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)
y = torch.cat((y0,y1),).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.predict = torch.nn.Linear(n_hidden,n_output)
# 前向传递
def forward(self,x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return x
net = Net(2,10,2)
print(net)
plt.ion()
plt.show()
optimizer = torch.optim.SGD(net.parameters(),lr=0.02)
loss_func = torch.nn.CrossEntropyLoss() # 预测标签误差
for t in range(100):
out = net(x)
loss = loss_func(out,y)
optimizer.zero_grad() # 将梯度降为0
loss.backward() # 反向传递过程
optimizer.step() # 优化梯度
if t % 2 == 0:
plt.cla()
prediction = torch.max(F.softmax(out),1)[1]
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,'Accuracy=%.2f'%accuracy,fontdict={'size':20,'color':'red'})
plt.pause(0.1)
plt.ioff()
plt.show()
