使用双向图层会导致错误：CancelledError：[_Derived_] RecvAsync被取消

如何解决使用双向图层会导致错误：CancelledError：[_Derived_] RecvAsync被取消

我遇到一个问题，每当我在模型中包括双向图层包装器时，它就会在训练期间导致崩溃，并出现以下错误：

CancelledError                            Traceback (most recent call last)
<ipython-input-7-7944b517869f> in <module>
      1 history = model.fit(train_dataset,epochs=10,2                     validation_data=test_dataset,----> 3                     validation_steps=30)

D:\Python\anaconda\envs\tf-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self,*args,**kwargs)
    106   def _method_wrapper(self,**kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self,**kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

D:\Python\anaconda\envs\tf-gpu\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self,x,y,batch_size,epochs,verbose,callbacks,validation_split,validation_data,shuffle,class_weight,sample_weight,initial_epoch,steps_per_epoch,validation_steps,validation_batch_size,validation_freq,max_queue_size,workers,use_multiprocessing)
   1096                 batch_size=batch_size):
   1097               callbacks.on_train_batch_begin(step)
-> 1098               tmp_logs = train_function(iterator)
   1099               if data_handler.should_sync:
   1100                 context.async_wait()

D:\Python\anaconda\envs\tf-gpu\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self,**kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args,**kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

D:\Python\anaconda\envs\tf-gpu\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self,**kwds)
    805       # In this case we have created variables on the first call,so we run the
    806       # defunned version which is guaranteed to never create variables.
--> 807       return self._stateless_fn(*args,**kwds)  # pylint: disable=not-callable
    808     elif self._stateful_fn is not None:
    809       # Release the lock early so that multiple threads can perform the call

D:\Python\anaconda\envs\tf-gpu\lib\site-packages\tensorflow\python\eager\function.py in __call__(self,**kwargs)
   2827     with self._lock:
   2828       graph_function,args,kwargs = self._maybe_define_function(args,kwargs)
-> 2829     return graph_function._filtered_call(args,kwargs)  # pylint: disable=protected-access
   2830 
   2831   @property

D:\Python\anaconda\envs\tf-gpu\lib\site-packages\tensorflow\python\eager\function.py in _filtered_call(self,kwargs,cancellation_manager)
   1846                            resource_variable_ops.BaseResourceVariable))],1847         captured_inputs=self.captured_inputs,-> 1848         cancellation_manager=cancellation_manager)
   1849 
   1850   def _call_flat(self,captured_inputs,cancellation_manager=None):

D:\Python\anaconda\envs\tf-gpu\lib\site-packages\tensorflow\python\eager\function.py in _call_flat(self,cancellation_manager)
   1922       # No tape is watching; skip to running the function.
   1923       return self._build_call_outputs(self._inference_function.call(
-> 1924           ctx,cancellation_manager=cancellation_manager))
   1925     forward_backward = self._select_forward_and_backward_functions(
   1926         args,D:\Python\anaconda\envs\tf-gpu\lib\site-packages\tensorflow\python\eager\function.py in call(self,ctx,cancellation_manager)
    548               inputs=args,549               attrs=attrs,--> 550               ctx=ctx)
    551         else:
    552           outputs = execute.execute_with_cancellation(

D:\Python\anaconda\envs\tf-gpu\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name,num_outputs,inputs,attrs,name)
     58     ctx.ensure_initialized()
     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle,device_name,op_name,---> 60                                         inputs,num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:

CancelledError:  [_Derived_]RecvAsync is cancelled.
     [[{{node gradient_tape/sequential/embedding/embedding_lookup/Reshape/_38}}]] [Op:__inference_train_function_5988]

Function call stack:
train_function

我正在运行Tensorflow教程中的确切代码：https://www.tensorflow.org/tutorials/text/text_classification_rnn#prepare_the_data_for_training。

此外，我尝试过将这些行包括在内 ''' physical_devices = tf.config.list_physical_devices（'GPU'） tf.config.experimental.set_memory_growth（physical_devices [0]，是） ''' 在我的程序开始时，我遇到了同样的问题。

我的Tensorflow版本是2.3.0，Cuda版本是10.1.243，CUDNN版本是7.6.5。

有人知道这个问题的可能解决方案吗？

解决方法

使用Google colab，上述tutorial对我来说很好用。

您的Tensorflow版本与Cuda和CUDNN版本兼容，这不成问题。

问题可能是内存使用错误，应解决此问题。

r"""
#Fra command prompt:
#C:\Users\David>C:\Users\David\Desktop\IN1900\uke38\quadratic_roots_cml.py 1 0 -1
#The quadratic formula with used values gives two roots 1.0 and -1.0
#(koden funker ikke på et eller annet magisk vis når jeg bruker
"""