U-Net图像分割运行时发生错误。

1. 检查代码并确保其符合U-Net模型的要求，包括正确的输入/输出形状、基本参数等。例如，以下是一个简单的Python示例，演示如何定义U-Net模型并对图像进行分割：

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

def unet(input_shape=(256,256,3)):
    inputs = Input(shape=input_shape)

    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool2)
    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool3)
    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same')(pool4)
    conv5 = Conv2D(1024, (3, 3), activation='