I've tried to rewrite neural network found here to javascript. My javascript code looks like this.
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 it how to resolve 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;
}
The problem is that all results that I get is close to 0.5 and pretty random, no matter what arguments I use. For example:
UseNet(0,0) => 0.5107701166677714
UseNet(0,1) => 0.4801498747476413
UseNet(1,0) => 0.5142463167153447
UseNet(1,1) => 0.4881829364416052
What can be wrong with my code?
I've tried to rewrite neural network found here to javascript. My javascript code looks like this.
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 it how to resolve 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;
}
The problem is that all results that I get is close to 0.5 and pretty random, no matter what arguments I use. For example:
UseNet(0,0) => 0.5107701166677714
UseNet(0,1) => 0.4801498747476413
UseNet(1,0) => 0.5142463167153447
UseNet(1,1) => 0.4881829364416052
What can be wrong with my code?
Share Improve this question edited Mar 18, 2014 at 22:34 Henk Holterman 274k32 gold badges351 silver badges533 bronze badges asked Mar 18, 2014 at 22:21 Łukasz W.Łukasz W. 9,7635 gold badges39 silver badges65 bronze badges 3- Off-topic: you could define most methods in the prototype instead of in each instance. – Oriol Commented Mar 21, 2014 at 22:12
- definitely give stackoverflow./questions/13998970/… a read-through – Mike 'Pomax' Kamermans Commented Mar 21, 2014 at 23:45
- Really impressive! Hi, I would really like to see this in action. Do you have this hosted somewhere with the functioning example code and some sort of interface (can be spartan) I don't know how to trigger this to start learning and how do you dump the output to see the results?...Fascinating stuff. – Frank Tudor Commented Mar 25, 2014 at 20:30
3 Answers
Reset to default 2 +50This network is big enough for the XOR problem and I can't see any obvious mistakes, so I suspect it's getting stuck in a local minimum.
Try going through the training set 10,000 times instead of 1000; this gives it a better chance of breaking out of any minima and converging. You can also increase convergence a lot by upping the number of hidden neurons, tweaking η (the learning rate) or adding momentum. To implement the latter, try using this as your training function:
this.train = function(learningRate) {
var momentum = 0 /* Some value, probably fairly small. */;
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.weight * momentum);
input.factor.delta = 0;
})
})
}
I've had good results changing the learning rate to 1.5 (which is pretty high) and momentum to 0.000001 (which is pretty small).
(Incidentally, have you tried running the .NET implementation with a few different seeds? It can take quite a while to converge too!)
This system uses fuzzy logic. As it says in the article don't use integers instead use "close" real numbers as the article suggests -- try
UseNet(0.1,0.1) =>
UseNet(0.1,0.9) =>
UseNet(0.9,0.1) =>
UseNet(0.9,0.9) =>
For the results anything above 0.5 is a 1 and below is 0
Hmmmm
Try instead of:
var testInputs = [[0,0], [0,1], [1,0], [1,1]];
var testOutputs = [[1],[0],[0],[1]];
This:
var testInputs = [[0.05,0.05], [0.05,0.95], [0.95,0.05], [0.95,0.95]];
var testOutputs = [[1],[0],[0],[1]];
or
var testInputs = [[0,0], [0,1], [1,0], [1,1]];
var testOutputs = [[0.95],[0.05],[0.05],[0.95]];
发布者：admin，转转请注明出处：http://www.yc00.com/questions/1745136880a4613245.html
评论列表（0条）