easy-nn

The easy-nn is "easy to use" lightweight implementation of a feed forward neural network. The network has to consist of at least one neuron, so called perceptron. Each neuron of the network counts with a bias, which is automatically generated and is not a part of the topology deffinition. It is possible to store and load a state of the network and of course, you can train it.

Installation

npm install easy-nn

Examples

Creating Neural Network

A simple example that shows how to create a neural network with given topology. The topology is defined as a set of layers, each layer has predefined number of neurons. The input and output layers always have to be defined, with at least one neuron. Hidden layers are optional. Every neuron weights all values from previous layer, insluding the bias.

{NeuralNetwork} = require "easy-nn"
 
# Each input has 1 value, i.e. input layer has 1 entry, 
# output layer has 1 neuron 
perceptron1 = new NeuralNetwork [1, 1]
 
# Each input has 3 values, i.e. input layer has 3 entries, 
# output layer has 1 neuron 
perceptron2 = new NeuralNetwork [3, 1]
 
# Each input has 74 values, i.e. input layer has 74 entries, 
# 1st hidden layer has 14 neurons, 
# output layer has 2 neurons 
network1 = new NeuralNetwork [74, 14, 2]
 
# Each input has 21 values, i.e. input layer has 21 entries, 
# 1st hidden layer has 7 neurons, 
# 2nd hidden layer has 9 neurons, 
# output layer has 3 neurons 
network2 = new NeuralNetwork [21, 7, 9, 3]

Previous code results in four neural networks with randomly set weights for each neuron. Each set of weights for neuron inputs contains weight for bias as well. The bias is implemented as invisible neuron in previous layer outputting constant 1. The first two neural networks are so called perceptrons, the simplest neural networks with only one neuron.

Using Neural Network

Once you have created a network, you can easily feed some input in and get a response of the neural network. Every input is an array of values and every output is an array of values, one value for each neuron.

{NeuralNetwork} = require "easy-nn"
 
# Perceptron that processes only one value on input 
perceptron = new NeuralNetwork [1, 1]
output = perceptron.feedForward [6]
console.log output # prints array with 1 value 
 
# Network that processes 4 values on input 
network = new NeuralNetwork [4, 3, 2]
output = network.feedForward [11, 7, 9, 5]
console.log output # prints array with 2 values 

Of course, responses (outputs) in this example contain nonsenses, because the networks are not trained yet.

Training Neural Network

You can use a neural network trainer to train the network. One trainer is built for one specific network, but it is configurable and can be used to train the network repeatedly with different data or settings. The trainer modifies weights of the original network and once the training is over, returns accuracy of the trained network.

{NeuralNetwork, NeuralNetworkTrainer} = require "easy-nn"
 
nn = new NeuralNetwork [2, 1]
nnTrainer = new NeuralNetworkTrainer nn
 
# inputs array is set of points, each with {x, y} coordinates 
# desired response is 0 if x-coordinate is < 4, else 1 
inputs = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 8], [6, 2]]
outputs = [[0], [0], [0], [1], [1], [1]]
 
# train the network using inputs and desired responses/outputs 
acc = nnTrainer.train inputs, outputs
 
console.log "Network trained with #{(acc * 100).toFixed(2)}% accuracy after #{nnTrainer.epochs} epochs.\n"
 
# the network is trained now, lets use it as it is and check the results 
for i in [0...inputs.length]
    output = nn.feedForward inputs[i]
    console.log "input: #{inputs[i]}, output: #{output}, desired output: #{outputs[i]}"

Output should look similar to this:

Network trained with 100.00% accuracy after 1045 epochs.

input: 1,2, output: 0.0031683220517143684, desired output: 0
input: 2,3, output: 0.08543480587781929, desired output: 0
input: 3,1, output: 0.09277863465043776, desired output: 0
input: 4,3, output: 0.9000008820059654, desired output: 1
input: 5,8, output: 0.9999529756701503, desired output: 1
input: 6,2, output: 0.9965586497259523, desired output: 1

Of course, the training took too many epochs, because the trainer is set to default. There are few options you can set, that should help with that:

• Training Parameters:
  • learningRate: learning rate (related to size of change of weights), default is 0.1
  • momentum: momentum (related to dumping of change of weights), default is 0.8
  • maxEpochs: max number of epochs until the training is stopped, default is 20,000
  • minAccuracy: min accuracy that has to be met to stop the training, default is 0.95
  • log: writes number of epochs and accuracy into console, default is false
  • logPeriod: writes only every logPeriod-th log line into console, default is 1

{NeuralNetwork, NeuralNetworkTrainer} = require "easy-nn"
 
nn = new NeuralNetwork [2, 1]
nnTrainer = new NeuralNetworkTrainer nn, {learningRate: 0.6, momentum: 0.8}
 
# or we can set some options this way 
nnTrainer.setOptions {maxEpochs: 500, minAccuracy: 0.9}
 
# inputs array is set of points, each with {x, y} coordinates 
# desired response is 0 if x-coordinate is < 4, else 1 
inputs = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 8], [6, 2]]
outputs = [[0], [0], [0], [1], [1], [1]]
 
# train the network using inputs and desired responses/outputs 
acc = nnTrainer.train inputs, outputs
 
console.log "Network trained with #{(acc * 100).toFixed(2)}% accuracy after #{nnTrainer.epochs} epochs.\n"
 
# the network is trained now, lets use it as it is and check the results 
for i in [0...inputs.length]
    output = nn.feedForward inputs[i]
    console.log "input: #{inputs[i]}, output: #{output}, desired output: #{outputs[i]}"

Output should look similar to this:

Network trained with 100.00% accuracy after 95 epochs.

input: 1,2, output: 0.003421933296556123, desired output: 0
input: 2,3, output: 0.08859437917377035, desired output: 0
input: 3,1, output: 0.09758939010565129, desired output: 0
input: 4,3, output: 0.9000483968432926, desired output: 1
input: 5,8, output: 0.9999475925358006, desired output: 1
input: 6,2, output: 0.9964863616017431, desired output: 1

Storing & Loading Neural Network

Once the neural network is created and trained it is quite handy to be able to store the structure and state of the network for later use. It is provided in a form of JSON string.

{NeuralNetwork, NeuralNetworkTrainer} = require "easy-nn"
 
nn = new NeuralNetwork [2, 1]
nnTrainer = new NeuralNetworkTrainer nn
 
# inputs array is set of points, each with {x, y} coordinates 
# desired response is 0 if x-coordinate is < 4, else 1 
inputs = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 8], [6, 2]]
outputs = [[0], [0], [0], [1], [1], [1]]
 
# train the network using inputs and desired responses/outputs 
acc = nnTrainer.train inputs, outputs
 
console.log "Network trained with #{(acc * 100).toFixed(2)}% accuracy after #{nnTrainer.epochs} epochs.\n"
 
nnState = nn.toJSON()
 
console.log nnState
 

The nnState string contains information about topology of the network, where layers object contains a key for each layer and each layer is represented by array of neurons. Every neuron has an array of weights for its inputs, including weight for bias - that is the last one. Input layer doesn't contain weights, there are no neurons, just inputs. Output should look like this:

Network trained with 100.00% accuracy after 1045 epochs.
{
	"layers": {
		"input": [
			[],
			[]
		],
		"output": [
			[
				2.284898341232388,
				1.0984011751180214,
				-10.237093429332361
			]
		]
	}
}

The neural network can be set to a state stored in a JSON string during initialisation or an existing neural network can be reconfigured into the new topology and state using the JSON string. Original topology and state will be lost.

{NeuralNetwork, NeuralNetworkTrainer} = require "easy-nn"
 
inputs = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 8], [6, 2]]
outputs = [[0], [0], [0], [1], [1], [1]]
 
 
nn = new NeuralNetwork [2, 1]
 
state1 = nn.toJSON() # not trained network stored as string 
 
nnTrainer = new NeuralNetworkTrainer nn
nnTrainer.train inputs, outputs
 
state2 = nn.toJSON() # trained network stored as string 
 
# if you want to, you can store the sate into file now 
# and load it later and restore the network 
 
restoredNN = new NeuralNetwork state2 # network initialised from string 
 
# or reset existing network 
 
restoredNN.fromJSON state1 # network reconfigured from string