本文整理汇总了Python中differential_privacy.privacy_accountant.tf.accountant.GaussianMomentsAccountant方法的典型用法代码示例。如果您正苦于以下问题：Python accountant.GaussianMomentsAccountant方法的具体用法？Python accountant.GaussianMomentsAccountant怎么用？Python accountant.GaussianMomentsAccountant使用的例子？那么恭喜您, 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.privacy_accountant.tf.accountant的用法示例。
在下文中一共展示了accountant.GaussianMomentsAccountant方法的1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: GAN_solvers
# 需要导入模块: from differential_privacy.privacy_accountant.tf import accountant [as 别名]
# 或者: from differential_privacy.privacy_accountant.tf.accountant import GaussianMomentsAccountant [as 别名]
def GAN_solvers(D_loss, G_loss, learning_rate, batch_size, total_examples,
l2norm_bound, batches_per_lot, sigma, dp=False):
"""
Optimizers
"""
discriminator_vars = [v for v in tf.trainable_variables() if v.name.startswith('discriminator')]
generator_vars = [v for v in tf.trainable_variables() if v.name.startswith('generator')]
if dp:
print('Using differentially private SGD to train discriminator!')
eps = tf.placeholder(tf.float32)
delta = tf.placeholder(tf.float32)
priv_accountant = accountant.GaussianMomentsAccountant(total_examples)
clip = True
l2norm_bound = l2norm_bound/batch_size
batches_per_lot = 1
gaussian_sanitizer = sanitizer.AmortizedGaussianSanitizer(
priv_accountant,
[l2norm_bound, clip])
# the trick is that we need to calculate the gradient with respect to
# each example in the batch, during the DP SGD step
D_solver = dp_optimizer.DPGradientDescentOptimizer(learning_rate,
[eps, delta],
sanitizer=gaussian_sanitizer,
sigma=sigma,
batches_per_lot=batches_per_lot).minimize(D_loss, var_list=discriminator_vars)
else:
D_loss_mean_over_batch = tf.reduce_mean(D_loss)
D_solver = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(D_loss_mean_over_batch, var_list=discriminator_vars)
priv_accountant = None
G_loss_mean_over_batch = tf.reduce_mean(G_loss)
G_solver = tf.train.AdamOptimizer().minimize(G_loss_mean_over_batch, var_list=generator_vars)
return D_solver, G_solver, priv_accountant
# --- to do with the model --- #