tf.app.flags

import tensorflow as tf
from tensroflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('./MNIST_data/',one_hot = True)

# 老司机疯狂立下FLAGS
FLAGS = tf.app.flags.FLAGS
# 根据参数类型不同,可以定义字符串DEFINE_string，也可以定义整数型DEFINE_integer
# DEFINE_integer('参数名称', 参数值， '参数的解释')
tf.app.flags.DEFINE_integer('batch_size',100,'batch size')
tf.app.flags.DEFINE_integer('epoch',20000,'how many epoch tf will run')
tf.app.flags.DEFINE_float('learning_rate',0.1,'learning rate')
tf.app.flags.DEFINE_string('log_dir','./path/log/','where to save tensorboard')

with tf.name_scope('input'):
     x = tf.placeholder(tf.float32,[None,784],name = 'x_input')
     y_ =  tf.placeholder(tf.float32,[None,10],name = 'y_input')

with tf.name_scope('nn'):
     with tf.name_scope('weight'):
          W = tf.Variable(tf.zeros([784,10]))
     with tf.name_scope('biases'):
          b = tf.Variable(tf.zeros([10]))

with tf.name_scope('loss'):
     cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y))
with tf.name_scope('train'):
     train_step = tf.train.GradientDescentOpitimizer(FLAGS.learning_rate).minimize(cross_entropy)

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

for _ in range(FLAGS.epoch):
        batch_xs, batch_ys = mnist.train.next_batcj(FLAGS.batch_size)
        fd = {x:batch_xs, y_:batch_ys}
        sess.run(train_step, feed_dict = tf)

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
fd2 = {x:mnist.test.images, y_:mnist.test.labels}
print(sess.run(accuracy, feed_dict = fd2))

merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)

输出：
    0.9221

并且，注意在定义了FLAGS后，
在任意地方都可以调用FLAGS里定义的变量，
只需import FLAGS定义所在文件即可。