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