pytorch网络参数 weight bias 初始化详解
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2021-01-19
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pytorch网络参数网络参数 weight bias 初始化详解初始化详解
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。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
补充知识：补充知识：Pytorch权值初始化及参数分组权值初始化及参数分组
1. 模型参数初始化
# ————————————————— 利用model.apply(weights_init)实现初始化
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif classname.find('BatchNorm') != -1:
m.weight.data.fill_(1)
m.bias.data.zero_()
elif classname.find('Linear') != -1:
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
m.bias.data = torch.ones(m.bias.data.size())
# ————————————————— 直接放在__init__构造函数中实现初始化
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
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