go-perceptron-go

command module
Version: v0.2.0 Latest Latest
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Published: Oct 4, 2017 License: GPL-3.0 Imports: 5 Imported by: 0

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 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
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 https://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:

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

To complete yet
  • test methods
Future features
  • mathgl for better vector space handling
  • some other cool neural model XD

Documentation

Overview

Main package provide main to test library

Source Files

Directories

Path Synopsis
model
neural
Neural provides struct to represents most common neural networks model and algorithms to train / test them.
Neural provides struct to represents most common neural networks model and algorithms to train / test them.
Util provides util to handle common tasks: file and struct operations, string manipulation, etc.
Util provides util to handle common tasks: file and struct operations, string manipulation, etc.

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