基于googlenet的cifar10数据集网络结构pytorch

以下是基于GoogLeNet的CIFAR-10数据集的网络结构示例代码，使用PyTorch实现：

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

class Inception(nn.Module):
    def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
        super(Inception, self).__init__()
        
        # 1x1 conv branch
        self.branch1 = nn.Sequential(
            nn.Conv2d(in_channels, ch1x1, kernel_size=1),
            nn.BatchNorm2d(ch1x1),
            nn.ReLU(inplace=True)
        )
        
        # 1x1 conv -> 3x3 conv branch
        self.branch2 = nn.Sequential(
            nn.Conv2d(in_channels, ch3x3red, kernel_size=1),
            nn.BatchNorm2d(ch3x3red),
            nn.ReLU(inplace=True),
            nn.Conv2d(ch3x3red, ch3x3, kernel_size=3, padding=1),
            nn.BatchNorm2d(ch3x3),
            nn.ReLU(inplace=True)
        )
        
        # 1x1 conv -> 5x5 conv branch
        self.branch3 = nn.Sequential(
            nn.Conv2d(in_channels, ch5x5red, kernel_size=1),
            nn.BatchNorm2d(ch5x5red),
            nn.ReLU(inplace=True),
            nn.Conv2d(ch5x5red, ch5x5, kernel_size=5, padding=2),
            nn.BatchNorm2d(ch5x5),
            nn.ReLU(inplace=True)
        )
        
        # 3x3 pool -> 1x1 conv branch
        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels, pool_proj, kernel_size=1),
            nn.BatchNorm2d(pool_proj),
            nn.ReLU(inplace=True)
        )
        
    def forward(self, x):
        branch1 = self.branch1(x)
        branch2 = self.branch2(x)
        branch3 = self.branch3(x)
        branch4 = self.branch4(x)
        outputs = [branch1, branch2, branch3, branch4]
        return torch.cat(outputs, 1)

class GoogLeNet(nn.Module):
    def __init__(self):
        super(GoogLeNet, self).__init__()
        
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        
        self.inception1 = Inception(64, 64, 96, 128, 16, 32, 32)
        self.inception2 = Inception(256, 128, 128, 192, 32, 96, 64)
        
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.inception3 = Inception(480, 192, 96, 208, 16, 48, 64)
        self.inception4 = Inception(512, 160, 112, 224, 24, 64, 64)
        self.inception5 = Inception(512, 128, 128, 256, 24, 64, 64)
        self.inception6 = Inception(512, 112, 144, 288, 32, 64, 64)
        self.inception7 = Inception(528, 256, 160, 320, 32, 128, 128)
        
        self.avgpool = nn.AdaptiveAvgPool2d((1,1))
        
        self.dropout = nn.Dropout(p=0.4)
        self.fc = nn.Linear(832, 10)
        
    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.maxpool(x)
        x = self.inception1(x)
        x = self.inception2(x)
        x = self.maxpool(x)
        x = self.inception3(x)
        x = self.inception4(x)
        x = self.inception5(x)
        x = self.inception6(x)
        x = self.inception7(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.dropout(x)
        x = self.fc(x)
        return x

这里使用了Inception模块和多个Inception模块的堆叠，与GoogLeNet的结构类似。同时，也使用了BatchNorm和Dropout等技术来提高模型的性能和泛化能力。