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

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

@@ -1,92 +1,30 @@
 # C-net ඞ
 
-
-
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://git-ainf.aau.at/jastornig/c-net.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://git-ainf.aau.at/jastornig/c-net/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
 ## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
+C-net ඞ is a Python project designed to read and predict numbers from the MNIST dataset using neural networks.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
 
 ## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
+![Insert GIF or Screenshot here](link_to_visual.gif)
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
 
 ## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
 
-## Contributing
+- [x] Implemented an Image Loader for MNIST dataset.
-State if you are open to contributions and what your requirements are for accepting them.
+- [x] Created a prediction function for recognizing handwritten digits.
+- [x] Developed matrix calculation methods to support neural network operations.
+- [x] Added functionality to load and save neural network models.
+- [x] Successfully trained the network on MNIST images.
+- [x] Achieved an accuracy rate with a confidence level above 90%.
+- [ ] Ongoing optimization and code refinement.
 
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
+## Authors and Acknowledgments
+This project was brought to you by the following contributors:
+- Stornig, Jakob
+- Schleicher, Thomas
+- Dworski, Daniel
+- Walcher, Raphael
 
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+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
 
-## Authors and acknowledgment
+## Project Status
-Show your appreciation to those who have contributed to the project.
+The project is considered finished, but ongoing optimizations and improvements may still be in progress.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.

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 "../matrix/matrix.h"
-#include "util.h"
+#include "../util.h"
 
 void big_endian_to_c_uint(const char * bytes, void * target, int size) {
     char* helper = (char*)target;
@@ -68,8 +68,8 @@ Image * load_pgm_image(char * image_file_string){
 Image** import_images(char* image_file_string, char* label_file_string, int* _number_imported, int count) {
     printf("Loading Images\n");
     // create file pointer for the image and label data
-    FILE* image_file = fopen(image_file_string, "r");
+    FILE* image_file = fopen(image_file_string, "rb");
-    FILE* label_file = fopen(label_file_string, "r");
+    FILE* label_file = fopen(label_file_string, "rb");
 
     // check if the file could be opened
     if(image_file == NULL || label_file == NULL) {
@@ -159,7 +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
@@ -183,7 +182,7 @@ void img_free (Image* img) {
     free(img);
 }
 
-void images_free (Image** images, int quantity){
+void images_free(Image** images, int quantity) {
     //frees every single image
     for(int i=0;i<quantity;i++){
         img_free(images[i]);

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

@@ -1,29 +1,64 @@
 #include <stdio.h>
 
-#include "image.h"
+#include "image/image.h"
 #include "neuronal_network.h"
 
 int main() {
-    Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
+
+    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_visualize(images[1]);
+//    img_print(images[0]);
 
-//    matrix_print(images[0]->pixel_values);
+    /*
-//    matrix_print(images[1]->pixel_values);
+     * Create a new network and randomize the weights
+     */
 
-    Neural_Network* nn = new_network(28*28, 40, 5, 10, 0.08);
+    Neural_Network* network = new_network(input_size, hidden_layer_size, hidden_layer_count, 10, learning_rate);
-    randomize_network(nn, 1);
+    randomize_network(network, 1);
-//        Neural_Network* nn = load_network("../networks/newest_network.txt");
-//    printf("Done loading!\n");
 
-//    batch_train(nn, images, 20000, 20);
+    /*
+     * Training
+     */
 
-    for (int i = 0; i < 30000; ++i) {
+    for (int i = 0; i < amount_of_images_used_to_train; i++) {
-        train_network(nn, images[i], images[i]->label);
+        train_network(network, images[i], images[i]->label);
     }
 
-    save_network(nn);
+    // 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);
 
-    printf("%lf\n", measure_network_accuracy(nn, images, 10000));
+    printf("Trinaing Done!\n");
 
+    /*
+     * Saving and Loading
+     */
+
+//    save_network(network);
+//    Neural_Network* network = load_network("../networks/newest_network.txt");
+
+    /*
+     * Measure Accuracy & predict single images
+     */
+
+    printf("Accuracy: %lf\n", measure_network_accuracy(network, images, amount_of_images_used_to_test));
+
+//    matrix_print(predict_image(network, images[0]));
+
+    images_free(images, amount_of_images_to_load);
+    free_network(network);
+
+    return 0;
 }

matrix.h

@@ -1,41 +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* addScalar(Matrix* matrix, double value);
-Matrix* transpose(Matrix* matrix);
-double matrix_sum(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) {
 
@@ -216,22 +134,6 @@ Matrix* scale(Matrix* matrix, double value) {
     return result_matrix;
 }
 
-Matrix* addScalar(Matrix* matrix, double value) {
-
-    // create a copy of the original matrix
-    Matrix* result_matrix = matrix_copy(matrix);
-
-    // iterate over all numbers in the matrix and add the scalar value
-    for (int i = 0; i < result_matrix->rows; i++) {
-        for (int j = 0; j < result_matrix->columns; j++) {
-            result_matrix->numbers[i][j] += value;
-        }
-    }
-
-    // return the copy
-    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
@@ -249,77 +151,6 @@ Matrix* transpose(Matrix* matrix) {
 
 }
 
-double matrix_sum(Matrix* matrix) {
-    double sum = 0;
-    for (int i = 0; i < matrix->rows; i++) {
-        for (int j = 0; j < matrix->columns; j++) {
-            sum += matrix->numbers[i][j];
-        }
-    }
-    return sum;
-}
-
-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;
@@ -343,10 +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

@@ -1,13 +1,14 @@
 #include <stdlib.h>
 #include "neuronal_network.h"
+#include "matrix\operations.h"
 #include <stdio.h>
 #include <math.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);
+Matrix *calculate_weights_delta(Matrix *previous_layer_output, Matrix *delta_matrix);
-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);
 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){
@@ -120,7 +121,7 @@ double measure_network_accuracy(Neural_Network* network, Image** images, int amo
     for (int i = 0; i < amount; i++) {
         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) {
@@ -167,22 +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 % batch_size != 0) {
-//
+//        printf("ERROR: Batch Size is not compatible with image amount! (batch_train)");
-//        if(amount % 1000 == 0) {
+//        exit(1);
-//            printf("1k pics!\n");
 //    }
 //
+//    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]);
 //
@@ -193,14 +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);
 //        }
 //    }
 //}
@@ -239,13 +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;
@@ -253,10 +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
@@ -267,9 +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);
@@ -308,7 +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)");
@@ -320,27 +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++) {
+        for (int j = 0; j < scaled_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
+            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_free(scaled_delta_weights_matrix);
 }
 
-Matrix *calculate_weights_delta(Matrix *previous_layer_output, Matrix *delta_matrix, double learning_rate) {
+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 "matrix/matrix.h"
-#include "image.h"
+#include "image/image.h"
 
 typedef struct {
     int input_size;