2层感知机（神经网络）实现非线性回归（非线性拟合）【pytorch】

import torch
import numpy
import random
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt x = torch.unsqueeze(torch.linspace(-1,1,100),dim=1)
y = x.pow(2)+0.2*torch.rand(x.size())
x,y = Variable(x),Variable(y)
plt.ion()
class Net(torch.nn.Module):
def __init__(self,n_feature,n_hidden,n_output):
super(Net,self).__init__()
#两层感知机
self.hidden = torch.nn.Linear(n_feature,n_hidden)
self.predict = torch.nn.Linear(n_hidden,n_output) def forward(self,x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return xnet = Net(1,8,1) #输入节点1个，隐层节点8个，输出节点1个
optimizer = torch.optim.SGD(net.parameters(),lr = 0.2)
loss_func = torch.nn.MSELoss()for t in range(200):
prediction = net(x)
loss = loss_func(prediction,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if t%5==0:
plt.cla()
plt.scatter(x.data.numpy(),y.data.numpy())
plt.plot(x.data.numpy(),prediction.data.numpy(),'r-',lw=5)
plt.pause(0.1)plt.ioff()
plt.show()