mindspore.ops.layer_norm

mindspore.ops.layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-05)[source]

Applies the Layer Normalization to the input tensor.

This operator will normalize the input tensor on given axis. LayerNorm is described in the paper Layer Normalization.

\[y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta\]

where \(\gamma\) is weight, \(\beta\) is bias, \(\epsilon\) is eps.

Parameters

  • input (Tensor) – Tensor of shape \((N, \ldots)\). The input of LayerNorm.

  • normalized_shape (Union(int, tuple[int], list[int])) – The normalized shape of input for LayerNorm. normalized_shape equal to input_shape[begin_norm_axis:], where begin_norm_axis represents the axis where normalization begins.

  • weight (Tensor, optional) – Learnable parameter \(\gamma\) . Tensor of shape normalized_shape. Default: None, has the same data type with input. Initialized to 1 when weight is None.

  • bias (Tensor, optional) – Learnable parameter \(\beta\) . Tensor of shape normalized_shape. Default: None, has the same data type with input. Initialized to 0 when bias is None.

  • eps (float, optional) – A value added to the denominator for numerical stability(\(\epsilon\)). Default: 1e-5 .

Returns

  • output (Tensor) - The normalized input, has the same type and shape as the input.

Raises

  • TypeError – If input is not a Tensor.

  • TypeError – If normalized_shape is not an integer, a list or a tuple.

  • TypeError – If eps is not a float.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]), mindspore.float32)
>>> normalized_shape = (3,)
>>> gamma = Tensor(np.ones(normalized_shape), mindspore.float32)
>>> beta = Tensor(np.zeros(normalized_shape), mindspore.float32)
>>> eps = 1e-7
>>> output = ops.layer_norm(input_x, normalized_shape, gamma, beta, eps)
>>> print(output)
[[-1.2247448 0. 1.2247448]
 [-1.2247448 0. 1.2247448]]