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https://github.com/tleyden/neurgo
Neural Network toolkit in Go
https://github.com/tleyden/neurgo
Last synced: 11 days ago
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Neural Network toolkit in Go
- Host: GitHub
- URL: https://github.com/tleyden/neurgo
- Owner: tleyden
- License: apache-2.0
- Created: 2013-06-05T07:24:52.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2016-03-09T18:24:39.000Z (over 8 years ago)
- Last Synced: 2024-08-01T04:02:19.952Z (3 months ago)
- Language: Go
- Size: 356 KB
- Stars: 37
- Watchers: 4
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-golang-ai - neurgo
README
# Neurgo
[](https://drone.io/github.com/tleyden/neurgo/latest) [](https://godoc.org/github.com/tleyden/neurgo)
A library for constructing Neural Networks in [Go](http://golang.org/), where Neurons are goroutines that communicate with each other via channels.
## What it can do
* Feedforward networks
* Recurrent networks
* JSON Marshal/Unmarshal ([example json](https://drone.io/github.com/tleyden/neurgo/files/xnor.json))
* Visualization network topology in SVG ([example svg](https://drone.io/github.com/tleyden/neurgo/files/xnor.svg))
## Learning mechanism
Neurgo does _not_ contain any code for learning/training.
The idea is to have a separation of concerns such that the code that does the training will live in it's own repo. Currently, there is only one training module:
* [neurvolve](https://github.com/tleyden/neurvolve) - An evolution based trainer that is essentially a port of [DXNN2](https://github.com/CorticalComputer/DXNN2) (a Topology & Parameter Evolving Universal Learning Network in Erlang).
## Roadmap
* Training module for Backpropagation based learning (contributions welcome!)
* Stress testing / benchmarks
## Example applications
* [Checkerlution - A Checkers Bot](https://github.com/tleyden/checkerlution)
## Example code
The following code creates a neural net with [this topology](https://drone.io/github.com/tleyden/neurgo/files/xnor.svg). It does not actually run the network (eg, feed inputs), so for a more complete example see `cortex_test.go`.
```go
sensor := &Sensor{
NodeId: NewSensorId("sensor", 0.0),
VectorLength: 2,
}
sensor.Init()
hiddenNeuron1 := &Neuron{
ActivationFunction: EncodableSigmoid(),
NodeId: NewNeuronId("hidden-neuron1", 0.25),
Bias: -30,
}
hiddenNeuron1.Init()
hiddenNeuron2 := &Neuron{
ActivationFunction: EncodableSigmoid(),
NodeId: NewNeuronId("hidden-neuron2", 0.25),
Bias: 10,
}
hiddenNeuron2.Init()
outputNeuron := &Neuron{
ActivationFunction: EncodableSigmoid(),
NodeId: NewNeuronId("output-neuron", 0.35),
Bias: -10,
}
outputNeuron.Init()
actuator := &Actuator{
NodeId: NewActuatorId("actuator", 0.5),
VectorLength: 1,
}
actuator.Init()
// wire up connections
sensor.ConnectOutbound(hiddenNeuron1)
hiddenNeuron1.ConnectInboundWeighted(sensor, []float64{20, 20})
sensor.ConnectOutbound(hiddenNeuron2)
hiddenNeuron2.ConnectInboundWeighted(sensor, []float64{-20, -20})
hiddenNeuron1.ConnectOutbound(outputNeuron)
outputNeuron.ConnectInboundWeighted(hiddenNeuron1, []float64{20})
hiddenNeuron2.ConnectOutbound(outputNeuron)
outputNeuron.ConnectInboundWeighted(hiddenNeuron2, []float64{20})
outputNeuron.ConnectOutbound(actuator)
actuator.ConnectInbound(outputNeuron)
// create cortex
nodeId := NewCortexId("cortex")
cortex := &Cortex{
NodeId: nodeId,
}
cortex.SetSensors([]*Sensor{sensor})
cortex.SetNeurons([]*Neuron{hiddenNeuron1, hiddenNeuron2, outputNeuron})
cortex.SetActuators([]*Actuator{actuator})
```
## Getting Started
* [Install Go](http://golang.org/doc/install)
* Clone repository with `$ git clone git://github.com/tleyden/neurgo.git`
* Run tests with `$ go test`
* To write code that uses neurgo, your code will need `import "github.com/tleyden/neurgo"` as described in the [API documentation](http://godoc.org/github.com/tleyden/neurgo)
## Documentation
* This README file
* [API documentation](http://godoc.org/github.com/tleyden/neurgo)
## Libraries that build on Neurgo
* [neurvolve](https://github.com/tleyden/neurvolve) builds on this library to support evolution-based learning.
## Related Work
[DXNN2](https://github.com/CorticalComputer/DXNN2) - Pure Erlang TPEULN (Topology & Parameter Evolving Universal Learning Network).
## Related Publications
[Handbook of Neuroevolution Through Erlang](http://www.amazon.com/Handbook-Neuroevolution-Through-Erlang-Gene/dp/1461444624) _by Gene Sher_.