Noggin

Ruby Neural Network implementation using backpropagation and gradient descent for training

network = Noggin::Network.new

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

network.run [0, 0]  # 0.0163
network.run [0, 1]  # 0.9573
network.run [1, 0]  # 0.9702
network.run [1, 1]  # 0.0142

Installation

gem install the_noggin

Options

Noggin::Network.new( 
    max_training_laps: 100000, # How many propgation of errors to do when training
    learning_rate: 0.1, # How fast the network learns
    hidden_layer_size: 1 , # Number of hidden layers
    hidden_layer_node_size: 2 # Number of nodes each hidden layer has
)

Print Network

network.pretty_print

 ------                                           ------                                          --------------      
|      | -EDGE--(w: 0.438443, d: 0.01759)        |      | -EDGE--(w: 0.515923, d: 0.09704)       | ed: 0.668486       
|      | -EDGE--(w: 0.746539, d: 0.013825)        ------                                         | d: 0.148145        
 ------                                           ------                                         | e: 0.223437        
 ------                                          |      | -EDGE--(w: 0.485781, d: 0.11099)       | o: 0.668486        
|      | -EDGE--(w: 0.199745, d: 0.01759)         ------                                          --------------      
|      | -EDGE--(w: 0.345684, d: 0.013825)       
 ------