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test3.py
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53 lines (51 loc) · 1.66 KB
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import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
from torch.autograd import Variable
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)
#plt.scatter(x.data.numpy()[:,0],x.data.numpy()[:,1],c=y.data.numpy(),s=100,lw=0.5,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)
# 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(n_feature=2,n_hidden=10,n_output=2)
#print(net)
net = torch.nn.Sequential(
torch.nn.Linear(2,10),
torch.nn.ReLU(),
torch.nn.Linear(10,2)
)
print(net)
optimizer = torch.optim.SGD(net.parameters(),lr=0.02)
loss_func = torch.nn.CrossEntropyLoss()
plt.ion()
for t in range(100):
out = net(x)
loss = loss_func(out,y)
optimizer.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=1,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()