def after_batch(self):
    'Update all metrics and records lr and smooth loss in training'
    if (len(self.yb) == 0):
        return
    mets = (self._train_mets if self.training else self._valid_mets)
    for met in mets:
        met.accumulate(self.learn)
    if (not self.training):
        return
    self.lrs.append(self.opt.hypers[(- 1)]['lr'])
    self.losses.append(self.smooth_loss.value)
    self.learn.smooth_loss = self.smooth_loss.value
Update all metrics and records lr and smooth loss in training
def after_batch(self):
    
    if (len(self.yb) == 0):
        return
    mets = (self._train_mets if self.training else self._valid_mets)
    for met in mets:
        met.accumulate(self.learn)
    if (not self.training):
        return
    self.lrs.append(self.opt.hypers[(- 1)]['lr'])
    self.losses.append(self.smooth_loss.value)
    self.learn.smooth_loss = self.smooth_loss.value