GitHub - Mojo0869/ABSL: ABSL or Adaptive Bitshift learning is a lerning method only using Integers for AI i made. The goal is to make a good Integer Neuronr neuron · GitHub
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Mojo0869
ABSL
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##ABSL - Adaptive bitshft learning ( v.1.0.0 XOR)
ABSL is an experimental, 100% integer-only learning algorithm for neural networks, written from scratch in Rust.
By avoiding floating-point math entirely, ABSL doesn't need an FPU. That makes it interesting for low-power embedded systems, 8-bit/16-bit microcontrollers, and neuromorphic hardware.
Instead of a fixed learning rate, ABSL scales weight updates using an adaptive bit-shift based on the integer error magnitude.
Solving XOR without floats
XOR is normally solved with continuous gradients. I wanted to see if a purely integer-based approach could get there too.
Using a 2-4-1 architecture (2 inputs, 4 hidden neurons, 1 output) instead of the minimal 2-2-1 gives the network enough redundancy to work around the rounding errors that come from integer quantization.
##Benchmarks
Metric---------------------- v5
Perfekt runs (4/4)---------- 98.7%<br>Glaobal Accuracy------------ 99.7%<br>Avg. correct cases / run---- 3.99<br>total failure ((look in results/ at the last dierctorys)
##Accuracy by Input
(0,0): 99.9%
(0,1): 99.9%
(1,0): 99.2%
(1,1): 99.6%
How does it Work?
I'm a bit lazy so look in src/learning_rule/absl.rs for the Code
The shift amount is calculated from the bit-length (ilog2) of the error and weight, which keeps weights from exploding while staying fast.
##Roadmap
Try to get the best run possible: 99%+ done (99.7% global accuracy with v5)
Try to scale it to MNIST
##About me
I am Mojo 15 years old and live in north germany im currently in 10th grade and love Computers an AI
My Goal is to study an land a good job and live in Japan
##License
Costum License, look LICENSE
About<br>ABSL or Adaptive Bitshift learning is a lerning method only using Integers for AI i made. The goal is to make a good Integer Neuronr neuron<br>Resources<br>Readme<br>License<br>Activity<br>Stars<br>2 stars<br>Watchers<br>0 watching<br>Forks<br>0 forks<br>Report repository
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