Skip to content

Latest commit

 

History

History
72 lines (45 loc) · 3.31 KB

README.md

File metadata and controls

72 lines (45 loc) · 3.31 KB
Raw

Neural-Network

My sixth attempt at a neural network.

There is a library included in the repo - I would not recommend using it in any production scenario.

But you can try it out by cloning the repo and importing the ./dist/lib.js file.

Constructor

It exposes a Network class as its default export. You can use this to construct networks based on certain parameters you provide in the constructor.

new Network(layerSizes: number[], activationFunctions: {f: (x: number) => number, fPrime: (x: number) => number}[], learningRate: number = 0.3, trainingSubset: number = 0);

The constructor takes 4 parameters:

  • layerSizes: number[]: The number of neurons in each layer. This includes the input and output layers.

    Note: each value in this array must be an integer greater than 0

  • activationFunctions: {f: (x: number) => number, fPrime: (x: number) => number}[]: The activation functions for each layer. The object’s f property can be any non-linear activation function where fPrime is its derivative.

    Note: As the input layer does no calculation on its inputs (it doesn’t take inputs) there is no need for activation functions in this layer. Resultantly, the activation set (f and fPrime) are omitted. Do not include a pair of activation functions for the input layer.

    Info: f and fPrime are stringified on export() and reused on load() (Neither functions exist yet but are planned for next commit) so should not rely on external variables and are ideally pure functions.

    ​ Using properties from the Math object is okay as every JS environment has one however other objects and variables cannot be guaranteed.

  • learningRate: number = 0.3: The learning rate of the network. Default is 0.3 and is optional.

  • trainingSubset: number = 0: The number of examples to randomly choose from the training set. If 0 is provided, the entire set is used for each training cycle. The default is 0 and is optional.

The Network

The network can be operated using several functions on the Network instance.

  • evaluate(inputs: number[]): number[]: Performs a feed-forward step on given inputs.
    • inputs: number[]: The values of the input neurons
    • Returns: number: []: The activations of the output layer.
  • train(trainingSet: example[], confirm: number | ((error: number) => boolean)): number
    • trainingSet: example[]: A complete list of training examples for the network to train on.
    • confirm: number | ((error: number) => boolean): A function to check error level and determine whether to continue training or a number to train a given amount of times.
    • Returns: number: The final error of the network after training.

Other Info

The lib.js file also exports an example type. It is an alias for

{
    inputs: number[], 
	expected: number[] 
}

  • inputs: number[]: A list of input values for each neuron of the input layer.

  • expected: number[]: A list of values expected to be returned from the network.

Contribution

Bugs

You will almost certainly find issues in the code. Please report them and I’ll try to address them as quickly as I can.

Code

Code updates are also welcome. Please let me know if you find an issue and would like to pull some code for it. Any help is welcome.