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How to checkpoint by minibatch in Keras



The 2019 Stack Overflow Developer Survey Results Are InDoes the time to train a model using keras increase linear with epoches?Keras Neural Network training is stuck (gets stuck around epoch 6)Keras Callback example for saving a model after every epoch?My Keras bidirectional LSTM model is giving terrible predictionsWhy model.fit_generator in keras is taking so much time even before picking the data?Understanding why my binary classification is approaching 50% accuracy using TensorFlow and KerasHow to train a multi inputs deep learning model using every combination of inputs?How to define a multi-dimensional neural network with kerasKeras: extreme spike in loss during trainingTensor input in keras model is array of tensors but won't agree to dimensions










1












$begingroup$


I know that I can use ModelCheckpoint in Keras for checkpointing a model every epoch (or every few epochs, depending on what I want).



I am getting my data for each minibatch from a fit_generator, and it takes a very long time to evaluate each minibatch. I'd like to be able to checkpoint by minibatch instead of by epoch. How can I do this in Keras?






keras







share|improve this question









$endgroup$
















    1












    $begingroup$


    I know that I can use ModelCheckpoint in Keras for checkpointing a model every epoch (or every few epochs, depending on what I want).



    I am getting my data for each minibatch from a fit_generator, and it takes a very long time to evaluate each minibatch. I'd like to be able to checkpoint by minibatch instead of by epoch. How can I do this in Keras?






    keras







    share|improve this question









    $endgroup$














      1












      1








      1





      $begingroup$


      I know that I can use ModelCheckpoint in Keras for checkpointing a model every epoch (or every few epochs, depending on what I want).



      I am getting my data for each minibatch from a fit_generator, and it takes a very long time to evaluate each minibatch. I'd like to be able to checkpoint by minibatch instead of by epoch. How can I do this in Keras?






      keras







      share|improve this question









      $endgroup$




      I know that I can use ModelCheckpoint in Keras for checkpointing a model every epoch (or every few epochs, depending on what I want).



      I am getting my data for each minibatch from a fit_generator, and it takes a very long time to evaluate each minibatch. I'd like to be able to checkpoint by minibatch instead of by epoch. How can I do this in Keras?






      keras




      keras






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Mar 30 at 1:32









      StatsSorceressStatsSorceress

      1,1253724




      1,1253724




















          1 Answer
          1






          active

          oldest

          votes


















          1












          $begingroup$

          You have to write a custom callback for this. Steps are :



          1. Subclass ModelCheckpoint (https://github.com/keras-team/keras/blob/master/keras/callbacks.py) or create new one if you do not need filename pattern etc.


          2. Add method that would be called at the end of each batch 



          class BatchModelCheckpoint(keras.callbacks.Callback):
           def on_batch_end(self, batch, logs=None):
           self.model.save(filepath, overwrite=True)






          share|improve this answer









          $endgroup$













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            1












            $begingroup$

            You have to write a custom callback for this. Steps are :



            1. Subclass ModelCheckpoint (https://github.com/keras-team/keras/blob/master/keras/callbacks.py) or create new one if you do not need filename pattern etc.


            2. Add method that would be called at the end of each batch 



            class BatchModelCheckpoint(keras.callbacks.Callback):
             def on_batch_end(self, batch, logs=None):
             self.model.save(filepath, overwrite=True)






            share|improve this answer









            $endgroup$

















              1












              $begingroup$

              You have to write a custom callback for this. Steps are :



              1. Subclass ModelCheckpoint (https://github.com/keras-team/keras/blob/master/keras/callbacks.py) or create new one if you do not need filename pattern etc.


              2. Add method that would be called at the end of each batch 



              class BatchModelCheckpoint(keras.callbacks.Callback):
               def on_batch_end(self, batch, logs=None):
               self.model.save(filepath, overwrite=True)






              share|improve this answer









              $endgroup$















                1












                1








                1





                $begingroup$

                You have to write a custom callback for this. Steps are :



                1. Subclass ModelCheckpoint (https://github.com/keras-team/keras/blob/master/keras/callbacks.py) or create new one if you do not need filename pattern etc.


                2. Add method that would be called at the end of each batch 



                class BatchModelCheckpoint(keras.callbacks.Callback):
                 def on_batch_end(self, batch, logs=None):
                 self.model.save(filepath, overwrite=True)






                share|improve this answer









                $endgroup$



                You have to write a custom callback for this. Steps are :



                1. Subclass ModelCheckpoint (https://github.com/keras-team/keras/blob/master/keras/callbacks.py) or create new one if you do not need filename pattern etc.


                2. Add method that would be called at the end of each batch 



                class BatchModelCheckpoint(keras.callbacks.Callback):
                 def on_batch_end(self, batch, logs=None):
                 self.model.save(filepath, overwrite=True)







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Mar 30 at 8:19









                Shamit VermaShamit Verma

                1,5191314




                1,5191314



























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