PyTorch实现AlexNet示例

PyTorch:https://github.com/shanglianlm0525/PyTorch-Networksimporttorchimporttorch.nnasnnimporttorchvisionclassAlexNet(nn

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脚本之家。

PyTorch AlexNet