TensorFLow 入门 - 用Saver保存和恢复变量

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建立文件tensor_save.py, 保存变量v1,v2的tensor到checkpoint files中，名称分别设置为v3,v4。

import tensorflow as tf

# Create some variables.
v1 = tf.Variable(3, name="v1")
v2 = tf.Variable(4, name="v2")

# Create model
y=tf.add(v1,v2)

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

# Add ops to save and restore all the variables.
saver = tf.train.Saver({'v3':v1,'v4':v2})

# Later, launch the model, initialize the variables, do some work, save the
# variables to disk.
with tf.Session() as sess:
  sess.run(init_op)
  print("v1 = ", v1.eval())
  print("v2 = ", v2.eval())
  # Save the variables to disk.
  save_path = saver.save(sess, "f:/tmp/model.ckpt")
  print ("Model saved in file: ", save_path)

建立文件tensor_restror.py, 将checkpoint files中名称分别为v3,v4的tensor分别恢复到变量v3,v4中。
import tensorflow as tf

# Create some variables.
v3 = tf.Variable(0, name="v3")
v4 = tf.Variable(0, name="v4")

# Create model
y=tf.mul(v3,v4)

# 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, "f:/tmp/model.ckpt")
  print ("Model restored.")
  print ("v3 = ", v3.eval())
  print ("v4 = ", v4.eval())
  print ("y = ",sess.run(y))