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Norms#

class ivy.data_classes.array.norms._ArrayWithNorms[source]#

Bases: ABC

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

ivy.Array 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 (Array) – Input array

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

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

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

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

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

  • 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 layer after applying layer normalization.

Examples

>>> x = ivy.array([[0.0976, -0.3452,  1.2740],
...                   [0.1047,  0.5886,  1.2732],
...                   [0.7696, -1.7024, -2.2518]])
>>> norm = x.layer_norm([0, 1], eps=0.001,
...                     new_std=1.5, scale=0.5, offset=[0.5, 0.02, 0.1])
>>> print(norm)
ivy.array([[ 0.826, -0.178, 0.981 ],
           [ 0.831,  0.421, 0.981 ],
           [ 1.26 , -1.05 , -1.28 ]])

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

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