tutorials/tensorflowTUT/tf19_saver.py at master · Snowdrop99/tutorials · GitHub

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"""

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))