nn.Parameter not getting updated not sure about the usage

I have declared two nn.Parameter() variables with requires_grad=True and I am using those in a different function that’s being called inside the init method of the class where variables are declared. lparam and rparam are not getting updated
My question is am I doing it the right way?
if not how it should be done?

here is the code example:

class LG(BaseNetwork):
    def __init__(self, opt):
        self.opt = opt
        self.lparam = nn.Parameter(torch.zeros(1), requires_grad=True).cuda(device=opt.gpu_ids[0])
        self.rparam = nn.Parameter(torch.zeros(1), requires_grad=True).cuda(device=opt.gpu_ids[0])
    def foo(self, a, b, k=1.0, lparam=0, rparam=0):
      t = bar(a, b, k=k, lparam=lparam, rparam=rparam)
      return t

    def forward(self, a, b):
       x = self.foo(a, b, k=self.opt.k, lparam=self.lparam, rparam=self.rparam)
       return x

BaseNetwork is just initializing functions and uses nn.Module

def bar(a, b, k=1.0, lparam=0, rparam=0):
    return n(a) * (b.std() * (k * lparam)) + (b.mean() * (k * rparam))

When I print the named params I can not get lparam and rparam

The .cuda() operation on the nn.Parameter is differentiable and will create a non-leaf tensor.
Remove the .cuda() operation and call it on the nn.Module instead or alternatively call it on the tensor before wrapping it into the nn.Parameter.

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Thank You so much @ptrblck you’re mind saver :smile:

Hi, thanks for this! my problem is also solved by not using cuda(). But I am confused by this. Does this mean that, if I use cuda(), the nn.Parameter won’t be updated?

Yes, since the cuda() and to() operations are differentiable as explained before with recommendations of a proper usage (call these ops on the module or on the tensor before wrapping it into an nn.Parameter):

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