PyTorch实现AlexNet示例

Beth ·

更新时间:2024-09-20

· 744 次阅读

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,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),
    )
  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)

以上这篇PyTorch实现AlexNet示例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持软件开发网。

您可能感兴趣的文章:pytorch 修改预训练model实例pytorch载入预训练模型后,实现训练指定层PyTorch加载预训练模型实例(pretrained)pytorch 实现在预训练模型的 input上增减通道使用pytorch搭建AlexNet操作(微调预训练模型及手动搭建)

pytorch alexnet

1024 个赞