How to work with multiple graphs in TensorFlow

In TensorFlow, handling multiple graphs typically occurs when you need to build multiple independent models within the same program. A TensorFlow graph is a collection of operations organized as nodes, which can be executed within a session. Each graph is independent, possessing its own variables, operations, collections, etc. The key to handling multiple graphs is to properly manage each graph and session to ensure they do not interfere with each other.

How to Create and Manage Multiple Graphs

1. Create Multiple Graphs: You can create multiple instances of tf.Graph to manage different models.

python
import tensorflow as tf

# Create the first graph
graph1 = tf.Graph()
with graph1.as_default():
    x = tf.constant(8, name='x_const')
    y = tf.constant(5, name='y_const')
    my_sum = tf.add(x, y, name='x_y_sum')

# Create the second graph
graph2 = tf.Graph()
with graph2.as_default():
    a = tf.constant(15, name='a_const')
    b = tf.constant(10, name='b_const')
    my_product = tf.multiply(a, b, name='a_b_product')

1. Run Graphs in Sessions: Each graph must be run within its own tf.Session to avoid conflicts.

python
# Run the first graph in a session
with tf.Session(graph=graph1) as session:
    print("Sum of x and y:", session.run(my_sum))

# Run the second graph in another session
with tf.Session(graph=graph2) as session:
    print("Product of a and b:", session.run(my_product))

Use Case

Suppose you are responsible for two parts in a machine learning project: one for image classification using a convolutional neural network (CNN), and the other for time series prediction using a recurrent neural network (RNN). Since these models differ significantly in structure and data, you can create separate graphs for each model, ensuring they do not share any variables or operations, making the project more modular and easier to manage.

Key Points

• Ensure Operations are in the Correct Graph: Use with graph.as_default(): to ensure your operations are defined within the correct graph.
• Session Management: Each graph should be run within its own session to ensure computations of one graph are not affected by sessions of other graphs.
• Resource Management: Each graph and session consumes system resources, including memory and computational resources; improper management can lead to resource wastage or contention.

By following this approach, you can effectively manage multiple independent models within the same project, each with its own graph and session, ensuring isolation and correct execution.