本文整理匯總了Python中differential_privacy.dp_sgd.dp_optimizer.sanitizer.ClipOption方法的典型用法代碼示例。如果您正苦於以下問題：Python sanitizer.ClipOption方法的具體用法？Python sanitizer.ClipOption怎麽用？Python sanitizer.ClipOption使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類differential_privacy.dp_sgd.dp_optimizer.sanitizer的用法示例。
在下文中一共展示了sanitizer.ClipOption方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: ComputeDPPrincipalProjection
# 需要導入模塊: from differential_privacy.dp_sgd.dp_optimizer import sanitizer [as 別名]
# 或者: from differential_privacy.dp_sgd.dp_optimizer.sanitizer import ClipOption [as 別名]
def ComputeDPPrincipalProjection(data, projection_dims,
sanitizer, eps_delta, sigma):
"""Compute differentially private projection.
Args:
data: the input data, each row is a data vector.
projection_dims: the projection dimension.
sanitizer: the sanitizer used for achieving privacy.
eps_delta: (eps, delta) pair.
sigma: if not None, use noise sigma; otherwise compute it using
eps_delta pair.
Returns:
A projection matrix with projection_dims columns.
"""
eps, delta = eps_delta
# Normalize each row.
normalized_data = tf.nn.l2_normalize(data, 1)
covar = tf.matmul(tf.transpose(normalized_data), normalized_data)
saved_shape = tf.shape(covar)
num_examples = tf.slice(tf.shape(data), [0], [1])
if eps > 0:
# Since the data is already normalized, there is no need to clip
# the covariance matrix.
assert delta > 0
saned_covar = sanitizer.sanitize(
tf.reshape(covar, [1, -1]), eps_delta, sigma=sigma,
option=san.ClipOption(1.0, False), num_examples=num_examples)
saned_covar = tf.reshape(saned_covar, saved_shape)
# Symmetrize saned_covar. This also reduces the noise variance.
saned_covar = 0.5 * (saned_covar + tf.transpose(saned_covar))
else:
saned_covar = covar
# Compute the eigen decomposition of the covariance matrix, and
# return the top projection_dims eigen vectors, represented as columns of
# the projection matrix.
eigvals, eigvecs = tf.self_adjoint_eig(saned_covar)
_, topk_indices = tf.nn.top_k(eigvals, projection_dims)
topk_indices = tf.reshape(topk_indices, [projection_dims])
# Gather and return the corresponding eigenvectors.
return tf.transpose(tf.gather(tf.transpose(eigvecs), topk_indices)) 示例2: ComputeDPPrincipalProjection
# 需要導入模塊: from differential_privacy.dp_sgd.dp_optimizer import sanitizer [as 別名]
# 或者: from differential_privacy.dp_sgd.dp_optimizer.sanitizer import ClipOption [as 別名]
def ComputeDPPrincipalProjection(data, projection_dims,
sanitizer, eps_delta, sigma):
"""Compute differentially private projection.
Args:
data: the input data, each row is a data vector.
projection_dims: the projection dimension.
sanitizer: the sanitizer used for acheiving privacy.
eps_delta: (eps, delta) pair.
sigma: if not None, use noise sigma; otherwise compute it using
eps_delta pair.
Returns:
A projection matrix with projection_dims columns.
"""
eps, delta = eps_delta
# Normalize each row.
normalized_data = tf.nn.l2_normalize(data, 1)
covar = tf.matmul(tf.transpose(normalized_data), normalized_data)
saved_shape = tf.shape(covar)
num_examples = tf.slice(tf.shape(data), [0], [1])
if eps > 0:
# Since the data is already normalized, there is no need to clip
# the covariance matrix.
assert delta > 0
saned_covar = sanitizer.sanitize(
tf.reshape(covar, [1, -1]), eps_delta, sigma=sigma,
option=san.ClipOption(1.0, False), num_examples=num_examples)
saned_covar = tf.reshape(saned_covar, saved_shape)
# Symmetrize saned_covar. This also reduces the noise variance.
saned_covar = 0.5 * (saned_covar + tf.transpose(saned_covar))
else:
saned_covar = covar
# Compute the eigen decomposition of the covariance matrix, and
# return the top projection_dims eigen vectors, represented as columns of
# the projection matrix.
eigvals, eigvecs = tf.self_adjoint_eig(saned_covar)
_, topk_indices = tf.nn.top_k(eigvals, projection_dims)
topk_indices = tf.reshape(topk_indices, [projection_dims])
# Gather and return the corresponding eigenvectors.
return tf.transpose(tf.gather(tf.transpose(eigvecs), topk_indices))