Class: Qoa::NeuralNetwork

Inherits:
Object
  • Object
show all
Includes:
Err::Validations, LossFunctions, Training, Utils
Defined in:
lib/qoa/neural_network.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from Err::Validations

#validate_calculate_loss_args, #validate_constructor_args, #validate_query_args, #validate_train_args

Methods included from LossFunctions

binary_cross_entropy, categorical_cross_entropy, cross_entropy_loss, mean_absolute_error, mean_squared_error

Methods included from Utils

#load_model, #save_model

Methods included from Training

#backward_pass, #calculate_regularization_penalty, #conv_weight_delta, #convolution, #forward_pass, #pool_weight_delta, #pooling, #train, #train_batch, #train_with_early_stopping

Methods included from MatrixHelpers

#apply_dropout, #apply_function, #matrix_add, #matrix_multiply, #matrix_multiply_element_wise, #matrix_pow, #matrix_subtract, #mean, #normalize, #scalar_add, #scalar_multiply, #scale_and_shift, #transpose, #update_beta, #update_gamma, #variance

Constructor Details

#initialize(input_nodes, hidden_layers, output_nodes, learning_rate, dropout_rate, activation_func = :leaky_relu, decay_rate = 0.9, epsilon = 1e-8, batch_size = 10, l1_lambda = 0.0, l2_lambda = 0.0) ⇒ NeuralNetwork

Returns a new instance of NeuralNetwork.



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# File 'lib/qoa/neural_network.rb', line 19

def initialize(input_nodes, hidden_layers, output_nodes, learning_rate, dropout_rate, activation_func = :leaky_relu, decay_rate = 0.9, epsilon = 1e-8, batch_size = 10, l1_lambda = 0.0, l2_lambda = 0.0)
  # validate_constructor_args(input_nodes, hidden_layers, output_nodes, learning_rate, dropout_rate, activation_func, decay_rate, epsilon, batch_size, l1_lambda, l2_lambda)

  @input_nodes = input_nodes
  @hidden_layers = hidden_layers
  @output_nodes = output_nodes
  @learning_rate = learning_rate
  @activation_func = activation_func
  @dropout_rate = dropout_rate
  @decay_rate = decay_rate
  @epsilon = epsilon
  @batch_size = batch_size
  @l1_lambda = l1_lambda
  @l2_lambda = l2_lambda

  @layers = []
  @layers << Qoa::Layers::Layer.new(input_nodes, hidden_layers[0].is_a?(Array) ? hidden_layers[0][1] : hidden_layers[0])

  hidden_layers.each_cons(2) do |l1, l2|
    l1_size = l1.is_a?(Array) ? l1[1] : l1
    l2_size = l2.is_a?(Array) ? l2[1] : l2

    if l1.is_a?(Array) && l2.is_a?(Array) && l1[0] == :conv && l2[0] == :conv
      @layers << Qoa::Layers::ConvolutionalLayer.new(l1_size, l2_size, l1[2], l1[3])
    elsif l1.is_a?(Array) && l1[0] == :conv && l2.is_a?(Numeric)
      @layers << Qoa::Layers::ConvolutionalLayer.new(l1_size, l2_size, l1[2], l1[3])
    elsif l1.is_a?(Numeric) && l2.is_a?(Array) && l2[0] == :conv
      @layers << Qoa::Layers::ConvolutionalLayer.new(l1_size, l2_size, l2[2], l2[3])
    elsif l1.is_a?(Array) && l1[0] == :pool && l2.is_a?(Numeric)
      @layers << Qoa::Layers::PoolingLayer.new(l1_size, l2_size, l1[2], l1[3])
    elsif l1.is_a?(Numeric) && l2.is_a?(Array) && l2[0] == :pool
      @layers << Qoa::Layers::PoolingLayer.new(l1_size, l2_size, l2[2], l2[3])
    else
      @layers << Qoa::Layers::Layer.new(l1_size, l2_size)
    end
  end
  @layers << Qoa::Layers::Layer.new(hidden_layers[-1].is_a?(Array) ? hidden_layers[-1][1] : hidden_layers[-1], output_nodes)
end

Instance Attribute Details

#activation_func ⇒ Object (readonly)

Returns the value of attribute activation_func.



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# File 'lib/qoa/neural_network.rb', line 17

def activation_func
  @activation_func
end

#batch_size ⇒ Object (readonly)

Returns the value of attribute batch_size.



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# File 'lib/qoa/neural_network.rb', line 17

def batch_size
  @batch_size
end

#decay_rate ⇒ Object (readonly)

Returns the value of attribute decay_rate.



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# File 'lib/qoa/neural_network.rb', line 17

def decay_rate
  @decay_rate
end

#dropout_rate ⇒ Object (readonly)

Returns the value of attribute dropout_rate.



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# File 'lib/qoa/neural_network.rb', line 17

def dropout_rate
  @dropout_rate
end

#epsilon ⇒ Object (readonly)

Returns the value of attribute epsilon.



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# File 'lib/qoa/neural_network.rb', line 17

def epsilon
  @epsilon
end

#hidden_layers ⇒ Object (readonly)

Returns the value of attribute hidden_layers.



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# File 'lib/qoa/neural_network.rb', line 17

def hidden_layers
  @hidden_layers
end

#input_nodes ⇒ Object (readonly)

Returns the value of attribute input_nodes.



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# File 'lib/qoa/neural_network.rb', line 17

def input_nodes
  @input_nodes
end

#l1_lambda ⇒ Object (readonly)

Returns the value of attribute l1_lambda.



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# File 'lib/qoa/neural_network.rb', line 17

def l1_lambda
  @l1_lambda
end

#l2_lambda ⇒ Object (readonly)

Returns the value of attribute l2_lambda.



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# File 'lib/qoa/neural_network.rb', line 17

def l2_lambda
  @l2_lambda
end

#learning_rate ⇒ Object (readonly)

Returns the value of attribute learning_rate.



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# File 'lib/qoa/neural_network.rb', line 17

def learning_rate
  @learning_rate
end

#output_nodes ⇒ Object (readonly)

Returns the value of attribute output_nodes.



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# File 'lib/qoa/neural_network.rb', line 17

def output_nodes
  @output_nodes
end

Instance Method Details

#calculate_loss(inputs, targets, loss_function = :cross_entropy_loss) ⇒ Object 



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# File 'lib/qoa/neural_network.rb', line 65

def calculate_loss(inputs, targets, loss_function = :cross_entropy_loss)
  validate_calculate_loss_args(inputs, targets, loss_function)

  total_loss = 0.0
  inputs.zip(targets).each do |input, target|
    prediction = query(input)
    total_loss += LossFunctions.send(loss_function, prediction, target)
  end

  total_loss / inputs.size
end

#query(inputs) ⇒ Object 



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# File 'lib/qoa/neural_network.rb', line 58

def query(inputs)
  validate_query_args(inputs)

  layer_outputs = forward_pass(inputs)
  layer_outputs.last.flatten
end