在VGG16中加入正则化的代码

可以使用以下代码在VGG16模型的构建中加入正则化：

from keras import regularizers
from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense
from keras.models import Model

input_shape = (224, 224, 3)

# input layer
inputs = Input(shape=input_shape)

# block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(inputs)
x = Conv2D(64, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = MaxPooling2D((2, 2))(x)

# block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = MaxPooling2D((2, 2))(x)

# block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = MaxPooling2D((2, 2))(x)

# block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = MaxPooling2D((2, 2))(x)

# block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same',
           kernel_regularizer=regularizers.l2(0.001))(x)
x = MaxPooling2D((2, 2))(x)

# fully connected layers
x = Flatten()(x)
x = Dense(4096, activation='relu', kernel_regularizer=regularizers.l2(0.001))(x)
x = Dense(4096, activation='relu', kernel_regularizer=regularizers.l2(0.001))(x)
outputs = Dense(1000, activation='softmax', kernel_regularizer=regularizers.l2(0.001))(x)

model = Model(inputs=inputs, outputs=outputs)

这里使用了L2正则化，它可以限制模型的权重，防止过拟合。在上述代码中，核正则化系数设置为0.001。这里在所有卷积层和全连接层中都加入了正则化，可以根据需求进行修改。