forked from 626_privacy/tensorflow_privacy
100 lines
4.1 KiB
Python
100 lines
4.1 KiB
Python
# Copyright 2018, The TensorFlow Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Differentially private optimizers for TensorFlow."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import tensorflow as tf
|
|
|
|
from privacy.optimizers import gaussian_query
|
|
|
|
|
|
def make_optimizer_class(cls):
|
|
"""Constructs a DP optimizer class from an existing one."""
|
|
if (tf.train.Optimizer.compute_gradients.__code__ is
|
|
not cls.compute_gradients.__code__):
|
|
tf.logging.warning(
|
|
'WARNING: Calling make_optimizer_class() on class %s that overrides '
|
|
'method compute_gradients(). Check to ensure that '
|
|
'make_optimizer_class() does not interfere with overridden version.',
|
|
cls.__name__)
|
|
|
|
class DPOptimizerClass(cls):
|
|
"""Differentially private subclass of given class cls."""
|
|
|
|
def __init__(self, l2_norm_clip, noise_multiplier, num_microbatches, *args,
|
|
**kwargs):
|
|
super(DPOptimizerClass, self).__init__(*args, **kwargs)
|
|
stddev = l2_norm_clip * noise_multiplier
|
|
self._num_microbatches = num_microbatches
|
|
self._private_query = gaussian_query.GaussianAverageQuery(
|
|
l2_norm_clip, stddev, num_microbatches)
|
|
self._global_state = self._private_query.initial_global_state()
|
|
|
|
def compute_gradients(self,
|
|
loss,
|
|
var_list,
|
|
gate_gradients=tf.train.Optimizer.GATE_OP,
|
|
aggregation_method=None,
|
|
colocate_gradients_with_ops=False,
|
|
grad_loss=None):
|
|
|
|
# Note: it would be closer to the correct i.i.d. sampling of records if
|
|
# we sampled each microbatch from the appropriate binomial distribution,
|
|
# although that still wouldn't be quite correct because it would be
|
|
# sampling from the dataset without replacement.
|
|
microbatches_losses = tf.reshape(loss, [self._num_microbatches, -1])
|
|
sample_params = (
|
|
self._private_query.derive_sample_params(self._global_state))
|
|
|
|
def process_microbatch(i, sample_state):
|
|
"""Process one microbatch (record) with privacy helper."""
|
|
grads, _ = zip(*super(cls, self).compute_gradients(
|
|
tf.gather(microbatches_losses, [i]), var_list, gate_gradients,
|
|
aggregation_method, colocate_gradients_with_ops, grad_loss))
|
|
grads_list = list(grads)
|
|
sample_state = self._private_query.accumulate_record(
|
|
sample_params, sample_state, grads_list)
|
|
return [tf.add(i, 1), sample_state]
|
|
|
|
i = tf.constant(0)
|
|
|
|
if var_list is None:
|
|
var_list = (
|
|
tf.trainable_variables() + tf.get_collection(
|
|
tf.GraphKeys.TRAINABLE_RESOURCE_VARIABLES))
|
|
sample_state = self._private_query.initial_sample_state(
|
|
self._global_state, var_list)
|
|
|
|
# Use of while_loop here requires that sample_state be a nested structure
|
|
# of tensors. In general, we would prefer to allow it to be an arbitrary
|
|
# opaque type.
|
|
_, final_state = tf.while_loop(
|
|
lambda i, _: tf.less(i, self._num_microbatches), process_microbatch,
|
|
[i, sample_state])
|
|
final_grads, self._global_state = (
|
|
self._private_query.get_noised_average(final_state,
|
|
self._global_state))
|
|
|
|
return zip(final_grads, var_list)
|
|
|
|
return DPOptimizerClass
|
|
|
|
|
|
DPAdagradOptimizer = make_optimizer_class(tf.train.AdagradOptimizer)
|
|
DPAdamOptimizer = make_optimizer_class(tf.train.AdamOptimizer)
|
|
DPGradientDescentOptimizer = make_optimizer_class(
|
|
tf.train.GradientDescentOptimizer)
|