使用保存的tensorflow模型预测图像并对图像运行攻击

Question

我有一个Tensorflow（完全训练的imagenet预训练模型）的预训练模型，采用inception\u resnet_v2网络架构。现在，我使用傻瓜箱对医学图像使用对抗性攻击并得到预测。在

但是每次模型返回一个不同的预测值（一个具有不同概率的不同类）图像。Am我重新初始化了重量？而且，对敌对图像的预测不起作用，每次都会抛出一个非类型错误，我无法使用任何攻击。在

import tensorflow as tf
import numpy as np
from PIL import Image
from nets import nets_factory
import matplotlib.pyplot as plt

image = np.asarray(Image.open('1.jpg'))
data_tf = tf.convert_to_tensor(image, np.float32)
data_tf = tf.reshape(data_tf, [1, 299, 299, 3])
arg_scope = nets_factory.arg_scopes_map['inception_resnet_v2']()

graph = tf.get_default_graph()
with slim.arg_scope(arg_scope):
(logits, end_points) = \
    nets_factory.networks_map['inception_resnet_v2'](inputs=data_tf, num_classes=2, is_training=False)

with foolbox.models.TensorFlowModel(data_tf, logits, (0, 255)) as model:
    init_op = tf.global_variables_initializer()
    model.session.run(init_op)
    attack = foolbox.attacks.FGSM(model)
    saver = tf.train.import_meta_graph(
    '/local-scratch/arkadeep/1_June/tf_classification/experiment/final_model/model.ckpt-30589.meta',
    clear_devices=True)
    saver.restore(model.session,
              '/local-scratch/arkadeep/1_June/tf_classification/experiment/final_model/model.ckpt-30589')

    example_label = np.argmax(model.predictions(image))
    print(example_label)
    print(foolbox.utils.softmax(model.predictions(image))[0])

    adversarial = attack(image, example_label, unpack=False)
    print(np.argmax(model.predictions(adversarial.image)))
    print(foolbox.utils.softmax(model.predictions(adversarial))[0])

    print(adversarial.distance)

这是堆栈跟踪：

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