Function scan

tensorflow/python/ops/functional_ops.py:281–529

scan on the list of tensors unpacked from `elems` on dimension 0. The simplest version of `scan` repeatedly applies the callable `fn` to a sequence of elements from first to last. The elements are made of the tensors unpacked from `elems` on dimension 0. The callable fn takes two tensors as

(fn,
         elems,
         initializer=None,
         parallel_iterations=10,
         back_prop=True,
         swap_memory=False,
         infer_shape=True,
         reverse=False,
         name=None)

@tf_export("scan")
def scan(fn,
         elems,
         initializer=None,
         parallel_iterations=10,
         back_prop=True,
         swap_memory=False,
         infer_shape=True,
         reverse=False,
         name=None):
  """scan on the list of tensors unpacked from `elems` on dimension 0.

  The simplest version of `scan` repeatedly applies the callable `fn` to a
  sequence of elements from first to last. The elements are made of the tensors
  unpacked from `elems` on dimension 0. The callable fn takes two tensors as
  arguments. The first argument is the accumulated value computed from the
  preceding invocation of fn, and the second is the value at the current
  position of `elems`. If `initializer` is None, `elems` must contain at least
  one element, and its first element is used as the initializer.

  Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
  of the result tensor is `[len(values)] + fn(initializer, values[0]).shape`.
  If reverse=True, it's fn(initializer, values[-1]).shape.

  This method also allows multi-arity `elems` and accumulator.  If `elems`
  is a (possibly nested) list or tuple of tensors, then each of these tensors
  must have a matching first (unpack) dimension.  The second argument of
  `fn` must match the structure of `elems`.

  If no `initializer` is provided, the output structure and dtypes of `fn`
  are assumed to be the same as its input; and in this case, the first
  argument of `fn` must match the structure of `elems`.

  If an `initializer` is provided, then the output of `fn` must have the same
  structure as `initializer`; and the first argument of `fn` must match
  this structure.

  For example, if `elems` is `(t1, [t2, t3])` and `initializer` is
  `[i1, i2]` then an appropriate signature for `fn` in `python2` is:
  `fn = lambda (acc_p1, acc_p2), (t1, [t2, t3]):` and `fn` must return a list,
  `[acc_n1, acc_n2]`.  An alternative correct signature for `fn`, and the
   one that works in `python3`, is:
  `fn = lambda a, t:`, where `a` and `t` correspond to the input tuples.

  Args:
    fn: The callable to be performed.  It accepts two arguments.  The first will
      have the same structure as `initializer` if one is provided, otherwise it
      will have the same structure as `elems`.  The second will have the same
      (possibly nested) structure as `elems`.  Its output must have the same
      structure as `initializer` if one is provided, otherwise it must have the
      same structure as `elems`.
    elems: A tensor or (possibly nested) sequence of tensors, each of which will
      be unpacked along their first dimension.  The nested sequence of the
      resulting slices will be the first argument to `fn`.
    initializer: (optional) A tensor or (possibly nested) sequence of tensors,
      initial value for the accumulator, and the expected output type of `fn`.
    parallel_iterations: (optional) The number of iterations allowed to run in
      parallel.
    back_prop: (optional) True enables support for back propagation.