Tensorflow中padding的两种类型SAME和VALID

按照下面的解释，“SAME”用的比较多。same的含义是：长度除以步长向上取整。

For the 

SAME

 padding,
the output height and width are computed as:

out_height = ceil(float(in_height) / float(strides[1]))

out_width = ceil(float(in_width) / float(strides[2]))
http://blog.csdn.net/jasonzzj/article/details/53930074
SAME means that the output feature map has the same spatial dimensions as the input feature map. Zero padding is introduced to make the shapes match as needed, equally on every side of the input map.
VALID means no padding.

Padding could be used in convolution and pooling operations.

Here, take pooling for example:

down vote

If you like ascii art: "VALID"  = without padding: inputs: 1 2 3 4 5 6 7 8 9 10 11 (12 13) |________________| dropped |_________________| "SAME"  = with zero padding: pad| |pad inputs: 0 |1 2 3 4 5 6 7 8 9 10 11 12 13|0 0 |________________| |_________________| |________________| In this example: Input width = 13 Filter width = 6 Stride = 5 Notes: "VALID"  only ever drops the right-most columns (or bottom-most rows). "SAME"  tries to pad evenly left and right, but if the amount of columns to be added is odd, it will add the extra column to the right, as is the case in this example (the same logic applies vertically: there may be an extra row of zeros at the bottom).

The TensorFlow Convolution example gives an overview about the difference
between 

SAME

 and 

VALID

 :

For the 

SAME

 padding,
the output height and width are computed as:

out_height = ceil(float(in_height) / float(strides[1]))

out_width = ceil(float(in_width) / float(strides[2]))

And

For the 

VALID

 padding,
the output height and width are computed as:

out_height = ceil(float(in_height - filter_height + 1) / float(strides1))

out_width = ceil(float(in_width - filter_width + 1) / float(strides[2]))