Raccoon 데이터 세트에서 GPU대신 CPU를 사용하려면 어떻게 해야하나요?

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AssertionError                            Traceback (most recent call last)
<ipython-input-66-4070cbde17ab> in <module>
     14 epochs_cnt = 100
     15 
---> 16 train_yolo(pretrained_path, annotation_path,classes_path, anchors_path, log_dir,trained_model_name, b_size, epochs_cnt)

<ipython-input-65-229740a9ec0a> in train_yolo(pretrained_path, annotation_path, classes_path, anchors_path, log_dir, trained_model_name, b_size, epochs_cnt)
     57                     epochs=int(epochs_cnt/2),
     58                     initial_epoch=0,
---> 59                     callbacks=[logging, checkpoint])
     60             model.save_weights(log_dir + trained_model_name+'_stage_1.h5')
     61 

~\anaconda3\envs\115t\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~\anaconda3\envs\115t\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1416             use_multiprocessing=use_multiprocessing,
   1417             shuffle=shuffle,
-> 1418             initial_epoch=initial_epoch)
   1419 
   1420     @interfaces.legacy_generator_methods_support

~\anaconda3\envs\115t\lib\site-packages\keras\engine\training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    179             batch_index = 0
    180             while steps_done < steps_per_epoch:
--> 181                 generator_output = next(output_generator)
    182 
    183                 if not hasattr(generator_output, '__len__'):

~\anaconda3\envs\115t\lib\site-packages\keras\utils\data_utils.py in get(self)
    707                     "`use_multiprocessing=False, workers > 1`."
    708                     "For more information see issue #1638.")
--> 709             six.reraise(*sys.exc_info())

~\anaconda3\envs\115t\lib\site-packages\six.py in reraise(tp, value, tb)
    701             if value.__traceback__ is not tb:
    702                 raise value.with_traceback(tb)
--> 703             raise value
    704         finally:
    705             value = None

~\anaconda3\envs\115t\lib\site-packages\keras\utils\data_utils.py in get(self)
    683         try:
    684             while self.is_running():
--> 685                 inputs = self.queue.get(block=True).get()
    686                 self.queue.task_done()
    687                 if inputs is not None:

~\anaconda3\envs\115t\lib\multiprocessing\pool.py in get(self, timeout)
    642             return self._value
    643         else:
--> 644             raise self._value
    645 
    646     def _set(self, i, obj):

~\anaconda3\envs\115t\lib\multiprocessing\pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
    117         job, i, func, args, kwds = task
    118         try:
--> 119             result = (True, func(*args, **kwds))
    120         except Exception as e:
    121             if wrap_exception and func is not _helper_reraises_exception:

~\anaconda3\envs\115t\lib\site-packages\keras\utils\data_utils.py in next_sample(uid)
    624         The next value of generator `uid`.
    625     """
--> 626     return six.next(_SHARED_SEQUENCES[uid])
    627 
    628 

C:\JupyterNotebook\DLCV\Detection\yolo\keras-yolo3\train.py in data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)
    179         image_data = np.array(image_data)
    180         box_data = np.array(box_data)
--> 181         y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
    182         yield [image_data, *y_true], np.zeros(batch_size)
    183 

C:\JupyterNotebook\DLCV\Detection\yolo\keras-yolo3\yolo3\model.py in preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes)
    246 
    247     '''
--> 248     assert (true_boxes[..., 4]<num_classes).all(), 'class id must be less than num_classes'
    249     num_layers = len(anchors)//3 # default setting
    250     anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]

AssertionError: class id must be less than num_classes