这是您的操作方式：
- 在模块的正向返回最终输出和要应用L1正则化的图层的输出中
loss
变量将是输出wrt目标的交叉熵损失与L1惩罚的总和。
这是一个示例代码
import torchfrom torch.autograd import Variablefrom torch.nn import functional as Fclass MLP(torch.nn.Module): def __init__(self): super(MLP, self).__init__() self.linear1 = torch.nn.Linear(128, 32) self.linear2 = torch.nn.Linear(32, 16) self.linear3 = torch.nn.Linear(16, 2) def forward(self, x): layer1_out = F.relu(self.linear1(x)) layer2_out = F.relu(self.linear2(layer1_out)) out = self.linear3(layer2_out) return out, layer1_out, layer2_outbatchsize = 4lambda1, lambda2 = 0.5, 0.01model = MLP()optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)# usually following pre is looped over all batches # but let's just do a dummy batch for brevityinputs = Variable(torch.rand(batchsize, 128))targets = Variable(torch.ones(batchsize).long())optimizer.zero_grad()outputs, layer1_out, layer2_out = model(inputs)cross_entropy_loss = F.cross_entropy(outputs, targets)all_linear1_params = torch.cat([x.view(-1) for x in model.linear1.parameters()])all_linear2_params = torch.cat([x.view(-1) for x in model.linear2.parameters()])l1_regularization = lambda1 * torch.norm(all_linear1_params, 1)l2_regularization = lambda2 * torch.norm(all_linear2_params, 2)loss = cross_entropy_loss + l1_regularization + l2_regularizationloss.backward()optimizer.step()
