README
¶
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.
Documentation
¶
Index ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type NN ¶
type NN struct {
// Whether the problem is regression or classification
Regression bool
// Number of nodes in each layer
NNodes []int
// Activations for each layer
Activations [][]float64
// Weights
Weights [][][]float64
// Last change in weights for momentum
Changes [][][]float64
}
NN struct is used to represent a neural network
func New ¶
New creates a new neural network
'nInputs' is number of nodes in input layer
'nHiddens' is array of numbers of nodes in hidden layers
'nOutputs' is number of nodes in output layer
'isRegression' is whether the problem is regression or classification
return the neural network
func (*NN) Config ¶
Config the neural network, also reset all trained weights
'nInputs' is number of nodes in input layer
'nHiddens' is array of numbers of nodes in hidden layers
'nOutputs' is number of nodes in output layer
'isRegression' is whether the problem is regression or classification
Source Files