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synaps v1.0.1

TypeScript Neural Network Library

Synaps

TypeScript Neural Network Library

Installation

  • In the browser

    <script type="text/javascript" src="https://unpkg.com/synaps"></script>
    
  • In Node.js

    $ npm install synaps
    

    and

    const synaps = require("synaps").default;
    

    or in es6 or TypeScript

    import synaps from "synaps";
    

Usage

  • Creating a new instance

    • Neural network with 3 input neurons and 1 output neuron

      let network = new synaps.Network.Type.FeedForward(3, [], 1);
      
    • Neural network with 4 input neurons, 3 hidden neurons and 2 output neurons

      let network = new synaps.Network.Type.FeedForward(4, [ 3 ], 2);
      
    • Neural network with 6 input neurons, two hidden layers with 4 and 2 neurons, and 3 output neurons

      let network = new synaps.Network.Type.FeedForward(6, [ 4, 2 ], 3);
      
  • Passing any number of additional options to the network

    // pass an object containing the desired options as the fourth parameter
    let network = new synaps.Network.Type.FeedForward(3, [ 4 ], 1, {
        seed: 501935,
        learningRate: 0.3,
        hiddenLayerActivationFunction: new synaps.Activation.HyperbolicTangent(),
        outputLayerActivationFunction: new synaps.Activation.BinaryStep()
    });
    
  • Available activation functions

    new synaps.Activation.ArcTangent();
    new synaps.Activation.BinaryStep();
    new synaps.Activation.GaussianFunction();
    new synaps.Activation.HyperbolicTangent();
    new synaps.Activation.Identity();
    new synaps.Activation.LogisticFunction();
    new synaps.Activation.RectifiedLinearUnit();
    new synaps.Activation.RectifiedLinearUnit(0.01);
    new synaps.Activation.SinusoidFunction();
    
  • Training the network using supervised batch ("all-at-once") learning

    // the first parameter is the array of inputs and the second parameter is the array of desired outputs
    // the third parameter is the optional number of iterations and the fourth parameter is the optional error threshold
    let error = network.trainBatch(
        [
            [0, 0, 1],
            [0, 1, 1],
            [1, 0, 1],
            [1, 1, 1]
        ],
        [
            [ 0 ],
            [ 1 ],
            [ 1 ],
            [ 0 ]
        ],
        60000,
        0.005
    );
    
  • Training the network using supervised online ("single-pattern") learning

    // the first parameter is the input and the second parameter is the desired output
    let error = network.train([0, 0, 1], [ 0 ]);
    
  • Asking the network to predict some output from a supplied input pattern

    // the single parameter is the input to process
    network.predict([ 0, 0, 1 ])
    
  • Saving the network with all its properties to a JSON string

    let jsonStr = JSON.stringify(network);
    
  • Restoring the network with all its properties from a JSON string

    let network = synaps.Network.Type.FeedForward.fromJson(jsonStr);
    

Development

  • Prerequisites

    $ npm install
    
  • Lint the js files

    $ npm lint
    

    or to fix some errors automatically

    $ npm lint:fix
    
  • Build the js files

    $ npm build
    
  • Running the Node.js examples

    $ node examples/node.js
    

Contributing

All contributions are welcome! If you wish to contribute, please create an issue first so that your feature, problem or question can be discussed.

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