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sigmoid#

ivy.sigmoid(x, /, *, complex_mode='jax', out=None)[source]#

Apply the sigmoid function element-wise.

Parameters:

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

  • complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') – optional specifier for how to handle complex data types. See ivy.func_wrapper.handle_complex_input for more detail.

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

Return type:

Array

Returns:

ret – an array containing the sigmoid activation of each element in x. sigmoid activation of x is defined as 1/(1+exp(-x)).

Examples

With ivy.Array input:

>>> x = ivy.array([-1.0, 1.0, 2.0])
>>> y = ivy.sigmoid(x)
>>> print(y)
ivy.array([0.2689414 , 0.7310586 , 0.88079703])

>>> x = ivy.array([-1.0, 1.0, 2.0])
>>> y = ivy.zeros(3)
>>> ivy.sigmoid(x, out=y)
>>> print(y)
ivy.array([0.2689414 , 0.7310586 , 0.88079703])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0.]),
...                   b=ivy.Container(c=ivy.array([1.]),
...                                   d=ivy.array([2.])))
>>> y = ivy.sigmoid(x)
>>> print(y)
{
    a: ivy.array([0.5]),
    b: {
        c: ivy.array([0.7310586]),
        d: ivy.array([0.88079703])
    }
}

>>> x = ivy.Container(a=ivy.array([0.]),
...                   b=ivy.Container(c=ivy.array([1.]),
...                                   d=ivy.array([2.])))
>>> y = ivy.Container(a=ivy.array([0.]),
...                   b=ivy.Container(c=ivy.array([0.]),
...                                   d=ivy.array([0.])))
>>> ivy.sigmoid(x, out=y)
>>> print(y)
{
    a: ivy.array([0.5]),
    b: {
        c: ivy.array([0.7310586]),
        d: ivy.array([0.88079703])
    }
}
Array.sigmoid(self, /, *, complex_mode='jax', out=None)[source]#

ivy.Array instance method variant of ivy.sigmoid.

This method simply wraps the function, and so the docstring for ivy.sigmoid also applies to this method with minimal changes.

Parameters:

  • self (Array) – Input array

  • complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') – optional specifier for how to handle complex data types. See ivy.func_wrapper.handle_complex_input for more detail.

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

Return type:

Array

Returns:

ret – an array with the sigmoid activation function applied element-wise.

Examples

>>> x = ivy.array([-1., 1., 2.])
>>> y = x.sigmoid()
>>> print(y)
ivy.array([0.269, 0.731, 0.881])
Container.sigmoid(self, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, complex_mode='jax', out=None)[source]#

ivy.Container instance method variant of ivy.sigmoid. This method simply wraps the function, and so the docstring for ivy.sigmoid 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.

  • complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') – optional specifier for how to handle complex data types. See ivy.func_wrapper.handle_complex_input for more detail.

  • 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 sigmoid unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([-1., 1., 2.]), b=ivy.array([0.5, 0., -0.1]))
>>> y = x.sigmoid()
>>> print(y)
{
    a: ivy.array([0.2689414, 0.7310586, 0.88079703]),
    b: ivy.array([0.62245935, 0.5, 0.4750208])
}
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