1 import torch
2 import torch.nn.functional as F
3 import matplotlib.pyplot as plt
4
5 # torch.manual_seed(1) # reproducible
6
7 # make fake data
8 n_data = torch.ones(100, 2)
9 x0 = torch.normal(2*n_data, 1) # class0 x data (tensor), shape=(100, 2)
10 y0 = torch.zeros(100) # class0 y data (tensor), shape=(100, 1)
11 x1 = torch.normal(-2*n_data, 1) # class1 x data (tensor), shape=(100, 2)
12 y1 = torch.ones(100) # class1 y data (tensor), shape=(100, 1)
13 x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # shape (200, 2) FloatTensor = 32-bit floating
14 y = torch.cat((y0, y1), ).type(torch.LongTensor) # shape (200,1) LongTensor = 64-bit integer
15
16 # The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors
17 # x, y = Variable(x), Variable(y)
18
19 # plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')
20 # plt.show()
21
22
23 class Net(torch.nn.Module):
24 def __init__(self, n_feature, n_hidden, n_output):
25 super(Net, self).__init__()
26 self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer
27 self.out = torch.nn.Linear(n_hidden, n_output) # output layer
28
29 def forward(self, x):
30 x = F.relu(self.hidden(x)) # activation function for hidden layer
31 x = self.out(x)
32 return x
33
34 net = Net(n_feature=2, n_hidden=10, n_output=2) # define the network
35 print(net) # net architecture
36
37 optimizer = torch.optim.SGD(net.parameters(), lr=0.02)
38 loss_func = torch.nn.CrossEntropyLoss() # the target label is NOT an one-hotted
39
40 plt.ion() # something about plotting
41
42 for t in range(100):
43 out = net(x) # input x and predict based on x
44 loss = loss_func(out, y) # must be (1. nn output, 2. target), the target label is NOT one-hotted
45
46 optimizer.zero_grad() # clear gradients for next train
47 loss.backward() # backpropagation, compute gradients
48 optimizer.step() # apply gradients
49
50 if t % 2 == 0:
51 # plot and show learning process
52 plt.cla()
53 prediction = torch.max(out, 1)[1]
54 pred_y = prediction.data.numpy()
55 target_y = y.data.numpy()
56 plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')
57 accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size)
58 plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color': 'red'})
59 plt.pause(0.1)
60
61 plt.ioff()
62 plt.show()
