U-Net语义分割模型在新图像上测试失败。

可能的解决方案之一是增加数据集中的图像数量，并使用数据扩充技术来增加训练图像数量。另一个方案是使用预训练模型来初始化U-Net模型权重，从而加快模型的收敛。以下是使用Keras实现的预训练模型初始化示例代码：

from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import Conv2DTranspose, Concatenate, BatchNormalization
from tensorflow.keras.models import Model

# define the U-Net model architecture
def unet(input_size):
    # load pretrained MobileNetV2
    base_model = MobileNetV2(input_shape=input_size, include_top=False, weights='imagenet')
    # get the layers to use for the encoder and decoder
    encoder_layers = base_model.layers
    decoder_layers = []

    # define the decoder layers
    for i in range(len(encoder_layers)-1, -1, -1):
        if isinstance(encoder_layers[i], Conv2DTranspose):
            decoder_layers.append(encoder_layers[i])
        elif isinstance(encoder_layers[i], Conv2D) and encoder_layers[i].strides == (2, 2):
            decoder_layers.append(Conv2DTranspose(filters=encoder_layers[i].filters,
                                                   kernel_size=encoder_layers[i].kernel_size,
                                                   strides=(2,2),
                                                   padding='same',
                                                   activation='relu'))

    # define the skip connections
    skip_connection_names = (
        'input_1', # skip connection from input image to block 1
        'block_6_expand_relu', # skip connection from block 6 to block 7
        'block_13_expand_relu', # skip connection from block 13 to block 14
        'block_16_project', # skip connection from block 16 to the output
    )
    skip_connections = []
    for layer_name in skip_connection_names:
        skip_connections.append(base_model.get_layer(layer_name).output)

    # define the decoder portion of the network
    x = decoder_layers[0](encoder_layers[-1].output)
    for i in range(1, len(decoder_layers)):