def SetParams(ss, sheet, setMsg):
"""
SetParams sets the params for "Base" and then current ParamSet.
If sheet is empty, then it applies all avail sheets (e.g., Network, Sim)
otherwise just the named sheet
if setMsg = true then we output a message for each param that was set.
"""
if sheet == "":
ss.Params.ValidateSheets(go.Slice_string(["Network", "Sim"]))
nt = ss.Net()
err = ss.SetParamsSet("Base", sheet, setMsg)
if ss.TrainedWts:
ss.SetParamsSet("Trained", sheet, setMsg)
else:
ss.SetParamsSet("Untrained", sheet, setMsg)
ffinhsc = ss.FFinhibWtScale
if nt == ss.NetBidir:
ffinhsc *= 0.5 # 2 inhib prjns so .5 ea
hid = leabra.LeabraLayer(nt.LayerByName("Hidden")).AsLeabra()
hid.Act.Gbar.I = ss.HiddenGbarI
hid.Act.Dt.GTau = ss.HiddenGTau
hid.Act.Update()
inh = leabra.LeabraLayer(nt.LayerByName("Inhib")).AsLeabra()
inh.Act.Gbar.I = ss.InhibGbarI
inh.Act.Dt.GTau = ss.InhibGTau
inh.Act.Update()
ff = leabra.LeabraPrjn(inh.RcvPrjns.SendName("Input")).AsLeabra()
ff.WtScale.Rel = ffinhsc
fb = leabra.LeabraPrjn(inh.RcvPrjns.SendName("Hidden")).AsLeabra()
fb.WtScale.Rel = ss.FBinhibWtScale
hid.Inhib.Layer.On = ss.FFFBInhib
inh.Inhib.Layer.On = ss.FFFBInhib
fi = leabra.LeabraPrjn(hid.RcvPrjns.SendName("Inhib")).AsLeabra()
fi.WtScale.Abs = ss.FmInhibWtScaleAbs
fi = leabra.LeabraPrjn(inh.RcvPrjns.SendName("Inhib")).AsLeabra()
fi.WtScale.Abs = ss.FmInhibWtScaleAbs
if nt == ss.NetBidir:
hid = leabra.LeabraLayer(nt.LayerByName("Hidden2")).AsLeabra()
hid.Act.Gbar.I = ss.HiddenGbarI
hid.Act.Dt.GTau = ss.HiddenGTau
hid.Act.Update()
inh = leabra.LeabraLayer(nt.LayerByName("Inhib2")).AsLeabra()
inh.Act.Gbar.I = ss.InhibGbarI
inh.Act.Dt.GTau = ss.InhibGTau
inh.Act.Update()
hid.Inhib.Layer.On = ss.FFFBInhib
inh.Inhib.Layer.On = ss.FFFBInhib
fi = leabra.LeabraPrjn(hid.RcvPrjns.SendName("Inhib2")).AsLeabra()
fi.WtScale.Abs = ss.FmInhibWtScaleAbs
fi = leabra.LeabraPrjn(inh.RcvPrjns.SendName("Inhib2")).AsLeabra()
fi.WtScale.Abs = ss.FmInhibWtScaleAbs
ff = leabra.LeabraPrjn(inh.RcvPrjns.SendName("Hidden")).AsLeabra()
ff.WtScale.Rel = ffinhsc
fb = leabra.LeabraPrjn(inh.RcvPrjns.SendName("Hidden2")).AsLeabra()
fb.WtScale.Rel = ss.FBinhibWtScale
inh = leabra.LeabraLayer(nt.LayerByName("Inhib")).AsLeabra()
ff = leabra.LeabraPrjn(inh.RcvPrjns.SendName("Hidden2")).AsLeabra()
ff.WtScale.Rel = ffinhsc
return err
def Lesion(ss, lay, locations, units):
"""
Lesion lesions given set of layers (or unlesions for NoLesion) and
locations and number of units (Half = partial = 1/2 units, Full = both units)
"""
ss.InitWts(ss.Net)
if lay == NoLesion:
return
if lay == LesionSpat1 or lay == LesionSpat12:
sp1 = leabra.LeabraLayer(ss.Net.LayerByName("Spat1"))
ss.LesionUnit(sp1, 3, 1)
ss.LesionUnit(sp1, 4, 1)
if units == LesionFull:
ss.LesionUnit(sp1, 3, 0)
ss.LesionUnit(sp1, 4, 0)
if locations == LesionFull:
ss.LesionUnit(sp1, 0, 1)
ss.LesionUnit(sp1, 1, 1)
ss.LesionUnit(sp1, 2, 1)
if units == LesionFull:
ss.LesionUnit(sp1, 0, 0)
ss.LesionUnit(sp1, 1, 0)
ss.LesionUnit(sp1, 2, 0)
if lay == LesionSpat2 or lay == LesionSpat12:
sp2 = leabra.LeabraLayer(ss.Net.LayerByName("Spat2"))
ss.LesionUnit(sp2, 2, 1)
if units == LesionFull:
ss.LesionUnit(sp2, 2, 0)
if locations == LesionFull:
ss.LesionUnit(sp2, 0, 1)
ss.LesionUnit(sp2, 1, 1)
if units == LesionFull:
ss.LesionUnit(sp2, 0, 0)
ss.LesionUnit(sp2, 1, 0)
def LogTstCyc(ss, dt, cyc):
"""
LogTstCyc adds data from current cycle to the TstCycLog table.
log always contains number of testing items
"""
if dt.Rows <= cyc:
dt.SetNumRows(cyc + 1)
row = cyc
harm = ss.Harmony(ss.Net)
ly = leabra.LeabraLayer(ss.Net.LayerByName("NeckerCube")).AsLeabra()
dt.SetCellFloat("Cycle", row, float(cyc))
dt.SetCellFloat("Harmony", row, float(harm))
# dt.SetCellFloat("GknaFast", row, float(ly.Neurons[0].GknaFast))
# dt.SetCellFloat("GknaMed", row, float(ly.Neurons[0].GknaMed))
# dt.SetCellFloat("GknaSlow", row, float(ly.Neurons[0].GknaSlow))
for lnm in ss.TstRecLays:
tsr = ss.ValsTsr(lnm)
ly = leabra.Layer(ss.Net.LayerByName(lnm))
ly.UnitValsTensor(tsr, "Act")
dt.SetCellTensor(lnm, row, tsr)
if cyc % 10 == 0:
ss.TstCycPlot.GoUpdate()
def ConfigTstTrlLog(ss, dt):
dt.SetMetaData("name", "TstTrlLog")
dt.SetMetaData("desc", "Record of testing per input pattern")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
nt = ss.TestEnv.Table.Len() # number in view
sch = etable.Schema(
[etable.Column("Trial", etensor.INT64, go.nil, go.nil),
etable.Column("TrialName", etensor.STRING, go.nil, go.nil)]
)
for lnm in ss.TstRecLays:
ly = leabra.LeabraLayer(ss.Net.LayerByName(lnm)).AsLeabra()
sch.append(etable.Column(lnm, etensor.FLOAT32, ly.Shp.Shp, go.nil))
dt.SetFromSchema(sch, nt)
def ApplyInputs(ss, en):
"""
ApplyInputs applies input patterns from given envirbonment.
It is good practice to have this be a separate method with appropriate
# going to the same layers, but good practice and cheap anyway
args so that it can be used for various different contexts
(training, testing, etc).
"""
ss.Net.InitExt()
lays = go.Slice_string(["Input", "Emotion", "Gender", "Identity"])
for lnm in lays :
ly = leabra.LeabraLayer(ss.Net.LayerByName(lnm)).AsLeabra()
pats = en.State(ly.Nm)
if pats != 0:
ly.ApplyExt(pats)
def ConfigTstCycLog(ss, dt):
dt.SetMetaData("name", "TstCycLog")
dt.SetMetaData("desc", "Record of testing per cycle")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
nt = 100
sch = etable.Schema([
etable.Column("Cycle", etensor.INT64, go.nil, go.nil),
etable.Column("TrialName", etensor.STRING, go.nil, go.nil),
etable.Column("Harmony", etensor.FLOAT64, go.nil, go.nil),
etable.Column("GknaFast", etensor.FLOAT64, go.nil, go.nil),
etable.Column("GknaMed", etensor.FLOAT64, go.nil, go.nil),
etable.Column("GknaSlow", etensor.FLOAT64, go.nil, go.nil)
])
for lnm in ss.TstRecLays:
ly = leabra.LeabraLayer(ss.Net.LayerByName(lnm)).AsLeabra()
sch.append(etable.Column(lnm, etensor.FLOAT64, ly.Shp.Shp, go.nil))
dt.SetFromSchema(sch, nt)