一、版本

[tensorflow1.10 到 tensorflow1.13] 中都有
tf.flags和tf.app.flags
[tensorflow1.14 到 tensorflow1.15] 以及 tensorflow2.0不在有了

二、作用

Tensorflow 采用tf.app.flags 来进行命令行参数传递.
如 - flags_test.py

import tensorflow as tf 

flags = tf.app.flags
FLAGS = flags.FLAGS

# Settings for some training parameters.
flags.DEFINE_enum('learning_policy', 'poly', ['poly', 'step'],  
                  'Learning rate policy for training.')
flags.DEFINE_float('base_learning_rate', .0001,  
                   'The base learning rate for model training.')
flags.DEFINE_integer('learning_rate_decay_step', 2000, 
                     'Decay the base learning rate at a fixed step.')
flags.DEFINE_integer('train_batch_size', 12,
                     'The number of images in each batch during training.')
flags.DEFINE_multi_integer('train_crop_size', [513, 513],
                           'Image crop size [height, width] during training.')
flags.DEFINE_boolean('upsample_logits', True,
                     'Upsample logits during training.')
flags.DEFINE_string('dataset', 'dataset_name',
                    'Name of the test dataset.')

def main(_):  
    print(FLAGS.learning_policy)
    print(FLAGS.base_learning_rate)
    print(FLAGS.learning_rate_decay_step)
    print(FLAGS.train_batch_size)
    print(FLAGS.train_crop_size)
    print(FLAGS.upsample_logits)
    print(FLAGS.dataset)

if __name__ == '__main__':
    tf.app.run()