我正在处理一个包含12个类别的图像数据集。我使用VGG16进行迁移学习。然而，我遇到了一个错误：形状（None, None）和（None, 28, 28, 12）不兼容。我的代码如下：
IMAGE_SHAPE = (224, 224)BATCH_SIZE = 32train = ImageDataGenerator()train_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale= 1./255, fill_mode= 'nearest')train_data = train_generator.flow_from_directory(directory="path",target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical', batch_size=BATCH_SIZE , shuffle = True )valid = ImageDataGenerator()validation_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)valid_data = validation_generator.flow_from_directory(directory="path, target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=BATCH_SIZE , shuffle = True )test = ImageDataGenerator()test_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)test_data = test_generator.flow_from_directory(directory='path',target_size=IMAGE_SHAPE , color_mode="rgb" , class_mode='categorical' , batch_size=1 , shuffle = False )test_data.reset()from keras.applications.vgg19 import VGG19vggmodel = VGG19(weights='imagenet', include_top=True)for layers in (vggmodel.layers)[:32]: print(layers) layers.trainable = Falseimport tensorflow as tftf.keras.Modeltf.keras.layers.Densefrom keras import optimizerstf.keras.preprocessing.image.ImageDataGeneratortf.keras.preprocessing.image.load_imgX= vggmodel.layers[-12].outputflatten = tf.keras.layers.Flatten()(X)out = vggmodel.layers(flatten)predictions = tf.keras.layers.Dense(num_classes, activation='softmax')model_final = Model(vggmodel.input, predictions) model_final.compile(optimizer = optimizers.Adam(lr=0.0003), loss='categorical_crossentropy', metrics=["accuracy"]) for image_batch, labels_batch in train_data: print(image_batch.shape) print(labels_batch.shape) breakfrom keras.callbacks import ModelCheckpoint, EarlyStoppingcheckpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)early = EarlyStopping(monitor='val_acc',patience=40, verbose=1, mode='auto')model_final.fit_generator(generator= train_data, steps_per_epoch= 2, epochs= 100, validation_data= valid_data, validation_steps=1, callbacks=[checkpoint,early])model_final.save_weights("vgg16_1.h5")错误： ValueError: Shapes (None, None) and (None, 28, 28, 12) are incompatible[![enter image description here]
错误详情：
ValueError Traceback (most recent call last)<ipython-input-39-938295cc34c4> in <module>() 2 checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1) 3 early = EarlyStopping(monitor='val_acc', min_delta=0, patience=40, verbose=1, mode='auto')----> 4 model_final.fit_generator(generator= train_images , steps_per_epoch= 2, epochs= 100, validation_data= val_images , validation_steps=1, callbacks=[checkpoint,early]) 5 model_final.save_weights("vgg16_1.h5")10 frames/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs) 984 except Exception as e: # pylint:disable=broad-except 985 if hasattr(e, "ag_error_metadata"):--> 986 raise e.ag_error_metadata.to_exception(e) 987 else: 988 raiseValueError: in user code: /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:830 train_function * return step_function(self, iterator) /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:813 run_step * outputs = model.train_step(data) /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:771 train_step * loss = self.compiled_loss( /usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:201 __call__ * loss_value = loss_obj(y_t, y_p, sample_weight=sw) /usr/local/lib/python3.7/dist-packages/keras/losses.py:142 __call__ * losses = call_fn(y_true, y_pred) /usr/local/lib/python3.7/dist-packages/keras/losses.py:246 call * return ag_fn(y_true, y_pred, **self._fn_kwargs) /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper ** return target(*args, **kwargs) /usr/local/lib/python3.7/dist-packages/keras/losses.py:1631 categorical_crossentropy y_true, y_pred, from_logits=from_logits) /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper return target(*args, **kwargs) /usr/local/lib/python3.7/dist-packages/keras/backend.py:4827 categorical_crossentropy target.shape.assert_is_compatible_with(output.shape) /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1161 assert_is_compatible_with raise ValueError("Shapes %s and %s are incompatible" % (self, other)) ValueError: Shapes (None, None) and (None, 28, 28, 12) are incompatible
更新后的代码：
image_dir = Path('../content/dataset')# 获取文件路径和标签filepaths = list(image_dir.glob(r'**/*.png'))labels = list(map(lambda x: os.path.split(os.path.split(x)[0])[1], filepaths))def create_gen(): # 使用生成器和数据增强加载图像 train_generator = tf.keras.preprocessing.image.ImageDataGenerator( preprocessing_function=tf.keras.applications.vgg16.preprocess_input, validation_split=0.1 ) test_generator = tf.keras.preprocessing.image.ImageDataGenerator( preprocessing_function=tf.keras.applications.vgg16.preprocess_input ) train_images = train_generator.flow_from_dataframe( dataframe=train_df, x_col='Filepath', y_col='Label', target_size=(224, 224), color_mode='rgb', class_mode='categorical', batch_size=32, shuffle=True, seed=0, subset='training', rotation_range=30, # 取消注释以使用数据增强 zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15, horizontal_flip=True, fill_mode="nearest" ) val_images = train_generator.flow_from_dataframe( dataframe=train_df, x_col='Filepath', y_col='Label', target_size=(224, 224), color_mode='rgb', class_mode='categorical', batch_size=32, shuffle=True, seed=0, subset='validation', rotation_range=30, # 取消注释以使用数据增强 zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15, horizontal_flip=True, fill_mode="nearest" ) test_images = test_generator.flow_from_dataframe( dataframe=test_df, x_col='Filepath', y_col='Label', target_size=(224, 224), color_mode='rgb', class_mode='categorical', batch_size=32, shuffle=False ) return train_generator,test_generator,train_images,val_images,test_imagesfrom keras.applications.vgg16 import VGG16vggmodel = VGG16(weights='imagenet', include_top=True)for layers in (vggmodel.layers)[:256]: print(layers) layers.trainable = FalseX= vggmodel.layers[-12].outputpredictions = Dense(12, activation="softmax")(X)model_final = Model(vggmodel.input, predictions)model_final.compile(optimizer = optimizers.Adam(lr=0.0003), loss='categorical_crossentropy', metrics=["accuracy"])# 分离训练和测试数据train_df, test_df = train_test_split(image_df, train_size=0.9, shuffle=True, random_state=1)# 创建生成器strain_generator,test_generator,train_images,val_images,test_images = create_gen()from keras.callbacks import ModelCheckpoint, EarlyStoppingcheckpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)early = EarlyStopping(monitor='val_acc', min_delta=0, patience=40, verbose=1, mode='auto')model_final.fit_generator(generator= train_images, steps_per_epoch= 2, epochs= 100, validation_data= val_images, validation_steps=1, callbacks=[checkpoint,early])model_final.save_weights("vgg16_1.h5")回答：
