深入浅出Pytorch函数——torch.nn.Linear

分���目录：《深入浅出Pytorch函数》总目录

对输入数据做线性变换 y = x A T + b y=xA^T+b y=xAT+b

语法

torch.nn.Linear(in_features, out_features, bias=True, device=None, dtype=None)

参数

• in_features：[int] 每个输入样本的大小
• out_features ：[int] 每个输出样本的大小
• bias：[bool] 若设置为False，则该层不会学习偏置项目，默认值为True

  变量形状

  • 输入变量： ( N , in_features ) (N, \text{in\_features}) (N,in_features)
  • 输出变量： ( N , out_features ) (N, \text{out\_features}) (N,out_features)

    变量

    • weight：模块中形状为 ( out_features , in_features ) (\text{out\_features}, \text{in\_features}) (out_features,in_features)的可学习权重项
    • bias ：模块中形状为 out_features \text{out\_features} out_features的可学习偏置项

      实例

      >>> m = nn.Linear(20, 30)>>> input = torch.randn(128, 20)>>> output = m(input)>>> print(output.size())torch.Size([128, 30])

      函数实现

      class Linear(Module):    r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`    This module supports :ref:`TensorFloat32`.    On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward.    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:`(*, H_{in})` where :math:`*` means any number of          dimensions including none and :math:`H_{in} = \text{in\_features}`.        - Output: :math:`(*, H_{out})` where all but the last dimension          are the same shape as the input and :math:`H_{out} = \text{out\_features}`.    Attributes:        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}}`    Examples::        >>> m = nn.Linear(20, 30)        >>> input = torch.randn(128, 20)        >>> output = m(input)        >>> print(output.size())        torch.Size([128, 30])    """    __constants__ = ['in_features', 'out_features']    in_features: int    out_features: int    weight: Tensor    def __init__(self, in_features: int, out_features: int, bias: bool = True,                 device=None, dtype=None) -> None:        factory_kwargs = {'device': device, 'dtype': dtype}        super().__init__()        self.in_features = in_features        self.out_features = out_features        self.weight = Parameter(torch.empty((out_features, in_features), **factory_kwargs))        if bias:            self.bias = Parameter(torch.empty(out_features, **factory_kwargs))        else:            self.register_parameter('bias', None)        self.reset_parameters()    def reset_parameters(self) -> None:        # Setting a=sqrt(5) in kaiming_uniform is the same as initializing with        # uniform(-1/sqrt(in_features), 1/sqrt(in_features)). For details, see        # https://github.com/pytorch/pytorch/issues/57109        init.kaiming_uniform_(self.weight, a=math.sqrt(5))        if self.bias is not None:            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)            bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0            init.uniform_(self.bias, -bound, bound)    def forward(self, input: Tensor) -> Tensor:        return F.linear(input, self.weight, self.bias)    def extra_repr(self) -> str:        return 'in_features={}, out_features={}, bias={}'.format(            self.in_features, self.out_features, self.bias is not None        )

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