基于torch，给出一个5层的cnn网络，输入大小为4*30，类别数为3

好的，以下是一个基于PyTorch的5层CNN网络，输入大小为4*30，输出类别数为3：

import torch
import torch.nn as nn

class CNN5(nn.Module):
    def __init__(self):
        super(CNN5, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu1 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(64)
        self.relu3 = nn.ReLU(inplace=True)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv4 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(128)
        self.relu4 = nn.ReLU(inplace=True)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn5 = nn.BatchNorm2d(256)
        self.relu5 = nn.ReLU(inplace=True)
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc = nn.Linear(256*1*3, 3)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = self.pool3(x)
        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu4(x)
        x = self.pool4(x)
        x = self.conv5(x)
        x = self.bn5(x)
        x = self.relu5(x)
        x = self.pool5(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

这个网络有5个卷积层和1个全连接层。第1个卷积层的输入通道数为1，输出通道数为16，卷积核大小为3x3，步长为1，padding为1。每个卷积层之后都有一个BatchNorm层和ReLU激活函数。随后进行2x2最大池化。最后一层的全连接层的输入大小为256\*1\*3，输出大小为3。