Why do we name variables in Tensorflow?

Question

In some of the places, I saw the syntax, where variables are initialized with names, sometimes without names. For example:

# With name var = tf.Variable(0, name="counter")  # Without one = tf.constant(1) 

What is the point of naming the variable var "counter"?

Answer 1

The name parameter is optional (you can create variables and constants with or without it), and the variable you use in your program does not depend on it. Names can be helpful in a couple of places:

When you want to save or restore your variables (you can save them to a binary file after the computation). From docs:

By default, it uses the value of the Variable.name property for each variable

matrix_1 = tf.Variable([[1, 2], [2, 3]], name="v1") matrix_2 = tf.Variable([[3, 4], [5, 6]], name="v2") init = tf.initialize_all_variables()  saver = tf.train.Saver()  sess = tf.Session() sess.run(init) save_path = saver.save(sess, "/model.ckpt") sess.close() 

Nonetheless you have variables matrix_1, matrix_2 they are saves as v1, v2 in the file.

Also names are used in TensorBoard to nicely show names of edges. You can even group them by using the same scope:

import tensorflow as tf  with tf.name_scope('hidden') as scope:   a = tf.constant(5, name='alpha')   W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0), name='weights')   b = tf.Variable(tf.zeros([1]), name='biases') 

Answer 2

You can imagine Python namespace and TensorFlow namespace as two parallel universes. Names in TensorFlow space are actually the "real" attributes belonging to any TensorFlow variables, while names in Python space are just temporary pointers pointing to TensorFlow variables during this run of your script. That is the reason why when saving and restoring variables, only TensorFlow names are used, because the Python namespace no longer exists after script being terminated, but Tensorflow namespace is still there in your saved files.