利用pytorch進行線性迴歸

import torch
import matplotlib.pyplot as plt

torch.manual_seed(10)

lr = 0.1 #learning rate

#train data
x = torch.rand(20,1)*10 #x data shape(20,1)
y = 2*x+(5+torch.randn(20,1))#y data shape(20,1)

#build model
w = torch.randn((1),requires_grad=True)
b = torch.zeros((1),requires_grad=True)

for iteration in range(1000):

    #前向傳播
    wx = torch.mul(w,x)
    y_pred = torch.add(wx,b)

    #loss
    loss = (0.5*(y-y_pred)**2).mean()  #求均值

    #反向傳播
    loss.backward()

    #更新參數
    b.data.sub_(lr*b.grad)
    w.data.sub_(lr*w.grad)

    #plot
    if iteration % 20 == 0:
        plt.scatter(x.data.numpy(),y.data.numpy())
        plt.plot(x.data.numpy(),y_pred.data.numpy(),'r-',lw=5)
        plt.text(2,20,'Loss=%.4f'%loss.data.numpy(),fontdict={'size':20,'color':'red'})
        plt.xlim(1.5,10)
        plt.ylim(8,28)
        plt.title("Iteration:{}\nw:{} b:{}".format(iteration,w.data.numpy(),b.data.numpy()))
        plt.pause(0.5)

        if loss.data.numpy() < 1:
            break