KaimingUniform¶
-
class
paddle.nn.initializer.
KaimingUniform
( fan_in=None ) [source] -
Implements the Kaiming Uniform initializer
This class implements the weight initialization from the paper Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a robust initialization method that particularly considers the rectifier nonlinearities.
In case of Uniform distribution, the range is [-x, x], where
\[\begin{split}x = \sqrt{\\frac{6.0}{fan\_in}}\end{split}\]- Parameters
-
fan_in (float32|None) – fan_in for Kaiming uniform Initializer. If None, it is
from the variable. default is None. (inferred) –
Note
It is recommended to set fan_in to None for most cases.
Examples
import paddle import paddle.nn as nn linear = nn.Linear(2, 4, weight_attr=nn.initializer.KaimingUniform()) data = paddle.rand([30, 10, 2], dtype='float32') res = linear(data)