Update to use TF 2.0 API in TensorFlow Privacy:

tf.logging -> Removed for absl
tf.assert_type -> tf.debugging.assert_type
tf.assert_less_equal -> tf.debugging.assert_less_equal
tf.global_norm -> tf.linalg.global_norm

PiperOrigin-RevId: 425730344

tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/tf_estimator_evaluation_example.py

@@ -17,7 +17,7 @@ from absl import app
 from absl import flags
 from absl import logging
 import numpy as np
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
 
 from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType
 from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import get_flattened_attack_metrics
@@ -91,10 +91,8 @@ def load_cifar10():
 
 
 def main(unused_argv):
-  tf.logging.set_verbosity(tf.logging.ERROR)
+  logger = tf.get_logger()
-  logging.set_verbosity(logging.ERROR)
+  logger.set_level(logging.ERROR)
-  logging.set_stderrthreshold(logging.ERROR)
-  logging.get_absl_handler().use_absl_log_file()
 
   # Load training and test data.
   x_train, y_train, x_test, y_test = load_cifar10()

tutorials/lm_dpsgd_tutorial.py

@@ -34,10 +34,10 @@ import os
 
 from absl import app
 from absl import flags
+from absl import logging
 import numpy as np
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
 import tensorflow_datasets as tfds
-
 from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
 from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
 from tensorflow_privacy.privacy.optimizers import dp_optimizer
@@ -157,7 +157,9 @@ def compute_epsilon(steps):
 
 
 def main(unused_argv):
-  tf.logging.set_verbosity(tf.logging.INFO)
+  logger = tf.get_logger()
+  logger.set_level(logging.INFO)
+
   if FLAGS.batch_size % FLAGS.microbatches != 0:
     raise ValueError('Number of microbatches should divide evenly batch_size')
 

tutorials/mnist_dpsgd_tutorial_vectorized.py

@@ -15,9 +15,9 @@
 
 from absl import app
 from absl import flags
+from absl import logging
 import numpy as np
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
-
 from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
 from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
 from tensorflow_privacy.privacy.optimizers import dp_optimizer_vectorized
@@ -140,7 +140,9 @@ def load_mnist():
 
 
 def main(unused_argv):
-  tf.logging.set_verbosity(tf.logging.INFO)
+  logger = tf.get_logger()
+  logger.set_level(logging.INFO)
+
   if FLAGS.dpsgd and FLAGS.batch_size % FLAGS.microbatches != 0:
     raise ValueError('Number of microbatches should divide evenly batch_size')
 

tutorials/mnist_lr_tutorial.py

@@ -25,10 +25,9 @@ import math
 
 from absl import app
 from absl import flags
-
+from absl import logging
 import numpy as np
 import tensorflow.compat.v1 as tf
-
 from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
 from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
 from tensorflow_privacy.privacy.optimizers import dp_optimizer
@@ -178,7 +177,9 @@ def print_privacy_guarantees(epochs, batch_size, samples, noise_multiplier):
 
 
 def main(unused_argv):
-  tf.logging.set_verbosity(tf.logging.INFO)
+  logger = tf.get_logger()
+  logger.set_level(logging.INFO)
+
   if FLAGS.data_l2_norm <= 0:
     raise ValueError('data_l2_norm must be positive.')
   if FLAGS.dpsgd and FLAGS.learning_rate > 8 / FLAGS.data_l2_norm**2:

tutorials/walkthrough/mnist_scratch.py

@@ -13,8 +13,9 @@
 # limitations under the License.
 """Scratchpad for training a CNN on MNIST with DPSGD."""
 
+from absl import logging
 import numpy as np
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
 
 tf.flags.DEFINE_float('learning_rate', .15, 'Learning rate for training')
 tf.flags.DEFINE_integer('batch_size', 256, 'Batch size')
@@ -86,7 +87,8 @@ def load_mnist():
 
 
 def main(unused_argv):
-  tf.logging.set_verbosity(tf.logging.INFO)
+  logger = tf.get_logger()
+  logger.set_level(logging.INFO)
 
   # Load training and test data.
   train_data, train_labels, test_data, test_labels = load_mnist()