train.Saver() restory Key not found in checkpoint

加上tf.reset_default_graph()在最前面就解决了

-----------------------saver----------------------------------

import tensorflow as tf
tf.reset_default_graph()

#v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
#v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)

v1 = tf.Variable(tf.constant(1,shape=[3]),name='v1')
v2 =tf.Variable(tf.constant(7,shape=[5]),name='v2')

inc_v1 = v1.assign(v1+1)
dec_v2 = v2.assign(v2-1)

# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()

# Add ops to save and restore all the variables.
saver = tf.train.Saver()

# Later, launch the model, initialize the variables, do some work, and save the
# variables to disk.
with tf.Session() as sess:
  sess.run(init_op)
  # Do some work with the model.
  inc_v1.op.run()
  dec_v2.op.run()
  # Save the variables to disk.
  save_path = saver.save(sess, "saver/model.ckpt")

  print("Model saved in path: %s" % save_path)


-----------------restore----------------------------------

import tensorflow as tf

tf.reset_default_graph()

# Create some variables.
v1 = tf.Variable(tf.constant(1,shape=[3]),name='v1')
v2 =tf.Variable(tf.constant(7,shape=[5]),name='v2')

# Add ops to save and restore all the variables.
saver = tf.train.Saver()

# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
  # Restore variables from disk.
  saver.restore(sess, "saver/model.ckpt")
  print("Model restored.")
  # Check the values of the variables
  print("v1 : %s" % v1.eval())
  print("v2 : %s" % v2.eval())