Theano conv2d的border_mode

文档是这么写的:

    border_mode: str, int or tuple of two int
    Either of the following:
    
    ``'valid'``: apply filter wherever it completely overlaps with the
    input. Generates output of shape: input shape - filter shape + 1
    ``'full'``: apply filter wherever it partly overlaps with the input.
    Generates output of shape: input shape + filter shape - 1
    ``'half'``: pad input with a symmetric border of ``filter rows // 2``
    rows and ``filter columns // 2`` columns, then perform a valid
    convolution. For filters with an odd number of rows and columns, this
    leads to the output shape being equal to the input shape.
    ``int``: pad input with a symmetric border of zeros of the given
    width, then perform a valid convolution.
    ``(int1, int2)``: pad input with a symmetric border of ``int1`` rows
    and ``int2`` columns, then perform a valid convolution.

首先, 这几种模式对应的padding都是zero-padding.

• int, (int1, int2): 它们最容易理解的: 手动指定行和列方向上的padding数量.
• valid: 其实就是不padding, 即(border\_mode = (0, 0))
  
• full: 它的padding为:(border\_mode=(k_h-1, k_w-1)), 其中(k)为kernel的行数(高)与列数(宽). 若(k_h=k_w=3), conv操作会全方位覆盖所有的((3, 3)), ((3, 1)), ((2,1)), ((1, 1))区域. 这也是它叫full的原因.
  
• half: (border\_mode = (frac {(k-1)}{2}, frac{k-1}{2})). 注意, (k)一般都是奇数. full模式的padding得到的conv输出比输入要大, 而half的输出形状与输入相同. 也有叫same的.
  

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* 原始图片来源: https://github.com/vdumoulin/conv_arithmetic * Reference: http://deeplearning.net/software/theano_versions/dev/tutorial/conv_arithmetic.html