Feedforward Artificial Neural Network Library
npm install fwd-annfwd-ann is a Feedforward Artificial Neural Network Library developed with TypeScript.bash
npm i --save fwd-ann
`Examples
Go to the to explore examples. To run example run ts-node
your console. Better way to understand how this library works is to explore these examples.
Another good way to understand this library — explore library and create
small projects. Feel free to create anything you want using this library!Below you can find short introduction to the API. It will help you to get
started with
fwd-ann.
API
LayerType
This enum represents possible types of ANN's layers. There are three possible types:* INPUT
* HIDDEN
* OUTPUT
These types are used to create ANN's instance with aim architecture.
activationFuncs
Activation function determines how signals will be modified in neurons. There are lots of
available activation functions:
* Logistic
* UnitLinear
* BinaryStep
* Areasinus
* ...and so onSignals
Signals class represents numbers vector that can be used as input for
ANN (Artificial Neural Network). To create Signals you simply should pass numbers array
to the Signals.create static method:
`typescript
import { Signals } from 'fwd-ann';const signals = new Signals([1, 0, 0, 1]);
`
You can also create by passing Matrix to Signal's class constructor. Matrix is a
special class that you can import from
matrix-calculus library.
`typescript
import { Signals } from 'fwd-ann';
import { SingleColMatrixFactory } from 'matrix-calculus/factories';const signalsMatrix = SingleColMatrixFactory.create([1, 0, 0, 1]);
const signals = new Signals(signalsMatrix);
`
You can pass second (optional) parameter to the or Signals's constructor that
represents names for every passed signal. In some cases it is useful to have signals' names.ANN, createANN
To create ANN object you can use ANN class or createANN utility. Prefer second one.
ANN instance represents Artificial Neural Network that can be taught by Teacher
and used to process some input and respond with some output Signals.
`typescript
import createANN, { LayerType, activationFuncs } from 'fwd-ann';const { ReLU } = activationFuncs;
const ann = createANN(
{
id: 'ExampleANN',
layersData: [
{
type: LayerType.INPUT,
unitsData: [{
qty: 6,
ActivationFunction: ReLU,
}],
},
{
type: LayerType.HIDDEN,
unitsData: [{
qty: 7,
ActivationFunction: ReLU,
}],
},
{
type: LayerType.OUTPUT,
unitsData: [{
qty: 1,
ActivationFunction: ReLU,
}],
},
],
},
{
learningSpeed: .01,
},
);
`
First parameter represents of the Neural Network. Second parameter (optional) represents
parameters such as learningSpeed.Teacher
The Teacher is that core thing that teaches your Neural Network to perform
some actions you need. Teacher's API is vary simple and allows you concentrate on
data and behavior but not on the teaching process implementation.
`typescript
import createANN, { Teacher } from 'fwd-ann';const ann = createANN({
id: 'ExampleANN',
layersData: [/.../],
});
Teacher.teach({
ann,
sets: [/.../],
}).then(({ ann, log }) => {/.../});
`
Do notice! mutates your ann` instance.