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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