Awesome
GEM: cerebrum
![Gem Version](https://badge.fury.io/rb/cerebrum.svg)
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.
Installation
Add this line to your application's Gemfile:
gem 'cerebrum'
And then execute:
$ bundle
Or install it yourself as:
$ gem install cerebrum
Usage
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]
Training
Use Cerebrum#train
to train the network with an array of training data.
Data format
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 Options
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]
})
Training Options
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.
Output
The output of Cerebrum#train
is a hash of information about how the training went:
network.train(data, options)
# => { error: 0.005324233132423, iterations: 9001 }
JSON
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.
Development
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.
Contributing
Bug reports and pull requests are welcome on GitHub at irfansharif.
License
The gem is available as open source under the terms of the MIT License.