# tf.map_fn

Transforms `elems` by applying `fn` to each element unstacked on axis 0. (deprecated arguments)

### Used in the notebooks

Used in the guide Used in the tutorials

See also `tf.scan`.

`map_fn` unstacks `elems` on axis 0 to obtain a sequence of elements; calls `fn` to transform each element; and then stacks the transformed values back together.

#### Mapping functions with single-Tensor inputs and outputs

If `elems` is a single tensor and `fn`'s signature is `tf.Tensor->tf.Tensor`, then `map_fn(fn, elems)` is equivalent to `tf.stack([fn(elem) for elem in tf.unstack(elems)])`. E.g.:

````tf.map_fn(fn=lambda t: tf.range(t, t + 3), elems=tf.constant([3, 5, 2]))`
`<tf.Tensor: shape=(3, 3), dtype=int32, numpy=`
`  array([[3, 4, 5],`
`         [5, 6, 7],`
`         [2, 3, 4]], dtype=int32)>`
```

`map_fn(fn, elems).shape = [elems.shape[0]] + fn(elems[0]).shape`.

#### Mapping functions with multi-arity inputs and outputs

`map_fn` also supports functions with multi-arity inputs and outputs:

• If `elems` is a tuple (or nested structure) of tensors, then those tensors must all have the same outer-dimension size (`num_elems`); and `fn` is used to transform each tuple (or structure) of corresponding slices from `elems`. E.g., if `elems` is a tuple `(t1, t2, t3)`, then `fn` is used to transform each tuple of slices `(t1[i], t2[i], t3[i])` (where `0 <= i < num_elems`).

• If `fn` returns a tuple (or nested structure) of tensors, then the result is formed by stacking corresponding elements from those structures.

#### Specifying `fn`'s output signature

If `fn`'s input and output signatures are different, then the output signature must be specified using `fn_output_signature`. (The input and output signatures are differ if their structures, dtypes, or tensor types do not match). E.g.:

````tf.map_fn(fn=tf.strings.length,  # input & output have different dtypes`
`          elems=tf.constant(["hello", "moon"]),`
`          fn_output_signature=tf.int32)`
`<tf.Tensor: shape=(2,), dtype=int32, numpy=array([5, 4], dtype=int32)>`
`tf.map_fn(fn=tf.strings.join,  # input & output have different structures`
`          elems=[tf.constant(['The', 'A']), tf.constant(['Dog', 'Cat'])],`
`          fn_output_signature=tf.string)`
`<tf.Tensor: shape=(2,), dtype=string,`
` numpy=array([b'TheDog', b'ACat'], dtype=object)>`
```

`fn_output_signature` can be specified using any of the following:

#### RaggedTensors

`map_fn` supports `tf.RaggedTensor` inputs and outputs. In particular:

````# Example: RaggedTensor input`
`rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]])`
`tf.map_fn(tf.reduce_sum, rt, fn_output_signature=tf.int32)`
`<tf.Tensor: shape=(4,), dtype=int32, numpy=array([6, 0, 9, 6], dtype=int32)>`
```
````# Example: RaggedTensor output`
`elems = tf.constant([3, 5, 0, 2])`
`tf.map_fn(tf.range, elems,`
`          fn_output_signature=tf.RaggedTensorSpec(shape=[None],`
`                                                  dtype=tf.int32))`
`<tf.RaggedTensor [[0, 1, 2], [0, 1, 2, 3, 4], [], [0, 1]]>`
```

E.g.:

````rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]])`
`tf.ragged.map_flat_values(lambda x: x + 2, rt)`
`<tf.RaggedTensor [[3, 4, 5], [], [6, 7], [8]]>`
```

#### SparseTensors

`map_fn` supports `tf.sparse.SparseTensor` inputs and outputs. In particular:

• If `elems` is a `SparseTensor`, then `fn` will be called with each row of that sparse tensor. In particular, the value passed to `fn` will be a `tf.sparse.SparseTensor` with one fewer dimension than `elems`.

• If the result of `map_fn` should be a `SparseTensor`, then use a `tf.SparseTensorSpec` to specify `fn_output_signature`. The individual `SparseTensor`s returned by `fn` will be stacked into a single `SparseTensor` with one more dimension.

````# Example: SparseTensor input`
`st = tf.sparse.SparseTensor([[0, 0], [2, 0], [2, 1]], [2, 3, 4], [4, 4])`
`tf.map_fn(tf.sparse.reduce_sum, st, fn_output_signature=tf.int32)`
`<tf.Tensor: shape=(4,), dtype=int32, numpy=array([2, 0, 7, 0], dtype=int32)>`
```
````# Example: SparseTensor output`
`tf.sparse.to_dense(`
`    tf.map_fn(tf.sparse.eye, tf.constant([2, 3]),`
`              fn_output_signature=tf.SparseTensorSpec(None, tf.float32)))`
`<tf.Tensor: shape=(2, 3, 3), dtype=float32, numpy=`
`  array([[[1., 0., 0.],`
`          [0., 1., 0.],`
`          [0., 0., 0.]],`
`         [[1., 0., 0.],`
`          [0., 1., 0.],`
`          [0., 0., 1.]]], dtype=float32)>`
```
• If the function is expressible as TensorFlow ops, use:

``````tf.sparse.SparseTensor(st.indices, fn(st.values), st.dense_shape)
``````
• Otherwise, use:

``````tf.sparse.SparseTensor(st.indices, tf.map_fn(fn, st.values),
st.dense_shape)
``````

#### `map_fn` vs. vectorized operations

`map_fn` will apply the operations used by `fn` to each element of `elems`, resulting in `O(elems.shape[0])` total operations. This is somewhat mitigated by the fact that `map_fn` can process elements in parallel. However, a transform expressed using `map_fn` is still typically less efficient than an equivalent transform expressed using vectorized operations.

`map_fn` should typically only be used if one of the following is true:

• It is difficult or expensive to express the desired transform with vectorized operations.
• `fn` creates large intermediate values, so an equivalent vectorized transform would take too much memory.
• Processing elements in parallel is more efficient than an equivalent vectorized transform.
• Efficiency of the transform is not critical, and using `map_fn` is more readable.

E.g., the example given above that maps `fn=lambda t: tf.range(t, t + 3)` across `elems` could be rewritten more efficiently using vectorized ops:

````elems = tf.constant([3, 5, 2])`
`tf.range(3) + tf.expand_dims(elems, 1)`
`<tf.Tensor: shape=(3, 3), dtype=int32, numpy=`
`  array([[3, 4, 5],`
`         [5, 6, 7],`
`         [2, 3, 4]], dtype=int32)>`
```

In some cases, `tf.vectorized_map` can be used to automatically convert a function to a vectorized eqivalent.

#### Eager execution

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.function` decorator:

````fn=lambda t: tf.range(t, t + 3)`
`@tf.function`
`def func(elems):`
`  return tf.map_fn(fn, elems, parallel_iterations=3)`
`func(tf.constant([3, 5, 2]))`
`<tf.Tensor: shape=(3, 3), dtype=int32, numpy=`
`  array([[3, 4, 5],`
`         [5, 6, 7],`
`         [2, 3, 4]], dtype=int32)>`
```

`fn` The callable to be performed. It accepts one argument, which will have the same (possibly nested) structure as `elems`. Its output must have the same structure as `fn_output_signature` 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 unstacked along their first dimension. `fn` will be applied to the nested sequence of the resulting slices. `elems` may include ragged and sparse tensors. `elems` must consist of at least one tensor.
`dtype` Deprecated: Equivalent to `fn_output_signature`.
`parallel_iterations` (optional) The number of iterations allowed to run in parallel. When graph building, the default value is 10. While executing eagerly, the default value is set to 1.
`back_prop` (optional) Deprecated: prefer using `tf.stop_gradient` instead. False disables support for back propagation.
`swap_memory` (optional) True enables GPU-CPU memory swapping.
`infer_shape` (optional) False disables tests for consistent output shapes.
`name` (optional) Name prefix for the returned tensors.
`fn_output_signature` The output signature of `fn`. Must be specified if `fn`'s input and output signatures are different (i.e., if their structures, dtypes, or tensor types do not match). `fn_output_signature` can be specified using any of the following:

A tensor or (possibly nested) sequence of tensors. Each tensor stacks the results of applying `fn` to tensors unstacked from `elems` along the first dimension, from first to last. The result may include ragged and sparse tensors.

`TypeError` if `fn` is not callable or the structure of the output of `fn` and `fn_output_signature` do not match.
`ValueError` if the lengths of the output of `fn` and `fn_output_signature` do not match, or if the `elems` does not contain any tensor.

#### Examples:

````elems = np.array([1, 2, 3, 4, 5, 6])`
`tf.map_fn(lambda x: x * x, elems)`
`<tf.Tensor: shape=(6,), dtype=int64, numpy=array([ 1,  4,  9, 16, 25, 36])>`
```
````elems = (np.array([1, 2, 3]), np.array([-1, 1, -1]))`
`tf.map_fn(lambda x: x[0] * x[1], elems, fn_output_signature=tf.int64)`
`<tf.Tensor: shape=(3,), dtype=int64, numpy=array([-1,  2, -3])>`
```
````elems = np.array([1, 2, 3])`
`tf.map_fn(lambda x: (x, -x), elems,`
`         fn_output_signature=(tf.int64, tf.int64))`
`(<tf.Tensor: shape=(3,), dtype=int64, numpy=array([1, 2, 3])>,`
` <tf.Tensor: shape=(3,), dtype=int64, numpy=array([-1, -2, -3])>)`
```