详细代码与注释:
import torch
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)
y = torch.cat((y0, y1), ).type(torch.LongTensor)
class Net(torch.nn.Module):
def __init__(self, n_features, hidden_features, out_features):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(in_features=n_features, out_features=hidden_features)
self.predict = torch.nn.Linear(in_features=hidden_features, out_features=out_features)
def forward(self, x):
x = torch.relu(self.hidden(x))
x = self.predict(x)
return x
net = Net(2, 2, 2)
print(net)
optimizer = torch.optim.SGD(net.parameters(), lr=0.02)
loss_fuc = torch.nn.CrossEntropyLoss()
plt.ion()
for t in range(100):
out = net.predict(x)
loss = loss_fuc(out, y)
net.zero_grad()
loss.backward()
optimizer.step()
if t % 2 == 0:
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(0.2)
plt.ioff()
plt.show()
效果展示: