Skip to content

Add support for convolutional\kernel\filter layers #33

@GiorgosXou

Description

@GiorgosXou

My thought proccess:

I could efficiently add support for convolutional\kernel\filter layers while preserving overall performance via just a fake-activation function and a simple condition when loading weights (i.e. "activation function" defined as FILTER...). Pottentialy by squeezing the logic of what-the-size-of-filter-is under the rest of the single byte of each activation function ... for example (considering all activation functions supported by this library are 14+5 custom ones and byte-size allows use up to 255) activation function defined by name FILTERX as 22 could be 2x2, or 98 9x8 or 101 10x1 and so on... (or even better i could also shift 22 to represent 1x2 [or even by a predifined shifting variable]) like who's going to use more than 255 on an MCU... let's be honest... although I could also add such a preference too... And finally by manipulating the output via that filter\fake-activation function. +additional destructor logic

Outro:
It always sounds exciting and funny until I start working on it and realise it isn't as easy as first thought lol

Metadata

Metadata

Assignees

No one assigned

    Labels

    enhancementNew feature or request

    Projects

    No projects

    Milestone

    No milestone

    Relationships

    None yet

    Development

    No branches or pull requests

    Issue actions