def setup_after_stop_training(self, model, final_evaluation):
self.rememberer.reset_to_best_model(self.epochs_df, model,
self.optimizer)
if final_evaluation:
loss_to_reach = float(self.epochs_df['train_loss'].iloc[-1])
self.stop_criterion = Or(stop_criteria=[
MaxEpochs(max_epochs=global_vars.get('final_max_epochs')),
ColumnBelow(column_name='valid_loss',
target_value=loss_to_reach)
])
def setup_after_stop_training(self):
"""
Setup training after first stop.
Resets parameters to best parameters and updates stop criterion.
"""
# also remember old monitor chans, will be put back into
# monitor chans after experiment finished
self.before_stop_df = deepcopy(self.epochs_df)
self.rememberer.reset_to_best_model(self.epochs_df, self.model,
self.optimizer)
loss_to_reach = float(self.epochs_df['train_loss'].iloc[-1])
self.stop_criterion = Or(stop_criteria=[
MaxEpochs(max_epochs=self.rememberer.best_epoch * 2),
ColumnBelow(column_name='valid_loss', target_value=loss_to_reach)])
log.info("Train loss to reach {:.5f}".format(loss_to_reach))
def setup_after_stop_training(self):
"""
Setup training after first stop.
Resets parameters to best parameters and updates stop criterion.
"""
# also remember old monitor chans, will be put back into
# monitor chans after experiment finished
self.before_stop_df = deepcopy(self.epochs_df)
self.rememberer.reset_to_best_model(self.epochs_df, self.model,
self.optimizer)
loss_to_reach = float(self.epochs_df['train_loss'].iloc[-1])
self.stop_criterion = Or(stop_criteria=[
MaxEpochs(max_epochs=self.rememberer.best_epoch * 2),
ColumnBelow(column_name='valid_loss', target_value=loss_to_reach)
])
log.info("Train loss to reach {:.5f}".format(loss_to_reach))
# def exp_lr_scheduler(optimizer, global_step, init_lr, decay_steps, decay_rate, lr_clip, staircase=True):
# if staircase:
# lr = init_lr * decay_rate
# else:
# lr = init_lr * decay_rate**(global_step / decay_steps)
# lr = max(lr, lr_clip)
# #print(lr)
# if global_step % decay_steps == 0:
# print('LR is set to {}'.format(lr))
# lr=0.1
# for param_group in optimizer.param_groups:
# param_group['lr'] = lr
# return optimizer
# def adjust_lr(optimizer, epoch):
# lr = init_lr * (0.1 ** (epoch // 20))
# for param_group in optimizer.param_groups:
# param_group['lr'] = lr
# return optimizer