這裏用torch 作一個最簡單的測試網絡
目標就是咱們用torch 創建一個一層的網絡,而後擬合一組能夠迴歸的數據app
import torch from torch.autograd import Variable 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) + 0.2*torch.rand(x.size()) x, y = Variable(x), Variable(y)
這裏咱們先早出來假數據,這裏須要注意的是,最新版本的torch已經不須要variable了測試
接着咱們來搭建咱們的網絡優化
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) # 前向傳播 def forward(self, x): x = F.relu(self.hidden(x)) x = self.predict(x) return x
咱們作了個 1-10-1這樣的單隱藏層的網絡spa
net = Net(n_feature=1, n_hidden=10, n_output=1) print(net) # define optimizer optimizer = torch.optim.SGD(net.parameters(), lr=0.5) loss_func = torch.nn.MSELoss()
接着咱們選SGD來優化,選MSE作loss functioncode
開始訓練blog
plt.ion() # begin training for t in range(200): prediction = net(x) loss = loss_func(prediction, y) # must be (1. nn output, 2. target) optimizer.zero_grad() # clear gradients for next train loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients 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()
大概效果是這樣get