https://github.com/made2591/go-perceptron-go

A single / multi layer / recurrent neural network written in Golang.
https://github.com/made2591/go-perceptron-go

go level-perceptron neural neural-network neural-networks perceptron weights-estimated

Last synced: 29 days ago
JSON representation

A single / multi layer / recurrent neural network written in Golang.

• Host: GitHub
• URL: https://github.com/made2591/go-perceptron-go
• Owner: made2591
• License: gpl-3.0
• Created: 2017-07-17T22:47:28.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-07-15T16:09:33.000Z (over 6 years ago)
• Last Synced: 2024-10-28T08:41:45.640Z (about 2 months ago)
• Topics: go, level-perceptron, neural, neural-network, neural-networks, perceptron, weights-estimated
• Language: Go
• Homepage:
• Size: 433 KB
• Stars: 167
• Watchers: 9
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-golang-ai - go-perceptron-go

README

        
# Go Perceptron

A single / multi level perceptron classifier with weights estimated from sonar training data set using stochastic gradient descent. Recently I added back propagation algorithm over multilayer perceptron network.

The implementation is in dev. Planned features:

- complete future features XD (see above)

- find co-workers

- create a ml library in openqasm (just kidding)

- brainstorming / devtesting other an models

### Updates

2017-10-04: Introduced Recurrent Neural Network (Elman Network) with "learn to sum integer" task. Big refactoring in code (working on)

2017-08-08: Introduced multi layer perceptron network definition with parametric number of hidden layer and neurons. Back propagation algorithm with different transfer function actived - I wanna thank you [dakk](https://github.com/dakk) because I was truly inspired by your code.

2017-08-01: Introduced validation package and k-fold cross validation.

2017-07-31: I started working on ```mlp``` branch for MLP + back prop. It doens't work yet but...I push first commit after some exp in dev. I delete ```dev``` because of some structs optimization.

2017-07-31: we started working on k-fold validation.

### Dependencies

- [logrus](https://github.com/sirupsen/logrus)

### Run test

To run a simple test just open a shell and run the following:

```

git clone https://github.com/made2591/go-perceptron-go

cd go-perceptron-go

go get github.com/sirupsen/logrus

go run main.go

```

You can setup a MultiLayerPerceptron using ```PrepareMLPNet```. The first parameter, a simple ```[]int```, define the entire network struct. Example:

- [4, 3, 3] will define a network struct with 3 layer: input, hidden, output, with respectively 4, 3 and 3 neurons. For classification problems the input layers has to be define with a number of neurons that match features of pattern shown to network. Of course, the output layer should have a number of unit equals to the number of class in training set.

The network will have this topology:

![](./media/first_example.png)

- [4, 6, 4, 3] will have this topology:

![](./media/second_example.png)

### To complete yet

- test methods

### Future features

- [mathgl](https://github.com/go-gl/mathgl.git) for better vector space handling

- some other cool neural model XD