https://github.com/mtchavez/nnet
Neural Net in Go
https://github.com/mtchavez/nnet
Last synced: about 2 months ago
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Neural Net in Go
- Host: GitHub
- URL: https://github.com/mtchavez/nnet
- Owner: mtchavez
- Created: 2013-06-02T03:21:21.000Z (almost 12 years ago)
- Default Branch: master
- Last Pushed: 2013-06-12T05:28:49.000Z (almost 12 years ago)
- Last Synced: 2025-02-04T15:32:40.689Z (4 months ago)
- Language: Go
- Size: 188 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
nnet [](https://travis-ci.org/mtchavez/nnet)
====
Neural Net in Go
## Installation
Use ```go get``` to install the package.
```
go get -u github.com/mtchavez/nnet/nnet
```
## Usage
There are some training examples in ```./nnet/training_sets.go``` to use.
An example using ```TrainingSet2``` (NAND) would look like this:
```go
package main
import (
"github.com/mtchavez/nnet/nnet"
"log"
"math/rand"
"time"
)
func init() {
rand.Seed(time.Now().UTC().UnixNano())
}
func main() {
nn := &nnet.NeuralNet{}
nn.SetupNeuralNet(4, 5, 1)
nn.Train(nnet.TrainingSet)
for _, ex := range nnet.TrainingSet {
input := ex[:4]
expected := ex[4:]
output := nn.Predict(input)
log.Printf("For %+v neural net predicts %+v and we expect %+v\n", input, output, expected)
}
}
```
The set named ```TrainingSet``` will take a while to run but will finish eventually
and using the same script above should give you similar output:
```
2013/06/09 19:08:49 Err: 3.874987320725568e-05
2013/06/09 19:09:26 Err: 1.6621031811903178e-05
2013/06/09 19:10:02 Err: 1.0078854035870953e-05
...
2013/06/09 19:19:31 Err: 1.1254870026927736e-06
2013/06/09 19:20:06 Err: 1.0652633938055432e-06
2013/06/09 19:20:41 Err: 1.0111589283784186e-06
2013/06/09 19:20:48 For [-0.5 0.75 0.4 0.8] neural net predicts [0.999999715590078] and we expect [1]
2013/06/09 19:20:48 For [-0.75 0.25 0.3 0.8] neural net predicts [0.0006310811073551809] and we expect [0]
2013/06/09 19:20:48 For [-0.5 0.75 0.3 0.8] neural net predicts [0.9997530594821283] and we expect [1]
2013/06/09 19:20:48 For [-0.75 0.5 0.4 0.8] neural net predicts [7.72391221796645e-10] and we expect [0]
2013/06/09 19:20:48 For [-1 0.25 0.3 0.8] neural net predicts [0.9995155065107615] and we expect [1]
2013/06/09 19:20:48 For [-0.75 0.5 0.4 0.9] neural net predicts [2.110466190106359e-11] and we expect [0]
2013/06/09 19:20:48 For [-0.5 0.5 0.3 0.9] neural net predicts [0.00037037185571059725] and we expect [0]
2013/06/09 19:20:48 For [-0.25 0.5 0.4 0.8] neural net predicts [0.9998935743831123] and we expect [1]
2013/06/09 19:20:48 For [-0.5 0.75 0.3 0.9] neural net predicts [0.0003226877510435841] and we expect [0]
2013/06/09 19:20:48 For [-0.25 0.75 0.3 0.9] neural net predicts [0.9999999953574958] and we expect [1]
2013/06/09 19:20:48 For [-0.5 0.75 0.4 0.8] neural net predicts [0.999999715590078] and we expect [1]
2013/06/09 19:20:48 For [-0.25 0.75 0.4 0.8] neural net predicts [0.9999998573667516] and we expect [1]
2013/06/09 19:20:48 For [-1 0.25 0.4 0.8] neural net predicts [1.2870298645547013e-09] and we expect [0]
2013/06/09 19:20:48 For [-0.5 0.75 0.3 0.8] neural net predicts [0.9997530594821283] and we expect [1]
2013/06/09 19:20:48 For [-0.5 0.25 0.3 0.8] neural net predicts [0.9992302273315177] and we expect [1]
2013/06/09 19:20:48 For [-1 0.25 0.3 0.9] neural net predicts [0.9999997050099974] and we expect [1]
2013/06/09 19:20:48 For [-0.75 0.5 0.4 0.9] neural net predicts [2.110466190106359e-11] and we expect [0]
2013/06/09 19:20:48 For [-0.25 0.25 0.4 0.9] neural net predicts [0.9999999919841681] and we expect [1]
2013/06/09 19:20:48 For [-0.25 0.5 0.3 0.8] neural net predicts [0.9997764205767077] and we expect [1]
2013/06/09 19:20:48 For [-0.5 0.25 0.4 0.9] neural net predicts [0.0005914493975230156] and we expect [0]
```
Which shows the NeuralNet has learned the example training set of detemining some outcome given
basic information of on a group of people.
## Documentation
Docs are on [Godoc](http://godoc.org/github.com/mtchavez/nnet/nnet)
## Test
You can run the tests using ```go test```
## TODO
* Allow multiple hidden layers
* Write function to export weights of neural net
* Write function to import previously known weights
* Allow Activation functions to be set to anything
## License
Written by Chavez
Released under the MIT License: http://www.opensource.org/licenses/mit-license.php