# View more python tutorials on my Youtube and Youku channel!!! # Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg # Youku video tutorial: http://i.youku.com/pythontutorial """ Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly. """ from __future__ import print_function import tensorflow as tf import numpy as np # Save to file # remember to define the same dtype and shape when restore # W = tf.Variable([[1,2,3],[3,4,5]], dtype=tf.float32, name='weights') # b = tf.Variable([[1,2,3]], dtype=tf.float32, name='biases') # tf.initialize_all_variables() no long valid from # 2017-03-02 if using tensorflow >= 0.12 # if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: # init = tf.initialize_all_variables() # else: # init = tf.global_variables_initializer() # # saver = tf.train.Saver() # # with tf.Session() as sess: # sess.run(init) # save_path = saver.save(sess, "my_net/save_net.ckpt") # print("Save to path: ", save_path) ################################################ # restore variables # redefine the same shape and same type for your variables W = tf.Variable(np.arange(6).reshape((2, 3)), dtype=tf.float32, name="weights") b = tf.Variable(np.arange(3).reshape((1, 3)), dtype=tf.float32, name="biases") # not need init step saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, "my_net/save_net.ckpt") print("weights:", sess.run(W)) print("biases:", sess.run(b))