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:
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