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https://github.com/totemstech/neuraln


https://github.com/totemstech/neuraln

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README 

          
# NeuralN

### Powerful Neural Network for Node.js



NeuralN is a C++ Neural Network library for Node.js with multiple advantages

compared to existing solutions:

  - Works with extra large datasets (>1Go allowed by nodejs)

  - Multi-Threaded training available.



#### Large datasets



With Node.js and the V8, it is not possible to work with large datasets since

the maximum allowed memory is around 512MB for 32-bits machines and 1GB for

64-bits machines. When you are working with datasets of several gigabytes, it

quickly becomes difficult to train you network with all your data.



NeuralN allows you to use datasets as big as your memory can contain.



#### Multi-Threaded



Working with large datasets increases the performances of your final network,

but the learning phase can sometimes take up to several days or even weeks to

obtain good results.



With the multi-threaded training method of NeuralN, you can significantly reduce

the duration of the learning phase, by training your network simultaneously on

different parts of your dataset. The results of each iteration are then combined.



## Install



```

npm install neuraln

```



## How it works



```javascript

var NeuralN = require('neuraln');



/* Create a neural network with 4 layers (2 hidden layers) */

var network = new NeuralN([ 1, 4, 3, 1 ]);



/* Add points to the training set */

for(var i = -1; i < 1; i+=0.1) {

  network.train_set_add([ i ], [ Math.abs(Math.sin(i)) ]);

}



/* Train the network with one of the two available methods */

/* monothread (blocking) vs multithread (non-blocking)     */

network.train({

  target_error: 0.01,

  iterations: 20000,



  multithread: true,

  /* Relevant only when multithread is true: */

  step_size: 100,

  threads: 4

}, function(err) {



});



/* Run */

var result = network.run([ (Math.random() * 2) - 1 ]);



/* Retrieve the network's string representation */

var string = network.to_string();



/* Retrieve the network's state string */

var state = network.get_state();

```



## Instantiation & Methods



```javascript

var network = new NeuralN(layers, momentum, learning_rate, bias);

var network = new NeuralN(network_string);

```



Instantiate a new network with the following parameters:

- `layers` is an array representing the layers of the network

- `momentum` is a number between 0 and 1. This parameter is optional and defaults to `0.3`

- `learning_rate` is a number. This parameter is optional and defaults to `0.1`

- `bias` is a number. This parameter is optional and defaults to `-1`



Or



- `network_string` a string from a previous network (using `to_string`)



```javascript

network.train_set_add(input, output);

```



Add a training data point with `input` and `output` being arrays of numbers.

`input` and `output` must contain as many values as the number of neurons of the

first and last layers



```javascript

network.train([options, ]callback);

```



Train the network with the training set until the `target_error` or the

`max_iterations` has been reached. The `options` are optional parameters:

- `target_error: 0.01`

- `iterations: 20000`

- `multithread` is a boolean, which defaults to `false`.

- `step_size` represents the number of points of the training set to use by

thread at each iteration. Default to `100` (only with `multithread: true`)

- `threads` represents the number of threads to be used for the training.

Default to `4`  (only with `multithread: true`)

- `callback(err)` is called once the training is done.



All these parameters are optional except for the `callback`



```javascript

network.run(input)

```



Runs the given `input` throught the network and returns its `output`



```javascript

network.to_string()

```



Returns a string representation of the network in order to save and reload it

later



```javascript

network.get_state()

```



Returns a string representation of each neuron of the network. It allows you to

understand which entrance neurons most impacted the final result.



```javascript

network.to_json();



// Example:

{ layers: [ 1, 4, 3, 1 ],

  momentum: 0.3,

  learning_rate: 0.1,

  bias: -1,

  biases:

   [ [],

     [ -0.00000901958, -0.00000414136, 0.00000156238, -0.00000275219 ],

     [ 0.000125352, 0.000145129, 0.000285706 ],

     [ -0.00914877 ] ],

  weights:

   [ [],

     [ [ 0.218714 ], [ 0.285424 ], [ 0.236087 ], [ 0.329174 ] ],

     [ [ 0.0541952, -0.057953, -0.0293854, 0.030311 ],

       [ -0.106412, -0.0125738, 0.0167244, -0.117874 ],

       [ -0.0977025, -0.0275803, 0.0262269, 0.00674729 ] ],

     [ [ -0.0480921, -0.0574143, -0.118449 ] ] ] }

```



Returns a json representation of the network. This is not recommended when the

network structure gets big.



```javascript

network.get_state_json();



// Example:

{ layers: [ 1, 4, 3, 1 ],

  values:

   [ ,

     [ [ 0.978927 ], [ 1.10844 ], [ 0.926947 ], [ 0.974797 ] ],

     [ [ 2.39933, -2.49017, -5.58942, 3.32711 ],

       [ -2.09446, -0.193907, 1.53176, -2.44019 ],

       [ -2.62491, -0.0477072, 2.40908, -3.03165 ] ],

     [ [ 1.38492, -0.294276, -0.362479 ] ] ] }

```



Returns a json representation of the network's state. This is not recommended

when the network structure gets big.



## Contact us



Feel free to contact us at `hello@totems.co`



## License



Distributed under the MIT License.



Copyright Teleportd Ltd. and other Contributors



Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:



The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.



THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.