitede, kullanacağız Adil Göstergelerini keşfetmeye Yabani veri kümesindeki Etiketli Faces üzerinde FaceSSD tahminlerde . Adil Göstergeler üzerine inşa araçları paketidir TensorFlow Modeli Analizi ürün boru hatları adalet ölçütlerinin düzenli değerlendirilmesini sağlar.
Veri Kümesi Hakkında
Bu alıştırmada, FaceSSD API tarafından oluşturulan yaklaşık 200 bin farklı görüntü tahmini ve temel gerçekler olan FaceSSD tahmin veri seti ile çalışacaksınız.
Araçlar Hakkında
TensorFlow Modeli Analizi TensorFlow olmayan TensorFlow makine öğrenme modellerinin her ikisi değerlendirmek için bir kütüphanedir. Kullanıcıların modellerini büyük miktarda veri üzerinde dağıtılmış bir şekilde değerlendirmelerine, grafik içi ve diğer ölçümleri farklı veri dilimleri üzerinde hesaplamalarına ve not defterlerinde görselleştirmelerine olanak tanır.
TensorFlow Veri Doğrulama verilerinizi analiz etmek için kullanabileceğiniz bir araçtır. Verilerinizdeki eksik değerler ve veri dengesizlikleri gibi Adalet eşitsizliklerine yol açabilecek olası sorunları bulmak için kullanabilirsiniz.
İle Adil Göstergeleri , kullanıcıların mümkün olacaktır:
- Tanımlanmış kullanıcı grupları arasında dilimlenmiş model performansını değerlendirin
- Güven aralıkları ve birden çok eşikteki değerlendirmeler ile sonuçlardan emin olun
içe aktarılıyor
fairness_indicators kitaplığını kurmak için aşağıdaki kodu çalıştırın. Bu paket, bu alıştırmada kullanacağımız araçları içerir. Çalışma Zamanını Yeniden Başlatma istenebilir ancak gerekli değildir.
pip install -q -U pip==20.2pip 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
Verileri İndirin ve Anlayın
Wild etiketli Yüzler da çift eşleştirme olarak bilinen bir yüz doğrulama için genel bir referans veri seti olup. LFW, web'den toplanan 13.000'den fazla yüz görüntüsü içerir.
Belirli bir görüntüde bir yüzün bulunup bulunmadığını tahmin etmek için bu veri kümesinde FaceSSD tahminlerini çalıştırdık. Bu İşbirliğinde, farklı cinsiyet grupları için model performansı arasında önemli farklılıklar olup olmadığını gözlemlemek için verileri cinsiyete göre dilimleyeceğiz.
Bir görüntüde birden fazla yüz varsa, cinsiyet "EKSİZ" olarak etiketlenir.
Kolaylık sağlamak için veri kümesini Google Cloud Platform'da barındırdık. Verileri GCP'den indirmek için aşağıdaki kodu çalıştırın; verilerin indirilmesi ve analiz edilmesi yaklaşık bir dakika sürecektir.
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)`
Sabitleri Tanımlama
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]),
}
TFMA için Modelden Agnostik Yapılandırma
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])])
]
Adalet Geri Aramaları ve Bilgi İşlem Adalet Metrikleri
# 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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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 WARNING:apache_beam.io.filebasedsink:Deleting 1 existing files in target path matching: WARNING:apache_beam.io.filebasedsink:Deleting 1 existing files in target path matching: WARNING:apache_beam.io.filebasedsink:Deleting 1 existing files in target path matching:
Hakkaniyet Göstergeleri Oluşturun
Dışa aktarılan değerlendirme sonuçlarıyla Adalet Göstergeleri pencere öğesini oluşturun.
Aşağıda, seçilen metriklerdeki verilerin her bir diliminin performansını gösteren çubuk grafikler göreceksiniz. Görselleştirmenin üst kısmındaki açılır menüleri kullanarak temel karşılaştırma dilimini ve görüntülenen eşik(ler)i ayarlayabilirsiniz.
Bu kullanım durumu için ilgili bir ölçüm, geri çağırma olarak da bilinen gerçek pozitif orandır. true_positive_rate grafiğini seçmek için sol taraftaki seçiciyi kullanın. Bu metrik değerler görüntülenen değerlerle eşleşen modeli kartına .
Bazı fotoğraflarda, fotoğraftaki kişi doğru bir şekilde açıklama yapılamayacak kadar gençse, cinsiyet erkek veya kadın yerine genç olarak etiketlenir.
widget_view.render_fairness_indicator(eval_result=eval_result,
slicing_column=slice_key)
FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'post_export…