时间戳

首页 > 解决方案 > Pytorch Conv2d 自动编码器输出形状

pytorch - Pytorch Conv2d 自动编码器输出形状

问题描述

我构建了波纹管卷积自动编码器并尝试对其进行调整以获得 [NxHxW] = 1024 的编码器输出形状 (x_encoder) 而不会增加损失。目前我的输出形状是 [4, 64, 64] 有什么想法吗？

# define the NN architecture
class ConvAutoencoder(nn.Module):
    def __init__(self):
        super(ConvAutoencoder, self).__init__()
        ## encoder layers ##
        # conv layer (depth from in --> 16), 3x3 kernels
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)  
        # conv layer (depth from 16 --> 4), 3x3 kernels
        self.conv2 = nn.Conv2d(16, 4, 3, padding=1)
        # pooling layer to reduce x-y dims by two; kernel and stride of 2
        self.pool = nn.MaxPool2d(2, 2)
        
        ## decoder layers ##
        ## a kernel of 2 and a stride of 2 will increase the spatial dims by 2
        self.t_conv1 = nn.ConvTranspose2d(4, 16, 2, stride=2)
        self.t_conv2 = nn.ConvTranspose2d(16, 1, 2, stride=2)

    def forward(self, x):
        ## encode ##
        # add hidden layers with relu activation function
        # and maxpooling after
        x = F.relu(self.conv1(x))
        x = self.pool(x)
        # add second hidden layer
        x = F.relu(self.conv2(x))
        x = self.pool(x)  # compressed representation
        x_encoder = x
        ## decode ##
        # add transpose conv layers, with relu activation function
        x = F.relu(self.t_conv1(x))
        # output layer (with sigmoid for scaling from 0 to 1)
        x = F.sigmoid(self.t_conv2(x))
                
        return x, x_encoder

标签： pytorchartificial-intelligenceconv-neural-networkautoencoder

解决方案

假设 x_encoder 的形状为 ，这应该可以工作(torch.Size([1, 4, 64, 64])。您可以添加一个转换。步幅设置为 2 或 Conv 的层。层后跟一个池化层。检查下面的代码：

# conv layer (depth from in --> 16), 3x3 kernels
self.conv1 = nn.Conv2d(1, 16, 3, padding=1)  
# conv layer (depth from 16 --> 4), 3x3 kernels
self.conv2 = nn.Conv2d(16, 4, 3, padding=1)
# pooling layer to reduce x-y dims by two; kernel and stride of 2
self.pool = nn.MaxPool2d(2, 2)

# The changes
self.conv3 = nn.Conv2d(4, 1, 1, 2)

# or 
self.conv3 = nn.Conv2d(4, 1, 1)
self.maxpool2d = nn.MaxPool2d((2, 2))

推荐阅读