print net.sim(tempTraces[i, POIs])
print output[i]
</pre>
and sample outputs (neural net output, followed by target output):
<pre>
[[ 0.00269868]
[ 0.06987566]
[ 0.1822478 ]
[ 0.27928454]
[ 0.23389815]
[ 0.14159774]
[ 0.0836733 ]
[ 0.01236645]
[-0.00336757]]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0.]
[[ 0.00185322]
[ 0.04963187]
[ 0.13283786]
[ 0.23381804]
[ 0.22884202]
[ 0.18269986]
[ 0.13702157]
[ 0.03021828]
[ 0.00238221]]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0.]
[[ 0.00110461]
[ 0.05152491]
[ 0.13646525]
[ 0.23203113]
[ 0.22911589]
[ 0.18913897]
[ 0.13414064]
[ 0.02664681]
[ 0.00179477]]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0.]
[[ 0.0014322 ]
[ 0.05234322]
[ 0.13706946]
[ 0.23290479]
[ 0.22914769]
[ 0.18871015]
[ 0.1334901 ]
[ 0.02694461]
[ 0.00150736]]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0.]
[[-0.00059773]
[ 0.03843651]
[ 0.10725462]
[ 0.1905264 ]
[ 0.22326898]
[ 0.23700908]
[ 0.16796544]
[ 0.02915104]
[ 0.00633207]]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0.]
[[ -1.78281921e-04]
[ 3.59578962e-02]
[ 9.94288173e-02]
[ 1.91547921e-01]
[ 2.24460384e-01]
[ 2.30580391e-01]
[ 1.75289176e-01]
[ 3.68336205e-02]
[ 6.44831449e-03]]
[ 0. 0. 0. 0. 0. 0. 0. 1. 0.]
[[ -2.69312107e-05]
[ 3.91174797e-02]
[ 1.08659542e-01]
[ 1.98308362e-01]
[ 2.24538767e-01]
[ 2.24923191e-01]
[ 1.65405991e-01]
[ 3.20631603e-02]
[ 5.85090474e-03]]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0.]
[[ 0.00340681]
[ 0.07477566]
[ 0.19291102]
[ 0.29022512]
[ 0.23452383]
[ 0.13103114]
[ 0.07176303]
[ 0.00888493]
[-0.00466867]]
[ 0. 1. 0. 0. 0. 0. 0. 0. 0.]
</pre>