nn.Linear()和nn.Conv2d()

今天看别人代码时，突然忘了nn.Linear()和nn.Conv2d()网络的相关信息，复习一下。
nn.Linear()用于设置全连接层，输入输出均为二维张量，形状为[batch_size, size]，nn.Conv2d()卷积层要求输入输出为四维张量.
截取部分源码

class Linear(Module):
    r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`

    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:`(N, *, H_{in})` where :math:`*` means any number of
          additional dimensions and :math:`H_{in} = \text{in\_features}`
        - Output: :math:`(N, *, H_{out})` where all but the last dimension
          are the same shape as the input and :math:`H_{out} = \text{out\_features}`.
    Examples::

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

in_features 是输入的二维张量的size，比如示例[128,20]中的20，
out_features 是输出的二维张量的size，比如示例[128,30]中的30,
最终得到[128,30]的张量.

nn.Conv2d()
卷积层参数比Linear多不少，这里只复习in_channels和out_channels
in_channel:　输入数据的通道数，例RGB图片通道数为3；
out_channel: 输出数据的通道数，这个根据模型调整；

class Conv2d(_ConvNd):
"""      
    Args:
            in_channels (int): Number of channels in the input image
            out_channels (int): Number of channels produced by the convolution
            kernel_size (int or tuple): Size of the convolving kernel
            stride (int or tuple, optional): Stride of the convolution. Default: 1
            padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
            padding_mode (string, optional). Accepted values `zeros` and `circular` Default: `zeros`
            dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
            groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
            bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True``
"""
            def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1,
                 bias=True, padding_mode='zeros'):
                kernel_size = _pair(kernel_size)
                stride = _pair(stride)
                padding = _pair(padding)
                dilation = _pair(dilation)
                super(Conv2d, self).__init__(
                    in_channels, out_channels, kernel_size, stride, padding, dilation,
                    False, _pair(0), groups, bias, padding_mode)