ann-js
Artificial neural network in vanilla JS
This is a 3-layer neural network that uses sigmoid activation function and stochastic gradient descent as its backpropagation algorithm.
Installation
Install from the npm repository:
npm install ann-js
Small example
Teaching the network logical XOR operation:
// require the network
const NeuralNetwork = require("ann-js");
// instantiate a network with two inputs, 7 hidden neurons and 1 output
// set learning rate to 0.6
const NN = NeuralNetwork(2, 7, 1, 0.6);
// define our training data
const inputs = [
{ input: [1, 1], expected: 0 },
{ input: [1, 0], expected: 1 },
{ input: [0, 1], expected: 1 },
{ input: [1, 1], expected: 0 }
];
// and let it run
for (let i = 0; i < 10000; i++) {
for (let j = 0; j < inputs.length; j++) {
NN.train(inputs[j]);
}
}
// let's check it out..
for(let i = 0; i < inputs.length; i++) {
console.log("input:", inputs[i].input, "| output:", NN.test(inputs[i].input));
}
// --> input: [ 1, 1 ] | output: 0.007655196970540926
// --> input: [ 1, 0 ] | output: 0.9918757893434477
// --> input: [ 0, 1 ] | output: 0.9925807646447175
// --> input: [ 1, 1 ] | output: 0.007655196970540926Methods
Constructor: NeuralNetwork(numInputs, numHidden, numOutputs, [ learningRate, [ bias ]])
Learning rate is by default set to 0.5 and bias si set to 1.
const NN = NeuralNetwork(1, 3, 2);
// 1 input neuron, 3 neurons in hidden layer, 2 output neurons
const NN = NeuralNetwork(2, 1, 2, 0.9, 2);
// 2 input neurons, 1 neuron in hidden layer, 2 output neurons, learning rate 0.9, bias set to 2.train(trainObject)
Trains the network on a single training example
NN.train({ input: [1, 0], expected: 1 });
NN.train({ input: [1, 1, 1], expected: [1, 0] });The .input property is what the network will be fed with, .expected is the result we're hoping to see.
.test(input)
Performs a single feed forward on the network and returns the result
const NN = NeuralNetwork(1, 2, 1);
NN.test(7);
// --> Numberconst NN = NeuralNetwork(1, 2, 2);
NN.test(14);
// --> Array(2).load(file, callback) && .save(file, callback)
Asynchronously saves or loads the weights of the network. The saved file is in a json format.
NN.load("saved.json", err => {
if(err) {
return console.error(err);
}
// do something with network
});NN.save("saved.json", err => {
if(err) {
return console.error(err);
}
// done saving4
});.loadSync(file) & .saveSync(file)
Synchronous versions of .load & .sync
// loading multiple networks
NN1.loadSync("saved1.json");
NN2.loadSync("saved2.json");
