TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It has a huge number of in-built functions that can be used for machine learning applications. There are functions to save and restore the model.

**To save the model:**

**tf.train.Saver**

The saver class used to save and restore variables. Every time we save a model. It forms a checkpoint. From checkpoints, we can restore our model. Saver can automatically number checkpoints with a provided counter.

For Example

v1 = tf.Variable(name='v1')

v2 = tf.Variable(name='v2')

# Pass the variables as a dict:

saver = tf.train.Saver({'v1': v1, 'v2': v2})

# Or pass them as a list.

saver = tf.train.Saver([v1, v2])

# Passing a list is equivalent to passing a dict with the variable op names

# as keys:

saver = tf.train.Saver({v.op.name: v for v in [v1, v2]})

**To restore the model**

The __tf.train.Saver__ object not only saves variables to checkpoint files, but it also restores variables. The __tf.train.Saver.restore__ method is used to restore variables from the checkpoint files:

tf.reset_default_graph()

# Create some variables.

v1 = tf.get_variable("v1", shape=[3])

v2 = tf.get_variable("v2", shape=[5])

# Add ops to save and restore all the variables.

saver = tf.train.Saver()

# Later, launch the model, use the saver to restore variables from disk, and

# do some work with the model.

with tf.Session() as sess:

# Restore variables from disk.

saver.restore(sess, "/tmp/model.ckpt")

print("Model restored.")

# Check the values of the variables

print("v1 : %s" % v1.eval())

print("v2 : %s" % v2.eval())

I hope you will understand TensorFlow save and restore function from this. More you can find __here__**. **

Learn TensorFlow with the help of this comprehensive video tutorial: