Building Tensorflow Graphs Inside of Functions

There are several options.

Option 1: just pass the name of the tensor instead of the tensor itself.

with tf.Session(graph=graph) as sess:
    feed = {"Placeholder:0": 3}      
    print(sess.run("Add:0", feed_dict=feed))

In this case, it's probably best to give the nodes meaningful names, instead of using the default names as above:

def build_graph():
     g = tf.Graph()
     with g.as_default():
         a = tf.placeholder(tf.int8, name="a")
         b = tf.add(a, tf.constant(1, dtype=tf.int8), name="b")
     return g

graph = build_graph()
with tf.Session(graph=graph) as sess:
     feed = {"a:0": 3}
     print(sess.run("b:0", feed_dict=feed))

Recall that the outputs of an operation named "foo" are tensors named "foo:0", "foo:1", and so on. Most operations have just one output.

Option 2: make your build_graph() function return all the important nodes.

def build_graph():
     g = tf.Graph()
     with g.as_default():
         a = tf.placeholder(tf.int8)
         b = tf.add(a, tf.constant(1, dtype=tf.int8))
     return g, a, b

graph, a, b = build_graph()
with tf.Session(graph=graph) as sess:
     feed = {a: 3}
     print(sess.run(b, feed_dict=feed))

Option 3: add important nodes to a collection

def build_graph():
     g = tf.Graph()
     with g.as_default():
         a = tf.placeholder(tf.int8)
         b = tf.add(a, tf.constant(1, dtype=tf.int8))
     for node in (a, b):
         g.add_to_collection("important_stuff", node)
     return g

graph = build_graph()
a, b = graph.get_collection("important_stuff")
with tf.Session(graph=graph) as sess:
     feed = {a: 3}
     print(sess.run(b, feed_dict=feed))

Option 4: as suggested by @pohe you can use get_tensor_by_name()

def build_graph():
     g = tf.Graph()
     with g.as_default():
         a = tf.placeholder(tf.int8, name="a")
         b = tf.add(a, tf.constant(1, dtype=tf.int8), name="b")
     return g

graph = build_graph()
a, b = [graph.get_tensor_by_name(name) for name in ("a:0", "b:0")]
with tf.Session(graph=graph) as sess:
     feed = {a: 3}
     print(sess.run(b, feed_dict=feed))

I personally use option 2 most often, it's pretty straightforward and doesn't require playing with names. I use option 3 when the graph is large and will live for a long time, because the collection gets saved along with the model, and it's a quick way to document what really matters. I don't really use option 1, because I prefer to have actual references to objects (not sure why). Option 4 is useful when you are working with a graph built by someone else, and they didn't give you direct references to tensors.

Hope this helps!

I'm looking for a better way as well, so my answer is probably not the best. Nevertheless, if you give a and b a name, such as

a = tf.placeholder(tf.int8, name='a')
b = tf.add(a, tf.constant(1, dtype=tf.int8), name='b')

Then you can do

graph = build_graph()

a = graph.get_tensor_by_name('a:0')
b = graph.get_tensor_by_name('b:0')

with tf.Session(graph=graph) as sess:
    feed = {a: 3}      
    print(sess.run(b, feed_dict=feed))

p.s. naming a and b is not necessary. it's just easier to reference later. Also, if you've found a better solution for it, please share it too.