Pytorch 初学---神经网络线性回归以及模型保存

import torch
import torch.nn as nn
import torch.optim as op
from torch.autograd import Variable

x = Variable(torch.Tensor([[1,2],[3,4],[4,2]]),requires_grad = False)

y = Variable(torch.Tensor([[3],[7],[6]]),requires_grad = False)

#model constract
def model():
    net = nn.Sequential(
        nn.Linear(2,4),
        nn.ReLU6(),
        nn.Linear(4,3),
        nn.ReLU(),
        nn.Linear(3,1)
    )
    optimizer = op.Adam(net.parameters(),lr= 0.01)
    loss_fun = nn.MSELoss()

    for i in range(2000):
        out = net(x)
        loss = loss_fun(out,y)
        print(loss)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print(net(x))
    #save model
    torch.save(net,'net.npy')


model()

一个简单的DNN网络，只有三层，输入，隐层和输出层