calumroy
commented
7 years ago Profiling just the np_predictCells calculator's function updatePredictiveState (note this is from a different test to the above profile hence the 50ms time vs the 122ms above).
38140 function calls in 0.050 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
4 0.000 0.000 0.000 0.000 np_predictCells.py:127(getSegmentActiveSynapses)
4 0.000 0.000 0.000 0.000 np_predictCells.py:150(checkCellPredicting)
4 0.000 0.000 0.000 0.000 np_predictCells.py:160(setPredictCell)
4 0.000 0.000 0.000 0.000 np_predictCells.py:170(setActiveSeg)
6051 0.007 0.000 0.007 0.000 np_predictCells.py:177(checkCellActive)
10230 0.032 0.000 0.040 0.000 np_predictCells.py:190(segmentNumSynapsesActive)
1 0.010 0.010 0.050 0.050 np_predictCells.py:209(updatePredictiveState)
10238 0.000 0.000 0.000 0.000 {len}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
4 0.000 0.000 0.000 0.000 {numpy.core.multiarray.zeros}
11599 0.002 0.000 0.002 0.000 {range}
The above profile was taken form a HTM layer with the following parameters
'desiredLocalActivity': 1,
'minOverlap': 2,
'wrapInput':1,
'inhibitionWidth': 30,
'inhibitionHeight': 2,
'centerPotSynapses': 1,
'connectPermanence': 0.3,
'potentialWidth': 34,
'potentialHeight': 31,
'spatialPermanenceInc': 0.1,
'spatialPermanenceDec': 0.01,
'activeColPermanenceDec': 0.0,
'tempDelayLength': 10,
'permanenceInc': 0.15,
'permanenceDec': 0.05,
'tempSpatialPermanenceInc': 0.04,
'tempSeqPermanenceInc': 0.1,
'minThreshold': 5,
'minScoreThreshold': 3,
'newSynapseCount': 10,
'maxNumSegments': 10,
'activationThreshold': 6,
'colSynPermanence': 0.1,
'cellSynPermanence': 0.4
The current calcualtor that calculates the predictive cells is just a numpy implementation. This calculators function updatePredictiveState uses about half the total calcualtion time for a HTM step.
See below profile