本文整理汇总了Python中differential_privacy.dp_sgd.dp_optimizer.utils.BatchClipByL2norm方法的典型用法代码示例。如果您正苦于以下问题：Python utils.BatchClipByL2norm方法的具体用法？Python utils.BatchClipByL2norm怎么用？Python utils.BatchClipByL2norm使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类differential_privacy.dp_sgd.dp_optimizer.utils的用法示例。
在下文中一共展示了utils.BatchClipByL2norm方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: sanitize
# 需要导入模块: from differential_privacy.dp_sgd.dp_optimizer import utils [as 别名]
# 或者: from differential_privacy.dp_sgd.dp_optimizer.utils import BatchClipByL2norm [as 别名]
def sanitize(self, x, eps_delta, sigma=None,
option=ClipOption(None, None), tensor_name=None,
num_examples=None, add_noise=True):
"""Sanitize the given tensor.
This santize a given tensor by first applying l2 norm clipping and then
adding Gaussian noise. It calls the privacy accountant for updating the
privacy spending.
Args:
x: the tensor to sanitize.
eps_delta: a pair of eps, delta for (eps,delta)-DP. Use it to
compute sigma if sigma is None.
sigma: if sigma is not None, use sigma.
option: a ClipOption which, if supplied, used for
clipping and adding noise.
tensor_name: the name of the tensor.
num_examples: if None, use the number of "rows" of x.
add_noise: if True, then add noise, else just clip.
Returns:
a pair of sanitized tensor and the operation to accumulate privacy
spending.
"""
if sigma is None:
# pylint: disable=unpacking-non-sequence
eps, delta = eps_delta
with tf.control_dependencies(
[tf.Assert(tf.greater(eps, 0),
["eps needs to be greater than 0"]),
tf.Assert(tf.greater(delta, 0),
["delta needs to be greater than 0"])]):
# The following formula is taken from
# Dwork and Roth, The Algorithmic Foundations of Differential
# Privacy, Appendix A.
# http://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf
sigma = tf.sqrt(2.0 * tf.log(1.25 / delta)) / eps
l2norm_bound, clip = option
if l2norm_bound is None:
l2norm_bound, clip = self._default_option
if ((tensor_name is not None) and
(tensor_name in self._options)):
l2norm_bound, clip = self._options[tensor_name]
if clip:
x = utils.BatchClipByL2norm(x, l2norm_bound)
if add_noise:
if num_examples is None:
num_examples = tf.slice(tf.shape(x), [0], [1])
privacy_accum_op = self._accountant.accumulate_privacy_spending(
eps_delta, sigma, num_examples)
with tf.control_dependencies([privacy_accum_op]):
saned_x = utils.AddGaussianNoise(tf.reduce_sum(x, 0),
sigma * l2norm_bound)
else:
saned_x = tf.reduce_sum(x, 0)
return saned_x 示例2: sanitize
# 需要导入模块: from differential_privacy.dp_sgd.dp_optimizer import utils [as 别名]
# 或者: from differential_privacy.dp_sgd.dp_optimizer.utils import BatchClipByL2norm [as 别名]
def sanitize(self, x, eps_delta, sigma=None,
option=ClipOption(None, None), tensor_name=None,
num_examples=None, add_noise=True):
"""Sanitize the given tensor.
This santize a given tensor by first applying l2 norm clipping and then
adding Gaussian noise. It calls the privacy accountant for updating the
privacy spending.
Args:
x: the tensor to sanitize.
eps_delta: a pair of eps, delta for (eps,delta)-DP. Use it to
compute sigma if sigma is None.
sigma: if sigma is not None, use sigma.
option: a ClipOption which, if supplied, used for
clipping and adding noise.
tensor_name: the name of the tensor.
num_examples: if None, use the number of "rows" of x.
add_noise: if True, then add noise, else just clip.
Returns:
a pair of sanitized tensor and the operation to accumulate privacy
spending.
"""
if sigma is None:
# pylint: disable=unpacking-non-sequence
eps, delta = eps_delta
with tf.control_dependencies(
[tf.Assert(tf.greater(eps, 0),
["eps needs to be greater than 0"]),
tf.Assert(tf.greater(delta, 0),
["delta needs to be greater than 0"])]):
# The following formula is taken from
# Dwork and Roth, The Algorithmic Foundations of Differential
# Privacy, Appendix A.
# http://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf
sigma = tf.sqrt(2.0 * tf.log(1.25 / delta)) / eps
l2norm_bound, clip = option
if l2norm_bound is None:
l2norm_bound, clip = self._default_option
if ((tensor_name is not None) and
(tensor_name in self._options)):
l2norm_bound, clip = self._options[tensor_name]
if clip:
x = utils.BatchClipByL2norm(x, l2norm_bound)
if add_noise:
if num_examples is None:
num_examples = tf.slice(tf.shape(x), [0], [1])
privacy_accum_op = self._accountant.accumulate_privacy_spending(
eps_delta, sigma, num_examples)
with tf.control_dependencies([privacy_accum_op]):
saned_x = utils.AddGaussianNoise(tf.reduce_sum(x, 0),
sigma * l2norm_bound)
else:
saned_x = tf.reduce_sum(x, 0)
return saned_x