feedforward

author: Arne (voidragon) Simon [arne_simon@gmx.de]

A simple layered feedforward network with momentum based backward propergation.

It learns fast and it is easy to train a whole population of networks.

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Example

$3

``js
const { Feedforward, Population } = require('feedforward');

const pattern = [
[[0, 0], [0]],
[[1, 0], [0]],
[[0, 1], [0]],
[[1, 1], [1]],
];

// --- train - feedforward ---

const net = new Feedforward({
model: [2, 1],
flexibility: 0.5
});

const measure = net.train({
pattern,
maxIterations: 70,
minError: 0.1,
});

console.log(iterations: ${measure.iterations} error: ${measure.error});

pattern.forEach(p => {
const res = net.map(p[0]);
console.log(${p[1][0]} = ${res[0]} error: ${Math.abs(p[1][0] - res[0])});
});
`

$3

`js
// --- train - population ---

const pop = new Population({
model: [2, 1],
spread: 10,
flexibility: 0.5
});

const measure = pop.train({
pattern,
maxIterations: 70,
minError: 0.1,
});

console.log(iterations: ${measure.iterations} error: ${measure.error});

pattern.forEach(p => {
const res = measure.net.map(p[0]);
console.log(${p[1][0]} = ${res[0]} error: ${Math.abs(p[1][0] - res[0])});
});
`

$3

`js
// every thing that is needed is the config and the connections.
const data = JSON.stringify({
config: net.config,
connections: net.connections,
});

const loaded = JSON.parse(data);
const newNet = Feedforward.fromConnections(loaded.config, loaded.connections);
`

API

$3

A simple feedforward neuronal network.

#### fromConnections(config, connections)[static] -> net

Creates a new network for the config and initializes the connection values.

+ config - A feedforward config object.
+ connections - 2d array of connection values.
+ net - The newly created feedforward network.

#### new Feedforward(config)

+ config
+ model - Array of layer depths.
+ flexibility - How fast the net adepts.
+ activationThreshold - Idicates when an output cell identifies as on, aka 1.0 .

#### map(inputs) -> outputs

+ inputs - Array of input values, normalized between 1.0 and 0.0 .
+ outputs - An array of 0.0 or 1.0, indicating if the output neuron is off or on.

#### correct(outputs)

+ outputs - Array of expected output values, normalized between 1.0 and 0.0 .

#### train(options) -> measure

+ options - Training options.
+ pattern - The pattern to learn, aka approximate.
+ maxInterations - Maximum interations for the learning process.
+ minError - The minimum error at wich learning ends.
+ measure -
+ error - The avarage error for the pattern.
+ iterations - How many iterations where nessecary for learning the pattern.

$3

#### new Population(config)

+ model - Array of layer depths.
+ spread - How many networks will be generated.
+ flexibility - How fast the net adepts.
+ activationThreshold - Indicates when an output cell identifies as on, aka 1.0 .

#### map(inputs) -> outputs

+ inputs - Array of input values, normalized between 1.0 and 0.0 .

#### correct(outputs)

+ outputs - Array of expected output values, normalized between 1.0 and 0.0 .

#### train(options) -> measure

+ options - Training options.
+ pattern - The pattern to learn, aka approximate.
+ maxInterations - Maximum interations for the learning process.
+ minError - The minimum error at wich learning ends.
+ measure -
+ error - The avarage error for the pattern.
+ iterations - How many iterations where nessecary for learning the pattern.
+ net` - The feedforward network with the lowest error.

#### nextGeneration()

Takes the best network from the last training and prodcues new population based on this network.