一共有12类不同的图像，分别被划分为训练集，验证集，分别在data文件夹下的train文件夹和val文件夹中。这12类图像中，其中有11类图像描述着各种各样的问题，分别在data文件夹下的train1文件夹和val1文件夹中。有1类图像是无缺陷的图像，分别在data文件夹下的train2文件夹和val2文件夹中。现在准备在一个分支上将11类有问题的图像用VGG16网络进行训练，再在另一个分支上将1类无问题的图像用VGG16网络进行训练，然后将这两个分支得到的结果进行相减，相减之后得到的结果，再次通过VGG16网络进行训练，对12类图像的数据集进行训练、验证。请给出相应代码。

首先，需要导入必要的库和模块：

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator

接下来，定义VGG16网络模型：

def create_vgg16():
    input_layer = Input(shape=(224, 224, 3))
    
    # Block 1
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(input_layer)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
    
    # Block 2
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
    
    # Block 3
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
    
    # Block 4
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
    
    # Block 5
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
    
    # Flatten
    x = Flatten(name='flatten')(x)
    x = Dense(4096, activation='relu', name='fc1')(x)
    x = Dense(4096, activation='relu', name='fc2')(x)
    output_layer = Dense(12, activation='softmax', name='predictions')(x)
    
    # Create model
    model = Model(input_layer, output_layer)
    return model

其中，输入图像的大小为224x224x3，输出层为12个类别，使用softmax激活。

接下来，定义训练、验证数据集的路径和参数：

train_dir1 = 'data/train1'
val_dir1 = 'data/val1'
train_dir2 = 'data/train2'
val_dir2 = 'data/val2'

batch_size = 32
epochs = 50

train_datagen = ImageDataGenerator(rescale=1./255)
train_generator1 = train_datagen.flow_from_directory(
        train_dir1,
        target_size=(224, 224),
        batch_size=batch_size,
        class_mode='categorical')

train_generator2 = train_datagen.flow_from_directory(
        train_dir2,
        target_size=(224, 224),
        batch_size=batch_size,
        class_mode='categorical')

val_datagen = ImageDataGenerator(rescale=1./255)
validation_generator1 = val_datagen.flow_from_directory(
        val_dir1,
        target_size=(224, 224),
        batch_size=batch_size,
        class_mode='categorical')

validation_generator2 = val_datagen.flow_from_directory(
        val_dir2,
        target_size=(224, 224),
        batch_size=batch_size,
        class_mode='categorical')

其中，使用ImageDataGenerator进行数据增强和归一化，并生成训练、验证数据集的迭代器。

接下来，定义两个分支的VGG16网络模型：

model1 = create_vgg16()
model2 = create_vgg16()

其中，model1用于训练11类有问题的图像，model2用于训练1类无问题的图像。

接下来，编译两个模型：

model1.compile(loss='categorical_crossentropy',
              optimizer=Adam(lr=1e-4),
              metrics=['acc'])
model2.compile(loss='categorical_crossentropy',
              optimizer=Adam(lr=1e-4),
              metrics=['acc'])

其中，损失函数使用交叉熵，优化器使用Adam，学习率为1e-4。

接下来，分别训练两个模型：

history1 = model1.fit(
        train_generator1,
        steps_per_epoch=train_generator1.samples // batch_size,
        epochs=epochs,
        validation_data=validation_generator1,
        validation_steps=validation_generator1.samples // batch_size)

history2 = model2.fit(
        train_generator2,
        steps_per_epoch=train_generator2.samples // batch_size,
        epochs=epochs,
        validation_data=validation_generator2,
        validation_steps=validation_generator2.samples // batch_size)

其中，每个模型的训练步数为训练集的样本数除以batch_size。

接下来，将两个模型的输出层相减，得到新的模型：

output_layer1 = model1.get_layer('fc2').output
output_layer2 = model2.get_layer('fc2').output

merged_layer = Dense(4096, activation='relu', name='merged_fc1')(output_layer1-output_layer2)
merged_layer = Dense(4096, activation='relu', name='merged_fc2')(merged_layer)
merged_output_layer = Dense(12, activation='softmax', name='merged_predictions')(merged_layer)

merged_model = Model(inputs=[model1.input, model2.input], outputs=merged_output_layer)

其中，使用model1和model2的前两个输出层作为输入层，将它们相减，并通过两个全连接层后，再接softmax输出层。

接下来，编译新的模型：

merged_model.compile(loss='categorical_crossentropy',
              optimizer=Adam(lr=1e-4),
              metrics=['acc'])

接下来，定义新的训练、验证数据集的迭代器：

train_datagen_merged = ImageDataGenerator(rescale=1./255)
train_generator_merged = train_datagen_merged.flow_from_directory(
        'data/train',
        target_size=(224, 224),
        batch_size=batch_size,
        class_mode='categorical')

val_datagen_merged = ImageDataGenerator(rescale=1./255)
validation_generator_merged = val_datagen_merged.flow_from_directory(
        'data/val',
        target_size=(224, 224),
        batch_size=batch_size,
        class_mode='categorical')

其中，训练、验证数据集的路径为'data/train'和'data/val'，包含有问题的图像和无问题的图像，共12类。

接下来，训练新的模型：

history_merged = merged_model.fit(
        [train_generator1, train_generator2],
        steps_per_epoch=train_generator_merged.samples // batch_size,
        epochs=epochs,
        validation_data=validation_generator_merged,
        validation_steps=validation_generator_merged.samples // batch_size)

其中，使用两个训练、验证数据集的迭代器作为输入，每个训练步数为训练集的样本数除以batch_size。

最后，可以对新模型进行测试：

test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
        'data/test',
        target_size=(224, 224),
        batch_size=batch_size,
        class_mode='categorical')

score = merged_model.evaluate_generator(test_generator, steps=test_generator.samples // batch_size)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

其中，测试集的路径为'data/test'，包含有问题的图像和无问题的图像，共12类。