.gitignore

@@ -54,3 +54,5 @@ modules.order
 Module.symvers
 Mkfile.old
 dkms.conf
+/.idea/.name
+/.idea/misc.xml

neuronal_network.c

@@ -1,11 +1,41 @@
-
+#include <stdlib.h>
 #include "neuronal_network.h"
 #include <stdio.h>
 #include <time.h>
 
-Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate);
-void print_network(Neural_Network* network);
-void free_network(Neural_Network* network);
+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
+    network.input_size = input_size;
+    network.hidden_size = hidden_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.bias_output = matrix_create(output_size, 1); // do we need it?
+
+    return network;
+}
+
+//void print_network(Neural_Network* network){};
+
+void free_network(Neural_Network* network){
+    matrix_free(network->weights_1);
+    matrix_free(network->weights_2);
+    matrix_free(network->weights_3);
+    matrix_free(network->weights_output);
+    matrix_free(network->bias_1);
+    matrix_free(network->bias_2);
+    matrix_free(network->bias_3);
+    free(network);
+}
+
 
 void save_network(Neural_Network* network) {
 
@@ -54,9 +84,38 @@ Neural_Network* load_network(char* file) {
 
 }
 
-double predict_images(Neural_Network* network, Image** images, int amount);
+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*);
-Matrix* predict(Neural_Network* network, Matrix* image_data);
+
+Matrix* predict(Neural_Network* network, Matrix* image_data) {
+    Matrix* hidden1_inputs = dot(network->weights_1, image_data);
+    Matrix* hidden1_outputs = apply(relu, hidden1_inputs);
+
+    Matrix* hidden2_inputs = dot(network->weights_2, hidden1_outputs);
+    Matrix* hidden2_outputs = apply(relu, hidden2_inputs);
+
+
+    Matrix* final_inputs = dot(net->output_weights, hidden_outputs);
+    Matrix* final_outputs = apply(sigmoid, final_inputs);
+    Matrix* result = softmax(final_outputs);
+
+    matrix_free(hidden_inputs);
+    matrix_free(hidden_outputs);
+    matrix_free(final_inputs);
+    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);

neuronal_network.h

@@ -26,7 +26,7 @@ typedef struct {
 } Neural_Network;
 
 Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate);
-void print_network(Neural_Network* network);
+//void print_network(Neural_Network* network);
 void free_network(Neural_Network* network);
 
 void save_network(Neural_Network* network);