Pytorch实现线性回归

import torch

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

class MyLinear(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = torch.nn.Linear(1,1)
        
    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred
    
model = MyLinear()
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred,y_data)
    print('epoch==' + str(epoch), 'loss==' + str(loss.item()))
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
print('w=',model.linear.weight.item())
print('b=',model.linear.bias.item())

x_test = torch.tensor([[4.0]])
y_test = model(x_test)
print("y_test=",y_test.data)

输出结果：