How to re-use kernel parameters in each iteration

autograd
1

I want to transfer the kernel parameters in each iteration into the regularization term part. I use the following code.

        model=MNISTnet()
        print(model.state_dict())

        for i, j in model.named_parameters():
            print(i)
            print(j[0][0][0])
            break

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The result is shown above. I found that the named_parameters() function get different kernel parameters. The last 2 weights are results of named_parameters() function. I don’t know why they get parameters by re-initialize the kernel rather than the training parameters in each iteration. Please give me some suggestions about this problem, thank you