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, n         


        

Answer 1

Consider the following use case code and its output

def f():
    a = tf.Variable(np.random.normal(), dtype = tf.float32, name = 'test123')

def run123():
    f()
    init = tf.global_variables_initializer()
    with tf.Session() as sess123:
        sess123.run(init)
        print(sess123.run(fetches = ['test123:0']))
        print(sess123.run(fetches = [a]))

run123()

output:

[0.10108799]

NameError Traceback (most recent call last) in () 10 print(sess123.run(fetches = [a])) 11 ---> 12 run123()

in run123() 8 sess123.run(init) 9 print(sess123.run(fetches = ['test123:0'])) ---> 10 print(sess123.run(fetches = [a])) 11 12 run123()

NameError: name 'a' is not defined

The 'a', as defined in the scope of f(), not available outside of its scope, i.e in run123(). But the default graph has to refer to them with something, so that the default graph can be referenced to, as needed, across various scopes and that is when its name comes handy.