function NeuralFactor(weight) {
var self = this;
this.weight = weight;
this.delta = 0;
}
function Sigmoid(value) {
return 1 / (1 + Math.exp(-value));
}
function Neuron(isInput) {
var self = this;
this.pulse = function() {
self.output = 0;
self.input.forEach(function(item) {
self.output += item.signal.output * item.factor.weight;
});
self.output += self.bias.weight;
self.output = Sigmoid(self.output);
};
this.bias = new NeuralFactor(isInput ? 0 : Math.random());
this.error = 0;
this.input = [];
this.output = 0;
this.findInput = function(signal) {
var input = self.input.filter(function(input) {
return signal == input.signal;
})[0];
return input;
};
}
function NeuralLayer() {
var self = this;
this.pulse = function() {
self.neurons.forEach(function(neuron) {
neuron.pulse();
});
};
this.neurons = [];
this.train = function(learningRate) {
self.neurons.forEach(function(neuron) {
neuron.bias.weight += neuron.bias.delta * learningRate;
neuron.bias.delta = 0;
neuron.input.forEach(function(input) {
input.factor.weight += input.factor.delta * learningRate;
input.factor.delta = 0;
})
})
}
function NeuralNet(inputCount, hiddenCount, outputCount) {
var self = this;
this.inputLayer = new NeuralLayer();
this.hiddenLayer = new NeuralLayer();
this.outputLayer = new NeuralLayer();
this.learningRate = 0.5;
for(var i = 0; i < inputCount; i++)
self.inputLayer.neurons.push(new Neuron(true));
for(var i = 0; i < hiddenCount; i++)
self.hiddenLayer.neurons.push(new Neuron());
for(var i = 0; i < outputCount; i++)
self.outputLayer.neurons.push(new Neuron());
for (var i = 0; i < hiddenCount; i++)
for (var j = 0; j < inputCount; j++)
self.hiddenLayer.neurons[i].input.push({
signal: self.inputLayer.neurons[j],
factor: new NeuralFactor(Math.random())
});
for (var i = 0; i < outputCount; i++)
for (var j = 0; j < hiddenCount; j++)
self.outputLayer.neurons[i].input.push({
signal: self.hiddenLayer.neurons[j],
factor: new NeuralFactor(Math.random())
});
this.pulse = function() {
self.hiddenLayer.pulse();
self.outputLayer.pulse();
};
this.backPropagation = function(desiredResults) {
for(var i = 0; i < self.outputLayer.neurons.length; i++) {
var outputNeuron = self.outputLayer.neurons[i];
var output = outputNeuron.output;
outputNeuron.error = (desiredResults[i] - output) * output * (1.0 - output);
for(var i = 0; i < self.hiddenLayer.neurons.length; i++) {
var hiddenNeuron = self.hiddenLayer.neurons[i];
var error = 0;
for(var j = 0; j < self.outputLayer.neurons.length; j++) {
var outputNeuron = self.outputLayer.neurons[j];
error += outputNeuron.error * outputNeuron.findInput(hiddenNeuron).factor.weight * hiddenNeuron.output * (1.0 - hiddenNeuron.output);
hiddenNeuron.error = error;
for(var j = 0; j < self.outputLayer.neurons.length; j++) {
var outputNeuron = self.outputLayer.neurons[j];
for(var i = 0; i < self.hiddenLayer.neurons.length; i++) {
var hiddenNeuron = self.hiddenLayer.neurons[i];
outputNeuron.findInput(hiddenNeuron).factor.delta += outputNeuron.error * hiddenNeuron.output;
outputNeuron.bias.delta += outputNeuron.error * outputNeuron.bias.weight;
for(var j = 0; j < self.hiddenLayer.neurons.length; j++) {
var hiddenNeuron = self.hiddenLayer.neurons[j];
for(var i = 0; i < self.inputLayer.neurons.length; i++) {
var inputNeuron = self.inputLayer.neurons[i];
hiddenNeuron.findInput(inputNeuron).factor.delta += hiddenNeuron.error * inputNeuron.output;
hiddenNeuron.bias.delta += hiddenNeuron.error * hiddenNeuron.bias.weight;
};
this.train = function(input, desiredResults) {
for(var i = 0; i < self.inputLayer.neurons.length; i++) {
var neuron = self.inputLayer.neurons[i];
neuron.output = input[i];
self.pulse();
self.backPropagation(desiredResults);
self.hiddenLayer.train(self.learningRate);
self.outputLayer.train(self.learningRate);
};
}
Now I'm trying to learn how to resolve the XOR problem. I'm teaching it like this:
var net = new NeuralNet(2,2,1);
var testInputs = [[0,0], [0,1], [1,0], [1,1]];
var testOutputs = [[1],[0],[0],[1]];
for (var i = 0; i < 1000; i++)
for(var j = 0; j < 4; j++)
net.train(testInputs[j], testOutputs[j]);
function UseNet(a, b) {
net.inputLayer.neurons[0].output = a;
net.inputLayer.neurons[1].output = b;
net.pulse();
return net.outputLayer.neurons[0].output;
}
