import torch
from torch.nn.parameter import Parameter
from .. import functional as F
from .module import Module
class Linear(Module):#继承自Module
r"""Applies a linear transformation to the incoming data: :math:`y = Ax + b`
Args:
in_features: size of each input sample
out_features: size of each output sample
bias: If set to False, the layer will not learn an additive bias. Default: True
Shape:
- Input: :math:`(N, in\_features)`
- Output: :math:`(N, out\_features)`
Attributes:
weight: the learnable weights of the module of shape (out_features x in_features)
bias: the learnable bias of the module of shape (out_features)
Examples::
>>> m = nn.Linear(20, 30)
>>> input = autograd.Variable(torch.randn(128, 20))
>>> output = m(input)
>>> print(output.size())
"""
def __setattr__(self, name, value):
def remove_from(*dicts):
for d in dicts:
if name in d:
del d[name]
params = self.__dict__.get('_parameters')
if isinstance(value, Parameter):#Parameter类型的属性
if params is None:
raise AttributeError(
"cannot assign parameters before Module.__init__() call")
remove_from(self.__dict__, self._buffers, self._modules)
self.register_parameter(name, value)
elif params is not None and name in params:
if value is not None:
raise TypeError("cannot assign '{}' as parameter '{}' "
"(torch.nn.Parameter or None expected)"
.format(torch.typename(value), name))
self.register_parameter(name, value)
else:
modules = self.__dict__.get('_modules')
if isinstance(value, Module):#Module类型的属性
if modules is None:
raise AttributeError(
"cannot assign module before Module.__init__() call")
remove_from(self.__dict__, self._parameters, self._buffers)
modules[name] = value
elif modules is not None and name in modules:
if value is not None:
raise TypeError("cannot assign '{}' as child module '{}' "
"(torch.nn.Module or None expected)"
.format(torch.typename(value), name))
modules[name] = value
else:
buffers = self.__dict__.get('_buffers')
if buffers is not None and name in buffers:
if value is not None and not torch.is_tensor(value):
raise TypeError("cannot assign '{}' as buffer '{}' "
"(torch.Tensor or None expected)"
.format(torch.typename(value), name))
buffers[name] = value
else:
object.__setattr__(self, name, value)
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【转载】
作者：CodeTutor
原文：https://blog.csdn.net/VictoriaW/article/details/72869680
