(model)
Iterative training of the model
     - To allow for finite RAM...
     - To allow infinite training data as the training noise is injected in runtime
Iterative training of the model
     - To allow for finite RAM...
     - To allow infinite training data as the training noise is injected in runtime
Iterative training of the model - To allow for finite RAM ... - To allow infinite training data as the training noise is injected in runtime
def itarative_train(model):
    """
    Iterative training of the model
     - To allow for finite RAM...
     - To allow infinite training data as the training noise is injected in runtime
    """
    model.fit_generator(generator(NEWS_FILE_NAME_TRAIN), steps_per_epoch=CONFIG.steps_per_epoch,
                        epochs=CONFIG.epochs,
                        verbose=1, callbacks=[ON_EPOCH_END_CALLBACK, ], validation_data=generator(NEWS_FILE_NAME_VALIDATE),
                        validation_steps=CONFIG.validation_steps,
                        class_weight=None, max_q_size=10, workers=1,
                        pickle_safe=False, initial_epoch=0)
def itarative_train ( model ) : model . fit_generator ( generator ( NEWS_FILE_NAME_TRAIN ) , steps_per_epoch = CONFIG . steps_per_epoch , epochs = CONFIG . epochs , verbose = 1 , callbacks = [ ON_EPOCH_END_CALLBACK , ] , validation_data = generator ( NEWS_FILE_NAME_VALIDATE ) , validation_steps = CONFIG . validation_steps , class_weight = None , max_q_size = 10 , workers = 1 , pickle_safe = False , initial_epoch = 0 )