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Update to use TF 2.0 API in TensorFlow Privacy:

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tf.logging -> Removed for absl
tf.assert_type -> tf.debugging.assert_type
tf.assert_less_equal -> tf.debugging.assert_less_equal
tf.global_norm -> tf.linalg.global_norm

PiperOrigin-RevId: 425730344
This commit is contained in:
Michael Reneer 2022-02-01 15:28:37 -08:00 committed by A. Unique TensorFlower
parent 438da5a09b
commit 8a6827b27c
5 changed files with 21 additions and 16 deletions
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8
tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/tf_estimator_evaluation_example.py
View file

@ -17,7 +17,7 @@ from absl import app
from absl import flags
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow as tf
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import get_flattened_attack_metrics
@ -91,10 +91,8 @@ def load_cifar10():
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.ERROR)
logging.set_verbosity(logging.ERROR)
logging.set_stderrthreshold(logging.ERROR)
logging.get_absl_handler().use_absl_log_file()
logger = tf.get_logger()
logger.set_level(logging.ERROR)
# Load training and test data.
x_train, y_train, x_test, y_test = load_cifar10()

8
tutorials/lm_dpsgd_tutorial.py
View file

@ -34,10 +34,10 @@ import os
from absl import app
from absl import flags
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
from tensorflow_privacy.privacy.optimizers import dp_optimizer
@ -157,7 +157,9 @@ def compute_epsilon(steps):
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
logger = tf.get_logger()
logger.set_level(logging.INFO)
if FLAGS.batch_size % FLAGS.microbatches != 0:
raise ValueError('Number of microbatches should divide evenly batch_size')

8
tutorials/mnist_dpsgd_tutorial_vectorized.py
View file

@ -15,9 +15,9 @@
from absl import app
from absl import flags
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow as tf
from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
from tensorflow_privacy.privacy.optimizers import dp_optimizer_vectorized
@ -140,7 +140,9 @@ def load_mnist():
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
logger = tf.get_logger()
logger.set_level(logging.INFO)
if FLAGS.dpsgd and FLAGS.batch_size % FLAGS.microbatches != 0:
raise ValueError('Number of microbatches should divide evenly batch_size')

7
tutorials/mnist_lr_tutorial.py
View file

@ -25,10 +25,9 @@ import math
from absl import app
from absl import flags
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
from tensorflow_privacy.privacy.optimizers import dp_optimizer
@ -178,7 +177,9 @@ def print_privacy_guarantees(epochs, batch_size, samples, noise_multiplier):
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
logger = tf.get_logger()
logger.set_level(logging.INFO)
if FLAGS.data_l2_norm <= 0:
raise ValueError('data_l2_norm must be positive.')
if FLAGS.dpsgd and FLAGS.learning_rate > 8 / FLAGS.data_l2_norm**2:

6
tutorials/walkthrough/mnist_scratch.py
View file

@ -13,8 +13,9 @@
# limitations under the License.
"""Scratchpad for training a CNN on MNIST with DPSGD."""
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow as tf
tf.flags.DEFINE_float('learning_rate', .15, 'Learning rate for training')
tf.flags.DEFINE_integer('batch_size', 256, 'Batch size')
@ -86,7 +87,8 @@ def load_mnist():
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
logger = tf.get_logger()
logger.set_level(logging.INFO)
# Load training and test data.
train_data, train_labels, test_data, test_labels = load_mnist()