angepasst

neuralNetworkTests.c

@@ -5,10 +5,74 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
-
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    /*
+    typedef struct
+    {
+        Matrix weights;
+        Matrix biases;
+        ActivationFunctionType activation;
+    } Layer;
+
+    typedef struct
+    {
+        Layer *layers;
+        unsigned int numberOfLayers;
+    } NeuralNetwork;
+
+    */
+    FILE *file = fopen(path, "wb");
+    if (!file)
+        return;
+
+    //---------------------------------------------------------------------------
+    const char *tag = "__info2_neural_network_file_format__";
+    fwrite(tag, 1, strlen(tag), file);
+
+    // Schreibe die Anzahl der Layer
+    if (nn.numberOfLayers == 0)
+    {
+        fclose(file);
+        return;
+    }
+
+    // Schreibe die Eingabe- und Ausgabegrößen des Netzwerks
+    int input = nn.layers[0].weights.cols;
+    int output = nn.layers[0].weights.rows;
+
+    fwrite(&input, sizeof(int), 1, file);
+    fwrite(&output, sizeof(int), 1, file);
+
+    // Schreibe die Layer-Daten
+    for (int i = 0; i < nn.numberOfLayers; i++)
+    {
+        const Layer *layer = &nn.layers[i];
+        int out = layer->weights.rows;
+        int in = layer->weights.cols;
+
+        fwrite(layer->weights.buffer, sizeof(MatrixType), out * in, file);
+
+        fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, file);
+
+        if (i + 1 < nn.numberOfLayers)
+        {
+            int nextOut = nn.layers[i + 1].weights.rows;
+            fwrite(&nextOut, sizeof(int), 1, file);
+        }
+    }
+    fclose(file);
+
+    // Debuging-Ausgabe
+    printf("prepareNeuralNetworkFile: Datei '%s' erstellt mit %u Layer(n)\n", path, nn.numberOfLayers);
+    for (unsigned int i = 0; i < nn.numberOfLayers; i++)
+    {
+        Layer layer = nn.layers[i];
+        printf("Layer %u: weights (%u x %u), biases (%u x %u)\n",
+               i, layer.weights.rows, layer.weights.cols, layer.biases.rows, layer.biases.cols);
+    }
+    //---------------------------------------------------------------------------
+
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -205,8 +269,8 @@ void test_predictReturnsCorrectLabels(void)
     Matrix biases1 = {.buffer = biasBuffer1, .rows = 2, .cols = 1};
     Matrix biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
     Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
-    Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
+    Layer layers[] = {{.weights = weights1, .biases = biases1, .activation = someActivation},
-                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
+                      {.weights = weights2, .biases = biases2, .activation = someActivation},
                       {.weights = weights3, .biases = biases3, .activation = someActivation}};
     NeuralNetwork netUnderTest = {.layers = layers, .numberOfLayers = 3};
     unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
@@ -216,11 +280,13 @@ void test_predictReturnsCorrectLabels(void)
     free(predictedLabels);
 }
 
-void setUp(void) {
+void setUp(void)
+{
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
-void tearDown(void) {
+void tearDown(void)
+{
     // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }