准备数据

        这里用一组比较简单的数据

x_data = torch.Tensor([[1.0],[2.0],[3.0]])
y_data = torch.Tensor([[3.0],[6.0],[9.0]])

构建线性回归模型

#定义一个线性回归类
class Model(torch.nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        #模型只包含一个线性回归模型
        self.linear = torch.nn.Linear(1,1)
    #前向运算
    def forward(self,x):
        y_pred = self.linear(x)
        return y_pred

构建目标函数与优化方法

#定义损失函数
criterion = torch.nn.MSELoss(size_average = False)
#随机梯度下降作为优化方法
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)

迭代过程

#开始迭代
for epoch in range(500):
    y_pred = model(x_data)
    求损失函数
    loss = criterion(y_pred,y_data)
    print("epoch: ",epoch,"   loss: ",loss.item())
    
    optimizer.zero_grad()
    #反向
    loss.backward()
    #优化
    optimizer.step()

全部代码

import torch
from torch.autograd import Variable

x_data = torch.Tensor([[1.0],[2.0],[3.0]])
y_data = torch.Tensor([[2.0],[4.0],[6.0]])

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

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

model = Model()

criterion = torch.nn.MSELoss(size_average = False)
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)

for epoch in range(500):
    y_pred = model(x_data)

    loss = criterion(y_pred,y_data)
    print("epoch: ",epoch,"   loss: ",loss.item())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

hour_val = torch.Tensor([[4.0]])
print("after training:",4,model.forward(hour_val).data[0][0])

结果

这里只展示最后的结果了

epoch:  496    loss:  0.0005511323106475174
epoch:  497    loss:  0.0005431824829429388
epoch:  498    loss:  0.0005353790475055575
epoch:  499    loss:  0.0005277082673273981
after training: 4 tensor(11.9736)

可以看出最后求得的结果是11.9736，很接近12