import torch

 import torch.nn.functional as F

 import matplotlib.pyplot as plt

 # torch.manual_seed(1)    # reproducible

 # make fake data

 n_data = torch.ones(100, 2)

 x0 = torch.normal(2*n_data, 1)      # class0 x data (tensor), shape=(100, 2)

 y0 = torch.zeros(100)               # class0 y data (tensor), shape=(100, 1)

 x1 = torch.normal(-2*n_data, 1)     # class1 x data (tensor), shape=(100, 2)

 y1 = torch.ones(100)                # class1 y data (tensor), shape=(100, 1)

 x = torch.cat((x0, x1), 0).type(torch.FloatTensor)  # shape (200, 2) FloatTensor = 32-bit floating

 y = torch.cat((y0, y1), ).type(torch.LongTensor)    # shape (200,1) LongTensor = 64-bit integer

 # 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()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')

 # 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.out = 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.out(x)

         return x

 net = Net(n_feature=2, n_hidden=10, n_output=2)     # define the network

 print(net)  # net architecture

 optimizer = torch.optim.SGD(net.parameters(), lr=0.02)

 loss_func = torch.nn.CrossEntropyLoss()  # the target label is NOT an one-hotted

 plt.ion()   # something about plotting

 for t in range(100):

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

     loss = loss_func(out, y)     # must be (1. nn output, 2. target), the target label is NOT one-hotted

     optimizer.zero_grad()   # clear gradients for next train

     loss.backward()         # backpropagation, compute gradients

     optimizer.step()        # apply gradients

     if t % 2 == 0:

         # plot and show learning process

         plt.cla()

         prediction = torch.max(out, 1)[1]

         pred_y = prediction.data.numpy()

         target_y = y.data.numpy()

         plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')

         accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size)

         plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color':  'red'})

         plt.pause(0.1)

 plt.ioff()

 plt.show()