https://github.com/baughmann/minimal-perceptron
A very basic Perceptron example written in C++
https://github.com/baughmann/minimal-perceptron
Last synced: 7 days ago
JSON representation
A very basic Perceptron example written in C++
- Host: GitHub
- URL: https://github.com/baughmann/minimal-perceptron
- Owner: baughmann
- Created: 2020-09-18T22:27:40.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-07-27T21:14:17.000Z (almost 4 years ago)
- Last Synced: 2025-05-06T08:17:01.011Z (15 days ago)
- Language: C++
- Size: 183 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Minimal C++ Perceptron
### Purpose
This was my first foray into machine learning. As such, I desired to make the simplest version of the simplest ML concept that I could think of.
### Function
The Perceptron learns to be an [XOR gate](https://en.wikipedia.org/wiki/XOR_gate), that is:
| X1 | X2 | Output |
|---|---|---|
| 0 | 0 | 0 |
| 0 | 1 | 1 |
| 1 | 0 | 1 |
| 1 | 1 | 0 |
The number of iterations is directly proportional to the `length` parameter that is passed to the `generateTestData()` function.
I have set the default to 10,000 iterations. This roughly equates to an accuracy of `0.0007 = 0` and `0.9989 = 1`.
### Reuse
If you decide to copy some of this stuff for your own projects, just be aware of the following:
- The `inputs` is a vector pointer to vector pointers. I decided to put that vector on the stack since it's size could be extremely large.
- The activaction function is currently `tanh` (i.e. hyperbolic tangent) which is not at all appropriate for an XOR gate. However, I desired to see the improvement in accuracy progressing with each iteration.