给我写个unet的代码

抱歉，我是一个语言模型AI，无法直接编写代码。但是，以下是一个简单的Python实现的U-Net模型的示例：

import tensorflow as tf

# 定义U-Net模型
def unet(input_shape):
    # 定义卷积块
    def conv_block(inputs, num_filters):
        x = tf.keras.layers.Conv2D(num_filters, 3, padding='same', activation='relu')(inputs)
        x = tf.keras.layers.Conv2D(num_filters, 3, padding='same', activation='relu')(x)
        return x
    
    # 定义上采样块
    def upsample_block(inputs, num_filters):
        x = tf.keras.layers.UpSampling2D(size=(2, 2))(inputs)
        x = tf.keras.layers.Conv2D(num_filters, 2, padding='same', activation='relu')(x)
        return x
    
    # 定义U-Net模型的编码器部分
    inputs = tf.keras.layers.Input(shape=input_shape)
    c1 = conv_block(inputs, 64)
    p1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c1)
    c2 = conv_block(p1, 128)
    p2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c2)
    c3 = conv_block(p2, 256)
    p3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c3)
    c4 = conv_block(p3, 512)
    p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)
    c5 = conv_block(p4, 1024)
    
    # 定义U-Net模型的解码器部分
    u6 = upsample_block(c5, 512)
    merge6 = tf.keras.layers.concatenate([u6, c4])
    c6 = conv_block(merge6, 512)
    u7 = upsample_block(c6, 256)
    merge7 = tf.keras.layers.concatenate([u7, c3])
    c7 = conv_block(merge7, 256)
    u8 = upsample_block(c7, 128)
    merge8 = tf.keras.layers.concatenate([u8, c2])
    c8 = conv_block(merge8, 128)
    u9 = upsample_block(c8, 64)
    merge9 = tf.keras.layers.concatenate([u9, c1])
    c9 = conv_block(merge9, 64)
    
    # 定义U-Net模型的输出
    outputs = tf.keras.layers.Conv2D(1, 1, activation='sigmoid')(c9)
    
    # 创建模型实例
    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    
    return model

该模型使用了TensorFlow 2.0+的Keras API来实现。模型的编码器部分由5个卷积块和最大池化层组成，解码器部分由4个上采样块和卷积块组成。模型的输出是一个sigmoid激活函数的单通道二进制图像，用于图像分割任务。