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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