迭代打印model.named_parameters()将会打印每一次迭代元素的名字和param(元素是 torch.nn.parameter.Parameter 类型) for name, param in model.named_parameters(): print(name,param.requires_grad) param.requires_grad=False # 顺便改下属性 model.parameters() [parameters(recurse: bool = True) → Iterator[t...
所以最后网络结构是预处理的conv层和bn层,以及接下去的三个stage,每个stage分别是三层,最后是avgpool和全连接层 1、model.named_parameters(),迭代打印model.named_parameters()将会打印每一次迭代元素的名字和param forname, paraminnet.named_parameters():print(name,param.requires_grad) param.requires_grad=False...
def named_parameters(self, prefix='', recurse=True): r"""Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. Args: prefix (str): prefix to prepend to all parameter names. recurse (bool): if True, then yields parameters of...
named_parameters():返回模块参数上的迭代器,产生参数的名称和参数本身 forname,parameterinmodel.named_parameters():print(name, parameter)rnn.weight_ih_l0Parametercontaining:[432, 34]float32@cuda:0tensor([[-0.0785, -0.0164, -0.0400, ..., -0.0276,0.0482, -0.0297], [0.0041,0.0281,0.0573, ..., ...
optimizer = torch.optim.AdamW(model.parameters(), lr=0.01) loss_form_c =torch.nn.BCELoss() ...
端庄的汤汤:pytorch中model、conv、linear、nn.Module和nn.optim模块参数方法一站式理解+finetune应用(中)0 赞同 · 0 评论文章 接上篇,我们继续看一下named_parameters(...)和parameters(...)还有这两个方法涉及的_named_members(...)方法。 先看named_parameters(...),代码如下。
{name: p for name, p in model.named_parameters()} tangents = {name: torch.rand_like(p) for name, p in params.items()} with fwAD.dual_level(): for name, p in params.items(): delattr(model, name) setattr(model, name, fwAD.make_dual(p, tangents[name])) out = model(input)...
# Configuration flags and hyperparametersUSE_MAMBA = 1DIFFERENT_H_STATES_RECURRENT_UPDATE_MECHANISM = 0 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 定义超参数和初始化 d_model = 8state_size = 128 # Example stat...
通过上面的例子可以看到,nn.parameter.Paramter的requires_grad属性值默认为True。另外上面例子给出了三种读取parameter的方法,推荐使用后面两种(这两种的区别可参阅Pytorch: parameters(),children(),modules(),named_*区别),因为是以...
# 例如,self.emb=nn.Embedding(5000,100)forname,paraminself.model.named_parameters():ifparam.requires_grad and emb_nameinname:self.backup[name]=param.data.clone()norm=torch.norm(param.grad)# 默认为2范数ifnorm!=0:r_at=epsilon*param.grad/norm ...