deepy/trainers/annealers.py
def invoke(self):
        """
        Run it, return whether to end training.
        """
        self._iter += 1
        if self._iter - max(self._trainer.best_iter, self._annealed_iter) >= self._patience:
            if self._annealed_times >= self._anneal_times:
                logging.info("ending")
                self._trainer.exit()
            else:
                self._trainer.set_params(*self._trainer.best_params)
                self._learning_rate.set_value(self._learning_rate.get_value() * 0.5)
                self._annealed_times += 1
                self._annealed_iter = self._iter
                logging.info("annealed learning rate to %f" % self._learning_rate.get_value())
def invoke(self):
        """
        Run it, return whether to end training.
        """
        self._iter += 1
        if self._iter - max(self._trainer.best_iter, self._annealed_iter) >= self._patience:
            if self._annealed_times >= self._anneal_times:
                logging.info("ending")
                self._trainer.exit()
            else:
                self._trainer.set_params(*self._trainer.best_params)
                self._learning_rate.set_value(self._learning_rate.get_value() * 0.5)
                self._annealed_times += 1
                self._annealed_iter = self._iter
                logging.info("annealed learning rate to %f" % self._learning_rate.get_value())
def invoke ( self ) : self . _iter += 1 if self . _iter - max ( self . _trainer . best_iter , self . _annealed_iter ) >= self . _patience : if self . _annealed_times >= self . _anneal_times : logging . info ( "ending" ) self . _trainer . exit ( ) else : self . _trainer . set_params ( * self . _trainer . best_params ) self . _learning_rate . set_value ( self . _learning_rate . get_value ( ) * 0.5 ) self . _annealed_times += 1 self . _annealed_iter = self . _iter logging . info ( "annealed learning rate to %f" % self . _learning_rate . get_value ( ) )
Run it, return whether to end training.
Run it return whether to end training .