权重衰退简洁实现
def train_concise(wd): net = nn.Sequential(nn.Linear(num_inputs, 1)) for param in net.parameters(): param.data.normal_() loss = nn.MSELoss() num_epochs, lr = 100, 0.003 # 随机梯度下降 trainer = torch.optim.SGD([{ "params": net[0].weight, # weight_decay:就是那个超参数 'weight_decay': wd}, { "params": net[0].bias}], lr=lr) animator = d2l.Animator(xlabel='epochs', ylabel='loss', yscale='log', xlim=[5, num_epochs], legend=['train', 'test']) for epoch in range(num_epochs): for X, y in train_iter: with torch.enable_grad(): trainer.zero_grad() l = loss(net(X), y) l.backward() trainer.step() if (epoch + 1) % 5 == 0: animator.add(epoch + 1, (d2l.evaluate_loss(net, train_iter, loss), d2l.evaluate_loss(net, test_iter, loss))) print('w的L2范数:', net[0].weight.norm().item())