Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/Fontinalis/fonet
fonet is a deep neural network package for Go.
https://github.com/Fontinalis/fonet
deep-neural-networks golang machine-learning
Last synced: 3 months ago
JSON representation
fonet is a deep neural network package for Go.
- Host: GitHub
- URL: https://github.com/Fontinalis/fonet
- Owner: Fontinalis
- License: mit
- Created: 2017-10-03T15:57:15.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-06-01T10:04:04.000Z (over 3 years ago)
- Last Synced: 2024-07-31T20:52:15.402Z (6 months ago)
- Topics: deep-neural-networks, golang, machine-learning
- Language: Go
- Size: 29.3 KB
- Stars: 82
- Watchers: 6
- Forks: 17
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-go - fonet - A Deep Neural Network library written in Go. (Machine Learning / Search and Analytic Databases)
- zero-alloc-awesome-go - fonet - A Deep Neural Network library written in Go. (Machine Learning / Search and Analytic Databases)
- awesome-go - fonet - fonet is a deep neural network package for Go. - ★ 21 (Machine Learning)
- awesome-go-extra - fonet - 10-03T15:57:15Z|2021-06-01T10:04:04Z| (Machine Learning / Advanced Console UIs)
- awesome-go-zh - fonet
README
# fonet
[](https://github.com/Fontinalis/fonet/actions/workflows/go.yml)
[](https://coveralls.io/github/Fontinalis/fonet?branch=master)
[](https://goreportcard.com/report/github.com/Fontinalis/fonet)
[](https://pkg.go.dev/github.com/Fontinalis/fonet)
`fonet` is a deep neural network package for Go. It's mainly created because I wanted to learn about neural networks and create my own package. I'm planning to continue the development of the package and add more function to it, for example exporting/importing a model.
## Install
It's the same as everywhere, you just have to run the
```
go get github.com/Fontinalis/fonet
```
## Usage
I focused (and still focusing) on creating an easy to use package, but let me know if something is not clear.
### Creating a network
As in the `xor` example, it's not so complicated to create a network.
When you creating the network, you always have to define the layers.
```go
n := fonet.NewNetwork([]int{2, 3, 1}, fonet.Sigmond)
/*
2 nodes in the INPUT LAYER
3 nodes in the HIDDEN LAYER
1 node in the OUTPUT LAYER
*/
```
But my goal was also to create a package, which can create deep neural networks too, so here is another example for that.
```go
n := fonet.NewNetwork([]int{6, 12, 8, 4}, fonet.Sigmond)
/*
6 nodes in the INPUT LAYER
12 nodes in the HIDDEN LAYER (1)
8 nodes in the HIDDEN LAYER (2)
4 nodes in the OUTPUT LAYER
*/
```
### Train the network
After creating the network, you have to train your network. To do that, you have to specify your training set, which should be like the next
```go
var trainingData = [][][]float64{
[][]float64{ // The actual training sample
[]float64{
/*
The INPUT data
*/
},
[]float64{
/*
The OUTPUT data
*/
},
},
}
```
After giving the training data, you can set the epoch and the learning rate.
```go
n.Train(trainingData, epoch, lrate, true)
// Train(trainingData [][][]float64, epochs int, lrate float64, debug bool)
```
`
Note: When 'debug' is true, it'll show when and which epoch is finished
`
### Predict the output
After training your network, using the `Predict(..)` function you can calculate the output for the given input.
In the case of XOR, it looks like the next
```go
input := []float64{
1,
1,
}
out := n.Predict(input)
```