parameters() 总述 model.parameters()返回的是一个生成器,该生成器中只保存了可学习、可被优化器更新的参数的具体的参数,可通过循环迭代打印参数。(参见代码示例一) 与model.named_parameters()相比,model.parameters()不会保存参数的名字。 该方法可以用来改变可学习、可被优化器更新参数的requires_grad属性,但由于...
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paramforname, paraminmodel.named_parameters():print(name,param.requires_grad) param.requires_grad=False2、model.parameters(),迭代打印model.parameters()将会打印每一次迭代元素的param而不会打印名字,这是他和named_p...
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paramforname, paraminmodel.named_parameters():print(name,param.requires_grad) param.requires_grad=False2、model.parameters(),迭代打印model.parameters()将会打印每一次迭代元素的param而不会打印名字,这是他和named_p...
named_parameters(): print(name, param) # 打印参数层名称、打印所有参数 print("---") for module in model.children(): print(module) # 打印网络第一代子模块 print("---") for name, module in model.named_children(): print(name, module) # 打印模块名称 网络第一代子模块 print("---"...
4. model.named_children() 5. model.parameters() 6. model.named_parameters() 7. model.state_dict() 模型示例: import torch import torch.nn as nn class Net(nn.Module): def __init__(self, num_class=10): super().__init__() self.features = nn.Sequential( nn.Conv2d(in_channels=3...
_modules()] In [16]: model_children = [x for x in model.children()] In [17]: model_named_children = [x for x in model.named_children()] In [18]: model_parameters = [x for x in model.parameters()] In [19]: model_named_parameters = [x for x in model.named_parameters()...
pytorch查看模型model参数parameters 示例1:pytorch自带的faster r-cnn模型 import torch import torchvision model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True) for name, p in model.named_parameters(): print(name) print(p.requires_grad) ...
🐛 Describe the bug Hello, when I am using DDP to train a model, I found that using multi-task loss and gradient checkpointing at the same time can lead to gradient synchronization failure between GPUs, which in turn causes the parameters...
for name, param in base_model.named_parameters(): if torch.isnan(param).any() or torch.isinf(param).any(): print(f"Warning: Found NaN or Inf in parameter {name}") # 检查输入数据 # if torch.isnan(inputs['input_ids']).any() or torch.isinf(inputs['input_ids']).any(): ...
>>> for name, param in self.named_parameters(): >>> if name in ['bias']: >>> print(param.size()) """ if memo is None: memo = set() #本身模块的参数 for name, p in self._parameters.items(): if p is not None and p not in memo: ...