PyTorch实现AlexNet示例
PyTorch:https://github.com/shanglianlm0525/PyTorch-Networks
importtorch importtorch.nnasnn importtorchvision classAlexNet(nn.Module): def__init__(self,num_classes=1000): super(AlexNet,self).__init__() self.feature_extraction=nn.Sequential( nn.Conv2d(in_channels=3,out_channels=96,kernel_size=11,stride=4,padding=2,bias=False), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3,stride=2,padding=0), nn.Conv2d(in_channels=96,out_channels=192,kernel_size=5,stride=1,padding=2,bias=False), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3,stride=2,padding=0), nn.Conv2d(in_channels=192,out_channels=384,kernel_size=3,stride=1,padding=1,bias=False), nn.ReLU(inplace=True), nn.Conv2d(in_channels=384,out_channels=256,kernel_size=3,stride=1,padding=1,bias=False), nn.ReLU(inplace=True), nn.Conv2d(in_channels=256,out_channels=256,kernel_size=3,stride=1,padding=1,bias=False), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3,stride=2,padding=0), ) self.classifier=nn.Sequential( nn.Dropout(p=0.5), nn.Linear(in_features=256*6*6,out_features=4096), nn.ReLU(inplace=True), nn.Dropout(p=0.5), nn.Linear(in_features=4096,out_features=4096), nn.ReLU(inplace=True), nn.Linear(in_features=4096,out_features=num_classes), ) defforward(self,x): x=self.feature_extraction(x) x=x.view(x.size(0),256*6*6) x=self.classifier(x) returnx if__name__=='__main__': #model=torchvision.models.AlexNet() model=AlexNet() print(model) input=torch.randn(8,3,224,224) out=model(input) print(out.shape)
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