Delete unused TF 1.0 API in TensorFlow Privacy.
PiperOrigin-RevId: 425900761
This commit is contained in:
Michael Reneer
A. Unique TensorFlower
parent
6fde7b0480
commit
778c804d1b
2 changed files with 36 additions and 97 deletions
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@ -14,19 +14,15 @@
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"""A hook and a function in tf estimator for membership inference attack."""
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import os
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from typing import Iterable
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from absl import logging
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import numpy as np
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import tensorflow.compat.v1 as tf
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import tensorflow as tf
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import data_structures
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import membership_inference_attack as mia
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackInputData
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import get_flattened_attack_metrics
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SlicingSpec
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.utils import log_loss
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.utils_tensorboard import write_results_to_tensorboard
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import utils
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import utils_tensorboard
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def calculate_losses(estimator, input_fn, labels):
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@ -47,23 +43,23 @@ def calculate_losses(estimator, input_fn, labels):
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loss: cross entropy loss of each sample
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"""
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pred = np.array(list(estimator.predict(input_fn=input_fn)))
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loss = log_loss(labels, pred)
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loss = utils.log_loss(labels, pred)
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return pred, loss
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class MembershipInferenceTrainingHook(tf.estimator.SessionRunHook):
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"""Training hook to perform membership inference attack on epoch end."""
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def __init__(
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self,
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estimator,
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in_train,
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out_train,
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input_fn_constructor,
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slicing_spec: SlicingSpec = None,
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attack_types: Iterable[AttackType] = (AttackType.THRESHOLD_ATTACK,),
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tensorboard_dir=None,
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tensorboard_merge_classifiers=False):
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def __init__(self,
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estimator,
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in_train,
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out_train,
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input_fn_constructor,
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slicing_spec: data_structures.SlicingSpec = None,
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attack_types: Iterable[data_structures.AttackType] = (
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data_structures.AttackType.THRESHOLD_ATTACK,),
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tensorboard_dir=None,
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tensorboard_merge_classifiers=False):
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"""Initialize the hook.
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Args:
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@ -112,7 +108,7 @@ class MembershipInferenceTrainingHook(tf.estimator.SessionRunHook):
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self._attack_types)
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logging.info(results)
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att_types, att_slices, att_metrics, att_values = get_flattened_attack_metrics(
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att_types, att_slices, att_metrics, att_values = data_structures.get_flattened_attack_metrics(
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results)
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print('Attack result:')
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print('\n'.join([
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@ -123,8 +119,9 @@ class MembershipInferenceTrainingHook(tf.estimator.SessionRunHook):
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# Write to tensorboard if tensorboard_dir is specified
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global_step = self._estimator.get_variable_value('global_step')
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if self._writers is not None:
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write_results_to_tensorboard(results, self._writers, global_step,
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self._tensorboard_merge_classifiers)
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utils_tensorboard.write_results_to_tensorboard(
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results, self._writers, global_step,
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self._tensorboard_merge_classifiers)
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def run_attack_on_tf_estimator_model(
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@ -132,8 +129,9 @@ def run_attack_on_tf_estimator_model(
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in_train,
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out_train,
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input_fn_constructor,
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slicing_spec: SlicingSpec = None,
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attack_types: Iterable[AttackType] = (AttackType.THRESHOLD_ATTACK,)):
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slicing_spec: data_structures.SlicingSpec = None,
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attack_types: Iterable[data_structures.AttackType] = (
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data_structures.AttackType.THRESHOLD_ATTACK,)):
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"""Performs the attack in the end of training.
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Args:
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@ -164,14 +162,14 @@ def run_attack_on_tf_estimator_model(
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return results
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def run_attack_helper(
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estimator,
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in_train_input_fn,
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out_train_input_fn,
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in_train_labels,
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out_train_labels,
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slicing_spec: SlicingSpec = None,
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attack_types: Iterable[AttackType] = (AttackType.THRESHOLD_ATTACK,)):
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def run_attack_helper(estimator,
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in_train_input_fn,
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out_train_input_fn,
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in_train_labels,
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out_train_labels,
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slicing_spec: data_structures.SlicingSpec = None,
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attack_types: Iterable[data_structures.AttackType] = (
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data_structures.AttackType.THRESHOLD_ATTACK,)):
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"""A helper function to perform attack.
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Args:
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@ -192,7 +190,7 @@ def run_attack_helper(
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out_train_pred, out_train_loss = calculate_losses(estimator,
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out_train_input_fn,
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out_train_labels)
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attack_input = AttackInputData(
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attack_input = data_structures.AttackInputData(
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logits_train=in_train_pred,
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logits_test=out_train_pred,
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labels_train=in_train_labels,
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@ -13,42 +13,13 @@
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# limitations under the License.
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"""Utility functions for writing attack results to tensorboard."""
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from typing import List
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from typing import Union
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from typing import List, Union
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import tensorflow as tf2
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import tensorflow.compat.v1 as tf1
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import tensorflow as tf
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResults
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from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import get_flattened_attack_metrics
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def write_to_tensorboard(writers, tags, values, step):
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"""Write metrics to tensorboard.
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Args:
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writers: a list of tensorboard writers or one writer to be used for metrics.
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If it's a list, it should be of the same length as tags
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tags: a list of tags of metrics
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values: a list of values of metrics with the same length as tags
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step: step for the tensorboard summary
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"""
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if writers is None or not writers:
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raise ValueError('write_to_tensorboard does not get any writer.')
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if not isinstance(writers, list):
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writers = [writers] * len(tags)
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assert len(writers) == len(tags) == len(values)
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for writer, tag, val in zip(writers, tags, values):
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summary = tf1.Summary()
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summary.value.add(tag=tag, simple_value=val)
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writer.add_summary(summary, step)
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for writer in set(writers):
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writer.flush()
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def write_to_tensorboard_tf2(writers, tags, values, step):
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"""Write metrics to tensorboard.
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@ -69,7 +40,7 @@ def write_to_tensorboard_tf2(writers, tags, values, step):
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for writer, tag, val in zip(writers, tags, values):
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with writer.as_default():
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tf2.summary.scalar(tag, val, step=step)
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tf.summary.scalar(tag, val, step=step)
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writer.flush()
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for writer in set(writers):
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@ -77,39 +48,9 @@ def write_to_tensorboard_tf2(writers, tags, values, step):
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writer.flush()
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def write_results_to_tensorboard(attack_results: AttackResults,
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writers: Union[tf1.summary.FileWriter,
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List[tf1.summary.FileWriter]],
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step: int, merge_classifiers: bool):
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"""Write attack results to tensorboard.
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Args:
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attack_results: results from attack
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writers: a list of tensorboard writers or one writer to be used for metrics
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step: step for the tensorboard summary
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merge_classifiers: if true, plot different classifiers with the same
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slicing_spec and metric in the same figure
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"""
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if writers is None or not writers:
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raise ValueError('write_results_to_tensorboard does not get any writer.')
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att_types, att_slices, att_metrics, att_values = get_flattened_attack_metrics(
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attack_results)
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if merge_classifiers:
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att_tags = ['attack/' + f'{s}_{m}' for s, m in zip(att_slices, att_metrics)]
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write_to_tensorboard([writers[t] for t in att_types], att_tags, att_values,
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step)
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else:
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att_tags = [
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'attack/' + f'{s}_{t}_{m}'
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for t, s, m in zip(att_types, att_slices, att_metrics)
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]
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write_to_tensorboard(writers, att_tags, att_values, step)
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def write_results_to_tensorboard_tf2(
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attack_results: AttackResults,
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writers: Union[tf2.summary.SummaryWriter, List[tf2.summary.SummaryWriter]],
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writers: Union[tf.summary.SummaryWriter, List[tf.summary.SummaryWriter]],
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step: int, merge_classifiers: bool):
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"""Write attack results to tensorboard.
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