我正在尝试使用cleverhans 进行显着性映射方法。
我的模型需要是 keras 顺序的，因此我搜索并找到了cleverhans.utils_keras，Sequential 使用 KerasModelWrapper。但是由于某种原因，我仍然认为它应该是cleverhans模型。这是堆栈跟踪
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" thecleverhans.model.Model class.") 59
TypeError：模型参数应该是cleverhans.model.Model 类的实例。
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。