https://www.tensorflow.org/responsible_ ... on_privacy
Ниже. — это код вместе с версиями TensorFlow и TensorFlow Privacy, установленными на моем компьютере.
Код: Выделить всё
!pip show tensorflow
Name: tensorflow
Version: 2.14.1
!pip show tensorflow_privacy
Name: tensorflow_privacy
Version: 0.9.0
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
import numpy as np
tf.get_logger().setLevel('ERROR')
WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/compat/v2_compat.py:108: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
import tensorflow_privacy
from tensorflow_privacy.privacy.analysis import compute_dp_sgd_privacy
train, test = tf.keras.datasets.mnist.load_data()
train_data, train_labels = train
test_data, test_labels = test
train_data = np.array(train_data, dtype=np.float32) / 255
test_data = np.array(test_data, dtype=np.float32) / 255
train_data = train_data.reshape(train_data.shape[0], 28, 28, 1)
test_data = test_data.reshape(test_data.shape[0], 28, 28, 1)
train_labels = np.array(train_labels, dtype=np.int32)
test_labels = np.array(test_labels, dtype=np.int32)
train_labels = tf.keras.utils.to_categorical(train_labels, num_classes=10)
test_labels = tf.keras.utils.to_categorical(test_labels, num_classes=10)
assert train_data.min() == 0.
assert train_data.max() == 1.
assert test_data.min() == 0.
assert test_data.max() == 1.
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 [==============================] - 2s 0us/step
epochs = 3
batch_size = 250
l2_norm_clip = 1.5
noise_multiplier = 1.3
num_microbatches = 250
learning_rate = 0.25
if batch_size % num_microbatches != 0:
raise ValueError('Batch size should be an integer multiple of the number of microbatches')
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(16, 8,
strides=2,
padding='same',
activation='relu',
input_shape=(28, 28, 1)),
tf.keras.layers.MaxPool2D(2, 1),
tf.keras.layers.Conv2D(32, 4,
strides=2,
padding='valid',
activation='relu'),
tf.keras.layers.MaxPool2D(2, 1),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(10)
])
optimizer = tensorflow_privacy.DPKerasSGDOptimizer(
l2_norm_clip=l2_norm_clip,
noise_multiplier=noise_multiplier,
num_microbatches=num_microbatches,
learning_rate=learning_rate)
loss = tf.keras.losses.CategoricalCrossentropy(
from_logits=True, reduction=tf.losses.Reduction.NONE)
model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
model.fit(train_data, train_labels,
epochs=epochs,
validation_data=(test_data, test_labels),
batch_size=batch_size)
Train on 60000 samples, validate on 10000 samples
Epoch 1/3
60000/60000 [==============================] - 204s 3ms/sample - loss: 0.8745 - acc: 0.7297 - val_loss: 0.3688 - val_acc: 0.8954
Epoch 2/3
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training_v1.py:2335: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
updates = self.state_updates
60000/60000 [==============================] - 204s 3ms/sample - loss: 0.3743 - acc: 0.9021 - val_loss: 0.3241 - val_acc: 0.9227
Epoch 3/3
60000/60000 [==============================] - 203s 3ms/sample - loss: 0.3471 - acc: 0.9199 - val_loss: 0.3105 - val_acc: 0.9339
compute_dp_sgd_privacy.compute_dp_sgd_privacy(n=train_data.shape[0],
batch_size=batch_size,
noise_multiplier=noise_multiplier,
epochs=epochs,
delta=1e-5)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
in ()
----> 1 compute_dp_sgd_privacy.compute_dp_sgd_privacy(n=train_data.shape[0],
2 batch_size=batch_size,
3 noise_multiplier=noise_multiplier,
4 epochs=epochs,
5 delta=1e-5)
AttributeError: module 'tensorflow_privacy.privacy.analysis.compute_dp_sgd_privacy' has no attribute 'compute_dp_sgd_privacy'
Однако TensorFlow показывает следующий результат для приведенного выше кода:
Код: Выделить всё
DP-SGD with sampling rate = 0.417% and noise_multiplier = 1.3 iterated over 720 steps satisfies differential privacy with eps = 0.563 and delta = 1e-05.
The optimal RDP order is 18.0.
(0.5631726490328062, 18.0)
Я хотел бы либо исправить ошибку в справочном коде, предоставленном веб-сайт TensorFlow и понять причину ее возникновения или, если устранить ее невозможно, найти альтернативный подход к конфиденциальности TensorFlow для реализации федеративного обучения (FL) с дифференциальной конфиденциальностью.
Примечание. Я также тестировал аналогичный код с другими версиями TensorFlow и TensorFlow Privacy, но возникли аналогичные проблемы. упорствовать.
Подробнее здесь: https://stackoverflow.com/questions/788 ... ow-privacy
