Here is an example of directly manipulating the layer.
Note that you shouldn’t use the .data attribute, but instead wrap the manipulation in a with torch.no_grad() block.
Alternatively, here is an example of the nn.ParameterList usage:
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.params = nn.ParameterList()
self.params.append(nn.Parameter(torch.randn(2, 10)))
def forward(self, x):
w = torch.cat(tuple(self.params.parameters()), 0)
x = F.linear(x, w)
return x
model = MyModel()
x = torch.randn(1, 10)
out = model(x)
print(out.shape)
# Add param
new_param = nn.Parameter(torch.randn(1, 10))
model.params.append(new_param)
out = model(x)
print(out.shape)
In that case you could use optimizer.add_param_group with your new parameter.