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Enables eager execution for the lifetime of this program.
`tf.contrib.eager.enable_eager_execution`Compat aliases for migration
See Migration guide for more details.
tf.enable_eager_execution( config=None, device_policy=None, execution_mode=None )
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
return concrete values (as opposed to symbolic references to a node in a
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).
(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
(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.
||If eager execution is enabled after creating/executing a TensorFlow graph, or if options provided conflict with a previous call to this function.|