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Move TensorFlow v1 imports to their own __init__.py file in a new subdirectory.

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PiperOrigin-RevId: 387156295
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Steve Chien 2021-07-27 11:28:15 -07:00 committed by A. Unique TensorFlower
parent 2cafe28d8d
commit 2f862eba9b
6 changed files with 53 additions and 36 deletions
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21
tensorflow_privacy/__init__.py
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@ -26,6 +26,9 @@ from tensorflow_privacy.version import __version__ # pylint: disable=g-bad-impo
if hasattr(sys, 'skip_tf_privacy_import'): # Useful for standalone scripts. if hasattr(sys, 'skip_tf_privacy_import'): # Useful for standalone scripts.
pass pass
else: else:
# TensorFlow v1 imports
import tensorflow_privacy.v1
# Analysis # Analysis
from tensorflow_privacy.privacy.analysis.compute_dp_sgd_privacy_lib import compute_dp_sgd_privacy from tensorflow_privacy.privacy.analysis.compute_dp_sgd_privacy_lib import compute_dp_sgd_privacy
from tensorflow_privacy.privacy.analysis.privacy_ledger import GaussianSumQueryEntry from tensorflow_privacy.privacy.analysis.privacy_ledger import GaussianSumQueryEntry
@ -54,7 +57,6 @@ else:
# Estimators # Estimators
from tensorflow_privacy.privacy.estimators.dnn import DNNClassifier from tensorflow_privacy.privacy.estimators.dnn import DNNClassifier
from tensorflow_privacy.privacy.estimators.v1.dnn import DNNClassifier as DNNClassifierV1
# Keras Models # Keras Models
from tensorflow_privacy.privacy.keras_models.dp_keras_model import DPModel from tensorflow_privacy.privacy.keras_models.dp_keras_model import DPModel
@ -62,14 +64,6 @@ else:
from tensorflow_privacy.privacy.keras_models.dp_keras_model import make_dp_model_class from tensorflow_privacy.privacy.keras_models.dp_keras_model import make_dp_model_class
# Optimizers # Optimizers
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdagradGaussianOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdagradOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdamGaussianOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdamOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import make_optimizer_class
from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasAdagradOptimizer from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasAdagradOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasAdamOptimizer from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasAdamOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasSGDOptimizer from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasSGDOptimizer
@ -80,15 +74,6 @@ else:
from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras_vectorized import VectorizedDPKerasSGDOptimizer from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras_vectorized import VectorizedDPKerasSGDOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras_vectorized import make_vectorized_keras_optimizer_class from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras_vectorized import make_vectorized_keras_optimizer_class
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPAdagradOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPAdamOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPSGDOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPAdagrad
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPAdam
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPSGD
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import make_vectorized_optimizer_class
try: try:
from tensorflow_privacy.privacy.bolt_on.models import BoltOnModel from tensorflow_privacy.privacy.bolt_on.models import BoltOnModel
from tensorflow_privacy.privacy.bolt_on.optimizers import BoltOn from tensorflow_privacy.privacy.bolt_on.optimizers import BoltOn

5
tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy.py
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@ -32,16 +32,11 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import sys
from absl import app from absl import app
from absl import flags from absl import flags
from tensorflow_privacy.privacy.analysis.compute_dp_sgd_privacy_lib import compute_dp_sgd_privacy from tensorflow_privacy.privacy.analysis.compute_dp_sgd_privacy_lib import compute_dp_sgd_privacy
# Opting out of loading all sibling packages and their dependencies.
sys.skip_tf_privacy_import = True
FLAGS = flags.FLAGS FLAGS = flags.FLAGS
flags.DEFINE_integer('N', None, 'Total number of examples') flags.DEFINE_integer('N', None, 'Total number of examples')

4
tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy_lib.py
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@ -19,13 +19,9 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import math import math
import sys
from absl import app from absl import app
# Opting out of loading all sibling packages and their dependencies.
sys.skip_tf_privacy_import = True
from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp # pylint: disable=g-import-not-at-top from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp # pylint: disable=g-import-not-at-top
from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent

5
tensorflow_privacy/privacy/analysis/compute_noise_from_budget.py
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@ -34,16 +34,11 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import sys
from absl import app from absl import app
from absl import flags from absl import flags
from tensorflow_privacy.privacy.analysis.compute_noise_from_budget_lib import compute_noise from tensorflow_privacy.privacy.analysis.compute_noise_from_budget_lib import compute_noise
# Opting out of loading all sibling packages and their dependencies.
sys.skip_tf_privacy_import = True
FLAGS = flags.FLAGS FLAGS = flags.FLAGS
flags.DEFINE_integer('N', None, 'Total number of examples') flags.DEFINE_integer('N', None, 'Total number of examples')

4
tensorflow_privacy/privacy/analysis/compute_noise_from_budget_lib.py
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@ -19,7 +19,6 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import math import math
import sys
from absl import app from absl import app
from scipy.optimize import bisect from scipy.optimize import bisect
@ -27,9 +26,6 @@ from scipy.optimize import bisect
from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp # pylint: disable=g-import-not-at-top from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp # pylint: disable=g-import-not-at-top
from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
# Opting out of loading all sibling packages and their dependencies.
sys.skip_tf_privacy_import = True
def apply_dp_sgd_analysis(q, sigma, steps, orders, delta): def apply_dp_sgd_analysis(q, sigma, steps, orders, delta):
"""Compute and print results of DP-SGD analysis.""" """Compute and print results of DP-SGD analysis."""

50
tensorflow_privacy/v1/__init__.py Normal file
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@ -0,0 +1,50 @@
# Copyright 2020, The TensorFlow Privacy 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.
"""TensorFlow Privacy library v1 imports.
This module includes classes designed to be compatible with TF1, based on
`tf.compat.v1.train.Optimizer` and `tf.estimator.Estimator`.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
# pylint: disable=g-import-not-at-top
if hasattr(sys, 'skip_tf_privacy_import'): # Useful for standalone scripts.
pass
else:
# Estimators
from tensorflow_privacy.privacy.estimators.v1.dnn import DNNClassifier as DNNClassifierV1
# Optimizers
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdagradGaussianOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdagradOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdamGaussianOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPAdamOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer import make_optimizer_class
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPAdagradOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPAdamOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPSGDOptimizer
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPAdagrad
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPAdam
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import VectorizedDPSGD
from tensorflow_privacy.privacy.optimizers.dp_optimizer_vectorized import make_vectorized_optimizer_class