Search Results
Search type | Search syntax |
---|---|
Tags | [tag] |
Exact | "words here" |
Author |
user:1234 user:me (yours) |
Score |
score:3 (3+) score:0 (none) |
Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
Views | views:250 |
Code | code:"if (foo != bar)" |
Sections |
title:apples body:"apples oranges" |
URL | url:"*.example.com" |
Saves | in:saves |
Status |
closed:yes duplicate:no migrated:no wiki:no |
Types |
is:question is:answer |
Exclude |
-[tag] -apples |
For more details on advanced search visit our help page |
For use when discussing the commutative and linear, but not associative operator interpreted on functions and distributions.
5
votes
Accepted
1x1 Convolution. How does the math work?
Let's go back at normal convolution: let's say you have a 28x28x3 image (3 = R,G,B).
I don't use torch, but keras, but the principle applies I think. … The same happens when, after a first layer of convolution with 100 filters, you obtain an image of size 28x28x100, at the second convolution layer you decide only the first two dimension of the filter, …