import torch

 import torch.nn.functional as F

 import matplotlib.pyplot as plt

 # torch.manual_seed(1)    # reproducible

 x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)

 y = x.pow(2) + 0.2*torch.rand(x.size())                 # noisy y data (tensor), shape=(100, 1)

 # torch can only train on Variable, so convert them to Variable

 # The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors

 # x, y = Variable(x), Variable(y)

 # plt.scatter(x.data.numpy(), y.data.numpy())

 # plt.show()

 class Net(torch.nn.Module):

     def __init__(self, n_feature, n_hidden, n_output):

         super(Net, self).__init__()

         self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer

         self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer

     def forward(self, x):

         x = F.relu(self.hidden(x))      # activation function for hidden layer

         x = self.predict(x)             # linear output

         return x

 net = Net(n_feature=1, n_hidden=10, n_output=1)     # define the network

 print(net)  # net architecture

 optimizer = torch.optim.SGD(net.parameters(), lr=0.2)

 loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss

 plt.ion()   # something about plotting

 for t in range(200):

     prediction = net(x)     # input x and predict based on x

     loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)

     optimizer.zero_grad()   # clear gradients for next train

     loss.backward()         # backpropagation, compute gradients

     optimizer.step()        # apply gradients

     if t % 5 == 0:

         # plot and show learning process

         plt.cla()

         plt.scatter(x.data.numpy(), y.data.numpy())

         plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)

         plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color':  'red'})

         plt.pause(0.1)

 plt.ioff()

 plt.show()