I made a 10 layers nn, with the MSELoss(), it works normally, but I have to add a regularization term, when I add it to the loss function directly, it report an error :
TypeError: add() received an invalid combination of arguments - got (MSELoss), but expected one of:

(Tensor other, float alpha)
(float other, float alpha)

when I transpose the regularization to numpy, it error too:
TypeError: unsupported operand type(s) for +: ‘MSELoss’ and ‘float’
I know why it happened, but I don`t know how to correct,
My question is that how to write a regularization?
Thank you!
here is my code:

params=model.state_dict()
for k,v in params.items():
print(k)
n1=params[‘layer1.weight’]
n2=params[‘layer2.weight’]
n3=params[‘layer3.weight’]
n4=params[‘layer4.weight’]
n5=params[‘layer5.weight’]
n6=params[‘layer6.weight’]
n7=params[‘layer7.weight’]
n8=params[‘layer8.weight’]
n9=params[‘layer9.weight’]
n=(
1**4*(params[‘layer1.weight’] -params[‘layer1.weight’] )2 +
0.94*(params[‘layer1.weight’] -params[‘layer1.weight’] )2 +
0.84*(params[‘layer1.weight’] -params[‘layer1.weight’] )2 +
0.74*(params[‘layer1.weight’] -params[‘layer1.weight’] )2 +
0.64*(params[‘layer1.weight’] -params[‘layer1.weight’] )2 +
0.54*(params[‘layer1.weight’] -params[‘layer1.weight’] )2 +
0.44*(params[‘layer1.weight’] -params[‘layer1.weight’] )2 +
0.34*(params[‘layer1.weight’] -params[‘layer1.weight’] )**2
)
E=0.003*n
def train(epoch):
model.train()
criterion = torch.nn.MSELoss()+E