在 Keras ResNet 上激活 FGSM 时形状等级不匹配
Shape rank mismatch when activating FGSM on Keras ResNet
我正在尝试使用 keras
的 ResNet 50 激活 FGSM,但出现错误:
ValueError: Shape must be rank 4 but is rank 5 for 'model_1/conv1_pad/Pad' (op: 'Pad') with input shapes: [2,1,224,224,3], [4,2].
我的代码是:
from keras.applications.resnet50 import ResNet50
model = ResNet50(weights='imagenet')
images = ['images/dog1.jpg', 'images/image_0001.jpg']
for image_path in images:
img = image.load_img(image_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
X.append(x)
wrap = KerasModelWrapper(model)
target = [np.zeros((1000,))]
target[0][0] = 1
target = np.repeat(target, len(X), axis=0)
fgsm_params = {
'eps': 0.05,
# 'clip_min': 0.,
# 'clip_max': 1.,
'y_target': target
}
X = np.array(X)
x_tensor = K.variable(X)
print(type(X))
print(X.shape)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
fgsm = FastGradientMethod(wrap, sess=sess)
adv = fgsm.generate(x_tensor, **fgsm_params)
系统配置
- OS
- Python 版本 3.7
- TensorFlow 版本 1.13
原来我需要将 x
更改为 0 和 1 之间的浮点数:
for image_path in file_list:
img = image.load_img(image_path, target_size=(224, 224))
x = image.img_to_array(img)
x = x.astype('float32')
x /= 255
X.append(x)
我正在尝试使用 keras
的 ResNet 50 激活 FGSM,但出现错误:
ValueError: Shape must be rank 4 but is rank 5 for 'model_1/conv1_pad/Pad' (op: 'Pad') with input shapes: [2,1,224,224,3], [4,2].
我的代码是:
from keras.applications.resnet50 import ResNet50
model = ResNet50(weights='imagenet')
images = ['images/dog1.jpg', 'images/image_0001.jpg']
for image_path in images:
img = image.load_img(image_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
X.append(x)
wrap = KerasModelWrapper(model)
target = [np.zeros((1000,))]
target[0][0] = 1
target = np.repeat(target, len(X), axis=0)
fgsm_params = {
'eps': 0.05,
# 'clip_min': 0.,
# 'clip_max': 1.,
'y_target': target
}
X = np.array(X)
x_tensor = K.variable(X)
print(type(X))
print(X.shape)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
fgsm = FastGradientMethod(wrap, sess=sess)
adv = fgsm.generate(x_tensor, **fgsm_params)
系统配置 - OS - Python 版本 3.7 - TensorFlow 版本 1.13
原来我需要将 x
更改为 0 和 1 之间的浮点数:
for image_path in file_list:
img = image.load_img(image_path, target_size=(224, 224))
x = image.img_to_array(img)
x = x.astype('float32')
x /= 255
X.append(x)