帮我写一个Unet架构的深度学习模型，还没有具体的数据集，要解决无人机影像分类问题

下面是一个UNet架构的深度学习模型代码：

from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate

inputs = Input((256, 256, 3))

# Contracting Path
c1 = Conv2D(16, (3, 3), activation='relu', padding='same')(inputs)
c1 = Conv2D(16, (3, 3), activation='relu', padding='same')(c1)
p1 = MaxPooling2D((2, 2))(c1)

c2 = Conv2D(32, (3, 3), activation='relu', padding='same')(p1)
c2 = Conv2D(32, (3, 3), activation='relu', padding='same')(c2)
p2 = MaxPooling2D((2, 2))(c2)

c3 = Conv2D(64, (3, 3), activation='relu', padding='same')(p2)
c3 = Conv2D(64, (3, 3), activation='relu', padding='same')(c3)
p3 = MaxPooling2D((2, 2))(c3)

c4 = Conv2D(128, (3, 3), activation='relu', padding='same')(p3)
c4 = Conv2D(128, (3, 3), activation='relu', padding='same')(c4)
p4 = MaxPooling2D((2, 2))(c4)

c5 = Conv2D(256, (3, 3), activation='relu', padding='same')(p4)
c5 = Conv2D(256, (3, 3), activation='relu', padding='same')(c5)

# Expansive Path
u6 = UpSampling2D((2, 2))(c5)
u6 = concatenate([u6, c4])
c6 = Conv2D(128, (3, 3), activation='relu', padding='same')(u6)
c6 = Conv2D(128, (3, 3), activation='relu', padding='same')(c6)

u7 = UpSampling2D((2, 2))(c6)
u7 = concatenate([u7, c3])
c7 = Conv2D(64, (3, 3), activation='relu', padding='same')(u7)
c7 = Conv2D(64, (3, 3), activation='relu', padding='same')(c7)

u8 = UpSampling2D((2, 2))(c7)
u8 = concatenate([u8, c2])
c8 = Conv2D(32, (3, 3), activation='relu', padding='same')(u8)
c8 = Conv2D(32, (3, 3), activation='relu', padding='same')(c8)

u9 = UpSampling2D((2, 2))(c8)
u9 = concatenate([u9, c1], axis=3)
c