oneflow.nn.LayerNorm¶
-
class
oneflow.nn.
LayerNorm
(normalized_shape: Union[int, Tuple[int], oneflow.Size], eps: float = 1e-05, elementwise_affine: bool = True)¶ Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization
\[y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta\]The mean and standard-deviation are calculated separately over the last certain number dimensions which have to be of the shape specified by
normalized_shape
. \(\gamma\) and \(\beta\) are learnable affine transform parameters ofnormalized_shape
ifelementwise_affine
isTrue
. The standard-deviation is calculated via the biased estimator.Note
Unlike Batch Normalization and Instance Normalization, which applies scalar scale and bias for each entire channel/plane with the
affine
option, Layer Normalization applies per-element scale and bias withelementwise_affine
.This layer uses statistics computed from input data in both training and evaluation modes.
- Parameters
normalized_shape (int or list or oneflow.Size) –
input shape from an expected input of size
\[[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]\]If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps – a value added to the denominator for numerical stability. Default: 1e-5
elementwise_affine – a boolean value that when set to
True
, this module has learnable per-element affine parameters initialized to ones (for weights) and zeros (for biases). Default:True
.
- Shape:
Input: \((N, *)\)
Output: \((N, *)\) (same shape as input)
For example:
>>> import numpy as np >>> import oneflow as flow >>> input_arr = np.array( ... [ ... [ ... [[-0.16046895, -1.03667831], [-0.34974465, 0.26505867]], ... [[-1.24111986, -0.53806001], [1.72426331, 0.43572459]], ... ], ... [ ... [[-0.77390957, -0.42610624], [0.16398858, -1.35760343]], ... [[1.07541728, 0.11008703], [0.26361224, -0.48663723]], ... ], ... ], ... dtype=np.float32, ... ) >>> x = flow.Tensor(input_arr) >>> m = flow.nn.LayerNorm(2) >>> y = m(x).numpy() >>> y array([[[[ 0.99997395, -0.99997395], [-0.999947 , 0.999947 ]], [[-0.99995965, 0.9999595 ], [ 0.99998784, -0.99998784]]], [[[-0.9998348 , 0.99983466], [ 0.9999914 , -0.9999914 ]], [[ 0.9999785 , -0.9999785 ], [ 0.9999646 , -0.9999646 ]]]], dtype=float32)