# 其中fan_in是指第i层神经元的个数,fan_out是指第i + 1层神经元的个数forminnet.modules():ifisinstance(m,(torch.nn.Linear,torch.nn.Conv1d,torch.nn.Conv2d)):torch.nn.init.xavier_uniform_(m.weight)forminnet.modules():ifisinstance(m,torch.nn.Conv2d):torch.nn.init.xavier_uniform_(m.w...
models.Conv2_1024_2048.weight models.BatchNorm2d_2048.weight models.BatchNorm2d_2048.bias models.Conv2_2048_100.weight 2.直接在定义网络时定义 import torch.nnasnn import torch.nn.initasinit import torch.nn.functionalasFclassDiscriminator(nn.Module):"""6层全连接层"""def __init__(self, z_...
然后定义初始化函数weight_init(net),其中参数net就是继承自Module的主网络Outer对象: defweight_init(net):# 递归获得net的所有子代Moduleforopinnet.modules():# 针对不同类型操作采用不同初始化方式ifisinstance(op, nn.Linear):nn.init.constant_(op.weight....
nn.init.xavier_uniform(conv1.weight) nn.init.constant(conv1.bias, 0.1) 1. 2. 3. 3. 模型初始化 def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv2d') != -1: nn.init.xavier_normal_(m.weight.data) nn.init.constant_(m.bias.data, 0.0) elif classn...
= -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) netG= _netG(ngpu)#生成模型实例netG.apply(weights_init)#递归的调用weights_init函数,遍历netG的submodule作为参数
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)
defweight_init(m):ifisinstance(m,nn.Conv2d):n=m.kernel_size[0]*m.kernel_size[1]*m.out_channelsm.weight.data.normal_(0,math.sqrt(2./n))elifisinstance(m,nn.BatchNorm2d):m.weight.data.fill_(1)m.bias.data.zero_() 但一般如果没有很大需求初始化参数,也没有问题(不确定性能是否有影响...
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 classnet.apply(weights_init)# apply weight init
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。 代码语言:javascript 复制 net=Residual()# generate an instance network from the Netclassnet.apply(weights_init)# apply weight init
在搭建模型的过程中，eager模式量化必须将用到的所有算子在__init__中定义，并且引入的是torch.nn的...