main.c

@@ -11,17 +11,19 @@ int main() {
 //    matrix_print(images[0]->pixel_values);
 //    matrix_print(images[1]->pixel_values);
 
-    Neural_Network* nn = new_network(28*28, 32, 2, 10, 0.1);
+    Neural_Network* nn = new_network(28*28, 40, 5, 10, 0.08);
     randomize_network(nn, 1);
-    //    save_network(nn);
-    //    Neural_Network* nn = load_network("../networks/test1.txt");
+//        Neural_Network* nn = load_network("../networks/newest_network.txt");
+//    printf("Done loading!\n");
 
 //    batch_train(nn, images, 20000, 20);
 
-    for (int i = 0; i < 1000; ++i) {
+    for (int i = 0; i < 30000; ++i) {
         train_network(nn, images[i], images[i]->label);
     }
 
-    printf("%lf\n", measure_network_accuracy(nn, images, 10));
+    save_network(nn);
+
+    printf("%lf\n", measure_network_accuracy(nn, images, 10000));
 
 }

networks/test1.txt

@@ -1,45 +0,0 @@
-4
-2
-3
-2
-1
--0.2195067977
--0.1657067977
-2
-4
-0.0297932023
-0.0289932023
--0.2106067977
--0.0132067977
--0.1003067977
--0.0923067977
--0.1315067977
-0.1174932023
-2
-1
--0.0374067977
-0.1903932023
-2
-2
--0.1219067977
--0.1745067977
-0.0758932023
-0.0761932023
-2
-1
--0.0955067977
-0.0071932023
-2
-2
--0.1881067977
--0.1272067977
--0.1149067977
--0.1048067977
-3
-2
-0.1665932023
--0.2083067977
--0.1944067977
-0.1201932023
-0.1768932023
--0.1408067977

neuronal_network.c

@@ -5,7 +5,6 @@
 
 double sigmoid(double input);
 Matrix* predict(Neural_Network* network, Matrix* image_data);
-double square(double input);
 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);
@@ -72,7 +71,7 @@ void save_network(Neural_Network* network) {
         matrix_save(network->weights[i], file_name);
     }
 
-    printf("Network Saved!");
+    printf("Network Saved!\n");
 }
 
 Neural_Network* load_network(char* file) {
@@ -121,9 +120,6 @@ 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]);
 
-        matrix_print(prediction);
-        printf("Label: %c\n", images[i]->label);
-
         int guess = matrix_argmax(prediction);
         int answer = (unsigned char) images[i]->label;
 
@@ -362,7 +358,3 @@ Matrix* sigmoid_derivative(Matrix* matrix) {
 double sigmoid(double input) {
     return 1.0 / (1 + exp(-1 * input));
 }
-
-double square(double input) {
-    return input * input;
-}