X - F + 2P S
@ejlbell
1 +
Convolution
1
0
4
ImageConvoluted
Feature
Image Filters
-1
-2
+1
+2
X
X = image size
F
F = filter size
XX = image size
P = padding
P
S = stride
Filter Size
1 × 1 3 × 3 7 × 7 9 × 95 × 5
Max Pooling
6
2
5
3
8
3X
Y
Example: VGG
19 layers
3x3 convolution
pad 1
stride 1
1 + = X
Resources • image size
• parameters (dense vs conv)
• parallelisation (data vs model)
thanks @ejlbell
References
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