Module: RubyBrain::Trainer

Defined in:
lib/ruby_brain.rb,
lib/ruby_brain/trainer.rb

Class Method Summary collapse

Class Method Details

.learn2(network_structure, input_training_set, output_training_set) ⇒ Object 



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# File 'lib/ruby_brain/trainer.rb', line 27

def learn2(network_structure, input_training_set, output_training_set)
  network = RubyBrain::Network.new(network_structure)
  network.learning_rate = 0.05
  network.init_network
  network.learn2(input_training_set, output_training_set, max_training_count=5000, tolerance=0.045)

  puts network.dump_weights_to_yaml

  input_training_set.each do |inputs|
    pp network.get_forward_outputs(inputs)[0]
  end
end

.normal_learning(network_structure, input_training_set, output_training_set, options = {}) ⇒ Object 



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# File 'lib/ruby_brain/trainer.rb', line 6

def normal_learning(network_structure, input_training_set, output_training_set, options={})
  default_options = {learning_rate: 0.05, max_training_count: 5000, tolerance: 0.045, initial_weights_file: nil}
  training_options = default_options.merge(options)
  puts "===== normal learnng ====="
  pp training_options
  network = RubyBrain::Network.new(network_structure)
  network.learning_rate = training_options[:learning_rate]
  network.init_network
  if training_options[:initial_weights_file]
    puts "loading weights from #{training_options[:initial_weights_file]}"
    network.load_weights_from_yaml_file(training_options[:initial_weights_file])
  end
  network.learn(input_training_set, output_training_set, max_training_count=training_options[:max_training_count], tolerance=training_options[:tolerance])

  network.dump_weights_to_yaml("weights_#{network_structure.join('_')}_#{Time.now.to_i}.yml")

  # input_training_set.each do |inputs|
  #   puts network.get_forward_outputs(inputs).join(',')
  # end
end

.stack_learning(network_structure, input_training_set, output_training_set) ⇒ Object 



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# File 'lib/ruby_brain/trainer.rb', line 40

def stack_learning(network_structure, input_training_set, output_training_set)
    ws = Array.new(network_structure.size-1, nil)
    (1..(network_structure.size-2)).each do |i|
      next_network_form = network_structure[0..i] + [network_structure[-1]]
      neuralnet = RubyBrain::Network.new(next_network_form)
      neuralnet.learning_rate = 0.05
      neuralnet.init_network
      neuralnet.overwrite_weights(ws)
      # neuralnet.learn_only_specified_layer(-1, input_training_set, output_training_set, max_training_count=1000, tolerance=0.06)
      neuralnet.learn2(input_training_set, output_training_set, max_training_count=500, tolerance=0.006)
      ws = neuralnet.get_weights_as_array
      ws[-1] = nil
    end
    neuralnet = RubyBrain::Network.new(network_structure)
    neuralnet.learning_rate = 0.05
    neuralnet.init_network
    neuralnet.overwrite_weights(ws)
    neuralnet.learn(input_training_set, output_training_set, max_training_count=2000, tolerance=0.0036)
    # neuralnet.learn2(input_training_set, output_training_set, max_training_count=1000, tolerance=0.036)

    puts neuralnet.dump_weights_to_yaml

    input_training_set.each do |inputs|
      pp neuralnet.get_forward_outputs(inputs)[0]
    end
    puts "==================================================================================================="
    input_training_set.each do |inputs|
      pp neuralnet.get_forward_outputs(inputs)[1]
    end
end