Multiple graphs

You can create your own graphs separate from the default graph and execute them in a session. However, creating and executing multiple graphs is not recommended, as it has the following disadvantages:

• Creating and using multiple graphs in the same program would require multiple TensorFlow sessions and each session would consume heavy resources
• You cannot directly pass data in between graphs

Hence, the recommended approach is to have multiple subgraphs in a single graph. In case you wish to use your own graph instead of the default graph, you can do so with the tf.graph() command. Here is an example where we create our own graph, g, and execute it as the default graph:

g = tf.Graph()
output = 0

# Assume Linear Model y = w * x + b

with g.as_default():
 # Define model parameters
 w = tf.Variable([.3], tf.float32)
 b = tf.Variable([-.3], tf.float32)
 # Define model input and output
 x = tf.placeholder(tf.float32)
 y = w * x + b

with tf.Session(graph=g) as tfs:
 # initialize and print the variable y
 tf.global_variables_initializer().run()
 output = tfs.run(y,{x:[1,2,3,4]})
 
print('output : ',output)