def METHOD_NAME(model_file, initial_learning_rate=0.0001, learning_rate_drop=0.5, learning_rate_epochs=None,
                  learning_rate_patience=50, logging_file="training.log", verbosity=1,
                  early_stopping_patience=None):
    callbacks = list()
    callbacks.append(ModelCheckpoint(model_file, save_best_only=True))
    callbacks.append(CSVLogger(logging_file, append=True))
    if learning_rate_epochs:
        callbacks.append(LearningRateScheduler(partial(step_decay, initial_lrate=initial_learning_rate,
                                                       drop=learning_rate_drop, epochs_drop=learning_rate_epochs)))
    else:
        callbacks.append(ReduceLROnPlateau(factor=learning_rate_drop, patience=learning_rate_patience,
                                           verbose=verbosity))
    if early_stopping_patience:
        callbacks.append(EarlyStopping(verbose=verbosity, patience=early_stopping_patience))
    return callbacks