PyData London CNN Lightning Talk

1. X - F + 2P
S
@ejlbell
1 +

2. Convolution
1
1
0
0
1
0
1
1
1
0
0
0
1
0
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1
0
0
0
0
1
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4
Image
Convoluted
Feature

3. Image Filters
-1
-2
-1
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0
+1
+2
+1
-1
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+1
-2
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+2
-1
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+1

4. X
X
X - F + 2P
S
X = image size
1 +

5. X
X
F
X = image size
F = filter size
X - F + 2P
S
1 +

6. X
XX = image size
F = filter size
P = padding
F
P
X - F + 2P
S
1 +

7. X
XX = image size
F = filter size
P = padding
S = stride
P
X - F + 2P
S
1 +

8. X
XX = image size
F = filter size
P = padding
S = stride
P
X - F + 2P
S
1 +

9. X
XX = image size
F = filter size
P = padding
S = stride
P
X - F + 2P
S
1 +

10. X
XX = image size
F = filter size
P = padding
S = stride
P
X - F + 2P
S
1 +

11. X
XX = image size
F = filter size
P = padding
S = stride
P
X - F + 2P
S
1 +

12. X
XX = image size
F = filter size
P = padding
S = stride
P
X - F + 2P
S
1 +

13. X
XX = image size
F = filter size
P = padding
S = stride
P
X - F + 2P
S
1 +

14. Filter Size
1 × 1 3 × 3 7 × 7 9 × 95 × 5

15. Max Pooling
1
6
2
2
4
1
0
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1
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3
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2
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6
3
X
Y

16. Example: VGG
19 layers
3x3 convolution
pad 1
stride 1

17. 19 layers
3x3 convolution
pad 1
stride 1
X - F + 2P
S
1 + = X
Example: VGG

18. Resources
• image size
• parameters (dense vs conv)
• parallelisation (data vs model)

19. thanks
@ejlbell

20. References