我正在尝试使用Keras中的VGG16模型对MNIST数字进行分类。生成的错误是：
---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-17-fd172601765f> in <module>() 1 # Train the the model----> 2 history=model.fit(train_features, train_labels, batch_size=128, epochs=100,callbacks=callback, verbose=0, validation_split=0.2)~\Anaconda3\lib\site-packages\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, **kwargs) 950 sample_weight=sample_weight, 951 class_weight=class_weight,--> 952 batch_size=batch_size) 953 # Prepare validation data. 954 do_validation = False~\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size) 787 feed_output_shapes, 788 check_batch_axis=False, # Don't enforce the batch size.--> 789 exception_prefix='target') 790 791 # Generate sample-wise weight values given the `sample_weight` and~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 126 ': expected ' + names[i] + ' to have ' + 127 str(len(shape)) + ' dimensions, but got array '--> 128 'with shape ' + str(data_shape)) 129 if not check_batch_axis: 130 data_shape = data_shape[1:]ValueError: Error when checking target: expected block5_pool to have 4 dimensions, but got array with shape (60000, 10)这是我进行的所有预处理和调整大小的代码。我通过简单地将图像堆叠三次，将28×28的单通道图像调整为48×48的三通道图像。由于我是这个领域的新手，我不明白我哪里做错了。
train_features=np.stack([train_features]*3,axis = -1)test_features=np.stack([test_features]*3,axis = -1)# Reshape images as per the tensor format required by tensorflowtrain_features = train_features.reshape(-1, 28,28,3)test_features= test_features.reshape (-1,28,28,3)# Resize the images 48*48 as required by VGG16from keras.preprocessing.image import img_to_array, array_to_imgtrain_features = np.asarray([img_to_array(array_to_img(im, scale=False).resize((48,48))) for im in train_features])test_features = np.asarray([img_to_array(array_to_img(im, scale=False).resize((48,48))) for im in test_features])train_features.shape, test_features.shape#normalising the training and testing features train_features = train_features.astype('float32')test_features = test_features .astype('float32')train_features /= 255test_features /= 255# Converting Labels to one hot encoded formattest_labels = to_categorical(test_labels,10)train_labels = to_categorical(train_labels,10)# Preprocessing the input train_features = preprocess_input(train_features)test_features = preprocess_input (test_features)model = VGG16(weights=None, include_top=False)input = Input(shape=(48,48,3),name = 'image_input')#Use the generated model output = model(input)#Add the fully-connected layers x = Flatten(name='flatten')(output)x = Dense(4096, activation='relu', name='fc1')(x)x = Dense(4096, activation='relu', name='fc2')(x)x = Dense(10, activation='softmax', name='predictions')(x)#Create your own model vgg16_model = Model(input=input, output=x)model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])# Train the the modelhistory=model.fit(train_features, train_labels, batch_size=128, epochs=100,callbacks=callback, verbose=0, validation_split=0.2)模型摘要如下：
Layer (type) Output Shape Param # =================================================================input_2 (InputLayer) (None, None, None, 3) 0 _________________________________________________________________block1_conv1 (Conv2D) (None, None, None, 64) 1792 _________________________________________________________________block1_conv2 (Conv2D) (None, None, None, 64) 36928 _________________________________________________________________block1_pool (MaxPooling2D) (None, None, None, 64) 0 _________________________________________________________________block2_conv1 (Conv2D) (None, None, None, 128) 73856 _________________________________________________________________block2_conv2 (Conv2D) (None, None, None, 128) 147584 _________________________________________________________________block2_pool (MaxPooling2D) (None, None, None, 128) 0 _________________________________________________________________block3_conv1 (Conv2D) (None, None, None, 256) 295168 _________________________________________________________________block3_conv2 (Conv2D) (None, None, None, 256) 590080 _________________________________________________________________block3_conv3 (Conv2D) (None, None, None, 256) 590080 _________________________________________________________________block3_pool (MaxPooling2D) (None, None, None, 256) 0 _________________________________________________________________block4_conv1 (Conv2D) (None, None, None, 512) 1180160 _________________________________________________________________block4_conv2 (Conv2D) (None, None, None, 512) 2359808 _________________________________________________________________block4_conv3 (Conv2D) (None, None, None, 512) 2359808 _________________________________________________________________block4_pool (MaxPooling2D) (None, None, None, 512) 0 _________________________________________________________________block5_conv1 (Conv2D) (None, None, None, 512) 2359808 _________________________________________________________________block5_conv2 (Conv2D) (None, None, None, 512) 2359808 _________________________________________________________________block5_conv3 (Conv2D) (None, None, None, 512) 2359808 _________________________________________________________________block5_pool (MaxPooling2D) (None, None, None, 512) 0 =================================================================Total params: 14,714,688Trainable params: 14,714,688Non-trainable params: 0__________________________________________________________________________________________________________________________________Layer (type) Output Shape Param # =================================================================image_input (InputLayer) (None, 48, 48, 3) 0 _________________________________________________________________vgg16 (Model) multiple 14714688 _________________________________________________________________flatten (Flatten) (None, 512) 0 _________________________________________________________________fc1 (Dense) (None, 4096) 2101248 _________________________________________________________________fc2 (Dense) (None, 4096) 16781312 _________________________________________________________________predictions (Dense) (None, 10) 40970 =================================================================Total params: 33,638,218Trainable params: 33,638,218Non-trainable params: 0_________________________________________________________________关于这个问题，任何帮助都将不胜感激。
回答：
Keras对你的目标提出了抱怨，这是因为你的模型的输出形状不正确，因为它没有分类（Dense）层，请尝试这样做：
model = VGG16(weights=None, include_top=False,input_shape=(48,48,3))model.add(Dense(64, activation='relu'))model.add(Dense(10, activation='softmax'))model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])