Pytroch學習筆記(1)--關係擬合(迴歸)|莫凡python
阿新 • • 發佈:2018-11-08
Pytroch學習筆記(1)–關係擬合(迴歸)|莫凡python
本文使用Pytorch構建一個簡單的神經網路,可以在資料當中找到他們的關係, 然後用神經網路模型來建立一個可以代表他們關係的線條
import torch import torch.nn.functional as F import matplotlib.pyplot as plt x = torch.unsqueeze(torch.linspace(-1,1,100),dim=1) y= x.pow(2) + torch.rand(x.size()) """" plt.scatter(x.data.numpy(),y.data.numpy()) 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.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(n_feature=1,n_hidden= 10,n_output = 1) print(net) optimizer = torch.optim.SGD(net.parameters(), lr=0.2) loss_func = torch.nn.MSELoss() plt.ion() for t in range(100): prediction = net(x) loss = loss_func(prediction, y) optimizer.zero_grad() loss.backward() optimizer.step() 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.data.numpy(),fontdict={'size':20,'color':'red'}) plt.pause(0.1) plt.ioff() plt.show()