(self)
puts whole network on wait while the parameters are synced, but evaluates while training the next iteration(s)
puts whole network on wait while the parameters are synced, but evaluates while training the next iteration(s)
puts whole network on wait while the parameters are synced but evaluates while training the next iteration ( s )
def evaluate(self):
        """
        puts whole network on wait while the parameters are synced, but evaluates while training the next iteration(s)
        """
        evaluators_to_run = []
        for kind in list(self.eval_masters.keys()):
            ev = self.eval_masters[kind][0]
            freq = self.eval_masters[kind][1]
            if self._cfr_iter % freq == 0:
                evaluators_to_run.append(ev)
                print("Evaluating vs.", kind.upper())
                self._ray.wait([
                    self._ray.remote(ev.update_weights)
                ])

        for ev in evaluators_to_run:
            self._ray.remote(ev.evaluate,
                             self._cfr_iter)
def evaluate ( self ) : evaluators_to_run = [ ] for kind in list ( self . eval_masters . keys ( ) ) : ev = self . eval_masters [ kind ] [ 0 ] freq = self . eval_masters [ kind ] [ 1 ] if self . _cfr_iter % freq == 0 : evaluators_to_run . append ( ev ) print ( "Evaluating vs." , kind . upper ( ) ) self . _ray . wait ( [ self . _ray . remote ( ev . update_weights ) ] ) for ev in evaluators_to_run : self . _ray . remote ( ev . evaluate , self . _cfr_iter )