├── LICENSE ├── README.md └── Source ├── ActivationFunction.cpp ├── BackPropagation.cpp ├── DrawNeuralNetwork.cpp ├── DrawText.cpp ├── ForwardPropagation.cpp ├── GetMaxElement.cpp ├── GetMinElement.cpp ├── Headers ├── ActivationFunction.hpp ├── BackPropagation.hpp ├── DrawNeuralNetwork.hpp ├── DrawText.hpp ├── ForwardPropagation.hpp ├── GetMaxElement.hpp ├── GetMinElement.hpp └── Global.hpp ├── Main.cpp └── Resources └── Images └── Font.png /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Neural-network 2 | A very simple neural network. 3 | 4 | Here's the video explaining how I did it: https://youtu.be/Zrrnqd0rCXg 5 | -------------------------------------------------------------------------------- /Source/ActivationFunction.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "Headers/ActivationFunction.hpp" 4 | 5 | float activation_function(bool i_derivative, float i_input) 6 | { 7 | //Machine learning requires calculus. 8 | //And I know what derivatives are. 9 | //So I know calculus. 10 | //Like 1% of it. 11 | //Oh yeah. 12 | if (0 == i_derivative) 13 | { 14 | if (0 >= i_input) 15 | { 16 | return pow(2, i_input - 1); 17 | } 18 | else 19 | { 20 | return 1 - pow(2, -1 - i_input); 21 | } 22 | } 23 | else 24 | { 25 | if (0 >= i_input) 26 | { 27 | //log(x) means ln(x) in the C++ standard library language. 28 | //By the way, when I found out that the backpropagation uses derivatives, I was like "BUT WHAT'S THE DERIVATIVE OF MY FUNCTION?!" 29 | //But thanks to the internet, I found out it's this thing. 30 | return log(2) * pow(2, i_input - 1); 31 | } 32 | else 33 | { 34 | return log(2) * pow(2, -1 - i_input); 35 | } 36 | } 37 | } -------------------------------------------------------------------------------- /Source/BackPropagation.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | 5 | #include "Headers/ActivationFunction.hpp" 6 | #include "Headers/Global.hpp" 7 | #include "Headers/BackPropagation.hpp" 8 | 9 | void back_propagation(const vector_1d& i_target_outputs, vector_2d& i_errors, const vector_2d& i_neural_network, vector_3d& i_weights) 10 | { 11 | for (unsigned char a = i_neural_network.size() - 1; 0 < a; a--) 12 | { 13 | for (unsigned b = 0; b < i_neural_network[a].size(); b++) 14 | { 15 | //The bias neurons aren't connected to the neurons of the previous layer. 16 | //And that caused a lot of headaches. 17 | //But I figured it out! 18 | //(I think...) 19 | unsigned bias_neurons = 0; 20 | 21 | if (a == i_neural_network.size() - 1) 22 | { 23 | //I forgot to say in the video that by taking a square or a cube of the error, we can prioritize large errors over small ones. 24 | i_errors[a - 1][b] = pow(i_neural_network[a][b] - i_target_outputs[b], 3); 25 | } 26 | else if (b >= BIAS_NEURONS[a]) 27 | { 28 | unsigned next_bias_neurons = 0; 29 | 30 | bias_neurons = BIAS_NEURONS[a]; 31 | 32 | if (a < i_neural_network.size() - 2) 33 | { 34 | next_bias_neurons = BIAS_NEURONS[1 + a]; 35 | } 36 | 37 | i_errors[a - 1][b - bias_neurons] = 0; 38 | 39 | for (unsigned c = next_bias_neurons; c < i_neural_network[1 + a].size(); c++) 40 | { 41 | i_errors[a - 1][b - bias_neurons] += i_errors[a][c - next_bias_neurons] * i_weights[a][c - next_bias_neurons][b - bias_neurons]; 42 | } 43 | } 44 | 45 | if (b >= bias_neurons) 46 | { 47 | //We used the activation function in forward propagation, so we need to recalculate the sum of the neuron. 48 | float neuron_output = 0; 49 | 50 | for (unsigned c = 0; c < i_weights[a - 1][b - bias_neurons].size(); c++) 51 | { 52 | neuron_output += i_neural_network[a - 1][c] * i_weights[a - 1][b - bias_neurons][c]; 53 | } 54 | 55 | for (unsigned c = 0; c < i_neural_network[a - 1].size(); c++) 56 | { 57 | //I have no idea what's happening here. 58 | //I just took this formula from the internet. 59 | //It worked and I was like, "Cool!" 60 | i_weights[a - 1][b - bias_neurons][c] -= LEARNING_RATE * i_errors[a - 1][b - bias_neurons] * i_neural_network[a - 1][c] * activation_function(1, neuron_output); 61 | } 62 | } 63 | } 64 | } 65 | } -------------------------------------------------------------------------------- /Source/DrawNeuralNetwork.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | 7 | #include "Headers/Global.hpp" 8 | #include "Headers/DrawNeuralNetwork.hpp" 9 | #include "Headers/DrawText.hpp" 10 | #include "Headers/GetMaxElement.hpp" 11 | #include "Headers/GetMinElement.hpp" 12 | 13 | void draw_neural_network(sf::RenderWindow& i_window, const sf::Texture& i_font_texture, const vector_2d& i_neural_network, const vector_3d& i_weights) 14 | { 15 | //When our max neuron is 9 and our min neuron is -1, I don't wanna draw them with as opposites of one another. 16 | //I want to have SYMMETRY! 17 | float max_neuron = std::max(abs(get_max_element(i_neural_network)), abs(get_min_element(i_neural_network))); 18 | float min_neuron = -max_neuron; 19 | 20 | sf::CircleShape neuron_shape(NEURON_SHAPE_RADIUS); 21 | neuron_shape.setOutlineColor(sf::Color(255, 255, 255)); 22 | neuron_shape.setOutlineThickness(-LINE_THICKNESS); 23 | 24 | sf::VertexArray connection_shape(sf::Quads, 4); 25 | 26 | //When our max neuron is 3 and our min neuron is 2, I don't wanna draw the min neuron as black. 27 | if (0 <= get_min_element(i_neural_network)) 28 | { 29 | min_neuron = 0; 30 | } 31 | 32 | for (unsigned char a = 0; a < i_neural_network.size(); a++) 33 | { 34 | unsigned short neuron_x = SCREEN_WIDTH * (1 + a) / (1 + i_neural_network.size()); 35 | 36 | for (unsigned b = 0; b < i_neural_network[a].size(); b++) 37 | { 38 | //Rounding the value of the neuron to the nearest [DECIMAL_DIGITS] digits after the decimal point. 39 | float neuron_value = round(i_neural_network[a][b] * pow(10, DECIMAL_DIGITS)) / pow(10, DECIMAL_DIGITS); 40 | 41 | unsigned char neuron_color = round(255 * (i_neural_network[a][b] - min_neuron) / (max_neuron - min_neuron)); 42 | unsigned char text_color = 255 * (128 > neuron_color); 43 | 44 | unsigned short neuron_y = SCREEN_HEIGHT * (1 + b) / (1 + i_neural_network[a].size()); 45 | 46 | std::ostringstream neuron_text_stream; 47 | neuron_text_stream << std::fixed << std::setprecision(DECIMAL_DIGITS) << neuron_value; 48 | 49 | neuron_shape.setFillColor(sf::Color(neuron_color, neuron_color, neuron_color)); 50 | neuron_shape.setPosition(neuron_x - NEURON_SHAPE_RADIUS, neuron_y - NEURON_SHAPE_RADIUS); 51 | 52 | if (a < i_neural_network.size() - 1) 53 | { 54 | float max_weight = std::max(abs(get_max_element(i_weights[a])), abs(get_min_element(i_weights[a]))); 55 | float min_weight = -max_weight; 56 | 57 | //I have mixed feelings towards bias neurons. 58 | //Sometimes I hate them. 59 | //And sometimes I hate them. 60 | unsigned bias_neurons = 0; 61 | 62 | if (a < i_neural_network.size() - 2) 63 | { 64 | bias_neurons = BIAS_NEURONS[1 + a]; 65 | } 66 | 67 | for (unsigned c = bias_neurons; c < i_neural_network[1 + a].size(); c++) 68 | { 69 | if (0 <= i_weights[a][c - bias_neurons][b]) 70 | { 71 | unsigned char connection_opacity = round(255 * i_weights[a][c - bias_neurons][b] / max_weight); 72 | 73 | //Why can't I just say: "connection_shape.color = sf::Color(0, 255, 0, connection_opacity)"? 74 | connection_shape[0].color = sf::Color(0, 255, 0, connection_opacity); 75 | connection_shape[1].color = sf::Color(0, 255, 0, connection_opacity); 76 | connection_shape[2].color = sf::Color(0, 255, 0, connection_opacity); 77 | connection_shape[3].color = sf::Color(0, 255, 0, connection_opacity); 78 | } 79 | else 80 | { 81 | unsigned char connection_opacity = round(255 * i_weights[a][c - bias_neurons][b] / min_weight); 82 | 83 | connection_shape[0].color = sf::Color(255, 0, 0, connection_opacity); 84 | connection_shape[1].color = sf::Color(255, 0, 0, connection_opacity); 85 | connection_shape[2].color = sf::Color(255, 0, 0, connection_opacity); 86 | connection_shape[3].color = sf::Color(255, 0, 0, connection_opacity); 87 | } 88 | 89 | unsigned short previous_neuron_x = SCREEN_WIDTH * (2 + a) / (1 + i_neural_network.size()); 90 | unsigned short previous_neuron_y = SCREEN_HEIGHT * (1 + c) / (1 + i_neural_network[1 + a].size()); 91 | 92 | //I should've used rectangles. 93 | connection_shape[0].position = sf::Vector2f(previous_neuron_x, previous_neuron_y - 0.5f * LINE_THICKNESS); 94 | connection_shape[1].position = sf::Vector2f(neuron_x, neuron_y - 0.5f * LINE_THICKNESS); 95 | connection_shape[2].position = sf::Vector2f(neuron_x, neuron_y + 0.5f * LINE_THICKNESS); 96 | connection_shape[3].position = sf::Vector2f(previous_neuron_x, previous_neuron_y + 0.5f * LINE_THICKNESS); 97 | 98 | i_window.draw(connection_shape); 99 | } 100 | } 101 | 102 | i_window.draw(neuron_shape); 103 | 104 | draw_text(1, 1, neuron_x, neuron_y, neuron_text_stream.str(), sf::Color(text_color, text_color, text_color), i_window, i_font_texture); 105 | } 106 | } 107 | } -------------------------------------------------------------------------------- /Source/DrawText.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "Headers/DrawText.hpp" 4 | 5 | void draw_text(bool i_horizontal_center, bool i_vertical_center, short i_x, short i_y, const std::string& i_text, const sf::Color& i_color, sf::RenderWindow& i_window, const sf::Texture& i_font_texture) 6 | { 7 | short character_x = i_x; 8 | short character_y = i_y; 9 | 10 | unsigned char character_height = i_font_texture.getSize().y; 11 | unsigned char character_width = i_font_texture.getSize().x / 96; 12 | 13 | sf::Sprite character_sprite(i_font_texture); 14 | 15 | character_sprite.setColor(i_color); 16 | 17 | if (1 == i_horizontal_center) 18 | { 19 | character_x -= round(0.5f * character_width * i_text.substr(0, i_text.find_first_of('\n')).size()); 20 | } 21 | 22 | if (1 == i_vertical_center) 23 | { 24 | character_y -= round(0.5f * character_height * (1 + std::count(i_text.begin(), i_text.end(), '\n'))); 25 | } 26 | 27 | for (std::string::const_iterator a = i_text.begin(); a != i_text.end(); a++) 28 | { 29 | if ('\n' == *a) 30 | { 31 | if (1 == i_horizontal_center) 32 | { 33 | character_x = i_x - round(0.5f * character_width * i_text.substr(0, i_text.find_first_of('\n')).size()); 34 | } 35 | else 36 | { 37 | character_x = i_x; 38 | } 39 | 40 | character_y += character_height; 41 | 42 | continue; 43 | } 44 | 45 | character_sprite.setPosition(character_x, character_y); 46 | character_sprite.setTextureRect(sf::IntRect(character_width * (*a - 32), 0, character_width, character_height)); 47 | 48 | character_x += character_width; 49 | 50 | i_window.draw(character_sprite); 51 | } 52 | } -------------------------------------------------------------------------------- /Source/ForwardPropagation.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | 5 | #include "Headers/ActivationFunction.hpp" 6 | #include "Headers/Global.hpp" 7 | #include "Headers/ForwardPropagation.hpp" 8 | 9 | vector_1d forward_propagation(bool i_update, const vector_1d& i_inputs, vector_2d& i_neural_network, const vector_3d& i_weights) 10 | { 11 | //When we visualize the outputs, we use a lot of forward propagation. 12 | //And during that time, I don't wanna change the neural network. 13 | //Because I also wanna draw the neural network. 14 | //So I came up with this. 15 | if (1 == i_update) 16 | { 17 | for (unsigned a = 0; a < i_neural_network[0].size(); a++) 18 | { 19 | if (a >= BIAS_NEURONS[0]) 20 | { 21 | i_neural_network[0][a] = i_inputs[a - BIAS_NEURONS[0]]; 22 | } 23 | else 24 | { 25 | //Bias neurons' values are always 1. 26 | i_neural_network[0][a] = 1; 27 | } 28 | } 29 | 30 | for (unsigned char a = 0; a < i_weights.size(); a++) 31 | { 32 | unsigned bias_neurons = 0; 33 | 34 | if (a < i_weights.size() - 1) 35 | { 36 | bias_neurons = BIAS_NEURONS[1 + a]; 37 | } 38 | 39 | //At the beginning we assume that each neuron is a bias neuron. 40 | std::fill(i_neural_network[1 + a].begin(), i_neural_network[1 + a].end(), 1); 41 | 42 | for (unsigned b = 0; b < i_weights[a].size(); b++) 43 | { 44 | i_neural_network[1 + a][b + bias_neurons] = 0; 45 | 46 | for (unsigned c = 0; c < i_weights[a][b].size(); c++) 47 | { 48 | i_neural_network[1 + a][b + bias_neurons] += i_neural_network[a][c] * i_weights[a][b][c]; 49 | } 50 | 51 | i_neural_network[1 + a][b + bias_neurons] = activation_function(0, i_neural_network[1 + a][b + bias_neurons]); 52 | } 53 | } 54 | 55 | return i_neural_network[i_neural_network.size() - 1]; 56 | } 57 | else 58 | { 59 | //Yes, I know this is super inefficient. 60 | //Yes, I know there are better ways of doing this. 61 | //Yes, I'm sorry you had to see this. 62 | vector_2d neural_network = i_neural_network; 63 | 64 | for (unsigned a = 0; a < neural_network[0].size(); a++) 65 | { 66 | if (a >= BIAS_NEURONS[0]) 67 | { 68 | neural_network[0][a] = i_inputs[a - BIAS_NEURONS[0]]; 69 | } 70 | else 71 | { 72 | neural_network[0][a] = 1; 73 | } 74 | } 75 | 76 | for (unsigned char a = 0; a < i_weights.size(); a++) 77 | { 78 | unsigned bias_neurons = 0; 79 | 80 | if (a < i_weights.size() - 1) 81 | { 82 | bias_neurons = BIAS_NEURONS[1 + a]; 83 | } 84 | 85 | std::fill(neural_network[1 + a].begin(), neural_network[1 + a].end(), 1); 86 | 87 | for (unsigned b = 0; b < i_weights[a].size(); b++) 88 | { 89 | neural_network[1 + a][b + bias_neurons] = 0; 90 | 91 | for (unsigned c = 0; c < i_weights[a][b].size(); c++) 92 | { 93 | neural_network[1 + a][b + bias_neurons] += neural_network[a][c] * i_weights[a][b][c]; 94 | } 95 | 96 | neural_network[1 + a][b + bias_neurons] = activation_function(0, neural_network[1 + a][b + bias_neurons]); 97 | } 98 | } 99 | 100 | return neural_network[neural_network.size() - 1]; 101 | } 102 | } -------------------------------------------------------------------------------- /Source/GetMaxElement.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | #include "Headers/Global.hpp" 7 | #include "Headers/GetMaxElement.hpp" 8 | 9 | float get_max_element(const vector_1d& i_vector) 10 | { 11 | return *std::max_element(i_vector.begin(), i_vector.end()); 12 | } 13 | 14 | float get_max_element(const vector_2d& i_vector) 15 | { 16 | float output = FLT_MIN; 17 | 18 | for (const vector_1d& a : i_vector) 19 | { 20 | //We're using recursion! --------- 21 | // | 22 | // | 23 | // \/ 24 | output = std::max(output, get_max_element(a)); 25 | } 26 | 27 | return output; 28 | } 29 | 30 | float get_max_element(const vector_3d& i_vector) 31 | { 32 | float output = FLT_MIN; 33 | 34 | for (const vector_2d& a : i_vector) 35 | { 36 | output = std::max(output, get_max_element(a)); 37 | } 38 | 39 | return output; 40 | } -------------------------------------------------------------------------------- /Source/GetMinElement.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | #include "Headers/Global.hpp" 7 | #include "Headers/GetMinElement.hpp" 8 | 9 | float get_min_element(const vector_1d& i_vector) 10 | { 11 | return *std::min_element(i_vector.begin(), i_vector.end()); 12 | } 13 | 14 | float get_min_element(const vector_2d& i_vector) 15 | { 16 | float output = FLT_MAX; 17 | 18 | for (const vector_1d& a : i_vector) 19 | { 20 | //We're using recursion! --------- 21 | // | 22 | // | 23 | // \/ 24 | output = std::min(output, get_min_element(a)); 25 | } 26 | 27 | return output; 28 | } 29 | 30 | float get_min_element(const vector_3d& i_vector) 31 | { 32 | float output = FLT_MAX; 33 | 34 | for (const vector_2d& a : i_vector) 35 | { 36 | output = std::min(output, get_min_element(a)); 37 | } 38 | 39 | return output; 40 | } -------------------------------------------------------------------------------- /Source/Headers/ActivationFunction.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | float activation_function(bool i_derivative, float i_input); -------------------------------------------------------------------------------- /Source/Headers/BackPropagation.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | void back_propagation(const vector_1d& i_target_outputs, vector_2d& i_errors, const vector_2d& i_neural_network, vector_3d& i_weights); -------------------------------------------------------------------------------- /Source/Headers/DrawNeuralNetwork.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | void draw_neural_network(sf::RenderWindow& i_window, const sf::Texture& i_font_texture, const vector_2d& i_neural_network, const vector_3d& i_weights); -------------------------------------------------------------------------------- /Source/Headers/DrawText.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | void draw_text(bool i_horizontal_center, bool i_vertical_center, short i_x, short i_y, const std::string& i_text, const sf::Color& i_color, sf::RenderWindow& i_window, const sf::Texture& i_font_texture); -------------------------------------------------------------------------------- /Source/Headers/ForwardPropagation.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | vector_1d forward_propagation(bool i_update, const vector_1d& i_inputs, vector_2d& i_neural_network, const vector_3d& i_weights); -------------------------------------------------------------------------------- /Source/Headers/GetMaxElement.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | float get_max_element(const vector_1d& i_vector); 4 | float get_max_element(const vector_2d& i_vector); 5 | float get_max_element(const vector_3d& i_vector); -------------------------------------------------------------------------------- /Source/Headers/GetMinElement.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | float get_min_element(const vector_1d& i_vector); 4 | float get_min_element(const vector_2d& i_vector); 5 | float get_min_element(const vector_3d& i_vector); -------------------------------------------------------------------------------- /Source/Headers/Global.hpp: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | //How many digits we'll show after the decimal point. 4 | constexpr unsigned char DECIMAL_DIGITS = 2; 5 | constexpr unsigned char LINE_THICKNESS = 2; 6 | constexpr unsigned char NEURON_SHAPE_RADIUS = 32; 7 | constexpr unsigned char SCREEN_RESIZE = 1; 8 | 9 | //The size of the outputs visualization. 10 | constexpr unsigned short OUTPUTS_HEIGHT = 64; 11 | constexpr unsigned short OUTPUTS_WIDTH = 64; 12 | constexpr unsigned short SCREEN_HEIGHT = 512; 13 | constexpr unsigned short SCREEN_WIDTH = 512; 14 | 15 | constexpr std::chrono::microseconds FRAME_DURATION(16667); 16 | 17 | //NN stuff 18 | 19 | //How big the learning step is gonna be. 20 | //A low number means: "I'll put $1 in my bank account at 2% interest, so my investment has a low risk of failure." 21 | //A high number means "I'LL SPEND ALL MY MONEY ON GAMBLING!" 22 | constexpr float LEARNING_RATE = 0.03125f; 23 | //I only used these values when generating the weights. So in the beginning all the weights are in the range of -1 to 1. 24 | constexpr float VALUE_MAX = 1; 25 | constexpr float VALUE_MIN = -1; 26 | 27 | constexpr unsigned INPUT_NEURONS = 2; 28 | constexpr unsigned OUTPUT_NEURONS = 3; 29 | 30 | constexpr unsigned short TRAININGS_PER_FRAME = 1024; 31 | 32 | //The size of the array is the total number of hidden layers. Each element is the number of neurons in each layer. 33 | constexpr std::array HIDDEN_NEURONS = {3, 3}; 34 | //THE OUTPUT LAYER DOESN'T NEED YOUR PATHETIC BIAS NEURONS! 35 | constexpr std::array BIAS_NEURONS = {1, 0, 0}; 36 | 37 | //I used typedef. 38 | //I deserve a cookie. 39 | //A BIG cookie. 40 | typedef std::vector vector_1d; 41 | typedef std::vector> vector_2d; 42 | typedef std::vector>> vector_3d; -------------------------------------------------------------------------------- /Source/Main.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | #include "Headers/Global.hpp" 7 | #include "Headers/BackPropagation.hpp" 8 | #include "Headers/DrawNeuralNetwork.hpp" 9 | #include "Headers/DrawText.hpp" 10 | #include "Headers/ForwardPropagation.hpp" 11 | 12 | //It's 11 PM and I'm writing these comments instead of sleeping. 13 | //What's wrong with me? 14 | int main() 15 | { 16 | bool train = 0; 17 | 18 | float total_error = 0; 19 | 20 | std::chrono::microseconds lag(0); 21 | 22 | std::chrono::steady_clock::time_point previous_time; 23 | 24 | std::mt19937_64 random_engine(std::chrono::system_clock::now().time_since_epoch().count()); 25 | 26 | //The distribution includes the min value and excludes the max value. 27 | //So I'm using nextafter. 28 | //Call me crazy. 29 | std::uniform_real_distribution value_distribution(VALUE_MIN, std::nextafter(VALUE_MAX, 1 + VALUE_MAX)); 30 | 31 | vector_2d errors; 32 | vector_2d inputs; 33 | vector_2d neural_network(2 + HIDDEN_NEURONS.size()); 34 | neural_network[0].resize(INPUT_NEURONS + BIAS_NEURONS[0], 0); 35 | neural_network[neural_network.size() - 1].resize(OUTPUT_NEURONS, 0); 36 | 37 | vector_2d target_outputs; 38 | 39 | vector_3d weights(neural_network.size() - 1); 40 | 41 | sf::Event event; 42 | 43 | //In order to draw pixels on the screen, I need to make 3 objects. 44 | //SFML is weird sometimes. 45 | sf::Image outputs_image; 46 | outputs_image.create(OUTPUTS_WIDTH, OUTPUTS_HEIGHT); 47 | 48 | sf::RenderWindow window(sf::VideoMode(2 * SCREEN_RESIZE * SCREEN_WIDTH, SCREEN_RESIZE * SCREEN_HEIGHT), "Neural networks", sf::Style::Close); 49 | window.setView(sf::View(sf::FloatRect(0, 0, 2 * SCREEN_WIDTH, SCREEN_HEIGHT))); 50 | 51 | sf::Sprite outputs_sprite; 52 | 53 | sf::Texture font_texture; 54 | font_texture.loadFromFile("Resources/Images/Font.png"); 55 | 56 | sf::Texture outputs_texture; 57 | outputs_texture.loadFromImage(outputs_image); 58 | 59 | outputs_sprite.setPosition(SCREEN_WIDTH, 0); 60 | outputs_sprite.setScale(SCREEN_WIDTH / OUTPUTS_WIDTH, SCREEN_HEIGHT / OUTPUTS_HEIGHT); 61 | outputs_sprite.setTexture(outputs_texture); 62 | 63 | for (unsigned a = 1; a < neural_network.size() - 1; a++) 64 | { 65 | neural_network[a].resize(BIAS_NEURONS[a] + HIDDEN_NEURONS[a - 1], 0); 66 | } 67 | 68 | for (unsigned char a = 0; a < weights.size(); a++) 69 | { 70 | unsigned size = neural_network[1 + a].size(); 71 | 72 | if (a < weights.size() - 1) 73 | { 74 | size -= BIAS_NEURONS[1 + a]; 75 | } 76 | 77 | weights[a].resize(size, vector_1d(neural_network[a].size())); 78 | 79 | for (unsigned b = 0; b < weights[a].size(); b++) 80 | { 81 | for (unsigned c = 0; c < weights[a][b].size(); c++) 82 | { 83 | //a - layer index. 84 | //b - neuron in layer a. 85 | //c - neuron in layer a - 1. 86 | //weights[a][b][c] - the weight of the connection from neuron c to neuron b. 87 | weights[a][b][c] = value_distribution(random_engine); 88 | } 89 | } 90 | } 91 | 92 | errors.resize(weights.size()); 93 | 94 | for (unsigned char a = 0; a < errors.size(); a++) 95 | { 96 | errors[a].resize(weights[a].size(), 0); 97 | } 98 | 99 | //Oh yeah, we're also gonna use rand(). 100 | srand(std::chrono::system_clock::now().time_since_epoch().count()); 101 | 102 | previous_time = std::chrono::steady_clock::now(); 103 | 104 | while (1 == window.isOpen()) 105 | { 106 | std::chrono::microseconds delta_time = std::chrono::duration_cast(std::chrono::steady_clock::now() - previous_time); 107 | 108 | lag += delta_time; 109 | 110 | previous_time += delta_time; 111 | 112 | while (FRAME_DURATION <= lag) 113 | { 114 | lag -= FRAME_DURATION; 115 | 116 | while (1 == window.pollEvent(event)) 117 | { 118 | switch (event.type) 119 | { 120 | case sf::Event::Closed: 121 | { 122 | window.close(); 123 | 124 | break; 125 | } 126 | case sf::Event::KeyReleased: 127 | { 128 | switch (event.key.code) 129 | { 130 | case sf::Keyboard::Enter: 131 | { 132 | //We start training when we press Enter. 133 | train = 1; 134 | } 135 | } 136 | 137 | break; 138 | } 139 | case sf::Event::MouseButtonPressed: 140 | { 141 | switch (event.mouseButton.button) 142 | { 143 | case sf::Mouse::Left: 144 | { 145 | int mouse_x = sf::Mouse::getPosition(window).x; 146 | int mouse_y = sf::Mouse::getPosition(window).y; 147 | 148 | if (mouse_x >= SCREEN_RESIZE * SCREEN_WIDTH && mouse_x < window.getSize().x) 149 | { 150 | if (0 <= mouse_y && mouse_y < window.getSize().y) 151 | { 152 | float dot_x = (mouse_x - SCREEN_RESIZE * SCREEN_WIDTH) / static_cast(SCREEN_RESIZE * SCREEN_WIDTH); 153 | float dot_y = mouse_y / static_cast(SCREEN_RESIZE * SCREEN_HEIGHT); 154 | 155 | if (sf::Keyboard::isKeyPressed(sf::Keyboard::E)) 156 | { 157 | inputs.push_back({dot_x, dot_y}); 158 | //{red, green, blue} 159 | target_outputs.push_back({0, 0, 1}); 160 | } 161 | else if (sf::Keyboard::isKeyPressed(sf::Keyboard::Q)) 162 | { 163 | inputs.push_back({dot_x, dot_y}); 164 | target_outputs.push_back({1, 0, 0}); 165 | } 166 | else if (sf::Keyboard::isKeyPressed(sf::Keyboard::W)) 167 | { 168 | inputs.push_back({dot_x, dot_y}); 169 | target_outputs.push_back({0, 1, 0}); 170 | } 171 | } 172 | } 173 | } 174 | } 175 | } 176 | } 177 | } 178 | 179 | if (1 == train) 180 | { 181 | total_error = 0; 182 | 183 | for (unsigned short a = 0; a < TRAININGS_PER_FRAME; a++) 184 | { 185 | //We select inputs randomly so that we don't give priority to inputs that are at the beginning of the inputs vector. 186 | unsigned input_index = rand() % inputs.size(); 187 | 188 | forward_propagation(1, inputs[input_index], neural_network, weights); 189 | back_propagation(target_outputs[input_index], errors, neural_network, weights); 190 | } 191 | 192 | for (unsigned a = 0; a < inputs.size(); a++) 193 | { 194 | vector_1d outputs = forward_propagation(0, inputs[a], neural_network, weights); 195 | 196 | for (unsigned b = 0; b < outputs.size(); b++) 197 | { 198 | total_error += abs(outputs[b] - target_outputs[a][b]); 199 | } 200 | } 201 | } 202 | 203 | if (FRAME_DURATION > lag) 204 | { 205 | sf::CircleShape dot_shape(0.5f * SCREEN_WIDTH / OUTPUTS_WIDTH); 206 | dot_shape.setOrigin(dot_shape.getRadius(), dot_shape.getRadius()); 207 | dot_shape.setOutlineColor(sf::Color(0, 0, 0)); 208 | dot_shape.setOutlineThickness(-LINE_THICKNESS); 209 | 210 | window.clear(); 211 | 212 | draw_neural_network(window, font_texture, neural_network, weights); 213 | 214 | for (unsigned short a = 0; a < OUTPUTS_WIDTH; a++) 215 | { 216 | for (unsigned short b = 0; b < OUTPUTS_HEIGHT; b++) 217 | { 218 | float input_1 = a / static_cast(OUTPUTS_WIDTH); 219 | float input_2 = b / static_cast(OUTPUTS_HEIGHT); 220 | 221 | vector_1d output_color = forward_propagation(0, {input_1, input_2}, neural_network, weights); 222 | 223 | outputs_image.setPixel(a, b, sf::Color(round(255 * output_color[0]), round(255 * output_color[1]), round(255 * output_color[2]))); 224 | } 225 | } 226 | 227 | outputs_texture.update(outputs_image); 228 | 229 | window.draw(outputs_sprite); 230 | 231 | for (unsigned a = 0; a < inputs.size(); a++) 232 | { 233 | dot_shape.setFillColor(sf::Color(round(255 * target_outputs[a][0]), round(255 * target_outputs[a][1]), round(255 * target_outputs[a][2]))); 234 | dot_shape.setPosition(SCREEN_WIDTH * (1 + inputs[a][0]), SCREEN_HEIGHT * inputs[a][1]); 235 | 236 | window.draw(dot_shape); 237 | } 238 | 239 | draw_text(0, 0, 8, 8, "Total error: " + std::to_string(total_error), sf::Color(255, 255, 255), window, font_texture); 240 | 241 | window.display(); 242 | } 243 | } 244 | } 245 | } -------------------------------------------------------------------------------- /Source/Resources/Images/Font.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Kofybrek/Neural-network/bdf9c8ac7641f3cf1377374d1062aed009421b85/Source/Resources/Images/Font.png --------------------------------------------------------------------------------