import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
n_data = torch.ones(100,2)
x0 = torch.normal(2*n_data, 1)
y0 = torch.zeros(100)
x1 = torch.normal(-2*n_data, 1)
y1 = torch.ones(100)
x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # 组装（连接）
y = torch.cat((y0, y1), 0).type(torch.LongTensor)
x, y = Variable(x), Variable(y)
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)
self.out = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
x = F.relu(self.hidden(x))
x = self.out(x)
return x
net = Net(2, 10, 2)
optimizer = torch.optim.SGD(net.parameters(), lr = 0.012)
for t in range(100):
out = net(x)
loss = torch.nn.CrossEntropyLoss()(out, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (t+1) % 20 == 0:
plt.cla()
prediction = torch.max(F.softmax(out), 1)[1] # 在第1维度取最大值并返回索引值
pred_y = prediction.data.numpy().squeeze()
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 = sum(pred_y == target_y)/200
plt.text(1.5, -4, 'Accu=%.2f' % accuracy, fontdict={'size': 20, 'color': 'red'})
plt.pause(0.1)
Processed:
0.010, SQL:
9
