基于MNIST 手写数字识别模型代码要有key(s) in state_dict: "fc3.weight", "fc3.bias"

好的，这是一个基于MNIST手写数字识别的LeNet模型代码示例，你可以参考一下：

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


class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
        self.pool1 = nn.MaxPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
        self.pool2 = nn.MaxPool2d(kernel_size=2)
        self.fc1 = nn.Linear(16 * 4 * 4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool1(x)
        x = F.relu(self.conv2(x))
        x = self.pool2(x)
        x = x.view(-1, 16 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

model = LeNet()

# 加载预训练模型权重
pretrained_dict = torch.load('lenet_mnist.pth')
model_dict = model.state_dict()

# 去掉不匹配的键
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}

# 更新模型权重
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)

当你加载预训练模型权重时，如果出现了"fc3.weight"和"fc3.bias"这两个键缺失的问题，可以将这两个键从模型权重中去掉，然后再更新模型权重。