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leaky_relu#

ivy.leaky_relu(x, /, *, alpha=0.2, complex_mode='jax', out=None)[source]#

Apply the leaky rectified linear unit function element-wise.

If the input is complex, then by default each element is scaled by alpha if either its real part is strictly negative or if its real part is zero and its imaginary part is negative. This behaviour can be changed by specifying a different complex_mode.

Parameters:

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

  • alpha (float, default: 0.2) – Negative slope for ReLU.

  • 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 inputs broadcast to.

Return type:

Array

Returns:

ret – The input array with leaky relu applied element-wise.

Examples

With ivy.Array input:

>>> x = ivy.array([0.39, -0.85])
>>> y = ivy.leaky_relu(x)
>>> print(y)
ivy.array([ 0.39, -0.17])

>>> x = ivy.array([1.5, 0.7, -2.4])
>>> y = ivy.zeros(3)
>>> ivy.leaky_relu(x, out=y)
>>> print(y)
ivy.array([ 1.5 ,  0.7 , -0.48])

>>> x = ivy.array([[1.1, 2.2, 3.3],
...                [-4.4, -5.5, -6.6]])
>>> ivy.leaky_relu(x, out=x)
>>> print(x)
ivy.array([[ 1.1 ,  2.2 ,  3.3 ],
   [-0.88, -1.1 , -1.32]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0.0, -1.2]), b=ivy.array([0.4, -0.2]))
>>> x = ivy.leaky_relu(x, out=x)
>>> print(x)
{
    a: ivy.array([0., -0.24000001]),
    b: ivy.array([0.40000001, -0.04])
}
Array.leaky_relu(self, /, *, alpha=0.2, complex_mode='jax', out=None)[source]#

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

Parameters:

  • self (Array) – input array.

  • alpha (float, default: 0.2) – the slope of the negative section.

  • 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 inputs broadcast to.

Return type:

Array

Returns:

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

Examples

>>> x = ivy.array([0.39, -0.85])
>>> y = x.leaky_relu()
>>> print(y)
ivy.array([ 0.39, -0.17])
Container.leaky_relu(self, /, *, alpha=0.2, 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.leaky_relu. This method simply wraps the function, and so the docstring for ivy.leaky_relu also applies to this method with minimal changes.

Parameters:

  • self (Container) – input container.

  • alpha (Container, default: 0.2) – array or scalar specifying the negative slope.

  • 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 leaky relu unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([0.39, -0.85]), b=ivy.array([1., -0.2]))
>>> y = x.leaky_relu()
>>> print(y)
{
    a: ivy.array([0.38999999, -0.17]),
    b: ivy.array([1., -0.04])
}
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