shared-brad

require 'torch'
require 'nn'

torch.setdefaulttensortype('torch.FloatTensor')
-------------- Model -------------------------
branch = nn.Sequential()
branch:add(nn.Linear(4,5))
branch:add(nn.Linear(5,1))

parallel=nn.Parallel(1,1);
parallel:add(branch);                         -- apply to first slice
parallel:add(branch:clone('weight', 'bias'))  -- apply to second slice

mlp = nn.Sequential()
mlp:add(parallel)
mlp:add(nn.Sum(1))
-------------- End of Model -------------------------

input = torch.rand(2,4)
print(mlp:forward(input))