Clean-up pass to eliminate warnings: replacing deprecated endpoints with recommended versions and annotating test sizes.

PiperOrigin-RevId: 246901723

privacy/dp_query/BUILD

@@ -13,17 +13,21 @@ py_library(
     deps = [
         ":dp_query",
         ":normalized_query",
+        "//third_party/py/distutils",
         "//third_party/py/tensorflow",
     ],
 )
 
 py_test(
     name = "gaussian_query_test",
+    size = "small",
     srcs = ["gaussian_query_test.py"],
     deps = [
         ":gaussian_query",
         ":test_utils",
         "//third_party/py/absl/testing:parameterized",
+        "//third_party/py/numpy",
+        "//third_party/py/six",
         "//third_party/py/tensorflow",
     ],
 )
@@ -33,12 +37,14 @@ py_library(
     srcs = ["no_privacy_query.py"],
     deps = [
         ":dp_query",
+        "//third_party/py/distutils",
         "//third_party/py/tensorflow",
     ],
 )
 
 py_test(
     name = "no_privacy_query_test",
+    size = "small",
     srcs = ["no_privacy_query_test.py"],
     deps = [
         ":no_privacy_query",
@@ -53,12 +59,14 @@ py_library(
     srcs = ["normalized_query.py"],
     deps = [
         ":dp_query",
+        "//third_party/py/distutils",
         "//third_party/py/tensorflow",
     ],
 )
 
 py_test(
     name = "normalized_query_test",
+    size = "small",
     srcs = ["normalized_query_test.py"],
     deps = [
         ":gaussian_query",
@@ -73,18 +81,22 @@ py_library(
     srcs = ["nested_query.py"],
     deps = [
         ":dp_query",
+        "//third_party/py/distutils",
         "//third_party/py/tensorflow",
     ],
 )
 
 py_test(
     name = "nested_query_test",
+    size = "small",
     srcs = ["nested_query_test.py"],
     deps = [
         ":gaussian_query",
         ":nested_query",
         ":test_utils",
         "//third_party/py/absl/testing:parameterized",
+        "//third_party/py/distutils",
+        "//third_party/py/numpy",
         "//third_party/py/tensorflow",
     ],
 )

privacy/optimizers/dp_optimizer.py

@@ -27,9 +27,9 @@ from privacy.dp_query import gaussian_query
 def make_optimizer_class(cls):
   """Constructs a DP optimizer class from an existing one."""
   if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
-    parent_code = tf.train.Optimizer.compute_gradients.__code__
+    parent_code = tf.compat.v1.train.Optimizer.compute_gradients.__code__
     child_code = cls.compute_gradients.__code__
-    GATE_OP = tf.train.Optimizer.GATE_OP  # pylint: disable=invalid-name
+    GATE_OP = tf.compat.v1.train.Optimizer.GATE_OP  # pylint: disable=invalid-name
   else:
     parent_code = tf.optimizers.Optimizer._compute_gradients.__code__  # pylint: disable=protected-access
     child_code = cls._compute_gradients.__code__  # pylint: disable=protected-access
@@ -211,9 +211,9 @@ def make_gaussian_optimizer_class(cls):
 
 # Compatibility with tf 1 and 2 APIs
 try:
-  AdagradOptimizer = tf.train.AdagradOptimizer
+  AdagradOptimizer = tf.compat.v1.train.AdagradOptimizer
-  AdamOptimizer = tf.train.AdamOptimizer
+  AdamOptimizer = tf.compat.v1.train.AdamOptimizer
-  GradientDescentOptimizer = tf.train.GradientDescentOptimizer
+  GradientDescentOptimizer = tf.compat.v1.train.GradientDescentOptimizer
 except:  # pylint: disable=bare-except
   AdagradOptimizer = tf.optimizers.Adagrad
   AdamOptimizer = tf.optimizers.Adam

tutorials/lm_dpsgd_tutorial.py

@@ -36,6 +36,10 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+
+from absl import app
+from absl import flags
+
 import numpy as np
 import tensorflow as tf
 import tensorflow_datasets as tfds
@@ -45,20 +49,22 @@ from privacy.analysis.rdp_accountant import compute_rdp
 from privacy.analysis.rdp_accountant import get_privacy_spent
 from privacy.optimizers import dp_optimizer
 
-tf.flags.DEFINE_boolean('dpsgd', True, 'If True, train with DP-SGD. If False, '
+flags.DEFINE_boolean(
-                        'train with vanilla SGD.')
+    'dpsgd', True, 'If True, train with DP-SGD. If False, '
-tf.flags.DEFINE_float('learning_rate', 0.001, 'Learning rate for training')
+    'train with vanilla SGD.')
-tf.flags.DEFINE_float('noise_multiplier', 0.001,
+flags.DEFINE_float('learning_rate', 0.001, 'Learning rate for training')
-                      'Ratio of the standard deviation to the clipping norm')
+flags.DEFINE_float('noise_multiplier', 0.001,
-tf.flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
+                   'Ratio of the standard deviation to the clipping norm')
-tf.flags.DEFINE_integer('batch_size', 256, 'Batch size')
+flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
-tf.flags.DEFINE_integer('epochs', 60, 'Number of epochs')
+flags.DEFINE_integer('batch_size', 256, 'Batch size')
-tf.flags.DEFINE_integer('microbatches', 256, 'Number of microbatches '
+flags.DEFINE_integer('epochs', 60, 'Number of epochs')
-                        '(must evenly divide batch_size)')
+flags.DEFINE_integer(
-tf.flags.DEFINE_string('model_dir', None, 'Model directory')
+    'microbatches', 256, 'Number of microbatches '
-tf.flags.DEFINE_string('data_dir', None, 'Directory containing the PTB data.')
+    '(must evenly divide batch_size)')
+flags.DEFINE_string('model_dir', None, 'Model directory')
+flags.DEFINE_string('data_dir', None, 'Directory containing the PTB data.')
 
-FLAGS = tf.flags.FLAGS
+FLAGS = flags.FLAGS
 
 SEQ_LEN = 80
 NB_TRAIN = 45000
@@ -217,4 +223,4 @@ def main(unused_argv):
       print('Trained with vanilla non-private SGD optimizer')
 
 if __name__ == '__main__':
-  tf.app.run()
+  app.run(main)

tutorials/mnist_dpsgd_tutorial.py

@@ -18,6 +18,9 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from absl import app
+from absl import flags
+
 import numpy as np
 import tensorflow as tf
 
@@ -28,26 +31,28 @@ from privacy.optimizers import dp_optimizer
 
 # Compatibility with tf 1 and 2 APIs
 try:
-  GradientDescentOptimizer = tf.train.GradientDescentOptimizer
+  GradientDescentOptimizer = tf.compat.v1.train.GradientDescentOptimizer
 except:  # pylint: disable=bare-except
   GradientDescentOptimizer = tf.optimizers.SGD  # pylint: disable=invalid-name
 
-tf.flags.DEFINE_boolean('dpsgd', True, 'If True, train with DP-SGD. If False, '
+FLAGS = flags.FLAGS
-                        'train with vanilla SGD.')
-tf.flags.DEFINE_float('learning_rate', .15, 'Learning rate for training')
-tf.flags.DEFINE_float('noise_multiplier', 1.1,
-                      'Ratio of the standard deviation to the clipping norm')
-tf.flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
-tf.flags.DEFINE_integer('batch_size', 256, 'Batch size')
-tf.flags.DEFINE_integer('epochs', 60, 'Number of epochs')
-tf.flags.DEFINE_integer('microbatches', 256, 'Number of microbatches '
-                        '(must evenly divide batch_size)')
-tf.flags.DEFINE_string('model_dir', None, 'Model directory')
 
-FLAGS = tf.flags.FLAGS
+flags.DEFINE_boolean(
+    'dpsgd', True, 'If True, train with DP-SGD. If False, '
+    'train with vanilla SGD.')
+flags.DEFINE_float('learning_rate', .15, 'Learning rate for training')
+flags.DEFINE_float('noise_multiplier', 1.1,
+                   'Ratio of the standard deviation to the clipping norm')
+flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
+flags.DEFINE_integer('batch_size', 256, 'Batch size')
+flags.DEFINE_integer('epochs', 60, 'Number of epochs')
+flags.DEFINE_integer(
+    'microbatches', 256, 'Number of microbatches '
+    '(must evenly divide batch_size)')
+flags.DEFINE_string('model_dir', None, 'Model directory')
 
 
-class EpsilonPrintingTrainingHook(tf.train.SessionRunHook):
+class EpsilonPrintingTrainingHook(tf.estimator.SessionRunHook):
   """Training hook to print current value of epsilon after an epoch."""
 
   def __init__(self, ledger):
@@ -203,4 +208,4 @@ def main(unused_argv):
     print('Test accuracy after %d epochs is: %.3f' % (epoch, test_accuracy))
 
 if __name__ == '__main__':
-  tf.app.run()
+  app.run(main)

tutorials/mnist_dpsgd_tutorial_eager.py

@@ -18,7 +18,9 @@ from __future__ import print_function
 
 from absl import app
 from absl import flags
+
 from distutils.version import LooseVersion
+
 import numpy as np
 import tensorflow as tf
 
@@ -28,8 +30,8 @@ from privacy.dp_query.gaussian_query import GaussianAverageQuery
 from privacy.optimizers.dp_optimizer import DPGradientDescentOptimizer
 
 if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
-  GradientDescentOptimizer = tf.train.GradientDescentOptimizer
+  GradientDescentOptimizer = tf.compat.v1.train.GradientDescentOptimizer
-  tf.enable_eager_execution()
+  tf.compat.v1.enable_eager_execution()
 else:
   GradientDescentOptimizer = tf.optimizers.SGD  # pylint: disable=invalid-name
 

tutorials/mnist_dpsgd_tutorial_keras.py

@@ -17,6 +17,9 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from absl import app
+from absl import flags
+
 import numpy as np
 import tensorflow as tf
 
@@ -27,23 +30,25 @@ from privacy.optimizers.dp_optimizer import DPGradientDescentOptimizer
 
 # Compatibility with tf 1 and 2 APIs
 try:
-  GradientDescentOptimizer = tf.train.GradientDescentOptimizer
+  GradientDescentOptimizer = tf.compat.v1.train.GradientDescentOptimizer
 except:  # pylint: disable=bare-except
   GradientDescentOptimizer = tf.optimizers.SGD  # pylint: disable=invalid-name
 
-tf.flags.DEFINE_boolean('dpsgd', True, 'If True, train with DP-SGD. If False, '
+flags.DEFINE_boolean(
-                        'train with vanilla SGD.')
+    'dpsgd', True, 'If True, train with DP-SGD. If False, '
-tf.flags.DEFINE_float('learning_rate', 0.15, 'Learning rate for training')
+    'train with vanilla SGD.')
-tf.flags.DEFINE_float('noise_multiplier', 1.1,
+flags.DEFINE_float('learning_rate', 0.15, 'Learning rate for training')
-                      'Ratio of the standard deviation to the clipping norm')
+flags.DEFINE_float('noise_multiplier', 1.1,
-tf.flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
+                   'Ratio of the standard deviation to the clipping norm')
-tf.flags.DEFINE_integer('batch_size', 250, 'Batch size')
+flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
-tf.flags.DEFINE_integer('epochs', 60, 'Number of epochs')
+flags.DEFINE_integer('batch_size', 250, 'Batch size')
-tf.flags.DEFINE_integer('microbatches', 250, 'Number of microbatches '
+flags.DEFINE_integer('epochs', 60, 'Number of epochs')
-                        '(must evenly divide batch_size)')
+flags.DEFINE_integer(
-tf.flags.DEFINE_string('model_dir', None, 'Model directory')
+    'microbatches', 250, 'Number of microbatches '
+    '(must evenly divide batch_size)')
+flags.DEFINE_string('model_dir', None, 'Model directory')
 
-FLAGS = tf.flags.FLAGS
+FLAGS = flags.FLAGS
 
 
 def compute_epsilon(steps):
@@ -146,4 +151,4 @@ def main(unused_argv):
     print('Trained with vanilla non-private SGD optimizer')
 
 if __name__ == '__main__':
-  tf.app.run()
+  app.run(main)