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Migrate lm_dpsgd_tutorial from deprecated TFP rdp_accountant to Google DP.

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PiperOrigin-RevId: 446846972
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Galen Andrew 2022-05-05 16:30:57 -07:00 committed by A. Unique TensorFlower
parent 65eadd3a02
commit afe676135e
2 changed files with 15 additions and 9 deletions
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3
tutorials/BUILD
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@ -105,10 +105,11 @@ py_binary(
python_version = "PY3", python_version = "PY3",
srcs_version = "PY3", srcs_version = "PY3",
deps = [ deps = [
"//tensorflow_privacy/privacy/analysis:rdp_accountant",
"//tensorflow_privacy/privacy/optimizers:dp_optimizer", "//tensorflow_privacy/privacy/optimizers:dp_optimizer",
"//third_party/py/tensorflow:tensorflow_compat_v1_estimator", "//third_party/py/tensorflow:tensorflow_compat_v1_estimator",
"//third_party/py/tensorflow:tensorflow_estimator", "//third_party/py/tensorflow:tensorflow_estimator",
"@com_google_differential_py//python/dp_accounting:dp_event",
"@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
], ],
) )

21
tutorials/lm_dpsgd_tutorial.py
View file

@ -40,9 +40,11 @@ import tensorflow as tf
from tensorflow import estimator as tf_estimator from tensorflow import estimator as tf_estimator
from tensorflow.compat.v1 import estimator as tf_compat_v1_estimator from tensorflow.compat.v1 import estimator as tf_compat_v1_estimator
import tensorflow_datasets as tfds import tensorflow_datasets as tfds
from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
from tensorflow_privacy.privacy.optimizers import dp_optimizer 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
flags.DEFINE_boolean( flags.DEFINE_boolean(
'dpsgd', True, 'If True, train with DP-SGD. If False, ' 'dpsgd', True, 'If True, train with DP-SGD. If False, '
@ -150,13 +152,16 @@ def compute_epsilon(steps):
return float('inf') return float('inf')
orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64)) orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
sampling_probability = FLAGS.batch_size / NB_TRAIN sampling_probability = FLAGS.batch_size / NB_TRAIN
rdp = compute_rdp(
q=sampling_probability, accountant = rdp_privacy_accountant.RdpAccountant(orders)
noise_multiplier=FLAGS.noise_multiplier, event = dp_event.SelfComposedDpEvent(
steps=steps, dp_event.PoissonSampledDpEvent(
orders=orders) sampling_probability,
dp_event.GaussianDpEvent(FLAGS.noise_multiplier)), steps)
accountant.compose(event)
# Delta is set to 1e-5 because Penn TreeBank has 60000 training points. # Delta is set to 1e-5 because Penn TreeBank has 60000 training points.
return get_privacy_spent(orders, rdp, target_delta=1e-5)[0] return accountant.get_epsilon(target_delta=1e-5)
def main(unused_argv): def main(unused_argv):