Roadmap

v2: Simplicity and Performance First

It must be easy enough to teach a child and fast enough to cause innovation.

Layer PlayGround

Currently we want to focus our efforts on creating a "layer playground", where we have tons of layers, and they can work with any network type, be it feedforward, or recurrent.

GPU Accelerated

We've put in a considerable amount of work to achieve gpu acceleration, and will eventually be fully gpu, in all networks. The desired library where much of the work has been done is http://gpu.rocks.

Easy concepts

The concepts of recurrent and feedforward have always seemed like completely different networks when really there are a few very simple things that make them different. We want to make them so easy anyone can use them:

FeedForward class overview

new FeedForward({
  inputLayer: () => { /* return an instantiated layer here */ }
  hiddenLayers: [
    (input) => { /* return an instantiated layer here */ },
    /* more layers? by all means... */
    /* `input` here is the output from the previous layer */
  ]
  outputLayer: (input) => { /* return an instantiated layer here */ }
});

FeedForward class usage example (xor)

import { FeedForward, layer } from 'brain.js';
const { input, feedForward, output } = layer;
const net = new FeedForward({
  inputLayer: () => input({ width: 2 })
  hiddenLayers: [
    input => feedForward({ width: 3 }, input),
  ]
  outputLayer: input => output({ width: 1 }, input)
});
net.train([
  { input: [0, 0], output: [0] },
  { input: [0, 1], output: [1] },
  { input: [1, 0], output: [1] },
  { input: [1, 1], output: [0] }
]);
net.run([0, 0]); // [0]
net.run([0, 1]); // [1]
net.run([1, 0]); // [1]
net.run([1, 1]); // [0]

Recurrent class overview

new Recurrent({
  inputLayer: () => { /* return an instantiated layer here */ }
  hiddenLayers: [
    (input, recurrentInput) => { /* return an instantiated layer here */ },
    /* more layers? by all means... */
    /* `input` here is the output from the previous layer */
    /* `recurrentInput` here is the output from the previous recurse, or zeros on first go */
  ]
  outputLayer: (input) => { /* return an instantiated layer here */ }
});

Recurrent class usage example (xor)

import { Recurrent, layer } from 'brain.js';
const { input, lstm, peephole, output } = layer;
const net = new Recurrent({
  inputLayer: () => input({ width: 2 }),
  hiddenLayers: [
    (input, recurrentInput) => lstm({ height: 3 }, recurrentInput, input)
  ],
  outputLayer: input => output({ width: 1 }, input)
});
net.train([
  { input: [0, 0], output: [0] },
  { input: [0, 1], output: [1] },
  { input: [1, 0], output: [1] },
  { input: [1, 1], output: [0] }
]);
net.run([0, 0]); // [0]
net.run([0, 1]); // [1]
net.run([1, 0]); // [1]
net.run([1, 1]); // [0]

Removal of brain.recurrent

brain.recurrent provided a nice means of learning to simplify how to expose the concept of recurrent to the public, in v2 recurrent will essentially become the Recurrent class, so we can remove brain.recurrent, and continue development there.

Distributed learning research

More to come.

v3: Unsupervised learning, spiking neural networks, distributed learning