neural

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

Install

  go get github.com/breskos/gopher-learn
  go get github.com/breskos/gopher-learn/persist
  go get github.com/breskos/gopher-learn/learn
  go get github.com/breskos/gopher-learn/engine
  go get github.com/breskos/gopher-learn/evaluation
  go get github.com/breskos/gopher-learn/online

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

data := learn.NewSet(neural.Classification)
ok, err := data.LoadFromCSV(dataFile)

Start engine

e := engine.NewEngine(neural.Classification, []int{hiddenNeurons}, data)
e.SetVerbose(true)
e.Start(neural.CriterionDistance, tries, epochs, trainingSplit, learningRate, decay)

Use evalation mode

evaluation.PrintSummary("name of class1")
evaluation.PrintSummary("name of class2")
evaluation.PrintConfusionMatrix()

Perform in production

x := net.CalculateWinnerLabel(vector)

Regression

Important note: Use regression just with a target value between 0 and 1.

Read training data from file

data := learn.NewSet(neural.Regression)
ok, err := data.LoadFromCSV(dataFile)

Start engine

e := engine.NewEngine(neural.Regression, []int{hiddenNeurons}, data)
e.SetVerbose(true)
e.Start(neural.CriterionDistance, tries, epochs, trainingSplit, learningRate, decay)

Use evalation mode

evaluation.GetRegressionSummary()

Perform in production

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:

• CriterionAccuracy - uses simple accuracy calculation to decide the best model. Not suitable with unbalanced data sets.
• CriterionBalancedAccuracy - uses balanced accuracy. Suitable for unbalanced data sets.
• CriterionFMeasure - uses F1 score. Suitable for unbalanced data sets.
• CriterionSimple - uses simple correct classified divided by all classified samples. Suitable for regression with thresholding.
• CriterionDistance - uses distance between ideal output and current output. Suitable for regression.

...
e := engine.NewEngine(neural.Classification, []int{100}, data)
e.Start(neural.CriterionDistance, tries, epochs, trainingSplit, learningRate, decay)
...

Some more basics

Train a network using engine

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 //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.Start(neural.CriterionDistance, tries, epochs, trainingSplit, learningRate, decay)
	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

  import "github.com/breskos/gopher-learn"
  // 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 := neural.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})