CODEWITHSUNDEEP
pythontensorflow
maininformer
maininformer

Reputation: 1077

Saving tensorflow model after training is finished

I have finished running a big model in tensorflow python. But I have not saved it inside the session. Now that the training is over, I want to save the variables. I am doing the following: 

saver=tf.train.Saver()
with tf.Session(graph=graph) as sess:  
    save_path = saver.save(sess, "86_model.ckpt")
    print("Model saved in file: %s" % save_path)

This returns : ValueError: No variables to save. According to their website what is missing is initialize_all_variables(). The documentation says little about what exactly that does. The word "initialize" scares me, I do not want to reset all my trained values. Any way to save my model without re-running it?

Upvotes: 0

Views: 2704

Answers (3)

Mia Hunsicker
Mia Hunsicker

Reputation: 51

I believe its because you are not initializing all of your variables in the saver. This should work 

with tf.Session() as sess:
      tf.initialize_all_variables().run()
      saver = tf.train.Saver(tf.all_variables())
     -------everything your session does -------------
      checkpoint_path = os.path.join(save_dir, 'model.ckpt')
      saver.save(sess, checkpoint_path, global_step = your_global_step)

Upvotes: 1

Luis Leal
Luis Leal

Reputation: 3534

How about using skflow ? With skflow(now skflow is integrated in tensorflow) you can specify the parameter model_dir on your constructor and that automatically will save your model while training(it will save checkpoints so if something goes wrong during training, you can restart from last checkpooint).

Upvotes: 0

mprat
mprat

Reputation: 2481

It seems like from the tensorflow documentation, the "session" is the thing that holds the information from the trained model. So presumably somewhere you called sess.run() to train your model - what you want to do is call sess.save() using THAT session, not a new one you create with this saver object.

Upvotes: 1

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