[人工智能]pytorch模拟线性回归

import torch

x = torch.randn(200, 1, 1)
y = x * 2 + 3 + torch.randn(x.shape) * 0.01

class Liner(torch.nn.Module):
    def __init__(self):
        super(Liner, self).__init__()
        self.layer1 = torch.nn.Linear(1, 1)

    def forward(self, x):
        y_pre = self.layer1(x)
        return y_pre


liner = Liner()
global_step = 0
for epoch in range(5):
    for ind, i in enumerate(x):
        y_pre = liner(i)
        loss = ((y_pre - y[ind]) ** 2) / 2
        optimizer = torch.optim.SGD(params=liner.parameters(), lr=0.01)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        print("loss= {}\tstep= {}\t w = {}\tb = {}".format(loss.item(), global_step, liner.layer1.weight.item(), liner.layer1.bias.item()))
        global_step += 1