ai 0.1.2

Library that help construct neural networks (AI) with Dart. Each type of network is one object and designed as close as possible to human brain.

Library for creating AI with Dart

Created under a MIT-style license.

Overview

This library represent an simple way to create neural network. Currently multilayer and single-layer perceptron can be created. In learning used backpropagation algorithm for both of them.

API

Perseptron is designed due to Rosenblatt's perseptron.

The main class is the MultilayerPerseptron which can contains Layers. Each layer consist of one or many Neurons. First layer always consist of InputNeurons where each of them take one input value and have weight equal to 1. All neurons of previous layer have contacts with each neurons of next layer.

Memory

Neural network have long-time and short-time memory. All information (knowledge - weights of synapces) of neural network during studying pass through short-time memory. When studying finished and knowledge is structured, then it pass to long-time memory. Knowledge is saved in JSON file knowledge.json in resources directory. For next time network take knowledge from file and initialize with proper weights.

Sample

This testing network recognize number 5 from numbers in range from 0 to 9. Also she detects distorted numbers of 5.

import 'package:ai/ai.dart';

void main() {
  final l1 = Layer<InputNeuron>(<InputNeuron>[
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron(),
    InputNeuron()
  ]);
  final l2 = Layer<Neuron>(<Neuron>[
    Neuron(15),
    Neuron(15),
    Neuron(15),
    Neuron(15),
    Neuron(15)
  ]);
  final l3 = Layer<Neuron>(<Neuron>[
    Neuron(5)
  ]);
  final n = MultilayerPerceptron(<Layer<NeuronBase>>[
    l1,
    l2,
    l3
  ]);

  final expected = <double>[0.01, 0.01, 0.01, 0.01, 0.01, 0.99, 0.01, 0.01, 0.01, 0.01];

  // Learning data
  final trainInput = <List<double>>[
    '111101101101111'.split('').map(double.parse).toList(),
    '001001001001001'.split('').map(double.parse).toList(),
    '111001111100111'.split('').map(double.parse).toList(),
    '111001111001111'.split('').map(double.parse).toList(),
    '101101111001001'.split('').map(double.parse).toList(),
    '111100111001111'.split('').map(double.parse).toList(), // 5
    '111100111101111'.split('').map(double.parse).toList(),
    '111001001001001'.split('').map(double.parse).toList(),
    '111101111101111'.split('').map(double.parse).toList(),
    '111101111001111'.split('').map(double.parse).toList()
  ];

  // Testing data
  final testInput = <List<double>>[
    '111100111000111'.split('').map(double.parse).toList(),
    '111100010001111'.split('').map(double.parse).toList(),
    '111100011001111'.split('').map(double.parse).toList(),
    '110100111001111'.split('').map(double.parse).toList(),
    '110100111001011'.split('').map(double.parse).toList(),
    '111100101001111'.split('').map(double.parse).toList()
  ];

  // Number which this network must recognize
  final num5 = '111100111001111'.split('').map(double.parse).toList();

  // This network trains
  n.train(input: trainInput, expected: expected, learningRate: 0.42, epoch: 5000);

  // This network predicts result
  print('Recognize 5? - ${n.predict(num5)}');
  for (var item in testInput) {
    print('Recognize distorted 5? - ${n.predict(item)[0]}');
  }
  print('Аnd 0? - ${n.predict(trainInput[0])}');
  print('Аnd 8? - ${n.predict(trainInput[8])}');
  print('Аnd 3? - ${n.predict(trainInput[3])}');

Features and bugs

Please file feature requests and bugs at the issue tracker.

With ❤️ to AI