import torch.nn as nn

class AlexNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1), nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 192, kernel_size=3, padding=1), nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(),
            nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(),
            nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Dropout(0.5),
            nn.Linear(256 * 4 * 4, 4096), nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(4096, 4096), nn.ReLU(),
            nn.Linear(4096, 10),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        return self.classifier(x)