tensorflow 計算圖模型的保存和恢復

定義計算圖並計算，保存其中的變量 。保存.ipynb

import tensorflow as tf
tf.reset_default_graph()
# Create some variables.
v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)

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, "./ckpt_test/model.ckpt")
  print("Model saved in path: %s" % save_path)

 

創建相同的圖結構，圖中的節點變量可以由已經保存的模型文件中的內容恢復處理，注意 首先要圖進行清空（感覺tf公用了變量空間，所以如果沒有清空會導致變量內容名稱不一致）恢復.ipynb

import tensorflow as tf
tf.reset_default_graph()

# Create some variables.
v1 = tf.get_variable("v1", shape=[3])
v2 = tf.get_variable("v2", shape=[5])

# 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, "./ckpt_test/model.ckpt")
  print("Model restored.")
  # Check the values of the variables
  print("v1 : %s" % v1.eval())
  print("v2 : %s" % v2.eval())

所以最好在保存和恢復的文件中都先對圖清空。