Tensorflow中padding的两种类型SAME和VALID
2017-10-29 15:39
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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:
The TensorFlow Convolution example gives an overview about the
difference between
For the
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
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]))
转载自:
http://blog.csdn.net/jasonzzj/article/details/53930074
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). |
difference between
SAMEand
VALID:
For the
SAMEpadding,
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
VALIDpadding,
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]))
转载自:
http://blog.csdn.net/jasonzzj/article/details/53930074
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