The difference in performance between nnet.conv2d() and nnet.conv.conv2d() 
seems to be about the same for 100x100 matrices. 

The profile is as follows:

Apply
------
<% time> <sum %> <apply time> <time per call> <#call> <id> <Apply name>
  88.0%    88.0%      18.591s       1.84e-01s    101     5   CorrMM{valid, 
(1, 1), (1, 1)}(InplaceDimShuffle{x,x,0,1}.0, Subtensor{::, ::, ::int64, 
::int64}.0)
   5.5%    93.5%       1.170s       1.16e-02s    101    11   
IncSubtensor{Set;int64:int64:, int64:int64:}(u, Reshape{2}.0, Constant{1}, 
Constant{-1}, Constant{1}, Constant{-1})
   4.8%    98.3%       1.022s       1.01e-02s    101    12   
Elemwise{Composite{sqr((i0 - i1))}}(IncSubtensor{Set;int64:int64:, 
int64:int64:}.0, u)
   1.7%   100.0%       0.352s       3.49e-03s    101    13   
Sum{acc_dtype=float64}(Elemwise{Composite{sqr((i0 - i1))}}.0)
...

Making `u` shared does not change the timings much, I expect it will be 
more important if I use the GPU backend.

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