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.asserttf.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
releases.
Valid values:
DEVICE_PLACEMENT_EXPLICIT: raises an error if the
placement is not correct.
DEVICE_PLACEMENT_WARN: copies the tensors which are not
on the right device but logs a warning.
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.
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:
SYNC: executes each operation synchronously.
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.