def _train_validdata(self, _skiptrain=False):
validf = self.getvalidfun(self.model)
trainf = self.buildtrainfun(self.model)
df = DataFeeder(*(self.traindata + [self.traingold])).numbats(
self.numbats)
vdf = DataFeeder(*(self.validdata + [self.validgold]), random=False)
vdf.batsize = df.batsize
#embed()
#dfvalid = df.osplit(split=self.validsplits, random=self.validrandom)
err, verr = self.trainloop(trainf=self.getbatchloop(trainf, df),
validf=self.getbatchloop(validf, vdf),
_skiptrain=_skiptrain)
return err, verr, None, None
def _train_validdata(self, _lambda=False, _skiptrain=False):
df = DataFeeder(*(self.traindata + [self.traingold])).numbats(
self.numbats)
vdf = DataFeeder(*(self.validdata + [self.validgold]), random=False)
vdf.batsize = df.batsize
trainf = self.buildtrainfun(self.model, df.batsize)
validf = self.getvalidfun(self.model, vdf.batsize)
#embed()
#dfvalid = df.osplit(split=self.validsplits, random=self.validrandom)
if _lambda:
return trainf, validf, df, vdf
else:
err, verr = self.trainloop(trainf=self.getbatchloop(trainf,
df,
phase="TRAIN"),
validf=self.getbatchloop(validf,
vdf,
phase="VALID"),
_skiptrain=_skiptrain)
return err, verr, None, None
