πAdvanced Concepts
Last updated
Last updated
Adding an additional one border or more to the image so the image is n+2 x n+2
and after convolution we end up with n x n
image which is the original size of the image
p
= number of added borders
For convention: it is filled by 0
For better understanding let's say that we have two concepts:
It means no padding so:
n x n
* f x f
β‘ n-f+1 x n-f+1
Pad so that output size is the same as the input size.
So we want that π§:
n+2p-f+1
= n
Hence:
p
= (f-1)/2
For convention f is chosen to be odd π©βπ
Another approach of convolutions, we calculate the output by applying filter on regions by some value s
.
For an n x n
image and f x f
filter, with p
padding and stride s
; the output image size can be calculated by the following formula
To apply convolution operation on an RGB image; for example on 10x10 px RGB image, technically the image's dimension is 10x10x3 so we can apply for example a 3x3x3 filter or fxfx3 π€³
Filters can be applied on a special color channel π¨
π©βπ« Usually when people report number of layers in an NN they just report the number of layers that have weights and params
Convention:
CONV1
+POOL1
=LAYER1
Better performance since they decrease the parameters that will be tuned π«
Term
Description
π· Padding
Adding additional border(s) to the image before convolution
π Strided Convolution
Convolving by s
steps
π Convolutions Over Volume
Applying convs on n-dimensional input (such as an RGB image)
Layer
Description
π« Convolution CONV
Filters to extract features
π Pooling POOL
A technique to reduce size of representation and to speed up the computations
β Fully Connected FC
Standard single neural network layer (one dimensional)