import tensorflow as tf
 
def model_1():
  with tf.variable_scope("var_a"):
    a = tf.Variable(initial_value=[1, 2, 3], name="a")
 
  vars = [var for var in tf.trainable_variables() if var.name.startswith("var_a")]
  print(len(vars))
  return vars
 
def model_2():
 
  vars1 = model_1()
 
  with tf.variable_scope("var_b"):
    a = tf.Variable(initial_value=[1, 2, 3], name="a")
 
  vars2 = [var for var in tf.trainable_variables() if var.name.startswith("var")]
  print(len(vars2))
  return vars1
 
 
def pretrain_model1():
  print("-------- model 1 ------")
  vars = model_1()
 
  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver = tf.train.Saver()
    saver.save(sess, "./model.ckpt")
 
def train_model2():
  print("-------- model 2 ------")
 
  model1_vars = model_2()
 
  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver = tf.train.Saver(var_list=model1_vars)
    saver.restore(sess, "./model.ckpt")
    vars = sess.run([model1_vars])
    for var in vars:
      print(var)
 
step = 2
if step == 1:
  pretrain_model1()
else:
  train_model2()