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https://github.com/vxern/synadart

A limited, but fully documented neural network library created for educational purposes.
https://github.com/vxern/synadart

complete confidence dart dartlang documented educational extensive neural-network nn

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A limited, but fully documented neural network library created for educational purposes.

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README 

        
# synadart



The `synadart` library can be used to create neural networks of any complexity,

as well as learn from the source code by studying its extremely clean

implementation.



## Launching our first network



To begin using the `synadart`, we must first import it into our project:



```dart

import 'package:synadart/synadart.dart';

```



Next, we must create a network of our chosen type. Let's create a sequential

network, in which every layer has one input and one output tensor. This should

be pretty easy:



```dart

final network = Sequential(learningRate: 0.3);

```



Our network is currently empty; it contains no layers and therefore no neurons.

Let's add three layers; the input layer, one hidden layer and the output layer:



```dart

network.addLayers([

  Dense(15, activationAlgorithm: ActivationAlgorithm.sigmoid),

  Dense(5, activationAlgorithm: ActivationAlgorithm.sigmoid),

  Dense(1, activationAlgorithm: ActivationAlgorithm.sigmoid),

]);

```



Now that our network has some structure to it, we can begin using it.. No, not

quite yet. Our network is still not trained, and has no clue what it is doing.

Time to train it.



Firstly, we will create a list of _expected_ values, i.e. _values we are

expecting the network to output_. Here, we are expecting to get the number '5'.



```dart

final expected = [

  [0.01], // 0

  [0.01], // 1

  [0.01], // 2

  [0.01], // 3

  [0.01], // 4

  [0.99], // 5 ( This is what we are anticipating )

  [0.01], // 6

  [0.01], // 7

  [0.01], // 8

  [0.01], // 9

];

```



Fantastic, we are now expecting our infantile network to magically output a

number 5, not having taught it a thing. Oh, right - that's where the training

data part comes in!



We must now tell the network what each of our expected output values is

associated with. Let's teach it some numbers:



```dart

final trainingData = [

  '111101101101111'.split('').map(double.parse).toList(), // Pixel representation of a 0,

  '001001001001001'.split('').map(double.parse).toList(), // a 1,

  '111001111100111'.split('').map(double.parse).toList(), // a 2,

  '111001111001111'.split('').map(double.parse).toList(), // a 3,

  '101101111001001'.split('').map(double.parse).toList(), // a 4,

  '111100111001111'.split('').map(double.parse).toList(), // a 5,

  '111100111101111'.split('').map(double.parse).toList(), // a 6,

  '111001001001001'.split('').map(double.parse).toList(), // a 7,

  '111101111101111'.split('').map(double.parse).toList(), // an 8,

  '111101111001111'.split('').map(double.parse).toList(), // and a 9.

];

```



Now that we granted our network a grand total of 10 numbers to learn, we can

begin training the network using the values we've set up:



```dart

network.train(inputs: trainingData, expected: expected, iterations: 5000);

```



Wonderful! We've trained our network using the pixel representation of number

images, and our network is now able to recognise the number '5' with relative

confidence. The last step is to test our network's capabilities ourselves.



Let's give our network a couple pixel representations of distorted images of the

number '5':



```dart

final testData = [

  '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(),

];

```



To check the confidence of the network in recognising distorted '5's:



```dart

for (final test in testData) {

  print('Confidence in recognising a distorted 5: ${network.process(test)}');

}

```