基于cifar-10数据集生成vgg16代码使得数据集各种类分辩率达85%使用pytorch

好的，首先我们需要导入必要的库：

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

然后，我们需要定义一些超参数：

# 超参数
batch_size = 128    # 批大小
num_epochs = 50     # 训练轮数
learning_rate = 0.001   # 学习率

接下来，我们需要下载并加载 CIFAR-10 数据集：

# 下载并加载 CIFAR-10 数据集
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, transform=transforms.ToTensor(), download=True)

# 数据加载器
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

现在，我们可以定义 VGG16 模型了：

# VGG16 模型
class VGG16(nn.Module):
    def __init__(self):
        super(VGG16, self).__init__()
        self.conv1 = nn.Sequential(nn.Conv2d(3, 64, 3, padding=1), nn.ReLU(inplace=True))
        self.conv2 = nn.Sequential(nn.Conv2d(64, 64, 3, padding=1), nn.ReLU(inplace=True))
        self.maxpool1 = nn.MaxPool2d(2, 2)
        self.conv3 = nn.Sequential(nn.Conv2d(64, 128, 3, padding=1), nn.ReLU(inplace=True))
        self.conv4 = nn.Sequential(nn.Conv2d(128, 128, 3, padding=1), nn.ReLU(inplace=True))
        self.maxpool2 = nn.MaxPool2d(2, 2)
        self.conv5 = nn.Sequential(nn.Conv2d(128, 256, 3, padding=1), nn.ReLU(inplace=True))
        self.conv6 = nn.Sequential(nn.Conv2d(256, 256, 3, padding=1), nn.ReLU(inplace=True))
        self.conv7 = nn.Sequential(nn.Conv2d(256, 256, 3, padding=1), nn.ReLU(inplace=True))
        self.maxpool3 = nn.MaxPool2d(2, 2)
        self.conv8 = nn.Sequential(nn.Conv2d(256, 512, 3, padding=1), nn.ReLU(inplace=True))
        self.conv9 = nn.Sequential(nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(inplace=True))
        self.conv10 = nn.Sequential(nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(inplace=True))
        self.maxpool4 = nn.MaxPool2d(2, 2)
        self.conv11 = nn.Sequential(nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(inplace=True))
        self.conv12 = nn.Sequential(nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(inplace=True))
        self.conv13 = nn.Sequential(nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(inplace=True))
        self.maxpool5 = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Sequential(nn.Linear(512, 4096), nn.ReLU(inplace=True), nn.Dropout())
        self.fc2 = nn.Sequential(nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Dropout())
        self.fc3 = nn.Linear(4096, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.maxpool1(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.maxpool2(x)
        x = self.conv5(x)
        x = self.conv6(x)
        x = self.conv7(x)
        x = self.maxpool3(x)
        x = self.conv8(x)
        x = self.conv9(x)
        x = self.conv10(x)
        x = self.maxpool4(x)
        x = self.conv11(x)
        x = self.conv12(x)
        x = self.conv13(x)
        x = self.maxpool5(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x

# 实例化模型
model = VGG16()

# 将模型移动到 GPU 上（如果有的话）
if torch.cuda.is_available():
    model.cuda()

接下来，我们需要定义损失函数和优化器：

# 损失函数
criterion = nn.CrossEntropyLoss()

# 优化器
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

最后，我们可以进行训练和测试：

# 训练
for epoch in range(num_epochs):
    # 训练模式
    model.train()
    train_loss = 0.0
    train_correct = 0
    train_total = 0
    for images, labels in train_loader:
        # 将数据移动到 GPU 上（如果有的话）
        if torch.cuda.is_available():
            images = images.cuda()
            labels = labels.cuda()

        # 前向传播和反向传播
        outputs = model(images)
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 统计训练损失和正确率
        train_loss += loss.item() * images.size(0)
        _, predicted = torch.max(outputs.data, 1)
        train_total += labels.size(0)
        train_correct += (predicted == labels).sum().item()

    # 计算训练损失和正确率
    train_loss = train_loss / len(train_loader.dataset)
    train_accuracy = 100.0 * train_correct / train_total

    # 测试模式
    model.eval()
    test_loss = 0.0
    test_correct = 0
    test_total = 0
    with torch.no_grad():
        for images, labels in test_loader:
            # 将数据移动到 GPU 上（如果有的话）
            if torch.cuda.is_available():
                images = images.cuda()
                labels = labels.cuda()

            # 前向传播
            outputs = model(images)
            loss = criterion(outputs, labels)

            # 统计测试损失和正确率
            test_loss += loss.item() * images.size(0)
            _, predicted = torch.max(outputs.data, 1)
            test_total += labels.size(0)
            test_correct += (predicted == labels).sum().item()

    # 计算测试损失和正确率
    test_loss = test_loss / len(test_loader.dataset)
    test_accuracy = 100.0 * test_correct / test_total

    # 输出训练和测试结果
    print('[Epoch %d] Train Loss: %.3f, Train Accuracy: %.2f%%, Test Loss: %.3f, Test Accuracy: %.2f%%' % (epoch+1, train_loss, train_accuracy, test_loss, test_accuracy))

这样，我们就可以使用 VGG16 模型对 CIFAR-10 数据集进行分类了。