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Fast AI

Neural Networks in ruby

Gem Version

NN eye candy

RubyFann, or "ruby-fann" is a Ruby Gem (no Rails required) that binds to FANN (Fast Artificial Neural Network) from within a ruby/rails environment. FANN is a is a free native open source neural network library, which implements multilayer artificial neural networks, supporting both fully-connected and sparsely-connected networks. It is easy to use, versatile, well documented, and fast. RubyFann makes working with neural networks a breeze using ruby, with the added benefit that most of the heavy lifting is done natively.

A talk given by our friend Ethan from Big-Oh Studios at Lone Star Ruby 2013:


Add this line to your application's Gemfile:

gem 'ruby-fann'

And then execute:

$ bundle

Or install it yourself as:

$ gem install ruby-fann


First, Go here & read about FANN. You don't need to install it before using the gem, but understanding FANN will help you understand what you can do with the ruby-fann gem:


ruby-fann documentation:

Example training & subsequent execution:

  require 'ruby-fann'
  train =>[[0.3, 0.4, 0.5], [0.1, 0.2, 0.3]], :desired_outputs=>[[0.7], [0.8]])
  fann =>3, :hidden_neurons=>[2, 8, 4, 3, 4], :num_outputs=>1)
  fann.train_on_data(train, 1000, 10, 0.1) # 1000 max_epochs, 10 errors between reports and 0.1 desired MSE (mean-squared-error)
  outputs =[0.3, 0.2, 0.4])

Save training data to file and use it later (continued from above)'verify.train')
  train =>'verify.train')
  # Train again with 10000 max_epochs, 20 errors between reports and 0.01 desired MSE (mean-squared-error)
  # This will take longer:
  fann.train_on_data(train, 10000, 20, 0.01)

Save trained network to file and use it later (continued from above)'')
  saved_nn =>"")[0.3, 0.2, 0.4])

Custom training using a callback method

This callback function can be called during training when using train_on_data, train_on_file or cascadetrain_on_data.

It is very useful for doing custom things during training. It is recommended to use this function when implementing custom training procedures, or when visualizing the training in a GUI etc. The args which the callback function takes is the parameters given to the train_on_data, plus an epochs parameter which tells how many epochs the training have taken so far.

The callback method should return an integer, if the callback function returns -1, the training will terminate.

The callback (training_callback) will be automatically called if it is implemented on your subclass as follows:

class MyFann < RubyFann::Standard
  def training_callback(args)
    puts "ARGS: #{args.inspect}"

A sample project using RubyFann to play tic-tac-toe


  1. Steven Miers
  2. Ole Krüger
  3. dignati
  4. Michal Pokorny
  5. Scott Li (locksley)
  6. alex.slotty


  1. Fork it
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create new Pull Request