卷积神经网络模型构建：包含池化层和卷积层的示例

model = Sequential()

# 第一块
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same', input_shape=(224,224,3)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

# 第二块
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

# 第三块
model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

# 第四块
model.add(Conv2D(512, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))