tf.executing_eagerly

Checks whether the current thread has eager execution enabled.

tf.executing_eagerly()

Used in the notebooks

Used in the tutorials
Text classification with TensorFlow Hub: Movie reviews Neural machine translation with attention Fast Style Transfer for Arbitrary Styles Text Classification with Movie Reviews Graph regularization for sentiment classification using synthesized graphs

Eager execution is enabled by default and this API returns True in most of cases. However, this API might return False in the following use cases.

• Executing inside tf.function, unless under tf.init_scope or tf.config.run_functions_eagerly(True) is previously called.
• Executing inside a transformation function for tf.dataset.
• tf.compat.v1.disable_eager_execution() is called.

General case:

print(tf.executing_eagerly())
True

Inside tf.function:

@tf.function
def fn():
  with tf.init_scope():
    print(tf.executing_eagerly())
  print(tf.executing_eagerly())
fn()
True
False

Inside tf.function after tf.config.run_functions_eagerly(True) is called:

tf.config.run_functions_eagerly(True)
@tf.function
def fn():
  with tf.init_scope():
    print(tf.executing_eagerly())
  print(tf.executing_eagerly())
fn()
True
True
tf.config.run_functions_eagerly(False)

Inside a transformation function for tf.dataset:

def data_fn(x):
  print(tf.executing_eagerly())
  return x
dataset = tf.data.Dataset.range(100)
dataset = dataset.map(data_fn)
False

Returns
True if the current thread has eager execution enabled.