PyTorch: %matplotlib inline
import torch
from torch import nn
from d2l import torch as d2l
n_train, n_test, num_inputs, batch_size = 20, 100, 200, 5
true_w, true_b = torch.ones((num_inputs, 1)) * 0.01, 0.05
train_data = d2l.synthetic_data(true_w, true_b, n_train)
train_iter = d2l.load_array(train_data, batch_size)
test_data = d2l.synthetic_data(true_w, true_b, n_test)
test_iter = d2l.load_array(test_data, batch_size, is_train=False)
def init_params():
    w = torch.normal(0, 1, size=(num_inputs, 1), requires_grad=True)
    b = torch.zeros(1, requires_grad=True)
    return [w, b]
def l2_penalty(w):
    return torch.sum(w.pow(2)) / 2
def train(lambd):
    w, b = init_params()
    net, loss = lambda X: d2l.linreg(X, w, b), d2l.squared_loss
    num_epochs, lr = 100, 0.003
    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:
            l = loss(net(X), y) + lambd * l2_penalty(w)
            l.sum().backward()
            d2l.sgd([w, b], lr, batch_size)
        if (epoch + 1) % 5 == 0:
            animator.add(epoch + 1, (d2l.evaluate_loss(net, train_iter, loss), d2l.evaluate_loss(net, test_iter, loss)))
def train_concise(wd):
    net = nn.Sequential(nn.Linear(num_inputs, 1))
    for param in net.parameters():
        param.data.normal_()
    loss = nn.MSELoss(reduction='none')
    num_epochs, lr = 100, 0.003
    trainer = torch.optim.SGD([{"params":net[0].weight,'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:
            trainer.zero_grad()
            l = loss(net(X), y)
            l.mean().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)))


Paddle: %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
n_train, n_test, num_inputs, batch_size = 20, 100, 200, 5
true_w, true_b = paddle.ones((num_inputs, 1)) * 0.01, 0.05
train_data = d2l.synthetic_data(true_w, true_b, n_train)
train_iter = d2l.load_array(train_data, batch_size)
test_data = d2l.synthetic_data(true_w, true_b, n_test)
test_iter = d2l.load_array(test_data, batch_size, is_train=False)
def init_params():
    w = paddle.normal(0, 1, shape=(num_inputs, 1))
    w.stop_gradient = False
    b = paddle.zeros(shape=[1])
    b.stop_gradient = False
    return [w, b]
def l2_penalty(w):
    return paddle.sum(w.pow(2)) / 2
def train(lambd):
    w, b = init_params()
    net, loss = lambda X: d2l.linreg(X, w, b), d2l.squared_loss
    num_epochs, lr = 100, 0.003
    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():
            l = loss(net(X), y) + lambd * l2_penalty(w)
            l.sum().backward()
            d2l.sgd([w, b], lr, batch_size)
        if (epoch + 1) % 5 == 0:
            animator.add(epoch + 1, (d2l.evaluate_loss(net, train_iter, loss), d2l.evaluate_loss(net, test_iter, loss)))
def train_concise(wd):
    weight_attr = paddle.framework.ParamAttr(initializer=paddle.nn.initializer.Normal(mean=0.0, std=1.0))
    bias_attr = paddle.framework.ParamAttr(initializer=paddle.nn.initializer.Normal(mean=0.0, std=1.0))
    net = nn.Sequential(nn.Linear(num_inputs, 1, weight_attr=weight_attr, bias_attr=bias_attr))
    loss = nn.MSELoss()
    num_epochs, lr = 100, 0.003
    trainer = paddle.optimizer.SGD(parameters=net[0].parameters(), learning_rate=lr, weight_decay=wd*1.0)
    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:
            l = loss(net(X), y)
            l.backward()
            trainer.step()
            trainer.clear_grad()
        if (epoch + 1) % 5 == 0:
            animator.add(epoch + 1, (d2l.evaluate_loss(net, train_iter, loss), d2l.evaluate_loss(net, test_iter, loss)))