Saving a model in tensorflow is pretty easy.

Let's say you have a linear model with input x and want to predict an output y. The loss here is the mean square error (MSE). The batch size is 16.

# Define the model
x = tf.placeholder(tf.float32, [16, 10])  # input
y = tf.placeholder(tf.float32, [16, 1])   # output

w = tf.Variable(tf.zeros([10, 1]), dtype=tf.float32)

res = tf.matmul(x, w)
loss = tf.reduce_sum(tf.square(res - y))

train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

---

Here comes the Saver object, which can have multiple parameters (cf. doc).

# Define the tf.train.Saver object
# (cf. params section for all the parameters)    
saver = tf.train.Saver(max_to_keep=5, keep_checkpoint_every_n_hours=1)

---

Finally we train the model in a tf.Session(), for 1000 iterations. We only save the model every 100 iterations here.

# Start a session
max_steps = 1000
with tf.Session() as sess:
    # initialize the variables
    sess.run(tf.initialize_all_variables())

    for step in range(max_steps):
        feed_dict = {x: np.random.randn(16, 10), y: np.random.randn(16, 1)}  # dummy input
        _, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)

        # Save the model every 100 iterations
        if step % 100 == 0:
            saver.save(sess, "./model", global_step=step)

---

After running this code, you should see the last 5 checkpoints in your directory:

• model-500 and model-500.meta
• model-600 and model-600.meta
• model-700 and model-700.meta
• model-800 and model-800.meta
• model-900 and model-900.meta

Note that in this example, while the saver actually saves both the current values of the variables as a checkpoint and the structure of the graph (*.meta), no specific care was taken w.r.t how to retrieve e.g. the placeholders x and y once the model was restored. E.g. if the restoring is done anywhere else than this training script, it can be cumbersome to retrieve x and y from the restored graph (especially in more complicated models). To avoid that, always give names to your variables / placeholders / ops or think about using tf.collections as shown in one of the remarks.

Restoring is also quite nice and easy.

Here's a handy helper function:

def restore_vars(saver, sess, chkpt_dir):
    """ Restore saved net, global score and step, and epsilons OR
    create checkpoint directory for later storage. """
    sess.run(tf.initialize_all_variables())

    checkpoint_dir = chkpt_dir 

    if not os.path.exists(checkpoint_dir):
        try:
            print("making checkpoint_dir")
            os.makedirs(checkpoint_dir)
            return False
        except OSError:
            raise

    path = tf.train.get_checkpoint_state(checkpoint_dir)
    print("path = ",path)
    if path is None:
        return False
    else:
        saver.restore(sess, path.model_checkpoint_path)
        return True

Main code:

path_to_saved_model = './'
max_steps = 1

# Start a session
with tf.Session() as sess:

    ... define the model here ...

    print("define the param saver")
    saver = tf.train.Saver(max_to_keep=5, keep_checkpoint_every_n_hours=1)

    # restore session if there is a saved checkpoint
    print("restoring model")
    restored = restore_vars(saver, sess, path_to_saved_model)
    print("model restored ",restored)

    # Now continue training if you so choose

    for step in range(max_steps):

        # do an update on the model (not needed)
        loss_value = sess.run([loss])
        # Now save the model
        saver.save(sess, "./model", global_step=step)