给出一个三输入特征三输出的基于ResNet网络逼近预测代码

由于缺乏题目中所需的具体数据集和需求，暂时无法提供完整的代码。以下是一个基于PyTorch实现的三输入一输出的ResNet网络的代码示例，供参考：

# 定义ResNet网络结构
import torch.nn as nn
import torch.nn.functional as F

class ResNet(nn.Module):

    def __init__(self):
        super(ResNet, self).__init__()
        
        # 模块1
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        # 模块2
        self.layer1 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )

        # 模块3
        self.layer2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True)
        )

        # 模块4
        self.layer3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True)
        )

        # 模块5
        self.layer4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True)
        )

        # 全连接层
        self.fc = nn.Linear(512 * 7 * 7, 3)
        

    def forward(self, x1, x2, x3):
        x = torch.cat((x1, x2, x3), dim=1) # 将三个输入特征拼接在一起

        # 前向传播过程
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x) + x # 将输出与输入残差相加
        x = self.layer2(x) + x
        x = self.layer3(x) + x
        x = self.layer4(x) + x

        x = F.avg_pool2d(x, kernel_size=7).view(x.size(0), -1)
        x = self.fc(x)

        return x

可以看到，这个ResNet网络接收三个输入特征x1、x2和x3作为输入，通过将它们拼接在一起，形成一个输入通道数为3的三维张量。网络经过一系列卷积、批归一化和激活等操作后，输出一个三维向量作为最终预测结果。在前向传播过程中，每个模块的输出都与输入残差相加，增加了网络的非线性可塑性，提高了网络拟合能力。