Stay organized with collections Save and categorize content based on your preferences.

View source on GitHub

Specifies additional arguments to be passed to the enclosing while_loop.

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.


def f():
  n = 0
  for i in tf.range(10):
    n += 1
  return n
def f():
  v = tf.constant((0,))
  for i in tf.range(3):
        shape_invariants=[(v, tf.TensorShape([None]))]
    v = tf.concat((v, [i]), 0)
  return v

Also see tf.while_loop.

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.