import torch

import matplotlib.pyplot as plt

learning_rate = 0.1

#准备数据 #y = 3x +0.8

x = torch.randn([500,1])

y_true = 3*x + 0.8

#计算预测值

w = torch.rand([],requires_grad=True)

b = torch.tensor(0,dtype=torch.float,requires_grad=True)

for i in range(50):

    #梯度默认会累加，梯度手动清零

    for j in [w,b]:

        if j.grad is not None:

            j.grad.data.zero_()

    y_predict = x*w +b

    #计算损失

    loss = (y_predict-y_true).pow(2).mean()

    loss.backward()

    #更新参数

    w.data = w.data - learning_rate * w.grad

    b.data = b.data - learning_rate * b.grad

    print(i,loss.item())

    print(w.data,b.data)

plt.figure(figsize=(20,8))

plt.scatter(x.numpy(),y_true.numpy())

y_predict = x*w + b

plt.plot(x.numpy(),y_predict.detach().numpy(),c="red")

plt.show()