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/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