Merge remote-tracking branch 'origin/Delta-Error-Test' into Delta-Error-Test

.gitlab-ci.yml

@@ -1,56 +0,0 @@
-# This file is a template, and might need editing before it works on your project.
-# You can copy and paste this template into a new `.gitlab-ci.yml` file.
-# You should not add this template to an existing `.gitlab-ci.yml` file by using the `include:` keyword.
-#
-# To contribute improvements to CI/CD templates, please follow the Development guide at:
-# https://docs.gitlab.com/ee/development/cicd/templates.html
-# This specific template is located at:
-# https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/ci/templates/C++.gitlab-ci.yml
-
-# use the official gcc image, based on debian
-# can use versions as well, like gcc:5.2
-# see https://hub.docker.com/_/gcc/
-
-image: gcc
-stages:
-  - build
-  - release
-
-build:
-  stage: build
-  # instead of calling g++ directly you can also use some build toolkit like make
-  # install the necessary build tools when needed
-  before_script:
-    - apt update && apt -y install cmake
-  script:
-    - echo BUILD_JOB_ID=$CI_JOB_ID >> CI_JOB_ID.env
-    - echo "Compiling the code..."
-    - cmake .
-    - cmake --build .
-
-  artifacts:
-    paths:
-      - c_net
-    reports:
-      dotenv: CI_JOB_ID.env
-     
-
-
-release:
-  image: registry.gitlab.com/gitlab-org/release-cli:latest
-  stage: release
-  needs:
-    - job: build
-
-  release:
-    tag_name: $CI_COMMIT_SHORT_SHA'
-    description: "latest"
-
-    assets:
-      links:
-        - name: c_net linux download (precompiled)
-          url: '${CI_PROJECT_URL}/-/jobs/${BUILD_JOB_ID}/artifacts/file/c_net'
-
-  script: echo "Define your deployment script!"
-  
-

CMakeLists.txt

@@ -3,5 +3,5 @@ project(c_net C)
 
 set(CMAKE_C_STANDARD 11)
 
-add_executable(c_net main.c matrix.c image.c neuronal_network.c util.c util.h)
+add_executable(c_net main.c matrix/matrix.c image/image.c neuronal_network.c util.c matrix/operations.c)
 target_link_libraries(c_net m)

README.md

@@ -1,10 +1,10 @@
 # C-net ඞ
 
 ## Description
-C-net ඞ is a C project designed to read and predict numbers from the MNIST dataset using neural networks.
+C-net ඞ is a Python project designed to read and predict numbers from the MNIST dataset using neural networks.
 
 ## Visuals
-![Insert GIF or Screenshot here](https://camo.githubusercontent.com/b308207b5c5ce0970b13c21609350fab21aa61a2fae56da2a6418d6fdcdbc079/68747470733a2f2f7777772e776f6c6672616d2e636f6d2f6d617468656d61746963612f6e65772d696e2d31302f656e68616e6365642d696d6167652d70726f63657373696e672f48544d4c496d616765732e656e2f68616e647772697474656e2d6469676974732d636c617373696669636174696f6e2f736d616c6c7468756d625f31302e676966)
+![Insert GIF or Screenshot here](link_to_visual.gif)
 
 ## Roadmap
 
@@ -23,14 +23,8 @@ This project was brought to you by the following contributors:
 - Dworski, Daniel
 - Walcher, Raphael
 
-We would like to express our gratitude to the following sources, which served as an inspiration and reference:
+We would like to express our gratitude to the following project, which served as an inspiration and reference:
 - [MNIST from Scratch](https://github.com/markkraay/mnist-from-scratch) by markkraay
-- [Neural Network Framework in C](https://medium.com/analytics-vidhya/building-neural-network-framework-in-c-using-backpropagation-8ad589a0752d)
-- [Simple Neural Network Implementation in C](https://towardsdatascience.com/simple-neural-network-implementation-in-c-663f51447547)
-- [3Blue1Brown Neural Network Series](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
-- [Brotcrunsher's YouTube Videos](https://www.youtube.com/watch?v=oCPT87SvkPM&pp=ygUbYnJvdCBjcnVzaGVyIG5ldXJhbCBuZXp3ZXJr), [Video 2](https://www.youtube.com/watch?v=YIqYBxpv53A&pp=ygUbYnJvdCBjcnVzaGVyIG5ldXJhbCBuZXp3ZXJr), [Video 3](https://youtu.be/EAtQCut6Qno)
 
 ## Project Status
 The project is considered finished, but ongoing optimizations and improvements may still be in progress.
-
-![amogus](https://media.tenor.com/7kpsm7kU330AAAAd/sussy-among-us.gif)

image.h

@@ -1,32 +0,0 @@
-#pragma once
-#include "matrix.h"
-
-#include "matrix.h"
-
-typedef struct {
-    Matrix* pixel_values;
-    char label;
-} Image;
-
-typedef struct {
-    const Image* image;
-    const size_t size;
-} Image_Container;
-
-static const int MAGIC_NUMBER_LABEL = 2049;
-
-static const int MAGIC_NUMBER_IMAGES = 2051;
-
-/**
- * reads a specified number of images out of the training dataset
- * @param image_file_string Path to the file containing the image data
- * @param label_file_string Path to the file containing the image labels
- * @param ptr via this pointer, the images can be accessed
- * @param count maximum number of images to be loaded. If it is 0, all available images are loaded.
- * @return
- */
-Image ** import_images(char* image_file_string, char* label_file_string, int* number_imported, int count);
-Image * load_pgm_image(char * image_file_string);
-void img_print (Image* image);
-void img_visualize(Image*image);
-void img_free (Image* image);

image/image.c

@@ -2,8 +2,8 @@
 #include <stdlib.h>
 
 #include "image.h"
-#include "matrix.h"
-#include "util.h"
+#include "../matrix/matrix.h"
+#include "../util.h"
 
 void big_endian_to_c_uint(const char * bytes, void * target, int size) {
     char* helper = (char*)target;
@@ -14,27 +14,23 @@ void big_endian_to_c_uint(const char * bytes, void * target, int size) {
 
 void read_until_space_or_newline(char * buff, int maxCount, FILE * fptr){
     int bufferOffset = 0;
-    char c;
-    int counter = 0;
+    char c = -1;
     do{
         c = (char)getc(fptr);
         buff[bufferOffset++] = c;
 
-    }while(!feof(fptr) && c != 0 && c != ' ' && c !='\n' && counter++ < maxCount);
+    }while(!feof(fptr) && c != 0 && c != ' ' && c !='\n');
     buff[bufferOffset-1] = 0;
 }
 
 Image * load_pgm_image(char * image_file_string){
     FILE * fptr = fopen(image_file_string, "r");
-    if(!fptr){
-        printf("could not open image file. exit\n");
-        exit(1);
-    }
     Image *image = malloc(sizeof(Image));
     image->label = -1;
 
 
-    char buffer[2048];
+    char buffer[100];
+    int magic_number = 0;
     fgets(buffer, 4, fptr);
     if(buffer[0] != 'P' || buffer[1] != '5'){
         printf("Wrong file Format");
@@ -44,16 +40,17 @@ Image * load_pgm_image(char * image_file_string){
         fgets(buffer, 1024, fptr);
     }
 
-    int image_width, image_height, image_white ;
+    int image_width, image_height, image_length, image_white ;
     read_until_space_or_newline(buffer, 10, fptr);
-    image_width = (int)strtol(buffer, NULL, 10);
+    image_width = strtol(buffer, NULL, 10);
 
     read_until_space_or_newline(buffer, 10, fptr);
-    image_height = (int)strtol(buffer, NULL, 10);
+    image_height = strtol(buffer, NULL, 10);
 
     read_until_space_or_newline(buffer, 10, fptr);
-    image_white = (int)strtol(buffer, NULL, 10);
+    image_white = strtol(buffer, NULL, 10);
 
+    image_length = image_width * image_height;
 
     image->pixel_values = matrix_create(image_height, image_width);
     for(int i = 0; i < image_height; i++){
@@ -162,7 +159,6 @@ Image** import_images(char* image_file_string, char* label_file_string, int* _nu
 }
 
 void img_print (Image* img) {
-
     //print the image
     matrix_print(img->pixel_values);
     //print the number of the image

image/image.h

@@ -0,0 +1,21 @@
+#include "../matrix/matrix.h"
+
+typedef struct {
+    Matrix* pixel_values;
+    char label;
+} Image;
+
+typedef struct {
+    const Image* image;
+    const size_t size;
+} Image_Container;
+
+static const int MAGIC_NUMBER_LABEL = 2049;
+static const int MAGIC_NUMBER_IMAGES = 2051;
+
+Image ** import_images(char* image_file_string, char* label_file_string, int* number_imported, int count);
+Image * load_pgm_image(char * image_file_string);
+void img_print (Image* image);
+void img_visualize(Image*image);
+void img_free (Image* image);
+void images_free (Image** images, int quantity);

main.c

@@ -1,122 +1,64 @@
 #include <stdio.h>
 
-#include "image.h"
+#include "image/image.h"
 #include "neuronal_network.h"
-#include <stdlib.h>
-#include <string.h>
-#include <errno.h>
-#include "util.h"
 
-void parsingErrorPrintHelp(){
-    printf("Syntax: c_net [train | predict]\n");
-    printf("commands:\n");
-    printf("train\t train the network\n");
-    printf("predict\t load a pgm image and predict_demo the number\n");
-    exit(1);
+int main() {
+
+    const int amount_of_images_to_load = 60000;
+    const int amount_of_images_used_to_train = 30000;
+    const int amount_of_images_used_to_test = 1000;
+    const int input_size = 28*28;
+    const int hidden_layer_size = 50;
+    const int hidden_layer_count = 3;
+    const double learning_rate = 0.1;
+
+    /*
+     * Loading Images from Dataset
+     */
+
+    Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, amount_of_images_to_load);
+
+//    img_visualize(images[0]);
+//    img_print(images[0]);
+
+    /*
+     * Create a new network and randomize the weights
+     */
+
+    Neural_Network* network = new_network(input_size, hidden_layer_size, hidden_layer_count, 10, learning_rate);
+    randomize_network(network, 1);
+
+    /*
+     * Training
+     */
+
+    for (int i = 0; i < amount_of_images_used_to_train; i++) {
+        train_network(network, images[i], images[i]->label);
     }
 
-void parsingErrorTrain(){
-    printf("invalid syntax\n");
-    printf("Syntax: c_net train [path_to_train-images.idx3-ubyte] [path_to_train-labels.idx1-ubyte] [hidden_layer_count] [neurons_per_layer] [epochs] [learning_rate] [path_to_save_network]\n");
-    exit(1);
-}
+    // Batch training works if you change the train_network method, but the results are not that good (needs further testing)
+    // batch_train(nn, images, 30000, 2);
 
-void parsingErrorDetect(){
-    printf("invalid syntax\n");
-    printf("Syntax: c_net predict_demo [path_to_network] [image_file]\n");
-}
+    printf("Trinaing Done!\n");
 
-void predict_demo(int argc, char** arguments){
-    if(argc != 2) parsingErrorDetect();
-    char * network_file = arguments[0];
-    char * image_file = arguments[1];
+    /*
+     * Saving and Loading
+     */
 
-    Neural_Network * nn = load_network(network_file);
-    Image * image = load_pgm_image(image_file);
-    Matrix * result = predict_image(nn, image);
-    int predicted = matrix_argmax(result);
-    printf("prediction result %d\n", predicted);
-    matrix_print(result);
-    matrix_free(result);
-}
+//    save_network(network);
+//    Neural_Network* network = load_network("../networks/newest_network.txt");
 
-void train(int argc, char** arguments) {
-    if (argc != 7) parsingErrorTrain();
-    char *image_file = arguments[0];
-    char *label_file = arguments[1];
-    int hidden_count = (int) strtol(arguments[2], NULL, 10);
-    int neurons_per_layer = (int) strtol(arguments[3], NULL, 10);
-    int epochs = (int) strtol(arguments[4], NULL, 10);
-    if (errno != 0) {
-        printf("hidden_count, neurons_per_layer or epochs could not be parsed!\n");
-        exit(1);
-    }
-    double learning_rate = strtod(arguments[5], NULL);
-    if (errno != 0) {
-        printf("learning_rate could not be parsed!\n");
-        exit(1);
-    }
-    char *save_path = arguments[6];
-    int imported = 0;
-    Image ** images = import_images(image_file, label_file, &imported, 60000);
-    Image ** evaluation_images = images+50000;
+    /*
+     * Measure Accuracy & predict single images
+     */
 
-    int training_image_count = 50000;
-    int testing_image_count = 10000;
+    printf("Accuracy: %lf\n", measure_network_accuracy(network, images, amount_of_images_used_to_test));
 
-    Neural_Network *nn = new_network(28 * 28, neurons_per_layer, hidden_count, 10, learning_rate);
-    randomize_network(nn, 1);
-    printf("training_network\n");
-    for(int epoch = 1; epoch <= epochs; epoch++){
-        printf("epoch %d\n", epoch);
-        for (int i = 0; i < training_image_count; i++) {
-            if (i % 1000 == 0) {
-                updateBar(i * 100 / imported);
-            }
-            train_network(nn, images[i], images[i]->label);
-        }
-        updateBar(100);
-        printf("\n");
-        printf("accuracy %lf\n", measure_network_accuracy(nn, evaluation_images, testing_image_count));
-    }
-    printf("done training!\n");
-    save_network(nn, save_path);
-}
+//    matrix_print(predict_image(network, images[0]));
+
+    images_free(images, amount_of_images_to_load);
+    free_network(network);
 
-int main(int argc, char** argv) {
-//    Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
-////    img_visualize(images[0]);
-////    img_visualize(images[1]);
-//
-////    matrix_print(images[0]->pixel_values);
-////    matrix_print(images[1]->pixel_values);
-//
-//    Neural_Network* nn = new_network(28*28, 40, 5, 10, 0.08);
-//    randomize_network(nn, 1);
-////        Neural_Network* nn = load_network("../networks/newest_network.txt");
-////    printf("Done loading!\n");
-//
-////    batch_train(nn, images, 20000, 20);
-//
-//    for (int i = 0; i < 30000; ++i) {
-//        train_network(nn, images[i], images[i]->label);
-//    }
-//
-//    save_network(nn);
-//
-//    printf("%lf\n", measure_network_accuracy(nn, images, 10000));
-    if(argc < 2){
-        parsingErrorPrintHelp();
-        exit(1);
-    }
-    if(strcmp(argv[1], "train") == 0){
-        train(argc-2, argv+2);
     return 0;
 }
-    if(strcmp(argv[1], "predict") == 0){
-        predict_demo(argc - 2, argv + 2);
-        return 0;
-    }
-    parsingErrorPrintHelp();
-
-}

matrix.h

@@ -1,39 +0,0 @@
-#pragma once
-#include <stdio.h>
-
-typedef struct {
-    int rows, columns;
-    double **numbers;
-} Matrix;
-
-static const int scaling_value = 10000;
-
-// operational functions
-Matrix* matrix_create(int rows, int columns);
-void matrix_fill(Matrix* matrix, double value);
-void matrix_free(Matrix* matrix);
-void matrix_print(Matrix *matrix);
-Matrix* matrix_copy(Matrix *matrix);
-void matrix_save(Matrix* matrix, char* file_string);
-Matrix* matrix_load(char* file_string);
-Matrix* load_next_matrix(FILE * save_file);
-
-void matrix_randomize(Matrix* matrix, int n); // don't understand the usage of the n
-int matrix_argmax(Matrix* matrix);
-Matrix* matrix_flatten(Matrix* matrix, int axis);
-Matrix* matrix_add_bias(Matrix* matrix);
-
-/*
- * These methods won't change or free the input matrix.
- * It creates a new matrix, which is modified and then returned.
- * If we don't need the original matrix, we should consider just changing the original matrix and changing the method signature to void.
- */
-
-// mathematical functions
-Matrix* multiply(Matrix* matrix1, Matrix* matrix2);
-Matrix* add(Matrix* matrix1, Matrix* matrix2);
-Matrix* subtract(Matrix* matrix1, Matrix* matrix2);
-Matrix* dot(Matrix* matrix1, Matrix* matrix2);
-Matrix* apply(double (*function)(double), Matrix* matrix);
-Matrix* scale(Matrix* matrix, double value);
-Matrix* transpose(Matrix* matrix);

matrix/matrix.c

@@ -0,0 +1,139 @@
+#include "matrix.h"
+#include <stdlib.h>
+#include <stdio.h>
+
+#define MAX_BYTES 100
+
+Matrix* matrix_create(int rows, int columns) {
+
+    // allocate memory for the matrix
+    Matrix* matrix = malloc(sizeof(Matrix));
+
+    // set size variables to the correct size
+    matrix->rows = rows;
+    matrix->columns = columns;
+
+    // allocate memory for the numbers (2D-Array)
+    matrix->numbers = malloc(sizeof(double*) * rows);
+    for (int i = 0; i < rows; i++) {
+        matrix->numbers[i] = calloc(sizeof(double), columns);
+    }
+
+    // return the pointer to the allocated memory
+    return matrix;
+}
+
+void matrix_fill(Matrix* matrix, double value) {
+
+    // simple for loop to populate the 2D-array with a value
+    for (int i = 0; i < matrix->rows; i++) {
+        for (int j = 0; j < matrix->columns; j++) {
+            matrix->numbers[i][j] = value;
+        }
+    }
+}
+
+void matrix_free(Matrix* matrix) {
+
+    // de-allocate every column
+    for (int i = 0; i < matrix->rows; i++) {
+        free(matrix->numbers[i]);
+    }
+
+    // de-allocate the rows
+    free(matrix->numbers);
+
+    // de-allocate the matrix
+    free(matrix);
+}
+
+void matrix_print(Matrix *matrix) {
+
+    // print the dimensions of the matrix
+    printf("Rows: %d, Columns: %d\n", matrix->rows, matrix->columns);
+
+    // loop through all values and format them into the correct matrix representation
+    for (int i = 0; i < matrix->rows; i++) {
+        for (int j = 0; j < matrix->columns; j++) {
+            printf("%lf ", matrix->numbers[i][j]);
+        }
+        printf("\n");
+    }
+}
+
+Matrix* matrix_copy(Matrix *matrix) {
+
+    // create another matrix of the same size
+    Matrix* copy_of_matrix = matrix_create(matrix->rows, matrix->columns);
+
+    // copy the values from the original matrix into the copy
+    for (int i = 0; i < matrix->rows; i++) {
+        for (int j = 0; j < matrix->columns; j++) {
+            copy_of_matrix->numbers[i][j] = matrix->numbers[i][j];
+        }
+    }
+
+    // return the pointer to the copy
+    return copy_of_matrix;
+}
+
+void matrix_save(Matrix* matrix, char* file_string){
+
+    // open the file in append mode
+    FILE *file = fopen(file_string, "a");
+
+    // check if the file could be found
+    if(file == NULL) {
+        printf("ERROR: Unable to get handle for \"%s\"! (matrix_save)", file_string);
+        exit(1);
+    }
+
+    // save the size of the matrix
+    fprintf(file, "%d\n", matrix->rows);
+    fprintf(file, "%d\n", matrix->columns);
+
+    // save all the numbers of the matrix into the file
+    for(int i = 0; i < matrix->rows; i++){
+        for(int j = 0; j < matrix->columns; j++){
+            fprintf(file, "%.10f\n", matrix->numbers[i][j]);
+        }
+    }
+
+    // close the file
+    fclose(file);
+}
+
+Matrix* matrix_load(char* file_string){
+
+    FILE *fptr = fopen(file_string, "r");
+
+    if(!fptr){
+        printf("Could not open \"%s\"", file_string);
+        exit(1);
+    }
+
+    Matrix * m = load_next_matrix(fptr);
+
+    fclose(fptr);
+    return m;
+}
+
+Matrix* load_next_matrix(FILE *save_file){
+
+    char buffer[MAX_BYTES];
+
+    fgets(buffer, MAX_BYTES, save_file);
+    int rows = (int)strtol(buffer, NULL, 10);
+    fgets(buffer, MAX_BYTES, save_file);
+    int cols = (int)strtol(buffer, NULL, 10);
+
+    Matrix *matrix = matrix_create(rows, cols);
+
+    for(int i = 0; i < rows; i++){
+        for(int j = 0; j < cols; j++){
+            fgets(buffer, MAX_BYTES, save_file);
+            matrix->numbers[i][j] = strtod(buffer, NULL);
+        }
+    }
+    return matrix;
+}

matrix/matrix.h

@@ -0,0 +1,15 @@
+#include <stdio.h>
+
+typedef struct {
+    int rows, columns;
+    double **numbers;
+} Matrix;
+
+Matrix* matrix_create(int rows, int columns);
+void matrix_fill(Matrix* matrix, double value);
+void matrix_free(Matrix* matrix);
+void matrix_print(Matrix *matrix);
+Matrix* matrix_copy(Matrix *matrix);
+void matrix_save(Matrix* matrix, char* file_string);
+Matrix* matrix_load(char* file_string);
+Matrix* load_next_matrix(FILE * save_file);

matrix/operations.c

@@ -1,92 +1,10 @@
-#include "matrix.h"
+#include <process.h>
 #include <stdlib.h>
-#include <stdio.h>
-#include <math.h>
 #include <time.h>
-#define MAX_BYTES 100
+#include "math.h"
+#include "operations.h"
 
 static int RANDOMIZED = 0;
-// operational functions
-Matrix* matrix_create(int rows, int columns) {
-
-    // allocate memory for the matrix
-    Matrix* matrix = malloc(sizeof(Matrix));
-
-    // set size variables to the correct size
-    matrix->rows = rows;
-    matrix->columns = columns;
-
-    // allocate memory for the numbers (2D-Array)
-    matrix->numbers = malloc(sizeof(double*) * rows);
-    for (int i = 0; i < rows; i++) {
-        matrix->numbers[i] = calloc(sizeof(double), columns);
-    }
-
-    // return the pointer to the allocated memory
-    return matrix;
-}
-
-void matrix_fill(Matrix* matrix, double value) {
-
-    // simple for loop to populate the 2D-array with a value
-    for (int i = 0; i < matrix->rows; i++) {
-        for (int j = 0; j < matrix->columns; j++) {
-            matrix->numbers[i][j] = value;
-        }
-    }
-}
-
-void matrix_free(Matrix* matrix) {
-
-    // de-allocate every column
-    for (int i = 0; i < matrix->rows; i++) {
-        free(matrix->numbers[i]);
-    }
-
-    // de-allocate the rows
-    free(matrix->numbers);
-
-    // de-allocate the matrix
-    free(matrix);
-}
-
-void matrix_print(Matrix *matrix) {
-
-    // print the dimensions of the matrix
-    printf("Rows: %d, Columns: %d\n", matrix->rows, matrix->columns);
-
-    // loop through all values and format them into the correct matrix representation
-    for (int i = 0; i < matrix->rows; i++) {
-        for (int j = 0; j < matrix->columns; j++) {
-            printf("%lf ", matrix->numbers[i][j]);
-        }
-        printf("\n");
-    }
-}
-
-Matrix* matrix_copy(Matrix *matrix) {
-
-    // create another matrix of the same size
-    Matrix* copy_of_matrix = matrix_create(matrix->rows, matrix->columns);
-
-    // copy the values from the original matrix into the copy
-    for (int i = 0; i < matrix->rows; i++) {
-        for (int j = 0; j < matrix->columns; j++) {
-            copy_of_matrix->numbers[i][j] = matrix->numbers[i][j];
-        }
-    }
-
-    // return the pointer to the copy
-    return copy_of_matrix;
-}
-
-// mathematical functions
-
-/*
- * These methods won't change or free the input matrix.
- * It creates a new matrix, which is modified and then returned.
- * If we don't need the original matrix, we should consider just changing the original matrix and changing the method signature to void.
- */
 
 Matrix* multiply(Matrix* matrix1, Matrix* matrix2) {
 
@@ -216,7 +134,6 @@ Matrix* scale(Matrix* matrix, double value) {
     return result_matrix;
 }
 
-
 Matrix* transpose(Matrix* matrix) {
 
     // create a new matrix of the size n-m, based on the original matrix of size m-n
@@ -234,67 +151,6 @@ Matrix* transpose(Matrix* matrix) {
 
 }
 
-void matrix_save(Matrix* matrix, char* file_string){
-
-    // open the file in append mode
-    FILE *file = fopen(file_string, "a");
-
-    // check if the file could be found
-    if(file == NULL) {
-        printf("ERROR: Unable to get handle for \"%s\"! (matrix_save)", file_string);
-        exit(1);
-    }
-
-    // save the size of the matrix
-    fprintf(file, "%d\n", matrix->rows);
-    fprintf(file, "%d\n", matrix->columns);
-
-    // save all the numbers of the matrix into the file
-    for(int i = 0; i < matrix->rows; i++){
-        for(int j = 0; j < matrix->columns; j++){
-            fprintf(file, "%.10f\n", matrix->numbers[i][j]);
-        }
-    }
-
-    // close the file
-    fclose(file);
-}
-
-Matrix* matrix_load(char* file_string){
-
-    FILE *fptr = fopen(file_string, "r");
-
-    if(!fptr){
-        printf("Could not open \"%s\"", file_string);
-        exit(1);
-    }
-
-    Matrix * m = load_next_matrix(fptr);
-
-    fclose(fptr);
-    return m;
-}
-
-Matrix* load_next_matrix(FILE *save_file){
-
-    char buffer[MAX_BYTES];
-
-    fgets(buffer, MAX_BYTES, save_file);
-    int rows = (int)strtol(buffer, NULL, 10);
-    fgets(buffer, MAX_BYTES, save_file);
-    int cols = (int)strtol(buffer, NULL, 10);
-
-    Matrix *matrix = matrix_create(rows, cols);
-
-    for(int i = 0; i < rows; i++){
-        for(int j = 0; j < cols; j++){
-            fgets(buffer, MAX_BYTES, save_file);
-            matrix->numbers[i][j] = strtod(buffer, NULL);
-        }
-    }
-    return matrix;
-}
-
 Matrix* matrix_flatten(Matrix* matrix, int axis) {
     // Axis = 0 -> Column Vector, Axis = 1 -> Row Vector
     Matrix* result_matrix;
@@ -318,10 +174,10 @@ Matrix* matrix_flatten(Matrix* matrix, int axis) {
     return result_matrix;
 }
 
-int matrix_argmax(Matrix* matrix) {
+int argmax(Matrix* matrix) {
     // Expects a Mx1 matrix
     if (matrix->columns != 1){
-        printf("ERROR: Matrix is not Mx1 (matrix_argmax)");
+        printf("ERROR: Matrix is not Mx1 (argmax)");
         exit(EXIT_FAILURE);
     }
 

matrix/operations.h

@@ -0,0 +1,25 @@
+#include "matrix.h"
+
+static const int scaling_value = 10000;
+
+Matrix* multiply(Matrix* matrix1, Matrix* matrix2);
+
+Matrix* add(Matrix* matrix1, Matrix* matrix2); //only used in the batch_training method
+
+Matrix* subtract(Matrix* matrix1, Matrix* matrix2);
+
+Matrix* dot(Matrix* matrix1, Matrix* matrix2);
+
+Matrix* apply(double (*function)(double), Matrix* matrix);
+
+Matrix* scale(Matrix* matrix, double value);
+
+Matrix* transpose(Matrix* matrix);
+
+Matrix* matrix_flatten(Matrix* matrix, int axis);
+
+int argmax(Matrix* matrix);
+
+void matrix_randomize(Matrix* matrix, int n);
+
+Matrix* matrix_add_bias(Matrix* matrix);

neuronal_network.c

@@ -1,14 +1,14 @@
 #include <stdlib.h>
 #include "neuronal_network.h"
+#include "matrix\operations.h"
 #include <stdio.h>
 #include <math.h>
-#include "util.h"
 
 double sigmoid(double input);
 Matrix* predict(Neural_Network* network, Matrix* image_data);
 Matrix* sigmoid_derivative(Matrix* matrix);
-Matrix *calculate_weights_delta(Matrix *previous_layer_output, Matrix *delta_matrix, double learning_rate);
-void apply_weights(Neural_Network* network, Matrix* delta_weights_matrix, int index);
+Matrix *calculate_weights_delta(Matrix *previous_layer_output, Matrix *delta_matrix);
+void apply_weights(Neural_Network *network, Matrix *delta_weights_matrix, int index, double learning_rate);
 Matrix* calculate_delta_hidden(Matrix* next_layer_delta, Matrix* weights, Matrix* current_layer_output);
 
 Neural_Network* new_network(int input_size, int hidden_size, int hidden_amount, int output_size, double learning_rate){
@@ -46,7 +46,9 @@ void free_network(Neural_Network* network){
     free(network);
 }
 
-void save_network(Neural_Network* network, char * file_name) {
+void save_network(Neural_Network* network) {
+
+    char* file_name = "../networks/newest_network.txt";
 
     // create file
     FILE* save_file = fopen(file_name, "w");
@@ -116,13 +118,10 @@ void print_network(Neural_Network* network) {
 double measure_network_accuracy(Neural_Network* network, Image** images, int amount) {
     int num_correct = 0;
 
-    printf("evaluating network\n");
-    if(amount > 10000) amount = 10000;
     for (int i = 0; i < amount; i++) {
-        updateBar(i*100/amount);
         Matrix* prediction = predict_image(network, images[i]);
 
-        int guess = matrix_argmax(prediction);
+        int guess = argmax(prediction);
         int answer = (unsigned char) images[i]->label;
 
         if (guess == answer) {
@@ -131,7 +130,6 @@ double measure_network_accuracy(Neural_Network* network, Image** images, int amo
 
         matrix_free(prediction);
     }
-    updateBar(100);
     return ((double) num_correct) / amount;
 }
 
@@ -170,22 +168,26 @@ Matrix* predict(Neural_Network* network, Matrix* image_data) {
 
 //void batch_train(Neural_Network* network, Image** images, int amount, int batch_size) {
 //
-//    for (int i = 0; i < amount; ++i) {
-//
-//        if(amount % 1000 == 0) {
-//            printf("1k pics!\n");
+//    if(amount % batch_size != 0) {
+//        printf("ERROR: Batch Size is not compatible with image amount! (batch_train)");
+//        exit(1);
 //    }
 //
+//    int image_index = 0;
+//
+//    for (int i = 0; i < amount / batch_size; ++i) {
+//
 //        Matrix* batch_weights[network->hidden_amount + 1];
 //
+//        for (int j = 0; j < network->hidden_amount + 1; j++) {
+//            batch_weights[j] = matrix_create(network->weights[j]->rows, network->weights[j]->columns);
+//            matrix_fill(batch_weights[j], 0);
+//        }
+//
 //        for (int j = 0; j < batch_size; ++j) {
-//            Matrix** delta_weights = train_network(network, images[i], images[i]->label);
+//            Matrix** delta_weights = train_network(network, images[image_index], images[image_index]->label);
 //
 //            for (int k = 0; k < network->hidden_amount + 1; k++) {
-//                if(j == 0) {
-//                    batch_weights[k] = delta_weights[k];
-//                    continue;
-//                }
 //
 //                Matrix* temp_result = add(batch_weights[k], delta_weights[k]);
 //
@@ -196,14 +198,16 @@ Matrix* predict(Neural_Network* network, Matrix* image_data) {
 //            }
 //
 //            free(delta_weights);
+//
+//            image_index++;
 //        }
 //
-//        for (int j = 0; j < network->hidden_amount + 1; ++j) {
+//        for (int j = 0; j < network->hidden_amount + 1; j++) {
 //            Matrix* average_delta_weight = scale(batch_weights[j], (1.0 / batch_size));
-//            apply_weights(network, average_delta_weight, j);
+//            apply_weights(network, average_delta_weight, j, network->learning_rate);
 //
-//            matrix_free(average_delta_weight);
 //            matrix_free(batch_weights[j]);
+//            matrix_free(average_delta_weight);
 //        }
 //    }
 //}
@@ -242,13 +246,13 @@ void train_network(Neural_Network* network, Image *image, int label) {
     Matrix* delta = multiply(sigmoid_prime, error);
 
     //calculate and apply the delta for all weights in out-put layer
-    delta_weights[network->hidden_amount] = calculate_weights_delta(output[network->hidden_amount - 1], delta, network->learning_rate);
+    delta_weights[network->hidden_amount] = calculate_weights_delta(output[network->hidden_amount - 1], delta);
 
     //hidden layers
     Matrix* previous_delta = delta;
     for (int i = network->hidden_amount; i > 1; i--) {
         delta = calculate_delta_hidden(previous_delta, network->weights[i], output[i - 1]);
-        delta_weights[i - 1] = calculate_weights_delta(output[i - 2], delta, network->learning_rate);
+        delta_weights[i - 1] = calculate_weights_delta(output[i - 2], delta);
 
         matrix_free(previous_delta);
         previous_delta = delta;
@@ -256,10 +260,16 @@ void train_network(Neural_Network* network, Image *image, int label) {
 
     // Input Layer
     delta = calculate_delta_hidden(previous_delta, network->weights[1], output[0]);
-    delta_weights[0] = calculate_weights_delta(image_data, delta, network->learning_rate);
+    delta_weights[0] = calculate_weights_delta(image_data, delta);
+
+
+    // if you want to use this method as a standalone method this part needs to be uncommented
+    for (int i = 0; i < network->hidden_amount + 1; ++i) {
+        apply_weights(network, delta_weights[i], i, network->learning_rate);
+    }
 
     for (int i = 0; i < network->hidden_amount + 1; ++i) {
-        apply_weights(network, delta_weights[i], i);
+        matrix_free(delta_weights[i]);
     }
 
     // De-allocate stuff
@@ -270,9 +280,7 @@ void train_network(Neural_Network* network, Image *image, int label) {
         matrix_free(output[i]);
     }
 
-    for (int i = 0; i < network->hidden_amount + 1; ++i) {
-        matrix_free(delta_weights[i]);
-    }
+
 
     matrix_free(sigmoid_prime);
     matrix_free(wanted_output);
@@ -311,7 +319,7 @@ Matrix* calculate_delta_hidden(Matrix* next_layer_delta, Matrix* weights, Matrix
     return new_deltas;
 }
 
-void apply_weights(Neural_Network* network, Matrix* delta_weights_matrix, int index) {
+void apply_weights(Neural_Network *network, Matrix *delta_weights_matrix, int index, double learning_rate) {
 
     if(index > network->hidden_amount || index < 0) {
         printf("ERROR: Index out of range! (apply_weights)");
@@ -323,27 +331,28 @@ void apply_weights(Neural_Network* network, Matrix* delta_weights_matrix, int in
         exit(1);
     }
 
+    // scale by learning rate
+    Matrix* scaled_delta_weights_matrix = scale(delta_weights_matrix, learning_rate);
+
     for (int i = 0; i < delta_weights_matrix->rows; i++) {
-        for (int j = 0; j < delta_weights_matrix->columns; j++) {
-            network->weights[index]->numbers[i][j] += delta_weights_matrix->numbers[i][j]; // multiply delta_weights_matrix with learning rate AND - instead of + because soll-ist
-        }
+        for (int j = 0; j < scaled_delta_weights_matrix->columns; j++) {
+            network->weights[index]->numbers[i][j] += scaled_delta_weights_matrix->numbers[i][j]; // multiply delta_weights_matrix with learning rate AND - instead of + because soll-ist
         }
     }
 
-Matrix *calculate_weights_delta(Matrix *previous_layer_output, Matrix *delta_matrix, double learning_rate) {
+    matrix_free(scaled_delta_weights_matrix);
+}
+
+Matrix *calculate_weights_delta(Matrix *previous_layer_output, Matrix *delta_matrix) {
 
     Matrix* previous_out_with_one = matrix_add_bias(previous_layer_output);
     Matrix* transposed_previous_out_with_bias = transpose(previous_out_with_one);
     Matrix* weights_delta_matrix = dot(delta_matrix, transposed_previous_out_with_bias);
 
-    // scale by learning rate
-    Matrix* result = scale(weights_delta_matrix, learning_rate);
-
     matrix_free(previous_out_with_one);
     matrix_free(transposed_previous_out_with_bias);
-    matrix_free(weights_delta_matrix);
 
-    return result;
+    return weights_delta_matrix;
 }
 
 Matrix* sigmoid_derivative(Matrix* matrix) {

neuronal_network.h

@@ -1,6 +1,6 @@
 
-#include "matrix.h"
-#include "image.h"
+#include "matrix/matrix.h"
+#include "image/image.h"
 
 typedef struct {
     int input_size;
@@ -21,7 +21,7 @@ Neural_Network* new_network(int input_size, int hidden_size, int hidden_amount,
 void randomize_network(Neural_Network* network, int scope);
 void free_network(Neural_Network* network);
 
-void save_network(Neural_Network* network, char * file_name);
+void save_network(Neural_Network* network);
 Neural_Network* load_network(char* file);
 
 void print_network(Neural_Network* network);