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やりたいこと

ニューラルネットワークのプログラムを書きたくなったので、
一番簡単な最急降下法でプログラムを書いてみる。
今日は、とりあえず枠組みだけ。

プログラム

入力データ

適当なinput layerのデータ。

1,2,3,4,5,15,7,11,9,10,1
14,21,6,8,2,9,4,5,10,7,2
19,5,13,9,2,8,18,11,7,3,3
11,9,7,13,19,3,8,2,12,10,4
9,2,3,15,11,17,12,8,9,10,5

nn.rb

require 'csv'

NUM_ITERATION = 10
NUM_INPUT_DATA = 5
NUM_INPUT_LAYER = 10
NUM_HIDDEN_LAYER = 15
NUM_OUTPUT_LAYER = 5

# Input Data
data = CSV.open("data.csv", "r")
line_num = 0
input_data = []
expected_result = []
data.each do |row|
    input_data << []
    field_cnt = 0
    NUM_INPUT_LAYER.times do |j|
        input_data[line_num] << row[field_cnt].to_i
        field_cnt += 1
    end
    expected_result << row[field_cnt].to_i
    line_num += 1
end

# Input Layer Initialize
input_layer = []
NUM_INPUT_LAYER.times do |i|
    input_layer << 0.0
end

# Hidden Layer Initialize
hidden_layer = []
NUM_HIDDEN_LAYER.times do |i|
    hidden_layer << 0.0
end

# Output Layer Initialize
output_layer = []
NUM_OUTPUT_LAYER.times do |i|
    output_layer << 0.0
end

# Initialize Parameter
weight_input_hidden = []
NUM_INPUT_LAYER.times do |i|
    weight_input_hidden << []
    NUM_HIDDEN_LAYER.times do |h|
        weight_input_hidden[i] << rand
    end
end

weight_hidden_output = []
NUM_HIDDEN_LAYER.times do |i|
    weight_hidden_output << []
    NUM_OUTPUT_LAYER.times do |h|
        weight_hidden_output[i] << rand
    end
end

threshold_hidden = []
NUM_HIDDEN_LAYER.times do |i|
    threshold_hidden << rand
end

# Sigmoid Function
def sigmoid(val)
    return 1.0/(1.0 + Math.exp(-val))
end

# Main Loop
NUM_ITERATION.times do |itr|
    error = 0.0
    NUM_INPUT_DATA.times do |d|
        # Set data to input layer
        NUM_INPUT_LAYER.times do |i|
            input_layer[i] = input_data[d][i]
        end

        # Calculate hidden layer's output
        NUM_HIDDEN_LAYER.times do |h|
            sum = 0.0
            NUM_INPUT_LAYER.times do |i|
                sum += weight_input_hidden[i][h] * input_layer[i]
            end
            sum += threshold_hidden[h]
            hidden_layer[h] = sigmoid(sum)
        end

        # Calculate output layer's output
        NUM_OUTPUT_LAYER.times do |o|
            sum = 0.0
            NUM_HIDDEN_LAYER.times do |h|
                sum += weight_hidden_output[h][o] * hidden_layer[h]
            end
            output_layer[o] = sigmoid(sum)
        end

        # Calculated total error
        NUM_OUTPUT_LAYER.times do |o|
            error += (expected_result[o] - output_layer[o])*(expected_result[o] - output_layer[o])
        end
    end

    #
    puts "Iteration #{itr+1} total error : #{error}"
end

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