Does a Keras checkpointer save the best weights when using chekpoints with restore_best_weights?












2












$begingroup$


I m training a sequence model in Keras using the tensorflow backend. I've also included some callbacks to save checkpoints and revert to best weights if the model starts to overfit (which it will).



My question - when fitting using this set of callbacks, does the final checkpoint contain the version of the model with the best weight? I know that the weights in classif_model will revert but I'm not sure if that also applies to the final saved state.



from keras import callbacks as kc



classif_model = my_model(input_shape)



# Set up callbacks

checkpointer = kc.ModelCheckpoint(filepath='results/'+name+'.h5', verbose=0)

earlystopping = kc.EarlyStopping(monitor='val_loss', patience=patience, restore_best_weights = True)

callbacks = [checkpointer, earlystopping]



# train the model

hist = classif_model.fit(x = X_tr, y = Y_tr, epochs = epochs, batch_size = batch_size, 

                         callbacks = callbacks, validation_data = (X_val, Y_val), 

                         verbose = 0)





keras







share|improve this question









$endgroup$

















    2












    $begingroup$


    I m training a sequence model in Keras using the tensorflow backend. I've also included some callbacks to save checkpoints and revert to best weights if the model starts to overfit (which it will).



    My question - when fitting using this set of callbacks, does the final checkpoint contain the version of the model with the best weight? I know that the weights in classif_model will revert but I'm not sure if that also applies to the final saved state.



    from keras import callbacks as kc
    

    
classif_model = my_model(input_shape)
    

    
# Set up callbacks
    
checkpointer = kc.ModelCheckpoint(filepath='results/'+name+'.h5', verbose=0)
    
earlystopping = kc.EarlyStopping(monitor='val_loss', patience=patience, restore_best_weights = True)
    
callbacks = [checkpointer, earlystopping]
    

    
# train the model
    
hist = classif_model.fit(x = X_tr, y = Y_tr, epochs = epochs, batch_size = batch_size, 
    
                         callbacks = callbacks, validation_data = (X_val, Y_val), 
    
                         verbose = 0)





    keras







    share|improve this question









    $endgroup$















      2












      2








      2





      $begingroup$


      I m training a sequence model in Keras using the tensorflow backend. I've also included some callbacks to save checkpoints and revert to best weights if the model starts to overfit (which it will).



      My question - when fitting using this set of callbacks, does the final checkpoint contain the version of the model with the best weight? I know that the weights in classif_model will revert but I'm not sure if that also applies to the final saved state.



      from keras import callbacks as kc
      

      
classif_model = my_model(input_shape)
      

      
# Set up callbacks
      
checkpointer = kc.ModelCheckpoint(filepath='results/'+name+'.h5', verbose=0)
      
earlystopping = kc.EarlyStopping(monitor='val_loss', patience=patience, restore_best_weights = True)
      
callbacks = [checkpointer, earlystopping]
      

      
# train the model
      
hist = classif_model.fit(x = X_tr, y = Y_tr, epochs = epochs, batch_size = batch_size, 
      
                         callbacks = callbacks, validation_data = (X_val, Y_val), 
      
                         verbose = 0)





      keras







      share|improve this question









      $endgroup$




      I m training a sequence model in Keras using the tensorflow backend. I've also included some callbacks to save checkpoints and revert to best weights if the model starts to overfit (which it will).



      My question - when fitting using this set of callbacks, does the final checkpoint contain the version of the model with the best weight? I know that the weights in classif_model will revert but I'm not sure if that also applies to the final saved state.



      from keras import callbacks as kc
      

      
classif_model = my_model(input_shape)
      

      
# Set up callbacks
      
checkpointer = kc.ModelCheckpoint(filepath='results/'+name+'.h5', verbose=0)
      
earlystopping = kc.EarlyStopping(monitor='val_loss', patience=patience, restore_best_weights = True)
      
callbacks = [checkpointer, earlystopping]
      

      
# train the model
      
hist = classif_model.fit(x = X_tr, y = Y_tr, epochs = epochs, batch_size = batch_size, 
      
                         callbacks = callbacks, validation_data = (X_val, Y_val), 
      
                         verbose = 0)





      keras




      keras






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 15 hours ago









      SledgeSledge

      1646




      1646






















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          $begingroup$

          It will if you set the save_best_only flag in your checkpoint callback definition:



          ModelCheckpoint(filepath, monitor='val_loss', save_best_only=True)


          From the docs:




          save_best_only: if save_best_only=True, the latest best model
          according to the quantity monitored will not be overwritten.







          share|improve this answer









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            $begingroup$

            It will if you set the save_best_only flag in your checkpoint callback definition:



            ModelCheckpoint(filepath, monitor='val_loss', save_best_only=True)


            From the docs:




            save_best_only: if save_best_only=True, the latest best model
            according to the quantity monitored will not be overwritten.







            share|improve this answer









            $endgroup$


















              2












              $begingroup$

              It will if you set the save_best_only flag in your checkpoint callback definition:



              ModelCheckpoint(filepath, monitor='val_loss', save_best_only=True)


              From the docs:




              save_best_only: if save_best_only=True, the latest best model
              according to the quantity monitored will not be overwritten.







              share|improve this answer









              $endgroup$
















                2












                2








                2





                $begingroup$

                It will if you set the save_best_only flag in your checkpoint callback definition:



                ModelCheckpoint(filepath, monitor='val_loss', save_best_only=True)


                From the docs:




                save_best_only: if save_best_only=True, the latest best model
                according to the quantity monitored will not be overwritten.







                share|improve this answer









                $endgroup$



                It will if you set the save_best_only flag in your checkpoint callback definition:



                ModelCheckpoint(filepath, monitor='val_loss', save_best_only=True)


                From the docs:




                save_best_only: if save_best_only=True, the latest best model
                according to the quantity monitored will not be overwritten.








                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 13 hours ago









                Simon LarssonSimon Larsson

                858214




                858214






























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