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 mode
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 1e-2}, {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] # AdamW是实现了权重衰减的优化器 ...
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...
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, ..., -0.0196,0.0507, -0.0302], [-0.0349, -0.0134, -0.0212, ...,0....
this module. Yields: Parameter: module parameter Example:: >>> for param inmodel.parameters(): >>> print(type(param), param.size()) <class 'torch.Tensor'> (20L,) <class 'torch.Tensor'> (20L, 1L, 5L, 5L) """ for name, param in self.named_parameters(recurse=recurse): yield ...
optimizer = torch.optim.AdamW(model.parameters(), lr=0.01) loss_form_c =torch.nn.BCELoss() ...
3.2.1 使用named_parameters()获取模型中的参数和参数名字---LogicNet_fun.py(第6部分)### 使用named_parameters()获取模型中的参数和参数名字 for name, param in model.named_parameters(): print(type(param.data),param.size(),name) # 输出 <class 'torch.Tensor'> torch.Size([3, 2]) Linear1.we...
fork,vinmodel.named_parameters():ifk!='XXX':v.requires_grad=False #固定参数 检查部分参数是否固定 代码语言:javascript 代码运行次数:0 运行 AI代码解释 fork,vinmodel.named_parameters():ifk!='xxx.weight'and k!='xxx.bias':print(v.requires_grad)#理想状态下,所有值都是False ...
parameters(module) is only needed in the # single-process multi device case, where it accesses replicated # parameters through _former_parameters. for param_name, param in module.named_parameters(recurse=False) if param.requires_grad and f"{module_name}.{param_name}" not in self.parameters_...
# 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...