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TM: cellsToColumns #397

Merged
merged 47 commits into from
Apr 25, 2019
Merged

TM: cellsToColumns #397

merged 47 commits into from
Apr 25, 2019

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breznak
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@breznak breznak commented Apr 11, 2019

that SDR.size matches TM.numberOfColumns
which is a convenience wrapper for TM::columnForCell()
these methods have been replaced by TM.cellsToColumns.
Because the conversion method is only valid for TM's cells.
which represents TM's output as mini-columns,
and is a union of active and predictive at the current time.

Used by Hotgym example, and TMRegion
@breznak breznak added enhancement New feature or request ready TM code code enhancement, optimization, cleanup..programmer stuff labels Apr 11, 2019
@breznak breznak self-assigned this Apr 11, 2019
src/nupic/algorithms/TemporalMemory.cpp Outdated Show resolved Hide resolved
*
* @return SDR size(COLS)
*/
sdr::SDR getOutputColumns() const;
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better name?

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I dont think that this is right. I dont think the TM should ever use the union of active & predicted cells.

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There are two use cases for converting from cells to minicolumns, decode and anomaly. We've decided not to implement decode. I would really like to see anomaly as part of this PR.

Which of the 2 methods do you objectify to?

  • cellsToColumns:

    • yes, a convenience method, columnForCell(cell) already exists
      • I propose removal of columnForCell
      • or either of them can be made private
    • this method is needed for TM::anomaly() which operates with columns.
    • we had the same functionality in VectorHelpers::cellsToColumns
      • but I decided to move it to TM, as it's the only user and the way of conversion (plain topology) makes sense only for TM. So as not to make it confusing and tempting for other, misused usages (if in VectorHelpers)
  • getOutputColumns ..TM output as union of active + predictive columns

    • conceptually, I think TM should output only columns (not cells)
      • as cols are needed for it to be used in a hierarchy (and in anomaly, decode,...)
        • what is Classifier using? It should probably use cols as well.
      • cells should be an implementation detail and hidden from the user
        • cells are still accessible for the likes of CP, etc.
    • I think we should provide all 3 of them:
      • getColumnsActive(), Predictive(), Both()
        • (both = current getOutputCols)
      • or fill them in compute method TM::compute(const SDR& input, bool learn, SDR& output)
        • impl details how to well decide which mode to fill in output (enum?), default param, etc...
      • Anomaly, NetworkAPI (TMRegion), Classifier(?) would work with these outputs

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I object to getOutputColumns. It should not mix the active & predictive cells together.

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It should not mix the active & predictive cells together.

why should not? A TM's minicolumn is activated by at least one active cell, be it feed forward activation, or predictive/contextual.

It's not like it's impossible to compute manually, we have access to the active, predictive cells; and will now have the cellsToColumns transformation.

  • but the user (for hierarchy, classifier, TMRegion, TM's direct output) has to do exactly what is this method about.

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ok, I quickly double checked with https://numenta.com/assets/pdf/biological-and-machine-intelligence/BaMI-Temporal-Memory.pdf
And you're right, there's no sense in mixing active+pred columns.

  • I'll remove getOutpuColumns
  • TMRegion outputs that with option orOutputCols, I then say we should rm that too.
  • instead, do we want to offer TM.activeCols, TM.predictiveCols ? That would be of use for anomaly

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@ctrl-z-9000-times ok, we agreed that mixing active + predictive cols/cells does not make sense. I'm removing the method from TM, not looking at TMRegion.
@dkeeney is it necessary for a Region to implement "bottomUpOutput"?

Currently we return either a union of cells (act+pred) or as columns, depending on a switch.

  • what should we return as bottom up output? (I'd say feed-forward activation as columns)
    • or should we remove the field altogether? (ideally)
  • currently, NetworkAPI has now way to obtain columns (active, predicted; separately), and it probably should.

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@dkeeney is it necessary for a Region to implement "bottomUpOutput"?

This is its only output. I think we need something. I think we could go with columns only. We could add additional outputs for active and predicted as separate outputs.
What makes since?

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any combination of {active, predictive} x {cells, columns} makes sense.
I'll probably open a new PR for that, and just remove the current orOutputColumns logic

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See #412 #413

src/nupic/algorithms/TemporalMemory.hpp Outdated Show resolved Hide resolved
src/nupic/regions/TMRegion.cpp Show resolved Hide resolved
* columns(binary vector). If any cell of a column is active (1), the column
* is considered active. See TP for details.
*/
static std::vector<UInt> cellsToColumns(const std::vector<UInt>& cellsBinary, const UInt cellsPerColumn)
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moved to TM

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There are two use cases for converting from cells to minicolumns, decode and anomaly. We've decided not to implement decode. I would really like to see anomaly as part of this PR. Please demonstrate how the user will interact with the TM to get the anomaly.

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Ok I read the unit tests and i think i understand how to use it. Would still like to see anomaly though, even if in psuedo code

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breznak commented Apr 12, 2019

Would still like to see anomaly though, even if in psuedo code

I tried to make smaller PRs :) Do you want to keep this separate, or add anomaly here?
Design:

TM {
  struct {
      float score;
      { //hide these
      SDR& prevPredCols;
      SDR& currentActiveCols;
      }
  } anomaly;

  TM::compute() {
    //...do all the stuff
    //for anomaly:
   //TODO Q: How to configure if anom should be done? a) always, b) TM(.., bool doAnomaly), c) getAnomalyScore() does not work that well, as we need to get predictiveColumns, which is expensive. I prefer B. 
  if(doAnomaly_) {
   anomaly.currentActiveCols = tm.cellsToColumns(active_); //or use active/pred cols, if the methods are available
   anomaly.score = computeRawAnomalyScore(activeCols, prevCols); //I'd keep this method in separate file 
   anomaly.prevPredCols = tm.cellsToColumns(getPredictiveCells())
  }
  } //-end TM::compute
}


// usage
TM tm(doAnomaly);
tm.compute(...);
if tm.anomaly.score > 0.9 { //run! }

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I tried to make smaller PRs :) Do you want to keep this separate, or add anomaly here?

Sorry for the conflicting advice. This PR can be just for cellsToColumns and anomaly can come later.

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TODO Q: How to configure if anom should be done?
a) always,
b) TM(.., bool doAnomaly),
c) getAnomalyScore() does not work that well, as we need to get predictiveColumns, which is expensive. I prefer B.

I prefer A, but B is acceptable too.

it does not make sense to mix feed-forward (active) activations
with contextual (predictive)
@breznak breznak removed the ready label Apr 15, 2019
@breznak breznak mentioned this pull request Apr 15, 2019
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I have a few comments and change requests. Overall this PR is good.

src/examples/hotgym/HelloSPTP.cpp Outdated Show resolved Hide resolved
src/examples/hotgym/HelloSPTP.cpp Outdated Show resolved Hide resolved
src/nupic/algorithms/TemporalMemory.cpp Outdated Show resolved Hide resolved
src/nupic/algorithms/SpatialPooler.cpp Outdated Show resolved Hide resolved
outSP SDR has to be initialized with dimensions, even if later
successfully assigned (outSP = outSPglobal)
for SP local,
also remove the time check for Windows CI
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breznak commented Apr 23, 2019

Added your feedback, this is now working. Ready for another round of reviews when you got time. Thanks

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breznak commented Apr 23, 2019

[----------] 1 test from HelloSPTPTest
[ RUN ] HelloSPTPTest.performance
starting test. DIM_INPUT=1000, DIM=2048, CELLS=8
EPOCHS = 5000
starting: 5000 iterations.Epoch = 4999
Anomaly = 0.940001
SP (g)= SDR( 2048 ) 72, 75, 284, 303, 305, 317, 329, 525, 1095, 2027
SP (l)= SDR( 2048 ) 6, 12, 26, 57, 63, 72, 75, 76, 77, 80, 82, 103, 105, 124, 135, 154, 171, 174, 175, 185, 192, 193, 195, 198, 263, 284, 296, 302, 303, 305, 313, 317, 319, 320, 356, 363, 364, 401, 403, 404, 410, 413, 425, 426, 428, 449, 491, 496, 511, 515, 516, 518, 520, 525, 529, 536, 550, 556, 574, 583, 592, 597, 598, 603, 609, 622, 626, 636, 645, 652, 704, 706, 722, 726, 727, 728, 729, 747, 751, 766, 779, 808, 833, 837, 838, 840, 848, 850, 853, 860, 908, 912, 918, 919, 923, 927, 929, 930, 931, 932, 970, 989, 1006, 1038, 1066, 1082, 1085, 1087, 1092, 1094, 1095, 1113, 1115, 1125, 1128, 1174, 1179, 1180, 1182, 1185, 1205, 1206, 1232, 1236, 1238, 1239, 1240, 1245, 1271, 1292, 1295, 1300, 1303, 1307, 1311, 1319, 1320, 1322, 1382, 1401, 1412, 1415, 1421, 1426, 1431, 1434, 1438, 1470, 1474, 1492, 1501, 1511, 1521, 1524, 1525, 1530, 1532, 1537, 1540, 1600, 1617, 1620, 1622, 1632, 1638, 1641, 1667, 1672, 1680, 1684, 1686, 1690, 1699, 1702, 1742, 1744, 1745, 1746, 1765, 1770, 1774, 1801, 1807, 1808, 1816, 1830, 1834, 1849, 1861, 1867, 1871, 1882, 1902, 1907, 1943, 1945, 1955, 1956, 1966, 1968, 1969, 1971, 1986, 2018, 2025, 2027
TM= SDR( 2048 ) 26, 75, 525
==============TIMERS============
Init: 0.0932824
Random: 0.211816
Encode: 0.0174722
SP (l): 39.4186
SP (g): 1.45276
TM: 1.43883
AN: 0.0270594
AN: 0.677287
ERR: CHECK FAILED: "outTM == goldTM" Deterministic output of TM failed!
SDR( 2048 ) 26, 75, 525
should be:
SDR( 2048 ) 26, 75
[c:\projects\nupic-cpp\src\examples\hotgym\hellosptp.cpp line 203]
unknown file: error: C++ exception with description "CHECK FAILED: "outTM == goldTM" Deterministic output of TM failed!
SDR( 2048 ) 26, 75, 525
should be:
SDR( 2048 ) 26, 75
" thrown in the test body.
[ FAILED ] HelloSPTPTest.performance (43378 ms)
[----------] 1 test from HelloSPTPTest (43378 ms total)

I'm not sure why the Windows run would fail deterministic checks .. :(

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Ok, this looks good to me.
It looks like the CI passed unit tests so I assume you figured out the problem with Windows. 👍

@@ -20,8 +20,8 @@ class BenchmarkHotgym {
bool useSPglobal=true,
bool useTM=true,
const UInt COLS = 2048, // number of columns in SP, TP
const UInt DIM_INPUT = 10000,
const UInt CELLS = 10 // cells per column in TP
const UInt DIM_INPUT = 1000,
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Oh, good. You reduced the size of the test :)

"* 'cellsPerColumn'.",
"* 'cellsPerColumn' by default; if orColumnOutputs is "
"set, then this returns only numberOfCols. "
"The activations come from TM::getActiveCells(). ",
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Good, I see you described what output 'bottomUpOut' contains. 👍

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breznak commented Apr 25, 2019

@ctrl-z-9000-times can you review as well when you've got time? You've requested changes, so I'll need your approval

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breznak commented Apr 25, 2019

It looks like the CI passed unit tests so I assume you figured out the problem with Windows. +1

unfortunately, I didn't. Just ifdefed Windows and delegated the fix to its dedicated issue #194

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This looks good to me. I can't see why the test is failing on one platform.

@breznak breznak merged commit b3243fc into master Apr 25, 2019
@breznak breznak deleted the columnForCell branch April 25, 2019 12:10
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breznak commented Apr 25, 2019

Thanks for your reviews!

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