PyTorch實現一個簡單的二分類網絡模型

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) # 組裝（連接） 
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維度取最大值並返回索引值 
        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)