Expose differentially private RMSPropOptimizer.

PiperOrigin-RevId: 311072544
Kritikalcoder · May 12, 2020 · da9fb28 · da9fb28
1 parent 10335f6
commit da9fb28
Show file tree

Hide file tree

Showing 2 changed files with 20 additions and 6 deletions.
diff --git a/tensorflow_privacy/privacy/optimizers/dp_optimizer.py b/tensorflow_privacy/privacy/optimizers/dp_optimizer.py
@@ -228,12 +228,15 @@ def ledger(self):
 AdagradOptimizer = tf.train.AdagradOptimizer
 AdamOptimizer = tf.train.AdamOptimizer
 GradientDescentOptimizer = tf.train.GradientDescentOptimizer
+RMSPropOptimizer = tf.train.RMSPropOptimizer
 
 DPAdagradOptimizer = make_optimizer_class(AdagradOptimizer)
 DPAdamOptimizer = make_optimizer_class(AdamOptimizer)
 DPGradientDescentOptimizer = make_optimizer_class(GradientDescentOptimizer)
+DPRMSPropOptimizer = make_optimizer_class(RMSPropOptimizer)
 
 DPAdagradGaussianOptimizer = make_gaussian_optimizer_class(AdagradOptimizer)
 DPAdamGaussianOptimizer = make_gaussian_optimizer_class(AdamOptimizer)
 DPGradientDescentGaussianOptimizer = make_gaussian_optimizer_class(
     GradientDescentOptimizer)
+DPRMSPropGaussianOptimizer = make_gaussian_optimizer_class(RMSPropOptimizer)
diff --git a/tensorflow_privacy/privacy/optimizers/dp_optimizer_test.py b/tensorflow_privacy/privacy/optimizers/dp_optimizer_test.py
@@ -48,7 +48,13 @@ def _loss(self, val0, val1):
       ('DPAdagrad 4', dp_optimizer.DPAdagradOptimizer, 4, [-2.5, -2.5]),
       ('DPAdam 1', dp_optimizer.DPAdamOptimizer, 1, [-2.5, -2.5]),
       ('DPAdam 2', dp_optimizer.DPAdamOptimizer, 2, [-2.5, -2.5]),
-      ('DPAdam 4', dp_optimizer.DPAdamOptimizer, 4, [-2.5, -2.5]))
+      ('DPAdam 4', dp_optimizer.DPAdamOptimizer, 4, [-2.5, -2.5]),
+      ('DPRMSPropOptimizer 1', dp_optimizer.DPRMSPropOptimizer, 1,
+       [-2.5, -2.5]),
+      ('DPRMSPropOptimizer 2', dp_optimizer.DPRMSPropOptimizer, 2,
+       [-2.5, -2.5]),
+      ('DPRMSPropOptimizer 4', dp_optimizer.DPRMSPropOptimizer, 4, [-2.5, -2.5])
+  )
   def testBaseline(self, cls, num_microbatches, expected_answer):
     with self.cached_session() as sess:
       var0 = tf.Variable([1.0, 2.0])
@@ -76,7 +82,8 @@ def testBaseline(self, cls, num_microbatches, expected_answer):
   @parameterized.named_parameters(
       ('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
       ('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
-      ('DPAdam', dp_optimizer.DPAdamOptimizer))
+      ('DPAdam', dp_optimizer.DPAdamOptimizer),
+      ('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer))
   def testClippingNorm(self, cls):
     with self.cached_session() as sess:
       var0 = tf.Variable([0.0, 0.0])
@@ -99,7 +106,8 @@ def testClippingNorm(self, cls):
   @parameterized.named_parameters(
       ('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
       ('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
-      ('DPAdam', dp_optimizer.DPAdamOptimizer))
+      ('DPAdam', dp_optimizer.DPAdamOptimizer),
+      ('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer))
   def testNoiseMultiplier(self, cls):
     with self.cached_session() as sess:
       var0 = tf.Variable([0.0])
@@ -182,7 +190,8 @@ def linear_model_fn(features, labels, mode):
   @parameterized.named_parameters(
       ('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
       ('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
-      ('DPAdam', dp_optimizer.DPAdamOptimizer))
+      ('DPAdam', dp_optimizer.DPAdamOptimizer),
+      ('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer))
   def testUnrollMicrobatches(self, cls):
     with self.cached_session() as sess:
       var0 = tf.Variable([1.0, 2.0])
@@ -213,7 +222,8 @@ def testUnrollMicrobatches(self, cls):
   @parameterized.named_parameters(
       ('DPGradientDescent', dp_optimizer.DPGradientDescentGaussianOptimizer),
       ('DPAdagrad', dp_optimizer.DPAdagradGaussianOptimizer),
-      ('DPAdam', dp_optimizer.DPAdamGaussianOptimizer))
+      ('DPAdam', dp_optimizer.DPAdamGaussianOptimizer),
+      ('DPRMSPropOptimizer', dp_optimizer.DPRMSPropGaussianOptimizer))
   def testDPGaussianOptimizerClass(self, cls):
     with self.cached_session() as sess:
       var0 = tf.Variable([0.0])
@@ -241,7 +251,8 @@ def testDPGaussianOptimizerClass(self, cls):
   @parameterized.named_parameters(
       ('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
       ('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
-      ('DPAdam', dp_optimizer.DPAdamOptimizer))
+      ('DPAdam', dp_optimizer.DPAdamOptimizer),
+      ('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer))
   def testAssertOnNoCallOfComputeGradients(self, cls):
     dp_sum_query = gaussian_query.GaussianSumQuery(1.0e9, 0.0)
     opt = cls(dp_sum_query, num_microbatches=1, learning_rate=1.0)