不能使用 utils_keras.Sequential 仍然认为它不是 Cleverhans 模型

Question

我正在尝试使用 cleverhans 进行显着图方法。

我的模型需要是 keras 顺序的，因此我搜索并发现 cleverhans.utils_keras，Sequential 使用 KerasModelWrapper。但出于某种原因，我仍然认为它应该是 cleverhans 模型。这是堆栈跟踪

TypeError Traceback (most recent call last) in 2 # https://github.com/tensorflow/cleverhans/blob/master/cleverhans/utils_keras.py 3 ----> 4 jsma = SaliencyMapMethod(model, sess=sess) 5 jsma_params = {'theta': 10.0, 'gamma': 0.15, 6 'clip_min': 0., 'clip_max': 1.,

c:\users\jeredriq\appdata\local\programs\python\python35\lib\site-packages\cleverhans\attacks__init__.py in init(self, model, sess, dtypestr, **kwargs) 911 """ 912 --> 913 super(SaliencyMapMethod, self).init(model, sess, dtypestr, **kwargs) 914 915 self.feedable_kwargs = ('y_target',)

c:\users\jeredriq\appdata\local\programs\python\python35\lib\site-packages\cleverhans\attacks__init__.py in init(self, model, sess, dtypestr, **kwargs) 55 56 if not isinstance(model, Model): ---> 57 raise TypeError("The model argument should be an instance of" 58 " the cleverhans.model.Model class.") 59

TypeError: The model argument should be an instance of the cleverhans.model.Model class.

这是我的代码


import numpy as np
from keras import backend
import tensorflow as tf
from keras.callbacks import ModelCheckpoint
from matplotlib import gridspec
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report
from keras.datasets import mnist
from keras.layers import Dense, Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from cleverhans.attacks import FastGradientMethod
from cleverhans.attacks import BasicIterativeMethod
from cleverhans.attacks import SaliencyMapMethod
from cleverhans.attacks import DeepFool

from cleverhans.utils_keras import Sequential


sess =  backend.get_session()
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
y = tf.placeholder(tf.float32, shape=(None, 10))
# Managing Mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train/=255
X_test/=255
y_train_cat = np_utils.to_categorical(y_train)
y_test_cat = np_utils.to_categorical(y_test)
num_classes = y_test_cat.shape[1]

### Defining Model ###

model = Sequential()      #  <-----  I use Sequential from CleverHans

model.add(Conv2D(32, (5, 5), input_shape=(28,28,1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()

history = model.fit(X_train, y_train_cat, epochs=10, batch_size=1024, verbose=1, validation_split=0.7)


### And the problem part ###

jsma = SaliencyMapMethod(model, sess=sess)  # <---- Where I get the exception

jsma_params = {'theta': 10.0, 'gamma': 0.15,
                   'clip_min': 0., 'clip_max': 1.,
                   'y_target': None}

sample_size = 20
one_hot_target = np.zeros((sample_size, 10), dtype=np.float32)
one_hot_target[:, 1] = 1
jsma_params['y_target'] = one_hot_target

X_test_small = X_test[0:sample_size,:]
y_test_small = y_test[0:sample_size]

adv_x = jsma.generate_np(X_test_small, **jsma_params)

我在 github 上也有同样的问题。

Answer 1

cleverhans.utils_keras中定义的Sequential仍然是keras的Sequential模型。需要的是cleverhans.model.Model。可以使用 KerasModelWrapper class.

包装 keras 模型以提供此行为

替换

jsma = SaliencyMapMethod(model, sess=sess)

和

jsma = SaliencyMapMethod(KerasModelWrapper(model), sess=sess)

不能使用 utils_keras.Sequential 仍然认为它不是 Cleverhans 模型

Cant Use utils_keras.Sequential still thinks its not Cleverhans model

python

keras

tensorflow

cleverhans