cerebrum is an implementation of ANNs in Ruby. There's no reason to train a neural network in Ruby, I'm using it to experiment and play around with the bare fundamentals of ANNs, original idea for this project here is currently unmaintained. Extensions on top of that are personal experimentation.

Add this line to your application's Gemfile:

gem 'cerebrum'

And then execute:

$ bundle

Or install it yourself as:

$ gem install cerebrum

require 'cerebrum'

network = Cerebrum.new

network.train([
  {input: [0, 0], output: [0]},
  {input: [0, 1], output: [1]},
  {input: [1, 0], output: [1]},
  {input: [1, 1], output: [0]}
])

result = network.run([1, 0])
# => [0.9333206724219677]

Use Cerebrum#train to train the network with an array of training data.

Each training pattern should have an input: and an output:, both of which can either be an array of numbers from 0 to 1 or a hash of numbers from 0 to 1. An example of the latter is demonstrated below:

network = Cerebrum.new

network.train([
    {input: { r: 0.03, g: 0.7, b: 0.5 }, output: { black: 1 }},
    {input: { r: 0.16, g: 0.09, b: 0.2 }, output: { white: 1 }},
    {input: { r: 0.5, g: 0.5, b: 1.0 }, output: { white: 1 }}
]);

result = network.run({ r: 1, g: 0.4, b: 0 })
# => { black: 0.011967728530458011, white: 0.9871010273923573 }

Cerebrum#new takes a hash of options that would set defaults if not specified in the Cerebrum#train procedure call:

network = Cerebrum.new({
  learning_rate:  0.3,
  momentum:       0.1,
  binary_thresh:  0.5,
  hidden_layers:  [3, 4]
})

Cerebrum#train optionally takes in a configuration hash as the second argument:

network.train(data, {
  error_threshold: 0.005,
  iterations:      20000,
  log:             true,
  log_period:      100,
  learning_rate:   0.3
})

The network will train until the training error has gone below the threshold or the max number of iterations has been reached, whichever comes first.

By default training won't let you know how its doing until the end, but set log to true to get periodic updates on the current training error of the network. The training error should decrease every time. The updates will be printed to console. If you set log to a function, this function will be called with the updates instead of printing to the console.

The learning_rate is a parameter that influences how quickly the network trains, a number from 0 to 1. If the learning rate is close to 0 it will take longer to train. If the learning rate is closer to 1 it will train faster but it's in danger of training to a local minimum and performing badly on new data.

The output of Cerebrum#train is a hash of information about how the training went:

network.train(data, options)
# => { error: 0.005324233132423, iterations: 9001 }

Serialize or load in the state of a trained network with JSON:

saved_state = network.save_state
nn = Cerebrum.new
nn.load_state(saved_state)

nn.run({ r: 1, g: 0.4, b: 0 })
# => { black: 0.011967728530458011, white: 0.9871010273923573 }

Additionally the new network can be separately trained whilst retaining all previous training from the other network.

After checking out the repo, run bin/setup to install dependencies. Then, run rake test to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment. To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

Bug reports and pull requests are welcome on GitHub at irfansharif.

The gem is available as open source under the terms of the MIT License.