مشاهده در TensorFlow.org | در Google Colab اجرا شود | در GitHub مشاهده کنید | دانلود دفترچه یادداشت |
بررسی اجمالی
در این فعالیت، شما با استفاده از شاخص عدالت برای TensorBoard . با این افزونه، میتوانید ارزیابیهای عادلانه را برای اجراهای خود تجسم کنید و به راحتی عملکرد را در گروهها مقایسه کنید.
واردات
کد زیر را برای نصب کتابخانه های مورد نیاز اجرا کنید.
pip install -q -U pip==20.2
pip install fairness_indicators 'absl-py<0.9,>=0.7'
pip install google-api-python-client==1.8.3
pip install tensorboard-plugin-fairness-indicators
pip install tensorflow-serving-api==2.7.0
زمان اجرا را مجدداً راه اندازی کنید. پس از شروع مجدد زمان اجرا، بدون اجرای مجدد سلول قبلی، به سلول های زیر ادامه دهید.
# %tf.disable_v2_behavior() # Uncomment this line if running in Google Colab.
import datetime
import os
import tempfile
from tensorboard_plugin_fairness_indicators import summary_v2
import tensorflow.compat.v1 as tf
# example_model.py is provided in fairness_indicators package to train and
# evaluate an example model.
from fairness_indicators import example_model
tf.compat.v1.enable_eager_execution()
داده ها و ثابت ها
# To know about dataset, check Fairness Indicators Example Colab at:
# https://github.com/tensorflow/fairness-indicators/blob/master/g3doc/tutorials/Fairness_Indicators_Example_Colab.ipynb
train_tf_file = tf.keras.utils.get_file('train.tf', 'https://storage.googleapis.com/civil_comments_dataset/train_tf_processed.tfrecord')
validate_tf_file = tf.keras.utils.get_file('validate.tf', 'https://storage.googleapis.com/civil_comments_dataset/validate_tf_processed.tfrecord')
BASE_DIR = tempfile.gettempdir()
TEXT_FEATURE = 'comment_text'
LABEL = 'toxicity'
FEATURE_MAP = {
# Label:
LABEL: tf.io.FixedLenFeature([], tf.float32),
# Text:
TEXT_FEATURE: tf.io.FixedLenFeature([], tf.string),
# Identities:
'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),
}
Downloading data from https://storage.googleapis.com/civil_comments_dataset/train_tf_processed.tfrecord 488161280/488153424 [==============================] - 11s 0us/step 488169472/488153424 [==============================] - 11s 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
مدل را آموزش دهید
model_dir = os.path.join(BASE_DIR, 'train',
datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
classifier = example_model.train_model(model_dir,
train_tf_file,
LABEL,
TEXT_FEATURE,
FEATURE_MAP)
INFO:tensorflow:Using default config. INFO:tensorflow:Using default config. INFO:tensorflow:Using config: {'_model_dir': '/tmp/train/20220107-180912', '_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/20220107-180912', '_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 18:09:22.433489: 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/20220107-180912/model.ckpt. INFO:tensorflow:Saving checkpoints for 0 into /tmp/train/20220107-180912/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:loss = 58.65023, step = 0 INFO:tensorflow:loss = 58.65023, step = 0 INFO:tensorflow:global_step/sec: 76.6785 INFO:tensorflow:global_step/sec: 76.6785 INFO:tensorflow:loss = 55.854782, step = 100 (1.306 sec) INFO:tensorflow:loss = 55.854782, step = 100 (1.306 sec) INFO:tensorflow:global_step/sec: 82.6 INFO:tensorflow:global_step/sec: 82.6 INFO:tensorflow:loss = 47.47064, step = 200 (1.210 sec) INFO:tensorflow:loss = 47.47064, step = 200 (1.210 sec) INFO:tensorflow:global_step/sec: 85.8208 INFO:tensorflow:global_step/sec: 85.8208 INFO:tensorflow:loss = 55.59231, step = 300 (1.166 sec) INFO:tensorflow:loss = 55.59231, step = 300 (1.166 sec) INFO:tensorflow:global_step/sec: 86.1252 INFO:tensorflow:global_step/sec: 86.1252 INFO:tensorflow:loss = 56.18415, step = 400 (1.161 sec) INFO:tensorflow:loss = 56.18415, step = 400 (1.161 sec) INFO:tensorflow:global_step/sec: 86.5735 INFO:tensorflow:global_step/sec: 86.5735 INFO:tensorflow:loss = 42.37696, step = 500 (1.155 sec) INFO:tensorflow:loss = 42.37696, step = 500 (1.155 sec) INFO:tensorflow:global_step/sec: 86.6948 INFO:tensorflow:global_step/sec: 86.6948 INFO:tensorflow:loss = 45.75257, step = 600 (1.153 sec) INFO:tensorflow:loss = 45.75257, step = 600 (1.153 sec) INFO:tensorflow:global_step/sec: 87.1878 INFO:tensorflow:global_step/sec: 87.1878 INFO:tensorflow:loss = 50.73873, step = 700 (1.147 sec) INFO:tensorflow:loss = 50.73873, step = 700 (1.147 sec) INFO:tensorflow:global_step/sec: 85.2284 INFO:tensorflow:global_step/sec: 85.2284 INFO:tensorflow:loss = 47.609695, step = 800 (1.173 sec) INFO:tensorflow:loss = 47.609695, step = 800 (1.173 sec) INFO:tensorflow:global_step/sec: 85.6373 INFO:tensorflow:global_step/sec: 85.6373 INFO:tensorflow:loss = 48.22233, step = 900 (1.168 sec) INFO:tensorflow:loss = 48.22233, step = 900 (1.168 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/20220107-180912/model.ckpt. INFO:tensorflow:Saving checkpoints for 1000 into /tmp/train/20220107-180912/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: 51.06088. INFO:tensorflow:Loss for final step: 51.06088.
تجزیه و تحلیل مدل TensorFlow را با شاخص های انصاف اجرا کنید
این مرحله ممکن است 2 تا 5 دقیقه طول بکشد.
tfma_eval_result_path = os.path.join(BASE_DIR, 'tfma_eval_result')
example_model.evaluate_model(classifier,
validate_tf_file,
tfma_eval_result_path,
'gender',
LABEL,
FEATURE_MAP)
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 18:09:39.350323: 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/20220107-180912/model.ckpt-1000 INFO:tensorflow:Restoring parameters from /tmp/train/20220107-180912/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-1641578979/assets INFO:tensorflow:Assets written to: /tmp/tfma_eval_model/temp-1641578979/assets INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1641578979/saved_model.pb INFO:tensorflow:SavedModel written to: /tmp/tfma_eval_model/temp-1641578979/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/1641578979/variables/variables INFO:tensorflow:Restoring parameters from /tmp/tfma_eval_model/1641578979/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)`
نشانگرهای انصاف را در TensorBoard تجسم کنید
در زیر شما نشانگرهای انصاف را در Tensorboard تجسم میکنید و عملکرد هر بخش از دادهها را در معیارهای انتخابی مقایسه میکنید. می توانید با استفاده از منوهای کشویی در بالای تجسم، برش مقایسه پایه و همچنین آستانه(های) نمایش داده شده را تنظیم کنید. همچنین می توانید با استفاده از منوی کشویی در گوشه بالا سمت چپ، مراحل ارزیابی مختلف را انتخاب کنید.
خلاصه شاخص های انصاف را بنویسید
فایل خلاصه ای بنویسید که حاوی تمام اطلاعات لازم برای تجسم نشانگرهای عدالت در TensorBoard باشد.
import tensorflow.compat.v2 as tf2
writer = tf2.summary.create_file_writer(
os.path.join(model_dir, 'fairness_indicators'))
with writer.as_default():
summary_v2.FairnessIndicators(tfma_eval_result_path, step=1)
writer.close()
TensorBoard را راه اندازی کنید
برای تجسم نشانگرهای انصاف، به تب "شاخص های انصاف" بروید.
%load_ext tensorboard
%tensorboard --logdir=$model_dir