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basic neural network with CrossEntropyLoss.py
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basic neural network with CrossEntropyLoss.py
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import torch
import torch.nn.functional as F
from matplotlib import 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)
'''
plt.scatter(x.data.numpy()[:,0],x.data.numpy()[:,1],c = y.data.numpy(),s = 100)
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)
optimizer = torch.optim.SGD(net.parameters(), lr = 0.02)
loss_fun = torch.nn.CrossEntropyLoss()
plt.ion()
for i in range(100):
out = net(x)
loss = loss_fun(out, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % 2 == 0:
plt.cla()
prediction = torch.max(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 = 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()