torch 搭建回归神经网络

image.png

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())

x,y = Variable(x),Variable(y)

# 打印散点图
# 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)

    # 前向传递
    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()
plt.show()

optimizer = torch.optim.SGD(net.parameters(),lr=0.5)
loss_func = torch.nn.MSELoss() # 用均方差计算误差

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

    optimizer.zero_grad() # 将梯度降为0
    loss.backward() # 反向传递过程
    optimizer.step() # 优化梯度

    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[0],fontdict={'size':20,'color':'red'})
        plt.pause(0.1)

plt.ioff()
plt.show()