Fix usage of logging API.

PiperOrigin-RevId: 463123944

tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/tf_estimator_evaluation_example.py

@@ -92,8 +92,7 @@ def load_cifar10():
 
 
 def main(unused_argv):
-  logger = tf.get_logger()
-  logger.set_level(logging.ERROR)
+  logging.set_verbosity(logging.ERROR)
 
   # Load training and test data.
   x_train, y_train, x_test, y_test = load_cifar10()

tutorials/lm_dpsgd_tutorial.py

@@ -163,8 +163,7 @@ def compute_epsilon(steps):
 
 
 def main(unused_argv):
-  logger = tf.get_logger()
-  logger.set_level(logging.INFO)
+  logging.set_verbosity(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

@@ -146,8 +146,7 @@ def load_mnist():
 
 
 def main(unused_argv):
-  logger = tf.get_logger()
-  logger.set_level(logging.INFO)
+  logging.set_verbosity(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

@@ -183,8 +183,7 @@ def print_privacy_guarantees(epochs, batch_size, samples, noise_multiplier):
 
 
 def main(unused_argv):
-  logger = tf.get_logger()
-  logger.set_level(logging.INFO)
+  logging.set_verbosity(logging.INFO)
 
   if FLAGS.data_l2_norm <= 0:
     raise ValueError('data_l2_norm must be positive.')

tutorials/walkthrough/mnist_scratch.py

@@ -89,8 +89,7 @@ def load_mnist():
 
 
 def main(unused_argv):
-  logger = tf.get_logger()
-  logger.set_level(logging.INFO)
+  logging.set_verbosity(logging.INFO)
 
   # Load training and test data.
   train_data, train_labels, test_data, test_labels = load_mnist()