生成基于pytorch的5层的一维cnn，输入大小为1*8，类别数为3

以下是基于PyTorch实现的5层一维CNN，输入大小为1*8，输出类别数为3的代码：

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(16),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.conv2 = nn.Sequential(
            nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(32),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.conv3 = nn.Sequential(
            nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.conv4 = nn.Sequential(
            nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.conv5 = nn.Sequential(
            nn.Conv1d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.fc = nn.Linear(256, 3)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

在这个网络中，我们使用了5层卷积层，每层都跟着一个BatchNorm层、ReLU激活函数和最大池化层。在网络的输出层，我们使用了一个线性层，输出维度为3（类别数为3）。我们可以通过实例化这个类来创建一个模型：

model = Net()

这里我们没有手动指定输入大小，这是因为PyTorch中的卷积层会根据输入的大小自动计算输出的大小。