How to save a trained tensorflow model for later use for application?

Question

I am a bit of a beginner with tensorflow so please excuse if this is a stupid question and the answer is obvious.

I have created a Tensorflow graph where starting with placeholders for X and y I have optimized some tensors which represent my model. Part of the graph is something where a vector of predictions can be calculated, e.g. for linear regression something like

y_model = tf.add(tf.mul(X,w),d)
y_vals = sess.run(y_model,feed_dict={....})

After training has been completed I have acceptable values for w and d and now I want to save my model for later. Then, in a different python session I want to restore the model so that I can again run

## Starting brand new python session
import tensorflow as tf
## somehow restor the graph and the values here: how????
## so that I can run this:
y_vals = sess.run(y_model,feed_dict={....})

for some different data and get back the y-values.

I want this to work in a way where the graph for calculating the y-values from the placeholders is also stored and restored - as long as the placeholders get fed the correct data, this should work transparently without the user (the one who applies the model) needing to know what the graph looks like).

As far as I understand tf.train.Saver().save(..) only saves the variables but I also want to save the graph. I think that tf.train.export_meta_graph could be relevant here but I do not understand how to use it correctly, the documentation is a bit cryptic to me and the examples do not even use export_meta_graph anywhere.

Answer 1

From the docs, try this:

# Create some variables.
v1 = tf.Variable(..., name="v1")
v2 = tf.Variable(..., name="v2")
...
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()

# Add ops to save and restore all the variables.
saver = tf.train.Saver()

# Later, launch the model, initialize the variables, do some work, save the
# variables to disk.
with tf.Session() as sess:
  sess.run(init_op)
  # Do some work with the model.
  ..
  # Save the variables to disk.
  save_path = saver.save(sess, "/tmp/model.ckpt")
  print("Model saved in file: %s" % save_path)

You can specify the path.

And if you want to restore the model, try:

with tf.Session() as sess:
    saver = tf.train.import_meta_graph('/tmp/model.ckpt.meta')
    saver.restore(sess, "/tmp/model.ckpt")