Как я могу реализовать VGG-net в наборе данных другой формы?

Я пытаюсь использовать часть модели VGG16 для передачи обучения с использованием набора данных Fashion MNIST. Данные обрабатываются, и модель указывается, как показано ниже:

    data = keras.datasets.fashion_mnist
    (train_img, train_labels), (test_img, test_labels) = data.load_data()

    train_img.shape, train_labels.shape, test_img.shape, test_labels.shape
    #((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))

    # transform to rgb as required by VGG
    train_img=tf.image.grayscale_to_rgb(tf.expand_dims(train_img, axis=3)) 
    test_img=tf.image.grayscale_to_rgb(tf.expand_dims(test_img, axis=3))

    #resize to minimum size of (32x32
    train_img=tf.image.resize_with_pad(train_img,32,32)
    test_img=tf.image.resize_with_pad(train_img,32,32)

    train_img = train_img / 255.
    test_img = test_img / 255.

    from keras.applications.vgg16 import preprocess_input
    train_img = tf.expand_dims(train_img, axis=0)
    test_img = tf.expand_dims(test_img, axis=0)

    #preprocessing as required by VGG16
    train_img=preprocess_input(train_img)
    test_img=preprocess_input(test_img)

    #using model without last layers
    vgg16=tf.keras.applications.VGG16(include_top=False, weights='imagenet', input_shape=(32,32,3))

    layer_dict = dict([(layer.name, layer) for layer in vgg16.layers])
    #stop at block3_pool and get output
    output = layer_dict['block3_pool'].output

    x = keras.layers.Flatten()(output)
    ...add some fully connected layers  here...
    x = keras.layers.Dense(10, activation='softmax')(x)

    final = keras.models.Model(inputs=vgg16.input, outputs=model)
    for layer in final.layers[:7]:
    layer.trainable = False

    final.fit(train_img, train_labels, epochs=50, validation_split=0.2)

Когда я пытаюсь подобрать модель, я получаю следующую ошибку:

   UnboundLocalError Traceback (most recent call last)
<ipython-input-65-6a0b99b56337> in <module>()
      1 early_stopping_cb=keras.callbacks.EarlyStopping(patience=3, verbose=1,restore_best_weights=True)
----> 2 vgg16_1.fit(train_img, train_labels, epochs=50, validation_split=0.2, callbacks=[early_stopping_cb])

1 frames
/usr/local/lib/python3.6/dist-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, **kwargs)
    857               logs = tmp_logs  # No error, now safe to assign to logs.
    858               callbacks.on_train_batch_end(step, logs)
--> 859         epoch_logs = copy.copy(logs)
    860 
    861         # Run validation.

UnboundLocalError: local variable 'logs' referenced before assignment

Я думал, что это может быть связано с ошибкой формы тренировочного набора, но если вместо этого я использую train_img[0], который имеет форму (60000,32,32,3), то вместо этого я получаю следующую ошибку:

ValueError Traceback (most recent call last)
<ipython-input-66-2b893ccd9ac9> in <module>()
      1 early_stopping_cb=keras.callbacks.EarlyStopping(patience=3, verbose=1,restore_best_weights=True)
----> 2 vgg16_1.fit(train_img[0], train_labels, epochs=50, validation_split=0.2, callbacks=[early_stopping_cb])

10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
     64   def _method_wrapper(self, *args, **kwargs):
     65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
---> 66       return method(self, *args, **kwargs)
     67 
     68     # Running inside `run_distribute_coordinator` already.

/usr/local/lib/python3.6/dist-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, **kwargs)
    849                 batch_size=batch_size):
    850               callbacks.on_train_batch_begin(step)
--> 851               tmp_logs = train_function(iterator)
    852               # Catch OutOfRangeError for Datasets of unknown size.
    853               # This blocks until the batch has finished executing.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    578         xla_context.Exit()
    579     else:
--> 580       result = self._call(*args, **kwds)
    581 
    582     if tracing_count == self._get_tracing_count():

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    625       # This is the first call of __call__, so we have to initialize.
    626       initializers = []
--> 627       self._initialize(args, kwds, add_initializers_to=initializers)
    628     finally:
    629       # At this point we know that the initialization is complete (or less

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    504     self._concrete_stateful_fn = (
    505         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 506             *args, **kwds))
    507 
    508     def invalid_creator_scope(*unused_args, **unused_kwds):

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2444       args, kwargs = None, None
   2445     with self._lock:
-> 2446       graph_function, _, _ = self._maybe_define_function(args, kwargs)
   2447     return graph_function
   2448 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   2775 
   2776       self._function_cache.missed.add(call_context_key)
-> 2777       graph_function = self._create_graph_function(args, kwargs)
   2778       self._function_cache.primary[cache_key] = graph_function
   2779       return graph_function, args, kwargs

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   2665             arg_names=arg_names,
   2666             override_flat_arg_shapes=override_flat_arg_shapes,
-> 2667             capture_by_value=self._capture_by_value),
   2668         self._function_attributes,
   2669         # Tell the ConcreteFunction to clean up its graph once it goes out of

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    979         _, original_func = tf_decorator.unwrap(python_func)
    980 
--> 981       func_outputs = python_func(*func_args, **func_kwargs)
    982 
    983       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    439         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    440         # the function a weak reference to itself to avoid a reference cycle.
--> 441         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    442     weak_wrapped_fn = weakref.ref(wrapped_fn)
    443 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    966           except Exception as e:  # pylint:disable=broad-except
    967             if hasattr(e, "ag_error_metadata"):
--> 968               raise e.ag_error_metadata.to_exception(e)
    969             else:
    970               raise

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function  *
        outputs = self.distribute_strategy.run(
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:533 train_step  **
        y, y_pred, sample_weight, regularization_losses=self.losses)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:204 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:143 __call__
        losses = self.call(y_true, y_pred)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:246 call
        return self.fn(y_true, y_pred, **self._fn_kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy
        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (32, 1) and (32, 10) are incompatible

Любые подсказки, откуда берутся эти ошибки и что я делаю неправильно? Такое ощущение, что я мог пропустить что-то очевидное, но, будучи новичком в Keras, я не могу понять, что это такое. Помощь очень ценится.