# tf.enable_eager_execution

### Aliases:

• tf.contrib.eager.enable_eager_execution
• tf.enable_eager_execution
tf.enable_eager_execution(
config=None,
device_policy=None,
execution_mode=None
)


Defined in tensorflow/python/framework/ops.py.

Enables eager execution for the lifetime of this program.

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.Session) and return concrete values (as opposed to symbolic references to a node in a computational graph).

For example:

tf.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.ndarrays 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.ConfigProto to use to configure the environment in which operations are executed. Note that tf.ConfigProto is also used to configure graph execution (via tf.Session) and many options within tf.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.