def TrainTrial(ss):
"""
TrainTrial runs one trial of training using TrainEnv
"""
if ss.NeedsNewRun:
ss.NewRun()
ss.TrainEnv.Step()
# Key to query counters FIRST because current state is in NEXT epoch
# if epoch counter has changed
epc = env.CounterCur(ss.TrainEnv, env.Epoch)
chg = env.CounterChg(ss.TrainEnv, env.Epoch)
if chg:
if ss.ViewOn and ss.TrainUpdt.value > leabra.AlphaCycle:
ss.UpdateView(True)
if epc%ss.TestInterval == 0: # note: epc is *next* so won't trigger first time
ss.TestAll()
ss.LogTrnEpc(ss.TrnEpcLog)
if epc >= ss.MaxEpcs:
# done with training..
ss.RunEnd()
if ss.TrainEnv.Run.Incr(): # we are done!
ss.StopNow = True
return
else:
ss.NeedsNewRun = True
return
ss.SetParamsSet("Hidden2Act", "Network", False) # 2 units active for training
ss.ApplyInputs(ss.TrainEnv)
ss.AlphaCyc(True) # train
ss.TrialStats(True) # accumulate
def TestAll(ss):
"""
TestAll runs through the full set of testing items
"""
ss.TestEnv.Init(0)
while True:
ss.TestTrial()
chg = env.CounterChg(ss.TestEnv, env.Epoch)
if chg or ss.StopNow:
break
def TestAll(ss):
"""
TestAll runs through the full set of testing items
"""
ss.TestEnv.Init(ss.TrainEnv.Run.Cur)
while True:
ss.TestTrial(True)
chg = env.CounterChg(ss.TestEnv, env.Epoch)
if chg or ss.StopNow:
break
ss.UniqPats = ss.UniquePatStat(ss.TstTrlLog)
def TestAll(ss):
"""
TestAll runs through the full set of testing items
"""
ss.SetParams("", False)
ss.UpdateEnv()
ss.TestEnv.Init(0)
while True:
ss.TestTrial()
chg = env.CounterChg(ss.TestEnv, env.Epoch)
if chg or ss.StopNow:
break
ss.TestStats()
def TestTrial(ss):
"""
TestTrial runs one trial of testing -- always sequentially presented inputs
"""
ss.TestEnv.Step()
chg = env.CounterChg(ss.TestEnv, env.Epoch)
if chg:
if ss.ViewUpdt.value > leabra.AlphaCycle:
ss.UpdateView()
return
ss.ApplyInputs(ss.TestEnv)
ss.AlphaCyc()
def TestTrial(ss, returnOnChg):
"""
TestTrial runs one trial of testing -- always sequentially presented inputs
"""
ss.TestEnv.Step()
chg = env.CounterChg(ss.TestEnv, env.Epoch)
if chg:
if ss.ViewOn and ss.TestUpdt.value > leabra.AlphaCycle:
ss.UpdateView(False)
ss.LogTstEpc(ss.TstEpcLog)
if returnOnChg:
return
ss.ApplyInputs(ss.TestEnv)
ss.AlphaCyc(False)
ss.TrialStats(False)
ss.LogTstTrl(ss.TstTrlLog)
def TrainTrial(ss):
"""
TrainTrial runs one trial of training using TrainEnv
"""
if ss.NeedsNewRun:
ss.NewRun()
ss.TrainEnv.Step()
# Key to query counters FIRST because current state is in NEXT epoch
# if epoch counter has changed
epc = env.CounterCur(ss.TrainEnv, env.Epoch)
chg = env.CounterChg(ss.TrainEnv, env.Epoch)
if chg:
if ss.ViewOn and ss.TrainUpdt.value > leabra.AlphaCycle:
ss.UpdateView(True)
if epc >= ss.MaxEpcs:
# done with training..
ss.RunEnd()
if ss.TrainEnv.Run.Incr(): # we are done!
ss.StopNow = True
return
else:
ss.NeedsNewRun = True
return
rch = leabra.Layer(ss.Net.LayerByName("Reach"))
if "choice" in ss.TrainEnv.TrialName.Cur:
rch.SetType(emer.Compare)
else:
rch.SetType(emer.Input)
if ss.TrainEnv.GroupName.Cur != ss.PrvGpName: # init at start of new group
ss.Net.InitActs()
ss.PrvGpName = ss.TrainEnv.GroupName.Cur
train = True
if "delay" in ss.TrainEnv.TrialName.Cur:
train = False # don't learn on delay trials
ss.ApplyInputs(ss.TrainEnv)
ss.AlphaCyc(train)
ss.TrialStats(True) # accumulate
ss.LogTrnTrl(ss.TrnTrlLog, ss.TrainEnv.Trial.Cur,
ss.TrainEnv.TrialName.Cur)
def TestTrial(ss):
"""
TestTrial runs one trial of testing -- always sequentially presented inputs
"""
ss.TestEnv.Step()
chg = env.CounterChg(ss.TestEnv, env.Epoch)
if chg:
if ss.ViewUpdt.value > leabra.AlphaCycle:
ss.UpdateView()
return
isCue = (ss.TestEnv.TrialName.Cur == "Cue")
if ss.TestEnv.TrialName.Prv != "Cue":
ss.Net.InitActs()
ss.ApplyInputs(ss.TestEnv)
if isCue:
ss.AlphaCycCue()
else:
ss.AlphaCyc()
ss.LogTstTrl(ss.TstTrlLog)
def TrainTrial(ss):
"""
TrainTrial runs one trial of training using TrainEnv
"""
if ss.NeedsNewRun:
ss.NewRun()
ss.TrainEnv.Step()
# Key to query counters FIRST because current state is in NEXT epoch
# if epoch counter has changed
epc = env.CounterCur(ss.TrainEnv, env.Epoch)
chg = env.CounterChg(ss.TrainEnv, env.Epoch)
if chg:
ss.LogTrnEpc(ss.TrnEpcLog)
if ss.ViewOn and ss.TrainUpdt.value > leabra.AlphaCycle:
ss.UpdateView(True)
if ss.TestInterval > 0 and epc % ss.TestInterval == 0: # note: epc is *next* so won't trigger first time
ss.TestAll()
if epc >= ss.MaxEpcs or (ss.NZeroStop > 0
and ss.NZero >= ss.NZeroStop):
# done with training..
ss.RunEnd()
if ss.TrainEnv.Run.Incr(): # we are done!
ss.StopNow = True
return
else:
ss.NeedsNewRun = True
return
# note: type must be in place before apply inputs
ss.Net.LayerByName("Output").SetType(emer.Target)
ss.ApplyInputs(ss.TrainEnv)
ss.AlphaCyc(True) # train
ss.TrialStats(True) # accumulate
