def ConfigRunLog(ss, dt):
dt.SetMetaData("name", "RunLog")
dt.SetMetaData("desc", "Record of performance at end of training")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
dt.SetFromSchema(etable.Schema(
[etable.Column("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Params", etensor.STRING, go.nil, go.nil)]
), 0)
def ConfigTstEpcLog(ss, dt):
dt.SetMetaData("name", "TstEpcLog")
dt.SetMetaData("desc", "Summary stats for testing trials")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
dt.SetFromSchema(etable.Schema(
[etable.Column("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Epoch", etensor.INT64, go.nil, go.nil)]
), 0)
def ConfigPats(ss):
dt = ss.Pats
dt.SetMetaData("name", "TrainPats")
dt.SetMetaData("desc", "Training patterns")
sch = etable.Schema([
etable.Column("Name", etensor.STRING, go.nil, go.nil),
etable.Column("Input", etensor.FLOAT32, go.Slice_int([10, 10]),
go.Slice_string(["Y", "X"]))
])
dt.SetFromSchema(sch, 1)
patgen.PermutedBinaryRows(dt.Cols[1], int(ss.InputPct), 1, 0)
def ConfigSpikeVsRateLog(ss, dt):
dt.SetMetaData("name", "SpikeVsRateLog")
dt.SetMetaData("desc", "Record spiking vs. rate-code activation")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
nt = 24 # typical number
sch = etable.Schema([
etable.Column("GBarE", etensor.FLOAT64, go.nil, go.nil),
etable.Column("Spike", etensor.FLOAT64, go.nil, go.nil),
etable.Column("Rate", etensor.FLOAT64, go.nil, go.nil)
])
dt.SetFromSchema(sch, nt)
def ConfigTrnEpcLog(ss, dt):
dt.SetMetaData("name", "TrnEpcLog")
dt.SetMetaData("desc", "Record of performance over epochs of training")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
sch = etable.Schema(
[etable.Column("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Epoch", etensor.INT64, go.nil, go.nil),
etable.Column("RewPredInputWts", etensor.FLOAT32, go.Slice_int([6, 1, 1, 6]), go.nil)]
)
dt.SetFromSchema(sch, 0)
ss.ConfigRewPredInput(ss.RewPredInputWts)
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))
ncy = 200 # max cycles
sch = etable.Schema(
[etable.Column("Cycle", etensor.INT64, go.nil, go.nil)])
for lnm in ss.TstRecLays:
sch.append(
etable.Column(lnm + "ActAvg", etensor.FLOAT64, go.nil, go.nil))
dt.SetFromSchema(sch, ncy)
def ConfigTrnEpcLog(ss, dt):
dt.SetMetaData("name", "TrnEpcLog")
dt.SetMetaData("desc", "Record of performance over epochs of training")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
sch = etable.Schema(
[etable.Column("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Epoch", etensor.INT64, go.nil, go.nil),
etable.Column("PerTrlMSec", etensor.FLOAT64, go.nil, go.nil)]
)
for lnm in ss.LayStatNms :
sch.append( etable.Column(lnm + " ActAvg", etensor.FLOAT64, go.nil, go.nil))
dt.SetFromSchema(sch, 0)
def ConfigPats(ss):
dt = ss.Pats
sch = etable.Schema(
[etable.Column("Name", etensor.STRING, go.nil, go.nil),
etable.Column("Input", etensor.FLOAT32, go.Slice_int([4, 5]), go.Slice_string(["Y", "X"])),
etable.Column("Output", etensor.FLOAT32, go.Slice_int([4, 5]), go.Slice_string(["Y", "X"]))]
)
dt.SetFromSchema(sch, 3)
ss.Pats.SetMetaData("name", "Pats")
ss.Pats.SetMetaData("desc", "Training patterns")
patgen.PermutedBinaryRows(dt.Cols[1], 6, 1, 0)
patgen.PermutedBinaryRows(dt.Cols[2], 6, 1, 0)
cn = etensor.String(dt.Cols[0])
cn.Values.copy(["any", "baker", "cheese"])
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 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))
sch = etable.Schema(
[etable.Column("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Epoch", etensor.INT64, go.nil, go.nil),
etable.Column("Trial", etensor.INT64, go.nil, go.nil),
etable.Column("TrialName", etensor.STRING, go.nil, go.nil)]
)
for lnm in ss.LayStatNms :
ly = leabra.Layer(ss.Net.LayerByName(lnm))
sch.append( etable.Column(lnm, etensor.FLOAT64, ly.Shp.Shp, go.nil))
dt.SetFromSchema(sch, 0)
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)]
)
for lnm in ss.TstRecLays:
ly = leabra.Layer(ss.Net.LayerByName(lnm))
sch.append(etable.Column(lnm, etensor.FLOAT64, ly.Shp.Shp, go.nil))
dt.SetFromSchema(sch, nt)
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("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Epoch", etensor.INT64, go.nil, go.nil),
etable.Column("Trial", etensor.INT64, go.nil, go.nil),
etable.Column("TrialName", etensor.STRING, go.nil, go.nil)
])
for lnm in ss.LayStatNms:
sch.append(
etable.Column(lnm + " ActM.Avg", etensor.FLOAT64, go.nil,
go.nil))
dt.SetFromSchema(sch, nt)
def ConfigTstTrlLog(ss, dt):
inp = leabra.Layer(ss.Net.LayerByName("Input"))
recv = leabra.Layer(ss.Net.LayerByName("RecvNeuron"))
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()
sch = etable.Schema(
[etable.Column("Trial", etensor.INT64, go.nil, go.nil),
etable.Column("TrialName", etensor.STRING, go.nil, go.nil),
etable.Column("Input", etensor.FLOAT64, inp.Shp.Shp, go.nil),
etable.Column("Ge", etensor.FLOAT64, recv.Shp.Shp, go.nil),
etable.Column("Act", etensor.FLOAT64, recv.Shp.Shp, go.nil)]
)
dt.SetFromSchema(sch, nt)
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 = ss.NCycles # max cycles
sch = etable.Schema([
etable.Column("Cycle", etensor.INT64, go.nil, go.nil),
etable.Column("Ge", etensor.FLOAT64, go.nil, go.nil),
etable.Column("Inet", etensor.FLOAT64, go.nil, go.nil),
etable.Column("Vm", etensor.FLOAT64, go.nil, go.nil),
etable.Column("Act", etensor.FLOAT64, go.nil, go.nil),
etable.Column("Spike", etensor.FLOAT64, go.nil, go.nil),
etable.Column("Gk", etensor.FLOAT64, go.nil, go.nil),
etable.Column("ISI", etensor.FLOAT64, go.nil, go.nil),
etable.Column("AvgISI", etensor.FLOAT64, go.nil, go.nil)
])
dt.SetFromSchema(sch, nt)
def ConfigRunLog(ss, dt):
dt.SetMetaData("name", "RunLog")
dt.SetMetaData("desc", "Record of performance at end of training")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
sch = etable.Schema([
etable.Column("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Params", etensor.STRING, go.nil, go.nil),
etable.Column("FirstZero", etensor.FLOAT64, go.nil, go.nil),
etable.Column("SSE", etensor.FLOAT64, go.nil, go.nil),
etable.Column("AvgSSE", etensor.FLOAT64, go.nil, go.nil),
etable.Column("PctErr", etensor.FLOAT64, go.nil, go.nil),
etable.Column("PctCor", etensor.FLOAT64, go.nil, go.nil),
etable.Column("CosDiff", etensor.FLOAT64, go.nil, go.nil)
])
dt.SetFromSchema(sch, 0)
def ConfigTstEpcLog(ss, dt):
dt.SetMetaData("name", "TstEpcLog")
dt.SetMetaData("desc", "Summary stats for testing trials")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
sch = etable.Schema([
etable.Column("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Epoch", etensor.INT64, go.nil, go.nil),
etable.Column("SSE", etensor.FLOAT64, go.nil, go.nil),
etable.Column("AvgSSE", etensor.FLOAT64, go.nil, go.nil),
etable.Column("PctErr", etensor.FLOAT64, go.nil, go.nil),
etable.Column("PctCor", etensor.FLOAT64, go.nil, go.nil),
etable.Column("CosDiff", etensor.FLOAT64, go.nil, go.nil)
])
dt.SetFromSchema(sch, 0)
def ConfigTstQuizLog(ss, dt):
dt.SetMetaData("name", "TstQuizLog")
dt.SetMetaData("desc", "Summary stats for testing trials")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
sch = etable.Schema(
[etable.Column("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Epoch", etensor.INT64, go.nil, go.nil),
etable.Column("QNo", etensor.INT64, go.nil, go.nil),
etable.Column("Resp", etensor.STRING, go.nil, go.nil),
etable.Column("Err", etensor.FLOAT64, go.nil, go.nil),
etable.Column("Correls", etensor.FLOAT64, go.Slice_int([3]), go.nil)]
)
dt.SetFromSchema(sch, 0)
def ConfigTrnTrlLog(ss, dt):
dt.SetMetaData("name", "TrnTrlLog")
dt.SetMetaData("desc", "Record of training per input event (time step)")
dt.SetMetaData("read-only", "true")
dt.SetMetaData("precision", str(LogPrec))
nt = 0
sch = etable.Schema(
[etable.Column("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Epoch", etensor.INT64, go.nil, go.nil),
etable.Column("Trial", etensor.INT64, go.nil, go.nil),
etable.Column("Event", etensor.INT64, go.nil, go.nil),
etable.Column("TD", etensor.FLOAT64, go.nil, go.nil),
etable.Column("RewPred", etensor.FLOAT64, go.nil, go.nil)]
)
for lnm in ss.TstRecLays:
ly = leabra.Layer(ss.Net.LayerByName(lnm))
sch.append( etable.Column(lnm, etensor.FLOAT64, ly.Shp.Shp, go.nil))
dt.SetFromSchema(sch, nt)
def ConfigPrjnTable(ss, dt):
dt.SetMetaData("name", "PrjnTable")
dt.SetMetaData("desc", "projection of data onto dimension")
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),
etable.Column("GendPrjn", etensor.FLOAT64, go.nil, go.nil),
etable.Column("EmotePrjn", etensor.FLOAT64, go.nil, go.nil),
etable.Column("RndPrjn0", etensor.FLOAT64, go.nil, go.nil),
etable.Column("RndPrjn1", etensor.FLOAT64, go.nil, go.nil)]
)
dt.SetFromSchema(sch, nt)
def ConfigTstTrlLog(ss, dt):
inp = leabra.Layer(ss.Net.LayerByName("Input"))
out = leabra.Layer(ss.Net.LayerByName("Output"))
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("Run", etensor.INT64, go.nil, go.nil),
etable.Column("Epoch", etensor.INT64, go.nil, go.nil),
etable.Column("Trial", etensor.INT64, go.nil, go.nil),
etable.Column("TrialName", etensor.STRING, go.nil, go.nil),
etable.Column("Err", etensor.FLOAT64, go.nil, go.nil),
etable.Column("SSE", etensor.FLOAT64, go.nil, go.nil),
etable.Column("AvgSSE", etensor.FLOAT64, go.nil, go.nil),
etable.Column("CosDiff", etensor.FLOAT64, go.nil, go.nil)
])
for lnm in ss.LayStatNms:
sch.append(
etable.Column(lnm + " ActM.Avg", etensor.FLOAT64, go.nil,
go.nil))
sch.append(etable.Column("InAct", etensor.FLOAT64, inp.Shp.Shp,
go.nil))
sch.append(
etable.Column("OutActM", etensor.FLOAT64, out.Shp.Shp, go.nil))
sch.append(
etable.Column("OutTarg", etensor.FLOAT64, out.Shp.Shp, go.nil))
dt.SetFromSchema(sch, nt)
