Hi,
I have a question about training in pytorch.
There is a pseudo code that I wanna ask.
model1 = Net()
model2 = copy.deepcopy(model1)

optimizer1 = torch.optim(model1.parameters(), lr)
optimizer2 = torch.optim(model2.parameters(), lr)

for i in range(dataset):

    x = random(dataset[i])
    y = dataset[i]

    update_step1(model1, optimizer1, x, y)
    update_step2(model2, optimizer2, x, y)

I thought this would be updated separately in each function(update_step1, 2), but the two values were the same.
How should I do to train model at the same time?
Note that the reason for doing this at the same time is to give the same random X value both models.
Thank you.