# softshrink#

ivy.softshrink(x, /, *, lambd=0.5, out=None)[source]#

Apply the softshrink function element-wise.

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

• lambd (`float`, default: `0.5`) – the value of the lower bound of the linear region range.

• 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 softshrink activation of each element in `x`.

Examples

With `ivy.Array` input: >>> x = ivy.array([-1.0, 1.0, 2.0]) >>> y = ivy.softshrink(x) >>> print(y) ivy.array([-0.5, 0.5, 1.5])

```>>> x = ivy.array([-1.0, 1.0, 2.0])
>>> y = x.softshrink()
>>> print(y)
ivy.array([-0.5,  0.5,  1.5])
```
```>>> x = ivy.array([[-1.3, 3.8, 2.1], [1.7, 4.2, -6.6]])
>>> y = ivy.softshrink(x)
>>> print(y)
ivy.array([[-0.79999995,  3.29999995,  1.59999991],
[ 1.20000005,  3.69999981, -6.0999999 ]])
```
Array.softshrink(self, /, *, lambd=0.5, out=None)[source]#

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

Parameters:
• self (`Array`) – input array.

• lambd (`float`, default: `0.5`) – the value of the lower bound of the linear region range.

• 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 with the softshrink activation function applied element-wise.

Examples

```>>> x = ivy.array([-1., 0., 1.])
>>> y = x.softshrink()
>>> print(y)
ivy.array([-0.5,  0. ,  0.5])
>>> x = ivy.array([-1., 0., 1.])
>>> y = x.softshrink(lambd=1.0)
>>> print(y)
ivy.array([0., 0., 0.])
```
Container.softshrink(self, /, *, lambd=0.5, key_chains=None, to_apply=False, prune_unapplied=True, map_sequences=False, out=None)[source]#

Apply the soft shrinkage function element-wise.

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

• lambd (`Container`, default: `0.5`) – Lambda value for soft shrinkage calculation.

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

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

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

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

• 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 – Container with soft shrinkage applied to the leaves.

Examples

```>>> import ivy.numpy as np
>>> x = ivy.Container(a=np.array([1., -2.]), b=np.array([0.4, -0.2]))
>>> y = ivy.Container.softshrink(x)
>>> print(y)
{
a: ivy.array([0.5, -1.5]),
b: ivy.array([0., 0.])
}
```