Keras中的MultiStepLR

Keras中没有多步调整学习率（MultiStepLR）的调度器，但是博主这里提供一个自己写的：

1.代码

  

   

    

     

    
    

     

      from tensorflow.python.keras.callbacks import Callback
     
    
   

    

     

    
    

     

      from tensorflow.python.keras import backend as K
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     

      import argparse
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      parser = argparse.ArgumentParser()
     
    
   

    

     

    
    

     

      parser.add_argument('--lr_decay_epochs', type=list, default=[2, 5, 7], help="For MultiFactorScheduler step")
     
    
   

    

     

    
    

     

      parser.add_argument('--lr_decay_factor', type=float, default=0.1)
     
    
   

    

     

    
    

     

      args, _ = parser.parse_known_args()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      def get_lr_scheduler(args):
     
    
   

    

     

    
    

     

       lr_scheduler = MultiStepLR(args=args)
     
    
   

    

     

    
    

     
 return lr_scheduler
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      class MultiStepLR(Callback):
     
    
   

    

     

    
    

     
 """Learning rate scheduler.
     
    
   

    

     

    
    

     

      
     
    
   

    

     

    
    

     

       Arguments:
     
    
   

    

     

    
    

     

       args: parser_setting
     
    
   

    

     

    
    

     

       verbose: int. 0: quiet, 1: update messages.
     
    
   

    

     

    
    

     

       """
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 def __init__(self, args, verbose=0):
     
    
   

    

     

    
    

     

       super(MultiStepLR, self).__init__()
     
    
   

    

     

    
    

     

       self.args = args
     
    
   

    

     

    
    

     

       self.steps = args.lr_decay_epochs
     
    
   

    

     

    
    

     

       self.factor = args.lr_decay_factor
     
    
   

    

     

    
    

     

       self.verbose = verbose
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 def on_epoch_begin(self, epoch, logs=None):
     
    
   

    

     

    
    

     
 if not hasattr(self.model.optimizer, 'lr'):
     
    
   

    

     

    
    

     
 raise ValueError('Optimizer must have a "lr" attribute.')
     
    
   

    

     

    
    

     

       lr = self.schedule(epoch)
     
    
   

    

     

    
    

     
 if not isinstance(lr, (float, np.float32, np.float64)):
     
    
   

    

     

    
    

     
 raise ValueError('The output of the "schedule" function '
     
    
   

    

     

    
    

     
 'should be float.')
     
    
   

    

     

    
    

     

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

    

     

    
    

     

       print("learning rate: {:.7f}".format(K.get_value(self.model.optimizer.lr)).rstrip('0'))
     
    
   

    

     

    
    

     
 if self.verbose > 0:
     
    
   

    

     

    
    

     

       print('\nEpoch %05d: MultiStepLR reducing learning '
     
    
   

    

     

    
    

     
 'rate to %s.' % (epoch + 1, lr))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 def schedule(self, epoch):
     
    
   

    

     

    
    

     

       lr = K.get_value(self.model.optimizer.lr)
     
    
   

    

     

    
    

     
 for i in range(len(self.steps)):
     
    
   

    

     

    
    

     
 if epoch == self.steps[i]:
     
    
   

    

     

    
    

     

       lr = lr * self.factor
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 return lr
     
    
  
 

2.调用（callbacks里append这个lr_scheduler，fit_generator里callbacks传入这个变量）

  

   

    

     

    
    

     

      callbacks = []
     
    
   

    

     

    
    

     

      lr_scheduler = get_lr_scheduler(args=args)
     
    
   

    

     

    
    

     

      callbacks.append(lr_scheduler)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      ...
     
    
   

    

     

    
    

     

      model.fit_generator(train_generator,
     
    
   

    

     

    
    

     

       steps_per_epoch=train_generator.samples // args.batch_size,
     
    
   

    

     

    
    

     

       validation_data=test_generator,
     
    
   

    

     

    
    

     

       validation_steps=test_generator.samples // args.batch_size,
     
    
   

    

     

    
    

     

       workers=args.num_workers,
     
    
   

    

     

    
    

     

       callbacks=callbacks,  # 你的callbacks， 包含了lr_scheduler
     
    
   

    

     

    
    

     

       epochs=args.epochs,
     
    
   

    

     

    
    

     

       )
     
    
  
 

大家可以拿去用~

文章来源: nickhuang1996.blog.csdn.net，作者：悲恋花丶无心之人，版权归原作者所有，如需转载，请联系作者。

原文链接：nickhuang1996.blog.csdn.net/article/details/103645204