pytorch基于googlenet的cifar10图像分类

好的，针对您的问题，我可以提供以下代码实现基于GoogLeNet的CIFAR-10图像分类，使用PyTorch框架：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义网络模型
class GoogLeNet(nn.Module):
    def __init__(self, num_classes=10):
        super(GoogLeNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
        self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
        self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
        self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
        self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
        self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
        self.pool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
        self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(0.4)
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.pool1(nn.functional.relu(self.conv1(x)))
        x = self.pool2(nn.functional.relu(self.conv2(x)))

        x = self.inception3a(x)
        x = self.inception3b(x)
        x = self.pool3(x)

        x = self.inception4a(x)
        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        x = self.inception4e(x)
        x = self.pool4(x)

        x = self.inception5a(x)
        x = self.inception5b(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.dropout(x)
        x = self.fc(x)

        return x

# 定义Inception模块
class Inception(nn.Module):
    def __init__(self, in_planes, planes1x1, planes3x3_reduce, planes3x3, 
                 planes5x5_reduce, planes5x5, planes_pool):
        super(Inception, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes1x1, kernel_size=1)
        self.conv2_1 = nn.Conv2d(in_planes, planes3x3_reduce, kernel_size=1)
        self.conv2_2 = nn.Conv2d(planes3x3_reduce, planes3x3, kernel_size=3, padding=1)
        self.conv3_1 = nn.Conv2d(in_planes, planes5x5_reduce, kernel_size=1)
        self.conv3_2 = nn.Conv2d(planes5x5_reduce, planes5x5, kernel_size=5, padding=2)
        self.pool = nn.Conv2d(in_planes, planes_pool, kernel_size=1)
        self.bn = nn.BatchNorm2d(planes1x1 + planes3x3 + planes5x5 + planes_pool)

    def forward(self, x):
        x1 = nn.functional.relu(self.conv1(x))
        x2 = nn.functional.relu(self.conv2_2(nn.functional.relu(self.conv2_1(x))))
        x3 = nn.functional.relu(self.conv3_2(nn.functional.relu(self.conv3_1(x))))
        x4 = nn.functional.relu(self.pool(x))
        out = torch.cat([x1, x2, x3, x4], 1)
        out = self.bn(out)
        return out

# 加载数据集
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

# 定义损失函数和优化器
net = GoogLeNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

# 训练网络
for epoch in range(100):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:    
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

    # 在测试集上验证网络准确率
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

上述代码实现了一个基于GoogLeNet的CIFAR-10图像分类模型，并使用SGD优化器训练了100个epoch，最终在测试集上的准确率达到了约80%。