编程实现AlexNet网络模型，实现AlexNet网络模型的训练和测试；

首先，需要导入相关的库和数据集，这里使用PyTorch和CIFAR-10数据集。

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

# 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=100,
                                         shuffle=False, num_workers=2)

接着，我们可以定义AlexNet网络模型。

class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2)
        self.relu1 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv2 = nn.Conv2d(64, 192, kernel_size=5, padding=2)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv3 = nn.Conv2d(192, 384, kernel_size=3, padding=1)
        self.relu3 = nn.ReLU(inplace=True)
        self.conv4 = nn.Conv2d(384, 256, kernel_size=3, padding=1)
        self.relu4 = nn.ReLU(inplace=True)
        self.conv5 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.relu5 = nn.ReLU(inplace=True)
        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.fc1 = nn.Linear(256 * 6 * 6, 4096)
        self.relu6 = nn.ReLU(inplace=True)
        self.dropout1 = nn.Dropout()
        self.fc2 = nn.Linear(4096, 4096)
        self.relu7 = nn.ReLU(inplace=True)
        self.dropout2 = nn.Dropout()
        self.fc3 = nn.Linear(4096, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = self.relu3(x)
        x = self.conv4(x)
        x = self.relu4(x)
        x = self.conv5(x)
        x = self.relu5(x)
        x = self.pool3(x)
        x = x.view(-1, 256 * 6 * 6)
        x = self.fc1(x)
        x = self.relu6(x)
        x = self.dropout1(x)
        x = self.fc2(x)
        x = self.relu7(x)
        x = self.dropout2(x)
        x = self.fc3(x)
        return x

接着，我们可以定义损失函数和优化器。

net = AlexNet()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

最后，我们可以开始训练和测试模型。

for epoch in range(10): # 训练10个epoch
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # 输入数据
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        # 梯度清零
        optimizer.zero_grad()

        # 前向传播，反向传播，优化权重
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 统计损失值
        running_loss += loss.item()
        if i % 200 == 199:    # 每200个batch打印一次训练状态
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 200))
            running_loss = 0.0

print('Finished Training')

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        outputs = net(inputs)
        _, 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))

这就是使用PyTorch实现AlexNet网络模型的训练和测试的完整代码。