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Finish migration of mnist_lr_tutorial to use differential_privacy library.

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PiperOrigin-RevId: 453258715
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Galen Andrew 2022-06-06 12:32:09 -07:00 committed by A. Unique TensorFlower
parent 97f5c2fdfb
commit fca208e514
1 changed files with 1 additions and 2 deletions
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tutorials/mnist_lr_tutorial.py
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@ -30,7 +30,6 @@ import numpy as np
import tensorflow as tf
from tensorflow import estimator as tf_estimator
from tensorflow.compat.v1 import estimator as tf_compat_v1_estimator
from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
from tensorflow_privacy.privacy.optimizers import dp_optimizer
from com_google_differential_py.python.dp_accounting import dp_event
from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
@ -167,7 +166,7 @@ def print_privacy_guarantees(epochs, batch_size, samples, noise_multiplier):
# Using RDP accountant to compute eps. Doing computation analytically is
# an option.
rdp = [order * coef for order in orders]
eps, _, _ = get_privacy_spent(orders, rdp, target_delta=delta)
eps = rdp_privacy_accountant.compute_epsilon(orders, rdp, delta)
print('\t{:g}% enjoy at least ({:.2f}, {})-DP'.format(p * 100, eps, delta))
accountant = rdp_privacy_accountant.RdpAccountant(orders)