deepy/trainers/controllers.py
def invoke(self):
        """
        This function will be called after each iteration.
        """
        self._counter += 1
        if self._counter % self._freq == 0:
            cnt = 0.
            sum_map = defaultdict(float)
            for x in self._trainer.get_data(self._data_split):
                val_map = self.run(x)
                if not isinstance(val_map, dict):
                    raise Exception("Monitor.run must return a dict.")
                for k, val in val_map.items():
                    sum_map[k] += val
                cnt += 1
            for k in sum_map:
                sum_map[k] /= cnt
            new_best = self.compare(sum_map)
            self._trainer.report(sum_map, self._data_split, new_best=new_best)
            if new_best:
                self._trainer.save_checkpoint(self._save_path)
def invoke(self):
        """
        This function will be called after each iteration.
        """
        self._counter += 1
        if self._counter % self._freq == 0:
            cnt = 0.
            sum_map = defaultdict(float)
            for x in self._trainer.get_data(self._data_split):
                val_map = self.run(x)
                if not isinstance(val_map, dict):
                    raise Exception("Monitor.run must return a dict.")
                for k, val in val_map.items():
                    sum_map[k] += val
                cnt += 1
            for k in sum_map:
                sum_map[k] /= cnt
            new_best = self.compare(sum_map)
            self._trainer.report(sum_map, self._data_split, new_best=new_best)
            if new_best:
                self._trainer.save_checkpoint(self._save_path)
def invoke ( self ) : self . _counter += 1 if self . _counter % self . _freq == 0 : cnt = 0. sum_map = defaultdict ( float ) for x in self . _trainer . get_data ( self . _data_split ) : val_map = self . run ( x ) if not isinstance ( val_map , dict ) : raise Exception ( "Monitor.run must return a dict." ) for k , val in val_map . items ( ) : sum_map [ k ] += val cnt += 1 for k in sum_map : sum_map [ k ] /= cnt new_best = self . compare ( sum_map ) self . _trainer . report ( sum_map , self . _data_split , new_best = new_best ) if new_best : self . _trainer . save_checkpoint ( self . _save_path )
This function will be called after each iteration.
This function will be called after each iteration .