Accuracy

var net = new brain.NeuralNetwork();

var trainingSet = [
  {
    input: [0.1, 0.1],
    output: [0.2]
  },
  {
    input: [0.2, 0.2],
    output: [0.4]
  },
  {
    input: [0.5, 0.1],
    output: [0.6]
  },
  {
    input: [0, 0.1],
    output: [0.1]
  },
  {
    input: [0.3, 0.1],
    output: [0.4]
  }
];

console.log(net.train(trainingSet, { // Object {error: 0.000099992701244754, iterations: 15197}
  errorThresh: 0.0001,
  learningRate: 0.3
}));

console.log(net.run([0.2, 0.4])); // [0.5480908025594973]
console.log(net.run([0.1, 0.4])); // [0.45124782633077687]
console.log(net.run([0.3, 0.4])); // [0.6239448854007088]
console.log(net.run([0.5, 0.4])); // [0.721436435561435]
console.log(net.run([0, 0.4])); // [0.3393225809755128]

I’ve tried a few different approaches and all sorts of input/outputs (and another library actually).

I really can’t get accurate data out. Any tips?

[wagenaartje]

@brandonros, you cannot expect a neural network to map a function with such a small dataset - you have included just 5 training samples. Note that a neural network will always minimize the error on the given dataset, so you cannot guarantee that a neural network will perform as good on a a dataset that it was not trained on.

There are infinite possibilities for addition. So at least, scale your dataset up to 100 samples - you can do this dynamically by generating them. Split these 100 samples up in óne group of 80, and one group of 20.

Train the network on the 80 samples until it has reachted a low error. Then, test your neural network on the 20 samples on which it wasn’t trained; if the error is low on those 20 samples, you know your neural network has successfully learned how to addition in some way or another.

[robertleeplummerjr]

The datset should be fine if you give it the loose principles of math, it just needs a good deal of them. Something I’ve tried that has worked nice is giving training results in every case from 0 to 10, for example. It depends what your ceiling is and your floor, but if it is a finite set you can give something general enough but specific to not get overfitting.