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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