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Neural network implementation on golang
| Date | Stars |
|---|---|
| 2026-07-31 | 254 |
| 2026-08-03 | 254 |
| 2026-08-04 | 254 |
| 2026-08-06 | 254 |
Today
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Momentum
0.0
growth rate 0.00%/day
go-neural
==============
# Install
```
go get github.com/NOX73/go-neural
go get github.com/NOX73/go-neural/persist
go get github.com/NOX73/go-neural/learn
```
# Neural Network
Create new network:
```go
import "github.com/NOX73/go-neural"
//...
// Network has 9 enters and 3 layers
// ( 9 neurons, 9 neurons and 4 neurons).
// Last layer is network output.
n := neural.NewNetwork(9, []int{9,9,4})
// Randomize sypaseses weights
n.RandomizeSynapses()
result := n.Calculate([]float64{0,1,0,1,1,1,0,1,0})
```
# Persist network
Save to file:
```go
import "github.com/NOX73/go-neural/persist"
persist.ToFile("/path/to/file.json", network)
```
Load from file:
```go
import "github.com/NOX73/go-neural/persist"
network := persist.FromFile("/path/to/file.json")
```
# Learning
```go
import "github.com/NOX73/go-neural/learn"
var input, idealOutput []float64
// Learning speed [0..1]
var speed float64
learn.Learn(network, in, idealOut, speed)
```
You can get estimate of learning quality:
```go
e := learn.Evaluation(network, in, idealOut)
```
# Engine
For concurrent learn, calculate & dump neural network.
```go
network := neural.NewNetwork(2, []int{2, 2})
engine := New(network)
engine.Start()
engine.Learn([]float64{1, 2}, []float64{3, 3}, 0.1)
out := engine.Calculate([]float64{1, 2})
```
# Live example
Dirty live example: [https://github.com/NOX73/go-neural-play]
Excerpt of 1,448 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a50523fb0eba2a46, llm:description: 'Neural network implementation on golang' (repo description)
matched fp:a50523fb0eba2a46, llm:description: 'Neural network implementation on golang' (repo description)