def advanced_one_step(self, *args, lr_list=None, **kwargs):
if lr_list is None: lr_list = [0.000088] * self.branches_num
for i in range(self.branches_num):
self.set_branch_index(i)
if i > 0:
FLAGS.overwrite = False
FLAGS.save_best = True
self._optimizer_lr_modify(lr_list[i])
Model.train(self, *args, **kwargs)
lr_list = [0.000088, 0.00088, 0.000088]
self.train(branch_index=self.branches_num, lr_list=lr_list, **kwargs)
def train(self, *args, branch_index=0, lr_list=None, **kwargs):
if lr_list is None: lr_list = [0.000088] * self.branches_num
self.set_branch_index(branch_index)
freeze = kwargs.get('freeze', True)
if not freeze:
train_step = []
for i in range(branch_index + 1):
self._optimizer_lr_modify(lr_list[i])
train_step.append(
self._optimizer.minimize(loss=self._loss,
var_list=self._var_list[i]))
self._train_step = train_step
Model.train(self, *args, **kwargs)