forked from 626_privacy/tensorflow_privacy
254 lines
9.9 KiB
Python
254 lines
9.9 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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"""Tests for differentially private optimizers."""
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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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from absl.testing import parameterized
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import mock
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import numpy as np
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import tensorflow as tf
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from privacy.analysis import privacy_ledger
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from privacy.dp_query import gaussian_query
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from privacy.optimizers import dp_optimizer
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class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
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def _loss(self, val0, val1):
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"""Loss function that is minimized at the mean of the input points."""
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return 0.5 * tf.reduce_sum(tf.squared_difference(val0, val1), axis=1)
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# Parameters for testing: optimizer, num_microbatches, expected answer.
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@parameterized.named_parameters(
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('DPGradientDescent 1', dp_optimizer.DPGradientDescentOptimizer, 1,
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[-2.5, -2.5]),
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('DPGradientDescent 2', dp_optimizer.DPGradientDescentOptimizer, 2,
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[-2.5, -2.5]),
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('DPGradientDescent 4', dp_optimizer.DPGradientDescentOptimizer, 4,
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[-2.5, -2.5]),
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('DPAdagrad 1', dp_optimizer.DPAdagradOptimizer, 1, [-2.5, -2.5]),
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('DPAdagrad 2', dp_optimizer.DPAdagradOptimizer, 2, [-2.5, -2.5]),
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('DPAdagrad 4', dp_optimizer.DPAdagradOptimizer, 4, [-2.5, -2.5]),
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('DPAdam 1', dp_optimizer.DPAdamOptimizer, 1, [-2.5, -2.5]),
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('DPAdam 2', dp_optimizer.DPAdamOptimizer, 2, [-2.5, -2.5]),
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('DPAdam 4', dp_optimizer.DPAdamOptimizer, 4, [-2.5, -2.5]),
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('DPAdam None', dp_optimizer.DPAdamOptimizer, None, [-2.5, -2.5]))
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def testBaseline(self, cls, num_microbatches, expected_answer):
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with self.cached_session() as sess:
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var0 = tf.Variable([1.0, 2.0])
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data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]])
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ledger = privacy_ledger.PrivacyLedger(
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1e6, num_microbatches / 1e6 if num_microbatches else None)
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dp_average_query = gaussian_query.GaussianAverageQuery(
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1.0e9, 0.0, num_microbatches, ledger)
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dp_average_query = privacy_ledger.QueryWithLedger(
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dp_average_query, ledger)
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opt = cls(
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dp_average_query,
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num_microbatches=num_microbatches,
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learning_rate=2.0)
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self.evaluate(tf.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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# Expected gradient is sum of differences divided by number of
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# microbatches.
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gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
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grads_and_vars = sess.run(gradient_op)
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self.assertAllCloseAccordingToType(expected_answer, grads_and_vars[0][0])
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@parameterized.named_parameters(
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('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
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('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
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('DPAdam', dp_optimizer.DPAdamOptimizer))
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def testClippingNorm(self, cls):
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with self.cached_session() as sess:
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var0 = tf.Variable([0.0, 0.0])
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data0 = tf.Variable([[3.0, 4.0], [6.0, 8.0]])
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ledger = privacy_ledger.PrivacyLedger(1e6, 1 / 1e6)
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dp_average_query = gaussian_query.GaussianAverageQuery(1.0, 0.0, 1)
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dp_average_query = privacy_ledger.QueryWithLedger(
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dp_average_query, ledger)
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opt = cls(dp_average_query, num_microbatches=1, learning_rate=2.0)
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self.evaluate(tf.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([0.0, 0.0], self.evaluate(var0))
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# Expected gradient is sum of differences.
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gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
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grads_and_vars = sess.run(gradient_op)
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self.assertAllCloseAccordingToType([-0.6, -0.8], grads_and_vars[0][0])
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@parameterized.named_parameters(
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('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
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('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
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('DPAdam', dp_optimizer.DPAdamOptimizer))
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def testNoiseMultiplier(self, cls):
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with self.cached_session() as sess:
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var0 = tf.Variable([0.0])
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data0 = tf.Variable([[0.0]])
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ledger = privacy_ledger.PrivacyLedger(1e6, 1 / 1e6)
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dp_average_query = gaussian_query.GaussianAverageQuery(4.0, 8.0, 1)
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dp_average_query = privacy_ledger.QueryWithLedger(
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dp_average_query, ledger)
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opt = cls(dp_average_query, num_microbatches=1, learning_rate=2.0)
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self.evaluate(tf.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([0.0], self.evaluate(var0))
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gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
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grads = []
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for _ in range(1000):
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grads_and_vars = sess.run(gradient_op)
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grads.append(grads_and_vars[0][0])
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# Test standard deviation is close to l2_norm_clip * noise_multiplier.
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self.assertNear(np.std(grads), 2.0 * 4.0, 0.5)
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@mock.patch.object(tf, 'logging')
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def testComputeGradientsOverrideWarning(self, mock_logging):
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class SimpleOptimizer(tf.train.Optimizer):
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def compute_gradients(self):
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return 0
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dp_optimizer.make_optimizer_class(SimpleOptimizer)
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mock_logging.warning.assert_called_once_with(
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'WARNING: Calling make_optimizer_class() on class %s that overrides '
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'method compute_gradients(). Check to ensure that '
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'make_optimizer_class() does not interfere with overridden version.',
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'SimpleOptimizer')
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def testEstimator(self):
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"""Tests that DP optimizers work with tf.estimator."""
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def linear_model_fn(features, labels, mode):
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preds = tf.keras.layers.Dense(
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1, activation='linear', name='dense').apply(features['x'])
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vector_loss = tf.squared_difference(labels, preds)
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scalar_loss = tf.reduce_mean(vector_loss)
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ledger = privacy_ledger.PrivacyLedger(1e6, 1 / 1e6)
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dp_average_query = gaussian_query.GaussianAverageQuery(1.0, 0.0, 1)
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dp_average_query = privacy_ledger.QueryWithLedger(
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dp_average_query, ledger)
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optimizer = dp_optimizer.DPGradientDescentOptimizer(
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dp_average_query,
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num_microbatches=1,
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learning_rate=1.0)
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global_step = tf.train.get_global_step()
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train_op = optimizer.minimize(loss=vector_loss, global_step=global_step)
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return tf.estimator.EstimatorSpec(
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mode=mode, loss=scalar_loss, train_op=train_op)
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linear_regressor = tf.estimator.Estimator(model_fn=linear_model_fn)
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true_weights = np.array([[-5], [4], [3], [2]]).astype(np.float32)
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true_bias = 6.0
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train_data = np.random.normal(scale=3.0, size=(200, 4)).astype(np.float32)
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train_labels = np.matmul(train_data,
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true_weights) + true_bias + np.random.normal(
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scale=0.1, size=(200, 1)).astype(np.float32)
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train_input_fn = tf.estimator.inputs.numpy_input_fn(
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x={'x': train_data},
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y=train_labels,
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batch_size=20,
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num_epochs=10,
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shuffle=True)
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linear_regressor.train(input_fn=train_input_fn, steps=100)
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self.assertAllClose(
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linear_regressor.get_variable_value('dense/kernel'),
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true_weights,
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atol=1.0)
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@parameterized.named_parameters(
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('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
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('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
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('DPAdam', dp_optimizer.DPAdamOptimizer))
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def testUnrollMicrobatches(self, cls):
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with self.cached_session() as sess:
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var0 = tf.Variable([1.0, 2.0])
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data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]])
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num_microbatches = 4
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ledger = privacy_ledger.PrivacyLedger(
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1e6, num_microbatches / 1e6)
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dp_average_query = gaussian_query.GaussianAverageQuery(1.0e9, 0.0, 4)
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dp_average_query = privacy_ledger.QueryWithLedger(
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dp_average_query, ledger)
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opt = cls(
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dp_average_query,
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num_microbatches=num_microbatches,
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learning_rate=2.0,
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unroll_microbatches=True)
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self.evaluate(tf.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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# Expected gradient is sum of differences divided by number of
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# microbatches.
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gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
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grads_and_vars = sess.run(gradient_op)
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self.assertAllCloseAccordingToType([-2.5, -2.5], grads_and_vars[0][0])
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@parameterized.named_parameters(
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('DPGradientDescent', dp_optimizer.DPGradientDescentGaussianOptimizer),
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('DPAdagrad', dp_optimizer.DPAdagradGaussianOptimizer),
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('DPAdam', dp_optimizer.DPAdamGaussianOptimizer))
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def testDPGaussianOptimizerClass(self, cls):
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with self.cached_session() as sess:
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var0 = tf.Variable([0.0])
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data0 = tf.Variable([[0.0]])
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opt = cls(
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l2_norm_clip=4.0,
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noise_multiplier=2.0,
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num_microbatches=1,
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learning_rate=2.0,
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ledger=privacy_ledger.DummyLedger())
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self.evaluate(tf.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([0.0], self.evaluate(var0))
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gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
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grads = []
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for _ in range(1000):
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grads_and_vars = sess.run(gradient_op)
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grads.append(grads_and_vars[0][0])
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# Test standard deviation is close to l2_norm_clip * noise_multiplier.
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self.assertNear(np.std(grads), 2.0 * 4.0, 0.5)
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if __name__ == '__main__':
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tf.test.main()
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