layer_norm

paddle.nn.functional. layer_norm ( x, normalized_shape, weight=None, bias=None, epsilon=1e-05, name=None ) [source]

nn.LayerNorm is recommended. For more information, please refer to LayerNorm .

Parameters

  • x (Tensor) – Input Tensor. It’s data type should be bfloat16, float16, float32, float64.

  • normalized_shape (int|list|tuple) – Input shape from an expected input of size \([*, normalized_shape[0], normalized_shape[1], ..., normalized_shape[-1]]\). If it is a single integer, this module will normalize over the last dimension which is expected to be of that specific size.

  • weight (Tensor, optional) – The weight tensor of batch_norm. Default: None.

  • bias (Tensor, optional) – The bias tensor of batch_norm. Default: None.

  • epsilon (float, optional) – The small value added to the variance to prevent division by zero. Default: 1e-05.

  • name (str, optional) – Name for the LayerNorm, default is None. For more information, please refer to Name .

Returns

None

Examples

import paddle

x = paddle.rand((2, 2, 2, 3))
layer_norm_out = paddle.nn.functional.layer_norm(x, x.shape[1:])
print(layer_norm_out)