regularization

[linhanxiao]

@michaelauli @jgehring
the following is my loss regularization code,I directly added the penal item to crit.output,is it right for model regularization?
net:forward(sample.input)
crit:forward(net.output, sample.target)
local A
for _,b in ipairs(_G.model.selfattentivesoftmax) do
A=b.output
end
local B=A:clone()
for i=1,B:size(1) do
A=B[i]:clone()
local AAT=torch.mm(A,A:t())
local I=torch.eye(A:size(1))
local P=torch.norm( AAT - I, 2 )
local penal=PP
penal = penal/A:size(2)
crit.output=crit.output+_G.model.selfattentivelamdapenal
end
crit:backward(net.output, sample.target)
net:backward(sample.input, crit.gradInput)

in the document，
-- Loss:
f = f + opt.coefL1 * norm(parameters,1)
f = f + opt.coefL2 * norm(parameters,2)^2/2

        -- Gradients:
        gradParameters:add( sign(parameters):mul(opt.coefL1) + parameters:clone():mul(opt.coefL2) )

but my regularization is not L1,L2 regularization.in my regularization code above the A is one network layer output ,is not the whole parameters,so what should i do to write right regularization code