Я пытаюсь использовать часть модели VGG16 для трансферного обучения с использованием набора данных Fashion MNIST. Данные обрабатываются, и модель указывается в соответствии с нижеприведенным описанием:
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 подумал, что это может быть связано если форма обучающего набора неверна, 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
Любой подсказки, откуда эти ошибки и что я делаю не так? Такое чувство, что я, возможно, пропустил что-то очевидное, но, будучи новичком в Keras, я не могу понять, что это такое. Помощь высоко ценится.