三输入特征三输出的一维卷积神经网络，使用ResNet网络的基于pytorch的代码示例

以下是基于PyTorch实现的三输入特征三输出的一维卷积神经网络，使用ResNet网络的代码示例。

import torch
import torch.nn as nn
import torch.nn.functional as F


class ResNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResNetBlock, self).__init__()
        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm1d(out_channels)
        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm1d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(self, in_channels, out_channels, num_blocks=2):
        super(ResNet, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels

        self.conv1 = nn.Conv1d(in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm1d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(64, 64, num_blocks)
        self.layer2 = self._make_layer(64, 128, num_blocks, stride=2)
        self.layer3 = self._make_layer(128, 256, num_blocks, stride=2)

        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.fc1 = nn.Linear(256, 128)
        self.fc2 = nn.Linear(128, out_channels)

    def _make_layer(self, in_channels, out_channels, num_blocks, stride=1):
        downsample = None
        if stride != 1 or in_channels != out_channels:
            downsample = nn.Sequential(
                nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(out_channels),
            )

        layers = [ResNetBlock(in_channels, out_channels, stride, downsample)]
        for _ in range(1, num_blocks):
            layers.append(ResNetBlock(out_channels, out_channels))

        return nn.Sequential(*layers)

    def forward(self, x1, x2, x3):
        x = torch.cat((x1.unsqueeze(1), x2.unsqueeze(1), x3.unsqueeze(1)), dim=1)

        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)

        return x

该代码实现了一个ResNet网络，其中包含了ResNetBlock和ResNet两个类。ResNetBlock是ResNet的基本模块，ResNet是整个网络。

在ResNet中，我们首先对输入的三个特征进行拼接，然后进入卷积层进行特征提取。之后，我们通过多个ResNetBlock来进行深度的特征提取。最后，我们通过全局平均池化将特征降维，然后经过两个全连接层获得最终的输出。

在_ResNetBlock_中，我们定义了两个卷积层，每个卷积层后面都跟着一个BatchNorm1d和ReLU激活函数。在_ResNet_中，我们定义了一个_make_layer方法来生成多个ResNetBlock。在这个方法中，我们也定义了一个downsample层，用于将输入的特征进行下采样，使得特征维度和输出维度相同。最后，我们将三个特征拼接成一个特征输入到ResNet中进行特征提取。