pytorch中的named_parameters(), nam

1. named_modules
   内部采用yield关键字，得到生成器。可以看到函数内部给出的例子，当外部迭代调用net.named_modules()时，会先返回prefix=''，以及net对象本身。然后下一步会递归的调用named_modules()，继而深度优先的返回每一个module。

def named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = ''):
        r"""Returns an iterator over all modules in the network, yielding
        both the name of the module as well as the module itself.

        Yields:
            (string, Module): Tuple of name and module

        Note:
            Duplicate modules are returned only once. In the following
            example, ``l`` will be returned only once.

        Example::

            >>> l = nn.Linear(2, 2)
            >>> net = nn.Sequential(l, l)
            >>> for idx, m in enumerate(net.named_modules()):
                    print(idx, '->', m)

            0 -> ('', Sequential(
              (0): Linear(in_features=2, out_features=2, bias=True)
              (1): Linear(in_features=2, out_features=2, bias=True)
            ))
            1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

        """

        if memo is None:
            memo = set()
        if self not in memo:
            memo.add(self)
            yield prefix, self
            for name, module in self._modules.items():
                if module is None:
                    continue
                submodule_prefix = prefix + ('.' if prefix else '') + name
                for m in module.named_modules(memo, submodule_prefix):
                    yield m