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class paddle.nn.initializer. KaimingUniform ( fan_in: Optional[float] = None, negative_slope: float = 0.0, nonlinearity: str = 'relu', mode: str = 'fan_in' ) [source]
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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

\[x = gain \times \sqrt{\frac{3}{fan\_in}}\]
Parameters

  • fan_in (float32|None, optional) – fan_in (in_features) of trainable Tensor, If None, it will be inferred automatically. If you don’t want to use in_features of the Tensor, you can set the value of ‘fan_in’ smartly by yourself. Default is None.

  • negative_slope (float, optional) – negative_slope (only used with leaky_relu). Default is 0.0.

  • nonlinearity (str, optional) – the non-linear function. Default is relu.

  • mode (str, optional) – the mode of initialization, can be ‘fan_in’ or ‘fan_out’. When set to ‘fan_in’, the fan_in parameter is used for initialization. When set to ‘fan_out’, the out_features of trainable Tensor will be used. Default is ‘fan_in’.

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)