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...
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...
model.named_parameters()、model.named_children()、model.named_modules() 实验: import torchvision import torch model = torchvision.models.resnet34(pretrained=True) print(model) # 打印模型结构 print("---") for param in model.parameters(): print(param) # 打印所有参数 print("---") for name,...
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) ...
_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()...
@VainF I have trained a custom YOLOv8 model. After training i have successfully pruned the model. for name, param in model.model.named_parameters(): param.requires_grad = True replace_c2f_with_c2f_v2(model.model) model.model.eval() examp...
fromtransformersimportSeq2SeqTrainer,Seq2SeqTrainingArgumentsimporttransformersfromdatasetsimportDatasetimporttorch.distributedasdistimporttorchclassCustomMultitaskSeq2SeqTrainer(Seq2SeqTrainer):defcompute_loss(self,model,inputs):# here print the info of modeldist.barrier()forname,paraminmodel.named_parameters(...
>>> 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: ...