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https://github.com/breskos/gopher-learn
Golang neural network - fast and easy classification and regression.
https://github.com/breskos/gopher-learn
analysis classification machine-learning neural-network online-learning
Last synced: about 2 months ago
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Golang neural network - fast and easy classification and regression.
- Host: GitHub
- URL: https://github.com/breskos/gopher-learn
- Owner: breskos
- License: gpl-3.0
- Created: 2020-06-14T12:30:58.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-06-07T18:02:37.000Z (over 3 years ago)
- Last Synced: 2024-07-09T00:10:26.391Z (3 months ago)
- Topics: analysis, classification, machine-learning, neural-network, online-learning
- Language: Go
- Homepage:
- Size: 392 KB
- Stars: 12
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# gopher-learn
## Quickstart
- See examples here: https://github.com/breskos/gopher-learn/tree/master/examples
## What is gopher-learn?
- Artificial neural network written in Golang with training / testing framework
- Rich measurement mechanisms to control the training
- Examples for fast understanding
- Can also be used for iterative online learning (using online module) for autonomous agents
- Encoders can be used to encoder string data or massive float slices
## Install
```
go get github.com/breskos/gopher-learn/...
```
## Examples
Find the examples in the examples folder.
All the data to run the examples [can be found here](https://github.com/breskos/gopher-learn-data).
## The gopher-learn engine
The engine helps you with optimizing the learning process.
Basically it starts with a high learning rate to make fast progress in the beginning.
After some rounds of learning (epochs) the learning rate declines (decay).
During the process the best network is saved.
After engine finished training you receive the ready to go network.
## Modes
Gopher-neural can be used to perform classification and regression. This sections helps to set up both modes. In general, you have to take care about the differences between both modes during these parts: read training data from file, start engine, use evaluation modes and perform in production.
### Classification
#### Read training data from file
```go
data := learn.NewSet(neural.Classification)
ok, err := data.LoadFromCSV(dataFile)
```
#### Start engine
This uses the standard config of the engine.
For finetuning use e.SetConfig() to set your own config.
```go
e := engine.NewEngine(neural.Classification, []int{hiddenNeurons}, data)
e.SetVerbose(true)
e.Start(neural.Distance)
```
#### Use evalation mode
```go
evaluation.PrintSummary("name of class1")
evaluation.PrintSummary("name of class2")
evaluation.PrintConfusionMatrix()
```
#### Perform in production
```go
x := net.CalculateWinnerLabel(vector)
```
### Regression
Important note: Use regression just with a target value between 0 and 1.
#### Read training data from file
```go
data := learn.NewSet(neural.Regression)
ok, err := data.LoadFromCSV(dataFile)
```
#### Start engine
This uses the standard config of the engine.
For finetuning use e.SetConfig() to set your own config.
```go
e := engine.NewEngine(neural.Regression, []int{hiddenNeurons}, data)
e.SetVerbose(true)
e.Start(neural.Distance)
```
#### Use evalation mode
```go
evaluation.GetRegressionSummary()
```
#### Perform in production
```go
x := net.Calculate(vector)
```
## Criterions
To let the engine decide for the best model, a few criterias were implemented. They are listed below together with a short regarding their application:
- **Accuracy** - uses simple accuracy calculation to decide the best model. Not suitable with unbalanced data sets.
- **BalancedAccuracy** - uses balanced accuracy. Suitable for unbalanced data sets.
- **FMeasure** - uses F1 score. Suitable for unbalanced data sets.
- **Simple** - uses simple correct classified divided by all classified samples. Suitable for regression with thresholding.
- **Distance** - uses distance between ideal output and current output. Suitable for regression.
```go
...
e := engine.NewEngine(neural.Classification, []int{100}, data)
e.Start(neural.Distance, tries, epochs, trainingSplit, learningRate, decay)
...
```
## Some more basics
### Train a network using engine
Using the engine makes sense for you if you want to fully use the training framework that gopher-learn offers you.
With engine package the network is learned using learning rate, decay, epochs.
Also in the engine you can choose between the criteria options to find the best network.
```go
import (
"fmt"
"github.com/breskos/gopher-learn"
"github.com/breskos/gopher-learn/engine"
"github.com/breskos/gopher-learn/learn"
"github.com/breskos/gopher-learn/persist"
)
const (
dataFile = "data.csv"
networkFile = "network.json"
tries = 1
epochs = 100
trainingSplit = 0.7
learningRate = 0.6
decay = 0.005
hiddenNeurons = 20
)
func main() {
data := learn.NewSet(neural.Classification)
ok, err := data.LoadFromCSV(dataFile)
if !ok || nil != err {
fmt.Printf("something went wrong -> %v", err)
}
e := engine.NewEngine(neural.Classification, []int{hiddenNeurons}, data)
e.SetVerbose(true)
e.SetConfig(&engine.Config{
Tries: tries,
Epochs: epochs,
TrainingSplit: trainingSplit,
LearningRate: learningRate,
Decay: decay,
})
e.Start(neural.Distance)
network, evaluation := e.GetWinner()
evaluation.PrintSummary("name of class1")
evaluation.PrintSummary("name of class2")
err = persist.ToFile(networkFile, network)
if err != nil {
fmt.Printf("error while saving network: %v\n", err)
}
network2, err := persist.FromFile(networkFile)
if err != nil {
fmt.Printf("error while loading network: %v\n", err)
}
// check the network with the first sample
w := network2.CalculateWinnerLabel(data.Samples[0].Vector)
fmt.Printf("%v -> %v\n", data.Samples[0].Label, w)
fmt.Println(" * Confusion Matrix *")
evaluation.PrintConfusionMatrix()
}
```
### Create simple network for classification
```go
import "github.com/breskos/gopher-learn/net"
// Network has 9 enters and 3 layers
// ( 9 neurons, 9 neurons and 2 neurons).
// Last layer is network output (2 neurons).
// For these last neurons we need labels (like: spam, nospam, positive, negative)
labels := make(map[int]string)
labels[0] = "positive"
labels[1] = "negative"
n := net.NewNetwork(9, []int{9,9,2}, map[int])
// Randomize sypaseses weights
n.RandomizeSynapses()
// now you can calculate on this network (of course it is not trained yet)
// (for the training you can use then engine)
result := n.Calculate([]float64{0,1,0,1,1,1,0,1,0})
```