weight initilzation

1. pytorch提供接口

method 1  torch.nn.init里面有很多初始化分布

 import torch.nn.init as init

 self.conv1 = nn.Conv2d(3, 20, 5, stride=1, bias=True)
 init.xavier_uniform(self.conv1.weight, gain=np.sqrt(2.0))
 init.constant(self.conv1.bias, 0.1)

method 2 http://pytorch.org/docs/master/nn.html

 def init_weights(m):
     print(m)
     if isinstance(m, nn.Linear):
         m.weight.data.fill_(1.0)
         print(m.weight)

 net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
 net.apply(init_weights)

conv.py中有定义函数

 def reset_parameters(self):
         n = self.in_channels
         for k in self.kernel_size:
             n *= k
         stdv = 1. / math.sqrt(n)
         self.weight.data.uniform_(-stdv, stdv)
         if self.bias is not None:
             self.bias.data.uniform_(-stdv, stdv)