matrix.c

@@ -249,6 +249,16 @@ 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

matrix.h

@@ -37,6 +37,7 @@ 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);
 
 
 

neuronal_network.c

@@ -4,6 +4,11 @@
 #include <time.h>
 #include <math.h>
 
+double relu(double input);
+Matrix* softmax(Matrix* matrix);
+double square(double input);
+double loss_function(Matrix* output_matrix, int image_label);
+
 Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){
     Neural_Network *network = malloc(sizeof(Neural_Network));
     // initialize networks variables
@@ -19,9 +24,7 @@ Neural_Network* new_network(int input_size, int hidden_size, int output_size, do
     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);
-
-
+    network->bias_output = matrix_create(output_size, 1);
 
     return network;
 }
@@ -51,7 +54,6 @@ void free_network(Neural_Network* network){
     free(network);
 }
 
-
 void save_network(Neural_Network* network) {
 
     char* file_name = "../networks/newest_network.txt";
@@ -168,6 +170,12 @@ Matrix* predict(Neural_Network* network, Matrix* image_data) {
     return result;
 }
 
+double cost_function(Matrix* calculated, int expected){
+    calculated->numbers[expected] -= 1;
+    apply(square, calculated);
+
+}
+
 //void train_network(Neural_Network* network, Matrix* input, Matrix* output);
 //void batch_train_network(Neural_Network* network, Image** images, int size);
 
@@ -178,6 +186,10 @@ double relu(double input) {
     return input;
 }
 
+double relu_derivative(double x) {
+    return (x > 0) ? 1 : 0;
+}
+
 Matrix* softmax(Matrix* matrix) {
     double total = 0;
 
@@ -193,4 +205,19 @@ Matrix* softmax(Matrix* matrix) {
         }
     }
     return result_matrix;
+}
+
+double square(double input) {
+    return input * input;
+}
+
+double loss_function(Matrix* output_matrix, int image_label) {
+    Matrix* temp = matrix_copy(output_matrix);
+
+    temp->numbers[1, image_label] -= 1;
+    apply(square, temp);
+
+    matrix_free(temp);
+
+    return matrix_sum(temp);;
 }

neuronal_network.h

@@ -41,6 +41,3 @@ Matrix* predict(Neural_Network* network, Matrix* image_data);
 
 void train_network(Neural_Network* network, Matrix* input, Matrix* output);
 void batch_train_network(Neural_Network* network, Image** images, int size);
-
-double relu(double input);
-Matrix* softmax(Matrix* matrix);