View source on GitHub
|
Enables eager execution for the lifetime of this program.
tf.compat.v1.enable_eager_execution(
config=None, device_policy=None, execution_mode=None
) -> None
Migrate to TF2
This function is not necessary if you are using TF2. Eager execution is enabled by default.
| Used in the tutorials |
|---|
Eager execution provides an imperative interface to TensorFlow. With eager
execution enabled, TensorFlow functions execute operations immediately (as
opposed to adding to a graph to be executed later in a tf.compat.v1.Session)
and
return concrete values (as opposed to symbolic references to a node in a
computational graph).
tf.compat.v1.enable_eager_execution()
# After eager execution is enabled, operations are executed as they are
# defined and Tensor objects hold concrete values, which can be accessed as
# numpy.ndarray`s through the numpy() method.
assert tf.multiply(6, 7).numpy() == 42
Eager execution cannot be enabled after TensorFlow APIs have been used to create or execute graphs. It is typically recommended to invoke this function at program startup and not in a library (as most libraries should be usable both with and without eager execution).
Args |
|---|
config
tf.compat.v1.ConfigProto to use to configure the
environment in which operations are executed. Note that
tf.compat.v1.ConfigProto is also used to configure graph execution (via
tf.compat.v1.Session) and many options within tf.compat.v1.ConfigProto
are not implemented (or are irrelevant) when eager execution is enabled.
device_policy
execution_mode
Raises |
|---|
ValueError
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Last updated 2024-04-26 UTC.