Fast and simple neural network for node.js
#Fast and simple Neural Network for node.js
nn is a Neural Network library built for performance and ease of use. It is easy to configure and has sane defaults. You can use it for tasks such as pattern recognition and function approximation.
npm install nn
var nn = require'nn'var net = nn// this example shows how we could train it to approximate sin(x)// from a random set of input/output data.nettraininput: 0.5248588903807104 output: 0.5010908941521808input: 0 output: 0input: 0.03929789311951026 output: 0.03928777911794752input: 0.07391509227454662 output: 0.07384780553540908input: 0.11062344848178328 output: 0.1103979598825075input: 0.14104655454866588 output: 0.14057935309092454input: 0.06176552915712819 output: 0.06172626426511784input: 0.23915000406559558 output: 0.2368769073277496input: 0.27090200221864513 output: 0.267600651550329input: 0.15760037200525404 output: 0.1569487719674096input: 0.19391102618537845 output: 0.19269808506017222input: 0.42272064974531537 output: 0.4102431360805792input: 0.5248469677288086 output: 0.5010805763172892input: 0.4685300185577944 output: 0.45157520770441445input: 0.6920387226855382 output: 0.6381082150316612input: 0.40666140150278807 output: 0.3955452139761714input: 0.011600911058485508 output: 0.011600650849602313input: 0.404806485096924 output: 0.39384089298297537input: 0.13447276877705008 output: 0.13406785820465852input: 0.22471809106646107 output: 0.222831550102815// send it a new input to see its trained outputvar output = netsend 0.5 // => 0.48031129953896595
var net = nn(opts)
Creates a Neural Network instance. Pass in an optional
opts object to configure the instance. Any values specified in
opts will override the corresponding defaults.
The default configuration is shown below:
// hidden layers eg. [ 4, 3 ] => 2 hidden layers, with 4 neurons in the first, and 3 in the second.layers: 3// maximum training epochs to perform on the training dataiterations: 20000// maximum acceptable error thresholderrorThresh: 0.0005// activation function ('logistic' and 'hyperbolic' supported)activation: 'logistic'// learning ratelearningRate: 0.4// learning momentummomentum: 0.5// logging frequency to show training progress. 0 = never, 10 = every 10 iterations.log: 0
nn instance, using
trainingData. You can pass in a single training entry as an object with
output keys, or an array of training entries. The network will train itself from the supplied training data, until the error threshold has been reached, or the max number of iterations has been reached.
Sends your neural network the input data and returns its output.
input is an array of numbers. Typically you'll call this function after training your network.
Runs your neural network against
testData and returns an object with statistics about the performance of the network against the test data.
testData can be a single object with
output keys, or an array of those objects. Typically you'll call this function after training your network.
Returns a JSON string representing the state of the neural network. You can later use
nn.fromJson() to get back the neural network from the JSON string.
Load a neural network instance from the JSON representation. Pass in
jsonString as a string.
(The MIT License)
Copyright (c) by Tolga Tezel email@example.com
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