Norms#

class ivy.data_classes.container.norms._ContainerWithNorms(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, **kwargs)[source]#

Bases: ContainerBase

_abc_impl = <_abc_data object>#
layer_norm(normalized_idxs, /, *, scale=None, offset=None, eps=1e-05, new_std=1.0, out=None)[source]#

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

Parameters:

  • self (Union[Array, NativeArray, Container]) – Input container

  • normalized_idxs (List[int]) – Indices to apply the normalization to.

  • scale (Optional[Union[Array, NativeArray, Container]]) – Learnable gamma variables for elementwise post-multiplication, (default: None) default is None.

  • offset (Optional[Union[Array, NativeArray, Container]]) – Learnable beta variables for elementwise post-addition, default is None. (default: None)

  • eps (float) – small constant to add to the denominator. Default is 1e-05. (default: 1e-05)

  • new_std (float) – The standard deviation of the new normalized values. Default is 1. (default: 1.0)

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

Return type:

Container

Returns:

ret – The layer after applying layer normalization.

Examples

With one ivy.Container input: >>> x = ivy.Container({‘a’: ivy.array([7., 10., 12.]), … ‘b’: ivy.array([[1., 2., 3.], [4., 5., 6.]])}) >>> normalized_idxs = [0] >>> norm = x.layer_norm(normalized_idxs, eps=1.25, scale=0.3) >>> print(norm) {

a: ivy.array([-0.342, 0.0427, 0.299]), b: ivy.array([[-0.241, -0.241, -0.241,

[0.241, 0.241, 0.241]])

} With multiple ivy.Container inputs: >>> x = ivy.Container({‘a’: ivy.array([7., 10., 12.]), … ‘b’: ivy.array([[1., 2., 3.], [4., 5., 6.]])}) >>> normalized_idxs = ivy.Container({‘a’: [0], ‘b’: [1]}) >>> new_std = ivy.Container({‘a’: 1.25, ‘b’: 1.5}) >>> bias = ivy.Container({‘a’: [0.2, 0.5, 0.7], ‘b’: 0.3}) >>> norm = x.layer_norm(normalized_idxs, new_std=new_std, offset=offset) >>> print(norm) {

a: ivy.array([-1.62, 0.203, 1.42]), b: ivy.array([[-1.84, 0., 1.84],

[-1.84, 0., 1.84]])

}