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1. 导入常用初始化方法
from torch.nn.init import xavier_uniform_, xavier_normal_ from torch.nn.init
import kaiming_uniform_, kaiming_normal_
2. 各种初始化方法分析
* xavier_uniform_(tensor, gain=1.0)
Note: 以均匀分布的值初始化输入tensor. 方法根据《Understanding the difficulty of training deep
feedforward neural networks - Glorot, X. & Bengio, Y. (2010)》论文实现。最终得到的Tesor值取样于
U(−a,a) ，
其中： \
参数：
gain: 缩放因素(optional)
* xavier_normal_(tensor, gain=1.0)
Note: 以正太分布的值初始化输入tensor. 方法根据《Understanding the difficulty of training deep
feedforward neural networks - Glorot, X. & Bengio, Y. (2010)》论文实现。最终得到的Tesor值取样于
,
其中：
* kaiming_uniform_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu')
Note: 以均匀分布的值初始化输入tensor. 方法根据《Delving deep into rectifiers: Surpassing
human-level performance on ImageNet classification - He, K. et al.
(2015)》论文实现。最终得到的Tesor值取样于U(−bound,bound) ，
其中：
参数：a:
mode: "fan_in" 或 "fan_out". 选择“fan_in" 在前向传播中保存权重方差的幅度， ”fan_out"
在后向传播中保存幅度。
nonlinearity: 非线性函数。推荐"relu" or "leaky_relu".
* kaiming_normal_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu')
Note: 以正太分布的值初始化输入tensor. 方法根据《Delving deep into rectifiers: Surpassing
human-level performance on ImageNet classification - He, K. et al.
(2015)》论文实现。最终得到的Tesor值取样于，
其中：
