TNn
my neural network library
Installation
Add this line to your application's Gemfile:
gem 't_nn'
And then execute:
$ bundle
Or install it yourself as:
$ gem install t_nn
Usage
simple feedforward_neural_network
respect for keras.
require "t_nn"
model = TNN::FeedForwardNeuralNetwork.new(learning_rate=0.1)
model.add_layer(node_num=2)
model.add_layer(node_num=3)
model.add_layer(node_num=1)
x_train = [[0.0, 0.0],[0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
y_train = [[ 0.0 ], [ 1.0 ],[ 1.0 ],[ 0.0 ]]
model.fit(x_train, y_train, epoch=50000)
x_test = x_train
y_test = y_train
err_rate = model.evaluate(x_test, y_test)
puts "err rate: #{err_rate}%"
x_test.zip(y_test).each do |x, y|
output = model.propagation(x)
puts "x#{x}, y#{y}, output#{output}"
end
result
....
err rate: 2.5190691608533524%
x [0.0, 0.0], y [0.0] , output [0.03286460161620565]
x [0.0, 1.0], y [1.0] , output [0.9733866321804969]
x [1.0, 0.0], y [1.0] , output [0.9731963536942299]
x [1.0, 1.0], y [0.0] , output [0.014481150692655216]
hop filed net
sample
require "t_nn"
data = [1.0, 1.0, -1.0, -1.0, 1.0] # teacher data
hop_field_net = TNN::HopFieldNetwork.new(0.0, data)
hop_field_net.memorize
noisedData = TNN.add_noise_data(data, 0.0) # make test data
puts "======[before]======"
puts "#{TNN.evaluate(data, noisedData)}%"
hop_field_net.remember(noisedData)
puts "======[after]======"
puts "#{ TNN.evaluate(data,hop_field_net.nodes) }%"
Development
After checking out the repo, run bin/setup
to install dependencies. Then, run rake spec
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 https://github.com/[USERNAME]/t_nn. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.
License
The gem is available as open source under the terms of the MIT License.