1. 
2. def scheduler(epoch):
3. # 每隔50个epoch，学习率减小为原来的1/10
4. if epoch % 50 == 0 and epoch != 0:
5.         lr = K.get_value(GRU.optimizer.lr)
6. if lr>1e-5:
7.             K.set_value(GRU.optimizer.lr, lr * 0.1)
8. print("lr changed to {}".format(lr * 0.1))
9. return K.get_value(GRU.optimizer.lr)
10. 
11. reduce_lr = LearningRateScheduler(scheduler)
12. early_stopping = EarlyStopping(monitor='loss', 
13.                                patience=20, 
14.                                min_delta=1e-5,
15.                                mode='auto',
16. #                                restore_best_weights=False,#是否从具有监测数量的最佳值的时期恢复模型权重
17.                                verbose=2)