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.
VALIDmeans no padding.
Padding could be used in convolution and pooling operations.
Here, take pooling for example:
|
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).
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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]))