get_submodule(target: str) -> 'Module' 从Module中获取子module,example: 2.3 模型参数(parameter)与缓冲区(buffer) register_parameter(self, name: str, param: Optional[Parameter]) 用于在当前模块中添加一个parameter变量,其中参数param是一个Parame
If there was no such class as :class:`Parameter`, these temporaries would get registered too. Arguments: data (Tensor): parameter tensor. requires_grad (bool, optional): if the parameter requires gradient. See :ref:`excluding-subgraphs` for more details. Default: `True` """ 注释的核心...
def__setattr__(self, name, value):...params = self.__dict__.get('_parameters')ifisinstance(value, Parameter):ifparamsisNone:raiseAttributeError("cannot assign parameters before Module.__init__() call")remove_from(self.__dict__, self._buffers, self._modules)self.register_parameter(name...
def__setattr__(self,name,value):...params=self.__dict__.get('_parameters')ifisinstance(value,Parameter):ifparams is None:raiseAttributeError("cannot assign parameters before Module.__init__() call")remove_from(self.__dict__,self._buffers,self._modules)self.register_parameter(name,value)....
一种是反向传播需要被optimizer更新的,称之为 parameter(如权重等) 一种是反向传播不需要被optimizer更新,称之为 buffer(一些阈值之类的) 注册:torch.nn.register_parameter()用于注册Parameter实例到当前Module中(一般可以用torch.nn.Parameter()代替);torch.nn.register_buffer()用于注册Buffer实例到当前Module中。此外...
defget_param_count(model):returnsum(p.numel()forpinmodel.parameters()ifp.requires_grad)param_count=get_param_count(model)print(f"Model Parameter Count:{param_count}") 1. 2. 3. 4. 5. 在上述代码中: model.parameters()返回所有参数对象的生成器。
register_parameter(name, value) else: modules = self.__dict__.get('_modules') if isinstance(value, Module): if modules is None: raise AttributeError( "cannot assign module before Module.__init__() call") remove_from(self.__dict__, self._parameters, self._buffers) modules[name] = ...
在撰写本文时,不可能创建双张量 nn.Parameter。作为解决方法,必须将双张量注册为模块的非参数属性。 import torch.nn as nn model = nn.Linear(5, 5) input = torch.randn(16, 5) params = {name: p for name, p in model.named_parameters()} tangents = {name: torch.rand_like(p) for name, ...
StepLR>>>model=[torch.nn.Parameter(torch.randn(2,2,requires_grad=True))]>>>optimizer=SGD(model,0.1)>>>scheduler1=ExponentialLR(optimizer,gamma=0.9)>>>scheduler2=StepLR(optimizer,step_size=3,gamma=0.1)>>>forepochinrange(4):>>>print(epoch,scheduler2.get_last_lr()[0])>>>optimizer...
Parameter: 是nn.parameter.Paramter,也就是组成Module的参数。例如一个nn.Linear通常由weight和bias参数组成。它的特点是默认requires_grad=True,也就是说训练过程中需要反向传播的,就需要使用这个 import torch.nn as nn fc = nn.Linear(2,2) # 读取参数的方式一 ...