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GitHub - dathoangnd/gonet: Neural Network for Go.
Neural Network for Go. Contribute to dathoangnd/gonet development by creating an account on GitHub.
Visit SiteGitHub - dathoangnd/gonet: Neural Network for Go.
Neural Network for Go. Contribute to dathoangnd/gonet development by creating an account on GitHub.
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gonet
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:
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:
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
License
This project is licensed under the MIT License - see the LICENSE file for details.
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