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我使用的是最新版本的傻瓜盒（3.3.1），我的代码只是加载了一个 RESNET-50 CNN，为迁移学习应用程序添加了一些层，并按如下方式加载权重。

from numpy.core.records import array
import tensorflow as tf
from keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
import cv2
import os
import numpy as np
import foolbox as FB
from sklearn.metrics import accuracy_score
from scipy.spatial.distance import cityblock
from sklearn.metrics import plot_confusion_matrix
from sklearn.metrics import confusion_matrix
from PIL import Image
import foolbox as FB
import math
from foolbox.criteria import Misclassification

#load model
num_classes = 12

#Load model and prepare it for testing
print("Step 1: Load model and weights")
baseModel = ResNet50(weights=None, include_top=False, input_tensor=Input(shape=(224, 224, 3)))
headModel = baseModel.output
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(512, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(num_classes, activation="softmax")(headModel)
model = Model(inputs=baseModel.input, outputs=headModel)
model.load_weights("RESNET-50/weights/train1-test1.h5")

print("Step 2: prepare testing data")
#features is a set of (1200,10,224,224,3) images
features=np.load("features.npy")
labels=np.load("labels.npy")

现在我想使用傻瓜3.3.1 Carlini和Wagner攻击来攻击它，这是我为傻瓜加载模型的方式

#Lets test the foolbox model
bounds = (0, 1)
fmodel = fb.TensorFlowModel(model, bounds=bounds)

我的数据集被分成每个文档 10 个图像，我将使用 Carlini 和 Wagner 攻击的傻瓜机批量大小为 10 来攻击这 10 个图像

#for each i, I have 10 images
for i in range(0, features.shape[0]):

    print("document "+str(i))

    #Receive current values
    #This is a batch of (10,224,224,3) images
    features_to_test=features[i,:]
    #Get their labels
    labels_to_test=labels[i,:]

    ######################ATTACK IN THE NORMALIZED DOMAIN###########################  
    #lets do the attack
    #We use an interval of epsilons

    epsilons = np.linspace(0.01, 1, num=2)
    attack = fb.attacks.L2CarliniWagnerAttack(fmodel)
    adversarials = attack(features_to_test, labels_to_test, criterion=Misclassification(labels=labels_to_test), epsilons=epsilons)

但是，每当我运行代码时，都会返回给我的错误

Traceback (most recent call last):
File "test_carlini_wagner.py", line 161, in <module>
adversarials = attack(features_to_test, labels_to_test, 
criterion=Misclassification(labels=labels_to_test), epsilons=epsilons)
File "/usr/local/lib/python3.8/dist-packages/foolbox/attacks/base.py", line 410, in 
__call__
xp = self.run(model, x, criterion, early_stop=early_stop, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/foolbox/attacks/carlini_wagner.py", line 100, in run
bounds = model.bounds
AttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 
'bounds'

应该是什么错误？我是否错误地加载了我的模型？我应该为调用的攻击添加新参数吗？如前所述，我在傻瓜3.3.1。

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1 回答 1

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我认为您可能混淆了L2CarliniWagnerAttack. 这是一个带有虚拟数据的简化工作示例：

import tensorflow as tf
import numpy as np

from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from sklearn.metrics import accuracy_score
from scipy.spatial.distance import cityblock
from sklearn.metrics import plot_confusion_matrix
from sklearn.metrics import confusion_matrix
from foolbox import TensorFlowModel
from foolbox.criteria import Misclassification
from foolbox.attacks import L2CarliniWagnerAttack

num_classes = 12

print("Step 1: Load model and weights")
baseModel = ResNet50(weights=None, include_top=False, input_tensor=Input(shape=(224, 224, 3)))
headModel = baseModel.output
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(512, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(num_classes, activation="softmax")(headModel)
model = Model(inputs=baseModel.input, outputs=headModel)

bounds = (0, 1)
fmodel = TensorFlowModel(model, bounds=bounds)
images, labels = tf.random.normal((64, 10, 224, 224, 3)), tf.random.uniform((64, 10,), maxval=13, dtype=tf.int32)

for i in range(0, images.shape[0]):

    print("document "+str(i))
    features_to_test=images[i,:]
    labels_to_test=labels[i,:]

    epsilons = np.linspace(0.01, 1, num=2)
    attack = L2CarliniWagnerAttack()
    adversarials = attack(fmodel, features_to_test, criterion=Misclassification(labels_to_test), epsilons=epsilons)

Step 1: Load model and weights
document 0
document 1
document 2
document 3
document 4
document 5
document 6
...
于 2021-11-23T09:34:52.547 回答