forked from 626_privacy/tensorflow_privacy
d5dcfec745
set_denominator was added so that the batch size doesn't need to be specified before constructing the optimizer, but it breaks the DPQuery abstraction. Now the optimizer uses a GaussianSumQuery instead of GaussianAverageQuery, and normalization by batch size is done inside the optimizer. Also instead of creating all DPQueries with a PrivacyLedger and then wrapping with QueryWithLedger, it is now sufficient to create the queries with no ledger and QueryWithLedger will construct the ledger and pass it to all inner queries. PiperOrigin-RevId: 251462353
98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
# Copyright 2019, The TensorFlow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Implements DPQuery interface for normalized queries.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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from distutils.version import LooseVersion
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import tensorflow as tf
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from privacy.dp_query import dp_query
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if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
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nest = tf.contrib.framework.nest
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else:
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nest = tf.nest
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class NormalizedQuery(dp_query.DPQuery):
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"""DPQuery for queries with a DPQuery numerator and fixed denominator."""
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# pylint: disable=invalid-name
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_GlobalState = collections.namedtuple(
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'_GlobalState', ['numerator_state', 'denominator'])
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def __init__(self, numerator_query, denominator):
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"""Initializer for NormalizedQuery.
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Args:
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numerator_query: A DPQuery for the numerator.
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denominator: A value for the denominator. May be None if it will be
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supplied via the set_denominator function before get_noised_result is
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called.
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"""
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self._numerator = numerator_query
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self._denominator = denominator
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def set_ledger(self, ledger):
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"""See base class."""
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self._numerator.set_ledger(ledger)
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def initial_global_state(self):
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"""See base class."""
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if self._denominator is not None:
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denominator = tf.cast(self._denominator, tf.float32)
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else:
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denominator = None
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return self._GlobalState(
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self._numerator.initial_global_state(), denominator)
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def derive_sample_params(self, global_state):
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"""See base class."""
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return self._numerator.derive_sample_params(global_state.numerator_state)
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def initial_sample_state(self, global_state, template):
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"""See base class."""
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# NormalizedQuery has no sample state beyond the numerator state.
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return self._numerator.initial_sample_state(
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global_state.numerator_state, template)
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def preprocess_record(self, params, record):
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return self._numerator.preprocess_record(params, record)
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def accumulate_preprocessed_record(
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self, sample_state, preprocessed_record):
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"""See base class."""
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return self._numerator.accumulate_preprocessed_record(
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sample_state, preprocessed_record)
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def get_noised_result(self, sample_state, global_state):
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"""See base class."""
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noised_sum, new_sum_global_state = self._numerator.get_noised_result(
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sample_state, global_state.numerator_state)
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def normalize(v):
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return tf.truediv(v, global_state.denominator)
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return (nest.map_structure(normalize, noised_sum),
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self._GlobalState(new_sum_global_state, global_state.denominator))
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def merge_sample_states(self, sample_state_1, sample_state_2):
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"""See base class."""
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return self._numerator.merge_sample_states(sample_state_1, sample_state_2)
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