advanced deep-learning best practices keras callbacks learningratescheduler keras callbacks reducelronplateau keras callbacks csvlogger let' review few of them to give you an idea of how to use themmodelcheckpointearlystoppingand reducelronplateau the odelcheckpoint and earlystopping callbacks you can use the earlystopping callback to interrupt training once target metric being monitored has stopped improving for fixed number of epochs for instancethis callback allows you to interrupt training as soon as you start overfittingthus avoiding having to retrain your model for smaller number of epochs this callback is typically used in combination with modelcheckpointwhich lets you continually save the model during training (andoptionallysave only the current best model so farthe version of the model that achieved the best performance at the end of an epoch)callbacks are passed to the model via the callbacks argument in fitwhich takes list of callbacks you can pass any number of callbacks interrupts training when improvement stops import keras monitors the model' validation accuracy interrupts training when callbacks_list accuracy has stopped keras callbacks earlystoppingimproving for more than one monitor='acc'epoch (that istwo epochspatience= )keras callbacks modelcheckpointsaves the current weights after every epoch filepath='my_model 'path to the destination model file monitor='val_loss'save_best_only=truethese two arguments mean you won' overwrite the model file unless val_loss has improvedwhich allows you to keep the best model seen during training model compile(optimizer='rmsprop'loss='binary_crossentropy'metrics=['acc']model fit(xyepochs= batch_size= callbacks=callbacks_listvalidation_data=(x_valy_val)you monitor accuracyso it should be part of the model' metrics note that because the callback will monitor validation loss and validation accuracyyou need to pass validation_data to the call to fit the reducelronplateau callback you can use this callback to reduce the learning rate when the validation loss has stopped improving reducing or increasing the learning rate in case of loss plateau is is an effective strategy to get out of local minima during training the following example uses the reducelronplateau callbacklicensed to