试图将“ x”转换为张量，但失败了。 错误：不支持任何值

Question

我正在尝试使用在cleverhans.attacks中实现的cleverhans.attacks攻击简单的前馈神经网络。 该网络是使用tensorflow实现的非常基本的网络，实现了抽象类cleverhans.model.Model ：

 import tensorflow as tf import numpy as np from cleverhans.model import Model class TFModel(Model): # A basic 2 layer NN. def __init__(self): self.x = tf.placeholder(tf.float32, shape=(None, 3), name='x') self.y = tf.placeholder(tf.float32, shape=(None, 2), name='y') self.w1 = tf.Variable(initial_value=[[1., 2.], [1., 2.], [1., 2.]], name='w1') self.b1 = tf.Variable(initial_value=[1., 2.], name='b1') self.dense1 = tf.add(tf.matmul(self.x, self.w1), self.b1, name='dense1') self.out1 = tf.nn.softmax(self.dense1, name='out1') self.w2 = tf.Variable(initial_value=[[1., 2.], [1., 2.]], name='w2') self.b2 = tf.Variable(initial_value=[1., 2.], name='b2') self.dense2 = tf.add(tf.matmul(self.out1, self.w2), self.b2, name='dense2') # should be called 'logits' self.out2 = tf.nn.softmax(self.dense2, name='out2') self.outputs = {'layer1': self.out1, 'logits': self.dense2, 'softmax': self.out2} def get_layer_names(self): """ :return: a list of names for the layers that can be exposed by this model abstraction. """ return list(self.outputs.keys()) def fprop(self, x): """ Exposes all the layers of the model returned by get_layer_names. :param x: A symbolic representation (Tensor) of the network input :return: A dictionary mapping layer names to the symbolic representation of their output. """ return self.outputs 

用FastGradientMethod实现的攻击效果很好，如下所示：

 model = TFModel() sess = tf.Session() from cleverhans.attacks import FastGradientMethod fgsm_params = {'eps': 0.3, 'clip_min': 0., 'clip_max': 1.} fgsm = FastGradientMethod(model, sess=sess) adv_x = fgsm.generate(model.x, **fgsm_params) 

但是使用BasicIterativeMethod攻击BasicIterativeMethod ：

 from cleverhans.attacks import BasicIterativeMethod bim_params = {'eps_iter': 0.01, 'nb_iter': 100, 'clip_min': 0., 'clip_max': 1.} bim = BasicIterativeMethod(model, sess=sess) adv_x = bim.generate(model.x, **bim_params) 

这是完整的错误消息：

 rror Traceback (most recent call last)~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords) 509 as_ref=input_arg.is_ref, --> 510 preferred_dtype=default_dtype) 511 except TypeError as err: ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx) 1103 if ret is None: -> 1104 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) 1105 ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref) 234 _ = as_ref --> 235 return constant(v, dtype=dtype, name=name) 236 ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py in constant(value, dtype, shape, name, verify_shape) 213 tensor_util.make_tensor_proto( --> 214 value, dtype=dtype, shape=shape, verify_shape=verify_shape)) 215 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype) ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape) 419 if values is None: --> 420 raise ValueError("None values not supported.") 421 # if dtype is provided, forces numpy array to be the type ValueError: None values not supported. During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last)~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords) 523 observed = ops.internal_convert_to_tensor( --> 524 values, as_ref=input_arg.is_ref).dtype.name 525 except ValueError as err: ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx) 1103 if ret is None: -> 1104 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) 1105 ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref) 234 _ = as_ref --> 235 return constant(v, dtype=dtype, name=name) 236 ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py in constant(value, dtype, shape, name, verify_shape) 213 tensor_util.make_tensor_proto( --> 214 value, dtype=dtype, shape=shape, verify_shape=verify_shape)) 215 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype) ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape) 419 if values is None: --> 420 raise ValueError("None values not supported.") 421 # if dtype is provided, forces numpy array to be the type ValueError: None values not supported. During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last)<ipython-input-7-8ab4a61acce1> in <module>() 8 print('hello') 9 ---> 10 adv_x = bim.generate(model.x, **bim_params) c:\\users\\mjafarnia\\src\\cleverhans\\cleverhans\\attacks.py in generate(self, x, **kwargs) 400 sess=self.sess) 401 # Compute this step's perturbation --> 402 adv_x = FGM.generate(x + eta, **fgm_params) 403 404 # Clipping perturbation according to clip_min and clip_max c:\\users\\mjafarnia\\src\\cleverhans\\cleverhans\\attacks.py in generate(self, x, **kwargs) 284 ord=self.ord, clip_min=self.clip_min, 285 clip_max=self.clip_max, --> 286 targeted=(self.y_target is not None)) 287 288 def parse_params(self, eps=0.3, ord=np.inf, y=None, y_target=None, c:\\users\\mjafarnia\\src\\cleverhans\\cleverhans\\attacks_tf.py in fgm(x, preds, y, eps, ord, clip_min, clip_max, targeted) 65 if ord == np.inf: 66 # Take sign of gradient ---> 67 normalized_grad = tf.sign(grad) 68 # The following line should not change the numerical results. 69 # It applies only because `normalized_grad` is the output of ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py in sign(x, name) 451 indices=x.indices, values=x_sign, dense_shape=x.dense_shape) 452 else: --> 453 return gen_math_ops.sign(x, name=name) 454 455 ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py in sign(x, name) 6874 if _ctx is None or not _ctx._eager_context.is_eager: 6875 _, _, _op = _op_def_lib._apply_op_helper( -> 6876 "Sign", x=x, name=name) 6877 _result = _op.outputs[:] 6878 _inputs_flat = _op.inputs ~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords) 526 raise ValueError( 527 "Tried to convert '%s' to a tensor and failed. Error: %s" % --> 528 (input_name, err)) 529 prefix = ("Input '%s' of '%s' Op has type %s that does not match" % 530 (input_name, op_type_name, observed)) ValueError: Tried to convert 'x' to a tensor and failed. Error: None values not supported. 

Answer 1

基本迭代方法（BIM）多次应用快速梯度符号方法（FGSM）（使用您指定的参数进行100次）。 BIM的每个步骤都将FGSM应用于BIM上一步的结果。 因此，您的模型对象需要具有方法fprop ，该方法将针对作为参数传递的任何输入张量返回模型的输出。 您实现的当前类始终在相同的占位符self.x上返回模型的输出。 您将必须使用范围来定义fprop方法，该方法可以采用任意张量x并在该输入上返回模型的输出。 您可以在tutorials文件夹中找到一个简单的模型实现ModelBasicCNN的示例： https : //github.com/tensorflow/cleverhans/blob/master/cleverhans_tutorials/tutorial_models.py