Tensorflow中padding的两种类型SAME和VALID

最新推荐文章于 2020-08-22 22:03:55 发布

jasonzzj

最新推荐文章于 2020-08-22 22:03:55 发布

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分类专栏： TensorFlow Network 文章标签： padding SAME VALID

原文链接：https://www.tensorflow.org/api_docs/python/tf/pad

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本文详细解释了在卷积神经网络中SAME与VALID两种填充方式的区别。SAME填充确保输出特征图与输入特征图尺寸相同，通过在输入周围添加零填充实现；而VALID则不使用填充。文章还提供了具体示例来帮助理解这两种填充方式。

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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]))