Function while_loop_v2

tensorflow/python/ops/control_flow_ops.py:2303–2478

Repeat `body` while the condition `cond` is true. `cond` is a callable returning a boolean scalar tensor. `body` is a callable returning a (possibly nested) tuple, namedtuple or list of tensors of the same arity (length and structure) and types as `loop_vars`. `loop_vars` is a (possibly nes

(cond,
                  body,
                  loop_vars,
                  shape_invariants=None,
                  parallel_iterations=10,
                  back_prop=True,
                  swap_memory=False,
                  maximum_iterations=None,
                  name=None)

# pylint: disable=redefined-outer-name
@tf_export("while_loop", v1=[])
def while_loop_v2(cond,
                  body,
                  loop_vars,
                  shape_invariants=None,
                  parallel_iterations=10,
                  back_prop=True,
                  swap_memory=False,
                  maximum_iterations=None,
                  name=None):
  """Repeat `body` while the condition `cond` is true.

  `cond` is a callable returning a boolean scalar tensor. `body` is a callable
  returning a (possibly nested) tuple, namedtuple or list of tensors of the same
  arity (length and structure) and types as `loop_vars`. `loop_vars` is a
  (possibly nested) tuple, namedtuple or list of tensors that is passed to both
  `cond` and `body`. `cond` and `body` both take as many arguments as there are
  `loop_vars`.

  In addition to regular Tensors or IndexedSlices, the body may accept and
  return TensorArray objects.  The flows of the TensorArray objects will
  be appropriately forwarded between loops and during gradient calculations.

  Note that `while_loop` calls `cond` and `body` *exactly once* (inside the
  call to `while_loop`, and not at all during `Session.run()`). `while_loop`
  stitches together the graph fragments created during the `cond` and `body`
  calls with some additional graph nodes to create the graph flow that
  repeats `body` until `cond` returns false.

  For correctness, `tf.while_loop()` strictly enforces shape invariants for
  the loop variables. A shape invariant is a (possibly partial) shape that
  is unchanged across the iterations of the loop. An error will be raised
  if the shape of a loop variable after an iteration is determined to be more
  general than or incompatible with its shape invariant. For example, a shape
  of [11, None] is more general than a shape of [11, 17], and [11, 21] is not
  compatible with [11, 17]. By default (if the argument `shape_invariants` is
  not specified), it is assumed that the initial shape of each tensor in
  `loop_vars` is the same in every iteration. The `shape_invariants` argument
  allows the caller to specify a less specific shape invariant for each loop
  variable, which is needed if the shape varies between iterations. The
  `tf.Tensor.set_shape`
  function may also be used in the `body` function to indicate that
  the output loop variable has a particular shape. The shape invariant for
  SparseTensor and IndexedSlices are treated specially as follows:

  a) If a loop variable is a SparseTensor, the shape invariant must be
  TensorShape([r]) where r is the rank of the dense tensor represented
  by the sparse tensor. It means the shapes of the three tensors of the
  SparseTensor are ([None], [None, r], [r]). NOTE: The shape invariant here
  is the shape of the SparseTensor.dense_shape property. It must be the shape of
  a vector.

  b) If a loop variable is an IndexedSlices, the shape invariant must be
  a shape invariant of the values tensor of the IndexedSlices. It means
  the shapes of the three tensors of the IndexedSlices are (shape, [shape[0]],
  [shape.ndims]).

  `while_loop` implements non-strict semantics, enabling multiple iterations
  to run in parallel. The maximum number of parallel iterations can be