Working with multiple graphs in TensorFlow

Question

Can someone explain to me how name_scope works in TensorFlow?

Suppose I have the following code:

Answer 1

I was having similar difficulties dealing with multiple graphs on a IPython notebook. What works for my purposes is to encapsulate each graph and its session in a function. I realize this is more of a hack I guess, I don't know anything about namespaces and I know OP wanted something along those lines. Maybe it will help someone I dunno, you can also pass results between computations.

import tensorflow as tf

def Graph1():
    g1 = tf.Graph()
    with g1.as_default() as g:
        matrix1 = tf.constant([[3., 3.]])
        matrix2 = tf.constant([[2.],[2.]])
        product = tf.matmul( matrix1, matrix2, name = "product")

    with tf.Session( graph = g ) as sess:
        tf.initialize_all_variables().run()
        return product


def Graph2(incoming):
    i = incoming
    g2 = tf.Graph()
    with g2.as_default() as g:
        matrix1 = tf.constant([[4., 4.]])
        matrix2 = tf.constant([[5.],[5.]])
        product = tf.matmul( matrix1, matrix2, name = "product" )

    with tf.Session( graph = g ) as sess:
        tf.initialize_all_variables().run()
        print product
        print i

print Graph1()

Graph2(Graph1())