logs.items():
            self.history.setdefault(k, []).append(v)

        K.set_value(self.model.optimizer.lr, self.clr())

        # annihilate learning rate
        prev_overhump = self.overhump
        iterations = (self.clr_iterations + 1) % (self.step_size * 2)
        if iterations / self.step_size &gt; 1:
            self.overhump = True
        else:
            self.overhump = False
        if not prev_overhump and self.overhump:
            self.base_lr = self.max_lr / 1000
        elif prev_ove