cifar10分成5批,每批有10000张进行生成PGD对抗样本,并将全部对抗样本保存为一个npy文件
时间: 2024-04-30 09:24:10 浏览: 6
以下是Python代码实现:
```python
import numpy as np
import tensorflow as tf
from cleverhans.attacks import ProjectedGradientDescent
from cleverhans.utils_keras import KerasModelWrapper
from keras.datasets import cifar10
# 加载CIFAR-10数据集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# 创建Keras模型
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=x_train.shape[1:]),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(10, activation='softmax')
])
# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# 训练模型
model.fit(x_train, tf.keras.utils.to_categorical(y_train), batch_size=128, epochs=10, validation_data=(x_test, tf.keras.utils.to_categorical(y_test)))
# 使用CleverHans库生成PGD对抗样本
sess = tf.keras.backend.get_session()
wrap = KerasModelWrapper(model)
pgd = ProjectedGradientDescent(wrap, sess=sess)
x_adv = pgd.generate_np(x_test, eps=0.3, eps_iter=0.05, nb_iter=10, clip_min=0., clip_max=1.)
# 将全部对抗样本保存为一个npy文件
np.save('cifar10_pgd_adv.npy', x_adv)
```