用Pytorch实现一个一维残差网络

实现一个一维残差网络在PyTorch中非常简单。你可以定义一个继承自`nn.Module`类的自定义模型类，并在其中定义一维残差块，然后将多个残差块通过堆叠在一起来构建整个模型。

下面是一个示例代码：

import torch
import torch.nn as nn

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

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

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += x
        out = self.relu(out)
        return out

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

        self.conv = nn.Conv1d(input_size, 64, kernel_size=7, stride=2, padding=3)
        self.bn = nn.BatchNorm1d(64)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(64, 128, 3)
        self.layer2 = self._make_layer(128, 256, 4)
        self.layer3 = self._make_layer(256, 512, 6)
        self.layer4 = self._make_layer(512, 512, 3)

        self.avg_pool = nn.AdaptiveAvgPool1d(1)
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, in_features, out_features, num_blocks):
        layers = []
        for i in range(num_blocks):
            layers.append(ResidualBlock(in_features))
            in_features = out_features