tf.executing_eagerly | TensorFlow v2.16.1 (original) (raw)
tf.executing_eagerly
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Checks whether the current thread has eager execution enabled.
tf.executing_eagerly()
Used in the notebooks
Eager execution is enabled by default and this API returns Truein most of cases. However, this API might return False in the following use cases.
- Executing inside tf.function, unless under tf.init_scope ortf.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. |
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Last updated 2024-04-26 UTC.