线性回归简洁实现

import torch
import numpy as np
from torch.utils import data
from d2l import torch as d2l
true_w = torch.tensor([2, -3.4])
true_b = 4.2
features, labels = d2l.synthetic_data(true_w, true_b, 1000)
print(features, labels)

def load_array(data_arrays, batch_size, is_train=True):
    dataset = data.TensorDataset(*data_arrays)
    return data.DataLoader(dataset, batch_size, shuffle=is_train)
batch_size = 10
data_iter = load_array((features, labels), batch_size)
next(iter(data_iter))

from torch import nn

net = nn.Sequential(nn.Linear(2, 1))
net[0].weight.data.normal_(0, 0.01)
net[0].bias.data.fill_(0)
loss = nn.MSELoss()

trainer = torch.optim.SGD(net.parameters(), lr=0.03)

num_epochs = 10
for epoch in range(num_epochs):
    for x,y in data_iter:
        l = loss(net(x), y)
        trainer.zero_grad()
        l.backward()
        trainer.step()
    l = loss(net(features), labels)
    print(f'epoch{epoch + 1}, loss {l:f}')