These are chat archives for FreeCodeCamp/DataScience
discussion on how we can use statistical methods to measure and improve the efficacy of http://freeCodeCamp.com
I guess you are trying a conv-NN? If so, kernel (also known as convolution matrix or mask) is usually a square matrix. It seems that tiny_dnn is set to work with square matrices only, so you need just one parameter value. I tried to go through the code of the Yolo exercise but I didn't find the variable "kernel" mentioned in the code of the example. For what I can get from the code, it seems an implementation of what the author later described in the website. If you could help me by pointing me to the section where those 2 values are given to the program?
Kernel size and Window size? Not sure, in convolutional NN jargon I think "window size" could refer to the size of the overlapping matrix when adding padding? Reading somewhere else I found out that it might be also related the size of the pooling matrices if you include one in your processing.
I agree with @erictleung and you that
darknet documentation is poor. And I would add there is enough mix up and overloading of terms in the whole DS / ML sector to bring you mad sometimes.
I saw https://orange.biolab.si/ long time ago when they were starting as a project. That is a really lightweight one and I think they don't have an API for cnn, you should implement yourself using their NN capabilities.
Have you training in cNN? If not, I would suggest you to get some. It might help you the make the whole reverse-engineering easier?
deanhu2 sends brownie points to @evaristoc :sparkles: :thumbsup: :sparkles:
rajathrao sends brownie points to @erictleung :sparkles: :thumbsup: :sparkles: