def fit_and_plot_pytorch(wd): # 对权重参数衰减。权重名称一般是以weight结尾 net = nn.Linear(num_inputs, 1) nn.init.normal_(net.weight, mean=0, std=1) nn.init.normal_(net.bias, mean=0, std=1) optimizer_w = torch.optim.SGD(params=[net.weight], lr=lr, weight_decay=wd) # 对权重参数衰减 optimizer_b = torch.optim.SGD(params=[net.bias], lr=lr) # 不对偏差参数衰减 train_ls, test_ls = [], [] for _ in range(num_epochs): for X, y in train_iter: l = loss(net(X), y).mean() optimizer_w.zero_grad() optimizer_b.zero_grad() l.backward() # 对两个optimizer实例分别调用step函数,从而分别更新权重和偏差 optimizer_w.step() optimizer_b.step() train_ls.append(loss(net(train_features), train_labels).mean().item()) test_ls.append(loss(net(test_features), test_labels).mean().item()) d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss', range(1, num_epochs + 1), test_ls, ['train', 'test']) print('L2 norm of w:', net.weight.data.norm().item())