Resuming training on Neural Network

Hi, Neural Network gives me weird results. Maybe I miss something. Please help.

What is wrong?

Neural Network result is incorrect.

Where does it happen?

I’m running 2.0.0-alpha.12 on Node 13 and Mac.

How do we replicate the issue?

Firstly I tried:

const brain = require('brain.js');

const net = new brain.NeuralNetwork();

const trainingData = {
	input: { a: 0, b: 1 },
	output: { good: 1 },
};

net.train(trainingData);

const trainingData2 = {
	input: { a: 1, b: 0 },
	output: { bad: 1 },
};

net.train(trainingData2);

const result = net.run({ a: 0, b: 1 });

console.log(result);
// Output: { good: 0.060428500175476074 }
// I expect: { good: 1 }

Then tried serialising as advised but still doesn’t work:

const brain = require('brain.js');

// first  training
const trainingData = {
	input: { a: 0, b: 1 },
	output: { good: 1 },
};
const net = new brain.NeuralNetwork();
net.train(trainingData);
const stringifiedNet = JSON.stringify(net.toJSON());

// second training
const trainingData2 = {
	input: { a: 1, b: 0 },
	output: { bad: 1 },
};
const net2 = new brain.NeuralNetwork();
net2.fromJSON(JSON.parse(stringifiedNet));
net2.train(trainingData2);
const stringifiedNet2 = JSON.stringify(net2.toJSON());

// prediction
const net3 = new brain.NeuralNetwork();
net3.fromJSON(JSON.parse(stringifiedNet2));
const result = net3.run({
	a: 0,
	b: 1,
});
console.log(result);
// Output: { good: 0.07364676147699356 }
// I expect: { bad: 0, good: 1 }

const result2 = net3.run({
	a: 1,
	b: 0,
});
console.log(result2);
// Output: { good: 0.07043380290269852 }
// I expect: { bad: 1, good: 0 }

How important is this (1-5)?

4

Expected behavior (i.e. solution)

I expect NN to return other results (described in code comments).

Other Comments

You guys do an awesome job building brain.js!

EDIT: Seen this comment on StackOverflow: The keepNetworkIntact has been renamed to reinforce but can’t see this property in INeuralNetworkTrainingOptions

EDIT2: I read somewhere I should pass all data every time I run train. Is there any way around it? I train the NN every day with new daily statistics. Seems insane to have to calculate them every time for all the previous days (i.e. past 2 years).

[robertleeplummerjr]

@GELight This is supported, but if new neurons (in this case, aka object keys) are added, this breaks the network’s design.

Are all the keys of the object you are feeding in known in advance?

To achieve a network that can train to learn new things:

1. after training, you need to obtain the json of the network, and put it in your storage.
2. when new data is being used to train, append it to the already existing training set.
3. instantiate the network using the json, net.fromJSON(json)
4. then just train the net.

If you find this functionality not working, then there is a bug, and if you could include the scripting used to find that bug, I can get a quick fix in.

[robertleeplummerjr]

It is a very high priority issue, and I plan on looking at it tomorrow morning.