README
¶
Varis
Neural Networks with GO
About Package
Some time ago I decided to learn Go language and neural networks. So it's my variation of Neural Networks library. I tried to make library for programmers (not for mathematics).
For now Varis is 0.1 version.
I would be happy if someone can find errors and give advices. Thank you. Artem.
Main features
- All neurons and synapses are goroutines.
- Golang channels for connecting neurons.
- No dependencies
Installation
go get github.com/Xamber/Varis
Usage
package main
import (
"github.com/Xamber/Varis"
)
func main() {
net := varis.CreatePerceptron(2, 3, 1)
dataset := varis.Dataset{
{varis.Vector{0.0, 0.0}, varis.Vector{1.0}},
{varis.Vector{1.0, 0.0}, varis.Vector{0.0}},
{varis.Vector{0.0, 1.0}, varis.Vector{0.0}},
{varis.Vector{1.0, 1.0}, varis.Vector{1.0}},
}
trainer := varis.PerceptronTrainer{
Network: &net,
Dataset: dataset,
}
trainer.BackPropagation(10000)
varis.PrintCalculation = true
net.Calculate(varis.Vector{0.0, 0.0}) // Output: [0.9816677167418877]
net.Calculate(varis.Vector{1.0, 0.0}) // Output: [0.02076530509106318]
net.Calculate(varis.Vector{0.0, 1.0}) // Output: [0.018253250887023762]
net.Calculate(varis.Vector{1.0, 1.0}) // Output: [0.9847884089930481]
}
Roadmap 0.2-0.5
- Add locks
- Add training channels
- Improve speed
- Add error return to functions.
- Create more tests and benchmarks.
- Create server and cli realization for use Varis as a application
Alternatives
Documentation
¶
Overview ¶
Example ¶
rand.Seed(1338)
PrintCalculation = false
n := CreatePerceptron(2, 3, 1)
dataset := Dataset{
{Vector{0.0, 0.0}, Vector{1.0}},
{Vector{1.0, 0.0}, Vector{0.0}},
{Vector{0.0, 1.0}, Vector{0.0}},
{Vector{1.0, 1.0}, Vector{1.0}},
}
trainer := PerceptronTrainer{&n, dataset}
trainer.BackPropagation(10000)
PrintCalculation = true
n.Calculate(Vector{0.0, 0.0})
n.Calculate(Vector{1.0, 0.0})
n.Calculate(Vector{0.0, 1.0})
n.Calculate(Vector{1.0, 1.0})
Output: Input: [0 0] Output: [0.9816677167418877] Input: [1 0] Output: [0.020765305091063144] Input: [0 1] Output: [0.01825325088702373] Input: [1 1] Output: [0.9847884089930483]
Index ¶
Examples ¶
Constants ¶
This section is empty.
Variables ¶
var ACTIVATION neuronFunction = func(x float64) float64 { return 1 / (1 + math.Exp(-x)) }
ACTIVATION store default activation function.
var DEACTIVATION neuronFunction = func(x float64) float64 { var fx = ACTIVATION(x) return fx * (1 - fx) }
DEACTIVATION store default deactivation function.
var PrintCalculation = false
PrintCalculation logs all calculate calls (print input and output).
Functions ¶
func ConnectNeurons ¶
ConnectNeurons connect two neurons. It creates synapse and add connection to input and output Neuron.
func ToJSON ¶
func ToJSON(network Perceptron) string
ToJSON dump and transform Perceptron to json string.
Types ¶
type CoreNeuron ¶
type CoreNeuron struct {
// contains filtered or unexported fields
}
CoreNeuron - entity with float64 weight (it is bias) and connection. Activation result store in cache for training.
type Neuron ¶
type Neuron interface {
// contains filtered or unexported methods
}
Neuron - interface for all Neuron. Each Neuron must have: - coreNeuron is a basic neuron for all types - getCore() is a the function for getting pointer to CoreNeuron - live() - method for running neuron's goroutine. All kind of Neurons implement functionality live - changeWeight is the method for training
type Perceptron ¶
type Perceptron struct {
// contains filtered or unexported fields
}
Perceptron implement Neural Network Perceptron by collect layers with Neurons and input/output channels.
func CreatePerceptron ¶
func CreatePerceptron(layers ...int) Perceptron
CreatePerceptron make new Perceptron NN with count of neurons in each Layer.
func FromJSON ¶
func FromJSON(jsonString string) Perceptron
FromJSON load json string and create Perceptron.
func (*Perceptron) Calculate ¶
func (n *Perceptron) Calculate(input Vector) Vector
Calculate run Network calculations by broadcasting signals to input channels and wait signals from output array of chan.
func (*Perceptron) ConnectLayers ¶
func (n *Perceptron) ConnectLayers()
ConnectLayers create all to all connection between layers.
func (*Perceptron) RunNeurons ¶
func (n *Perceptron) RunNeurons()
RunNeurons create goroutines for all Neuron in Perceptron.
type PerceptronTrainer ¶
type PerceptronTrainer struct {
Network *Perceptron
Dataset Dataset
}
PerceptronTrainer is a trainer for Perceptron networks
func (*PerceptronTrainer) BackPropagation ¶
func (t *PerceptronTrainer) BackPropagation(times int)
BackPropagation train Network input Dataset for 'times' times.
Source Files