alextnet模型的训练准确率代码

下面是使用PyTorch框架实现alextnet模型的训练准确率的代码片段：

import torch

# 定义alextnet模型
class AlexNet(torch.nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2)
        self.pool1 = torch.nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv2 = torch.nn.Conv2d(64, 192, kernel_size=5, padding=2)
        self.pool2 = torch.nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv3 = torch.nn.Conv2d(192, 384, kernel_size=3, padding=1)
        self.conv4 = torch.nn.Conv2d(384, 256, kernel_size=3, padding=1)
        self.conv5 = torch.nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.pool3 = torch.nn.MaxPool2d(kernel_size=3, stride=2)
        self.fc1 = torch.nn.Linear(256 * 6 * 6, 4096)
        self.fc2 = torch.nn.Linear(4096, 4096)
        self.fc3 = torch.nn.Linear(4096, 1000)

    def forward(self, x):
        x = self.conv1(x)
        x = torch.nn.functional.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = torch.nn.functional.relu(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = torch.nn.functional.relu(x)
        x = self.conv4(x)
        x = torch.nn.functional.relu(x)
        x = self.conv5(x)
        x = torch.nn.functional.relu(x)
        x = self.pool3(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = torch.nn.functional.relu(x)
        x = self.fc2(x)
        x = torch.nn.functional.relu(x)
        x = self.fc3(x)
        return x

# 计算alextnet模型的训练准确率
def compute_accuracy(model, data_loader):
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in data_loader:
            images = images.cuda()
            labels = labels.cuda()
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    accuracy = 100 * correct / total
    return accuracy

在上面的代码中，我们首先定义了一个名为AlexNet的类，它继承自torch.nn.Module，用于构建alextnet模型。然后，我们定义了一个名为compute_accuracy的函数，用于计算alextnet模型在数据集上的训练准确率。在这个函数中，我们首先使用torch.no_grad()上下文管理器关闭梯度计算，然后遍历数据集中的所有样本，将其输入模型进行预测，并计算预测结果与真实标签相符的样本数量。最后，我们根据预测正确的样本数和总样本数计算出训练准确率，并返回该值。