pytorch深度学习：线性回归

深度学习版线性回归，哈哈哈哈。

在潘神的嘲笑下，我发了这篇博客，呜呜。

import torch
from torch import nn,optim

class LR(nn.Module):
    def __init__(self):
        super(LR, self).__init__()
        self.linear=nn.Linear(1,1)

    def forward(self, x):
        out = self.linear(x)
        return out

    def train(self,inputs,target,criterion,optimizer,epoches):
        for epoch in range(epoches):
            output = model.forward(inputs)
            loss = criterion(output, target)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        return model, loss

x=torch.Tensor([1,2,3,4,5,6])
y=x+torch.rand(6)
print(x)
print(y)
model=LR()
print(list(model.parameters()))

inputs=torch.unsqueeze(x,dim=1)
target=torch.unsqueeze(y,dim=1)
criterion=nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=1e-3)

new_model,loss=model.train(inputs=inputs,target=target,criterion=criterion,optimizer=optimizer,epoches=10000)
print(list(new_model.parameters()))
print(loss.item())

 最后%一下来自华科的陈巨。