/KNN-TFjs

This is a from scratch implementation of the K-nearest neighbors model in TensorFlowJs

Primary LanguageTypeScriptMIT LicenseMIT

KNN-TFjs

This is a from scratch implementation of the K-nearest neighbors model in TensorFlowJs.

In this example, we have a training set of letters that belong to one class or another. After training on those images, we then classify letters as seen in the example below

This implementation also allows you to hyperparameter tune the number of neighbors used in approximation.

To Run

  • Download this code to a folder
  • Open terminal in that folder (ie. cd into this folder)
  • Setup all dependencies with npm i
    • This will install the project dependencies to node_modules/
    • Dependencies like: tensorflow.js, nodemon, colors, etc
  • To run the code, please run: 
    npm start

Example

$ npm start

> knnmodel@1.0.0 start C:\Users\tvdar\Desktop\Final Project\KnnTF
> ts-node ./index


============================
=== Training Iteration 0  :  | Train Accuracy: 100%
  Labels Expected: 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
  Labels Guessed : 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
=== Training Iteration 1  :  | Train Accuracy: 80.0000011920929%
  Labels Expected: 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
  Labels Guessed : 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
=== Training Iteration 2  :  | Train Accuracy: 80.0000011920929%
  Labels Expected: 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
  Labels Guessed : 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
=== Training Iteration 3  :  | Train Accuracy: 69.9999988079071%
  Labels Expected: 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
  Labels Guessed : 0, 0, 0, 0, 0, 0, 0, 0, 1, 1
=== Training Iteration 4  :  | Train Accuracy: 69.9999988079071%
  Labels Expected: 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
  Labels Guessed : 0, 0, 0, 0, 0, 0, 0, 0, 1, 1
=== Training Iteration 5  :  | Train Accuracy: 69.9999988079071%
  Labels Expected: 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
  Labels Guessed : 0, 0, 0, 0, 0, 0, 0, 0, 1, 1
=== Training Iteration 6  :  | Train Accuracy: 69.9999988079071%
  Labels Expected: 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
  Labels Guessed : 0, 0, 0, 0, 0, 0, 0, 0, 1, 1
=== Training Iteration 7  :  | Train Accuracy: 50%
  Labels Expected: 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
  Labels Guessed : 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
=== Training Iteration 8  :  | Train Accuracy: 50%
  Labels Expected: 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
  Labels Guessed : 0, 1, 0, 0, 1, 1, 0, 0, 1, 0
=== Training Iteration 9  :  | Train Accuracy: 50%
  Labels Expected: 0, 0, 0, 0, 0, 1, 1, 1, 1, 1
  Labels Guessed : 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
Picking:  1  as the most optimal number of neighbors based on the fit method above
Created a model and fit it to the supplied Data and labels


Predicted Values are:  [ 1, 0, 1, 1, 1 ]
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