forked from 626_privacy/tensorflow_privacy
122 lines
4.9 KiB
Python
122 lines
4.9 KiB
Python
# Copyright 2018, 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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"""DPAdamOptimizer for TensorFlow."""
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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 tensorflow as tf
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import privacy.optimizers.gaussian_average_query as ph
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class DPAdamOptimizer(tf.train.AdamOptimizer):
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"""Optimizer that implements the DP Adam algorithm.
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"""
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def __init__(self,
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learning_rate,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8,
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use_locking=False,
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l2_norm_clip=1e9,
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noise_multiplier=0.0,
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nb_microbatches=1,
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name='DPAdam'):
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"""Construct a new DP Adam optimizer.
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Args:
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learning_rate: A Tensor or a floating point value. The learning rate to
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use.
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beta1: A float value or a constant float tensor.
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The exponential decay rate for the 1st moment estimates.
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beta2: A float value or a constant float tensor.
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The exponential decay rate for the 2nd moment estimates.
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epsilon: A small constant for numerical stability. This epsilon is
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"epsilon hat" in the Kingma and Ba paper (in the formula just before
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Section 2.1), not the epsilon in Algorithm 1 of the paper.
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use_locking: If True use locks for update operations.
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l2_norm_clip: Clipping parameter for DP-SGD.
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noise_multiplier: Noise multiplier for DP-SGD.
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nb_microbatches: Number of microbatches in which to split the input.
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name: Optional name prefix for the operations created when applying
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gradients. Defaults to "DPAdam". @compatibility(eager) When eager
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execution is enabled, `learning_rate` can be a callable that takes no
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arguments and returns the actual value to use. This can be useful for
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changing these values across different invocations of optimizer
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functions. @end_compatibility
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"""
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super(DPAdamOptimizer, self).__init__(
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learning_rate,
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beta1,
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beta2,
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epsilon,
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use_locking,
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name)
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stddev = l2_norm_clip * noise_multiplier
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self._nb_microbatches = nb_microbatches
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self._privacy_helper = ph.GaussianAverageQuery(l2_norm_clip, stddev,
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nb_microbatches)
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self._ph_global_state = self._privacy_helper.initial_global_state()
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def compute_gradients(self,
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loss,
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var_list,
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gate_gradients=tf.train.Optimizer.GATE_OP,
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aggregation_method=None,
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colocate_gradients_with_ops=False,
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grad_loss=None):
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# Note: it would be closer to the correct i.i.d. sampling of records if
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# we sampled each microbatch from the appropriate binomial distribution,
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# although that still wouldn't be quite correct because it would be sampling
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# from the dataset without replacement.
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microbatches_losses = tf.reshape(loss, [self._nb_microbatches, -1])
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sample_params = (
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self._privacy_helper.derive_sample_params(self._ph_global_state))
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def process_microbatch(i, sample_state):
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"""Process one microbatch (record) with privacy helper."""
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grads, _ = zip(*super(DPAdamOptimizer, self).compute_gradients(
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tf.gather(microbatches_losses, [i]), var_list, gate_gradients,
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aggregation_method, colocate_gradients_with_ops, grad_loss))
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sample_state = self._privacy_helper.accumulate_record(
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sample_params, sample_state, grads)
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return [tf.add(i, 1), sample_state]
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i = tf.constant(0)
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if var_list is None:
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var_list = (
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tf.trainable_variables() +
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tf.get_collection(tf.GraphKeys.TRAINABLE_RESOURCE_VARIABLES))
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sample_state = self._privacy_helper.initial_sample_state(
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self._ph_global_state, var_list)
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# Use of while_loop here requires that sample_state be a nested structure of
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# tensors. In general, we would prefer to allow it to be an arbitrary
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# opaque type.
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_, final_state = tf.while_loop(
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lambda i, _: tf.less(i, self._nb_microbatches), process_microbatch,
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[i, sample_state])
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final_grads, self._ph_global_state = (
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self._privacy_helper.get_noised_average(final_state,
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self._ph_global_state))
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return zip(final_grads, var_list)
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