Table of Contents

Note: extended design for callbcaks here

Problem and Goals

Background

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class EventHandler:
     def __init__(self,estimator):
        self._train_stats= estimator.train_stats
    
     
def train_begin(self):
        pass
     def train_end(self):
        pass
     def batch_begin(self):
        pass
     def batch_end(self):
        pass
     def epoch_begin(self):
        pass
     def epoch_end(self):
        pass

class LoggingHandler(EventHandler):
    def __init__(self, estimator, log_loc = './'):
        # setup logging
    def epoch_end:
         ## log the train stats to log location 

 class CheckpointHandler(EventHandler):
    def __init__(self, estimator, checkpoint_interval=5 , ckpt_loc='./', monitor= "val_loss"):
        super.__init__()
        train_stats = {"lr" = [0.1], "train_acc" = [0.85], "val_acc" = [0.99], ... } 
     def epoch_end:
       ## save the model params to the checkpointing location

class MetricHandler(EventHandler):
    def __init__(self, estimator):
        super.__init__()
        train_stats = {"lr" = [0.1], "train_acc" = [0.85], "val_acc" = [0.99], ... } 
    def epoch_end:
       ## calculate and update metrics for thr training dataset
       ## update_metrics(pred, labels)- default implementation can be overriden in case of multi-output cases
       ## update validation metrics for validation dataset

class EarlyStopping(EventHandler):
    def __init__(self, monitor= "val_loss", min_delta=0, patience=0, mode="auto", baseline=None, restore_best_params=False):
        # setup early stopping rules based on the metric/loss monitor and the mode
        # e.g. if "acc" use greater mode else use lesser 
    def on_epoch_end:
        # if metric improved, record the best value
        # else wait n epochs(n=patience) and stop trainning
        # restore net parameters from the best epoch accordingly
    def on_train_end:
        # let user know if early stopping is triggered
        
   

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By supporting the following models, we believe we can cover most basic use cases for Gluon users

APPENDIX C - Tensorflow estimators

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