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Small updates in preparation for auto-generating documentation.

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PiperOrigin-RevId: 367073829
This commit is contained in:
Steve Chien 2021-04-06 13:29:05 -07:00 committed by A. Unique TensorFlower
parent 693dd666c3
commit c8b1c97b47
4 changed files with 27 additions and 15 deletions
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15
tensorflow_privacy/privacy/optimizers/dp_optimizer.py
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@ -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
split. If None, will default to the size of the minibatch, and
num_microbatches: Number of microbatches into which each minibatch is
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
raises an exception.
loop instead of a `tf.while_loop`. Can be used if using a
`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

9
tensorflow_privacy/privacy/optimizers/dp_optimizer_keras.py
View file

@ -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)
noise_multiplier: Ratio of the standard deviation to the clipping norm
num_microbatches: The number of microbatches into which each minibatch
l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients).
noise_multiplier: Ratio of the standard deviation to the clipping norm.
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

8
tensorflow_privacy/privacy/optimizers/dp_optimizer_keras_vectorized.py
View file

@ -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)
noise_multiplier: Ratio of the standard deviation to the clipping norm
num_microbatches: The number of microbatches into which each minibatch
l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients).
noise_multiplier: Ratio of the standard deviation to the clipping norm.
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

10
tensorflow_privacy/privacy/optimizers/dp_optimizer_vectorized.py
View file

@ -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)
noise_multiplier: Ratio of the standard deviation to the clipping norm
num_microbatches: How many microbatches into which the minibatch is
split. If None, will default to the size of the minibatch, and
l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients).
noise_multiplier: Ratio of the standard deviation to the clipping norm.
num_microbatches: Number of microbatches into which each minibatch is
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