我试图获得相对于Keras输入的损耗梯度。我正在执行以下操作：

import tensorflow as tf
from tensorflow.python.keras.models import Model,Sequential
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.losses import mean_squared_error

# the Keras autoencoder model is defined below
model = Sequential()
model.add(Conv1D(filters=64,kernel_size=10,strides=1,activation='relu',input_shape=(data_shape[1],data_shape[2]),padding="same"))
model.add(Conv1D(32,10,padding="same"))
model.add(MaxPooling1D(4))
model.add(Conv1D(32,padding="same"))
model.add(UpSampling1D(4))
model.add(Conv1D(data_shape[2],padding="same"))
model.add(Conv1D(data_shape[2],activation=None,padding="same"))

# get gradient of the loss w.r.t. the input
loss = mean_squared_error(model.input,model.output)
grad = K.gradients(loss,model.input)[0]
func = K.function(model.input,[loss,grad])

session = K.get_session()
session.run(tf.global_variables_initializer())
loss_gradient = session.run(func,feed_dict={model.input: x})

我收到以下错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\Anaconda3\envs\tf_gpu_pytorch_gpu\lib\site-packages\tensorflow\python\client\session.py in __init__(self,fetches,contraction_fn)
    299         self._unique_fetches.append(ops.get_default_graph().as_graph_element(
--> 300             fetch,allow_tensor=True,allow_operation=True))
    301       except TypeError as e:

~\Anaconda3\envs\tf_gpu_pytorch_gpu\lib\site-packages\tensorflow\python\framework\ops.py in as_graph_element(self,obj,allow_tensor,allow_operation)
   3477     with self._lock:
-> 3478       return self._as_graph_element_locked(obj,allow_operation)
   3479 

~\Anaconda3\envs\tf_gpu_pytorch_gpu\lib\site-packages\tensorflow\python\framework\ops.py in _as_graph_element_locked(self,allow_operation)
   3566       raise TypeError("Can not convert a %s into a %s." % (type(obj).__name__,-> 3567                                                            types_str))
   3568 

TypeError: Can not convert a GraphExecutionFunction into a Tensor or Operation.

During handling of the above exception,another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-28-28014aeac718> in <module>
----> 1 a = session.run(func,feed_dict={model.input: x})

~\Anaconda3\envs\tf_gpu_pytorch_gpu\lib\site-packages\tensorflow\python\client\session.py in run(self,feed_dict,options,run_metadata)
    927     try:
    928       result = self._run(None,options_ptr,--> 929                          run_metadata_ptr)
    930       if run_metadata:
    931         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~\Anaconda3\envs\tf_gpu_pytorch_gpu\lib\site-packages\tensorflow\python\client\session.py in _run(self,handle,run_metadata)
   1135     # Create a fetch handler to take care of the structure of fetches.
   1136     fetch_handler = _FetchHandler(
-> 1137         self._graph,feed_dict_tensor,feed_handles=feed_handles)
   1138 
   1139     # Run request and get response.

~\Anaconda3\envs\tf_gpu_pytorch_gpu\lib\site-packages\tensorflow\python\client\session.py in __init__(self,graph,feeds,feed_handles)
    469     """
    470     with graph.as_default():
--> 471       self._fetch_mapper = _FetchMapper.for_fetch(fetches)
    472     self._fetches = []
    473     self._targets = []

~\Anaconda3\envs\tf_gpu_pytorch_gpu\lib\site-packages\tensorflow\python\client\session.py in for_fetch(fetch)
    269         if isinstance(fetch,tensor_type):
    270           fetches,contraction_fn = fetch_fn(fetch)
--> 271           return _ElementFetchMapper(fetches,contraction_fn)
    272     # Did not find anything.
    273     raise TypeError('Fetch argument %r has invalid type %r' % (fetch,~\Anaconda3\envs\tf_gpu_pytorch_gpu\lib\site-packages\tensorflow\python\client\session.py in __init__(self,contraction_fn)
    302         raise TypeError('Fetch argument %r has invalid type %r,'
    303                         'must be a string or Tensor. (%s)' %
--> 304                         (fetch,type(fetch),str(e)))
    305       except ValueError as e:
    306         raise ValueError('Fetch argument %r cannot be interpreted as a '

TypeError: Fetch argument <tensorflow.python.keras.backend.GraphExecutionFunction object at 0x000002641A998240> has invalid type <class 'tensorflow.python.keras.backend.GraphExecutionFunction'>,must be a string or Tensor. (Can not convert a GraphExecutionFunction into a Tensor or Operation.)

我已经搜索了一些计算所需梯度的解决方案：K.gradients(loss,input_img)[0] return "None". (Keras CNN visualization with tensorflow backend)

How to compute loss gradient w.r.t to model inputs in a Keras model?

此人谈论使用我正在使用的K.get_session（）：keras/tensorflow model: gradient w.r.t. input return the same (wrong?) value for all input data

我想计算损失w.r.t.输入，然后我将使用它来更新输入样本x。我只想使用Keras，而避免使用Keras模型包装器在Tensorflow中进行梯度计算，这在此博客中建议：https://github.com/tensorflow/cleverhans/blob/master/cleverhans_tutorials/mnist_tutorial_keras_tf.py

我找不到解决方案或发布有关此错误的信息。请帮忙，非常感谢！