Pytorch 关系拟合

跟着莫凡大神学习

code:

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) # x data (tensor) ,shape (100,1)
y=x.pow(2)+0.2*torch.rand(x.size())  # noisy y data (tensor) ,shape=(100,1)
# torch can only train on Variable,so convert them to Variable
x,y=Variable(x),Variable(y)

# 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(1,10,1)

print(net)
plt.ion()  # something about plotting
plt.show()


optimizer =torch.optim.SGD(net.parameters(),lr=0.5)  #优化参数
loss_func = torch.nn.MSELoss()  # 计算误差  （MSE：均方差）

for t in range(100):
    prediction =net(x)   #开始训练

    loss = loss_func(prediction,y)  # 一定要预测的值在前，真实值在后

# below are
    optimizer.zero_grad()  # clear gradients for next train
    loss.backward()        # backpropagation, compute gradients
    optimizer.step()       # 以学习效率 0.5来优化梯度
    if t % 5==0:  # 每训练5次 ，打印一次
        # plot and show learning process
        plt.cla()
        plt.scatter(x.data.numpy(),y.data.numpy())
        plt.plot(x.data.numpy(),prediction.data.numpy())
        plt.text(0.5,0,'Loss=%.4f' % loss.data[0],fontdict={'size':20,'color':'red'})
        plt.pause(0.1)

    plt.ioff()
    plt.show()