code: import torch
  import torch.nn.functional as F
  import numpy as np
  import matplotlib.pyplot as plt
  import torch.nn as nn
  import torch.optim as optim
  
  
  x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)
  y = x.pow(2) + 0.2 * torch.rand(x.size())
  
  # plt.scatter(x.numpy(), y.numpy())
  # plt.show()
  
  
  class Net(nn.Module):
      def __init__(self, n_features, n_hidden, n_output):
          super(Net, self).__init__()
          self.hidden = nn.Linear(n_features, n_hidden)
          self.predict = nn.Linear(n_hidden, n_output)
  
      def forward(self, x):
          x = F.relu(self.hidden(x))
          x = self.predict(x)
          return x
  
  
  net = Net(1, 20, 1)
  print(net)
  
  plt.ion()
  plt.show()
  
  optimizer = optim.SGD(net.parameters(), lr=0.02, momentum=0.9)
  loss_function = nn.MSELoss()
  
  for t in range(200):
      prediction = net(x)
  
      loss = loss_function(prediction, y)
  
      optimizer.zero_grad()
      loss.backward()
      optimizer.step()
  
      if (t % 5) == 0:
          print("Loss : %f" % loss.data)
          # plot and show learning process
          plt.cla()
          plt.scatter(x.numpy(), y.numpy())
          plt.plot(x.numpy(), prediction.data.numpy(), 'r-', lw=5)
          plt.text(0.5, 0, 'Loss=%.4f' % loss.data, fontdict={'size': 20, 'color': 'red'})
          plt.pause(0.1)
  
  plt.ioff()
  plt.show()
  
apis: matplotlib.pyplot.pause torch.nn.Linear torch.nn.MSELoss matplotlib.pyplot.cla matplotlib.pyplot.ioff torch.linspace matplotlib.pyplot.show matplotlib.pyplot.text matplotlib.pyplot.ion