That one does contain all parameters whereas model._parameters is empty:
>>>dict(net1.named_parameters())

{'fc2.bias': Parameter containing:
tensor([-0.1455,  0.3597], requires_grad=True), 'fc1.weight': Parameter containing:
tensor([[ 0.5153],
        [-0.4414]], requires_grad=True), 'fc3.weight': Parameter containing:
tensor([[ 0.0983, -0.0866],
        [ 0.1961,  0.0349],
        [ 0.2583, -0.2756],
        [-0.0516, -0.0637],
        [ 0.1025, -0.0028]], requires_grad=True), 'fc2.weight': Parameter containing:
tensor([[-0.1371,  0.3319],
        [-0.6657,  0.4241]], requires_grad=True), 'fc3.bias': Parameter containing:
tensor([ 0.6181,  0.2200, -0.2633, -0.4271, -0.1185], requires_grad=True)}


>>>dict(net1._parameters)

{}

Is there maybe a way to change a model’s parameters for my case without directly modifying model._parameters?