Function map_fn

tensorflow/python/ops/map_fn.py:38–285

map on the list of tensors unpacked from `elems` on dimension 0. The simplest version of `map_fn` 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 `f

(fn, elems, dtype=None, parallel_iterations=None, back_prop=True,
           swap_memory=False, infer_shape=True, name=None)

@tf_export("map_fn")
def map_fn(fn, elems, dtype=None, parallel_iterations=None, 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_fn` 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

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

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

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

  instead.

  When executing eagerly, map_fn does not execute in parallel even if
  `parallel_iterations` is set to a value > 1. You can still get the
  performance benefits of running a function in parallel by using the
  `tf.contrib.eager.defun` decorator,

  ```python
  # Assume the function being used in map_fn is fn.
  # To ensure map_fn calls fn in parallel, use the defun decorator.
  @tf.contrib.eager.defun
  def func(tensor):
    return tf.map_fn(fn, tensor)
  ```

  Note that if you use the defun decorator, any non-TensorFlow Python code
  that you may have written in your function won't get executed. See
  `tf.contrib.eager.defun` for more details. The recommendation would be to