Tensorflow: how to save/restore a model?

Question

After you train a model in Tensorflow:

1. How do you save the trained model?
2. How do you later restore this saved model?

Answer 1

Here's my simple solution for the two basic cases differing on whether you want to load the graph from file or build it during runtime.

This answer holds for Tensorflow 0.12+ (including 1.0).

Rebuilding the graph in code

Saving

graph = ... # build the graph
saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.save(sess, 'my-model')

Loading

graph = ... # build the graph
saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    # now you can use the graph, continue training or whatever

Loading also the graph from a file

When using this technique, make sure all your layers/variables have explicitly set unique names. Otherwise Tensorflow will make the names unique itself and they'll be thus different from the names stored in the file. It's not a problem in the previous technique, because the names are "mangled" the same way in both loading and saving.

Saving

graph = ... # build the graph

for op in [ ... ]:  # operators you want to use after restoring the model
    tf.add_to_collection('ops_to_restore', op)

saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.save(sess, 'my-model')

Loading

with ... as sess:  # your session object
    saver = tf.train.import_meta_graph('my-model.meta')
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    ops = tf.get_collection('ops_to_restore')  # here are your operators in the same order in which you saved them to the collection