浅析PyTorch中nn.Linear的使用

(编辑：jimmy 日期: 2025/1/19 浏览：3 次 )

查看源码

Linear 的初始化部分：

class Linear(Module):
 ...
 __constants__ = ['bias']
 
 def __init__(self, in_features, out_features, bias=True):
   super(Linear, self).__init__()
   self.in_features = in_features
   self.out_features = out_features
   self.weight = Parameter(torch.Tensor(out_features, in_features))
   if bias:
     self.bias = Parameter(torch.Tensor(out_features))
   else:
     self.register_parameter('bias', None)
   self.reset_parameters()
 ...

需要实现的内容：

计算步骤：

@weak_script_method
  def forward(self, input):
    return F.linear(input, self.weight, self.bias)

返回的是：input * weight + bias

对于 weight

weight: the learnable weights of the module of shape
  :math:`(\text{out\_features}, \text{in\_features})`. The values are
  initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
  :math:`k = \frac{1}{\text{in\_features}}`

对于 bias

bias:  the learnable bias of the module of shape :math:`(\text{out\_features})`.
    If :attr:`bias` is ``True``, the values are initialized from
    :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
    :math:`k = \frac{1}{\text{in\_features}}`

实例展示

举个例子：

> import torch
> nn1 = torch.nn.Linear(100, 50)
> input1 = torch.randn(140, 100)
> output1 = nn1(input1)
> output1.size()
torch.Size([140, 50])

张量的大小由 140 x 100 变成了 140 x 50

执行的操作是：

[140,100]×[100,50]=[140,50]

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