Pytroch学习笔记（1）--关系拟合（回归）|莫凡python

Pytroch学习笔记（1）–关系拟合（回归）|莫凡python

本文使用Pytorch构建一个简单的神经网络,可以在数据当中找到他们的关系, 然后用神经网络模型来建立一个可以代表他们关系的线条

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()