ValueError Traceback (most recent call last) Cell In[10], line 30 27 net = Net() 29 criterion = nn.CrossEntropyLoss() ---> 30 optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) 32 folder_path = 'random_matrices2' 34 # 创建空的tensor File C:\ProgramData\anaconda3\lib\site-packages\torch\optim\sgd.py:105, in SGD.__init__(self, params, lr, momentum, dampening, weight_decay, nesterov, maximize, foreach) 103 if nesterov and (momentum <= 0 or dampening != 0): 104 raise ValueError("Nesterov momentum requires a momentum and zero dampening") --> 105 super(SGD, self).__init__(params, defaults) File C:\ProgramData\anaconda3\lib\site-packages\torch\optim\optimizer.py:49, in Optimizer.__init__(self, params, defaults) 47 param_groups = list(params) 48 if len(param_groups) == 0: ---> 49 raise ValueError("optimizer got an empty parameter list") 50 if not isinstance(param_groups[0], dict): 51 param_groups = [{'params': param_groups}] ValueError: optimizer got an empty parameter list ​

这个错误可能是因为你没有给优化器提供要优化的参数。请检查一下你的 `net` 模型是否已经被正确地定义并且已经被传递给了优化器。另外，你也可以在创建 `Net` 类的时候，确保你的模型有可训练的参数，例如：

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

如果你还是无法解决问题，请提供更多的代码细节和上下文。