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Delete unused TF 1.0 API in TensorFlow Privacy.

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PiperOrigin-RevId: 425900761
This commit is contained in:
Michael Reneer 2022-02-02 09:15:28 -08:00 committed by A. Unique TensorFlower
parent 6fde7b0480
commit 778c804d1b
2 changed files with 36 additions and 97 deletions
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66
tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/tf_estimator_evaluation.py
View file

@ -14,19 +14,15 @@
"""A hook and a function in tf estimator for membership inference attack.""" """A hook and a function in tf estimator for membership inference attack."""
import os import os
from typing import Iterable from typing import Iterable
from absl import logging from absl import logging
import numpy as np import numpy as np
import tensorflow.compat.v1 as tf import tensorflow as tf
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import data_structures
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import membership_inference_attack as mia from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import membership_inference_attack as mia
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackInputData from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import utils
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import utils_tensorboard
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import get_flattened_attack_metrics
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SlicingSpec
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.utils import log_loss
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.utils_tensorboard import write_results_to_tensorboard
def calculate_losses(estimator, input_fn, labels): def calculate_losses(estimator, input_fn, labels):
@ -47,23 +43,23 @@ def calculate_losses(estimator, input_fn, labels):
loss: cross entropy loss of each sample loss: cross entropy loss of each sample
""" """
pred = np.array(list(estimator.predict(input_fn=input_fn))) pred = np.array(list(estimator.predict(input_fn=input_fn)))
loss = log_loss(labels, pred) loss = utils.log_loss(labels, pred)
return pred, loss return pred, loss
class MembershipInferenceTrainingHook(tf.estimator.SessionRunHook): class MembershipInferenceTrainingHook(tf.estimator.SessionRunHook):
"""Training hook to perform membership inference attack on epoch end.""" """Training hook to perform membership inference attack on epoch end."""
def __init__( def __init__(self,
self, estimator,
estimator, in_train,
in_train, out_train,
out_train, input_fn_constructor,
input_fn_constructor, slicing_spec: data_structures.SlicingSpec = None,
slicing_spec: SlicingSpec = None, attack_types: Iterable[data_structures.AttackType] = (
attack_types: Iterable[AttackType] = (AttackType.THRESHOLD_ATTACK,), data_structures.AttackType.THRESHOLD_ATTACK,),
tensorboard_dir=None, tensorboard_dir=None,
tensorboard_merge_classifiers=False): tensorboard_merge_classifiers=False):
"""Initialize the hook. """Initialize the hook.
Args: Args:
@ -112,7 +108,7 @@ class MembershipInferenceTrainingHook(tf.estimator.SessionRunHook):
self._attack_types) self._attack_types)
logging.info(results) logging.info(results)
att_types, att_slices, att_metrics, att_values = get_flattened_attack_metrics( att_types, att_slices, att_metrics, att_values = data_structures.get_flattened_attack_metrics(
results) results)
print('Attack result:') print('Attack result:')
print('\n'.join([ print('\n'.join([
@ -123,8 +119,9 @@ class MembershipInferenceTrainingHook(tf.estimator.SessionRunHook):
# Write to tensorboard if tensorboard_dir is specified # Write to tensorboard if tensorboard_dir is specified
global_step = self._estimator.get_variable_value('global_step') global_step = self._estimator.get_variable_value('global_step')
if self._writers is not None: if self._writers is not None:
write_results_to_tensorboard(results, self._writers, global_step, utils_tensorboard.write_results_to_tensorboard(
self._tensorboard_merge_classifiers) results, self._writers, global_step,
self._tensorboard_merge_classifiers)
def run_attack_on_tf_estimator_model( def run_attack_on_tf_estimator_model(
@ -132,8 +129,9 @@ def run_attack_on_tf_estimator_model(
in_train, in_train,
out_train, out_train,
input_fn_constructor, input_fn_constructor,
slicing_spec: SlicingSpec = None, slicing_spec: data_structures.SlicingSpec = None,
attack_types: Iterable[AttackType] = (AttackType.THRESHOLD_ATTACK,)): attack_types: Iterable[data_structures.AttackType] = (
data_structures.AttackType.THRESHOLD_ATTACK,)):
"""Performs the attack in the end of training. """Performs the attack in the end of training.
Args: Args:
@ -164,14 +162,14 @@ def run_attack_on_tf_estimator_model(
return results return results
def run_attack_helper( def run_attack_helper(estimator,
estimator, in_train_input_fn,
in_train_input_fn, out_train_input_fn,
out_train_input_fn, in_train_labels,
in_train_labels, out_train_labels,
out_train_labels, slicing_spec: data_structures.SlicingSpec = None,
slicing_spec: SlicingSpec = None, attack_types: Iterable[data_structures.AttackType] = (
attack_types: Iterable[AttackType] = (AttackType.THRESHOLD_ATTACK,)): data_structures.AttackType.THRESHOLD_ATTACK,)):
"""A helper function to perform attack. """A helper function to perform attack.
Args: Args:
@ -192,7 +190,7 @@ def run_attack_helper(
out_train_pred, out_train_loss = calculate_losses(estimator, out_train_pred, out_train_loss = calculate_losses(estimator,
out_train_input_fn, out_train_input_fn,
out_train_labels) out_train_labels)
attack_input = AttackInputData( attack_input = data_structures.AttackInputData(
logits_train=in_train_pred, logits_train=in_train_pred,
logits_test=out_train_pred, logits_test=out_train_pred,
labels_train=in_train_labels, labels_train=in_train_labels,

67
tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/utils_tensorboard.py
View file

@ -13,42 +13,13 @@
# limitations under the License. # limitations under the License.
"""Utility functions for writing attack results to tensorboard.""" """Utility functions for writing attack results to tensorboard."""
from typing import List from typing import List, Union
from typing import Union
import tensorflow as tf2 import tensorflow as tf
import tensorflow.compat.v1 as tf1
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResults from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResults
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import get_flattened_attack_metrics from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import get_flattened_attack_metrics
def write_to_tensorboard(writers, tags, values, step):
"""Write metrics to tensorboard.
Args:
writers: a list of tensorboard writers or one writer to be used for metrics.
If it's a list, it should be of the same length as tags
tags: a list of tags of metrics
values: a list of values of metrics with the same length as tags
step: step for the tensorboard summary
"""
if writers is None or not writers:
raise ValueError('write_to_tensorboard does not get any writer.')
if not isinstance(writers, list):
writers = [writers] * len(tags)
assert len(writers) == len(tags) == len(values)
for writer, tag, val in zip(writers, tags, values):
summary = tf1.Summary()
summary.value.add(tag=tag, simple_value=val)
writer.add_summary(summary, step)
for writer in set(writers):
writer.flush()
def write_to_tensorboard_tf2(writers, tags, values, step): def write_to_tensorboard_tf2(writers, tags, values, step):
"""Write metrics to tensorboard. """Write metrics to tensorboard.
@ -69,7 +40,7 @@ def write_to_tensorboard_tf2(writers, tags, values, step):
for writer, tag, val in zip(writers, tags, values): for writer, tag, val in zip(writers, tags, values):
with writer.as_default(): with writer.as_default():
tf2.summary.scalar(tag, val, step=step) tf.summary.scalar(tag, val, step=step)
writer.flush() writer.flush()
for writer in set(writers): for writer in set(writers):
@ -77,39 +48,9 @@ def write_to_tensorboard_tf2(writers, tags, values, step):
writer.flush() writer.flush()
def write_results_to_tensorboard(attack_results: AttackResults,
writers: Union[tf1.summary.FileWriter,
List[tf1.summary.FileWriter]],
step: int, merge_classifiers: bool):
"""Write attack results to tensorboard.
Args:
attack_results: results from attack
writers: a list of tensorboard writers or one writer to be used for metrics
step: step for the tensorboard summary
merge_classifiers: if true, plot different classifiers with the same
slicing_spec and metric in the same figure
"""
if writers is None or not writers:
raise ValueError('write_results_to_tensorboard does not get any writer.')
att_types, att_slices, att_metrics, att_values = get_flattened_attack_metrics(
attack_results)
if merge_classifiers:
att_tags = ['attack/' + f'{s}_{m}' for s, m in zip(att_slices, att_metrics)]
write_to_tensorboard([writers[t] for t in att_types], att_tags, att_values,
step)
else:
att_tags = [
'attack/' + f'{s}_{t}_{m}'
for t, s, m in zip(att_types, att_slices, att_metrics)
]
write_to_tensorboard(writers, att_tags, att_values, step)
def write_results_to_tensorboard_tf2( def write_results_to_tensorboard_tf2(
attack_results: AttackResults, attack_results: AttackResults,
writers: Union[tf2.summary.SummaryWriter, List[tf2.summary.SummaryWriter]], writers: Union[tf.summary.SummaryWriter, List[tf.summary.SummaryWriter]],
step: int, merge_classifiers: bool): step: int, merge_classifiers: bool):
"""Write attack results to tensorboard. """Write attack results to tensorboard.