pytorch 中网络参数 weight bias 初始化方法

权重初始化对于训练神经网络至关重要，好的初始化权重可以有效的避免梯度消失等问题的发生。

在pytorch的使用过程中有几种权重初始化的方法供大家参考。
注意：第一种方法不推荐。尽量使用后两种方法。

# not recommend
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        m.weight.data.normal_(0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        m.weight.data.normal_(1.0, 0.02)
        m.bias.data.fill_(0)

# recommend
def initialize_weights(m):
    if isinstance(m, nn.Conv2d):
        m.weight.data.normal_(0, 0.02)
        m.bias.data.zero_()
    elif isinstance(m, nn.Linear):
        m.weight.data.normal_(0, 0.02)
        m.bias.data.zero_()

# recommend
def weights_init(m): 
    if isinstance(m, nn.Conv2d): 
        nn.init.xavier_normal_(m.weight.data) 
        nn.init.xavier_normal_(m.bias.data)
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight,1)
        nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.BatchNorm1d):
        nn.init.constant_(m.weight,1)
        nn.init.constant_(m.bias, 0)

编写好weights_init函数后，可以使用模型的apply方法对模型进行权重初始化。

net = Residual() # generate an instance network from the Net class
net.apply(weights_init) # apply weight init