forked from 626_privacy/tensorflow_privacy
ec18db5ec5
-- moving to Bolton Optimizer Model is now just a convenient wrapper and example for users. Optimizer holds ALL Bolton privacy requirements. Optimizer is used as a context manager, and must be passed the model's layers. Unit tests incomplete, committing for visibility into the design.
344 lines
10 KiB
Python
344 lines
10 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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"""Unit testing for optimizer.py"""
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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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from tensorflow.python.platform import test
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from tensorflow.python.keras.optimizer_v2.optimizer_v2 import OptimizerV2
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras.regularizers import L1L2
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from tensorflow.python.keras import losses
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from tensorflow.python.keras.models import Model
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from tensorflow.python.framework import ops as _ops
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from tensorflow.python.framework import test_util
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from absl.testing import parameterized
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from privacy.bolton.loss import StrongConvexMixin
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from privacy.bolton import optimizer as opt
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class TestModel(Model):
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"""
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Bolton episilon-delta model
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Uses 4 key steps to achieve privacy guarantees:
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1. Adds noise to weights after training (output perturbation).
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2. Projects weights to R after each batch
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3. Limits learning rate
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4. Use a strongly convex loss function (see compile)
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For more details on the strong convexity requirements, see:
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Bolt-on Differential Privacy for Scalable Stochastic Gradient
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Descent-based Analytics by Xi Wu et. al.
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"""
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def __init__(self, n_classes=2):
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"""
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Args:
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n_classes: number of output classes to predict.
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epsilon: level of privacy guarantee
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noise_distribution: distribution to pull weight perturbations from
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weights_initializer: initializer for weights
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seed: random seed to use
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dtype: data type to use for tensors
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"""
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super(TestModel, self).__init__(name='bolton', dynamic=False)
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self.n_classes = n_classes
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self.layer_input_shape = (16, 1)
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self.output_layer = tf.keras.layers.Dense(
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self.n_classes,
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input_shape=self.layer_input_shape,
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kernel_regularizer=L1L2(l2=1),
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kernel_initializer='glorot_uniform',
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)
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# def call(self, inputs):
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# """Forward pass of network
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#
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# Args:
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# inputs: inputs to neural network
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#
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# Returns:
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#
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# """
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# return self.output_layer(inputs)
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class TestLoss(losses.Loss, StrongConvexMixin):
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"""Test loss function for testing Bolton model"""
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def __init__(self, reg_lambda, C, radius_constant, name='test'):
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super(TestLoss, self).__init__(name=name)
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self.reg_lambda = reg_lambda
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self.C = C
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self.radius_constant = radius_constant
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def radius(self):
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"""Radius of R-Ball (value to normalize weights to after each batch)
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Returns: radius
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"""
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return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
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def gamma(self):
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""" Gamma strongly convex
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Returns: gamma
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"""
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return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
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def beta(self, class_weight):
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"""Beta smoothess
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Args:
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class_weight: the class weights used.
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Returns: Beta
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"""
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return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
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def lipchitz_constant(self, class_weight):
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""" L lipchitz continuous
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Args:
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class_weight: class weights used
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Returns: L
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"""
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return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
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def call(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.math.squared_difference(val0, val1), axis=1)
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def max_class_weight(self, class_weight):
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if class_weight is None:
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return 1
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def kernel_regularizer(self):
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return L1L2(l2=self.reg_lambda)
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class TestOptimizer(OptimizerV2):
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"""Optimizer used for testing the Bolton optimizer"""
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def __init__(self):
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super(TestOptimizer, self).__init__('test')
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self.not_private = 'test'
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self.iterations = tf.constant(1, dtype=tf.float32)
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self._iterations = tf.constant(1, dtype=tf.float32)
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def _compute_gradients(self, loss, var_list, grad_loss=None):
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return 'test'
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def get_config(self):
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return 'test'
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def from_config(self, config, custom_objects=None):
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return 'test'
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def _create_slots(self):
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return 'test'
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def _resource_apply_dense(self, grad, handle):
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return 'test'
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def _resource_apply_sparse(self, grad, handle, indices):
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return 'test'
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def get_updates(self, loss, params):
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return 'test'
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def apply_gradients(self, grads_and_vars, name=None):
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return 'test'
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def minimize(self, loss, var_list, grad_loss=None, name=None):
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return 'test'
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def get_gradients(self, loss, params):
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return 'test'
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def limit_learning_rate(self):
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return 'test'
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class BoltonOptimizerTest(keras_parameterized.TestCase):
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"""Bolton Optimizer tests"""
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@test_util.run_all_in_graph_and_eager_modes
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@parameterized.named_parameters([
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{'testcase_name': 'branch beta',
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'fn': 'limit_learning_rate',
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'args': [tf.Variable(2, dtype=tf.float32),
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tf.Variable(1, dtype=tf.float32)],
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'result': tf.Variable(0.5, dtype=tf.float32),
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'test_attr': 'learning_rate'},
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{'testcase_name': 'branch gamma',
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'fn': 'limit_learning_rate',
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'args': [tf.Variable(1, dtype=tf.float32),
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tf.Variable(1, dtype=tf.float32)],
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'result': tf.Variable(1, dtype=tf.float32),
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'test_attr': 'learning_rate'},
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{'testcase_name': 'getattr',
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'fn': '__getattr__',
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'args': ['dtype'],
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'result': tf.float32,
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'test_attr': None},
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{'testcase_name': 'project_weights_to_r',
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'fn': 'project_weights_to_r',
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'args': ['dtype'],
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'result': tf.float32,
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'test_attr': None},
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])
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def test_fn(self, fn, args, result, test_attr):
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"""test that a fn of Bolton optimizer is working as expected.
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Args:
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fn: method of Optimizer to test
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args: args to optimizer fn
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result: the expected result
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test_attr: None if the fn returns the test result. Otherwise, this is
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the attribute of Bolton to check against result with.
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"""
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tf.random.set_seed(1)
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loss = TestLoss(1, 1, 1)
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private = opt.Bolton(TestOptimizer(), loss)
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res = getattr(private, fn, None)(*args)
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if test_attr is not None:
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res = getattr(private, test_attr, None)
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if hasattr(res, 'numpy') and hasattr(result, 'numpy'): # both tensors/not
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res = res.numpy()
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result = result.numpy()
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self.assertEqual(res, result)
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@parameterized.named_parameters([
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{'testcase_name': 'fn: get_updates',
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'fn': 'get_updates',
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'args': [0, 0]},
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{'testcase_name': 'fn: get_config',
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'fn': 'get_config',
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'args': []},
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{'testcase_name': 'fn: from_config',
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'fn': 'from_config',
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'args': [0]},
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{'testcase_name': 'fn: _resource_apply_dense',
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'fn': '_resource_apply_dense',
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'args': [1, 1]},
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{'testcase_name': 'fn: _resource_apply_sparse',
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'fn': '_resource_apply_sparse',
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'args': [1, 1, 1]},
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{'testcase_name': 'fn: apply_gradients',
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'fn': 'apply_gradients',
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'args': [1]},
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{'testcase_name': 'fn: minimize',
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'fn': 'minimize',
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'args': [1, 1]},
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{'testcase_name': 'fn: _compute_gradients',
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'fn': '_compute_gradients',
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'args': [1, 1]},
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{'testcase_name': 'fn: get_gradients',
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'fn': 'get_gradients',
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'args': [1, 1]},
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])
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def test_rerouted_function(self, fn, args):
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""" tests that a method of the internal optimizer is correctly routed from
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the Bolton instance to the internal optimizer instance (TestOptimizer,
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here).
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Args:
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fn: fn to test
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args: arguments to that fn
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"""
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loss = TestLoss(1, 1, 1)
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optimizer = TestOptimizer()
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optimizer = opt.Bolton(optimizer, loss)
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model = TestModel(2)
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model.compile(optimizer, loss)
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model.layers[0].kernel_initializer(model.layer_input_shape)
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print(model.layers[0].__dict__)
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with optimizer('laplace', 2, model.layers, 1, 1, model.n_classes):
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self.assertEqual(
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getattr(optimizer, fn, lambda: 'fn not found')(*args),
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'test'
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)
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@parameterized.named_parameters([
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{'testcase_name': 'fn: limit_learning_rate',
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'fn': 'limit_learning_rate',
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'args': [1, 1, 1]},
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{'testcase_name': 'fn: project_weights_to_r',
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'fn': 'project_weights_to_r',
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'args': []},
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{'testcase_name': 'fn: get_noise',
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'fn': 'get_noise',
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'args': [1, 1, 1, 1]},
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])
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def test_not_reroute_fn(self, fn, args):
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"""Test that a fn that should not be rerouted to the internal optimizer is
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in face not rerouted.
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Args:
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fn: fn to test
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args: arguments to that fn
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"""
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optimizer = TestOptimizer()
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loss = TestLoss(1, 1, 1)
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optimizer = opt.Bolton(optimizer, loss)
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self.assertNotEqual(getattr(optimizer, fn, lambda: 'test')(*args),
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'test')
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@parameterized.named_parameters([
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{'testcase_name': 'attr: _iterations',
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'attr': '_iterations'}
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])
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def test_reroute_attr(self, attr):
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""" test that attribute of internal optimizer is correctly rerouted to
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the internal optimizer
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Args:
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attr: attribute to test
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result: result after checking attribute
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"""
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loss = TestLoss(1, 1, 1)
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internal_optimizer = TestOptimizer()
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optimizer = opt.Bolton(internal_optimizer, loss)
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self.assertEqual(getattr(optimizer, attr),
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getattr(internal_optimizer, attr)
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)
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@parameterized.named_parameters([
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{'testcase_name': 'attr does not exist',
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'attr': '_not_valid'}
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])
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def test_attribute_error(self, attr):
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""" test that attribute of internal optimizer is correctly rerouted to
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the internal optimizer
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Args:
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attr: attribute to test
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result: result after checking attribute
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"""
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loss = TestLoss(1, 1, 1)
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internal_optimizer = TestOptimizer()
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optimizer = opt.Bolton(internal_optimizer, loss)
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with self.assertRaises(AttributeError):
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getattr(optimizer, attr)
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if __name__ == '__main__':
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test.main()
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