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

You have 3 graphs , not 2 graphs ( g1 or g2 ) .

You can see 3 graphs by id()

...

print 'g1           ', id(g1)
print 'g2           ', id(g2)
print 'current graph', id ( tf.get_default_graph() )

with tf.Session( graph = g1 ) as sess:
    result = sess.run( product )
    print( result )

Using "with g1.as_default(): " make the same error

...

with g1.as_default() :
    with tf.Session( graph = g1 ) as sess:
        result = sess.run( product )
        print( result )

Because, you have two 'product' , not one.

g1 = tf.Graph()
with g1.as_default() as g:
    ...
    print 'g1 product', id ( product )

...

g2 = tf.Graph()
with g2.as_default() as g:
    ...
    print 'g2 product', id ( product )

with tf.Session( graph = g1 ) as sess:
    print 'last product', id(product)
    ...

last product == g2 product

...
    product = tf.matmul(matrix1, matrix2, name='g1_product')
...
with g1.as_default() as g:
    with tf.Session() as sess:
        product = g.get_tensor_by_name(  "g1_product:0" )
        result = sess.run( product )
        print( result )

Above code work.

But, two variables with the same name ( product )

Encapsulating with class is good?