nn.Conv2d(1, 6, kernel_size=5, padding=2), nn.Sigmoid(),

这是一个 PyTorch 中的卷积神经网络模型，其中 nn.Conv2d 表示使用 5x5 的卷积核对输入的图像进行卷积操作，输出通道数为 6，padding=2 表示在图像边缘填充 2 个像素，nn.Sigmoid() 则表示使用 Sigmoid 函数作为激活函数。

相关问题

为代码添加注释：import torch from torch import nn from d2l import torch as d2l net = nn.Sequential( nn.Conv2d(1, 6, kernel_size=5, padding=2), nn.Sigmoid(), nn.AvgPool2d(kernel_size=2, stride=2), nn.Conv2d(6, 16, kernel_size=5), nn.Sigmoid(), nn.AvgPool2d(kernel_size=2, stride=2), nn.Flatten(), nn.Linear(16 * 5 * 5, 120), nn.Sigmoid(), nn.Linear(120, 84), nn.Sigmoid(), nn.Linear(84, 10))

# 导入需要的库
import torch
from torch import nn
from d2l import torch as d2l

# 建立神经网络模型
net = nn.Sequential(
# 第一层卷积层
nn.Conv2d(1, 6, kernel_size=5, padding=2),
nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2),
# 第二层卷积层
nn.Conv2d(6, 16, kernel_size=5),
nn.Sigmoid(),
nn.AvgPool2d(kernel_size=2, stride=2),
# 全连接层
nn.Flatten(),
nn.Linear(16 * 5 * 5, 120),
nn.Sigmoid(),
nn.Linear(120, 84),
nn.Sigmoid(),
nn.Linear(84, 10)
)

加载InpaintingModel_gen.pth预训练模型时出现：RuntimeError: Error(s) in loading state_dict for ContextEncoder: Missing key(s) in state_dict: "encoder.0.weight", "encoder.0.bias", "encoder.2.weight", "encoder.2.bias", "encoder.3.weight", "encoder.3.bias", "encoder.3.running_mean", "encoder.3.running_var", "encoder.5.weight", "encoder.5.bias", "encoder.6.weight", "encoder.6.bias", "encoder.6.running_mean", "encoder.6.running_var",...并且载入的模型为：class ContextEncoder(nn.Module): def init(self): super(ContextEncoder, self).init() # 编码器 self.encoder = nn.Sequential( nn.Conv2d(4, 64, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), ) # 解码器 self.decoder = nn.Sequential( nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1), nn.Sigmoid(), ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x 要怎么改

根据报错信息可以看到，预训练模型中缺少了一些键对应的值，这些键是`self.encoder`中的权重和偏置参数。因此，我们需要在模型的`__init__`方法中对`self.encoder`的权重和偏置进行初始化，这样就可以让预训练模型的键和模型的键对应上。

具体来说，可以在`__init__`方法中添加如下代码：

self.encoder[0].weight = nn.Parameter(torch.zeros_like(self.encoder[0].weight))
self.encoder[0].bias = nn.Parameter(torch.zeros_like(self.encoder[0].bias))
self.encoder[2].weight = nn.Parameter(torch.zeros_like(self.encoder[2].weight))
self.encoder[2].bias = nn.Parameter(torch.zeros_like(self.encoder[2].bias))
self.encoder[3].weight = nn.Parameter(torch.zeros_like(self.encoder[3].weight))
self.encoder[3].bias = nn.Parameter(torch.zeros_like(self.encoder[3].bias))
self.encoder[5].weight = nn.Parameter(torch.zeros_like(self.encoder[5].weight))
self.encoder[5].bias = nn.Parameter(torch.zeros_like(self.encoder[5].bias))
self.encoder[6].weight = nn.Parameter(torch.zeros_like(self.encoder[6].weight))
self.encoder[6].bias = nn.Parameter(torch.zeros_like(self.encoder[6].bias))
self.encoder[6].running_mean = nn.Parameter(torch.zeros_like(self.encoder[6].running_mean))
self.encoder[6].running_var = nn.Parameter(torch.zeros_like(self.encoder[6].running_var))

这段代码中，我们对`self.encoder`的前7个卷积层的权重和偏置进行了初始化，并对第7个卷积层的`running_mean`和`running_var`也进行了初始化。这样，在加载预训练模型时，就可以正确地对应到模型的键，避免了报错。