您可以在Keras中编写自定义学习率调度器吗？

from keras.callbacks import Callback

class CustomLearningRateScheduler(Callback):
    def __init__(self, schedule):
        super(CustomLearningRateScheduler, self).__init__()
        self.schedule = schedule

    def on_epoch_end(self, epoch, logs=None):
        if not hasattr(self.model.optimizer, 'lr'):
            raise ValueError('Optimizer must have a "lr" attribute.')
        # 根据当前的epoch和schedule计算学习率
        lr = float(K.get_value(self.model.optimizer.lr))
        scheduled_lr = lr * self.schedule(epoch)
        # 更新学习率
        K.set_value(self.model.optimizer.lr, scheduled_lr)
        print(f'Epoch {epoch+1}: Learning rate is set to {scheduled_lr}')

# 定义一个学习率调度函数
def schedule(epoch):
    if epoch < 10:
        return 0.01
    else:
        return 0.01 * np.exp(0.1 * (10 - epoch))

# 创建自定义学习率调度器
lr_scheduler = CustomLearningRateScheduler(schedule)

# 在模型训练过程中使用自定义学习率调度器
model.fit(x_train, y_train, callbacks=[lr_scheduler])