Source code for torch.nn.parameter

import torch
from torch._C import _disabled_torch_function_impl
from collections import OrderedDict

[docs]class Parameter(torch.Tensor): r"""A kind of Tensor that is to be considered a module parameter. Parameters are :class:`~torch.Tensor` subclasses, that have a very special property when used with :class:`Module` s - when they're assigned as Module attributes they are automatically added to the list of its parameters, and will appear e.g. in :meth:`~Module.parameters` iterator. Assigning a Tensor doesn't have such effect. This is because one might want to cache some temporary state, like last hidden state of the RNN, in the model. 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` """ def __new__(cls, data=None, requires_grad=True): if data is None: data = torch.Tensor() return torch.Tensor._make_subclass(cls, data, requires_grad) def __deepcopy__(self, memo): if id(self) in memo: return memo[id(self)] else: result = type(self)(, self.requires_grad) memo[id(self)] = result return result def __repr__(self): return 'Parameter containing:\n' + super(Parameter, self).__repr__() def __reduce_ex__(self, proto): # See Note [Don't serialize hooks] return ( torch._utils._rebuild_parameter, (, self.requires_grad, OrderedDict()) ) __torch_function__ = _disabled_torch_function_impl


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