How to handle maxpool layer backpropagation with recurring max values in same position
$begingroup$
Say I have a layer a
:
3 4 2
1 5 0
8 6 4
The maxpool using 2x2
filter is:
5 5
8 6
Thus the derivative of the max pooling layer is (in respect to layer a
):
0 0 0
0 1 0
1 1 0
Now say doing backpropagation I have the following deltas:
-1.4 0.8
0.2 0.3
I know that -1.4
and 0.8
is associated with 5
. While 0.2
and 0.3
corresponds to 8
and 6
respectively.
Currently what I have done, for repeating max values in the same position, I just sum up the corresponding deltas associated with that position, i.e., -1.4 + 0.8 = -0.6
. So the final delta being back propagated is the following matrix.
0 0 0 0
0 -0.6 0
0.2 0.3 0
Please let me know if this is the correct procedure.
neural-network deep-learning cnn backpropagation
New contributor
$endgroup$
add a comment |
$begingroup$
Say I have a layer a
:
3 4 2
1 5 0
8 6 4
The maxpool using 2x2
filter is:
5 5
8 6
Thus the derivative of the max pooling layer is (in respect to layer a
):
0 0 0
0 1 0
1 1 0
Now say doing backpropagation I have the following deltas:
-1.4 0.8
0.2 0.3
I know that -1.4
and 0.8
is associated with 5
. While 0.2
and 0.3
corresponds to 8
and 6
respectively.
Currently what I have done, for repeating max values in the same position, I just sum up the corresponding deltas associated with that position, i.e., -1.4 + 0.8 = -0.6
. So the final delta being back propagated is the following matrix.
0 0 0 0
0 -0.6 0
0.2 0.3 0
Please let me know if this is the correct procedure.
neural-network deep-learning cnn backpropagation
New contributor
$endgroup$
add a comment |
$begingroup$
Say I have a layer a
:
3 4 2
1 5 0
8 6 4
The maxpool using 2x2
filter is:
5 5
8 6
Thus the derivative of the max pooling layer is (in respect to layer a
):
0 0 0
0 1 0
1 1 0
Now say doing backpropagation I have the following deltas:
-1.4 0.8
0.2 0.3
I know that -1.4
and 0.8
is associated with 5
. While 0.2
and 0.3
corresponds to 8
and 6
respectively.
Currently what I have done, for repeating max values in the same position, I just sum up the corresponding deltas associated with that position, i.e., -1.4 + 0.8 = -0.6
. So the final delta being back propagated is the following matrix.
0 0 0 0
0 -0.6 0
0.2 0.3 0
Please let me know if this is the correct procedure.
neural-network deep-learning cnn backpropagation
New contributor
$endgroup$
Say I have a layer a
:
3 4 2
1 5 0
8 6 4
The maxpool using 2x2
filter is:
5 5
8 6
Thus the derivative of the max pooling layer is (in respect to layer a
):
0 0 0
0 1 0
1 1 0
Now say doing backpropagation I have the following deltas:
-1.4 0.8
0.2 0.3
I know that -1.4
and 0.8
is associated with 5
. While 0.2
and 0.3
corresponds to 8
and 6
respectively.
Currently what I have done, for repeating max values in the same position, I just sum up the corresponding deltas associated with that position, i.e., -1.4 + 0.8 = -0.6
. So the final delta being back propagated is the following matrix.
0 0 0 0
0 -0.6 0
0.2 0.3 0
Please let me know if this is the correct procedure.
neural-network deep-learning cnn backpropagation
neural-network deep-learning cnn backpropagation
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