AlexNet(Pytorch实现)

最新推荐文章于 2025-10-22 13:36:07 发布

原创最新推荐文章于 2025-10-22 13:36:07 发布 · 1.2w 阅读

43 ·

CC 4.0 BY-SA版权

文章标签：

#AlexNet #Pytorch

PyTorch相关专栏收录该内容

7 篇文章

订阅专栏

本文深入探讨了AlexNet模型的架构与原理，该模型是深度学习历史上的里程碑，显著提升了图像识别的准确性。文章详细介绍了AlexNet的各层结构，包括卷积层、激活函数、池化层及全连接层，并提供了使用PyTorch框架实现AlexNet的代码示例。

部署运行你感兴趣的模型镜像

github博客传送门
 博客园传送门

论文在此: ImageNet Classification with Deep Convolutional Neural Networks

网络结构图:

AlexNet网络结构图
AlexNet

Pytorch代码实现:

import torch.nn as nn


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.classifier = nn.Sequential(
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)
        return x


if __name__ == '__main__':
    # Example
    net = AlexNet()
    print(net)