diff --git a/CMakeLists.txt b/CMakeLists.txt
index 847c636..113e5e8 100644
--- a/CMakeLists.txt
+++ b/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)
+add_executable(c_net main.c matrix.c image.c neuronal_network.c)
 target_link_libraries(c_net m)
diff --git a/image.c b/image.c
index e6a6d44..2ff1f67 100644
--- a/image.c
+++ b/image.c
@@ -4,29 +4,15 @@
 #include "image.h"
 #include "matrix.h"
 
-int endian_swap(int input) {
-    return ((input >> 24) & 0xff) |      // move byte 3 to byte 0
-           ((input << 8) & 0xff0000) |   // move byte 1 to byte 2
-           ((input >> 8) & 0xff00) |     // move byte 2 to byte 1
-           ((input << 24) & 0xff000000); // byte 0 to byte 3
-}
-
-int validate_files(FILE* image_file, FILE* label_file) {
-
-    // read magic number from files
-    int magic_number_label, magic_number_images;
-    fread(&magic_number_label, 4, 1, label_file);
-    fread(&magic_number_images, 4, 1, image_file);
-
-    // compare magic numbers with pre-defined value
-    if(endian_swap(magic_number_label) != 2049 || endian_swap(magic_number_images) != 2051) {
-        return 0;
+void big_endian_to_c_uint(const char * bytes, void * target, int size) {
+    char* helper = (char*)target;
+    for(int i = 0; i < size; i++){
+        *(helper+i) = *(bytes+size-i-1);
     }
-
-    return 1;
 }
 
-Image** import_images(char* image_file_string, char* label_file_string, int number_of_images) {
+
+Image** import_images(char* image_file_string, char* label_file_string, unsigned int* _number_imported, unsigned int count) {
 
     // create file pointer for the image and label data
     FILE* image_file = fopen(image_file_string, "r");
@@ -39,20 +25,79 @@ Image** import_images(char* image_file_string, char* label_file_string, int numb
     }
 
     // check magic number of the files
-    if(validate_files(image_file, label_file)) {
-        printf("ERROR: File validation failed! (validate_files)");
+    char word_buffer[4];
+    int buffer_size = sizeof(word_buffer);
+
+    unsigned int magic_number_label, magic_number_images, label_count, image_count;
+
+    //Read description of label file
+    fread(word_buffer, buffer_size, 1, label_file);
+    big_endian_to_c_uint(word_buffer, &magic_number_label, buffer_size);
+
+    fread(word_buffer, 4, 1, label_file);
+    big_endian_to_c_uint(word_buffer, &label_count, buffer_size);
+
+
+    //Read description of file
+    fread(word_buffer, 4, 1, image_file);
+    big_endian_to_c_uint(word_buffer, &magic_number_images, buffer_size);
+
+    fread(word_buffer, 4, 1, image_file);
+    big_endian_to_c_uint(word_buffer, &image_count, buffer_size);
+
+    // compare magic numbers with pre-defined value
+    if(magic_number_label != MAGIC_NUMBER_LABEL || magic_number_images != MAGIC_NUMBER_IMAGES) {
+        printf("TrainingData or Labels are malformed. Exiting...");
+        exit(1);
+    }
+    if(label_count != image_count){
+        printf("Number of images and labels does not match. Exiting...");
         exit(1);
     }
 
+    if(count == 0){
+        count = image_count;
+    }
 
-    // Jakob Section
+    if(count > image_count){
+        count = image_count;
+        printf("Number of images exceeds number of available images. Loading all available images");
+    }
 
+    int image_height, image_width, image_length;
+    //read image dimensions;
+    fread(word_buffer, 4, 1, image_file);
+    big_endian_to_c_uint(word_buffer, &image_height, buffer_size);
+
+    fread(word_buffer, 4, 1, image_file);
+    big_endian_to_c_uint(word_buffer, &image_width, 4);
+
+    image_length = image_height*image_width;
 
     // allocate memory for the storage of images
-    Image** images = malloc(sizeof(Image) * number_of_images);
+    Image** images = malloc(sizeof(Image*) * count);
+    if(!images){
+        printf("not enough memory");
+        exit(1);
+    }
+
+    unsigned char byteBuffer[image_length];
+    for(int i = 0; i < count; i++){
+        images[i] = malloc(sizeof(Image));
+        fread(&images[i]->label, 1, 1, label_file);
+        fread(&byteBuffer, image_width*image_height, 1, image_file);
+        images[i]->pixel_values = matrix_create(image_height, image_width);
+
+        for(int j = 0; j < image_length; j++) {
+            images[i]->pixel_values->numbers[j / image_width][j % image_width] = byteBuffer[j] / 255.0;
+        }
+    }
+
+    if(_number_imported != NULL)*_number_imported = count;
 
     fclose(image_file);
     fclose(label_file);
+    return images;
 }
 
 void img_print (Image* img) {
@@ -60,7 +105,17 @@ void img_print (Image* img) {
     //print the image
     matrix_print(img->pixel_values);
     //print the number of the image
-    printf("Number it is supposed to be: %d\n", img->image_label);
+    printf("Number it is supposed to be: %d\n", img->label);
+}
+
+void img_visualize(Image* img){
+    for(int i = 0; i < img->pixel_values->rows; i++){
+        for(int j = 0; j < img->pixel_values->columns; j++){
+            img->pixel_values->numbers[i][j] > 0.5 ? putc('#', stdout) : putc(' ', stdout);
+        }
+        putc('\n', stdout);
+    }
+    printf("Should be %d", img->label);
 }
 
 void img_free (Image* img) {
diff --git a/image.h b/image.h
index b80b4d9..969251a 100644
--- a/image.h
+++ b/image.h
@@ -5,9 +5,27 @@
 
 typedef struct {
     Matrix* pixel_values;
-    int image_label;
+    char label;
 } Image;
 
-Image** import_images(char* image_file_string, char* label_file_string, int number_of_images);
+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, unsigned int* number_imported, unsigned int count);
 void img_print (Image* image);
+void img_visualize(Image*image);
 void img_free (Image* image);
\ No newline at end of file
diff --git a/main.c b/main.c
index 82750fe..0330abf 100644
--- a/main.c
+++ b/main.c
@@ -1,10 +1,12 @@
 #include <stdio.h>
 #include <stdio.h>
+#include <malloc.h>
 
 #include "matrix.h"
 #include "image.h"
 
 int main() {
-    Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", 20);
+    Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 2);
+    img_visualize(images[1]);
 
 }
\ No newline at end of file
diff --git a/neuronal_network.c b/neuronal_network.c
index 6a352d6..72acda4 100644
--- a/neuronal_network.c
+++ b/neuronal_network.c
@@ -4,20 +4,20 @@
 #include <time.h>
 
 Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){
-    Neural_Network network = malloc(sizeof(Neural_Network));
+    Neural_Network *network = malloc(sizeof(Neural_Network));
     // initialize networks variables
-    network.input_size = input_size;
-    network.hidden_size = hidden_size;
-    network.output_size = output_size;
-    network.learning_rate = learning_rate;
+    network->hidden_size = hidden_size;
+    network->input_size = input_size;
+    network->output_size = output_size;
+    network->learning_rate = learning_rate;
 
-    network.weights_1 = matrix_randomize(matrix_create(hidden_size, input_size));
-    network.weights_2 = matrix_randomize(matrix_create(hidden_size, hidden_size));
-    network.weights_3 = matrix_randomize(matrix_create(hidden_size, hidden_size));
-    network.weights_output = matrix_randomize(matrix_create(output_size, hidden_size));
-    network.bias_1 = matrix_randomize(matrix_create(hidden_size, 1));
-    network.bias_2 = matrix_randomize(matrix_create(hidden_size, 1));
-    network.bias_3 = matrix_randomize(matrix_create(hidden_size, 1));
+    network->weights_1 = matrix_create(hidden_size, input_size);
+    network->weights_2 = matrix_create(hidden_size, hidden_size);
+    network->weights_3 = matrix_create(hidden_size, hidden_size);
+    network->weights_output = matrix_create(output_size, hidden_size);
+    network->bias_1 = matrix_create(hidden_size, 1);
+    network->bias_2 = matrix_create(hidden_size, 1);
+    network->bias_3 = matrix_create(hidden_size, 1);
     //network.bias_output = matrix_create(output_size, 1); // do we need it?
 
     return network;
@@ -60,7 +60,7 @@ void save_network(Neural_Network* network) {
     fprintf(save_file, "%d\n", network->output_size);
 
     // close the file
-    fclose(file_name);
+    fclose(save_file);
 
     // save first layer
     matrix_save(network->bias_1, file_name);
@@ -81,40 +81,41 @@ void save_network(Neural_Network* network) {
 }
 
 Neural_Network* load_network(char* file) {
-
+    return NULL;
 }
 
-double predict_images(Neural_Network* network, Image** images, int amount) {
-    int num_correct = 0;
-    for (int i = 0; i < amount; i++) {
-        Matrix* prediction = predict_image(network, images[i]);
-        if (matrix_argmax(prediction) == images[i]->image_label) {
-            num_correct++;
-        }
-        matrix_free(prediction);
-    }
-    return 1.0 * num_correct / amount;
-}
-Matrix* predict_image(Neural_Network* network, Image*);
+//double predict_images(Neural_Network* network, Image** images, int amount) {
+//    int num_correct = 0;
+//    for (int i = 0; i < amount; i++) {
+//        Matrix* prediction = predict_image(network, images[i]);
+//        if (matrix_argmax(prediction) == images[i]->label) {
+//            num_correct++;
+//        }
+//        matrix_free(prediction);
+//    }
+//    return 1.0 * num_correct / amount;
+//}
 
-Matrix* predict(Neural_Network* network, Matrix* image_data) {
-    Matrix* hidden1_outputs = apply(relu, add(dot(network->weights_1, image_data), network->bias_1));
+//Matrix* predict_image(Neural_Network* network, Image*);
 
-    Matrix* hidden2_outputs = apply(relu, add(dot(network->weights_2, hidden1_outputs), network->bias_2));
-
-    Matrix* hidden3_outputs = apply(relu, add(dot(network->weights_3, hidden2_outputs), network->bias_3));
-
-    Matrix* final_outputs = apply(relu, dot(network->weights_output, hidden3_outputs));
-
-    Matrix* result = softmax(final_outputs);
-
-    matrix_free(hidden1_outputs);
-    matrix_free(hidden2_outputs);
-    matrix_free(hidden3_outputs);
-    matrix_free(final_outputs);
-
-    return result;
-}
+//Matrix* predict(Neural_Network* network, Matrix* image_data) {
+//    Matrix* hidden1_outputs = apply(relu, add(dot(network->weights_1, image_data), network->bias_1));
+//
+//    Matrix* hidden2_outputs = apply(relu, add(dot(network->weights_2, hidden1_outputs), network->bias_2));
+//
+//    Matrix* hidden3_outputs = apply(relu, add(dot(network->weights_3, hidden2_outputs), network->bias_3));
+//
+//    Matrix* final_outputs = apply(relu, dot(network->weights_output, hidden3_outputs));
+//
+//    Matrix* result = softmax(final_outputs);
+//
+//    matrix_free(hidden1_outputs);
+//    matrix_free(hidden2_outputs);
+//    matrix_free(hidden3_outputs);
+//    matrix_free(final_outputs);
+//
+//    return result;
+//}
 
 void train_network(Neural_Network* network, Matrix* input, Matrix* output);
 void batch_train_network(Neural_Network* network, Image** images, int size);
\ No newline at end of file