https://github.com/dathoangnd/gonet

Neural Network for Go.
https://github.com/dathoangnd/gonet

deep-learning go machine-learning neural-network

Last synced: 3 months ago
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Neural Network for Go.

• Host: GitHub
• URL: https://github.com/dathoangnd/gonet
• Owner: dathoangnd
• License: mit
• Created: 2020-01-11T18:27:28.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-04-05T16:08:18.000Z (almost 5 years ago)
• Last Synced: 2024-07-31T20:52:16.000Z (5 months ago)
• Topics: deep-learning, go, machine-learning, neural-network
• Language: Go
• Homepage: https://pkg.go.dev/github.com/dathoangnd/gonet
• Size: 21.5 KB
• Stars: 82
• Watchers: 5
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

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• awesome-go-extra - gonet - 01-11T18:27:28Z|2020-04-05T16:08:18Z| (Machine Learning / Advanced Console UIs)

README

        
# gonet

[![Documentation](https://godoc.org/github.com/dathoangnd/gonet?status.svg)](https://pkg.go.dev/github.com/dathoangnd/gonet)

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

[![CircleCI](https://circleci.com/gh/dathoangnd/gonet.svg?style=svg)](https://circleci.com/gh/dathoangnd/gonet)

[![Mentioned in Awesome Go](https://awesome.re/mentioned-badge.svg)](https://github.com/avelino/awesome-go)  

gonet is a Go module implementing multi-layer Neural Network.

## Install

Install the module with:

```

go get github.com/dathoangnd/gonet

```

Import it in your project:

```go

import "github.com/dathoangnd/gonet"

```

## Example

This example will train a neural network to predict the outputs of XOR logic gates given two binary inputs:

```go

package main

import (

	"fmt"

	"log"

	"github.com/dathoangnd/gonet"

)

func main() {

	// XOR traning data

	trainingData := [][][]float64{

		{{0, 0}, {0}},

		{{0, 1}, {1}},

		{{1, 0}, {1}},

		{{1, 1}, {0}},

	}

	// Create a neural network

	// 2 nodes in the input layer

	// 2 hidden layers with 4 nodes each

	// 1 node in the output layer

	// The problem is classification, not regression

	nn := gonet.New(2, []int{4, 4}, 1, false)

	// Train the network

	// Run for 3000 epochs

	// The learning rate is 0.4 and the momentum factor is 0.2

	// Enable debug mode to log learning error every 1000 iterations

	nn.Train(trainingData, 3000, 0.4, 0.2, true)

	// Predict

	testInput := []float64{1, 0}

	fmt.Printf("%f XOR %f => %f\n", testInput[0], testInput[1], nn.Predict(testInput)[0])

	// 1.000000 XOR 0.000000 => 0.943074

	// Save the model

	nn.Save("model.json")

	// Load the model

	nn2, err := gonet.Load("model.json")

	if err != nil {

		log.Fatal("Load model failed.")

	}

	fmt.Printf("%f XOR %f => %f\n", testInput[0], testInput[1], nn2.Predict(testInput)[0])

	// 1.000000 XOR 0.000000 => 0.943074

}

```

## Documentation

See: [https://pkg.go.dev/github.com/dathoangnd/gonet](https://pkg.go.dev/github.com/dathoangnd/gonet)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.