create, save, destroy, load

tf_serialze.py

import tensorflow as tf


# save
dummy = tf.Variable(tf.truncated_normal(shape=[10]), name='dummy')
with tf.Session() as sess:
  saver = tf.train.Saver()
  sess.run(tf.global_variables_initializer())
  expected = sess.run(tf.reduce_sum(dummy))
  saver.save(sess, 'model')

# destroy
tf.reset_default_graph()

# read Graph + Checkpoint
with tf.Session() as sess:
  loader = tf.train.import_meta_graph('model.meta')
  loader.restore(sess, tf.train.latest_checkpoint('.'))
  dummy =  tf.get_default_graph().get_tensor_by_name("dummy:0")
  actual = sess.run(tf.reduce_sum(dummy))

  assert expected == actual

# destroy
tf.reset_default_graph()

# read Checkpoint
with tf.Session() as sess:
  dummy = tf.Variable(tf.truncated_normal(shape=[10]), name='dummy')
  sess.run(tf.global_variables_initializer())
  actual = sess.run(tf.reduce_sum(dummy))

  assert expected != actual

  loader = tf.train.Saver()
  loader.restore(sess, tf.train.latest_checkpoint('.'))
  actual = sess.run(tf.reduce_sum(dummy))

  assert expected == actual