def custom_predictions(path):
    img = ig.load_img(path, target_size=(64, 64), grayscale=False, color_mode='rgb', interpolation='nearest')
    plt.imshow(img)
    img = np.expand_dims(img, axis=0)
    img.reshape(1,64,64,3)
    print(np.shape(img))
    prediction = np.argmax(model.predict(np.array(img)))
#     result=loaded_model.predict_classes(img)
    plt.title(labels[prediction])
    plt.show()



custom_predictions('Desktop/data1a/training/00-damage/0007.JPEG')

(1, 64, 64, 3)
---------------------------------------------------------------------------
InternalError                             Traceback (most recent call last)
Input In [7], in <cell line: 1>()
----> 1 custom_predictions('Desktop/data1a/training/00-damage/0007.JPEG')

Input In [6], in custom_predictions(path)
      5     img.reshape(1,64,64,3)
      6     print(np.shape(img))
----> 7     prediction = np.argmax(model.predict(np.array(img)))
      8 #     result=loaded_model.predict_classes(img)
      9     plt.title(labels[prediction])

File ~\anaconda3\envs\tf2.5\lib\site-packages\tensorflow\python\keras\engine\training.py:1696, in Model.predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1690   except ValueError:
   1691     warnings.warn('Using Model.predict with '
   1692                   'MultiWorkerDistributionStrategy or TPUStrategy and '
   1693                   'AutoShardPolicy.FILE might lead to out-of-order result'
   1694                   '. Consider setting it to AutoShardPolicy.DATA.')
-> 1696 data_handler = data_adapter.get_data_handler(
   1697     x=x,
   1698     batch_size=batch_size,
   1699     steps_per_epoch=steps,
   1700     initial_epoch=0,
   1701     epochs=1,
   1702     max_queue_size=max_queue_size,
   1703     workers=workers,
   1704     use_multiprocessing=use_multiprocessing,
   1705     model=self,
   1706     steps_per_execution=self._steps_per_execution)
   1708 # Container that configures and calls `tf.keras.Callback`s.
   1709 if not isinstance(callbacks, callbacks_module.CallbackList):

字符串
文件~\anaconda3\envs\tf2.5\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py：1364，in get_data_handler（*args，**kwargs）1362 if getattr（kwargs[“model”]，“_cluster_coordinator”，None）：1363 return _transferterCoordinatorDataController（*args，**kwargs）-> 1364 return DataController（*args，**kwargs）

File ~\anaconda3\envs\tf2.5\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py:1150, in DataHandler.__init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)
       1148 else:
       1149   self._steps_per_execution = steps_per_execution
    -> 1150   self._steps_per_execution_value = steps_per_execution.numpy().item()
       1152 adapter_cls = select_data_adapter(x, y)
       1153 self._verify_data_adapter_compatibility(adapter_cls)
    
    File ~\anaconda3\envs\tf2.5\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:628, in BaseResourceVariable.numpy(self)
    626 def numpy(self):
    627   if context.executing_eagerly():
--> 628     return self.read_value().numpy()
    629   raise NotImplementedError(
    630       "numpy() is only available when eager execution is enabled.")

型
File ~\anaconda 3\envs\tf 2.5\lib\site-packages\tensorflow\python\framework\ops.py：1094，in _AncherTensorBase.numpy（self）1071“将此Tensor的内容复制到NumPy数组或标量中。1072 1073与NumPy数组不同，Tensor是不可变的，因此此方法必须复制（...）1091 NumPy dtype. 1092“1093 # TODO（slebedev）：考虑避免对非CPU或远程Tensor进行复制。-> 1094 maybe_numpy = self._numpy（）# pylint：disable=protected-access 1095 return maybe_bytes.copy（）if isinstance（maybe_bytes，np.ndarray）else maybe_bytes

File ~\anaconda3\envs\tf2.5\lib\site-packages\tensorflow\python\framework\ops.py:1062, in _EagerTensorBase._numpy(self)
   1060   return self._numpy_internal()
   1061 except core._NotOkStatusException as e:  # pylint: disable=protected-access
-> 1062   six.raise_from(core._status_to_exception(e.code, e.message), None)

型
文件：3，在raise_from（value，from_value）中
InternalError：流在完成之前没有阻止主机;已经处于错误状态