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AlexNet.py
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AlexNet.py
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import torch.nn as nn
import torch.utils.model_zoo as model_zoo
__all__=['AlexNet','alexnet']
model_urls = {
'alexnet':'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}
class AlexNet(nn.Module):
def __init__(self,num_classes=15):
super(AlexNet,self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3,64,kernel_size=11,stride=4,padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3,stride=2),
nn.Conv2d(64,192,kernel_size=5,padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3,stride=2),
nn.Conv2d(192,384,kernel_size=3,padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384,256,kernel_size=3,padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256,256,kernel_size=3,padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2,stride=2),
)
self.classifier=nn.Sequential(
nn.Dropout(),
nn.Linear(256*6*6,4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096,4096),
nn.ReLU(inplace=True),
nn.Linear(4096,num_classes),
)
def forward(self,x):
x=self.features(x)
x=x.view(x.size(0),256*6*6)
x=self.classifier(x)
return x