شاخص‌های انصاف در جاسازی‌های متن TF-Hub

مشاهده در TensorFlow.org

در Google Colab اجرا شود

در GitHub مشاهده کنید

دانلود دفترچه یادداشت

مدل TF Hub را ببینید

در این آموزش، شما یاد بگیرند که چگونه به استفاده از شاخص انصاف به ارزیابی درونه گیریها از TF توپی . این نوت بوک با استفاده از عمران نظرات مجموعه داده .

برپایی

کتابخانه های مورد نیاز را نصب کنید.

!pip install -q -U pip==20.2

!pip install fairness-indicators \
  "absl-py==0.12.0" \
  "pyarrow==2.0.0" \
  "apache-beam==2.34.0" \
  "avro-python3==1.9.1"

سایر کتابخانه های مورد نیاز را وارد کنید.

import os
import tempfile
import apache_beam as beam
from datetime import datetime
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_model_analysis as tfma
from tensorflow_model_analysis.addons.fairness.view import widget_view
from tensorflow_model_analysis.addons.fairness.post_export_metrics import fairness_indicators
from fairness_indicators import example_model
from fairness_indicators.tutorial_utils import util

ERROR: 
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/apache_beam/io/gcp/bigquery.py", line 341, in <module>
    import google.cloud.bigquery_storage_v1 as bq_storage
ModuleNotFoundError: No module named 'google.cloud.bigquery_storage_v1'

مجموعه داده

در این نوت بوک، شما کار با عمران نظرات مجموعه داده که شامل حدود 2 میلیون نظر عمومی عمومی توسط ساخته شده داخلی نظرات پلت فرم در سال 2017 برای تحقیقات در حال انجام. این تلاش توسط Jigsaw، که میزبان مسابقات در Kaggle برای کمک به طبقه بندی نظرات سمی و همچنین به حداقل رساندن سوگیری مدل ناخواسته، حمایت مالی شد.

هر نظر متنی جداگانه در مجموعه داده دارای یک برچسب سمیت است که اگر نظر سمی باشد، برچسب 1 و اگر نظر غیرسمی باشد 0 است. در داده‌ها، زیرمجموعه‌ای از نظرات با انواع ویژگی‌های هویتی، از جمله دسته‌هایی برای جنسیت، گرایش جنسی، مذهب، و نژاد یا قومیت برچسب‌گذاری می‌شوند.

داده ها را آماده کنید

TensorFlow تجزیه ویژگی های از داده ها با استفاده tf.io.FixedLenFeature و tf.io.VarLenFeature . ویژگی ورودی، ویژگی خروجی و سایر ویژگی های برش مورد علاقه را ترسیم کنید.

BASE_DIR = tempfile.gettempdir()

# The input and output features of the classifier
TEXT_FEATURE = 'comment_text'
LABEL = 'toxicity'

FEATURE_MAP = {
    # input and output features
    LABEL: tf.io.FixedLenFeature([], tf.float32),
    TEXT_FEATURE: tf.io.FixedLenFeature([], tf.string),

    # slicing features
    'sexual_orientation': tf.io.VarLenFeature(tf.string),
    'gender': tf.io.VarLenFeature(tf.string),
    'religion': tf.io.VarLenFeature(tf.string),
    'race': tf.io.VarLenFeature(tf.string),
    'disability': tf.io.VarLenFeature(tf.string)
}

IDENTITY_TERMS = ['gender', 'sexual_orientation', 'race', 'religion', 'disability']

به طور پیش فرض، نوت بوک یک نسخه از پیش پردازش شده این مجموعه داده را دانلود می کند، اما می توانید از مجموعه داده اصلی استفاده کنید و در صورت تمایل مراحل پردازش را دوباره اجرا کنید.

در مجموعه داده اصلی، هر نظر با درصد ارزیابی‌کنندگانی که معتقد بودند یک نظر با یک هویت خاص مطابقت دارد، برچسب‌گذاری می‌شود. برای مثال، یک نظر ممکن است با موارد زیر برچسب: { male: 0.3, female: 1.0, transgender: 0.0, heterosexual: 0.8, homosexual_gay_or_lesbian: 1.0 } .

مرحله پردازش هویت را بر اساس دسته بندی (جنسیت، گرایش_جنسی و غیره) گروه بندی می کند و هویت هایی با امتیاز کمتر از 0.5 حذف می کند. بنابراین مثال بالا به صورت زیر تبدیل می‌شود: ارزیابی‌کنندگانی که معتقد بودند یک نظر با یک هویت خاص مطابقت دارد. به عنوان مثال، نظر بالا خواهد بود با موارد زیر برچسب: { gender: [female], sexual_orientation: [heterosexual, homosexual_gay_or_lesbian] }

مجموعه داده را دانلود کنید.

download_original_data = False

if download_original_data:
  train_tf_file = tf.keras.utils.get_file('train_tf.tfrecord',
                                          'https://storage.googleapis.com/civil_comments_dataset/train_tf.tfrecord')
  validate_tf_file = tf.keras.utils.get_file('validate_tf.tfrecord',
                                             'https://storage.googleapis.com/civil_comments_dataset/validate_tf.tfrecord')

  # The identity terms list will be grouped together by their categories
  # (see 'IDENTITY_COLUMNS') on threshold 0.5. Only the identity term column,
  # text column and label column will be kept after processing.
  train_tf_file = util.convert_comments_data(train_tf_file)
  validate_tf_file = util.convert_comments_data(validate_tf_file)

else:
  train_tf_file = tf.keras.utils.get_file('train_tf_processed.tfrecord',
                                          'https://storage.googleapis.com/civil_comments_dataset/train_tf_processed.tfrecord')
  validate_tf_file = tf.keras.utils.get_file('validate_tf_processed.tfrecord',
                                             'https://storage.googleapis.com/civil_comments_dataset/validate_tf_processed.tfrecord')

Downloading data from https://storage.googleapis.com/civil_comments_dataset/train_tf_processed.tfrecord
488161280/488153424 [==============================] - 2s 0us/step
488169472/488153424 [==============================] - 2s 0us/step
Downloading data from https://storage.googleapis.com/civil_comments_dataset/validate_tf_processed.tfrecord
324943872/324941336 [==============================] - 9s 0us/step
324952064/324941336 [==============================] - 9s 0us/step

یک خط لوله تحلیل مدل TensorFlow ایجاد کنید

کتابخانه انصاف شاخص به اجرا در TensorFlow تجزیه و تحلیل مدل (TFMA) مدل . مدل‌های TFMA مدل‌های TensorFlow را با قابلیت‌های اضافی برای ارزیابی و تجسم نتایج آن‌ها می‌پوشانند. ارزیابی واقعی در داخل یک رخ می دهد خط لوله آپاچی پرتو .

مراحلی که برای ایجاد خط لوله TFMA دنبال می کنید عبارتند از:

1. یک مدل TensorFlow بسازید
2. یک مدل TFMA در بالای مدل TensorFlow بسازید
3. تحلیل مدل را در یک ارکستراتور اجرا کنید. مدل نمونه در این نوت بوک از Apache Beam به عنوان ارکستراتور استفاده می کند.

def embedding_fairness_result(embedding, identity_term='gender'):

  model_dir = os.path.join(BASE_DIR, 'train',
                         datetime.now().strftime('%Y%m%d-%H%M%S'))

  print("Training classifier for " + embedding)
  classifier = example_model.train_model(model_dir,
                                         train_tf_file,
                                         LABEL,
                                         TEXT_FEATURE,
                                         FEATURE_MAP,
                                         embedding)

  # Create a unique path to store the results for this embedding.
  embedding_name = embedding.split('/')[-2]
  eval_result_path = os.path.join(BASE_DIR, 'eval_result', embedding_name)

  example_model.evaluate_model(classifier,
                               validate_tf_file,
                               eval_result_path,
                               identity_term,
                               LABEL,
                               FEATURE_MAP)
  return tfma.load_eval_result(output_path=eval_result_path)

TFMA & Fairness Indicators را اجرا کنید

معیارهای شاخص های انصاف

برخی از معیارهای موجود با شاخص های انصاف عبارتند از:

• نرخ منفی، نرخ منفی کاذب (FNR) و نرخ منفی واقعی (TNR)
• نرخ مثبت، نرخ مثبت کاذب (FPR) و نرخ مثبت واقعی (TPR)
• دقت
• دقت و یادآوری
• دقت فراخوان AUC
• ROC AUC

جاسازی متن

TF-توپی فراهم می کند چندین درونه گیریها متن. این تعبیه‌ها به عنوان ستون ویژگی برای مدل‌های مختلف عمل می‌کنند. این آموزش از تعبیه‌های زیر استفاده می‌کند:

• تصادفی nnlm-EN-dim128 : درونه گیریها متن تصادفی، به عنوان یک پایه مناسب این عمل می کند.
• nnlm-EN-dim128 : متن تعبیه بر اساس نورونهای احتمالاتی مدل زبان .
• جهانی-جمله-رمزگذار : متن جاسازی بر اساس حکم جهانی رمزگذار .

نتایج نشانگر انصاف

شاخص عدالت محاسبه با embedding_fairness_result خط لوله، و پس از آن ارائه نتایج در انصاف شاخص UI با ویجت widget_view.render_fairness_indicator برای همه درونه گیریها کنید.

تصادفی NNLM

eval_result_random_nnlm = embedding_fairness_result('https://tfhub.dev/google/random-nnlm-en-dim128/1')

Training classifier for https://tfhub.dev/google/random-nnlm-en-dim128/1
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20220107-182244', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20220107-182244', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:397: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:397: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2022-01-07 18:22:54.196242: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 26 into an existing graph with producer version 987. Shape inference will have run different parts of the graph with different producer versions.
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
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/head.py:400: NumericColumn._get_dense_tensor (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/head.py:400: NumericColumn._get_dense_tensor (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/feature_column/feature_column.py:2188: NumericColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/feature_column/feature_column.py:2188: NumericColumn._transform_feature (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/adagrad.py:139: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/adagrad.py:139: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
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/train/20220107-182244/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/train/20220107-182244/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 60.23522, step = 0
INFO:tensorflow:loss = 60.23522, step = 0
INFO:tensorflow:global_step/sec: 78.2958
INFO:tensorflow:global_step/sec: 78.2958
INFO:tensorflow:loss = 67.36491, step = 100 (1.279 sec)
INFO:tensorflow:loss = 67.36491, step = 100 (1.279 sec)
INFO:tensorflow:global_step/sec: 85.8245
INFO:tensorflow:global_step/sec: 85.8245
INFO:tensorflow:loss = 57.875557, step = 200 (1.165 sec)
INFO:tensorflow:loss = 57.875557, step = 200 (1.165 sec)
INFO:tensorflow:global_step/sec: 83.7495
INFO:tensorflow:global_step/sec: 83.7495
INFO:tensorflow:loss = 61.091763, step = 300 (1.194 sec)
INFO:tensorflow:loss = 61.091763, step = 300 (1.194 sec)
INFO:tensorflow:global_step/sec: 83.0013
INFO:tensorflow:global_step/sec: 83.0013
INFO:tensorflow:loss = 62.251183, step = 400 (1.205 sec)
INFO:tensorflow:loss = 62.251183, step = 400 (1.205 sec)
INFO:tensorflow:global_step/sec: 83.4782
INFO:tensorflow:global_step/sec: 83.4782
INFO:tensorflow:loss = 56.21132, step = 500 (1.198 sec)
INFO:tensorflow:loss = 56.21132, step = 500 (1.198 sec)
INFO:tensorflow:global_step/sec: 87.0099
INFO:tensorflow:global_step/sec: 87.0099
INFO:tensorflow:loss = 57.211937, step = 600 (1.149 sec)
INFO:tensorflow:loss = 57.211937, step = 600 (1.149 sec)
INFO:tensorflow:global_step/sec: 86.7988
INFO:tensorflow:global_step/sec: 86.7988
INFO:tensorflow:loss = 62.16255, step = 700 (1.152 sec)
INFO:tensorflow:loss = 62.16255, step = 700 (1.152 sec)
INFO:tensorflow:global_step/sec: 88.1099
INFO:tensorflow:global_step/sec: 88.1099
INFO:tensorflow:loss = 58.081688, step = 800 (1.135 sec)
INFO:tensorflow:loss = 58.081688, step = 800 (1.135 sec)
INFO:tensorflow:global_step/sec: 85.3134
INFO:tensorflow:global_step/sec: 85.3134
INFO:tensorflow:loss = 57.763985, step = 900 (1.172 sec)
INFO:tensorflow:loss = 57.763985, step = 900 (1.172 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20220107-182244/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20220107-182244/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...
INFO:tensorflow:Loss for final step: 59.963802.
INFO:tensorflow:Loss for final step: 59.963802.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/encoding.py:132: 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/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/encoding.py:132: 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:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2022-01-07 18:23:11.033169: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 26 into an existing graph with producer version 987. Shape inference will have run different parts of the graph with different producer versions.
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
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/head.py:640: 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/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/head.py:640: 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:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
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/train/20220107-182244/model.ckpt-1000
INFO:tensorflow:Restoring parameters from /tmp/train/20220107-182244/model.ckpt-1000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfma_eval_model/temp-1641579790/assets
INFO:tensorflow:Assets written to: /tmp/tfma_eval_model/temp-1641579790/assets
INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1641579790/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1641579790/saved_model.pb
WARNING:absl:Tensorflow version (2.8.0-rc0) found. Note that TFMA support for TF 2.0 is currently in beta
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:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:164: load (from tensorflow.python.saved_model.loader_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.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:164: load (from tensorflow.python.saved_model.loader_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.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1641579790/variables/variables
INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1641579790/variables/variables
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:184: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.get_tensor_from_tensor_info or tf.compat.v1.saved_model.get_tensor_from_tensor_info.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:184: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.get_tensor_from_tensor_info or tf.compat.v1.saved_model.get_tensor_from_tensor_info.
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/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:107: 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/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:107: 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)`

widget_view.render_fairness_indicator(eval_result=eval_result_random_nnlm)

FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'post_export…

NNLM

eval_result_nnlm = embedding_fairness_result('https://tfhub.dev/google/nnlm-en-dim128/1')

Training classifier for https://tfhub.dev/google/nnlm-en-dim128/1
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20220107-182524', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20220107-182524', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2022-01-07 18:25:24.785154: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 26 into an existing graph with producer version 987. Shape inference will have run different parts of the graph with different producer versions.
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:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
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/train/20220107-182524/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/train/20220107-182524/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 58.637047, step = 0
INFO:tensorflow:loss = 58.637047, step = 0
INFO:tensorflow:global_step/sec: 75.6907
INFO:tensorflow:global_step/sec: 75.6907
INFO:tensorflow:loss = 56.208035, step = 100 (1.323 sec)
INFO:tensorflow:loss = 56.208035, step = 100 (1.323 sec)
INFO:tensorflow:global_step/sec: 85.4193
INFO:tensorflow:global_step/sec: 85.4193
INFO:tensorflow:loss = 47.563675, step = 200 (1.170 sec)
INFO:tensorflow:loss = 47.563675, step = 200 (1.170 sec)
INFO:tensorflow:global_step/sec: 85.3916
INFO:tensorflow:global_step/sec: 85.3916
INFO:tensorflow:loss = 56.227097, step = 300 (1.171 sec)
INFO:tensorflow:loss = 56.227097, step = 300 (1.171 sec)
INFO:tensorflow:global_step/sec: 85.7359
INFO:tensorflow:global_step/sec: 85.7359
INFO:tensorflow:loss = 55.668434, step = 400 (1.166 sec)
INFO:tensorflow:loss = 55.668434, step = 400 (1.166 sec)
INFO:tensorflow:global_step/sec: 85.6231
INFO:tensorflow:global_step/sec: 85.6231
INFO:tensorflow:loss = 41.7245, step = 500 (1.168 sec)
INFO:tensorflow:loss = 41.7245, step = 500 (1.168 sec)
INFO:tensorflow:global_step/sec: 85.1399
INFO:tensorflow:global_step/sec: 85.1399
INFO:tensorflow:loss = 45.596313, step = 600 (1.174 sec)
INFO:tensorflow:loss = 45.596313, step = 600 (1.174 sec)
INFO:tensorflow:global_step/sec: 83.6346
INFO:tensorflow:global_step/sec: 83.6346
INFO:tensorflow:loss = 51.108143, step = 700 (1.196 sec)
INFO:tensorflow:loss = 51.108143, step = 700 (1.196 sec)
INFO:tensorflow:global_step/sec: 85.4834
INFO:tensorflow:global_step/sec: 85.4834
INFO:tensorflow:loss = 47.63583, step = 800 (1.170 sec)
INFO:tensorflow:loss = 47.63583, step = 800 (1.170 sec)
INFO:tensorflow:global_step/sec: 86.7353
INFO:tensorflow:global_step/sec: 86.7353
INFO:tensorflow:loss = 48.044117, step = 900 (1.153 sec)
INFO:tensorflow:loss = 48.044117, step = 900 (1.153 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20220107-182524/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20220107-182524/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...
INFO:tensorflow:Loss for final step: 50.57175.
INFO:tensorflow:Loss for final step: 50.57175.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
2022-01-07 18:25:40.091474: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 26 into an existing graph with producer version 987. Shape inference will have run different parts of the graph with different producer versions.
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
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
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/train/20220107-182524/model.ckpt-1000
INFO:tensorflow:Restoring parameters from /tmp/train/20220107-182524/model.ckpt-1000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets written to: /tmp/tfma_eval_model/temp-1641579940/assets
INFO:tensorflow:Assets written to: /tmp/tfma_eval_model/temp-1641579940/assets
INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1641579940/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1641579940/saved_model.pb
WARNING:absl:Tensorflow version (2.8.0-rc0) found. Note that TFMA support for TF 2.0 is currently in beta
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1641579940/variables/variables
INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1641579940/variables/variables

widget_view.render_fairness_indicator(eval_result=eval_result_nnlm)

FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'label/mean'…

رمزگذار جملات جهانی

eval_result_use = embedding_fairness_result('https://tfhub.dev/google/universal-sentence-encoder/2')

Training classifier for https://tfhub.dev/google/universal-sentence-encoder/2
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20220107-182759', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20220107-182759', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
2022-01-07 18:28:15.955057: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 26 into an existing graph with producer version 987. Shape inference will have run different parts of the graph with different producer versions.
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:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
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/train/20220107-182759/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/train/20220107-182759/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 59.228935, step = 0
INFO:tensorflow:loss = 59.228935, step = 0
INFO:tensorflow:global_step/sec: 8.64079
INFO:tensorflow:global_step/sec: 8.64079
INFO:tensorflow:loss = 50.28162, step = 100 (11.575 sec)
INFO:tensorflow:loss = 50.28162, step = 100 (11.575 sec)
INFO:tensorflow:global_step/sec: 8.72597
INFO:tensorflow:global_step/sec: 8.72597
INFO:tensorflow:loss = 46.290745, step = 200 (11.460 sec)
INFO:tensorflow:loss = 46.290745, step = 200 (11.460 sec)
INFO:tensorflow:global_step/sec: 9.02825
INFO:tensorflow:global_step/sec: 9.02825
INFO:tensorflow:loss = 48.490734, step = 300 (11.076 sec)
INFO:tensorflow:loss = 48.490734, step = 300 (11.076 sec)
INFO:tensorflow:global_step/sec: 9.01342
INFO:tensorflow:global_step/sec: 9.01342
INFO:tensorflow:loss = 44.54372, step = 400 (11.095 sec)
INFO:tensorflow:loss = 44.54372, step = 400 (11.095 sec)
INFO:tensorflow:global_step/sec: 8.952
INFO:tensorflow:global_step/sec: 8.952
INFO:tensorflow:loss = 35.568554, step = 500 (11.171 sec)
INFO:tensorflow:loss = 35.568554, step = 500 (11.171 sec)
INFO:tensorflow:global_step/sec: 9.09908
INFO:tensorflow:global_step/sec: 9.09908
INFO:tensorflow:loss = 42.5132, step = 600 (10.990 sec)
INFO:tensorflow:loss = 42.5132, step = 600 (10.990 sec)
INFO:tensorflow:global_step/sec: 9.02127
INFO:tensorflow:global_step/sec: 9.02127
INFO:tensorflow:loss = 40.52431, step = 700 (11.085 sec)
INFO:tensorflow:loss = 40.52431, step = 700 (11.085 sec)
INFO:tensorflow:global_step/sec: 9.09376
INFO:tensorflow:global_step/sec: 9.09376
INFO:tensorflow:loss = 37.5485, step = 800 (10.996 sec)
INFO:tensorflow:loss = 37.5485, step = 800 (10.996 sec)
INFO:tensorflow:global_step/sec: 9.11679
INFO:tensorflow:global_step/sec: 9.11679
INFO:tensorflow:loss = 32.65558, step = 900 (10.968 sec)
INFO:tensorflow:loss = 32.65558, step = 900 (10.968 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20220107-182759/model.ckpt.
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20220107-182759/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...
INFO:tensorflow:Loss for final step: 46.92047.
INFO:tensorflow:Loss for final step: 46.92047.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
2022-01-07 18:30:32.176628: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 26 into an existing graph with producer version 987. Shape inference will have run different parts of the graph with different producer versions.
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
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
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/train/20220107-182759/model.ckpt-1000
INFO:tensorflow:Restoring parameters from /tmp/train/20220107-182759/model.ckpt-1000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1641580231/saved_model.pb
INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1641580231/saved_model.pb
WARNING:absl:Tensorflow version (2.8.0-rc0) found. Note that TFMA support for TF 2.0 is currently in beta
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1641580231/variables/variables
INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1641580231/variables/variables

widget_view.render_fairness_indicator(eval_result=eval_result_use)

FairnessIndicatorViewer(slicingMetrics=[{'sliceValue': 'Overall', 'slice': 'Overall', 'metrics': {'post_export…

مقایسه جاسازی ها

همچنین می توانید از Fairness Indicators برای مقایسه مستقیم جاسازی ها استفاده کنید. به عنوان مثال، مدل های تولید شده از تعبیه های NNLM و USE را مقایسه کنید.

widget_view.render_fairness_indicator(multi_eval_results={'nnlm': eval_result_nnlm, 'use': eval_result_use})

FairnessIndicatorViewer(evalName='nnlm', evalNameCompare='use', slicingMetrics=[{'sliceValue': 'Overall', 'sli…