o Qeq@s4ddlmZgZGdddeZGdddeZdS))XavierInitializerc"eZdZdZdfdd ZZS) XavierNormala This class implements the Xavier weight initializer from the paper `Understanding the difficulty of training deep feedforward neural networks `_ by Xavier Glorot and Yoshua Bengio, using a normal distribution whose mean is :math:`0` and standard deviation is .. math:: \sqrt{\frac{2.0}{fan\_in + fan\_out}}. Args: fan_in (float, optional): fan_in for Xavier initialization, which is inferred from the Tensor. The default value is None. fan_out (float, optional): fan_out for Xavier initialization, which is inferred from the Tensor. The default value is None. name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: A parameter initialized by Xavier weight, using a normal distribution. Examples: .. code-block:: python import paddle data = paddle.ones(shape=[3, 1, 2], dtype='float32') weight_attr = paddle.framework.ParamAttr( name="linear_weight", initializer=paddle.nn.initializer.XavierNormal()) bias_attr = paddle.framework.ParamAttr( name="linear_bias", initializer=paddle.nn.initializer.XavierNormal()) linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr) # inear.weight: [[ 0.06910077 -0.18103665] # [-0.02546741 -1.0402188 ]] # linear.bias: [-0.5012929 0.12418364] res = linear(data) # res: [[[-0.4576595 -1.0970719]] # [[-0.4576595 -1.0970719]] # [[-0.4576595 -1.0970719]]] Nctt|jd||dddS)NFuniformfan_infan_outseed)superr__init__selfr r name __class__LD:\Projects\ConvertPro\env\Lib\site-packages\paddle/nn/initializer/xavier.pyr A  zXavierNormal.__init__NNN__name__ __module__ __qualname____doc__r __classcell__rrrrrs,rcr) XavierUniforma This class implements the Xavier weight initializer from the paper `Understanding the difficulty of training deep feedforward neural networks `_ by Xavier Glorot and Yoshua Bengio. This initializer is designed to keep the scale of the gradients approximately same in all the layers. In case of Uniform distribution, the range is :math:`[-x,x]`, where .. math:: x = \sqrt{\frac{6.0}{fan\_in + fan\_out}}. Args: fan_in (float, optional): fan_in for Xavier initialization, which is inferred from the Tensor. The default value is None. fan_out (float, optional): fan_out for Xavier initialization, which is inferred from the Tensor. The default value is None. name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: A parameter initialized by Xavier weight, using a uniform distribution. Examples: .. code-block:: python import paddle data = paddle.ones(shape=[3, 1, 2], dtype='float32') weight_attr = paddle.framework.ParamAttr( name="linear_weight", initializer=paddle.nn.initializer.XavierUniform()) bias_attr = paddle.framework.ParamAttr( name="linear_bias", initializer=paddle.nn.initializer.XavierUniform()) linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr) # linear.weight: [[-0.04229349 -1.1248565 ] # [-0.10789523 -0.5938053 ]] # linear.bias: [ 1.1983747 -0.40201235] res = linear(data) # res: [[[ 1.0481861 -2.1206741]] # [[ 1.0481861 -2.1206741]] # [[ 1.0481861 -2.1206741]]] Ncr)NTrr)r rr rrrrr xrzXavierUniform.__init__rrrrrrrHs/rN)Zfluid.initializerr__all__rrrrrrs 4