def _build_lr_scheduler(self):
if self.args.force_anneal > 0:
def anneal(e):
if e < self.args.force_anneal:
return 1
else:
return self.args.lrshrink ** (e + 1 - self.args.force_anneal)
lr_scheduler = LambdaLR(self.optimizer, anneal)
lr_scheduler.best = None
else:
# decay the LR by 0.1 every time the validation loss plateaus
lr_scheduler = ReduceLROnPlateau(self.optimizer, patience=0)
return lr_scheduler
def _build_lr_scheduler(self):
if len(self.args.lr) > 1 or self.args.force_anneal > 0:
lrs = self.args.lr
def anneal(e):
if e < self.args.force_anneal:
# use fixed LR schedule
next_lr = lrs[min(e, len(lrs) - 1)]
else:
next_lr = lrs[-1] * self.args.lrshrink ** (e + 1 - self.args.force_anneal)
return next_lr / lrs[0] # correct for scaling from LambdaLR
lr_scheduler = LambdaLR(self.optimizer, anneal)
lr_scheduler.best = None
else:
# decay the LR by a factor every time the validation loss plateaus
lr_scheduler = ReduceLROnPlateau(self.optimizer, patience=0,
factor=self.args.lrshrink)
return lr_scheduler
