def main(args):
    hyperparams = load_hyperparams(args.config)
    logger = DebugLogger(hyperparams.data_log_output_dir)
    tf_version = check_tensorflow_version()
    logger.log_print('# tensorflow verison is {0}'.format(tf_version))
    if (args.mode == 'train'):
        train(logger, hyperparams, enable_eval=False, enable_debug=False)
    elif (args.mode == 'train_eval'):
        train(logger, hyperparams, enable_eval=True, enable_debug=False)
    elif (args.mode == 'train_debug'):
        train(logger, hyperparams, enable_eval=False, enable_debug=True)
    elif (args.mode == 'eval'):
        evaluate(logger, hyperparams, enable_debug=False)
    elif (args.mode == 'eval_debug'):
        evaluate(logger, hyperparams, enable_debug=True)
def main(args):
    hyperparams = load_hyperparams(args.config)
    logger = DebugLogger(hyperparams.data_log_output_dir)
    tf_version = check_tensorflow_version()
    logger.log_print('# tensorflow verison is {0}'.format(tf_version))
    if (args.mode == 'train'):
        train(logger, hyperparams, enable_eval=False, enable_debug=False)
    elif (args.mode == 'train_eval'):
        train(logger, hyperparams, enable_eval=True, enable_debug=False)
    elif (args.mode == 'train_debug'):
        train(logger, hyperparams, enable_eval=False, enable_debug=True)
    elif (args.mode == 'eval'):
        evaluate(logger, hyperparams, enable_debug=False)
    elif (args.mode == 'eval_debug'):
        evaluate(logger, hyperparams, enable_debug=True)