CODEWITHSUNDEEP
pythontensorflowmachine-learning
Zan Huang
Zan Huang

Reputation: 663

Tensorflow ValueError: No variables to save from

I have written a tensorflow CNN and it is already trained. I wish to restore it to run it on a few samples but unfortunately its spitting out:

ValueError: No variables to save

My eval code can be found here:

import tensorflow as tf

import main
import Process
import Input

eval_dir = "/Users/Zanhuang/Desktop/NNP/model.ckpt-30"
checkpoint_dir = "/Users/Zanhuang/Desktop/NNP/checkpoint"

init_op = tf.initialize_all_variables()
saver = tf.train.Saver()

def evaluate():
  with tf.Graph().as_default() as g:
    sess.run(init_op)

    ckpt = tf.train.get_checkpoint_state(checkpoint_dir)

    saver.restore(sess, eval_dir)

    images, labels = Process.eval_inputs(eval_data = eval_data)

    forward_propgation_results = Process.forward_propagation(images)

    top_k_op = tf.nn.in_top_k(forward_propgation_results, labels, 1)

    print(top_k_op)

def main(argv=None):
    evaluate()

if __name__ == '__main__':
  tf.app.run()

Upvotes: 18

Views: 36032

Answers (3)

mrry
mrry

Reputation: 126154

The tf.train.Saver must be created after the variables that you want to restore (or save). Additionally it must be created in the same graph as those variables.

Assuming that Process.forward_propagation(…) also creates the variables in your model, adding the saver creation after this line should work:

forward_propgation_results = Process.forward_propagation(images)

In addition, you must pass the new tf.Graph that you created to the tf.Session constructor so you'll need to move the creation of sess inside that with block as well.

The resulting function will be something like:

def evaluate():
  with tf.Graph().as_default() as g:
    images, labels = Process.eval_inputs(eval_data = eval_data)
    forward_propgation_results = Process.forward_propagation(images)
    init_op = tf.initialize_all_variables()
    saver = tf.train.Saver()
    top_k_op = tf.nn.in_top_k(forward_propgation_results, labels, 1)

  with tf.Session(graph=g) as sess:
    sess.run(init_op)
    saver.restore(sess, eval_dir)
    print(sess.run(top_k_op))

Upvotes: 28

Redfox-Codder
Redfox-Codder

Reputation: 183

Simply, there should be at least one tf.variable that is defined before you create your saver object.

You can get the above code running by adding the following line of code before the saver object definition.

The code that you need to add has come between the two ###.

import tensorflow as tf

import main
import Process
import Input

eval_dir = "/Users/Zanhuang/Desktop/NNP/model.ckpt-30"
checkpoint_dir = "/Users/Zanhuang/Desktop/NNP/checkpoint"

init_op = tf.initialize_all_variables()

### Here Comes the fake variable that makes defining a saver object possible.
_ = tf.Variable(initial_value='fake_variable')

###
saver = tf.train.Saver()
...

Upvotes: 3

P-Gn
P-Gn

Reputation: 24581

Note that since TF 0.11 — a long time ago yet after the currently accepted answer — tf.train.Saver gained a defer_build argument in its constructor that allows you to define variables after it has been constructed. However you now need to call its build member function when all variables have been added, typically just before finilizeing your graph.

saver = tf.train.Saver(defer_build=True)
# build you graph here
saver.build()
graph.finalize()
# now entering training loop

Upvotes: 2

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