description: Specifies additional arguments to be passed to the enclosing while_loop.

tf.autograph.experimental.set_loop_options

Specifies additional arguments to be passed to the enclosing while_loop.

tf.autograph.experimental.set_loop_options(
    parallel_iterations=UNSPECIFIED, swap_memory=UNSPECIFIED,
    maximum_iterations=UNSPECIFIED, shape_invariants=UNSPECIFIED
)

The parameters apply to and only to the immediately enclosing loop. It only has effect if the loop is staged as a TF while_loop; otherwise the parameters have no effect.

Usage:

>>> @tf.function(autograph=True)
... def f():
...   n = 0
...   for i in tf.range(10):
...     tf.autograph.experimental.set_loop_options(maximum_iterations=3)
...     n += 1
...   return n

>>> @tf.function(autograph=True)
... def f():
...   v = tf.constant((0,))
...   for i in tf.range(3):
...     tf.autograph.experimental.set_loop_options(
...         shape_invariants=[(v, tf.TensorShape([None]))]
...     )
...     v = tf.concat((v, [i]), 0)
...   return v

Also see tf.while_loop.

Args

parallel_iterations The maximum number of iterations allowed to run in parallel at any given time. Note that this does not guarantee parallel execution.
swap_memory Whether to store intermediate values needed for gradients on the CPU instead of GPU.
maximum_iterations Allows limiting the total number of iterations executed by the loop.
shape_invariants Allows controlling the argument with the same name passed to tf.while_loop. Unlike tf.while_loop, this is a list of (tensor, shape) pairs.