pytorch 卷积自动编码器

引用中提到了一个基于PyTorch的卷积自动编码器的实现。这个自动编码器模型使用了卷积层、最大池化层、上采样层和激活函数作为主要组成部分。代码如下:

class AutoEncoder2(torch.nn.Module):
    def __init__(self):
        super(AutoEncoder2, self).__init__()
        self.encoder = torch.nn.Sequential(
            torch.nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2, stride=2),
            torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.decoder = torch.nn.Sequential(
            torch.nn.Upsample(scale_factor=2, mode='nearest'),
            torch.nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.Upsample(scale_factor=2, mode='nearest'),
            torch.nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1)
        )

    def forward(self, input):
        output = self.encoder(input)
        output = self.decoder(output)
        return output

这个模型的编码部分由两个卷积层和最大池化层组成，用于提取输入数据的核心特征。解码部分由两个上采样层和卷积层组成，用于实现数据的重构。整个模型的作用是对输入数据进行清洗，如去除噪声数据、增强关键特征等。通过实例化这个模型并调用`forward`方法，可以对输入数据进行自动编码的处理。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [Pytorch基础-07-自动编码器](https://blog.csdn.net/Galen_xia/article/details/109593783)[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^v92^chatsearchT0_1"}}] [.reference_item style="max-width: 100%"]
[ .reference_list ]