How to reassign modified model.parameters() back to the model?

Hello, I trained a network and played on its weights by making a list using model.parameters(). Now I want to reassign new weights to the model and see new prediction values in case of classification. How can I do that?

Hi,

I wrote a snippet as follow:

model = Net()
old_params = {}

for name, params in model.named_parameters():
    old_params[name] = params.clone()

# do some modification here
old_params['conv1.weight'][0] = 0.

random_input = torch.randn(1,3,3,3)
random_target = torch.randn(3,3,3)
criterion = nn.MSELoss()
opt = torch.optim.SGD(model.parameters(), lr=0.1)

for i in range(3):
    opt.zero_grad()
    random_out = model(random_input)
    loss = criterion(random_out, random_target)
    loss.backward()
    opt.step()

# leaf variable with requires_grad=True can not used inplace operation
for name, params in model.named_parameters():
    params.data.copy_(old_params[name])

# do another predict

model is a convnet with three layers stack.

Thanks but why did you train the network again after cloning and modifying parameters? I just need to clone, modify, reassign and predict. My aim is to see how modifying some of the weights alters the accuracy of the network? Or am I missing something in your snippet? Or can you say your answer is the the part of snippet in which random data generation and training parts are excluded?

Oh, it seems the snippet is in a wrong order. You can change the train phase before modification or omit the train phase.

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