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Activations#

class ivy.data_classes.container.activations._ContainerWithActivations(dict_in=None, queues=None, queue_load_sizes=None, container_combine_method='list_join', queue_timeout=None, print_limit=10, key_length_limit=None, print_indent=4, print_line_spacing=0, ivyh=None, default_key_color='green', keyword_color_dict=None, rebuild_child_containers=False, types_to_iteratively_nest=None, alphabetical_keys=True, dynamic_backend=None, build_callable=False, **kwargs)[source]#

Bases: ContainerBase

_abc_impl = <_abc._abc_data object>#
static _static_gelu(x, /, *, approximate=False, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, complex_mode='jax', out=None)[source]#

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

Parameters:

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

  • approximate (Union[bool, Container], default: False) – whether to use the gelu approximation algorithm or exact formulation.

  • 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 gelu unit function applied element-wise.

Examples

>>> x = ivy.Container(a =ivy.array([0.3, -0.1]))
>>> y = ivy.Container.static_gelu(x)
>>> print(y)
{
     a: ivy.array([0.185, -0.046])
}
static _static_hardswish(x, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, complex_mode='jax', out=None)[source]#

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

Parameters:

  • x (Union[Array, NativeArray, 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 hardswish activation function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([-3., 4., 5.]), b=ivy.array([0., 5.]))
>>> x = ivy.hardswish(x, out=x)
>>> x
{
    a: ivy.array([-0.,  4.,  5.]),
    b: ivy.array([0., 5.])
}
static _static_leaky_relu(x, /, *, alpha=0.2, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, complex_mode='jax', out=None)[source]#

ivy.Container static 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:

  • x (Union[Array, NativeArray, 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 = x = ivy.Container(a=ivy.array([0.39, -0.85]), b=ivy.array([1., -0.2]))
>>> y = ivy.Container.static_leaky_relu(x)
>>> print(y)
{
    a: ivy.array([0.38999999, -0.17]),
    b: ivy.array([1., -0.04])
}
static _static_log_softmax(x, /, *, axis=-1, complex_mode='jax', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

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

Parameters:

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

  • axis (Optional[Container], default: -1) – the axis or axes along which the log_softmax should be computed

  • 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.

  • 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 log_softmax unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([-1.0, -0.98, 2.3]))
>>> y = ivy.Container.static_log_softmax(x)
>>> print(y)
{
    a: ivy.array([-3.37, -3.35, -0.0719])
}

>>> x = ivy.Container(a=ivy.array([1.0, 2.4]), b=ivy.array([-0.2, -1.0]))
>>> y = ivy.Container.static_log_softmax(x)
>>> print(y)
{
    a: ivy.array([-1.62, -0.22]),
    b: ivy.array([-0.371, -1.17])
}
static _static_mish(x, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, complex_mode='jax', out=None)[source]#

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

Parameters:

  • x (Union[Array, NativeArray, 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 rectified linear activation unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([1.0, -1.2]), b=ivy.array([0.4, -0.2]))
>>> y = ivy.Container.static_mish(x)
>>> print(y)
{
    a: ivy.array([0.86509842, -0.30883577]),
    b: ivy.array([0.28903052, -0.10714479])
}
static _static_relu(x, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, complex_mode='jax', out=None)[source]#

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

Parameters:

  • x (Union[Array, NativeArray, 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 rectified linear activation unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([1.0, -1.2]), b=ivy.array([0.4, -0.2]))
>>> y = ivy.Container.static_relu(x)
>>> print(y)
{
    a: ivy.array([1., 0.]),
    b: ivy.array([0.40000001, 0.])
}
static _static_sigmoid(x, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, complex_mode='jax', out=None)[source]#

ivy.Container static 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:

  • x (Union[Array, NativeArray, 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

>>> ivy.Container(a=ivy.array([-1., 1., 2.]), b=ivy.array([0.5, 0., -0.1]))
>>> y = ivy.Container.static_sigmoid(x)
>>> print(y)
{
    a: ivy.array([0.2689414, 0.7310586, 0.88079703]),
    b: ivy.array([0.62245935, 0.5, 0.4750208])
}
static _static_softmax(x, /, *, axis=None, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, complex_mode='jax', out=None)[source]#

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

Parameters:

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

  • axis (Optional[Container], default: None) – the axis or axes along which the softmax should be computed

  • 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 softmax unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([1.0, 0]), b=ivy.array([1.3, 0, -1.0]))
>>> y = ivy.Container.static_softmax(x)
>>> print(y)
{
    a: ivy.array([0.7310586, 0.2689414]),
    b: ivy.array([0.72844321, 0.19852395, 0.07303288])
}
static _static_softplus(x, /, *, beta=None, threshold=None, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, complex_mode='jax', out=None)[source]#

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

Parameters:

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

  • beta (Optional[Union[int, float, Container]], default: None) – The beta value for the softplus formation. Default: None.

  • threshold (Optional[Union[int, float, Container]], default: None) – values above this revert to a linear function. Default: None.

  • 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 softplus unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([-0.3461, -0.6491]), b=ivy.array([1., 0.]))
>>> y = ivy.Container.static_softplus(x)
>>> print(y)
{
    a: ivy.array([0.53499615, 0.42036411]),
    b: ivy.array([1.31326175, 0.69314718])
}

>>> x = ivy.Container(a=ivy.array([-1., 2., 4.]))
>>> y = ivy.Container.static_softplus(x, beta=0.5, threshold=2)
>>> print(y)
{
    a: ivy.array([0.948, 2.63, 4.25])
}
gelu(*, approximate=False, 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.gelu. This method simply wraps the function, and so the docstring for ivy.gelu also applies to this method with minimal changes.

Parameters:

  • self (Container) – input container.

  • approximate (Union[bool, Container], default: False) – whether to use the gelu approximation algorithm or exact formulation.

  • 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 gelu unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([1., 2.]), b=ivy.array([-0.9, -1.]))
>>> y = x.gelu()
    print(y)
    {
         a: ivy.array([0.841, 1.95]),
         b: ivy.array([-0.166, -0.159])
    }
hardswish(*, 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.hardswish. This method simply wraps the function, and so the docstring for ivy.hardswish 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 hardswish activation function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([-3., 4., 5.]), b=ivy.array([0., 5.]))
>>> x = ivy.hardswish(x, out=x)
>>> x
{
    a: ivy.array([-0.,  4.,  5.]),
    b: ivy.array([0., 5.])
}
leaky_relu(*, 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])
}
log_softmax(*, axis=-1, complex_mode='jax', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

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

Parameters:

  • self (Container) – input container.

  • axis (Optional[Container], default: -1) – the axis or axes along which the log_softmax should be computed

  • 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.

  • 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.

Returns:

ret – a container with the log_softmax unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([-1.0, -0.98, 2.3]))
>>> y = x.log_softmax()
>>> print(y)
{
    a: ivy.array([-3.37, -3.35, -0.0719])
}

>>> x = ivy.Container(a=ivy.array([1.0, 2.4]), b=ivy.array([-0.2, -1.0]))
>>> y = x.log_softmax()
>>> print(y)
{
    a: ivy.array([-1.62, -0.22]),
    b: ivy.array([-0.371, -1.17])
}
mish(*, 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.mish. This method simply wraps the function, and so the docstring for ivy.mish 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 rectified linear activation unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([1.0, -1.2]), b=ivy.array([0.4, -0.2]))
>>> y = x.mish()
>>> print(y)
{
    a: ivy.array([0.86509842, -0.30883577]),
    b: ivy.array([0.28903052, -0.10714479])
}
relu(*, 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.relu. This method simply wraps the function, and so the docstring for ivy.relu 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 rectified linear activation unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([1.0, -1.2]), b=ivy.array([0.4, -0.2]))
>>> y = x.relu()
>>> print(y)
{
    a: ivy.array([1., 0.]),
    b: ivy.array([0.40000001, 0.])
}
sigmoid(*, 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])
}
softmax(*, axis=None, key_chains=None, to_apply=True, prune_unapplied=False, complex_mode='jax', map_sequences=False, out=None)[source]#

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

Parameters:

  • self (Container) – input container.

  • axis (Optional[Container], default: None) – the axis or axes along which the softmax should be computed

  • 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 softmax unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([1.0, 0]), b=ivy.array([1.3, 0, -1.0]))
>>> y = x.softmax()
>>> print(y)
{
    a: ivy.array([0.7310586, 0.2689414]),
    b: ivy.array([0.72844321, 0.19852395, 0.07303288])
}
softplus(*, beta=None, threshold=None, 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.softplus. This method simply wraps the function, and so the docstring for ivy.softplus also applies to this method with minimal changes.

Parameters:

  • self (Container) – input container.

  • beta (Optional[Union[int, float, Container]], default: None) – The beta value for the softplus formation. Default: None.

  • threshold (Optional[Union[int, float, Container]], default: None) – values above this revert to a linear function. Default: None.

  • 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 softplus unit function applied element-wise.

Examples

>>> x = ivy.Container(a=ivy.array([-0.3461, -0.6491]))
>>> y = x.softplus()
>>> print(y)
{
    a: ivy.array([0.535, 0.42])
}

>>> x = ivy.Container(a=ivy.array([-1., 2., 4.]))
>>> y = x.softplus(beta=0.5, threshold=2)
>>> print(y)
{
    a: ivy.array([0.948, 2.63, 4.25])
}

This should have hopefully given you an overview of the activations submodule, if you have any questions, please feel free to reach out on our discord!

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