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https://github.com/surenderthakran/gomind

A simplistic Neural Network Library in Go
https://github.com/surenderthakran/gomind

go golang machine-learning neural-network

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A simplistic Neural Network Library in Go

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# GoMind



[![Build Status](https://travis-ci.com/surenderthakran/gomind.svg?branch=master)](https://travis-ci.com/surenderthakran/gomind)

[![GoDoc](https://godoc.org/github.com/surenderthakran/gomind?status.png)](https://godoc.org/github.com/surenderthakran/gomind)

[![codecov](https://codecov.io/gh/surenderthakran/gomind/branch/master/graph/badge.svg)](https://codecov.io/gh/surenderthakran/gomind)

[![Go Report Card](https://goreportcard.com/badge/github.com/surenderthakran/gomind)](https://goreportcard.com/report/github.com/surenderthakran/gomind)

[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://github.com/singhsurender/gomind/blob/master/LICENSE)



## Installation

```

go get github.com/surenderthakran/gomind

```



## About

GoMind is a neural network library written entirely in Go.

It only supports a single hidden layer (for now).

The network learns from a training set using back-propagation algorithm.



Some of the salient features of GoMind are:

- Supports following activation functions:

  - Linear (Default)

  - [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function)

  - [ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks))

  - [Leaky ReLU](https://en.wikipedia.org/wiki/Rectifier_%28neural_networks%29#Leaky_ReLUs)

- Smartly estimates ideal number of hidden layer neurons (if a count is not given during model configuration) for given input and output sizes.

- Uses [Mean Squared Error function](https://en.wikipedia.org/wiki/Mean_squared_error) to calculate error while back propagating.



Note: To understand the basic functioning of back-propagation in neural networks, one can refer to my blog [here](https://www.surenderthakran.com/articles/tech/implement-back-propagation-neural-network).



## Usage

```

package main



import (

	"github.com/singhsurender/gomind"

)



func main() {

	trainingSet := [][][]float64{

		[][]float64{[]float64{0, 0}, []float64{0}},

		[][]float64{[]float64{0, 1}, []float64{1}},

		[][]float64{[]float64{1, 0}, []float64{1}},

		[][]float64{[]float64{1, 1}, []float64{0}},

	}



	mind, err := gomind.New(&gomind.ModelConfiguration{

		NumberOfInputs:                    2,

		NumberOfOutputs:                   1,

		NumberOfHiddenLayerNeurons:        16,

		HiddenLayerActivationFunctionName: "relu",

		OutputLayerActivationFunctionName: "sigmoid",

	})

	if err != nil {

		return nil, fmt.Errorf("unable to create neural network. %v", err)

	}



	for i := 0; i < 500; i++ {

		rand := rand.Intn(4)

		input := trainingSet[rand][0]

		output := trainingSet[rand][1]



		if err := mind.LearnSample(input, output); err != nil {

			mind.Describe(true)

			return nil, fmt.Errorf("error while learning from sample input: %v, target: %v. %v", input, output, err)

		}



		actual := mind.LastOutput()

		outputError, err := mind.CalculateError(output)

		if err != nil {

			mind.Describe(true)

			return nil, fmt.Errorf("error while calculating error for input: %v, target: %v and actual: %v. %v", input, output, actual, err)

		}



		outputAccuracy, err := mind.CalculateAccuracy(output)

		if err != nil {

			mind.Describe(true)

			return nil, fmt.Errorf("error while calculating error for input: %v, target: %v and actual: %v. %v", input, output, actual, err)

		}



		fmt.Println("Index: %v, Input: %v, Target: %v, Actual: %v, Error: %v, Accuracy: %v\n", i, input, output, actual, outputError, outputAccuracy)

	}

}

```



## API Documentation

The documentation for various methods exposed by the library can be found at: [https://godoc.org/github.com/surenderthakran/gomind](https://godoc.org/github.com/surenderthakran/gomind)