pytorch實現回歸任務

完整代碼：

import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
import torch.optim as optim

#生成數據
#隨機取100個-1到1之間的數，利用unsqueeze將一維變成二維
x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)
y = x.pow(2) + 0.2*torch.rand(x.size())

#神經網絡只能接受Variable的數
x,y = Variable(x),Variable(y)

#繪制圖像
plt.scatter(x.data.numpy(), y.data.numpy())
#plt.show()


#定義網絡
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)

    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x

net = Net(1, 10, 1)
#print(net)  


#優化
optimizer = optim.SGD(net.parameters(), lr=0.5)
loss_func = torch.nn.MSELoss()

#可視化
plt.ion()
#plt.show()

for t in range(100):
    prediction = net(x)
    loss = loss_func(prediction, y) #預測值和真實值

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    #可視化
    if t % 5 == 0:
        plt.cla()
        plt.scatter(x.data.numpy(), y.data.numpy())
        plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
        plt.text(0.5, 0, 'Loss=%.4f' % loss.item(), fontdict={'size': 20, 'color':  'red'})
        plt.pause(0.1)

plt.ioff()
plt.show()

 

在我運行代碼是，出現過以下報錯：

IndexError: invalid index of a 0-dim tensor. Use tensor.item() to convert a 0-dim tensor to a Python

這是因為我的pytorch版本比較高，將代碼plt.text(0.5, 0, 'Loss=%.4f' % loss.data[0], fontdict={'size': 20, 'color': 'red'})改為plt.text(0.5, 0, 'Loss=%.4f' % loss.item(), fontdict={'size': 20, 'color': 'red'})就好了