用pytorch实现AlexNet

AlexNet经典网络由Alex Krizhevsky、Hinton等人在2012年提出，发表在NIPS，论文名为《ImageNet Classification with Deep Convolutional Neural Networks》，论文见：http://www.cs.toronto.edu/~hinton/absps/imagenet.pdf ，论文中的网络结构截图如下：

import torch
import torch.nn as nn# 定义AlexNet模型
class AlexNet(nn.Module):def __init__(self, num_classes=1000):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=3, stride=2))self.avgpool = nn.AdaptiveAvgPool2d((6, 6))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 = self.avgpool(x)x = torch.flatten(x, 1)x = self.classifier(x)return x# 创建AlexNet模型实例
model = AlexNet()