layer_norm( x, normalized_shape, weight=None, bias=None, epsilon=1e-05, name=None )
see more detail in paddle.nn.LayerNorm
x (Tensor) – Input Tensor. It’s data type should be float32, float64.
normalized_shape (int|list|tuple) – Input shape from an expected input of size \([*, normalized_shape, normalized_shape, ..., normalized_shape[-1]]\). If it is a single integer, this module will normalize over the last dimension which is expected to be of that specific size.
epsilon (float, optional) – The small value added to the variance to prevent division by zero. Default: 1e-05.
weight (Tensor, optional) – The weight tensor of batch_norm. Default: None.
bias (Tensor, optional) – The bias tensor of batch_norm. Default: None.
name (str, optional) – Name for the LayerNorm, default is None. For more information, please refer to Name..
import paddle import numpy as np np.random.seed(123) x_data = np.random.random(size=(2, 2, 2, 3)).astype('float32') x = paddle.to_tensor(x_data) layer_norm_out = paddle.nn.functional.layer_norm(x, x.shape[1:]) print(layer_norm_out)