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For MIA in seq2seq model, add support for graph mode, add data information and fix small typo in seq2seq_membership_inference_codelab.ipynb.

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PiperOrigin-RevId: 422909904
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Shuang Song 2022-01-19 14:50:20 -08:00 committed by A. Unique TensorFlower
parent f47200f60d
commit 3a4c4400a6
2 changed files with 1246 additions and 1195 deletions
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2414
tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/codelabs/third_party/seq2seq_membership_inference/seq2seq_membership_inference_codelab.ipynb vendored
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25
tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/seq2seq_mia.py
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@ -19,9 +19,9 @@ Contains seq2seq specific logic for attack data structures, attack data
generation, generation,
and the logistic regression membership inference attack. and the logistic regression membership inference attack.
""" """
import dataclasses
from typing import Iterator, List from typing import Iterator, List
from dataclasses import dataclass
import numpy as np import numpy as np
from scipy.stats import rankdata from scipy.stats import rankdata
from sklearn import metrics from sklearn import metrics
@ -46,7 +46,7 @@ def _is_iterator(obj, obj_name):
raise ValueError('%s should be a generator.' % obj_name) raise ValueError('%s should be a generator.' % obj_name)
@dataclass @dataclasses.dataclass
class Seq2SeqAttackInputData: class Seq2SeqAttackInputData:
"""Input data for running an attack on seq2seq models. """Input data for running an attack on seq2seq models.
@ -229,8 +229,13 @@ def _get_batch_loss_metrics(batch_logits: np.ndarray,
tf.keras.backend.constant(sequence_labels), tf.keras.backend.constant(sequence_labels),
tf.keras.backend.constant(sequence_logits), tf.keras.backend.constant(sequence_logits),
from_logits=True) from_logits=True)
batch_loss += sequence_loss.numpy().sum() if tf.executing_eagerly():
batch_loss += sequence_loss.numpy().sum()
else:
batch_loss += tf.reduce_sum(sequence_loss)
if not tf.executing_eagerly():
batch_loss = batch_loss.eval(session=tf.compat.v1.Session())
return batch_loss / batch_length, batch_length return batch_loss / batch_length, batch_length
@ -250,9 +255,15 @@ def _get_batch_accuracy_metrics(batch_logits: np.ndarray,
preds = tf.metrics.sparse_categorical_accuracy( preds = tf.metrics.sparse_categorical_accuracy(
tf.keras.backend.constant(sequence_labels), tf.keras.backend.constant(sequence_labels),
tf.keras.backend.constant(sequence_logits)) tf.keras.backend.constant(sequence_logits))
batch_correct_preds += preds.numpy().sum() if tf.executing_eagerly():
batch_correct_preds += preds.numpy().sum()
else:
batch_correct_preds += tf.reduce_sum(preds)
batch_total_preds += len(sequence_labels) batch_total_preds += len(sequence_labels)
if not tf.executing_eagerly():
batch_correct_preds = batch_correct_preds.eval(
session=tf.compat.v1.Session())
return batch_correct_preds, batch_total_preds return batch_correct_preds, batch_total_preds
@ -302,10 +313,8 @@ def create_seq2seq_attacker_data(
labels_all = np.concatenate(((np.zeros(ntrain)), (np.ones(ntest)))) labels_all = np.concatenate(((np.zeros(ntrain)), (np.ones(ntest))))
# Perform a train-test split # Perform a train-test split
features_train, features_test, \ features_train, features_test, is_training_labels_train, is_training_labels_test = model_selection.train_test_split(
is_training_labels_train, is_training_labels_test = \ features_all, labels_all, test_size=test_fraction, stratify=labels_all)
model_selection.train_test_split(
features_all, labels_all, test_size=test_fraction, stratify=labels_all)
# Populate accuracy, loss fields in privacy report metadata # Populate accuracy, loss fields in privacy report metadata
privacy_report_metadata.loss_train = loss_train privacy_report_metadata.loss_train = loss_train