tf.autograph.experimental.set_loop_options

Specifies additional arguments to be passed to the enclosing while_loop.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.autograph.experimental.set_loop_options

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.