PyTorch中nn.Linear()理解 | StriveZs的博客

PyTorch中nn.Linear()理解

PyTorch中nn.Linear()理解

计算公式

这里A为weight,b为bias。

代码部分

初始化部分代码

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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()

计算部分

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

返回值为: input * weight + bias

bias和weight

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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}}`
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}}`

示例

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

对于上述描述,我们创建一个input的维度为[140,100], 通过声明线性层会得到根据维度初始化的权重和偏差,其中weight的维度为[50,100]。对于公式中A表示的就是weight,而b表示的就是bias。由于对A进行了转置所以这里weight的维度为[50,100]而不是[100,50]。

具体计算为[140,100] × [50,100]的转置 + bias = [140,100] × [100,50] + bias最后得到的维度为[140,50]。

至于对于bias和weight的初始化,根绝网上所讲的是来有关维度值得均匀分布。

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