def _train_split(self): trainf = self.buildtrainfun(self.model) validf = self.buildvalidfun(self.model) df = DataFeeder(*(self.traindata + [self.traingold])) dftrain, dfvalid = df.split(self.validsplits, self.validrandom) err, verr = self.trainloop( trainf=self.getbatchloop(trainf, dftrain.numbats(self.numbats)), validf=self.getbatchloop(validf, dfvalid)) return err, verr, None, None
def _train_split(self): trainf = self.buildtrainfun(self.model) validf = self.buildvalidfun(self.model) df = DataFeeder(*(self.traindata + [self.traingold])) dftrain, dfvalid = df.split(self.validsplits, self.validrandom) err, verr = self.trainloop(trainf=self.getbatchloop( trainf, dftrain.numbats(self.numbats)), validf=self.getbatchloop(validf, dfvalid)) return err, verr, None, None
def _train_split(self, _skiptrain=False): trainf = self.buildtrainfun(self.model) validf = self.getvalidfun(self.model) df = DataFeeder(*(self.traindata + [self.traingold])) dftrain, dfvalid = df.split(self.validsplits, self.validrandom, df_randoms=(True, False)) dftrain.numbats(self.numbats) dfvalid.batsize = dftrain.batsize err, verr = self.trainloop(trainf=self.getbatchloop(trainf, dftrain), validf=self.getbatchloop(validf, dfvalid), _skiptrain=_skiptrain) return err, verr, None, None
def _train_split(self, _lambda=False, _skiptrain=False): df = DataFeeder(*(self.traindata + [self.traingold])) dftrain, dfvalid = df.split(self.validsplits, self.validrandom, df_randoms=(True, False)) dftrain.numbats(self.numbats) dfvalid.batsize = dftrain.batsize trainf = self.buildtrainfun(self.model, dftrain.batsize) validf = self.getvalidfun(self.model, dfvalid.batsize) if _lambda: return trainf, validf, dftrain, dfvalid else: err, verr = self.trainloop(trainf=self.getbatchloop(trainf, dftrain, phase="TRAIN"), validf=self.getbatchloop(validf, dfvalid, phase="VALID"), _skiptrain=_skiptrain) return err, verr, None, None