PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks

import torch
import torch.nn as nn
import torchvision

class AlexNet(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,nn.Conv2d(in_channels=192,out_channels=384,kernel_size=3,padding=1,nn.Conv2d(in_channels=384,out_channels=256,nn.Conv2d(in_channels=256,)
    self.classifier = nn.Sequential(
      nn.Dropout(p=0.5),nn.Linear(in_features=256*6*6,out_features=4096),nn.Dropout(p=0.5),nn.Linear(in_features=4096,out_features=num_classes),)
  def forward(self,x):
    x = self.feature_extraction(x)
    x = x.view(x.size(0),256*6*6)
    x = self.classifier(x)
    return x


if __name__ =='__main__':
  # model = torchvision.models.AlexNet()
  model = AlexNet()
  print(model)

  input = torch.randn(8,3,224,224)
  out = model(input)
  print(out.shape)

以上這篇PyTorch實現AlexNet示例就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。