tf.compat.v1.enable_control_flow

Enables eager execution for the lifetime of this program.

tf.compat.v1.enable_eager_execution(
    config=None, device_policy=None, execution_mode=None
)

Used in the notebooks

Used in the tutorials
TensorFlow Constrained Optimization Example Using CelebA Dataset Credit Card Interest TensorFlow Distributions: A Gentle Introduction Use XLA with tf.function

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).

For example:

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`	(Optional.) A `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`	(Optional.) Policy controlling how operations requiring inputs on a specific device (e.g., a GPU 0) handle inputs on a different device (e.g. GPU 1 or CPU). When set to None, an appropriate value will be picked automatically. The value picked may change between TensorFlow releases. Valid values: tf.contrib.eager.DEVICE_PLACEMENT_EXPLICIT: raises an error if the placement is not correct. tf.contrib.eager.DEVICE_PLACEMENT_WARN: copies the tensors which are not on the right device but logs a warning. tf.contrib.eager.DEVICE_PLACEMENT_SILENT: silently copies the tensors. Note that this may hide performance problems as there is no notification provided when operations are blocked on the tensor being copied between devices. tf.contrib.eager.DEVICE_PLACEMENT_SILENT_FOR_INT32: silently copies int32 tensors, raising errors on the other ones.
`execution_mode`	(Optional.) Policy controlling how operations dispatched are actually executed. When set to None, an appropriate value will be picked automatically. The value picked may change between TensorFlow releases. Valid values: tf.contrib.eager.SYNC: executes each operation synchronously. tf.contrib.eager.ASYNC: executes each operation asynchronously. These operations may return "non-ready" handles.

Raises
`ValueError`	If eager execution is enabled after creating/executing a TensorFlow graph, or if options provided conflict with a previous call to this function.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.enable_control_flow_v2

Used in the notebooks

For example:

Args

Raises