Assume that an input RGB image below is entering a Convolutional Neural Network: 3 2 2 80 3 5 3 7 3 1 1 5 72 3 7 0 3 5 1 0 2 3 2 11 5 2 3 4 4 6 0 We use the following vertical edge filter to convolve with the image. 2 7 2 1 0 4 2 7 0 4 2 5 7 2 1 1 8 4 7 2 3 3 7 1 0 5 1 1 0 -1 1 0 -1 1 0 -1 If the padding size is 3 and the stride is also 3. The size of the output will be nxn where n is 4 |0 3 0 1 0 -1 0 -1 -1 2 3 5 3 2 7 6 1 1 1 1 0 4 4 7 2 5 6 1 2 5 4 1 8 2 0 0 -1 0 -1 -1 6 4 3 1 4 7 2 8 3 3 5 1 0 7 3 2 1 1 2 6 3 4

Computer Networking: A Top-Down Approach (7th Edition)
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Author:James Kurose, Keith Ross
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Chapter1: Computer Networks And The Internet
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Assume that an input RGB image below is entering a Convolutional Neural Network:
3
2
2
4
6
1
8
7
7
3
0
1
0
3
3 5
3
1 1 5
2
7
0
3
5
1
2 3 2
5
2
3
We use the following vertical edge filter to convolve with the image.
2
7
0
4
2
1
0
4
0
7
1
0 -1
1 0 -1
1 0 -1
4 2 5
2 1 1
8 4 7
2
3
3
2
7 05
7 1 4
3
0
1
If the padding size is 3 and the stride is also 3. The size of the output will be nxn where n is
2
3
1
0 -1
1
0
-1
1 0 -1
5
3
2
7
6
1
1 0
1
0
1 0
0 4
4
7
2
8
5
2
6 1
5 4
-1
-1
-1
6 4
1
3
4
2
2
1
5
3
3
| 1
7 0
8
1
2
3
7
3
2
1
6
4
Transcribed Image Text:Assume that an input RGB image below is entering a Convolutional Neural Network: 3 2 2 4 6 1 8 7 7 3 0 1 0 3 3 5 3 1 1 5 2 7 0 3 5 1 2 3 2 5 2 3 We use the following vertical edge filter to convolve with the image. 2 7 0 4 2 1 0 4 0 7 1 0 -1 1 0 -1 1 0 -1 4 2 5 2 1 1 8 4 7 2 3 3 2 7 05 7 1 4 3 0 1 If the padding size is 3 and the stride is also 3. The size of the output will be nxn where n is 2 3 1 0 -1 1 0 -1 1 0 -1 5 3 2 7 6 1 1 0 1 0 1 0 0 4 4 7 2 8 5 2 6 1 5 4 -1 -1 -1 6 4 1 3 4 2 2 1 5 3 3 | 1 7 0 8 1 2 3 7 3 2 1 6 4
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