A simple model like this one:
model = torch.nn.Sequential(torch.nn.Linear(10, 1, bias=False))
A simple model like this one:
model = torch.nn.Sequential(torch.nn.Linear(10, 1, bias=False))
You would just need to wrap it in a torch.no_grad()
block and manipulate the parameters as you want:
model = torch.nn.Sequential(nn.Linear(10, 1, bias=False))
with torch.no_grad():
model[0].weight = nn.Parameter(torch.ones_like(model[0].weight))
model[0].weight[0, 0] = 2.
model[0].weight.fill_(3.)
...