weight.data, 1.0, 0.02) nn.init.constant_(m.bias.data, 0) 在这里对该代码学习后的理解做一些记录。首先是apply(fn),根据官网解释该方法是Module类的方法,作用是将fn递归地应用于每个子模块(由.children()返回),以及其自身。典型用途便是初始化模型的参数。我们这里来写一个简单的神经网络 net 并将其...
elifclassname.find("BatchNorm2d") !=-1: torch.nn.init.normal_(m.weight.data,1.0,0.02) torch.nn.init.constant_(m.bias.data,0.0) 这里的意思是选择module是conv或者是batchNorm2d的层进行初始化
model.apply(weights_init_normal)方法 应用把方法应用于每一个module,这里意思是进行初始化 def weights_init_normal(m): classname = m.__class__.__name__ if classname.find("Conv") != -1: torch.nn.init.normal_(m.weight.data, 0.0, 0.02) elif classname.find("BatchNorm2d") != -1: ...
下面是一个简单的权重初始化函数,它使用均匀分布和正态分布: definit_weights(m):ifisinstance(m,nn.Linear):# 判断该层是否为全连接层nn.init.kaiming_uniform_(m.weight,a=0.1)# 使用Kaiming均匀分布初始化权重nn.init.constant_(m.bias,0)# 将偏置初始化为0 1. 2. 3. 4. 4. 应用初始化 在模型实...
m.weight.fill_(1.0) print(m.weight) net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) net.apply(init_weights) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 #输出: Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[ 1., 1.], [ 1....
@torch.no_grad() def init_weights(m): print(m) if type(m) == torch.nn.LazyLinear: m.weight.fill_(1.0) print(m.weight) linear = torch.nn.LazyLinear(2) linear.apply(init_weights) # this fails, because `linear` (a lazy-module) hasn't been initialized yet input = torch.randn...
def weights_init_normal(m): classname = m.__class__.__name__ if classname.find("Conv") != -1: torch.nn.init.normal_(m.weight.data, 0.0, 0.02) elif classname.find("BatchNorm2d") != -1: torch.nn.init.normal_(m.weight.data, 1.0, 0.02) torch.nn.init.constant_(m.bias.data,...
典型用途包括初始化模型的参数(另见 torch.nn.init)。 例子: >>> @torch.no_grad() >>> def init_weights(m): >>> print(m) >>> if type(m) == nn.Linear: >>> m.weight.fill_(1.0) >>> print(m.weight) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> ...
if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0)# 创建模型实例model = SimpleNet()# 使用 apply 方法初始化权重model.apply(init_weights)# 检查初始化是否成功for name, ...
尝试此解决方案 class Calculator: def __init__(self): self.df = pd.DataFrame({'date_time': [date(2021,6,1), date(2021,6,2), date(2021,6,3), date(2021,6,4), date(2021,6,1), date(2021,6,2), date(2021,6,3), date(2021,6,4)], 'ticker': ['A', 'A', 'A', 'A'...