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，包含训练和测试部分

以下是一个基于PyTorch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，包含训练和测试部分。

首先，我们需要导入必要的库和数据集：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import Compose, ToTensor, Normalize

# 定义数据集
train_dataset = MNIST(root='./data', train=True, download=True, transform=Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]))
test_dataset = MNIST(root='./data', train=False, download=True, transform=Compose([ToTensor(), Normalize((0.1307,), (0.3081,))]))

# 定义数据加载器
batch_size = 128
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=4)

接下来，我们定义一个ResNet网络类，它包含一个三输入特征三输出的一维卷积层和ResNet块。

class ResNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResNetBlock, self).__init__()

        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm1d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm1d(out_channels)

        if stride != 1 or in_channels != out_channels:
            self.downsample = nn.Sequential(
                nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=stride),
                nn.BatchNorm1d(out_channels)
            )
        else:
            self.downsample = nn.Sequential()

    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)

        identity = self.downsample(identity)

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

        return out


class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()

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

        self.layer1 = nn.Sequential(
            ResNetBlock(64, 64),
            ResNetBlock(64, 64)
        )
        self.layer2 = nn.Sequential(
            ResNetBlock(64, 128, stride=2),
            ResNetBlock(128, 128)
        )
        self.layer3 = nn.Sequential(
            ResNetBlock(128, 256, stride=2),
            ResNetBlock(256, 256)
        )

        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.fc = nn.Linear(256, 10)

    def forward(self, x):
        x1 = x[:, 0:1, :]
        x2 = x[:, 1:2, :]
        x3 = x[:, 2:3, :]

        x1 = self.conv1(x1)
        x1 = self.bn1(x1)
        x1 = self.relu(x1)
        x1 = self.maxpool(x1)

        x2 = self.conv1(x2)
        x2 = self.bn1(x2)
        x2 = self.relu(x2)
        x2 = self.maxpool(x2)

        x3 = self.conv1(x3)
        x3 = self.bn1(x3)
        x3 = self.relu(x3)
        x3 = self.maxpool(x3)

        x = torch.cat([x1, x2, x3], dim=1)

        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.fc(x)

        return x

接下来，我们定义训练函数和测试函数。训练函数将对网络进行训练，并在每个epoch结束时计算损失和准确度。测试函数将在测试集上对网络进行评估，并计算准确度。

def train(model, optimizer, criterion, train_loader, device):
    model.train()

    train_loss = 0.0
    train_acc = 0.0

    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)

        optimizer.zero_grad()

        output = model(data)
        loss = criterion(output, target)
        loss.backward()

        optimizer.step()

        train_loss += loss.item() * data.size(0)
        pred = output.argmax(dim=1, keepdim=True)
        train_acc += pred.eq(target.view_as(pred)).sum().item()

    train_loss /= len(train_loader.dataset)
    train_acc /= len(train_loader.dataset)

    return train_loss, train_acc


def test(model, criterion, test_loader, device):
    model.eval()

    test_loss = 0.0
    test_acc = 0.0

    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)

            output = model(data)
            loss = criterion(output, target)

            test_loss += loss.item() * data.size(0)
            pred = output.argmax(dim=1, keepdim=True)
            test_acc += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    test_acc /= len(test_loader.dataset)

    return test_loss, test_acc

最后，我们定义主函数来训练和测试网络。

def main():
    # 定义设备
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # 定义模型、损失函数和优化器
    model = ResNet().to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

    # 训练和测试网络
    n_epochs = 10
    for epoch in range(1, n_epochs + 1):
        train_loss, train_acc = train(model, optimizer, criterion, train_loader, device)
        test_loss, test_acc = test(model, criterion, test_loader, device)

        print('Epoch: {}  Train Loss: {:.6f}  Train Acc: {:.6f}  Test Loss: {:.6f}  Test Acc: {:.6f}'.format(epoch, train_loss, train_acc, test_loss, test_acc))

if __name__ == '__main__':
    main()

这就是一个使用ResNet网络的三输入特征三输出的一维卷积神经网络的PyTorch代码示例。运行主函数，即可训练和测试网络。