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
