не удалось создать дескриптор примитива представления

Question

Я получил эту ошибку, когда запустил код «Проверка костей». Ссылка на исходный код: https://github.com/foamliu/Keypoints Ошибка:

Traceback (most recent call last):
  File "train.py", line 95, in <module>
    use_multiprocessing=False
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1418, in fit_generator
    initial_epoch=initial_epoch)
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training_generator.py", line 217, in fit_generator
    class_weight=class_weight)
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1217, in train_on_batch
    outputs = self.train_function(ins)
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 2715, in __call__
    return self._call(inputs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 2675, in _call
    fetched = self._callable_fn(*array_vals)
  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1439, in __call__
    run_metadata_ptr)
  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 528, in __exit__
    c_api.TF_GetCode(self.status.status))
# tensorflow.python.framework.errors_impl.AbortedError: Operation received an exception:Status: 5, message: could not create a view primitive descriptor, in file tensorflow/core/kernels/mkl_slice_op.cc:435
      [[{{nodetraining/Adam/gradients/concatenate_5/concat_grad/Slice_2}}]]  #



new_model.fit_generator(train_gen(),
                        steps_per_epoch=num_train_samples // batch_size,
                        validation_data=valid_gen(),
                        validation_steps=num_valid_samples // batch_size,
                        epochs=epochs,
                        verbose=1,
                        callbacks=callbacks,
                        use_multiprocessing=False
                        )

def fit_generator(self, generator,
                  steps_per_epoch=None,
                  epochs=1,
                  verbose=1,
                  callbacks=None,
                  validation_data=None,
                  validation_steps=None,
                  class_weight=None,
                  max_queue_size=10,
                  workers=1,
                  use_multiprocessing=False,
                  shuffle=True,
                  initial_epoch=0):

    return training_generator.fit_generator(
        self, generator,
        steps_per_epoch=steps_per_epoch,
        epochs=epochs,
        verbose=verbose,
        callbacks=callbacks,
        validation_data=validation_data,
        validation_steps=validation_steps,
        class_weight=class_weight,
        max_queue_size=max_queue_size,
        workers=workers,
        use_multiprocessing=use_multiprocessing,
        shuffle=shuffle,
        initial_epoch=initial_epoch)