pytorch 中网络参数 weight bias 初始化方法

# 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)

net = Residual() # generate an instance network from the Net class

net.apply(weights_init) # apply weight init