tf.wrap_function(
fn,
signature,
name=None
)
Defined in tensorflow/python/eager/wrap_function.py
.
Wraps the TF 1.x function fn into a graph function.
The python function fn
will be called once with symbolic arguments specified
in the signature
, traced, and turned into a graph function. Any variables
created by fn
will be owned by the object returned by wrap_function
. The
resulting graph function can be called with tensors which match the
signature.
def f(x, do_add):
v = tf.Variable(5.0)
if do_add:
op = v.assign_add(x)
else:
op = v.assign_sub(x)
with tf.control_dependencies([op]):
return v.read_value()
f_add = tf.compat.v1.wrap_function(f, [tf.TensorSpec((), tf.float32), True])
assert float(f_add(1.0)) == 6.0
assert float(f_add(1.0)) == 7.0
# Can call tf.compat.v1.wrap_function again to get a new trace, a new set
# of variables, and possibly different non-template arguments.
f_sub= tf.compat.v1.wrap_function(f, [tf.TensorSpec((), tf.float32), False])
assert float(f_sub(1.0)) == 4.0
assert float(f_sub(1.0)) == 3.0
Both tf.compat.v1.wrap_function
and tf.function
create a callable
TensorFlow graph. But while tf.function
runs all stateful operations
(e.g. tf.print
) and sequences operations to provide the same semantics as
eager execution, wrap_function
is closer to the behavior of session.run
in
TensorFlow 1.x. It will not run any operations unless they are required to
compute the function's outputs, either through a data dependency or a control
dependency. Nor will it sequence operations.
Unlike tf.function
, wrap_function
will only trace the Python function
once. As with placeholders in TF 1.x, shapes and dtypes must be provided to
wrap_function
's signature
argument.
Since it is only traced once, variables and state may be created inside the function and owned by the function wrapper object.
Args:
fn
: python function to be wrappedsignature
: the placeholder and python arguments to be passed to the wrapped functionname
: Optional. The name of the function.
Returns:
the wrapped graph function.