pytorch实现线性回归算法

‘a = Variable(torch.rand(1),requires_grad = True)

b = Variable(torch.rand(1),requires_grad = True)

print("initial paraments",[a,b])

learning_rate = 0.0001

for i in range(10000):

    predictions = a.expand_as(x) * x + b.expand_as(x)

    loss = torch.mean((predictions-y)**2)

    print("loss",loss)

    loss.backward()

    a.data.add_(-learning_rate * a.grad.data)

    b.data.add_(-learning_rate * b.grad.data)

    a.grad.data.zero_()

    b.grad.data.zero_()’