 / float(self.step_size)
                new_momentum = self.max_momentum - current_percentage * (
                    self.max_momentum - self.min_momentum
                )
            K.set_value(self.model.optimizer.beta_1, new_momentum)
            self.history.setdefault(&#34;momentum&#34;, []).append(
                K.get_value(self.model.optimizer.beta_1)
            )

    def on_epoch_end(self, epoch, logs=None):
        # print(K.eval(self.model.optimizer.lr))

        # Stop training if trainin