foldl on the list of tensors unpacked from elems
on dimension 0.
Aliases:
tf.compat.v1.foldl
tf.compat.v2.foldl
tf.foldl(
fn,
elems,
initializer=None,
parallel_iterations=10,
back_prop=True,
swap_memory=False,
name=None
)
This foldl operator 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 fn(initializer, values
[0]).shape. This method also allows multi-arity
elemsand output of
fn. If
elemsis a (possibly nested) list or tuple of tensors, then each of these tensors must have a matching first (unpack) dimension. The signature of
fnmay match the structure of
elems. That is, if
elemsis (
t1, [t2, t3, [t4, t5]]t1, [t2, t3, [t4, t5]]), then an appropriate signature for
fnis:
fn= lambda (
t1, [t2, t3, [t4, t5]]t1, [t2, t3, [t4, t5]]`):.
Args:
fn
: The callable to be performed.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 tofn
.initializer
: (optional) A tensor or (possibly nested) sequence of tensors, as the initial value for the accumulator.parallel_iterations
: (optional) The number of iterations allowed to run in parallel.back_prop
: (optional) True enables support for back propagation.swap_memory
: (optional) True enables GPU-CPU memory swapping.name
: (optional) Name prefix for the returned tensors.
Returns:
A tensor or (possibly nested) sequence of tensors, resulting from applying fn
consecutively to the list of tensors unpacked from elems
, from first to last.
Raises:
TypeError
: iffn
is not callable.
Example:
elems = tf.constant([1, 2, 3, 4, 5, 6])
sum = foldl(lambda a, x: a + x, elems)
# sum == 21