Small updates in preparation for auto-generating documentation.

PiperOrigin-RevId: 367073829

tensorflow_privacy/privacy/optimizers/dp_optimizer.py

@@ -53,14 +53,14 @@ def make_optimizer_class(cls):
       """Initialize the DPOptimizerClass.
 
       Args:
-        dp_sum_query: DPQuery object, specifying differential privacy
+        dp_sum_query: `DPQuery` object, specifying differential privacy
           mechanism to use.
-        num_microbatches: How many microbatches into which the minibatch is
+        num_microbatches: Number of microbatches into which each minibatch is
-          split. If None, will default to the size of the minibatch, and
+          split. If `None`, will default to the size of the minibatch, and
           per-example gradients will be computed.
         unroll_microbatches: If true, processes microbatches within a Python
-          loop instead of a tf.while_loop. Can be used if using a tf.while_loop
+          loop instead of a `tf.while_loop`. Can be used if using a
-          raises an exception.
+          `tf.while_loop` raises an exception.
       """
       super(DPOptimizerClass, self).__init__(*args, **kwargs)
       self._dp_sum_query = dp_sum_query
@@ -205,6 +205,8 @@ def make_optimizer_class(cls):
       return super(DPOptimizerClass,
                    self).apply_gradients(grads_and_vars, global_step, name)
 
+  DPOptimizerClass.__doc__ = ('DP subclass of {}.').format(cls.__name__)
+
   return DPOptimizerClass
 
 
@@ -265,6 +267,9 @@ def make_gaussian_optimizer_class(cls):
     def ledger(self):
       return self._dp_sum_query.ledger
 
+  DPGaussianOptimizerClass.__doc__ = ('DP subclass of {} using Gaussian '
+                                      'averaging.').format(cls.__name__)
+
   return DPGaussianOptimizerClass
 
 AdagradOptimizer = tf.train.AdagradOptimizer

tensorflow_privacy/privacy/optimizers/dp_optimizer_keras.py

@@ -49,9 +49,9 @@ def make_keras_optimizer_class(cls):
       """Initialize the DPOptimizerClass.
 
       Args:
-        l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients)
+        l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients).
-        noise_multiplier: Ratio of the standard deviation to the clipping norm
+        noise_multiplier: Ratio of the standard deviation to the clipping norm.
-        num_microbatches: The number of microbatches into which each minibatch
+        num_microbatches: Number of microbatches into which each minibatch
           is split.
       """
       super(DPOptimizerClass, self).__init__(*args, **kwargs)
@@ -169,6 +169,9 @@ def make_keras_optimizer_class(cls):
       return super(DPOptimizerClass,
                    self).apply_gradients(grads_and_vars, global_step, name)
 
+  DPOptimizerClass.__doc__ = ('DP subclass of {} using Gaussian '
+                              'averaging.').format(cls.__name__)
+
   return DPOptimizerClass
 
 

tensorflow_privacy/privacy/optimizers/dp_optimizer_keras_vectorized.py

@@ -62,9 +62,9 @@ def make_vectorized_keras_optimizer_class(cls):
       """Initialize the DPOptimizerClass.
 
       Args:
-        l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients)
+        l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients).
-        noise_multiplier: Ratio of the standard deviation to the clipping norm
+        noise_multiplier: Ratio of the standard deviation to the clipping norm.
-        num_microbatches: The number of microbatches into which each minibatch
+        num_microbatches: Number of microbatches into which each minibatch
           is split.
       """
       super(DPOptimizerClass, self).__init__(*args, **kwargs)
@@ -177,6 +177,8 @@ def make_vectorized_keras_optimizer_class(cls):
       return super(DPOptimizerClass,
                    self).apply_gradients(grads_and_vars, global_step, name)
 
+  DPOptimizerClass.__doc__ = ('Vectorized DP subclass of {} using Gaussian '
+                              'averaging.').format(cls.__name__)
   return DPOptimizerClass
 
 

tensorflow_privacy/privacy/optimizers/dp_optimizer_vectorized.py

@@ -51,10 +51,10 @@ def make_vectorized_optimizer_class(cls):
       """Initialize the DPOptimizerClass.
 
       Args:
-        l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients)
+        l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients).
-        noise_multiplier: Ratio of the standard deviation to the clipping norm
+        noise_multiplier: Ratio of the standard deviation to the clipping norm.
-        num_microbatches: How many microbatches into which the minibatch is
+        num_microbatches: Number of microbatches into which each minibatch is
-          split. If None, will default to the size of the minibatch, and
+          split. If `None`, will default to the size of the minibatch, and
           per-example gradients will be computed.
       """
       super(DPOptimizerClass, self).__init__(*args, **kwargs)
@@ -136,6 +136,8 @@ def make_vectorized_optimizer_class(cls):
 
         return list(zip(final_grads, var_list))
 
+  DPOptimizerClass.__doc__ = ('Vectorized DP subclass of {} using Gaussian '
+                              'averaging.').format(cls.__name__)
   return DPOptimizerClass