TensorFlow provides several higher order operators to simplify the common map-reduce programming patterns.

`tf.map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True, swap_memory=False, infer_shape=True, name=None)`

map on the list of tensors unpacked from `elems`

on dimension 0.

The simplest version of `map`

repeatedly applies the callable `fn`

to a
sequence of elements from first to last. The elements are made of the
tensors unpacked from `elems`

. `dtype`

is the data type of the return
value of `fn`

. Users must provide `dtype`

if it is different from
the data type of `elems`

.

Suppose that `elems`

is unpacked into `values`

, a list of tensors. The shape
of the result tensor is `[values.shape[0]] + fn(values[0]).shape`

.

This method also allows multi-arity `elems`

and output of `fn`

. If `elems`

is a (possibly nested) list or tuple of tensors, then each of these tensors
must have a matching first (unpack) dimension. The signature of `fn`

may
match the structure of `elems`

. That is, if `elems`

is
`(t1, [t2, t3, [t4, t5]])`

, then an appropriate signature for `fn`

is:
`fn = lambda (t1, [t2, t3, [t4, t5]]):`

.

Furthermore, `fn`

may emit a different structure than its input. For example,
`fn`

may look like: `fn = lambda t1: return (t1 + 1, t1 - 1)`

. In this case,
the `dtype`

parameter is not optional: `dtype`

must be a type or (possibly
nested) tuple of types matching the output of `fn`

.

To apply a functional operation to the nonzero elements of a SparseTensor one of the following methods is recommended. First, if the function is expressible as TensorFlow ops, use

```
result = SparseTensor(input.indices, fn(input.values), input.shape)
```

If, however, the function is not expressible as a TensorFlow op, then use

```
result = SparseTensor(input.indices, map_fn(fn, input.values), input.shape)
```

instead.

##### Args:

: The callable to be performed. It accepts one argument, which will have the same (possibly nested) structure as`fn`

`elems`

. Its output must have the same structure as`dtype`

if one is provided, otherwise it must have the same structure as`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 applied to`elems`

`fn`

.: (optional) The output type(s) of`dtype`

`fn`

. If`fn`

returns a structure of Tensors differing from the structure of`elems`

, then`dtype`

is not optional and must have the same structure as the output of`fn`

.: (optional) The number of iterations allowed to run in parallel.`parallel_iterations`

: (optional) True enables support for back propagation.`back_prop`

: (optional) True enables GPU-CPU memory swapping.`swap_memory`

: (optional) False disables tests for consistent output shapes.`infer_shape`

: (optional) Name prefix for the returned tensors.`name`

##### Returns:

A tensor or (possibly nested) sequence of tensors. Each tensor packs the
results of applying `fn`

to tensors unpacked from `elems`

along the first
dimension, from first to last.

##### Raises:

: if`TypeError`

`fn`

is not callable or the structure of the output of`fn`

and`dtype`

do not match, or if elems is a SparseTensor.: if the lengths of the output of`ValueError`

`fn`

and`dtype`

do not match.

##### Examples:

```
python
elems = np.array([1, 2, 3, 4, 5, 6])
squares = map_fn(lambda x: x * x, elems)
# squares == [1, 4, 9, 16, 25, 36]
```

```
python
elems = (np.array([1, 2, 3]), np.array([-1, 1, -1]))
alternate = map_fn(lambda x: x[0] * x[1], elems, dtype=tf.int64)
# alternate == [-1, 2, -3]
```

```
python
elems = np.array([1, 2, 3])
alternates = map_fn(lambda x: (x, -x), elems, dtype=(tf.int64, tf.int64))
# alternates[0] == [1, 2, 3]
# alternates[1] == [-1, -2, -3]
```

`tf.foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)`

foldl on the list of tensors unpacked from `elems`

on dimension 0.

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. 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`.

##### Args:

: The callable to be performed.`fn`

: A tensor to be unpacked on dimension 0.`elems`

: (optional) The initial value for the accumulator.`initializer`

: (optional) The number of iterations allowed to run in parallel.`parallel_iterations`

: (optional) True enables support for back propagation.`back_prop`

: (optional) True enables GPU-CPU memory swapping.`swap_memory`

: (optional) Name prefix for the returned tensors.`name`

##### Returns:

A tensor resulting from applying `fn`

consecutively to the list of tensors
unpacked from `elems`

, from first to last.

##### Raises:

: if`TypeError`

`fn`

is not callable.

##### Example:

```
python
elems = [1, 2, 3, 4, 5, 6]
sum = foldl(lambda a, x: a + x, elems)
# sum == 21
```

`tf.foldr(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)`

foldr on the list of tensors unpacked from `elems`

on dimension 0.

This foldr operator repeatedly applies the callable `fn`

to a sequence
of elements from last to first. The elements are made of the tensors
unpacked from `elems`

. The callable fn takes two tensors as arguments.
The first argument is the accumulated value computed from the preceding
invocation of fn. 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`

.

##### Args:

: The callable to be performed.`fn`

: A tensor that is unpacked into a sequence of tensors to apply`elems`

`fn`

.: (optional) The initial value for the accumulator.`initializer`

: (optional) The number of iterations allowed to run in parallel.`parallel_iterations`

: (optional) True enables support for back propagation.`back_prop`

: (optional) True enables GPU-CPU memory swapping.`swap_memory`

: (optional) Name prefix for the returned tensors.`name`

##### Returns:

A tensor resulting from applying `fn`

consecutively to the list of tensors
unpacked from `elems`

, from last to first.

##### Raises:

: if`TypeError`

`fn`

is not callable.

##### Example:

```
python
elems = [1, 2, 3, 4, 5, 6]
sum = foldr(lambda a, x: a + x, elems)
# sum == 21
```

`tf.scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, infer_shape=True, 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. 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`

.

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:

: The callable to be performed. It accepts two arguments. The first will have the same structure as`fn`

`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`

.: 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`elems`

`fn`

.: (optional) A tensor or (possibly nested) sequence of tensors, initial value for the accumulator, and the expected output type of`initializer`

`fn`

.: (optional) The number of iterations allowed to run in parallel.`parallel_iterations`

: (optional) True enables support for back propagation.`back_prop`

: (optional) True enables GPU-CPU memory swapping.`swap_memory`

: (optional) False disables tests for consistent output shapes.`infer_shape`

: (optional) Name prefix for the returned tensors.`name`

##### Returns:

A tensor or (possibly nested) sequence of tensors. Each tensor packs the
results of applying `fn`

to tensors unpacked from `elems`

along the first
dimension, and the previous accumulator value(s), from first to last.

##### Raises:

: if`TypeError`

`fn`

is not callable or the structure of the output of`fn`

and`initializer`

do not match.: if the lengths of the output of`ValueError`

`fn`

and`initializer`

do not match.

##### Examples:

```
python
elems = np.array([1, 2, 3, 4, 5, 6])
sum = scan(lambda a, x: a + x, elems)
# sum == [1, 3, 6, 10, 15, 21]
```

```
python
elems = np.array([1, 2, 3, 4, 5, 6])
initializer = np.array(0)
sum_one = scan(
lambda a, x: x[0] - x[1] + a, (elems + 1, elems), initializer)
# sum_one == [1, 2, 3, 4, 5, 6]
```

```
python
elems = np.array([1, 0, 0, 0, 0, 0])
initializer = (np.array(0), np.array(1))
fibonaccis = scan(lambda a, _: (a[1], a[0] + a[1]), elems, initializer)
# fibonaccis == ([1, 1, 2, 3, 5, 8], [1, 2, 3, 5, 8, 13])
```