Class: Net

Net(argmap)

The constructor and methods of neural networks.

Create a neural network:

let net = new Net({layers: [3, 4, 5});

Get the prediction for an input:

net.predict([0.4, 0.05, 0.2]);

Fit network to a single observation:

net.fit(0.1, [0.4, 0.05, 0.2], 0.03. 0.8);

Get the JSON representation of the network:

net.json();

Constructor

new Net(argmap)

Creates a neural network.

  • The argmap can have the single property layers (the size of each layer). In this case a random neural network is created.

  • The argmap can have the single property json (the JSON representation of a neural network). In this case a specific neural network is created.

  • The argmap can have the properties layers and seed (the number used to initialize the internal pseudorandom number generator). In this case a non-random neural network is created.

  • The argmap can have the properties layers, activation (the function that accepts the index of a layer and returns an activation function for its neurons) and weight (the function that accepts the index of a layer and returns a weight for the synapses of its neurons). In this case a customized neural network is created.

Parameters:
Name Type Description
argmap

An object that hold the parameters for the constructor.
Source:

Methods

fit(learningRate, inputValues, expectedOutput)

Adjust the weights of the neural network to the provided observation.

In order for it to be trained, it should fit with multiple observations.

Parameters:
Name Type Description
learningRate

A number that controls how much the weights are adjusted to the observation.
inputValues

The feature values of the observation.
expectedOutput

The expected output of the observation. It's size should be equal to the size of the output layer.
Source:

json()

The JSON representation of the neural network.

Source:
Returns:

The JSON representation of the neural network.

predict(inputValues)

Makes a prediction for the provided input.

Parameters:
Name Type Description
inputValues

The values of the features. Their size should be equal to the size of the input layer.
Source:
Returns:

The prediction. It's size should be equal to the size of the output layer.

svg()

An SVG representation of the neural network.

Source:
Returns:

The SVG representation of the neural network. The color of each neuron depends on its activation function while the transparency of the synapses depends on their weight.