synapses.js

A neural networks library for JavaScript!

Basic usage

Install synapses

npm i [email protected]

Require the library

const syn = require('synapses');

Create a random neural network by providing its layer sizes

let randNet = new syn.Net({layers: [2, 3, 1]});

Input layer: the first layer of the network has 2 nodes.
Hidden layer: the second layer has 3 neurons.
Output layer: the third layer has 1 neuron.

Get the json of the random neural network

randNet.json();
// "[[{\"activationF\" : \"sigmoid\", \"weights\" : [-0.5,0.1,0.8]}" +
// " ,{\"activationF\" : \"sigmoid\", \"weights\" : [0.7,0.6,-0.1]}," +
// "  {\"activationF\" : \"sigmoid\", \"weights\" : [-0.8,-0.1,-0.7]}]," +
// "[{\"activationF\" : \"sigmoid\", \"weights\" : [0.5,-0.3,-0.4,-0.5]}]]"

Create a neural network by providing its json

let net = new syn.Net({
    json:
        "[[{\"activationF\" : \"sigmoid\", \"weights\" : [-0.5,0.1,0.8]}" +
        " ,{\"activationF\" : \"sigmoid\", \"weights\" : [0.7,0.6,-0.1]}," +
        "  {\"activationF\" : \"sigmoid\", \"weights\" : [-0.8,-0.1,-0.7]}]," +
        " [{\"activationF\" : \"sigmoid\", \"weights\" : [0.5,-0.3,-0.4,-0.5]}]]"
});

Make a prediction

net.predict([0.2, 0.6]);
// [ 0.49131100324012494 ]

Train a neural network

net.fit(0.1, [0.2, 0.6], [0.9]);

The fit method adjusts the weights of the neural network to a single observation.

In practice, for a neural network to be fully trained, it should be fitted with multiple observations.

Advanced usage

Create a neural network for testing

new syn.Net({layers: [2, 3, 1], seed: 1000});

We can provide a seed to create a non-random neural network. This way, we can use it for testing.

Define the activation functions and the weights

function activation(layerIndex) {
    switch (layerIndex) {
        case 0:
            return syn.fun.SIGMOID;
        case 1:
            return syn.fun.IDENTITY;
        case 2:
            return syn.fun.LEAKY_RE_LU;
        case 3:
            return syn.fun.TANH;
    }
}

function weight(_layerIndex) {
    return 1.0 - 2.0 * Math.random();
}

let customNet = new syn.Net({
    layers: [4, 6, 8, 5, 3],
    activation: activation,
    weight: weight
});

The activation function accepts the index of a layer and returns an activation function for its neurons.
The weight function accepts the index of a layer and returns a weight for the synapses of its neurons.

If we don't provide these functions, the activation function of all neurons is sigmoid, and the weight distribution of the synapses is normal between -1.0 and 1.0.

Draw a neural network

customNet.svg();

With its svg drawing, we can see what a neural network looks like. The color of each neuron depends on its activation function while the transparency of the synapses depends on their weight.

Measure the difference between the expected and predicted values

let expAndPredVals = [
    [[0.0, 0.0, 1.0], [0.0, 0.1, 0.9]],
    [[0.0, 1.0, 0.0], [0.8, 0.2, 0.0]],
    [[1.0, 0.0, 0.0], [0.7, 0.1, 0.2]],
    [[1.0, 0.0, 0.0], [0.3, 0.3, 0.4]],
    [[0.0, 0.0, 1.0], [0.2, 0.2, 0.6]]
];

Root-mean-square error

syn.stats.rmse(expAndPredVals);
// 0.6957010852370435

Classification accuracy score

syn.stats.score(expAndPredVals);
// 0.6

Create a `Codec` by providing the attributes and the data points

One hot encoding is a process that turns discrete attributes into a list of 0.0 and 1.0.
Minmax normalization scales continuous attributes into values between 0.0 and 1.0.

You can use a codec to encode and decode a data point.

let setosa = {
    petal_length: "1.5",
    petal_width: "0.1",
    sepal_length: "4.9",
    sepal_width: "3.1",
    species: "setosa"
};

let versicolor = {
    petal_length: "3.8",
    petal_width: "1.1",
    sepal_length: "5.5",
    sepal_width: "2.4",
    species: "versicolor"
};

let virginica = {
    petal_length: "6.0",
    petal_width: "2.2",
    sepal_length: "5.0",
    sepal_width: "1.5",
    species: "virginica"
};

let dataset = [setosa, versicolor, virginica];

let attributes = [
    ["petal_length", false],
    ["petal_width", false],
    ["sepal_length", false],
    ["sepal_width", false],
    ["species", true],
];

let codec = new syn.Codec({attributes: attributes, data: dataset});

The first parameter is a list of pairs that define the name and the type (discrete or not) of each attribute.
The second parameter is an iterator that contains the data points.

Get the json of the codec

let codecJson = codec.json();
// "[{\"Case\" : \"SerializableContinuous\", " +
//   "\"Fields\" : [{\"key\" : \"petal_length\",\"min\" : 1.5,\"max\" : 6.0}]}," +
//  "{\"Case\" : \"SerializableContinuous\", " +
//   "\"Fields\" : [{\"key\" : \"petal_width\",\"min\" : 0.1,\"max\" : 2.2}]}," +
//  "{\"Case\" : \"SerializableContinuous\", " +
//   "\"Fields\" : [{\"key\" : \"sepal_length\",\"min\" : 4.9,\"max\" : 5.5}]}," +
//  "{\"Case\" : \"SerializableContinuous\", " +
//   "\"Fields\" : [{\"key\" : \"sepal_width\",\"min\" : 1.5,\"max\" : 3.1}]}," +
//  "{\"Case\" : \"SerializableDiscrete\", " +
//   "\"Fields\" : [{\"key\" : \"species\",\"values\" : [\"virginica\",\"versicolor\",\"setosa\"]}]}]"

Create a `Codec` by providing its json

new syn.Codec({json: codecJson})

Encode a data point

let encodedSetosa = codec.encode(setosa);
// [ 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0 ]

Decode a data point

codec.decode(encodedSetosa);
// {
//     petal_length: "1.5",
//     petal_width: "0.1",
//     sepal_length: "4.9",
//     sepal_width: "3.1",
//     species: "setosa"
// }

mrdimosthenis / synapses.js Goto Github PK

synapses.js's Introduction

synapses.js

Basic usage

Install synapses

Require the library

Create a random neural network by providing its layer sizes

Get the json of the random neural network

Create a neural network by providing its json

Make a prediction

Train a neural network

Advanced usage

Create a neural network for testing

Define the activation functions and the weights

Draw a neural network

Measure the difference between the expected and predicted values

Create a Codec by providing the attributes and the data points

Get the json of the codec

Create a Codec by providing its json

Encode a data point

Decode a data point

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Create a `Codec` by providing the attributes and the data points

Create a `Codec` by providing its json