net.py

import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt

class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()  # 继承__init__功能
        # 对输入数据进行线性变换 y=ax+b
        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)
x = torch.unsqueeze(torch.linspace(-1,1,100), dim=1)
y = x.pow(2) + 0.2*torch.rand(x.size())

optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
loss_func = torch.nn.MSELoss()

plt.ion()   # 画图
plt.show()

for t in range(100):
    prediction = net(x)
    loss = loss_func(prediction, y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # 接着上面来
    if t % 5 == 0:
        # plot and show learning process
        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)