alexnet代码实现pytorch

AlexNet的PyTorch实现代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F

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.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 = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)
        return x

model = AlexNet()

以上是一个基于PyTorch实现的AlexNet模型的代码。该代码定义了一个名为AlexNet的类，继承自nn.Module。该模型包含了8个层，其中5个是卷积层，3个是全连接层。在模型的前向传播方法forward中，首先将输入数据通过卷积层和池化层进行特征提取，然后通过全连接层进行分类预测。最后返回预测结果。

请注意，这只是一个简化的示例代码，并没有包含完整的训练和测试过程。如果你想要完整的AlexNet的实现代码，建议参考PyTorch官方文档或其他开源项目。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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- *2* *3* [pytorch实现AlexNet（含完整代码）](https://blog.csdn.net/weixin_45836809/article/details/121690604)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
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