self.weight1 = torch.nn.Parameter(torch.FloatTensor(1), requires_grad = True) self.weight1.data.fill_(0.1) 其中fill_ 和 normal_()用法: 比如有一个Tensor a,那么a.normal_()表示用标准正态分布填充a (下划线表示是in_place操作) a.fill_(0.1)则表示用0.1填充a,也是in_place操作 在神经网络的初...
torch.nn.init.normal_(m.weight.data,0,0.01) # m.weight.data.normal_(0,0.01) m.bias.data.zero_() 这段代码的基本流程就是,先从self.modules()中遍历每一层,然后判断更曾属于什么类型,是否是Conv2d,是否是BatchNorm2d,是否是Linear的,然...
next(iter(data_iter)):表示使用 next() 函数从迭代器对象中取出下一个数据样本。在这里,iter(data_iter) 的作用是将数据迭代器转换为迭代器对象,以便使用 next() 函数逐个取出数据样本。 net[0].weight.data.normal_(0,0.01) net[0].bias.data.fill_(0) net[0].weight.data.normal_(0,0.01):使用正...
torch.lerp(start, end, weight, out=None):对两个张量以start, end做线性插值,将结果返回到输出张量out = start + weight*(end - start) torch.addcdiv(tensor, value=1, tensor1, tensor2, out=None):用tensor2对tensor1逐元素相除,然后乘以标量值value并加到tensor上。 torch.addcmul(tensor, value=1...
mod.weight.data.normal_(0.0,0.02) elif classname.find('BatchNorm')!= -1: mod.weight.data.normal_(1.0,0.02) #bn层里初始化γ,服从(1,0.02)的正态分布 mod.bias.data.fill_(0) #bn层里初始化β,默认为0classEncoder(nn.Module): def __init__(self, input_size, input_channels, base_chann...
padding=1)#将原来的fc层改成fclass层self.fclass=nn.Linear(2048,num_classes)#原来的fc层:self.fc = nn.Linear(512 * block.expansion, num_classes)forminself.modules():#ifisinstance(m,nn.Conv2d):n=m.kernel_size[0]*m.kernel_size[1]*m.out_channels#m.weight.data.normal_(0,math.sqrt(...
nn.init.kaiming_normal_(tensor, a=0, mode=‘fan_in’, nonlinearity=‘leaky_relu’) 具体代码如下: def initialize(self):for m in self.modules():if isinstance(m, nn.Linear):#Pytorch提供的初始化方法nn.init.kaiming_normal_(m.weight.data)# 下面这个是手动写出的,这两句话其实作用一样,不过自己...
m.weight.data=torch.ones(m.weight.data.shape)*300 # 这样是可以修改的 # 修改方法二 # nn.init.kaiming_normal_(m.weight.data, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1.)
m.weight.data.normal_(0, sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.GroupNorm): m.weight.data.fill_(1) ...
m.weight.data.normal_(0,math.sqrt(2./n))elifisinstance(m,nn.BatchNorm2d):m.weight.data.fill_(1)m.bias.data.zero_()def_make_layer(self,block,planes,blocks,stride=1):downsample=Noneifstride!=1or self.inplanes!=planes*block.expansion:downsample=nn.Sequential(nn.Conv2d(self.inplanes,plan...