layer_norm#

ivy.layer_norm(x, normalized_idxs, /, *, scale=None, offset=None, eps=1e-05, new_std=1.0, out=None)[source]#

Apply Layer Normalization over a mini-batch of inputs.

Parameters:

x (Union[Array, NativeArray]) – Input array
normalized_idxs (List[int]) – Indices to apply the normalization to.
scale (Optional[Union[Array, NativeArray]]) – Learnable gamma variables for elementwise post-multiplication, (default: None) default is None.
offset (Optional[Union[Array, NativeArray]]) – 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[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) inputs broadcast to.

Return type:

Array

Returns:

ret – The layer after applying layer normalization.

Examples

With ivy.Array input: >>> x = ivy.array([[1.0, 2.0], [3.0, 4.0]]) >>> y = ivy.layer_norm(x, [0, 1], new_std=2.0) >>> print(y) ivy.array([[-2.68 , -0.894],

[ 0.894, 2.68 ]])

>>> x = ivy.array([[1., 2., 3.], [4., 5., 6.]])
>>> y = ivy.zeros((2, 3))
>>> ivy.layer_norm(x, [0], out=y)
>>> print(y)
ivy.array([[-1., -1., -1.],
           [ 1.,  1.,  1.]])
>>> x = ivy.array([[0.0976, -0.3452,  1.2740],
...                [0.1047,  0.5886,  1.2732],
...                [0.7696, -1.7024, -2.2518]])
>>> y = ivy.layer_norm(x, [0, 1], eps=0.001,
...                       new_std=1.5, scale=0.5, offset=[0.5, 0.02, 0.1])
>>> print(y)
ivy.array([[ 0.826, -0.178, 0.981 ],
           [ 0.831,  0.421, 0.981 ],
           [ 1.26 , -1.05 , -1.28 ]])
With a mix of :class:`ivy.Array` and :class:`ivy.Container` inputs:
>>> x = ivy.array([[1., 2., 3.], [4., 5., 6.]])
>>> normalized_idxs = ivy.Container({'a': [0], 'b': [1]})
>>> y = ivy.layer_norm(x, normalized_idxs, new_std=1.25, offset=0.2)
>>> print(y)
{
    a: ivy.array([[-1.25, -1.25, -1.25],
                  [1.25, 1.25, 1.25]]),
    b: ivy.array([[-1.53, 0., 1.53],
                  [-1.53, 0., 1.53]])
}
With one :class:`ivy.Container` input:
>>> x = ivy.Container({'a': ivy.array([7., 10., 12.]),
...                    'b': ivy.array([[1., 2., 3.], [4., 5., 6.]])})
>> normalized_idxs = [0]
>>> y = ivy.layer_norm(x, normalized_idxs, eps=1.25, scale=0.3)
>>> print(y)
{
    a: ivy.array([-0.342, 0.0427, 0.299]),
    b: ivy.array([[-0.217, 0., 0.217],
                  [-0.217, 0., 0.217]])
}
With multiple :class:`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})
>>> y = ivy.layer_norm(x, normalized_idxs, new_std=new_std, offset=offset)
>>> print(y)
{
    a: ivy.array([-1.62, 0.203, 1.42]),
    b: ivy.array([[-1.84, 0., 1.84],
                  [-1.84, 0., 1.84]])
}
Both the description and the type hints above assumes an array input for simplicity,
but this function is *nestable*, and therefore also accepts :class:`ivy.Container`
instances in place of any of the arguments.

Array.layer_norm(self, normalized_idxs, /, *, scale=None, offset=None, eps=1e-05, new_std=1.0, out=None)#

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]]) – Learnable gamma variables for elementwise post-multiplication, (default: None) default is None.
offset (Optional[Union[Array, NativeArray]]) – 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[Array]) – optional output array, for writing the result to. It must have a shape that (default: None) 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 ]])

Container.layer_norm(self, normalized_idxs, /, *, scale=None, offset=None, eps=1e-05, new_std=1.0, out=None)#

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]])

}