Skip to content

Instantly share code, notes, and snippets.

@seanbenhur
Last active November 7, 2020 12:28
Show Gist options
  • Save seanbenhur/76e4a49328f8034253692979cc667103 to your computer and use it in GitHub Desktop.
Save seanbenhur/76e4a49328f8034253692979cc667103 to your computer and use it in GitHub Desktop.
class AlexNet(nn.Module):
def __init__(self):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(),
nn.LocalResponseNorm(size=5,alpha=0.0001,beta=0.75,k=2),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, 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=3, stride=2),
)
self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(),
nn.Linear(4096, 1000),
)
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment