Defining model parameters

2

Greetings, you can follow this prescription.

For example simply wrap this in a nn.Parameter:

def normal(shape):
    return nn.Parameter(torch.randn(size=shape)*0.01)

for foo in gnn.parameters():
    print(foo)

Output:
Parameter containing:
tensor([[-0.0022]], requires_grad=True)
Parameter containing:
tensor([[0.0037]], requires_grad=True)
1 Like