TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.
executing_eagerly() is used check if eager execution is enabled or disabled in current thread. By default eager execution is enabled so in most cases it will return true. This will return false in following cases:
- If it is executing inside tensorflow.function and tf.init_scope or tf.config.experimental_run_functions_eagerly(True) is not called previously.
- Executing inside a transformation function for tensorflow.dataset.
- tensorflow.compat.v1.disable_eager_execution() is called.
Syntax: tensorflow.executing_eagerly()
Parameters: This doesn’t accept any parameters.
Returns: It returns true if eager execution is enabled otherwise it will return false.
Example 1:
Python3
# Importing the libraryimport tensorflow as tf# Checking eager executionres = tf.executing_eagerly()# Printing the resultprint('res: ', res) |
Output:
res: True
Example 2: This example checks eager execution for tensorflow.function with and without init_scope.
Python3
# Importing the libraryimport tensorflow as tf@tf.functiondef gfg(): with tf.init_scope(): # Checking eager execution inside init_scope res = tf.executing_eagerly() print("res 1:", res) # Checking eager execution outside init_scope res = tf.executing_eagerly() print("res 2:", res)gfg() |
Output:
res 1: True res 2: False
