batch训练回调函数：

 def _batch_callback(param):
      #global global_step
      global_step[0]+=1
      mbatch = global_step[0]
      for _lr in lr_steps:
        if mbatch==args.beta_freeze+_lr:
          opt.lr *= 0.1
          print('lr change to', opt.lr)
          break

      _cb(param)
      if mbatch%1000==0:
        print('lr-batch-epoch:',opt.lr,param.nbatch,param.epoch)

 

调用代码：

    model.fit(train_dataiter,
        begin_epoch        = begin_epoch,
        num_epoch          = end_epoch,
        eval_data          = val_dataiter,
        eval_metric        = eval_metrics,
        kvstore            = 'device',
        optimizer          = opt,
        #optimizer_params   = optimizer_params,
        initializer        = initializer,
        arg_params         = arg_params,
        aux_params         = aux_params,
        allow_missing      = True,
        batch_end_callback = _batch_callback,
        epoch_end_callback = epoch_cb )

 

可以在_batch_callback中加自己需要输出的日志，比如学习率，loss，ap。