What 's the difference between tf. Session and tf. InteractiveSession?

In TensorFlow, both tf.Session() and tf.InteractiveSession() are used to create a session (Session), but they have some differences in usage:

1. tf.Session()

tf.Session() is the most basic way to create a session in TensorFlow. Typically, when using tf.Session(), you should use the with statement within a session block to ensure the session is properly closed after use. For example:

python
import tensorflow as tf

# Build the computational graph
x = tf.constant([1, 2, 3])
y = tf.constant([4, 5, 6])
z = tf.add(x, y)

# Execute the computational graph within a session
with tf.Session() as sess:
    result = sess.run(z)
    print(result)

In this example, we first define a simple computational graph, then create a session using tf.Session(), and execute sess.run() within the with statement block to compute the result. This approach ensures the session is automatically closed after use.

2. tf.InteractiveSession()

tf.InteractiveSession() provides a more interactive way to use sessions, allowing you to continuously create and run computational graphs while working with TensorFlow. This is particularly useful in interactive environments, such as Jupyter Notebook. When using tf.InteractiveSession(), you can directly use Tensor.eval() and Operation.run() methods without explicitly passing the session object. For example:

python
import tensorflow as tf

# Create an interactive session
sess = tf.InteractiveSession()

# Build the computational graph
x = tf.constant([1, 2, 3])
y = tf.constant([4, 5, 6])
z = tf.add(x, y)

# Directly use eval() to run
print(z.eval())

# Close the session
sess.close()

In this example, we avoid using the with statement and directly create an interactive session, using eval() to compute z immediately. Finally, remember to manually close the session.

Summary

tf.Session() is suitable for traditional scripts and programs, requiring explicit session opening and closing; while tf.InteractiveSession() is better suited for interactive environments, making TensorFlow operations more intuitive and flexible. However, in practice, pay attention to resource management to ensure each session is properly closed and resources are released.