o Qe@s4ddlmZgZGdddeZGdddeZdS))MSRAInitializerc"eZdZdZdfdd ZZS) KaimingNormalaImplements the Kaiming Normal 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 Normal distribution, the mean is 0 and the standard deviation is .. math:: \frac{gain}{\sqrt{{fan\_in}}} Args: fan_in (float32|None, optional): fan_in (in_features) of trainable Tensor, If None, it will be infered automaticly. 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. Note: It is recommended to set fan_in to None for most cases. Examples: .. code-block:: python import paddle import paddle.nn as nn linear = nn.Linear(2, 4, weight_attr=nn.initializer.KaimingNormal()) data = paddle.rand([30, 10, 2], dtype='float32') res = linear(data) Nreluctt|jd|d||ddS)NFuniformfan_inseednegative_slope nonlinearity)superr__init__selfr r r __class__MD:\Projects\ConvertPro\env\Lib\site-packages\paddle/nn/initializer/kaiming.pyr<  zKaimingNormal.__init__Nrr__name__ __module__ __qualname____doc__r __classcell__rrrrrs&rcr)KaimingUniformaImplements 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 .. math:: x = gain \times \sqrt{\frac{3}{fan\_in}} Args: fan_in (float32|None, optional): fan_in (in_features) of trainable Tensor, If None, it will be infered automaticly. 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. Note: It is recommended to set fan_in to None for most cases. Examples: .. code-block:: python 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) Nrrcr)NTrr )rrrrrrrrjrzKaimingUniform.__init__rrrrrrrDs%rN)Zfluid.initializerr__all__rrrrrrs /