# selu#

ivy.selu(x, /, *, out=None)[source]#

Apply the scaled exponential linear unit function element-wise.

Parameters:
• x (`Union`[`Array`, `NativeArray`]) – input array

• out (`Optional`[`Array`], default: `None`) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Array`

Returns:

ret – an array containing the scaled exponential linear unit activation of each element in `x`.

Examples

With `ivy.Array` input: >>> x = ivy.array([-1., 0., 1., 2., 3., 4., 5., 6., 7.]) >>> y = ivy.selu(x) >>> print(y) ivy.array([-1.11133075, 0. , 1.05070102, 2.10140204, 3.15210295,

4.20280409, 5.25350523, 6.30420589, 7.35490704])

```>>> x = ivy.array([-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.])
>>> y = ivy.zeros(9)
>>> ivy.selu(x, out = y)
>>> print(y)
ivy.array([-1.11133075,  0.        ,  1.05070102,  2.10140204,  3.15210295,
4.20280409,  5.25350523,  6.30420589,  7.35490704])
```

With `ivy.Container` input: >>> x = ivy.Container(a=ivy.array([-3., -2., -1., 0., 1., 2., 3., 4., 5.]), … b=ivy.array([1., 2., 3., 4., 5., 6., 7., 8., 9.]) … ) >>> x = ivy.selu(x, out=x) >>> print(x) {

a: ivy.array([-1.6705687, -1.52016652, -1.11133075, 0., 1.05070102,

2.10140204, 3.15210295, 4.20280409, 5.25350523]),

b: ivy.array([1.05070102, 2.10140204, 3.15210295, 4.20280409, 5.25350523,

6.30420589, 7.35490704, 8.40560818, 9.45630932])

}

Array.selu(self, /, *, out=None)[source]#

Apply the scaled exponential linear unit function element-wise.

Parameters:
• self – input array

• out (`Optional`[`Array`], default: `None`) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Array`

Returns:

ret – an array containing the scaled exponential linear unit activation of each element in input.

Examples

With `ivy.Array` input:

```>>> x = ivy.array([-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.])
>>> y = x.selu()
>>> print(y)
ivy.array([-1.11133075,  0.,  1.05070102,  2.10140204,  3.15210295,
4.20280409,  5.25350523,  6.30420589,  7.35490704])
```
```>>> x = ivy.array([-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.])
>>> y = ivy.zeros(9)
>>> x.selu(out = y)
>>> print(y)
ivy.array([-1.11133075,  0.,  1.05070102,  2.10140204,  3.15210295,
4.20280409,  5.25350523,  6.30420589,  7.35490704])
```
Container.selu(self, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container instance method variant of ivy.selu. This method simply wraps the function, and so the docstring for ivy.selu also applies to this method with minimal changes.

Parameters:
• self (`Container`) – input container.

• key_chains (`Optional`[`Union`[`List`[`str`], `Dict`[`str`, `str`], `Container`]], default: `None`) – The key-chains to apply or not apply the method to. Default is `None`.

• to_apply (`Union`[`bool`, `Container`], default: `True`) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is `True`.

• prune_unapplied (`Union`[`bool`, `Container`], default: `False`) – Whether to prune key_chains for which the function was not applied. Default is `False`.

• map_sequences (`Union`[`bool`, `Container`], default: `False`) – Whether to also map method to sequences (lists, tuples). Default is `False`.

• out (`Optional`[`Container`], default: `None`) – optional output container, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Container`

Returns:

ret – a container with the scaled exponential linear unit activation function applied element-wise.

Examples

```>>> x = ivy.Container(a=ivy.array([1.0, -1.2]), b=ivy.array([0.4, -0.2]))
>>> y = x.selu()
>>> print(y)
{
a: ivy.array([1.05070102, -1.22856998]),
b: ivy.array([0.42028043, -0.31868932])
}
```